Note: Descriptions are shown in the official language in which they were submitted.
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METHODS AND COMPOSITIONS FOR
DIAGNOSING, PROGNOSING, AND CONFIRMING PREECLAMPSIA
[0001] This application claims the benefit of and priority from United States
provisional
patent application 62/031,132, filed July 30, 2014, United States provisional
patent
application 62/031,834, filed July 31, 2014, and United States provisional
patent application
62/115,077, filed February 11, 2015. The contents and disclosures of each of
the foregoing
patent applications are incorporated herein by reference in their entirety.
RELEVANT FIELD
[0002] This disclosure pertains to providing a preeclampsia diagnosis and
prognosis.
BACKGROUND
[0003] Preeclampsia (PE) is a serious multisystem complication of pregnancy
with adverse
effects for mothers and babies. The incidence of the disorder is around 5-8%
of all
pregnancies in the U.S. and worldwide, and the disorder is responsible for 18%
of all
maternal deaths in the U.S. The causes and pathogenesis of preeclampsia remain
uncertain,
and current laboratory signs and clinical symptoms of PE occur late in the
disease process,
sometimes making the determination of PE and clinical management decisions
difficult.
Specifically, it is crucial to distinguish preeclampsia from complication of
pregnancy
symptoms, such as gestational hypertension, chronic hypertension, and
gestational diabetes,
each of which require different treatment options. Earlier and more reliable
diagnosis,
prognosis, confirmation and monitoring of the disease will lead to more timely
and
personalized preeclampsia treatments and as such, will significantly advance
the
understanding of preeclampsia pathogenesis.
SUMMARY
[0004] The disclosure provides a method for confirming preeclampsia in any
subject,
preferably a pregnant subject, comprising: evaluating a plurality of
biomarkers in a sample
derived from the subject to calculate an index or to confirm if the subject
has preeclampsia
wherein the confirmation has a sensitivity of greater than 90%, a specificity
of greater than
90%, or greater than 0.9 area under the receiver operating characteristic
curve (ROC and/or
AUC).
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[0005] The disclosure provides a test for confirming preeclampsia in a
subject, preferably a
pregnant subject, wherein the test is able to discern subjects not having PE
but having one or
more symptoms associated with PE from subjects having by PE, with a ROC value
of at least
0.80, 0.85, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 0.995
or more. The one
or more symptoms associated with PE can be diabetes (e.g. gestational, type I
or type II),
higher than normal glucose level, hypertension (e.g. chronic or non-chronic),
excessive or
sudden weight gain, higher than normal weight, obesity, higher than normal
body mass index
(BMI), abnormal weight gain, abnormal blood pressure, water retention,
hereditary factors,
abnormal proteinurea, headache, edema, abnormal protein/creatinine ratio,
abnormal platelet
count, stress, nulliparity, abnormal Papanicolaou test results (Pap smear),
prior preeclampsia
episodes (e.g., personal history of PE), familial history of PE, PE in prior
pregnancies, renal
disease, thrombophilia, or any combination thereof.
[0006] The disclosure provides a test for confirming preeclampsia in a
subject, preferably a
pregnant subject, wherein the test is able to discern subjects not having PE
but having one or
more symptoms associated with PE from subjects having PE, with a sensitivity,
specificity
and/or negative predictive value (NPV) of at least 80%, 85%, 90%, 95%, 96%,
97%, 98%,
99%, 99.5% or more. The one or more symptoms associated with PE can be
diabetes (e.g.
gestational, type I or type II), higher than normal glucose level,
hypertension (e.g. chronic or
non-chronic), excessive or sudden weight gain, higher than normal weight,
obesity, higher
than normal body mass index (BMI), abnormal weight gain, abnormal blood
pressure, water
retention, hereditary factors, abnormal proteinurea, headache, edema, abnormal
protein/creatinine ratio, abnormal platelet count, stress, nulliparity,
abnormal Papanicolaou
test results (Pap smear), prior preeclampsia episodes (i.e. personal history
of PE), familial
history of PE, PE in prior pregnancies, renal disease, thrombophilia, or any
combination
thereof.
[0007] The disclosure provides a method for confirming preeclampsia in a
subject,
preferably a pregnant subject, comprising performing a test on a sample
derived from the
subject, wherein the test comprises measuring the levels of a plurality of
markers and using
the levels to confirm PE with a sensitivity, specificity and/or negative
predictive value (NPV)
of at least 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or more, or a ROC
value of at
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least 0.80, 0.85, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99,
0.995 or more. The
one or more symptoms associated with PE can be diabetes (e.g. gestational,
type I or type II),
higher than normal glucose level, hypertension (e.g. chronic or non-chronic),
excessive or
sudden weight gain, higher than normal weight, obesity, higher than normal
body mass index
(BMI), abnormal weight gain, abnormal blood pressure, water retention,
hereditary factors,
abnormal proteinurea, headache, edema, abnormal proteinicreatinine ratio,
abnormal platelet
count, stress, nulliparity, abnormal Papanicolaou test results (Pap smear),
prior preeclampsia
episodes (i.e. personal history of PE), familial history of PE, PE in prior
pregnancies, renal
disease, thrombophilia, or any combination thereof.
[0008] The disclosure further provides a method for confirming if a subject,
preferably a
pregnant subject, does not have preeclampsia, the method comprising:
evaluating a sample
derived from the subject to determine levels of a plurality of biomarkers in
the sample, using
the levels of the plurality of biomarkers to calculate an index representative
of a likelihood
that the subject does not have preeclampsia; and based upon the index,
confirming if the
subject does not have preeclampsia.
[0009] A method for confirming if a subject, preferably a pregnant subject,
does have
preeclampsia, the method comprising: (a) evaluating a sample derived from the
subject to
determine levels of a plurality of biomarkers in the sample, (b) using the
levels of the
plurality of biomarkers to calculate an index representative of a likelihood
that the subject
does have preeclampsia; and (c) based upon the index, confirming if the
subject does have
preeclampsia.
[0010] The disclosure further provides method for confirming if a subject,
preferably a
pregnant subject, does not have preeclampsia, the method comprising: (a)
evaluating a
sample derived from the subject to determine a level of a biomarker in the
sample; and (b)
using the level of the biomarker to calculate an index representative of the
likelihood that the
subject does not have preeclampsia, wherein the biomarker is not ferritin
(FT), cathepsin B
(CTSB), cathepsin C (CTSC), haptoglobin (HP), alpha-2-macroglobulin (A2M),
apolipoprotein E (ApoE), apolipoprotein C-III (Apo-C3), apolipoprotein A-1
(ApoA1),
retinol binding protein 4 (RBP4), hemoglobin (HB), fibrinogen alpha (FGA),
pikachurin
(EGFLAM) or heme.
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[0011] The disclosure provides a method for confirming if a subject,
preferably a pregnant
subject, does have preeclampsia, the method comprising: (a) evaluating a
sample derived
from the subject to determine a level of a biomarker in the sample; and (b)
using the level of
the biomarker to calculate an index representative of the likelihood that the
subject does have
preeclampsia, wherein the biomarker is not ferritin (FT), cathepsin B (CTSB),
cathepsin C
(CTSC), haptoglobin (HP), alpha-2-macroglobulin (A2M), apolipoprotein E
(ApoE),
apolipoprotein C-III (Apo-C3), apolipoprotein A-1 (ApoA1), retinol binding
protein 4
(RBP4), hemoglobin (HB), fibrinogen alpha (FGA), pikachurin (EGFLAM) or heme.
[0012] The disclosure further provides a method for confirming if a subject,
preferably a
pregnant subject, having at least one symptom associated with PE has PE, the
method
comprising: (a) evaluating a sample derived from the subject to determine a
level of one or
more biomarkers in the sample; and (b) calculating an index representative of
a likelihood
that the woman does have PE using the levels of the one or more biomarkers to
calculate an
index, wherein the one or more biomarkers are not ferritin (FT), cathepsin B
(CTSB),
cathepsin C (CTSC), haptoglobin (HP), alpha-2-macroglobulin (A2M),
apolipoprotein E
(ApoE), apolipoprotein C-III (Apo-C3), apolipoprotein A-1 (ApoA1), retinol
binding protein
4 (RBP4), hemoglobin (HB), fibrinogen alpha (FGA), pikachurin (EGFLAM) or
heme.
[0013] The disclosure further provides a method for confirming if a subject,
preferably a
pregnant subject, not having at least one symptom associated with PE does not
have PE, the
method comprising: (a) evaluating a sample derived from the subject to
determine a level of
one or more biomarkers in the sample; and (b) calculating an index
representative of a
likelihood that the subject does not have PE using the levels of the one or
more biomarkers to
calculate an index, wherein the one or more biomarkers are not ferritin (FT),
cathepsin B
(CTSB), cathepsin C (CTSC), haptoglobin (HP), alpha-2-macroglobulin (A2M),
apolipoprotein E (ApoE), apolipoprotein C-III (Apo-C3), apolipoprotein A-1
(ApoA1),
retinol binding protein 4 (RBP4), hemoglobin (HB), fibrinogen alpha (FGA),
pikachurin
(EGFLAM) or heme.
[0014] The disclosure further provides a method for diagnosing or confirming a
presence of
preeclampsia in a subject, preferably a pregnant subject, the method
comprising: (a)
performing a plurality of different assays that determine a level of
fibronectin in a sample
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derived from the subject; and (b) evaluating the sample and using the levels
from the plurality
of different assays to diagnose or confirm the existence of preeclampsia and
calculate an
index.
[0015] The disclosure further provides a method for confirming that a subject,
preferably a
pregnant subject, does not have preeclampsia, the method comprising:
performing a plurality
of different assays that determine a level of fibronectin in a sample derived
from the subject;
and evaluating the sample and using the levels from the plurality of assays to
confirm the
subject does not have preeclampsia and calculate an index.
[0016] The disclosure further provides a method for diagnosing or confirming a
presence of
preeclampsia in a subject, preferably a pregnant subject, the method
comprising: (a)
performing at least one assay which utilizes an antibody which binds
fibronectin or an
antibody that selectively binds a same antigen of fibronectin as the antibody,
wherein the
binding of the antibody determines a level of fibronectin in a sample derived
from the
subject; and (b) evaluating the sample and using the level of fibronectin from
the at least one
assay to diagnose or confirm the existence of preeclampsia and calculate an
index.
[0017] The disclosure further provides a method for diagnosing or confirming a
presence of
preeclampsia in a subject, preferably a pregnant subject, the method
comprising: (a)
measuring a level of a ratio of sFlt-1 and P1GF (PLGF) and a level a plurality
of different
biomarkers in a sample derived from the subject, wherein none of the different
biomarkers is
ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC), haptoglobin (HP), alpha-
2-
macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III (Apo-C3),
apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4), hemoglobin (HB),
fibrinogen
alpha (FGA), pikachurin (EGFLAM) or heme; and (b) evaluating the sample and
using the
level from step (a) to determine an index to diagnose or confirm the presence
of preeclampsia
and calculate an index.
[0018] The disclosure further provides a method for diagnosing, monitoring,
characterizing
or confirming preeclampsia by evaluating a sample derived from a subject,
preferably a
pregnant subject, by using the levels of at least 2, 3, 4, 5, 6, 7, 8, 9, or
10 markers (e.g.,
biomarkers) selected from Figures 5A-5F (or Table 2) to calculate an index
value, wherein
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the index value is used to determine a diagnosis, relative level, or
characterization
preeclampsia in the subject.
[0019] The disclosure further provides a method for confirming a subject,
preferably a
pregnant subject, does not have preeclampsia consisting of: measuring a level
of a ratio of
sFlt-1 and P1GF and a level of a plurality of different biomarkers in a sample
derived from
the subject, wherein none of the different biomarkers is ferritin (FT),
cathepsin B (CTSB),
cathepsin C (CTSC), haptoglobin (HP), alpha-2-macroglobulin (A2M),
apolipoprotein E
(ApoE), apolipoprotein C-III (Apo-C3), apolipoprotein A-1 (ApoA1), retinol
binding protein
4 (RBP4), hemoglobin (HB), fibrinogen alpha (FGA), pikachurin (EGFLAM) or
heme; and
evaluating the sample and using the level from step (a) to determine an index
to confirm the
absence of preeclampsia and calculate an index.
[0020] The disclosure further provides a method for distinguishing a subject
having
preeclampsia from a subject not having preeclampsia but having symptoms
associated with
preeclampsia. Such symptoms associated with preeclampsia include, e.g.,
chronic
hypertension, gestational hypertension, autoimmune disorders and/or
gestational diabetes.
The methods and tests herein have a specificity, sensitivity and/or negative
predictive value
(NPV) of at least 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,
97%,
98%, 99%, or 99.5%. The methods and tests herein preferably distinguish
between a subject
having preeclampsia from a subject not having preeclampsia but having symptoms
associated
with preeclampsia with a ROC value or area under the curve value of at least
0.8, 0.85, 0.9, or
0.95. The methods herein comprise measuring the level of a plurality of
different biomarkers
(e.g., such as those selected from the list in Figures 5A-5F (or Table 2)) in
a sample derived
from a subject, generating an index using the levels of the different index,
and using the index
as a means to confirm the presence of preeclampsia, absence of preeclampsia,
and/or severity
of preeclampsia.
[0021] The disclosure further provides a method for confirming the presence,
absence or
severity of preeclampsia in a subject, preferably a pregnant subject, the
method comprising:
utilizing a monoclonal antibody that selectively binds fibronectin to
determine the levels of
fibronectin in a sample derived from the subject, generating a report
indicating the presence,
absence or severity of preeclampsia based on the levels and containing an
index; evaluating
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the sample; and based upon the index, suggesting a treatment for preeclampsiaõ
wherein the
treatment involves aspirin, preterm labor, treatment with anti-hypertensive or
anti-
preeclampsia drugs, or bedrest.
[0022] The disclosure further provides a method for confirming the presence,
absence or
severity of preeclampsia in a sample derived from a subject, preferably a
pregnant subject,
the method comprising: utilizing an antibody directed to the antigen of the
fibronectin
antibody in at least one fibronectin ELISA kit to analyze and evaluate a
sample derived from
the subject.
[0023] The disclosure further provides a method for confirming the presence,
absence or
severity of preeclampsia in a subject, preferably a pregnant subject, the
method comprising:
performing at least one assay which utilizes an antibody that selectively
binds fibronectin, a
portion of fibronection, a part of fibronectin, or a fragment of fibronectin,
wherein the
binding of the antibody determines the level of fibronectin in a sample
derived from the
subject; evaluating the sample; and using the level of fibronectin from the at
least one assay
to confirm the presence, absence, or severity of preeclampsia and calculate an
index.
[0024] The disclosure further provides a test for confirming an absence of
preeclampsia in a
subject, preferably a female subject, wherein the test measures one or more
biomarkers from
a sample derived from the subject, wherein the test has an overall ROC value
of at least 0.8.
In some examples, the test measures one or more biomarkers from a sample
derived from a
subject, wherein the test has an overall ROC value of at least 0.7, 0.8, 0.85,
0.9, 0.95, 0.96,
0.97, 0.98, 0.99 or more. In some examples, the test measures one or more
biomarkers from a
sample derived from a subject and has an overall ROC value of at 0.81, 0.82,
0.83, 0.84, 0.85,
0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,
0.99, 0.995 or more.
[0025] The disclosure further provides a kit for confirming, diagnosing,
prognosing,
monitoring or characterizing preeclampsia in a subject, preferably a pregnant
subject, said kit
comprising at least two reagents that are specific for determining level of
fibronectin in a
sample derived from the subject.
[0026] The disclosure further provides a business method comprising the step
of
determining presence, absence, forecast, severity or character of preeclampsia
in a subject,
preferably a pregnant subjectõ said method comprising the steps of: (a)
evaluating levels of
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sFLT-1, P1GF and a plurality of different biomarkers in a sample derived from
the subject,
wherein the none of the different biomarkers is ferritin (FT), cathepsin B
(CTSB), cathepsin
C (CTSC), haptoglobin (HP), alpha-2-macroglobulin (A2M), apolipoprotein E
(ApoE),
apolipoprotein C-III (Apo-C3), apolipoprotein A-1 (ApoA1), retinol binding
protein 4
(RBP4), hemoglobin (HB), fibrinogen alpha (FGA), pikachurin (EGFLAM) or heme,
(b)
determining a biomarker index value, said index comprising sFLT-1/P1GF and the
addition of
a plurality of different biomarkers, (c) employing said biomarker index to
provide a
preeclampsia determination, confirmation of preeclampsia diagnosis,
confirmation of
preeclampsia absence, prognosis of preeclampsia, or characteristics of
preeclampsia, and (d)
providing a report in exchange for a fee, wherein the report indicates the
index value based
on the analysis of said biomarkers, and specifies whether said subject is at
low risk of
preeclampsia, high risk of preeclampsia, or has preeclampsia.
[0027] The disclosure further provides a system for confirming , diagnosing,
prognosing,
monitoring or characterizing preeclampsia in a subject, preferably a pregnant
subject,
comprising: (a) an input module for receiving as an input levels of sFLT-1,
P1GF and a
plurality of different biomarkers, (b) a processor optionally configured to
perform a log
transformation of said levels to obtain log transformed levels, normalize each
of said log
transformed levels to normalized levels, adjust each of said normalized levels
to a weighted
normalized level, total each of the adjusted levels, average each of the
adjusted levels; and
provide a preeclampsia index based on said score wherein the index score
comprises sFLT-
1/P1GF and an addition of the plurality of different biomarkers. In some
instances, the log
transformation is a natural, common, binary, rational or irrational log
transformation. In some
examples, the log transformation is 10g2, logo or loge transformation. In some
instances, the
log transformation is logb, where the logarithmic base is any real number
(including natural
numbers, rational number or irrational number).
[0028] This disclosure further provides a method for confirming that a
subject, preferably a
pregnant subject, does not have preeclampsia comprising: evaluating a
plurality of
biomarkers in a sample derived from the subject to confirm the subject does
not have
preeclampsia wherein the confirmation has a specificity of greater than 90% or
has an 0.9
AUC and is used to calculate an index.
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[0029] Some embodiments of this disclosure are:
1. A method for determining the severity of preeclampsia or for
confirming the
presence or absence of preeclampsia in a female subject comprising:
a) measuring levels of one or more biomarkers in a sample derived from the
pregnant female;
b) calculating an index based on the levels of the one or more biomarkers;
and
c) confirming whether the pregnant female is experiencing preeclampsia or
whether the pregnant female is not experiencing preeclampsia, based on the
index.
2. A method for diagnosing, pronging, monitoring, characterizing,
determining
the severity of preeclampsia or confirming the presence or absence of
preeclampsia in
a female subject the presence or absence of preeclampsia in a female subject
comprising:
a) measuring levels of one or more biomarkers in a sample derived from the
pregnant female;
b) comparing the levels of one or more biomarkers to a respective
recombinant
protein level; and
c) confirming whether the pregnant female is experiencing preeclampsia or
whether the pregnant female is not experiencing preeclampsia, based on the
comparing.
3. The method of claim 2, further comprising calculating an index based on
the
levels of the one or more biomarkers.
4. A method for diagnosing, pronging, monitoring, characterizing,
determining
the severity of preeclampsia or confirming the presence or absence of
preeclampsia in
a female subject comprising:
a) measuring levels of fibronectin (FN) and two or more biomarkers in a
sample
derived from the pregnant female, wherein at least two of the two or more
biomarkers
are different from fibronectin,
b) calculating an index based on the levels of fibronectin and the two or
more
biomarkers; and
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c) confirming whether the female subject is experiencing
preeclampsia or
whether the female subject is not experiencing preeclampsia, based on the
comparing.
5. The method of claim 4, wherein the two or more biomarkers are
selected from
the group consisting of sFLT-1, P1GF, ADAM-12, HPX and PAPP-A.
6. The method of claim 4, wherein the one or more biomarkers are sFLT-1,
P1GF and PAPP-A.
7. The method of claim 4, wherein the one or more biomarkers are sFLT-1,
P1GF, PAPP-A and ADAM-12.
8. The method of claim 4, wherein the one or more biomarkers are sFLT-1,
P1GF, PAPP-A and HPX.
9. The method of claim 4, wherein the one or more biomarkers are P1GF, PAPP-
A and ADAM-12.
10. The method of claim 4, wherein the one or more biomarkers are sFLT-1
and
P1GF.
11. The method of claim 4, wherein the one or more biomarkers are P1GF and
PAPP-A.
12. The method of claim 4, wherein the one or more biomarkers are sFLT-1,
P1GF and ADAM-12.
13. The method of claim 4, wherein the one or more biomarkers are sFLT-1
and
ADAM-12.
14. The method of claim 4, 5, 6, 7, 8, 9, 10, 11, 12 or 13 further
comprising
comparing the index to a threshold value.
15. A method for confirming preeclampsia or the absence of preeclampsia in
a
female subject comprising:
a) measuring levels of fibronectin (FN) or FN fragment in a sample derived
from
the female subject using a monoclonal antibody that selectively binds FN or FN
fragment; and
b) confirming whether the female subject is experiencing
preeclampsia or
whether the female subject is not experiencing preeclampsia, wherein the
confirming
is based on the comparing.
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16. The method of claim 15, further comprising measuring levels of two or
more
biomarkers in a sample derived from the female subject.
17. The method of claim 16, wherein the two or more biomarkers are selected
from the group consisting of sFLT-1, P1GF, ADAM-12, HPX and PAPP-A.
18. The method of claim 16, wherein the one or more biomarkers are sFLT-1,
P1GF and PAPP-A.
19. The method of claim 16, wherein the one or more biomarkers are sFLT-1,
P1GF, PAPP-A and ADAM-12.
20. The method of claim 16, wherein the one or more biomarkers are sFLT-1,
P1GF, PAPP-A and HPX.
21. The method of claim 16, wherein the one or more biomarkers are P1GF,
PAPP-A and ADAM-12.
22. The method of claim 16, wherein the one or more biomarkers are sFLT-1
and
P1GF.
23. The method of claim 16, wherein the one or more biomarkers are P1GF and
PAPP-A.
24. The method of claim 16, wherein the one or more biomarkers are sFLT-1,
P1GF and ADAM-12.
25. The method of claim 16, wherein the one or more biomarkers are sFLT-1
and
ADAM-12.
26. The method of claim 15, further comprising calculating an index based
on the
levels of the bound monoclonal antibodies.
27. The method of claim 16, 18, 19, 20, 21, 22, 23, 24 or 25 further
comprising
calculating an index based on the levels of the bound monoclonal antibodies
and the
two or more biomarkers.
28. The method of claims 26 or 27 further comprising comparing the index to
a
threshold value, wherein the index is indicative of the presence or absence of
preeclampsia in a female subject.
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29. A method for diagnosing, pronging, characterizing, monitoring,
determining
the severity of preeclampsia or confirming preeclampsia or the absence of
preeclampsia in a female subject comprising:
a) measuring levels of sFLT, P1GF and one or more biomarkers in a sample
derived from the pregnant female, wherein the one or more biomarker is
different
from only VEGF, wherein VEGF excludes VEGF R-1.
b) calculating an index based on the levels of sFLT, P1GF and the one or
more
biomarkers; and
c) confirming whether the female subject is experiencing preeclampsia or
whether the female subject is not experiencing preeclampsia, based on the
index.
30. The method of claim 29, wherein the one or more biomarkers are
selected
from the group consisting of fibronectin (FN), ADAM-12, HPX and PAPP-A.
31. The method of claim 29, wherein the one or more biomarkers are
ADAM-12.
32. The method of claim 29, wherein the one or more biomarkers are
PAPP-A.
33. The method of claim 29, wherein the one or more biomarkers are
fibronectin
(FN).
34. The method of claim 29, wherein the one or more biomarkers are
fibronectin
(FN) and PAPP-A.
35. The method of claim 29, wherein the one or more biomarkers are
fibronectin
(FN) and ADAM-12.
36. The method of claim 29, wherein the one or more biomarkers are
fibronecting
(FN), ADAM-12 and PAPP-A.
37. The method of claim 29, wherein the one or more biomarkers are
fibronecting
(FN), HPX and PAPP-A.
38. The method of claims 29, 30, 32, 33, 34, 35, 36 or 37 further
comprising
comparing the index to a threshold value.
39. A method for diagnosing, pronging, characterizing, monitoring,
determining
the severity of preeclampsia or confirming preeclampsia or the absence of
preeclampsia in a female subject comprising:
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a) measuring levels of least one fibronectin (FN) fragment in two different
assays, wherein the assays determine the level of FN in a sample derived from
the
pregnant female; and
b) diagnosing, pronging, characterizing, monitoring, determining the
severity of
preeclampsia or confirming preeclampsia or the absence of preeclampsia in a
female
subject, based on the FN levels measured in the two different assays.
40. The method of claim 39, wherein each of the different assays
utilizes a
different monoclonal antibody.
41. The method of claim 40, further comprising measuring levels of one or
more
biomarkers in the sample derived from the pregnant female, wherein the one or
more
biomarkers are different from fibronectin (FN).
42. The method of claim 41, wherein the one or more biomarkers are
selected
from the group consisting of sFLT-1, P1GF, ADAM-12, HPX and PAPP-A.
43. The method of claim 40, wherein the one or more biomarkers are sFLT-1
and
P1GF.
44. The method of claim 40, wherein the one or more biomarkers are sFLT-1,
P1GF and PAPP-A.
45. The method of claim 40, wherein the one or more biomarkers are sFLT-1,
P1GF and ADAM-12.
46. The method of claims 41, 43, 44 or 45, further comprising, calculating
an
index based on the levels of the bound monoclonal antibodies and the two or
more
biomarkers.
47. The method of claim 1, 3, or 46 further comprising comparing the index
to a
threshold value.
48. The method of claim 14, 28, 38, 47, wherein the index is calculated by
a real
function algorithm for totaling the one or more biomarker levels comprising
one or
more variables multiplied by one or more corresponding weight factors,
wherein the level of each of the one or more biomarker levels is input into a
specific
variable of the one or more variables,
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wherein the corresponding weight factor is unique for each specific variable,
wherein at least one of the corresponding weight factor is different from one.
49. The method of claim 48, wherein the algorithm comprises at
least one binary
operation.
50. The method of claim 49, wherein at least one binary operation is
division.
51. The method of claim 49, wherein at least one binary operation is
addition or
subtraction.
52. The method of claim 1, 3, 4, 15, 29 or 39, further comprising
generating a
report indicative of the presence or absence of preeclampsia.
53. The method of claim 1, 3, 4, 15, 29 or 39, wherein the method excludes
measuring blood pressure, sugar blood level, urine protein level, familial
preeclampsia history, or weight gain.
54. The method of claim 1, 3, 4, 15, 29 or 39, wherein the female subject
was
diagnosed as having least one of the symptoms of the group consisting of:
blood
pressure above 140/90 mm Hg, sugar blood level above 100 mg/dL while fasting,
urine protein level more than 5 gram in a 24 hour collection or more than 3+
on two
random urine samples collected at least four hours apart, weight gain of more
than
two pounds in a week, platelets level below 155,000(per microliter) in the
second
trimester or below 145,000 (per microliter) during the third trimester,
oliguria of less
than 400 mililiters in 24 hours, high body-mass index above 25, familial
preeclampsia
history or preeclampsia, maternal history of preeclampsia, pulmonary edema,
cyanosis and change in vision.
55. The method of claim 1, or 2, wherein the one or more biomarkers are
selected
from the group consisting of sFLT-1, P1GF, fibronectin (FN), ADAM-12, HPX and
PAPP-A.
56. The method of claim 1, 3, 4, 15, 29 or 39, wherein the biomarkers
exclude
ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC), haptoglobin (HP), alpha-
2-
macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III (Apo-C3),
apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4), hemoglobin (HB),
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fibrinogen alpha (FGA), pikachurin (EGFLAM), free human chorionic gonadotropin
(free beta hCG) and heme.
57. The method of claim 14, 38 or 47, wherein the comparing is comparing of
the
one or more biomarkers to a single pregnant female or to a group of pregnant
females
experience PE and a group of pregnant females not experiencing PE.
58. The method of claim 57, wherein the single pregnant female is the
female
being tested.
59. The method of claim 57, wherein the comparing comprises comparing of
the
one or more biomarkers to a respective recombinant protein index value.
60. The method of claim 1, 3, 4, 15, 29 or 39, wherein the biomarkers
comprise
one or more proteins or protein fragments.
61. The method of claim 1, 3, 4, 15, 29 or 39, wherein the biomarkers
comprise
polynucleotides.
62. The method of claim 1, 3, 4, 15, 29 or 39, wherein the measuring is
measuring
by a method selected from the group consisting of an immunological assay, mass
spectrometry, chromatography, nephelometry, radial immunodiffusion and single
radial immunodiffusion assay.
63. The method of claim 1, 3, 4, 15, 29 or 39, wherein the measuring is
measuring
by an immunological assay.
64. The method of claim 63, wherein the immunological assay is selected
from the
group consisting of ELISA, sandwich ELISA, competitive ELISA and IgM antibody
capture ELISA.
65. A kit for diagnosing, prognosing, monitoring, characterizing,
determining the
severity of preeclampsia or for confirming the presence or absence of
preeclampsia in
a pregnant female, the kit comprising: at least two different reagents that
are specific
for determining a level of fibronectin (FN) in a sample from the pregnant
female.
66. The kit of claim 65, further comprising two or more reagents measuring
levels
of two or more biomarkers in a sample derived from the female subject.
67. The kit of claim 66, wherein the two or more biomarkers are sFLT-1,
P1GF
and PAPP-A.
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68. The kit of claim 66, wherein the two or more biomarkers are sFLT-1,
P1GF,
PAPP-A and ADAM-12.
69. The kit of claim 66, wherein the two or more biomarkers are sFLT-1,
P1GF,
PAPP-A and HPX.
70. The kit of claim 66, wherein the two or more biomarkers are P1GF, PAPP-
A
and ADAM-12.
71. The kit of claim 66, wherein the two or more biomarkers are sFLT-1 and
P1GF.
72. The kit of claim 66, wherein the two or more biomarkers are P1GF and
PAPP-
A.
73. The kit of claim 66, wherein the two or more biomarkers are sFLT-1,
P1GF
and ADAM-12.
74. The kit of claim 66, wherein the two or more biomarkers are sFLT-1 and
ADAM-12.
75. The kit of claim 65 or 66, wherein the kit does not include a reagent
measure
the levels of biomarkers selected from the group consisting of ferritin (FT),
cathepsin
B (CTSB), cathepsin C (CTSC), haptoglobin (HP), alpha-2-macroglobulin (A2M),
apolipoprotein E (ApoE), apolipoprotein C-III (Apo-C3), apolipoprotein A-1
(ApoA1), retinol binding protein 4 (RBP4), hemoglobin (HB), fibrinogen alpha
(FGA), pikachurin (EGFLAM), free beta hPC and heme.
76. A kit for confirming the presence or absence of preeclampsia
in a pregnant
female, the kit comprises
a) a first reagent specific for determining level of PAPP-A; and
b) a second reagent specific for determining ADAM12.
77. The kit of claim 76, further comprising one or more reagents measuring
levels
of one or more biomarkers in a sample derived from the female subject.
78. The kit of claim 77, wherein the one or more biomarkers are sFLT-1,
P1GF
and fibronecting (FN).
79. The kit of claim 77, wherein the one or more biomarkers are P1GF and
fibronecting (FN).
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80. The kit of claim 76 or 77, wherein the kit does not include a
reagent measuring
the levels of biomarkers selected from the group consisting of ferritin (FT),
cathepsin
B (CTSB), cathepsin C (CTSC), haptoglobin (HP), alpha-2-macroglobulin (A2M),
apolipoprotein E (ApoE), apolipoprotein C-III (Apo-C3), apolipoprotein A-1
(ApoA1), retinol binding protein 4 (RBP4), hemoglobin (HB), fibrinogen alpha
(FGA), pikachurin (EGFLAM), free beta hPC and heme.
81. A kit for diagnosing, prognosing, monitoring, characterizing,
determining the
severity of preeclampsia, or for confirming the presence or absence of
preeclampsia
in a pregnant female, the kit comprises:
a) a first reagent specific for determining level of one of sFLT-1 or P1GF;
b) a second reagent specific for determining fibronectin (FN); and
c) a third reagent specific for determining a level of a biomarker that is
different
from the biomarker determined by the first and second reagent.
82. The kit of claim 81, wherein the first reagent determines the
levels of sFLT-1,
the third reagent determines the levels of P1GF, and the kit further comprises
a fourth
reagent determining the level of a biomarker different from sFLT-1, P1GF and
FN.
83. The kit of claim 82, wherein the fourth reagent determines the
levels of PAPP-
A, HPX or ADAM12.
84. The kit of claim 81, 82 or 83 wherein the kit does not include
reagents which
determine the levels of the biomarkers selected from the group consisting of
ferritin
(FT), cathepsin B (CTSB), cathepsin C (CTSC), haptoglobin (HP), alpha-2-
macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III (Apo-C3),
apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4), hemoglobin (HB),
fibrinogen alpha (FGA), pikachurin (EGFLAM), free beta hPC and heme.
85. A test for confirming the presence or the absence of preeclampsia in a
pregnant female, wherein the test measures one or more biomarkers from a
sample
derived from the pregnant female and has an overall ROC value of at least 0.8
or
more.
86. The test of claim 85, wherein the overall ROC value is at
least 0.9 or more.
87. The test of claim 85, wherein the overall ROC value is at least 0.95 or
more.
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88. The test of claim 85, wherein the overall ROC value is at
least 0.98 or more.
89. The test of claim 85, wherein the overall ROC value is at
least 0.984 or more.
90. A computer readable medium having an executable logic for
diagnosing,
pronging, characterizing, monitoring, determining the severity of preeclampsia
or
confirming the presence or absence of preeclampsia in a female subject
comprising:
(a) input fields for providing levels of one or more biomarkers,
(b) algorithm for adjusting the levels of one or more biomarkers to a
training set,
thereby providing one or more adjusted biomarker levels;
(c) algorithm comprising at least one binary operation performed using the
adjusted biomarker levels, wherein the algorithm is a real function that
results in an
index value; and
(d) output field presenting the index value, wherein the index
value indicates
diagnosis, prognosis, characterization, monitor, determining the severity of
preeclampsia or confirmation of the presence or absence of preeclampsia in a
female
subject.
91. A computer readable medium having an executable logic for
confirming the
presence or absence of preeclampsia in a female subject comprising:
(e) input fields for providing levels of one or more biomarkers,
(0 algorithm for adjusting the levels of one or more biomarkers to a
control
value, thereby providing one or more adjusted biomarker levels;
(g) algorithm comprising adding or subtracting the one or more
adjusted
biomarker levels, wherein the algorithm is a real function that results in an
index
value; and
(h) output field presenting the index value, wherein the index value
indicates the
absence or presence of preeclampsia in the female subject.
92. A computer readable medium having an executable logic for
confirming the
presence or absence of preeclampsia in a female subject comprising:
(i) input fields for providing levels of one or more biomarkers,
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(.0 algorithm for adjusting the levels of one or more biomarkers
to a control
value, thereby providing one or more adjusted biomarker levels;
(k) algorithm comprising a ratio between two of the one or more
adjusted
biomarker levels, wherein the algorithm is a real function that results in an
index
value; and
(1) output field presenting the index value, wherein the index
value indicates the
absence or presence of preeclampsia in the female subject.
93. The computer readable medium of claim 90, 91 or 92 wherein the
real
function is a complex statistical algorithm.
94. The computer readable medium of claim 90, 91 or 92, wherein the real
function comprises at least one additional binary operation.
95. The computer readable medium of claim 94, wherein the real function
comprises a variable multiplied by a corresponding weight factor.
96. The computer readable medium of claim 95, wherein the level of each
biomarker of the one or more biomarkers is input into a specific variable
corresponding to the specific biomarker, and wherein the corresponding weight
factor
is unique for each specific variable or unique for each specific ratio of two
variables.
97. The computer readable medium of claim 90, further comprising an
algorithm
for averaging each of the one or more adjusted biomarker levels.
The computer readable medium of claim 90, wherein the computer readable medium
further comprises an algorithm for performing a logarithmic transformation of
the
levels to obtain log transformed levels; algorithm for normalizing each of the
log
transformed levels to normalized levels; algorithm for adjusting each of the
normalized levels to a weighted normalized level.
98. A computer readable medium having an executable logic for diagnosing,
prognosing, monitoring, characterizing, determining the severity of
preeclampsia or
for confirming the presence or absence of preeclampsia in a pregnant female
comprising:
(a) input fields for providing levels of one or more biomarkers,
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(b) adjusting algorithm for adjusting the levels of each of the one or more
biomarkers to a corresponding control value, wherein the adjusting algorithm
provides one or more adjusted biomarker levels;
(c) real function algorithm for manipulating the one or more adjusted
biomarker
levels comprising one or more variables multiplied by one or more
corresponding
weight factors,
wherein the level of each of the one or more adjusted biomarker levels is
input into a
specific variable of the one or more variables,
wherein the corresponding weight factor is unique for each specific variable,
wherein at least one of the corresponding weight factor is different from one;
and
(d) output field presenting the index value, wherein the index value
indicates the
absence or presence of preeclampsia in the female subject.
99. The computer readable medium of claims 90. 91, 92 or 98
wherein the control
value is established using a training set.
100. The computer readable medium of claim 98, wherein the training set is
based
on a model.
101. The computer readable medium of claim 98, wherein the training set is
based
on real values obtained from subjects.
102. The computer readable medium of claim 98, wherein the subjects are at
least
150 subjects or more.
103. The computer readable medium of claim 98, wherein the actual subjects
comprise complex subjects.
104. The computer readable medium of claim 98, wherein the complex subjects
comprise sat least 10% of the total subjects used for the training set.
105. The computer readable medium of claim 98, wherein the algorithm comprises
at least one binary operation.
106. The computer readable medium of claim 105, wherein at least one binary
operation is division.
107. The computer readable medium of claim 105, wherein at least one binary
operation is addition or subtraction.
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[0030] Some embodiments of this disclosure are:
1. A method for confirming a presence or absence of preeclampsia
in a female
subject, the method comprising:
a) measuring levels of one or more biomarkers in a sample derived from the
female subject;
b) calculating an index based on the levels of the one or more biomarkers;
and
c) confirming the presence or absence of preeclampsia in the female
subject,
based on the index.
2. The method of claim 1, wherein the measuring levels of one or more
biomarkers comprise measuring levels of three or more biomarkers.
3. The method of claim 1, wherein the measuring levels of one or more
biomarkers comprise measuring levels of four or more biomarkers.
4. The method of claim 1, wherein the measuring levels of one or more
biomarkers comprise measuring levels of five or more biomarkers.
5. A method for confirming a presence or absence of preeclampsia in a
female
subject, the method comprising:
a) measuring levels of one or more biomarkers in a sample derived
from the
female subject;
b) comparing the levels of the one or more biomarkers to a respective
recombinant protein level or to a standard value; and
c) confirming the presence or absence of preeclampsia in the
female subject,
based on the comparing.
6. The method of claim 5, further comprising calculating an index based on
the
levels of the one or more biomarkers.
7. A method for confirming a presence or absence of preeclampsia in a
female
subject, the method comprising:
a) measuring levels of fibronectin (FN) and two or more
biomarkers in a sample
derived from the female subject, wherein at least two of the two or more
biomarkers are different from fibronectin,
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b) calculating an index based on the levels of FN and the two or more
biomarkers; and
c) confirming the presence or absence of preeclampsia in the female
subject,
based on the index.
8. The method of claim 7, wherein the two or more biomarkers are selected
from
the group consisting of sFLT-1, P1GF, ADAM-12, HPX and PAPP-A.
9. The method of claim 7, wherein the biomarkers are sFLT-1, P1GF and PAPP-
A.
10. The method of claim 7, wherein the biomarkers are sFLT-1, P1GF, PAPP-A
and ADAM-12.
11. The method of claim 7, wherein the biomarkers are sFLT-1, P1GF, PAPP-A
and HPX.
12. The method of claim 7, wherein the biomarkers are P1GF, PAPP-A and
ADAM-12.
13. The method of claim 7, wherein the biomarkers are sFLT-1 and P1GF.
14. The method of claim 7, wherein the biomarkers are P1GF and PAPP-A.
15. The method of claim 7, wherein the biomarkers are sFLT-1, P1GF and
ADAM-12.
16. The method of claim 7, wherein the biomarkers are sFLT-1 and ADAM-12.
17. The method of claim 7, wherein the biomarkers are P1GF, ADAM-12, sFLT1,
PAPP-A2, and HPX.
18. The method of claim 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17, further
comprising comparing the index to a threshold value.
19. A method for confirming a presence or absence of preeclampsia in a
female
subject, the method comprising:
a) measuring levels of fibronectin (FN) or FN fragment in a sample derived
from
the female subject using a monoclonal antibody that selectively binds the FN
or the FN fragment;
b) comparing the levels of fibronectin (FN) or FN fragment to a respective
recombinant protein level or to a standard value; and
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c) confirming the presence or the absence of preeclampsia, based
on the
comparing.
20. The method of claim 19, further comprising measuring levels of
two or more
biomarkers in a sample derived from the female subject.
21. The method of claim 20, wherein the two or more biomarkers are selected
from the group consisting of sFLT-1, P1GF, ADAM-12, HPX and PAPP-A.
22. The method of claim 20, wherein the biomarkers are sFLT-1, P1GF and
PAPP-A.
23. The method of claim 20, wherein the biomarkers are sFLT-1, P1GF, PAPP-A
and ADAM-12.
24. The method of claim 20, wherein the biomarkers are sFLT-1, P1GF, PAPP-A
and HPX.
25. The method of claim 20, wherein the biomarkers are P1GF, PAPP-A and
ADAM-12.
26. The method of claim 20, wherein the biomarkers are sFLT-1 and P1GF.
27. The method of claim 20, wherein the biomarkers are P1GF and PAPP-A.
28. The method of claim 20, wherein the biomarkers are sFLT-1, P1GF and
ADAM-12.
29. The method of claim 20, wherein the biomarkers are sFLT-1 and ADAM-12.
30. The method of claim 20, wherein the biomarkers are P1GF, FN, ADAM-12,
sFLT1, PAPP-A2, and HPX.
31. The method of claim 19, further comprising calculating an index based
on
levels of bound monoclonal antibodies.
32. The method of claim 20, 22, 23, 24, 25, 26, 27, 28 or 29 further
comprising
calculating an index based on levels of (1) bound monoclonal antibodies and
(2) the two or more biomarkers.
33. The method of claims 31 or 32 further comprising comparing the index to
a
threshold value, wherein the index is indicative of the presence or absence of
preeclampsia in the female subject.
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34. A method for confirming a presence or absence of preeclampsia
in a female
subject, the method comprising:
a) measuring levels of sFLT, P1GF and one or more biomarkers in a sample
derived from the female subject, wherein the one or more biomarker is
different from VEGF, wherein VEGF excludes VEGF R-1;
b) calculating an index based on the levels of sFLT, P1GF and the one or
more
biomarkers; and
c) confirming the presence or absence of preeclampsia in the female
subject,
based on the index.
35. The method of claim 34, wherein the one or more biomarkers are selected
from the group consisting of fibronectin (FN), ADAM-12, HPX and PAPP-A.
36. The method of claim 34, wherein the biomarkers are ADAM-12.
37. The method of claim 34, wherein the biomarkers are PAPP-A.
38. The method of claim 34, wherein the biomarkers are fibronectin (FN).
39. The method of claim 34, wherein the biomarkers are fibronectin (FN) and
PAPP-A.
40. The method of claim 34, wherein the biomarkers are fibronectin (FN) and
ADAM-12.
41. The method of claim 34, wherein the biomarkers are fibronectin (FN),
ADAM-12 and PAPP-A.
42. The method of claim 34, wherein the biomarkers are fibronectin (FN),
HPX
and PAPP-A.
43. The method of claim 34, wherein the biomarkers are FN, ADAM-12, PAPP-A2,
and HPX.
44. The method of claims 34, 35, 36, 37, 38, 39, 40,41 or 42 further
comprising
comparing the index to a threshold value.
45. A method for confirming a presence or absence of preeclampsia in a female
subject, the method comprising:
a. measuring levels of biomarkers consisting of: sFLT and P1GF;
b. calculating an index based on the levels of sFLT and P1GF; and
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c. confirming the presence or absence of preeclampsia in the
female subject,
based on the index.
46. The method of claim 45, wherein the calculating comprises multiplying each
of
the measured levels of sFLT and P1GF by a unique weight factor, and
applying one or more binary functions to weighted measured levels of sFLT
and P1GF.
47. A method for diagnosing, prognosing, characterizing, monitoring,
determining
a severity of, confirming a presence of, or confirming an absence of
preeclampsia in a female subject, the method comprising:
a) measuring levels of least one fibronectin (FN) fragment in two different
assays, wherein the assays determine the level of FN in a sample derived from
the female subject; and
b) diagnosing, prognosing, characterizing, monitoring,
determining the severity
of, confirming the presence of, or confirming the absence of preeclampsia in
the female subject, based on the levels of at least one FN fragment measured
in the two different assays.
48. The method of claim 47, wherein each of the two different assays
utilizes a
different monoclonal antibody.
49. The method of claim 48, further comprising measuring levels of one or
more
biomarkers in the sample derived from the female subject, wherein the one or
more biomarkers are different from fibronectin (FN).
50. The method of claim 49, wherein the biomarkers are selected from the
group
consisting of sFLT-1, P1GF, ADAM-12, HPX and PAPP-A.
51. The method of claim 49, wherein the biomarkers are sFLT-1 or P1GF.
52. The method of claim 49, wherein the biomarkers are sFLT-1, P1GF or PAPP-
A.
53. The method of claim 49, wherein the biomarkers are sFLT-1, P1GF or
ADAM-12.
54. The method of claim 49, wherein the biomarkers are P1GF, ADAM-12, sFLT1,
PAPP-A2, and HPX.
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55. The method of claims 49, 50, 51, 52, 53, or 54, further comprising
calculating
an index based on the levels of (1) bound monoclonal antibodies and (2) the
one or more biomarkers.
56. The method of claims 1, 5, 7, 34, or 47, wherein the measuring comprises:
coating an immunoassay plate with antibodies exhibiting an affinity for a
biomarker
to be measured; and
coating the immunoassay plate with a non-specific blocking protein.
57. The method of claim 56, further comprising:
mixing labeled biomarkers with the sample resulting in a mixture; and
adding the mixture to an immunoassay plate.
58. The method of claim 56, further comprising:
introducing the sample to the immunoassay plate; and
introducing secondary conjugated antibodies to the immunoassay plate.
59. The method of claim 1, 6 or 55 further comprising comparing the index
to a
threshold value.
60. The method of claim 7, 18, 31, 32, 33, 34, 44 , or 59, wherein the
index is
calculated by a real function algorithm for totaling measured levels of
biomarker levels, wherein the algorithm comprises multiplying one or more
variables by one or more corresponding weight factors,
wherein the level of each of the biomarker levels is input into a specific
variable of
the one or more variables,
wherein a corresponding weight factor is unique for each specific variable,
wherein at least one of the one or more corresponding weight factors is
different from
one.
61. The method of claim 60, wherein the algorithm comprises at least one
binary
operation.
62. The method of claim 61, wherein the at least one binary operation is
division.
63. The method of claim 61, wherein the at least one binary operation is
addition
or subtraction.
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64. The method of claim 60, wherein the one or more weight factors is a ratio
of
measured levels of two biomarkers.
65. The method of claim 1, 5, 7, 19, 34 or 47, further comprising
generating a
report indicating the presence or absence of preeclampsia in the female
subject.
66. The method of claim 1, 5, 7, 19, 34 or 47, wherein the method excludes
consideration of blood pressure, sugar blood level, urine protein level,
familial
preeclampsia history, or weight gain.
67. The method of claim 1, 5, 7, 19, 34 or 47, wherein the female subject
has at
least one symptom in the group consisting of: blood pressure above 140/90
mm Hg, sugar blood level above 100 mg/dL while fasting, urine protein level
more than 5 grams in a 24 hour collection or more than 3+ on two random
urine samples collected at least four hours apart, weight gain of more than
two
pounds in a week, platelets level below 155,000 (per microliter) in a second
trimester or below 145,000 (per microliter) during a third trimester, oliguria
of
less than 400 milliliters in 24 hours, high body-mass index above 25, familial
history of preeclampsia, pulmonary edema, cyanosis and change in vision.
68. The method of claim 1 or 5, wherein the one or more biomarkers are
selected
from the group consisting of sFLT-1, P1GF, fibronectin (FN), ADAM-12,
HPX and PAPP-A.
69. The method of claim 1, 5, 7, 19, 34 or 47, wherein the biomarkers
exclude
ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC), haptoglobin (HP),
alpha-2-macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III
(Apo-C3), apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4),
hemoglobin (HB), fibrinogen alpha (FGA), pikachurin (EGFLAM), free
human chorionic gonadotropin (free beta hCG) and heme.
70. The method of claim 18, 44 or 59, wherein the comparing comprises
comparing the biomarkers to (1) that of a single pregnant female or a group of
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pregnant females having preeclampsia and (2) that of a group of pregnant
females not having preeclampsia.
71. The method of claim 70, wherein the single pregnant female is the
female
subject.
72. The method of claim 70, wherein the comparing comprises comparing the
biomarkers to a respective recombinant protein index value.
73. The method of claim 1, 5, 7, 19, 34 or 47, wherein biomarkers comprise
one or
more proteins or protein fragments.
74. The method of claim 1, 5, 7, 19, 34 or 47, wherein the biomarkers
comprise
polynucleotides.
75. The method of claim 1, 5, 7, 19, 34 or 47, wherein the measuring
comprises
utilizing an immunological assay, mass spectrometry, chromatography,
nephelometry, radial immunodiffusion or single radial immunodiffusion assay.
76. The method of claim 1, 5, 7, 19, 34 or 47, wherein the measuring
comprises
measuring by an immunological assay.
77. The method of claim 76, wherein the immunological assay is selected
from the
group consisting of ELISA, sandwich ELISA, competitive ELISA and IgM
antibody capture ELISA.
78. A kit for diagnosing, prognosing, monitoring, characterizing,
determining a
severity of, confirming a presence of, or confirming an absence of
preeclampsia in a female subject, the kit comprising: at least two different
reagents that are specific for determining a level of fibronectin (FN) in a
sample derived from the female subject.
79. The kit of claim 78, further comprising two or more reagents for
measuring
levels of two or more biomarkers in the sample derived from the female
subject.
80. The kit of claim 79, wherein the biomarkers are sFLT-1, P1GF and PAPP-
A.
81. The kit of claim 79, wherein the biomarkers are sFLT-1, P1GF, PAPP-A
and
ADAM-12.
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82. The kit of claim 79, wherein the biomarkers are sFLT-1, P1GF, PAPP-A
and
HPX.
83. The kit of claim 79, wherein the biomarkers are P1GF, PAPP-A and ADAM-
12.
84. The kit of claim 79, wherein the biomarkers are sFLT-1 and P1GF.
85. The kit of claim 79, wherein the biomarkers are P1GF and PAPP-A.
86. The kit of claim 79, wherein the biomarkers are sFLT-1, P1GF and ADAM-
12.
87. The kit of claim 79, wherein the biomarkers are sFLT-1 and ADAM-12.
88. The kit of claim 79, wherein the biomarkers are P1GF, FN, ADAM-12, sFLT1,
PAPP-A2, and HPX.
89. The kit of claim 78 or 79, wherein the kit does not include a reagent
for
measuring the levels of biomarkers selected from the group consisting of
ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC), haptoglobin (HP),
alpha-2-macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III
(Apo-C3), apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4),
hemoglobin (HB), fibrinogen alpha (FGA), pikachurin (EGFLAM), free beta
hPC and heme.
90. A kit for confirming the presence or absence of preeclampsia in a
female
subject, the kit comprising:
a) a first reagent specific for determining level of PAPP-A; and
b) a second reagent specific for determining level of ADAM12.
91. The kit of claim 90, further comprising one or more reagents for
measuring
levels of one or more biomarkers in a sample derived from the female subject.
92. The kit of claim 91, wherein the biomarkers are sFLT-1, P1GF and
fibronectin
(FN).
93. The kit of claim 91 wherein the biomarkers are P1GF and fibronectin
(FN).
94. The kit of claim 90 or 91, wherein the kit does not include a reagent
for
measuring levels of biomarkers selected from the group consisting of ferritin
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(FT), cathepsin B (CTSB), cathepsin C (CTSC), haptoglobin (HP), alpha-2-
macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III (Apo-
C3), apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4),
hemoglobin (HB), fibrinogen alpha (FGA), pikachurin (EGFLAM), free beta
hPC and heme.
95. A kit for diagnosing, prognosing, monitoring, characterizing,
determining a
severity of, confirming a presence of, or confirming an absence of
preeclampsia, the kit comprising:
a) a first reagent specific for determining a level of one of
sFLT-1 or P1GF;
b) a second reagent specific for determining fibronectin (FN); and
c) a third reagent specific for determining a level of a
biomarker that is different
from biomarker determined by the first and second reagent.
96. The kit of claim 95, wherein the first reagent is specific for
determining the
level of sFLT-1, the third reagent is specific for determining the level of
P1GF, and the kit further comprises a fourth reagent specific for determining
a
level of a biomarker different from sFLT-1, P1GF and FN.
97. The kit of claim 96, wherein the fourth reagent is specific for
determining the
levels of PAPP-A, HPX or ADAM12.
98. The kit of claim 95, 96 or 97 wherein the kit does not include reagents
specific
for determining levels of biomarkers selected from the group consisting of
ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC), haptoglobin (HP),
alpha-2-macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III
(Apo-C3), apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4),
hemoglobin (HB), fibrinogen alpha (FGA), pikachurin (EGFLAM), free beta
hPC and heme.
99. A test for confirming a presence or absence of preeclampsia in a female
subject, wherein the test measures one or more biomarkers from a sample
derived from the female subject, wherein a receiver operating characteristic
(ROC) value associated with the biomarkers is at least 0.8.
100. The test of claim 99, wherein the ROC value is at least 0.9.
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101. The test of claim 99, wherein the ROC value is at least 0.95.
102. The test of claim 99, wherein the ROC value is at least 0.98.
103. The test of claim 99, wherein the ROC value is at least 0.984.
104. A test for confirming a presence or absence of preeclampsia in a subject,
wherein the test measures one or more biomarkers from a sample derived from
the subject, wherein a receiver operating characteristic (ROC) value
associated
with the biomarkers is greater than a ROC value associated with sFLT/P1GF.
105. The test of claim 104, wherein the female subject exhibits clinical
symptoms
of preeclampsia.
106. The test of claim 104, wherein the test comprises measuring a ratio of
measured levels of sFLT/P1GF.
107. The test of claim 104, wherein the ratio of measured levels of sFLT/P1GF
is
normalized, raw, adjusted, or a combination thereof
108. A system for diagnosing, prognosing, characterizing, monitoring,
determining
a severity of, confirming a presence of, or confirming an absence of
preeclampsia in a female subject, the system comprising:
(a) an input module for receiving as an input levels of one or more
biomarkers;
(b) a processor configured to:
perform a first algorithm adjusting the levels of the one or more biomarkers
to a
training set, thereby providing one or more adjusted biomarker levels; and
perform a second algorithm that applies at least one binary operation using
the
adjusted biomarker levels, wherein the second algorithm is a real function
that
results in an index value; and
(c) an output module for outputting the index value, wherein the index
value
indicates diagnosis, prognosis, characterization, a monitored aspect,
determination of the severity, confirmation of the presence, or confirmation
of
the absence, of preeclampsia in the female subject.
109. A system for confirming a presence or absence of preeclampsia in a female
subject, the system comprising:
(a) an input module for receiving as an input levels of one or more
biomarkers;
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(b) a processor configured to:
perform a first algorithm adjusting the levels of the one or more biomarkers
to a
control value; and
perform a second algorithm adding or subtracting the one or more adjusted
biomarker levels, wherein the second algorithm is a real function that results
in an index value; and
(c) an output module for outputting the index value, wherein the index
value
indicates the absence or the presence of preeclampsia in the female subject.
110. A system for confirming a presence or absence of preeclampsia in a female
subject, the system comprising:
(a) an input module for receiving as an input levels of two or more
biomarkers;
(b) a processor configured to:
perform a first algorithm adjusting the levels of the two or more biomarkers
to a
control value, thereby providing two or more adjusted biomarker levels; and
perform a second algorithm calculating a ratio between two of the two or more
adjusted biomarker levels, wherein the second algorithm is a real function
that
results in an index value; and
(c) an output module for outputting the index value, wherein the index
value
indicates the absence or presence of preeclampsia in the female subject.
111. The system of claim 108, 109 or 110 wherein the real function comprises a
complex statistical algorithm.
112. The system of claim 108, 109 or 110, wherein the real function comprises
at
least one binary operation.
113. The system of claim 112, wherein the real function comprises a
multiplying a
variable by a corresponding weight factor.
114. The system of claim 113, wherein a level of each biomarker is input into
a
specific variable corresponding to the biomarkers, and wherein the
corresponding weight factor is unique for each variable or unique for each
ratio of two variables.
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115. The system of claim 108, wherein the processor is further configured to
perform a third algorithm averaging each of the one or more adjusted
biomarker levels.
116. The system of claim 108, wherein the processor is further configured to
perform a third algorithm applying a logarithmic transformation of the levels
to obtain log transformed levels; a fourth algorithm normalizing each of the
log transformed levels to normalized levels; and a fifth algorithm adjusting
each of the normalized levels to a weighted normalized level.
117. A system for diagnosing, prognosing, monitoring, characterizing,
determining
a severity of, confirming a presence of, or confirming an absence of
preeclampsia in a female subject, the system comprising:
(a) an input module for receiving as an input levels of one or more
biomarkers;
(b) a processor configured to:
perform an algorithm adjusting the levels of each of the one or more
biomarkers to a
corresponding control value, thereby providing one or more adjusted
biomarker levels;
perform a real function algorithm manipulating the one or more adjusted
biomarker
levels by multiplying one or more variables by one or more corresponding
weight factors, wherein a level of each of the one or more adjusted biomarker
levels is input into a specific variable of the one or more variables, wherein
the
corresponding weight factor is unique for each specific variable, wherein at
least one of the corresponding weight factors is different from one; and
(c) an output module for outputting an index value, wherein the index value
indicates diagnosis, prognosis, characterization, a monitored aspect,
determination of the severity, confirmation of the presence, or confirmation
of
the absence, of preeclampsia in the female subject.
118. The system of claims 109, 110 or 117 wherein the control value is
established
using a training set.
119. The system of claim 117, wherein the training set is based on a model.
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120. The system of claim 117, wherein the training set is based on real values
obtained from subjects.
121. The system of claim 120, wherein the subjects comprise at least 150
subjects.
122. The system of claim 120, wherein subjects comprise complex subjects.
123. The system of claim 122, wherein the complex subjects comprise at least
10%
of all subjects used for the training set.
124. The system of claim 117, wherein the algorithm applies at least one
binary
operation.
125. The system of claim 124, wherein the at least one binary operation is
division.
126. The system of claim 124, wherein the at least one binary operation is
addition
or subtraction.
127. A test for confirming preeclampsia in a subject, wherein the test is able
to
discern subjects that do not have preeclampsia but have one or more
symptoms associated with preeclampsia, from subjects having preeclampsia,
with a receiving operating characteristic (ROC) value of at least 0.8.
128. The test of claim 127, wherein the ROC value is of at least 0.9.
129. The test of claim 127, wherein the one or more symptoms associated with
preeclampsia are selected from the group consisting of diabetes, higher than
normal glucose level, hypertension, excess or sudden weight gain,
overweight, obesity, higher than normal body mass index, abnormal weight
gain, abnormal blood pressure, water retention, hereditary factors, abnormal
proteinurea, headache, edema, abnormal proteinicreatinine ratio, abnormal
platelet count, stress, nulliparity, abnormal Papanicolaou test results, prior
preeclampsia episodes, familial history of PE, renal disease and
thrombophilia.
130. The test of claim 129, wherein the diabetes is gestational, type I or
type II.
131. The test of claim 129, wherein the hypertension is chronic hypertension.
132. A test for confirming preeclampsia in a subject, wherein the test is able
to
discern subjects not having preeclampsia but having one or more symptoms
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associated with preeclampsia, from subjects having preeclampsia, with a
sensitivity of at least 80% .
133. A test for confirming preeclampsia in a subject, wherein the test is able
to
discern subjects not having preeclampsia but having one or more symptoms
associated with preeclampsia, from subjects having preeclampsia, with
specificity of at least 80%.
134. A test for confirming preeclampsia in a subject, wherein the test is able
to
discern subjects not having preeclampsia but having one or more symptoms
associated with preeclampsia, from subjects having preeclampsia, with a
negative predictive value (NPV) of at least 80%.
135. The test of claim 132, 133 or 134, wherein the one or more symptoms
associated with preeclampsia are selected from the group consisting of
diabetes, higher than normal glucose level, hypertension, excess or sudden
weight gain, overweight, obesity, higher than normal body mass index,
abnormal weight gain, abnormal blood pressure, water retention, hereditary
factors, abnormal proteinurea, headache, edema, abnormal proteinicreatinine
ratio, abnormal platelet count, stress, nulliparity, abnormal Papanicolaou
test
results, prior preeclampsia episodes, familial history of PE, renal disease
and
thrombophilia.
136. The test of claim 135, wherein the diabetes is gestational, type I or
type II.
137. The test of claim 135, wherein the hypertension is chronic hypertension.
138. The test of claim 135, wherein the sensitivity is of at least 90%.
139. The test of claim 133, wherein the specificity is of at least 90%.
140. The test of claim 134, wherein the NPV is of at least 90%.
141. A method for confirming preeclampsia, the method comprising performing a
test on a sample derived from a female subject wherein the test comprises
measuring levels of a plurality of markers and using the levels of the
plurality
of markers to confirm preeclampsia with a receiving operating characteristic
(ROC) value of at least 0.90.
142. The method of claim 141, wherein the ROC value is at least 0.95.
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143. A method for confirming preeclampsia, the method comprising performing a
test on a sample derived from a female subject wherein the test comprises
measuring levels of a plurality of markers and using the levels of the
plurality
of markers to confirm preeclampsia with a specificity of at least 80%.
144. The method of claim 143, wherein the specificity is at least 90%.
145. A method for confirming preeclampsia, the method comprising performing a
test on a sample derived from a female subject wherein the test comprises
measuring levels of a plurality of markers and using the levels of the
plurality
of markers to confirm preeclampsia with a sensitivity of at least 80%.
146. The method of claim 145, wherein the sensitivity is at least 90%.
147. A method for confirming preeclampsia, the method comprising performing a
test on a sample derived from a female subject wherein the test comprises
measuring levels of a plurality of markers and using said levels to confirm
preeclampsia with a negative predictive value of at least 80%.
148. The method of claim 147, wherein the negative predictive value is at
least
90%.
149. The test as in any of claims 127-140, wherein the sample is selected from
the
group consisting of whole blood, urine, serum and plasma.
150. The method as in one of claims 141-148, wherein the sample is selected
from
the group consisting of whole blood, urine, serum and plasma.
151. The method as in any of claims 1-7, 31, 34, 60, 141, 143 or 147, wherein
the
biomarker comprises a biomarker of Group-1.
152. The test as in any of claims 30, 126 or 131-134, wherein the biomarker
comprises a biomarker of Group-1.
153. The system as in any of claims 108-110 or 116, wherein the biomarker
comprises a biomarker of Group-1.
154. The kit as in any of claims 78, 90 or 95, wherein the biomarker comprises
a
biomarker of Group-1.
155. A computer readable medium containing instructions which, when executed
by a computer system, cause the computer system to:
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receive a first data set pertaining to first levels of a plurality of
preeclampsia
biomarkers in a first biological sample derived from a subject at a first
point-
in-time;
perform a first analysis on the first levels to obtain a first assessment of
preeclampsia
in the subject;
receive a second set of data pertaining to second levels of the plurality of
preeclampsia biomarkers in a second biological sample derived from the
subject at a second point-in-time;
perform a second analysis on the second levels to obtain an assessment of
preeclampsia in the subject;
compare the first assessment with the second assessment; and
confirm preeclampsia or lack thereof based on the comparison.
156. A method for diagnosing or confirming preeclampsia in a subject, the
method
comprising:
detecting protein levels of sFLT, PIG, and a protein or protein fragment
binding to
pikachurin antibody in a biological sample derived from the subject; and
calculating a preeclampsia index score using the detected protein levels,
wherein the
preeclampsia score is indicative of the presence or absence of preeclampsia in
the subject.
157. The method of claim 156, further comprising diagnosing or confirming
preeclampsia in the subject.
158. The method of claim 156, wherein the calculating comprises multiplying
the
detected protein levels by a unique weight factor, and applying one or more
binary functions to weighted detected protein levels.
159. The method of claim 3, wherein each of the four or more biomarkers is
selected from the group consisting of sFLT-1, PIGF, abFN, PAPP-A, TakFN,
ADAM-12, and HPX, wherein each of the four or more biomarkers is different
from each other.
160. The method of claim 5 or 49, wherein the one or more biomarkers comprise
four or more biomarkers, wherein each of the four or more biomarkers is
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selected from the group consisting of sFLT-1, PIGF, abFN, PAPP-A, TakFN,
ADAM-12, and HPX, wherein each of the four or more biomarkers is different
from each other.
161. The method of claim 7 or 20, wherein the two or more biomarkers comprise
four or more biomarkers, wherein each of the four or more biomarkers is
selected from the group consisting of sFLT-1, PIGF, abFN, PAPP-A, TakFN,
ADAM-12, and HPX, wherein each of the four or more biomarkers is different
from each other.
162. The kit of claim 79, wherein the two or more biomarkers comprise four or
more biomarkers, wherein each of the four or more biomarkers is selected
from the group consisting of sFLT-1, PIGF, abFN, PAPP-A, TakFN, ADAM-
12, and HPX, wherein each of the four or more biomarkers is different from
each other.
INCORPORATION BY REFERENCE
[0031] All publications, patents, and patent applications mentioned in this
specification are
herein incorporated by reference to the same extent as if each individual
publication, patent,
or patent application was specifically and individually indicated to be
incorporated by
reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0032] The disclosure is best understood from the following detailed
description when read
in conjunction with the accompanying drawings. It is emphasized that,
according to common
practice, the various features of the drawings are not to-scale. On the
contrary, the
dimensions of the various features are arbitrarily expanded or reduced for
clarity. Included in
the drawings are the following figures.
[0033] Figure 1 depicts the performance of a series of penalized models with
increasing
number of markers. The figure shows the mean cross-validated performance and
corresponding standard error. The number on the top represents the size of the
model while
the arrows identify the models with the highest area under the curve (AUC) and
the one with
a mean AUC within 1-se ("1-se" rule for model selection) of the maximum.
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[0034] Figure 2 depicts the performance of a series of penalized models with
increasing
number of markers excluding sFlt-1/P1GF. The figure shows the mean cross-
validated
performance and corresponding standard error. The number on the top represents
the size of
the model while the arrows identify the models with the highest AUC and the
one with mean
AUC within 1-se ("1-se" rule for model selection) of the maximum.
[0035] Figure 3 is a diagram of a duplicate template depicting 32 samples with
2X duplicate
Hi and Lo quality control.
[0036] Figure 4 is a diagram of a triplicate template depicting 20 samples
with 3X triplicate
Hi and Lo quality control.
[0037] Figures 5A-5F provide a listing of various PE biomarkers (see also
Table 2).
[0038] Figure 6 is a diagram of an example 20 compound Master Block.
DETAILED DESCRIPTION
[0039] Provided herein are methods, compositions, systems and software for
confirming
preeclampsia diagnosis, confirming the presence and/or absence of "pre-
eclampsia" or
"preeclampsia" or "PE", predicting the likelihood that a subject will develop
PE, determining
and/or confirming the severity of PE, determining the susceptibility of a
subject not pregnant
developing PE if the subject becomes pregnant, and monitoring PE progression
in a subject
already diagnosed with PE, all with greater sensitivity, specificity,
confidence, accuracy, or
area under the curve values than traditional PE tests.
[0040] The methods involve analyzing a sample or samples derived from a
subject to
confirm presence, absence, quantity and/or conformation of one or more PE
biomarkers. A
sample derived from the subject can be whole blood, urine, serum, plasma and
other liquid
samples of biological origin or cells derived therefrom and the progeny
thereof Samples can
be manipulated in any way after their procurement, such as by treatment with
reagents,
solubilization, or enrichment for certain components. A "marker" or
"biomarker" is any
biological entity that is represented differently in a sample from an
individual that will get or
has preeclampsia as compared to an individual that will not get preeclampsia.
A biomarker is
differently represented if, e.g., it is found in a different level (e.g.,
amount of protein, RNA or
DNA), different three dimensional state (native form, mis-folded, alternative
conformation),
or different arrangements (e.g., complex, aggregates, mis-folded assembles).
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[0041] A PE biomarker can be a protein, a protein fragment, a peptide, a
polynucleotide, a
gene (DNA) or a gene fragment, an RNA transcript, or other forms of RNA such
as snRNA,
siRNA and micro-RNA. The terms "protein", "peptide" and "polypeptide" as used
in this
application are interchangeable. "Polypeptide" refers to a polymer of amino
acids and
includes post-translationally modified polypeptides, glycosylated polypeptide,
acetylated
polypeptide, phosphorylated polypeptide and the like.
[0042] Examples of PE biomarkers contemplated herein include, but are not
limited to the
markers listed in Figures 5A-5F (or Table 2).
[0043] The presence, absence, monitoring or prediction of PE can be performed
by
obtaining a "preeclampsia profile", "PE profile" or a "profile". A PE profile
is the level of
one or more preeclampsia biomarkers in a patient sample. PE biomarkers can be
determined
by measuring protein levels or expression levels. A PE profile can include any
one or more
of the following sets (panels) of PE biomarkers shown in Table 1.
Table 1.
ADAM12
FN
FN, ADAM12
HPX
HPX, ADAM12, PAPPA
HPX, FN, ADAM12
HPX, PAPPA
PAPPA
P1GF
P1GF, ADAM12
P1GF, ADAM12, PAPPA, FN
P1GF, ADAM12, PAPPA, TakFN
P1GF, abFN, PAPPA, TakFN
P1GF, FN, PAPPA
P1GF, FN, ADAM12, sFLT1,
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PAPP-A2, HPX
P 1GF, PAPPA, TakFN
sFLT, PAPPA, FN
sFLT, P1GF
sFLT, P1GF, ADAM12, PAPPA,
FN
sFLT, P1GF, FN
sFLT, P1GF, FN, ADAM12
sFLT, P1GF, FN, PAPPA
sFLT, P1GF, HPX, PAPPA, FN
sFltl
sFltl, FN, ADAM12
sFltl, abFN, ADAM12, TakFN
sFLT 1 /P 1 GF, abFN, PAPPA,
TakFN
sFLT 1 /P 1 GF, ADAM12, PAPPA,
TakFN
sFLT 1 /P 1 GF, sFltl, PAPPA,
TakFN
sFLT 1 /P 1 GF, PAPPA, TakFN
sFLT 1 /P 1 GF, P 1 GF, PAPPA,
TakFN
sFLT 1 /P 1 GF, sFltl, abFN,
PAPPA
sFLT 1 /P 1 GF, HPX, PAPPA,
TakFN
sFLT 1 /P 1 GF, abFN, TakFN
sFLT 1 /P 1 GF, abFN, ADAM12,
TakFN
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sFLT1/P1GF, P1GF, abFN,
TakFN
sFLT1/P1GF, sFlt 1 , abFN, TakFN
[0044] Other examples of PE profiles include any one or more of the following:
= the ratio sFLT1/P1GF and at least one biomarker selected from the group
consisting of FN, PAPP-A, HPX, and ADAM12,
= at least two biomarkers selected from the group consisting of P1GF,
ADAM12, FN, PAPP-A and HPX.
= FN, FG and at least one or two biomarkers selected from the group
consisting of HPX, sFlt-1, PAPP-A, VEGF (excluding VEGF-R1), P1GF
and ADAM12.
= HPX in combination with FN, FG, sFlt-1, PAPP-A, VEGF (excluding
VEGF-R1), P1GF and/or ADAM12
= sFlt-1 in combination with FN, FG, HPX, PAPP-A, VEGF (excluding
VEGF-R1), P1GF and/or ADAM12.
= PAPP-A in combination with FN, FG, HPX, sFlt-1, VEGF (excluding
VEGF-R1), P1GF and/or ADAM12.
= VEGF (excluding VEGF-R1) may be measured in combination with FN, FG,
HPX, PAPP-A, sFlt-1, P1GF and/or ADAM12.
= P1GF in combination with FN, FG, HPX, PAPP-A, sFlt-1, VEGF (excluding
VEGF-R1) and/or ADAM12.
= ADAM12 in combination with FN, FG, HPX, PAPP-A, sFlt-1, P1GF and/or
VEGF (excluding VEGF-R1).
= sFlt-1, P1GF, FN, FG and PAPPA-A;
= sFlt-1, P1GF, ADAM12 and FN, FG,;
= sFlt-1, P1GF, PAPPA-A and FN, FG;
= sFlt-1, P1GF, HPX, PAPP-A and FN, FG;
= P1GF, ADAM12, PAPP-A, FN, FG;
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= sFlt-1, P1GF and FN, FG, P1GF, FN, FG, PAPP-A
= FN, FG, sFlt-1, P1GF and FN, FG;
= sFlt-1, FN, FG, and ADAM12;
= P1GF, FN, FG, and PAPP-A;
= P1GF and ADAM12;
= FN, FG, and ADAM12, HPX, FN, FG, and ADAM12, HPX, ADAM12 and
PAPPA, HPX and PAPPA, sFlt-1 and P1GF.
Any of the PE biomarker profiles or panels herein can optionally exclude one
or more of the
following biomarkers: ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC),
haptoglobin
(HP), alpha-2-macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-
III (Apo-
C3), apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4), hemoglobin
(HB),
fibrinogen alpha (FGA), pikachurin (EGFLAM) and heme.
In some cases, the panel of biomarkers includes at least 2, 3, 4, 5, 6, 7, 8,
9, 10 or 15
biomarkers. Preferably, methods of the disclosure comprise determining protein
levels of
the one or more panels described herein. When a panel includes a ratio of
sFlt1 and P1GF, the
ratio can be of raw levels of sFlt-1 and P1GF, normalized or adjusted levels
of sFlt-1 and
P1GF (e.g., relative to housekeeping genes, e.g., ABL1, GAPDH, PGK1, or
relative to signal
across a whole panel), averaged levels, or levels as compared to a control
(e.g., purified,
recombinant proteins).
When a plurality of PE biomarkers is analyzed, the results can be subjected to
an algorithm,
or PE score function, that provides a single score, e.g.õ a PE score. A
preeclampsia score is a
single metric value that represents the one or more preeclampsia biomarkers in
a patient
sample. The PE score can be reported in a report to the subject or a
healthcare service
provider of the subject.
[0045] The PE score can be reported as a PE index. A "PE index" or an "index"
is a metric
system that indicates the likelihood PE is confirmed, severity of PE or the
degree of
likelihood of developing PE. The PE index can be calculated from the PE score,
using a
classification algorithm. Examples of supervised and unsupervised
classification processes
are described in Jain, "Statistical Pattern Recognition: A Review," IEEE
Transactions on
Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000.
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[0046] The PE score or index can be indicative of the diagnosis, prognosis, or
confirmation
of PE diagnosis. The report can provide, in addition to the PE score or PE
index, a set of
suggested treatments or assessment of effectiveness of current treatment. The
term
"diagnosing" or "diagnosis" as used herein refers to a determination of
whether a subject has
or does not have PE. The term "confirming" or "confirmation" as used herein
generally
includes a determination of whether a subject suspected of having PE or
previously
diagnosed with PE has or does not have PE. The term "prognosing" or
"prognosis" as used
herein generally includes a prediction of the likely course of PE in a
subject, such as the
likelihood of increasing severity of PE or a subject's responsiveness to
treatment. The term
"treating" or "treatment" as used herein generally means obtaining a desired
pharmacologic
and/or physiologic effect. The effect may be prophylactic in terms of
completely or partially
preventing a disease or symptom thereof (e.g., reducing the likelihood of
incidence), reducing
the incidence or severity of the disease, and/or therapeutic in terms of a
partial or complete
cure for a disease and/or adverse effect attributable to the disease. In
determining a PE score,
raw levels of the biomarkers are obtained by obtaining e.g., an optical
density (OD) value.
The raw data may be used to determine the concentration of a biomarker in a
sample using
the methods described herein which may include a comparison against a standard
curve. The
standard curve may have a coefficient of determination. In some cases, the
coefficient of
determination may be an R2value, for example, an R2value of > 0.5, 0.6, 0.7,
0.75, 0.8, 0.85,
0.9 or 0.95 may be used with the methods described herein. In an exemplary
case, the R2 of a
standard curve using the methods described herein is > 0.95. Additional cases
of the data
may be evaluated using statistical methods known to those of ordinary skill in
the art.
Software such as SoftMax Pro may be used to perform at least some of the
calculations and
analysis described herein. Acquired data which falls outside of the range of
the standard
curve will not be analyzed or calculated further.
[0047] Raw levels of a biomarker can be optionally normalized, e.g., to a
blank, a control or
to another sample described herein. In some cases, normalization may include
subtracting
the OD value of a blank, a control or to another sample from the OD value of
the sample.
Normalization may also include first taking an average, mean or median of the
OD of the
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sample and second, taking an average, mean or median OD of the blank, control
or another
sample before subtracting the two OD values.
[0048] In some cases, the raw OD values of individual samples, average, mean
or median of
a set of samples, a blank, a control, another sample, a set of blanks, a set
of controls or a set
of another samples are log transformed. Often, the log transformation may
include a
comparison with a standard curve.
[0049] Biomarker levels can be adjusted relative to one or more of the
following: a control
derived from a training set as discussed in more detail below, a subject being
tested prior to
pregnancy, a subject being tested prior to onset of PE, a mean value from
pregnant subjects
not having PE, a value derived from specified laboratory subjects, a
calculated value, a
corresponding purified biomarker or a corresponding recombinant biomarker, a
control level
derived from a sample of a pregnant subject that does not have PE or does not
have
symptoms of PE, control level derived from a sample of a pregnant subject that
is not
diagnosed as having PE or does not have symptoms of PE, a control level
derived from a
sample of a pregnant subject that has (or is diagnosed as having) symptoms of
PE, such as
complications of pregnancy symptoms, but does not have (or is not diagnosed as
having) PE,
a control level derived from a sample of a pregnant subject that does not have
PE but has one
or more preeclampsia symptoms (e.g., a pregnant subject having complicated
pregnancy)
such as diabetes (e.g., gestational, type I or type II), higher than normal
glucose level,
hypertension (e.g., chronic or non-chronic), higher than normal weight,
obesity, higher than
normal body mass index (BMI), abnormal weight gain, abnormal blood pressure,
water
retention, hereditary factors, abnormal proteinurea, headache, edema, abnormal
proteinicreatinine ratio, abnormal platelet count, stress, nulliparity,
abnormal Papanicolaou
test results (Pap smear), prior preeclampsia episodes (e.g., personal history
of PE), familial
history of PE, PE in prior pregnancies, renal disease, thrombophilia, or any
combination
thereof; a control level derived from a sample of a pregnant subject that is
not diagnosed with
PE but has one or more preeclampsia symptoms (e.g., a pregnant subject having
complicated
pregnancy) such as those mentioned above.
[0050] When a control value is derived from a training set, the training set
can be based on a
theoretical model, real (measured) values obtained from subjects, or a
combination of both.
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Preferably, a training set is based on real values obtained from at least 10,
50, 100, 150, 200,
250, 300, 400, or 500 subjects. More preferably, at least 5, 10, 20, 30, 40,
50, or 60% of the
subjects in the training set have PE while the remaining subjects are pregnant
and do not have
PE.
[0051] In one instance, PE biomarker levels are adjusted by multiplying each
biomarker
level (or normalized level) by a weighting factor, or "weight", to arrive at
weighted levels.
[0052] A biomarker score or biomarker index is then calculated using an
algorithm, a
function, a real function, a polynomial or the like. Such algorithm is
referred to herein as PE
score function. The weight for each biomarker in the PE score function can be
unique. The
weight for each biomarker can be a positive or a negative real number. The
weight can be a
ratio of two or more biomarkers. The PE score function can be a real function
algorithm
comprising binary operations such as addition, subtraction, multiplication and
division. In
some instances, the PE score function comprises at most one, two, three, four,
five, six or
seven binary operations. In some instances all binary operations are additions
or subtractions
of variables (the variables being biomarker values whether adjusted or non-
adjusted). In some
instances the binary operations include at least one division of variables. In
some instances
the binary operations include only one division of variables. In some
examples, the binary
operations exclude division of variables. In some instances, the binary
operations exclude
multiplication of variables.
[0053] PE score functions can be linear, exponential, logarithmic, quadratic,
or any
combination thereof
[0054] A PE score function for determining a PE score can be represented
according to the
following formula:
[0055] PE Score =a0 + ai(ratio) + a2(sFLT-1) + a3(P1GF) + a4(FN1) + a5(FN2) +
a6(PAPPA)
+a7(HPX) +a8(ADAM12) + a9(FG) + an(biomarker or ratio of biomarkers selected
from Table
1), wherein, (i) a0 is zero or at most -0.5, -1, -5, -10, -20, -30, -40, -50, -
60, -70, -80, -90, -
100, -110, -120, -130, -140, or -150 or a0 is zero, (ii) al is at least 0.01,
0.02, 0.03, 0.04, 0.05,
0.06, 0.07, 0.08, 0.09, or 0.10 , (iii) a2 is zero or at least -5.0, -4.0, -
3.5, -3.0, -2.5, -2.0, -1.5, -
1.0, 0.001, 0.01, 0.05, 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, or 5.0,
(iv) a3 is zero or at least
-5.0, -4.0, -3.5, -3.0, -2.5, -2.0, -1.5, -1.0, 0.001, 0.01, 0.05, 0.1, 0.5,
1.0, 1.5, 2.0, 2.5, 3.0,
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3.5, 4.0, or 5.0, (iv) a4 is zero or at least 2.0, 2.5, 3.0, 3.5, 4.0, 4.5,
5.0, 5.5, 6.0, 6.5, 7.0, 7.5,
8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0,
14.5, 15.0, or 15.5, (v) a5
is zero or at least -13.0, -12.0, -11.0, -10.0, -9.0, -8.0, -7.0, -6.0, -5.0, -
4.0, -3.5, -3.0, -2.5, -
2.0, -1.5, -1.0, 0.001, 0.01, 0.05, 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5,
4.0, or 5.0, (vi) a6 is zero
or at least -5.0, -4.0, -3.5, -3.0, -2.5, -2.0, -1.5, -1.0, -0.5, 0.5, 1.0,
1.5, 2.0, 2.5, 3.0, 3.5, 4.0,
4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, or 8.0, (vii) a7 is zero or at least 0.01,
0.05, 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 2.0, or 2.5,
(viii) a8 is zero or at least
-13.0, -12.0, -11.0, -10.0, -9.0, -8.0, -7.0, -6.0, -5.0, -4.0, -3.5, -3.0, -
2.5, -2.0, -1.5, -1.0,
0.001, 0.01, 0.05, 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5,
6.0, 6.5, 7.0, 7.5, 8.0,
8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5,
15.0, or 15.5, (ix) a9 is
zero or at least -13.0, -12.0, -11.0, -10.0, -9.0, -8.0, -7.0, -6.0, -5.0, -
4.0, -3.5, -3.0, -2.5, -2.0, -
1.5, -1.0, 0.001, 0.01, 0.05, 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0,
4.5, 5.0, 5.5, 6.0, 6.5, 7.0,
7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0,
14.5, 15.0, or 15.5,
(x) an is zero or at least -13.0, -12.0, -11.0, -10.0, -9.0, -8.0, -7.0, -6.0,
-5.0, -4.0, -3.5, -3.0, -
2.5, -2.0, -1.5, -1.0, 0.001, 0.01, 0.05, 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0,
3.5, 4.0, 5.0, 5.5, 6.0,
6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0,
13.5, 14.0, 14.5, 15.0,
or 15.5, and (xi) a ratio can be any ratio or two or more biomarkers,
including, e.g., sFLT-
1/P1GF, P1GF/sFLT-1, (sFLT-1)/(VEGF excluding VEGF-R1), (VEGF excluding VEGF-
R1)/P1GF, (VEGF excluding VEGF-R1)/sFLT-1, P1GF/(VEGF excluding VEGF-R1);
(xii)
FN1 designates an isoform of fibronectin detected by a first antibody and FN2
designates a
fibronectin isoform detected by a second antibody, wherein the two isoforms
can be the same
or different, and wherein the two antibodies are different.
[0056] Non-limiting examples of linear models for determining a PE score are
provided as
following: L = -52.651 + 0.214*ratio -0.877 P1GF -0.715 * HPX-
0.884*ADAM12+PAPPA*3.85+ 4.473*FN; L = -46.789 + 0.018*ratio -0.879*P1GF -
0.587 *
HPX-0.973*ADAM12+PAPPA*3.121 + 4.007*FN; L = -49.789 + 0.012*ratio -0.984*P1GF
-0.652 * HPX-0.968*ADAM12+PAPPA*3.22+ 4.105*FN; L = -50.789 + 0.019*ratio -
0.977
P1GF -0.695 * HPX-0.910*ADAM12+PAPPA*3.62+ 4.351*FN; L = -48.989 + 0.022*ratio
-
0.998 P1GF -0.731 * HPX-0.971*ADAM12+PAPPA*3.41+ 4.317*FN; L = -58.899 +
0.014*ratio -0.912*P1GF -0.486 * HPX-0.853*ADAM12+PAPPA*2.191 + 4.097*FN; L = -
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49.211 + 0.017*ratio -0.974 P1GF -0.785 * HPX-0.957*ADAM12+PAPPA*3.68+
4.324*FN;
L =-48.789 + 0.011*ratio -0.899*P1GF -0.487 * HPX-0.873*ADAM12+PAPPA*2.121 +
4.007*FN; L =-52.838 + 0.009*ratio -0.942*P1GF -0.533 * HPX-
0.899*ADAM12+PAPPA*2.460 + 4.212*FN; L =-51.828 + 0.119*ratio -0.762*P1GF -
0.618* HPX-0.711*ADAM12+PAPPA*2.243 + 5.921*FN; L =-47.298 + 0.122*ratio -
1.298*P1GF -0.723 * HPX-0.932*ADAM12+PAPPA*1.920 + 3.929*FN; L =-47.562 +
1.292*ratio -0.298*P1GF -0.722* HPX-0.921*ADAM12+PAPPA*3.291+ 4.118*FN.
[0057] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -20.0484 + 0.1478(ratio) + 3.2970(PAPPA); L= -
20.1484 +
0.0478(ratio) + 3.0970(PAPPA); L= -21.0484 + 2.1478(ratio) + 4.2970(PAPPA); L=
-
20.3484 + 0.4478(ratio) + 3.5970(PAPPA); L= -77.6525 + 0.1405(ratio) +
5.1705(FN1); L= -
79.6525 + 1.0405(ratio) + 7.1705(FN1); L= -78.7525 + 0.2405(ratio) +
6.2705(FN1); and L=
-78.6525 + 0.0405(ratio) + 6.1705(FN1).
[0058] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -77.0827 + 9.0491(ratio) + 8.5207(P1GF) +
7.1632(FN1); L= -
79.0827 + 0.1491(ratio) + 0.2207(P1GF) + 6.3632(FN1); L= -78.0827 +
0.0491(ratio) +
0.5207(P1GF) + 6.1632(FN1); L= -76.0827 + 0.4491(ratio) + 0.6207(P1GF) +
6.7632(FN1);
[0059] L= -87.8431 + 0.0422(ratio) + 0.9659(HPX) + 6.3886(FN1). L= -86.8431 +
0.1422(ratio) + 0.8659(HPX) + 6.2886(FN1); L= -88.8431 + 0.1422(ratio) +
1.9659(HPX) +
7.3886(FN1); and = -85.8431 + 0.2422(ratio) + 0.3659(HPX) + 6.4886(FN1);
[0060] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -49.0767 + 0.0462(ratio) + 3.2997(FN2) +
3.8873(PAPPA); L= -
48.0767 + 0.0462(ratio) + 2.2997(FN2) + 2.8873(PAPPA); L= -48.1767 +
0.2462(ratio) +
2.3997(FN2) + 2.4873(PAPPA) and L= -48.5767 + 0.6462(ratio) + 2.7997(FN2) +
2.9873(PAPPA).
[0061] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -85.7092 + 0.0492(ratio) -5.8358(FN2) +
12.7388(FN1); L= -
85.1092 + 0.2492(ratio) -5.3358(FN2) + 12.4388(FN1); L= -86.7092 +
0.1492(ratio) -
6.8358(FN2) + 13.7388(FN1); and L= -85.5092 + 0.6492(ratio) -5.7358(FN2) +
12.8388(FN1).
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[0062] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -68.2829 + 1.0405(ratio) + 2.0848(ADAM12) +
3.1148(FN1); L=
-88.2829 + 2.0405(ratio) + 3.0848(ADAM12) + 4.1148(FN1); L= -79.2829 +
0.1405(ratio) +
0.2848(ADAM12) + 7.1148(FN1); and L= -78.2829 + 0.0405(ratio) + 0.0848(ADAM12)
+
6.1148(FN1).
[0063] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -12.2190 + 2.0500(ratio) + 3.7240(PAPPA) +
4.2174(FN1); L= -
82.2190 + 0.0500(ratio) + 2.7240(PAPPA) + 5.2174(FN1); L= -22.2190 +
3.0500(ratio) +
4.7240(PAPPA) + 6.2174(FN1); and L= -32.2190 + 4.0500(ratio) + 5.7240(PAPPA) +
7.2174(FN1).
[0064] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -29.9815 + 4.0348(sFLT1) -5.5288(FN2) +
16.2160(FN1); L= -
39.9815 + 4.0348(sFLT1) -5.5288(FN2) + 17.2160(FN1); L= -19.9815 +
2.0348(sFLT1) -
3.5288(FN2) + 14.2160(FN1); and L = -59.9815 + 3.0348(sFLT1) -7.5288(FN2) +
12.2160(FN1).
[0065] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -12.4750 + -3.8232(P1GF) + 4.8961(PAPPA) +
6.7670(FN1) L= -
92.4750 + -2.8232(P1GF) + 2.8961(PAPPA) + 5.7670(FN1) L= -22.4750 + -
3.8232(P1GF) +
4.8961(PAPPA) + 7.7670(FN1); and L= -32.4750 + -4.8232(P1GF) + 5.8961(PAPPA) +
9.7670(FN1).
[0066] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -14.0940 + 0.0441(ratio) + 2.4773(sFLT1) +
3.5748(P1GF) +
4.8088(FN1); L= -24.0940 + 3.0441(ratio) + 4.4773(sFLT1) + 5.5748(P1GF) +
6.8088(FN1);
L= -74.0940 + 0.0441(ratio) + 0.4773(sFLT1) + 0.5748(P1GF) + 5.8088(FN1); and
L= -
34.0940 + 4.0441(ratio) + 5.4773(sFLT1) + 6.5748(P1GF) + 7.8088(FN1).
[0067] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -70.3822 + 1.0796(ratio) -3.7798(sFLT1) +
4.8134(FN2) +
6.2025(PAPPA); L= -77.3822 + 0.0796(ratio) -2.7798(sFLT1) + 3.8134(FN2) +
5.2025(PAPPA); L= -71.3822 + 2.0796(ratio) -3.7798(sFLT1) + 4.8134(FN2) +
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6.2025(PAPPA); and L= -72.3822 + 3.0796(ratio) -4.7798(sFLT1) + 5.8134(FN2) +
7.2025(PAPPA).
[0068] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -70.3510 + 1.0348(ratio) + 2.4503(sFLT1) -
3.5868(FN2) +
14.8250(FN1); L= -78.3510 + 0.0348(ratio) + 1.4503(sFLT1) -7.5868(FN2) +
13.8250(FN1); L= -71.3510 + 2.0348(ratio) + 3.4503(sFLT1) -4.5868(FN2) +
15.8250(FN1); and L= -72.3510 + 3.0348(ratio) + 4.4503(sFLT1) -5.5868(FN2) +
16.8250(FN1).
[0069] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -74.5657 + 0.0300(ratio) + 0.8356(sFLT1) -
0.5594(ADAM12) +
5.9467(FN1); L= -72.5657 + 0.0370(ratio) + 0.2756(sFLT1) -0.3894(ADAM12) +
4.6467(FN1); L= -79.5657 + 0.0350(ratio) + 0.8756(sFLT1) -0.5894(ADAM12) +
5.6467(FN1); and L= -67.5657 + 0.0230(ratio) + 0.4756(sFLT1) -0.1494(ADAM12) +
4.7867(FN1).
[0070] Additional non-limiting examples of linear models for determining a PE
score
include the following: L = -100.6298 + 1.0845(ratio) + -3.8124(sFLT1) +
4.0643(PAPPA)
+6.5308(FN1); L= -108.6298 + 0.0845(ratio) + -2.8124(sFLT1) + 5.0643(PAPPA)
+6.5308(FN1); L= -101.6298 + 2.0845(ratio) + -3.8124(sFLT1) + 4.0643(PAPPA)
+5.5308(FN1); and L= -102.6298 + 3.0845(ratio) + -4.8124(sFLT1) +
6.0643(PAPPA)
+7.5308(FN1).
[0071] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -41.5704 + 2.0489(ratio) + 3.1839(P1GF) +
4.2834(FN2)
+5.8683(PAPPA); L= -42.5704 + 3.0489(ratio) + 4.1839(P1GF) + 5.2834(FN2)
+6.8683(PAPPA); L= -40.5704 + 1.0489(ratio) + 2.1839(P1GF) + 3.2834(FN2)
+4.8683(PAPPA); L= -47.5704 + 0.0489(ratio) + 0.1839(P1GF) + 2.2834(FN2)
+2.8683(PAPPA); L= -80.6889 + 1.0607(ratio) + 2.6545(P1GF) -3.9105(FN2) +
14.8608(FN1); L= -85.6889 + 0.0607(ratio) + 0.6545(P1GF) -5.9105(FN2) +
12.8608(FN1);
L= -81.6889 + 2.0607(ratio) + 3.6545(P1GF) -4.9105(FN2) + 15.8608(FN1); and L=
-
82.6889 + 3.0607(ratio) + 4.6545(P1GF) -6.9105(FN2) + 17.8608(FN1).
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[0072] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -77.8703 + 0.0491(ratio) + 0.5191(P1GF) +
0.0523(ADAM12) +
6.1300(FN1); L= -71.8703 + 2.0491(ratio) + 3.5191(P1GF) + 4.0523(ADAM12) +
5.1300(FN1); L= -72.8703 + 3.0491(ratio) + 4.5191(P1GF) + 5.0523(ADAM12) +
7.1300(FN1); and L= -70.8703 + 1.0491(ratio) + 2.5191(P1GF) + 3.0523(ADAM12) +
4.1300(FN1).
[0073] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -81.9135 + 0.0433(ratio) + 0.3154(P1GF) +
1.7019(PAPPA) +
5.2189(FN1); L= -81.9125 + 0.0533(ratio) + 0.2154(P1GF) + 2.7019(PAPPA) +
5.2199(FN1); L= -82.9125 + 0.0553(ratio) + 0.3154(P1GF) + 2.8019(PAPPA) +
4.2199(FN1); and L= -71.9125 + 0.0633(ratio) + 0.2254(P1GF) + 2.7519(PAPPA) +
5.2399(FN1).
[0074] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -49.5169 + 0.0464(ratio) + 0.1807(HPX) + 2.3236(FN2)
+
2.8744(PAPPA); L= -47.5169 + 0.1464(ratio) + 0.1857(HPX) + 2.4236(FN2) +
2.6744(PAPPA); L= -45.2169 + 0.0344(ratio) + 0.2707(HPX) + 1.2236(FN2) +
2.4544(PAPPA); and L= -48.5169 + 0.0564(ratio) + 0.2807(HPX) + 2.1236(FN2) +
2.7744(PAPPA).
[0075] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -68.9243 + 0.0583(ratio) -0.7927(HPX) -6.2143(FN2) +
10.7478(FN1); L= -78.9243 + 0.0483(ratio) -0.6927(HPX) -6.0143(FN2) +
12.7478(FN1);
L= -77.9243 + 0.0583(ratio) -0.7727(HPX) -6.1243(FN2) + 11.8478(FN1); and L= -
79.9243
+ 0.1483(ratio) -0.6227(HPX) -6.2143(FN2) + 12.9478(FN1).
[0076] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -88.0679 + 0.0309(ratio) + 0.5208(HPX) +
2.6887(PAPPA) +
5.2737(FN1); L= -97.0679 + 0.0589(ratio) + 0.6708(HPX) + 2.7787(PAPPA) +
5.2937(FN1); L= -77.0679 + 0.0609(ratio) + 0.6308(HPX) + 2.8787(PAPPA) +
5.3937(FN1); and L= -87.0679 + 0.0509(ratio) + 0.6208(HPX) + 2.6787(PAPPA) +
5.2937(FN1).
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[0077] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -53.7575 + 0.0542(ratio) + 3.2134(FN2) -
1.5150(ADAM12) +
3.6894(PAPPA); L= -56.7575 + 0.0342(ratio) + 4.0134(FN2) -1.4160(ADAM12) +
3.7094(PAPPA); L= -55.7575 + 0.0442(ratio) + 3.0134(FN2) -1.4150(ADAM12) +
3.6094(PAPPA); and L= -45.7575 + 0.0443(ratio) + 3.2134(FN2) -1.6150(ADAM12) +
3.6084(PAPPA).
[0078] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -85.1599 + 0.0529(ratio) -6.5276(FN2) +
0.8455(ADAM12) +
13.1454(FN1); L= -85.1499 + 0.0509(ratio) -6.5286(FN2) + 0.8435(ADAM12) +
13.1434(FN1); L= -85.1399 + 0.0409(ratio) -6.2286(FN2) + 0.7435(ADAM12) +
13.0434(FN1); and L= -87.1499 + 1.0509(ratio) -8.5286(FN2) + 1.8435(ADAM12) +
11.1434(FN1).
[0079] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -94.7950 + 0.0632(ratio) -7.5691(FN2) +
3.0857(PAPPA) +
13.8372(FN1); L= -95.7950 + 0.1632(ratio) -7.5491(FN2) + 3.2857(PAPPA) +
15.8372(FN1); L= -94.8950 + 0.0642(ratio) -7.5591(FN2) + 3.0957(PAPPA) +
13.2372(FN1); and L= -94.7960 + 0.0631(ratio) -7.5791(FN2) + 4.0857(PAPPA) +
11.8372(FN1).
[0080] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -91.5034 + 0.0566(ratio) -1.8810(ADAM12) +
3.5597(PAPPA) +
6.1198(FN1); L= -92.5034 + 0.0466(ratio) -1.8910(ADAM12) + 3.6597(PAPPA) +
6.2198(FN1); L= -93.5034 + 0.0467(ratio) -1.7910(ADAM12) + 3.6797(PAPPA) +
7.2198(FN1); and L= -82.5034 + 0.0476(ratio) -1.9910(ADAM12) + 4.6597(PAPPA) +
6.5198(FN1).
[0081] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -73.6418 + 2.3065(sFLT1) -1.3341(P1GF) -7.4383(FN2)
+
13.0395(FN1); L= -72.3418 + 2.2365(sFLT1) -1.3131(P1GF) -7.4332(FN2) +
13.3595(FN1);
L= -71.6418 + 2.1065(sFLT1) -1.1141(P1GF) -7.1382(FN2) + 13.1595(FN1); and L= -
72.6418 + 2.2065(sFLT1) -1.3141(P1GF) -7.4382(FN2) + 13.0595(FN1).
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[0082] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -103.5305 + -0.3764(sFLT1) -3.3328(P1GF) +
3.4350(PAPPA) +
6.3890(FN1); L= -105.5305 + -0.6564(sFLT1) -3.5928(P1GF) + 3.4550(PAPPA) +
6.5890(FN1); L= -104.5305 + -0.6764(sFLT1) -3.3928(P1GF) + 3.4850(PAPPA) +
6.4890(FN1); and L= -104.4305 + -0.6464(sFLT1) -3.4928(P1GF) + 3.4450(PAPPA) +
6.4490(FN1).
[0083] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -74.1756 + 3.9467(sFLT1) -8.4664(FN2) -
2.4423(ADAM12) +
15.3474(FN1); L= -75.1756 + 3.9167(sFLT1) -8.7664(FN2) -2.4923(ADAM12) +
15.3674(FN1); L= -76.1756 + 3.9667(sFLT1) -8.6664(FN2) -2.4623(ADAM12) +
15.6674(FN1); and L= -73.1756 + 3.9367(sFLT1) -8.7364(FN2) -2.4323(ADAM12) +
13.3674(FN1).
[0084] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -76.5467 + 2.6634(sFLT1) -3.2682(ADAM12) +
1.9649(PAPPA)
+ 6.6178(FN1); L= -68.5467 + 2.6634(sFLT1) -3.2682(ADAM12) + 1.6349(PAPPA) +
6.6178(FN1); L= -74.5467 + 2.4634(sFLT1) -3.4282(ADAM12) + 1.9449(PAPPA) +
6.4178(FN1); L= -78.5467 + 2.5634(sFLT1) -3.2282(ADAM12) + 1.9349(PAPPA) +
6.1178(FN1)
L= -78.8718 -2.6772(P1GF) -1.7373(HPX) + 2.9707(PAPPA) + 5.5077(FN1); L= -
75.8618
-2.5762(P1GF) -1.5573(HPX) + 2.9507(PAPPA) + 5.5557(FN1); L= -77.8618 -
2.6772(P1GF) -1.5673(HPX) + 2.9106(PAPPA) + 5.5056(FN1); and L= -78.8618 -
2.6762(P1GF) -1.5373(HPX) + 2.9107(PAPPA) + 5.5057(FN1).
[0085] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -109.2319 -3.7617(P1GF) -6.7308(FN2) + 3.5194(PAPPA)
+
13.6500(FN1); L= -107.2319 -3.5617(P1GF) -6.3308(FN2) + 3.4194(PAPPA) +
13.6700(FN1); L= -108.2319 -3.6617(P1GF) -6.6308(FN2) + 3.2194(PAPPA) +
11.6500(FN1); and L= -119.2319 -3.7117(P1GF) -6.1308(FN2) + 3.5114(PAPPA) +
13.7500(FN1).
[0086] Additional non-limiting examples of linear models for determining a PE
score
include the following: L= -112.4589 -2.7282(P1GF) -2.7122(ADAM12) +
4.4379(PAPPA)
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+ 7.6259(FN1); L= -113.4589 -2.7281(P1GF) -2.7121(ADAM12) + 4.4279(PAPPA) +
7.6159(FN1); L= -114.4589 -2.7381(P1GF) -2.7221(ADAM12) + 4.5379(PAPPA) +
7.7159(FN1); and L= -133.4589 -2.8281(P1GF) -2.7131(ADAM12) + 4.5279(PAPPA) +
7.6169(FN1).
[0087] In some cases, a linear and an exponential model is utilized to
determine the
probability using the following algorithm: PE = 1/(1+exp(-L)).
[0088] Weighted levels of all biomarkers in a panel can then be totaled and in
some cases,
such as in the levels of sFlt and P1GF, the weighted levels can be formed into
a ratio. The
sum of the weighted levels and optionally a ratio results in a single weighted
level or "PE
score". Each biomarker can have a unique weighting factor, or a combination of
biomarkers
can have a unique weighting factor. In some instances, a preeclampsia score
may be
determined by methods similar to those described for a preeclampsia signature,
e.g. the levels
of each of the one or more preeclampsia markers in a patient sample may be
10g2, loge or
logo transformed and normalized as described above for generating a
preeclampsia profile.
[0089] The weighted levels for calculating the score can be defined by a
reference dataset,
"training dataset," or "training set." The training set can be based on a
model, actual values
obtained from subjects, or a combination thereof. A training set can comprise
subjects
diagnosed as having symptoms of PE. A training set can comprise subjects
having symptoms
of PE, but not PE. A training set can comprise subjects having symptoms of PE,
but not PE,
and having one or more other disorders (e.g., subjects having pregnancies with
complications) such as diabetes (e.g., gestational, type I or type II), higher
than normal
glucose level, hypertension (e.g., chronic or non-chronic), higher than normal
weight,
obesity, higher than normal body mass index (BMI), abnormal weight gain,
abnormal blood
pressure, water retention, hereditary factors, abnormal proteinurea, headache,
edema,
abnormal proteinicreatinine ratio, abnormal platelet count, stress,
nulliparity, abnormal
Papanicolaou test results (Pap smear), prior preeclampsia episodes (e.g.,
personal history of
PE), familial history of PE, PE in prior pregnancies, renal disease,
thrombophilia, or any
combination thereof A training set can comprise subjects diagnosed as having
PE with one
or more disorders (e.g., subjects having pregnancies with complications) such
as diabetes
(e.g., gestational, type I or type II), higher than normal glucose level,
hypertension (e.g.,
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chronic or non-chronic), higher than normal, obesity, higher than normal body
mass index
(BMI), abnormal weight gain, abnormal blood pressure, water retention,
hereditary factors,
abnormal proteinurea, headache, edema, abnormal protein/creatinine ratio,
abnormal platelet
count, stress, nulliparity, abnormal Papanicolaou test results (Pap smear),
prior preeclampsia
episodes (e.g., personal history of PE), familial history of PE, PE in prior
pregnancies, renal
disease, thrombophilia, or any combination thereof The training set can
include at least 30,
40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, or 400 subjects. The
training set can
include data from at least 15, 50, 100, 150, 200 or 250 subjects having a
normal pregnancy,
and at least 15, 50, 100, 150, 200 or 250 pregnant subjects with PE. In some
instances, more
than 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50% of the preeclamptic subjects
have at least one
additional condition (e.g., hypertension, diabetes, overweight, etc.).
[0090] In some instances, the classification of PE that is used to provide the
"preeclampsia
index" described herein is not based on for example blood pressure, weight
gain, water
retention, hereditary factors, proteinurea, headache, edema,
protein/creatinine ratio, platelet
count, stress, PE in prior pregnancies, nulliparity, age, age less than 20
years, age greater than
35, race, African-American and Filipino decent, serotype, Papanicolaou test
results (Pap
smear), prior preeclampsia episodes (e.g., personal history of PE), familial
history of PE,
number of pregnancies, number of miscarriages, body mass index (BMI),
gestational
diabetes, type I diabetes, obesity, glucose level, current and past
medications, stress, PE in
prior pregnancies (of the subject or her family members), chronic
hypertension, renal disease
and thrombophilia. In some examples, the classification of PE is not based on
any of the
characteristics of pregnancy just mentioned. In some examples, the
classification of PE is
based on at least one of the characteristics of pregnancy just mentioned. In
some cases, the
PE index may be based on the female subject's gestational period.
[0091] A "report," as described herein, is an electronic or tangible document
which includes
report elements that provide information relating to a subject. A subject
report optionally
includes one or more of the following: information about the subject, a PE
profile, a PE
score, a PE index, PE confirmation, PE diagnosis, PE prognosis, PE monitoring
status, and/or
suggested treatments. A subject report can further include one or more of: 1)
information
regarding the testing facility; 2) service provider information; 3) patient
data; 4) sample data;
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5) an assessment report, which can include various information including: a)
reference values
employed, and b) test data, where test data can include, e.g., a protein level
determination;
and 6) other features. The report may be for positive confirmation of PE,
negative
confirmation of PE, diagnosis of PE, characteristics of PE, progress of PE,
severity of PE, or
prognosis of PE. Positive confirmation of PE refers to a situation where a
subject having PE
symptoms is confirmed as having PE. Negative confirmation of PE refers to a
situation where
a subject not having symptoms of PE is confirmed as not having PE. Such report
may include
relative weight or signature values of biomarkers, PE score or PE index score.
The report
may include recommendation as to treatment recommendations (e.g., bed-rest,
aspirin,
drinking extra water, a low salt diet, medicines to control blood pressure,
corticosteroids, or
recommendation for early delivery).
Subjects and Samples
[0092] The term "biological sample" and "sample" encompasses blood, urine,
serum,
plasma, and other liquid samples of biological origin or cells derived
therefrom. Once a
sample is derived from a subject, it can be used directly, frozen, or
maintained in appropriate
culture medium for short periods of time. A sample that is derived from blood
may be
allowed to clot, and the serum separated and collected to be used in the
assay.
[0093] A sample volume of blood, serum, or urine between 2 1 to 2,000 1, may
be
sufficient for determining the PE score. In some examples, the sample volume
ranges from
10 1 to 1,750 1, from 20 1 to 1,500 1, from 40 1 to 1,250 1, from 60 1 to
1,000 1, from
100 1 to 900 1, from 200 1 to 800 1, from 400 1 to 600 1. In some instances a
sample
volume is 2-10mL or 0.5-5 mL or up to 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 mL.
[0094] A subject analyzed may have zero, or at least one, two, three, four, or
five factors
which confound a diagnosis of preeclampsia. In some cases, a confounding
factor may be
selected from the group consisting of: high blood pressure, age over 35 years,
higher than
normal weight, quick weight gain, gestational period greater than 20 weeks,
ethnicity,
diabetes (Type I or II), high proteinurea, kidney disease, autoimmune disease,
prior PE by the
subject in an earlier pregnancy, and a family or maternal history of PE.
[0095] In practicing the subject methods, a sample from a subject is evaluated
to obtain a
representation of the level(s) of one or more PE biomarkers. The levels of one
or more PE
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biomarkers can be used to provide, for example, a PE profile, PE signature, PE
score, or PE
index as described in greater detail below.
[0096] A subject sample may be treated in a variety of ways so as to enhance
detection of
the preeclampsia marker. For example, where the sample is blood, the red blood
cells may be
removed from the sample (e.g., by centrifugation) prior to assaying. Such a
treatment may
serve to reduce the non-specific background levels of detecting the level of a
preeclampsia
marker using an affinity reagent. Detection of a preeclampsia marker may also
be enhanced
by concentrating the sample using procedures well known in the art (e.g., acid
precipitation,
alcohol precipitation, salt precipitation, hydrophobic precipitation,
filtration (using a filter
which is capable of retaining molecules greater than 30 kD, e.g., Centrim
3OTM) or affinity
purification. In some cases, the pH of the test and control samples will be
adjusted to, and
maintained at, a pH which approximates neutrality (e.g., pH 6.5-8.0). Such a
pH adjustment
will prevent complex formation, thereby providing a more accurate quantitation
of the level
of marker in the sample. In cases where the sample is urine, the pH of the
sample is adjusted
and the sample is concentrated in order to enhance the detection of the
marker. pH may be
adjusted using methods known to those of ordinary skill in the art, for
example, adding an
acid to a basic or neutral pH sample or adding a base to an acidic or neutral
pH sample.
[0097] Buffers and/or other reagents may be added to the sample to facilitate
preparation of
the sample prior to determining a level of at least one biomarker in the
sample. In some
cases, a buffer and/or other reagent may include at least, but is not limited
to, one of the
following: ethylenediaminetetraacetic acid (EDTA), phosphate buffered saline,
Hanks
balanced salt solution, Ficoll, sodium chloride, sodium citrate, silica,
thrombin, tehophylline,
adenosine, dipyridamole, aprotinine, heparin, lithium heparin, fluoride,
potassium oxalate,
tri-sodium citrate, citric acid, and/or dextrose. The buffer and/or other
reagent may be
compounded in an inert base, for example, a gel, water, saline or the like.
[0098] In a particular case, a sample of blood may be collected using a serum
separator tube
(SST). The SST may contain a buffer and/or other reagent. In another
particular case, a
sample of blood may be collected using a clot-2 serum separator tube which may
contain a
buffer and/or reagent. The SST and/or clot-2 tube may be obtained from a
manufacturer such
as Becton Dickenson although any comparable tube may be used. In some cases,
the sample
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may be treated using a method, reagent or chemical known to one of ordinary
skill in the art
such that components of the sample become separated from one another.
Sometimes, the
sample of blood is separated such that the serum is in a layer comprising the
top of the
sample.
[0099] A subject sample is typically obtained from the individual during the
second or third
trimester of gestation. By "gestation" it is meant the duration of pregnancy
in a mammal,
e.g., the period of development in the uterus from conception until birth. A
subject sample
may be derived early in gestation, for example, on or before 34 weeks of
gestation, e.g., at
weeks 20-34 of gestation, at 24-34 weeks of gestation, at weeks 30-34 weeks of
gestation. A
subject sample may be derived late in gestation, for example, after 34, 35,
36, 37, or 38
weeks of gestation.
[00100] A PE profile, signature, score, or index may be determined soon after
or at least 2, 3,
or 4, weeks from the time a sample is derived from a subject. In some cases, a
PE profile,
signature, score, or index is determined at most 1, 2, 3, or 4 days from the
time a sample is
derived from a subject.
[00101] Once a sample is derived from a subject, the sample can be processed
(e.g., plasma
or serum isolated). The sample, or portion thereof, can further be diluted. A
sample, or
portion of a sample, can be diluted by a factor of at least 5, 10, 50, 100,
500, 1,000, 5,000,
10,000, 15,000, 20,000, 30,000, 35,000, 40,000, 45,000, 50,000, 55,000,
60,000, 65,000,
70,000, 75,000 or 80,00 fold.
[00102] Techniques for Detecting Biomarkers.
[00103] When a biomarker is a differentially expressed protein level, the
biomarker can be
detected by measuring the levels or the amounts of one or more proteins,
protein fragments,
peptides, nucleic acid transcripts (e.g. mRNA), genes, or gene fragments.
[00104] Each biomarker assayed using the methods and protocols described
herein may have
a threshold. Often the threshold of a biomarker is the performance of a
biomarker in an
assay, method or protocol. In some cases, the methods, protocols and assays
described herein
may be accurate, sensitive, and specific and may be used as a positive or a
negative predictive
value. The methods, protocols and assays described herein may be at least 70%,
75%, 80%,
85%, 90%, 95% or 99% accurate. The methods, protocols and assays described
herein may
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be at most 70%, 75%, 80%, 85%, 90%, 95% or at most 99% accurate. The methods,
protocols and assays described herein may be at least 70%, 75%, 80%, 85%, 90%,
95% or
99% sensitive. The methods, protocols and assays described herein may be at
most 70%,
75%, 80%, 85%, 90%, 95% or about 99% sensitive. The methods, protocols and
assays
described herein may be at least 70%, 75%, 80%, 85%, 90%, 95%, 99% or more
specific.
The methods, protocols and assays described herein may be at most 70%, 75%,
80%, 85%,
90%, 95%, 99% or less specific. The methods, protocols and assays described
herein may
have a positive predictive value of at least 70%, 75%, 80%, 85%, 90%, 95%, 99%
or more.
The methods, protocols and assays described herein may have a positive
predictive value of
at most 70%, 75%, 80%, 85%, 90%, 95%, 99% or less. The methods, protocols and
assays
described herein may have a negative predictive value of at least 70%, 75%,
80%, 85%, 90%,
95%, 99% or more. The methods, protocols and assays described herein may have
a negative
predictive value of at most 70%, 75%, 80%, 85%, 90%, 95%, 99% or less.
[00105] In some cases, a biomarker assayed using the methods and protocols
described herein
may be present in a sample above the lowest concentration of a standard curve
sample. In
other cases, a biomarker assayed using the methods and protocols described
herein may be
present in a sample below the lowest concentration of a standard curve sample.
In some
cases, a biomarker assayed using the methods and protocols described herein
may be present
in a sample above the highest concentration of a standard curve sample. In
other cases, a
biomarker assayed using the methods and protocols described herein may be
present in a
sample below the highest concentration of a standard curve sample. In some
cases, a
biomarker assayed using the methods and protocols described herein may be
present in a
sample above the lowest concentration of a high quality control sample. In
other cases, a
biomarker assayed using the methods and protocols described herein may be
present in a
sample below the lowest concentration of a high quality control sample. In
some cases, a
biomarker assayed using the methods and protocols described herein may be
present in a
sample above the highest concentration of a high quality control sample. In
other cases, a
biomarker assayed using the methods and protocols described herein may be
present in a
sample below the highest concentration of a high quality control sample. In
some cases, a
biomarker assayed using the methods and protocols described herein may be
present in a
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sample above the lowest concentration of a low quality control sample. In
other cases, a
biomarker assayed using the methods and protocols described herein may be
present in a
sample below the lowest concentration of a low quality control sample. In some
cases, a
biomarker assayed using the methods and protocols described herein may be
present in a
sample above the highest concentration of a low quality control sample. In
other cases, a
biomarker assayed using the methods and protocols described herein may be
present in a
sample below the highest concentration of a low quality control sample.
[00106] Often, a level of a biomarker assayed using the methods and protocols
described
herein may increase between a first assay and a second assay. In some cases, a
level of a
biomarker assayed using the methods and protocols described herein may
increase by about
two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16,
17, 18, 19,
20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150,
175,
200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 600, 700,
800,
900, 1000, 1250, 1500, 1750, 2000, 2500, 5000, 7500 or 10 000 fold between the
first
assay and the second assay. In some cases, a level of a biomarker assayed
using the methods
and protocols described herein may increase by at least two, three, four,
five, six, seven,
eight, nine, ten, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45,
50, 55,
60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300,
325,
350, 375, 400, 425, 450, 475, 500, 600, 700, 800, 900, 1000, 1250, 1500, 1750,
2000, 2500, 5000, 7500, 10,000 fold or more between the first assay and the
second assay.
In some cases, a level of a biomarker assayed using the methods and protocols
described
herein may increase by at most two, three, four, five, six, seven, eight,
nine, ten, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70,
75, 80,
85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400,
425,
450, 475, 500, 600, 700, 800, 900, 1000, 1250, 1500, 1750, 2000, 2500, 5000,
7500
or less than 10 000 fold between the first assay and the second assay.
[00107] Often, a level of a biomarker assayed using the methods and protocols
described
herein may decrease between a first assay and a second assay. In some cases, a
level of a
biomarker assayed using the methods and protocols described herein may
decrease by about
two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 25, 30,
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35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200,
225, 250, 275, 300,
325, 350, 375, 400, 425, 450, 475, 500, 600, 700, 800, 900, 1000, 1250, 1500,
1750, 2000,
2500, 5000, 7500 or 10 000 fold between the first assay and the second assay.
In some cases,
a level of a biomarker assayed using the methods and protocols described
herein may
decrease at least two, three, four, five, six, seven, eight, nine, ten, 11,
12, 13, 14, 15,
16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90,
95, 100,
125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475,
500,
600, 700, 800, 900, 1000, 1250, 1500, 1750, 2000, 2500, 5000, 7500 or 10 000
fold or
more between the first assay and the second assay. In some cases, a level of a
biomarker
assayed using the methods and protocols described herein may decrease at most
two, three,
four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 25, 30,
35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200,
225,
250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 600, 700, 800, 900,
1000,
1250, 1500, 1750, 2000, 2500, 5000, 7500, 10 000 fold or less, between the
first assay
and the second assay.
[00108] Often, a level of a biomarker assayed using the methods and protocols
described
herein may increase between a first assay and a second assay. In some cases, a
level of a
biomarker assayed using the methods and protocols described herein may
increase by about
two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 25, 30,
35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200,
225, 250, 275, 300,
325, 350, 375, 400, 425, 450, 475, 500, 600, 700, 800, 900, 1000, 1250, 1500,
1750, 2000,
2500, 5000, 7500 or 10 000 percent between the first assay and the second
assay. In some
cases, a level of a biomarker assayed using the methods and protocols
described herein may
increase at least by two, three, four, five, six, seven, eight, nine, ten, 11,
12, 13, 14,
15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,
90, 95,
100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450,
475,
500, 600, 700, 800, 900, 1000, 1250, 1500, 1750, 2000, 2500, 5000, 7500 or 10
000
percent or more between the first assay and the second assay. In some cases, a
level of a
biomarker assayed using the methods and protocols described herein may
increase by at most
two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16,
17, 18, 19,
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20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150,
175,
200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 600, 700,
800,
900, 1000, 1250, 1500, 1750, 2000, 2500, 5000, 7500 or 10 000 percent or less,
between the first assay and the second assay.
[00109] Often, a level of a biomarker assayed using the methods and protocols
described
herein may decrease between a first assay and a second assay. In some cases, a
level of a
biomarker assayed using the methods and protocols described herein may
decrease by about
two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 25, 30,
35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200,
225, 250, 275, 300,
325, 350, 375, 400, 425, 450, 475, 500, 600, 700, 800, 900, 1000, 1250, 1500,
1750, 2000,
2500, 5000, 7500 or 10 000 percent between the first assay and the second
assay. In some
cases, a level of a biomarker assayed using the methods and protocols
described herein may
decrease by at least two, three, four, five, six, seven, eight, nine, ten, 11,
12, 13, 14,
15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,
90, 95,
100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450,
475,
500, 600, 700, 800, 900, 1000, 1250, 1500, 1750, 2000, 2500, 5000, 7500, 10
000
percent or more between the first assay and the second assay. In some cases, a
level of a
biomarker assayed using the methods and protocols described herein may
decrease by at most
two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16,
17, 18, 19,
20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150,
175,
200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 600, 700,
800,
900, 1000, 1250, 1500, 1750, 2000, 2500, 5000, 7500 or 10 000 percent or less,
between
the first assay and the second assay.
[00110] The methods and protocols described herein may be considered in
combination with
clinical measures. Clinical measures may be associated with, but are not
limited to, total
blood pressure, diastolic blood pressure, systolic blood pressure, mean
arterial blood
pressure, proteinurea detected by, for example, dipstick method or 24-hour
collection
method, body mass index, swelling, abdominal pressure, uterine pulsatility
index, uterine
Doppler measurements, circulating free DNA, circulating free fetal DNA, fetal
DNA and/or
thrombocytopenia, fetal abnormalities, gestational period, age of mother,
previous case of
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preeclampsia during pregnancy, race or ethnicity, history of preeclampsia with
mother or
farther, multiple births, first birth, and subject's smoking history.
[00111] The disclosure provides a method for diagnosing or confirming an
existence of
preeclampsia in a subject comprising: performing at least two different assays
that determine
a level of fibronectin in a sample from the subject; and evaluating the sample
and using the
levels from the plurality of assays to diagnose or confirm the existence of
preeclampsia and
calculate an index. In some cases, the method further comprises, based upon
the index,
suggesting a treatment for preeclampsia, the treatment selected from the group
consisting of
aspirin, preterm labor or bedrest.
[00112] The disclosure provides a method for diagnosing or confirming an
existence of
preeclampsia in a subject comprising: evaluating a level of a ratio of sFlt-1
and P1GF and a
level of a plurality of biomarkers in a sample derived from the subject,
wherein the different
biomarker is not ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC),
haptoglobin (HP),
alpha-2-macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III
(Apo-C3),
apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4), hemoglobin (HB),
fibrinogen
alpha (FGA), pikachurin (EGFLAM), free human chorionic gonadotropin (free beta
hCG) or
heme; and evaluating the sample and using the levels from the previous step to
determine an
index to diagnose or confirm the existence of preeclampsia and calculating an
index. In some
cases, the method further comprises, based upon the index, suggesting a
treatment for
preeclampsia, the treatment involving aspirin, preterm labor or bedrest. In
some cases, the
method further comprises performing replicates of identical assays for each
biomarker using
the sample. In some cases, the method further comprises determining a mean
level for each of
the biomarkers using levels derived from each of the multiple identical
assays. In some cases,
the method further comprises performing a 10g2, loge or logio transformation
of the mean
levels. In some cases, the method further comprises comparing the levels of
each biomarker
to a standard curve for that biomarker. In some cases, the method further
comprises
weighting each of the biomarkers, wherein the weighting includes providing
numbers into a
polynomial such that each marker has a distinct weight. In some cases, the
calculating
comprises determining a ratio of adjusted levels of sFlt-1 and P1GF. In some
cases, the
calculating comprises determining a ratio of normalized levels of sFlt-1 and
P1GF. In some
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cases, the calculating comprises determining a ratio of raw levels of sFlt-1
and P1GF. In
some cases, the calculating comprises determining a ratio of adjusted levels
of sFlt-1 and
P1GF compared to a control. In some cases, the calculating comprises
determining a ratio of
normalized levels of sFlt-1 and P1GF compared to a control. In some cases, the
calculating
comprises determining a ratio of raw levels of sFlt-1 and P1GF compared to a
control.
[00113] The disclosure provides a method for diagnosing or confirming an
existence of
preeclampsia in a subject comprising: evaluating a level of a ratio of sFlt-1
and P1GF and a
level of a plurality of biomarkers in a sample derived from the subject,
wherein the different
biomarker is not ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC),
haptoglobin (HP),
alpha-2-macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III
(Apo-C3),
apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4), hemoglobin (HB),
fibrinogen
alpha (FGA), pikachurin (EGFLAM), free human chorionic gonadotropin (free beta
hCG) or
heme; and evaluating the sample and using the levels from step (a) to
determine an index to
diagnose or confirm the presence of preeclampsia. In some cases, the plurality
of biomarkers
is selected from the group consisting of sFlt-1,P1GF, FN and PAPP-A; sFlt-1,
P1GF, FN,
ADAM12 and PAPP-A; sFlt-1, P1GF, PAPP-A and FN; sFlt-1, P1GF, HPX, FN and PAPP-
A; P1GF, ADAM12, FN and PAPP-A; P1GF, FN and PAPP-A; sFlt-1, P1GF and FN;
P1GF,
FN and PAPP-A; sFlt-1, P1GF, FN and ADAM12; sFlt-1, P1GF and FN; P1GF, sFlt-1
and
FN; sFlt-1, FN and ADAM12; P1GF, FN and PAPP-A. In some cases, the method
further
comprises, based upon the index, suggesting a treatment for preeclampsia, the
treatment
involving aspirin, preterm labor or bedrest. In some cases, the method further
comprises
performing replicates of identical assays for each biomarker using the sample.
In some cases,
the method further comprises determining a mean level for each of the
biomarkers using
levels derived from each of the multiple identical assays. In some cases, the
method further
comprises performing a 10g2, loge or logio transformation of the mean levels.
In some cases,
the method further comprises comparing the levels of each biomarker to a
standard curve for
that biomarker. In some cases, the method further comprises weighting each of
the
biomarkers, wherein the weighting includes providing numbers into a polynomial
such that
each marker has a distinct weight. In some cases, the calculating comprises
determining a
ratio of adjusted levels of sFlt-1 and P1GF. In some cases, the calculating
comprises
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determining a ratio of normalized levels of sFlt-1 and P1GF. In some cases,
the calculating
comprises determining a ratio of raw levels of sFlt-1 and P1GF. In some cases,
the
calculating comprises determining a ratio of adjusted levels of sFlt-1 and
P1GF compared to
a control. In some cases, the calculating comprises determining a ratio of
normalized levels of
sFlt-1 and P1GF compared to a control. In some cases, the calculating
comprises determining
a ratio of raw levels of sFlt-1 and P1GF compared to a control.
Detection Reagents and Antibodies
[00114] The disclosure includes detection reagents and antibodies that may be
used to
determine levels of a plurality of biomarkers disclosed herein. Often, the
detection reagents
may include reagents useful for performing an ELISA.
[00115] Examples of commercially available antibody kits that can be used in
an ELISA
protocol include, but are not limited to, anti-P1GF (distributed by USCN Life
Science Inc.,
Roche and R&D Systems), anti-sFlt-1 (distributed by Boster Bio., R&D systems,
MyBioSource.com, antibodies-online, Biotrend Chemikalien GmbH, and Enzo Life
Sciences), anti- PAPP-A (distributed by R&D systems, RayBioTech, IBL Japan,
DRG
International, Abnova, USCN Life Science, Novus Bio, Rapid Test, MyBioSource,
antibodies-online.com, Fisher Scientific, elabscience, Sigma Aldrich, C USA
Bio, ANSH
Labs, Demeditec, Alpco, AMS Bio, NovaTeinBio, Creative Biomart, Biorbyt,
Biomatic
Corporation), anti-VEGF (distributed by AMS Bio, Mybiosource, Abnova,
antibodies-
online.com, United States Biological, Biomatik Corporation, Cloud-Clone Corp,
Biovendor,
Boster Immunoleader, Enzo Life Sciences, Fitzgerald, Abnova, Aviva Systems
Biology and
Creative Biomart), anti-fibronectin (distributed by Biovendor, Boster
Immunoleader, QED
Bioscience, eBioscience, Biorbyt, Fitzgerald, Amsbio, MyBioSource, Nova
TeinBio,
Abnova, Aviva Systems Biology, Creative Biomart, antibodies-online.com, Abcam,
Novus
Biologicals, United States Biological, EIAAB (Hong Kong) Company Limited,
Biomatik
Corporation, Cloud-Clone Corp), anti-fibrinogen (distributed by Molecular
Innovations,
Fitzgerald, Ams Bio, Biorbyt, MyBioSource, NovaTein Bio, Abnova, Creative
BioMart,
Aviva Systems Biology, antibodies-online.com, Abcam, Novus Biologicals, United
States
Biological, EIAAB (Hong Kong) Company Limited, Biomatik Corporation, Cloud-
Clone
Corp) and anti-ADAM12 (distributed by Boster Immunoleader, AMS Bio,
MyBioSource,
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Abnova, Creative Biomart, antibodies-online.com, GeneTex, Biorbyt, United
States
Biological, EIAAB (Hong Kong) Company Limited, R&D Systems and Cloud-Clone
Corp).
[00116] The methods include detecting the levels of one biomarker described
herein using
one ELISA kit and/or antibody described herein. In some cases, the methods
include
detecting the levels of one biomarker described herein using two ELISA kits
and/or
antibodies described herein. In other cases, the methods include detecting the
levels of one
biomarker described herein using three ELISA kits and/or antibodies described
herein. In
other cases, the methods include detecting the levels of one biomarker
described herein using
four ELISA kits and/or antibodies described herein. In other cases, the
methods include
detecting the levels of one biomarker described herein using five ELISA kits
and/or
antibodies described herein. In other cases, the methods include detecting the
levels of one
biomarker described herein using more than five ELISA kits and/or antibodies
described
herein. Often the one biomarker is independently measured using one, two,
three, four, five
or more than five ELISA kits. In some cases, the one, two, three, four, five
or more than five
ELISA kits may be the same or different ELISA kits.
[00117] The methods include detecting the levels of two biomarkers described
herein using
two ELISA kits and/or antibodies described herein. In other cases, the methods
include
detecting the levels of two biomarkers described herein using three ELISA kits
and/or
antibodies described herein. In other cases, the methods include detecting the
levels of two
biomarkers described herein using four ELISA kits and/or antibodies described
herein. In
other cases, the methods include detecting the levels of two biomarkers
described herein
using five ELISA kits and/or antibodies described herein. In other cases, the
methods include
detecting the levels of two biomarkers described herein using more than five
ELISA kits
and/or antibodies described herein. Often the two biomarkers are independently
measured
using two, three, four, five or more than five ELISA kits. In some cases, the
two, three, four,
five or more than five ELISA kits may be the same or different ELISA kits.
[00118] In other cases, the methods include detecting the levels of three
biomarkers described
herein using three ELISA kits and/or antibodies described herein. In other
cases, the methods
include detecting the levels of three biomarkers described herein using four
ELISA kits
and/or antibodies described herein. In other cases, the methods include
detecting the levels of
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three biomarkers described herein using five ELISA kits and/or antibodies
described herein.
In other cases, the methods include detecting the levels of three biomarkers
described herein
using more than five ELISA kits and/or antibodies described herein. Often the
three
biomarkers are independently measured using three, four, five or more than
five ELISA kits.
In some cases, the three, four, five or more than five ELISA kits may be the
same or different
ELISA kits.
[00119] In other cases, the methods include detecting the levels of four
biomarkers described
herein using four ELISA kits and/or antibodies described herein. In other
cases, the methods
include detecting the levels of four biomarkers described herein using five
ELISA kits and/or
antibodies described herein. In other cases, the methods include detecting the
levels of four
biomarkers described herein using more than five ELISA kits and/or antibodies
described
herein. Often the four biomarkers are independently measured using four, five
or more than
five ELISA kits. In some cases, the four, five or more than five ELISA kits
may be the same
or different ELISA kits.
[00120] In certain cases, the antibodies against the selected biomarkers may
be monoclonal
antibodies. In some cases the antibodies against the selected biomarkers may
be polyclonal
antibodies. Of particular interest are antibodies against a plurality
biomarkers selected from a
group consisting of P1GF, HPX, sFlt-1, PAPP-A, VEGF (excluding VEGF-R1), FN,
FG, and
ADAM12. Such antibodies may be monoclonal or polyclonal antibodies. Such
antibodies
may be commercially available or generated by the user using methods known to
those of
ordinary skill in the art.
[00121] Any commercially available antibody known to one of ordinary skill in
the art may
be used to detect a biomarker listed herein and may be used in combination
with the subject
methods. For example, commercially available antibodies can include, but are
not limited to,
anti-P1GF (distributed by Amb, Novus Biologicals, Nordic Biosite or Tebu
Biologicals),
anti-HPX (distributed by Sino Biological, Pierce, Sigma Aldrich, Origene,
Lifespan,
Proteintech Group, AbD Sertotec, BioRad, ThermoFisher, Agrisera, Angio-
Proteomie, Enzo
Life Sciences, Aviva Systems Biology, Everest Biotech, R&D systems, St. John's
Laboratory, Abbiotec, Biorbyt, Acris Antibodies, MyBioSource, AmsBio, Abgent,
Santa
Cruz Biotechnology, Creative Biomart, Nova TeinBio, Raybiotech, Gene Tex,
United States
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Biological, Abcam, Cedarlane Labs, Gallus Immunotech, Abnova and Cloud-Clone
Corp),
anti-sFlt-1 (distributed by Cell Signaling Technology, Lifespan Biosciences,
Antibodies-
online.com, Sino Biological, AbD Serotec, Proteintech Group, Boster
Immunoleader,
Thermo Fischer Scientific, Merck Millipore, Agrisera, Atlas Antibodies,
Fitzgerald, Aviva
Systems Biology, Angio-Proteomie, eBioscience, Genway, Biorbyt, R&D systems,
Life
Technologies, St. John's Laboratory, Abbiotec, Acris Antibodies, MyBioSource,
Amsbio,
Santa Cruz Biotechnology, Creative Biomart, Origene, Nova TeinBio, Raybiotech,
Novus
Biologicals, ProSci, Gene Tex, United States Biological, Abbexa, Abcam,
Bioworld
Technology Inc., Abnova, Spring Bioscience and Cloud-Clone Corp), anti- PAPP-A
(distributed by Lifespan Biosciences, Thermo Fisher Scientific, AbD Serotec,
Bio-Rad,
Merck Millipore, antibodies-online.com, R&D systems, Genway, Atlas Antibodies,
Abbiotec, Amsbio, MyBioSource, SantaCruz Biotechnology, Aviva Systems Biology,
Biorbyt, Creative Biomart, Nova TeinBio, Raybiotech, GeneTex, United States
Biological,
Fitzgerald, Novus Biologicals, Abcam, BBI Solutions, Abnova and Cloud-Clone
Corp), anti-
VEGF (distributed by Cell Signaling Technology, Lifespan Biosciences,
antibodies-
online.com, Epigentek, Angio-Proteomie, Aviva Systems Biology, R&D Systems,
St. John's
Laboratory, Thermo Fisher Scientific Inc., Biorbyt, Acris Antibodies,
MyBioSource,
SantaCruz Biotechnology, Gene Tex, United States Biological, Abbexa,
Fitzgerald, Novus
Biologicals, Abcam, Bioworld Technology Inc., Abnova, Sino Biological, Merck
Millipore,
Aviva Systems Biology, Origene, Life Technologies, Amsbio, Abgent, Creative
Biomart,
Raybiotech, IBL America, Boster Immunoleader, Atlas Antibodies, Bioworld
Technology
Inc., and Cloud-Clone Corp), anti-fibronectin (distributed by Proteintech
Group, Fitzgerald,
Lifespan Biosciences, Boster Immunoleader, Abcam, QED Bioscience, Sino
Biological, AbD
Serotech, Bio-Rad, Proten Biotechnik GmbH, Beckman Coulter, antibodies-
online.com,
Takara, Merck Millipore, Atlas Antibodies, Agrisera, Thermo Fisher Scientific,
Inc.,
Rockland, Immuquest, Enzo Life Sciences, Aviva Systems Biology, Genway,
eBioscience,
Biorbyt, R&D Systems, Amsbio, St. John's Laboratory, Abbiotec, Acris
Antibodies,
MyBioSource, Santa Cruz Biotechnology, Novus Biologicals, Creative Biomart,
Nova
TeinBio, Raybiotech, GeneTex, ProSci, United States Biological, Abbexa, Abcam,
Cedarlane
Labs, Molecular Innovations, Southern Biotech, Bioworld Technology Inc.,
Gallus
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Immunotech, Abnova, Alfa Aesar and Cloud-Clone Corp), anti-fibrinogen
(distributed by
Lifespan Biosciences, antibodies-online, AbD Serotec, Bio-Rad, Absea
Biotechnology,
Thermo Fischer Scientific Inc., Merck Millipore, Agrisera, Atlas Antibodies,
Aviva Systems
Biology, Enzo Life Sciences, Rockland, Genway, R&D Systems, St. John's
Laboratory,
Abbiotec, Amsbio, Acris Antibodies, MyBioSource, Abgent, Biorbyt, Santa Cruz
Biotechnology, Creative Biomart, Origene, Nova TeinBio, Raybiotech, Gene Tex,
Pro Sci,
United States Biological, Fitzgerald, Cedarlane Labs, Novus Biologicals,
Molecular
Innovations, Haematologic Technologies Inc., Dako, Oxford Biomedical Research,
Gallus
Immunotech, Bioworld Technology Inc., Abnova, Nordic Immunological
Laboratories, and
Cloud-Clone Corp) and anti-ADAM12 (distributed by Lifespan Biosciences, Sino
Biological,
AbD Serotec, Bio-Rad, Thermo Fischer Scientific Inc., Merck Millipore,
antibodies-
online.com, Atlas Antibodies, Enzo Life Sciences, Everest Biotech, Angio-
Proteomie, Aviva
Systems Biology, Proteintech Group, Genway, R&D Systems, St. John's
Laboratory, Acris
Antibodies, MyBio Source, Amsbio, Santa Cruz Biotechnology, Biorbyt, Creative
Biomart,
Origene, Nova Tein Bio, Raybiotech, GeneTex, ProSci, United States Biological,
Fitzgerald,
Novus Biologicals, Abcam, Abnova and Cloud-Clone Corp).
[00122] Monoclonal antibodies that specifically bind to any of the biomarkers
listed herein
may be produced using methods known to those of ordinary skill in the art.
These methods
include the methods of Kohler and Milstein (Nature, 256: 495-497, 1975 ) and
Campbell
("Monoclonal Antibody Technology, The Production and Characterization of
Rodent and
Human Hybridomas" in Burdon et al., Eds., Laboratory Techniques in
Biochemistry and
Molecular Biology, Volume 13, Elsevier Science Publishers, Amsterdam, 1985 ),
as well as
methods described by Huse et al. (Science, 246, 1275-1281, 1989).
[00123] Monoclonal antibodies may be prepared from supernatants of cultured
hybridoma
cells or from ascites induced by intra-peritoneal inoculation of hybridoma
cells into mice.
These methods are described in Kohler and Milstein (Eur. J. Immunol, 6, 511-
519, 1976).
The route and schedule of immunization of the host animal or cultured antibody-
producing
cells may follow with route and schedules known to those of ordinary skill in
the art for
antibody stimulation and production. Typically, mice are used as the test
model, however,
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any mammalian subject or antibody producing cells therefrom can be used for
production of
mammalian, including human, hybrid cell lines.
[00124] Following immunization, immune lymphoid cells can be fused with
myeloma cells
to generate a hybrid cell line that can be cultured indefinitely, to produce
monoclonal
antibodies. For example, lymphocytes may be selected for fusion and may be
isolated either
from lymph node tissue or the spleens of immunized animals. Murine myeloma
cell lines can
be obtained, for example, from the American Type Culture Collection (ATCC;
Manassas,
VA). Human myeloma and mouse-human heteromyeloma cell lines have also been
described
(Kozbor et al., J. Immunol., 133:3001-3005, 1984; Brodeur et al., Monoclonal
Antibody
Production Techniques and Applications, Marcel Dekker, Inc., New York, pp. 51-
63, 1987).
[00125] The hybrid cell lines can be maintained in vitro and stored and
preserved in any
number of conventional ways, including freezing and storage under liquid
nitrogen. Frozen
cell lines can be revived and cultured indefinitely. The secreted antibody can
be recovered
from tissue culture supernatant by conventional methods such as precipitation,
ion exchange
chromatography, affinity chromatography, or the like. The antibody may be from
any of one
of the following immunoglobulin classes: IgG, IgM, IgA, IgD, or IgE, and the
subclasses
thereof, and preferably is an IgG antibody.
[00126] In certain cases, a first antibody set may be included, that is
specifically designed to
interact with selected biomarkers. For example, the first antibody set may be
designed to
interact with proteins selected from the group consisting of P1GF, HPX, sFlt-
1, PAPP-A,
VEGF (excluding VEGF-R1), FN, FG, and ADAM12.
[00127] In some cases, detection reagents other than antibodies may be used to
practice the
methods described herein. A detection reagent specifically binds to a
biomarker as described
herein. In addition to antibodies, detection reagents may further comprise
aptamers, Fc
fragments, Fab fragments, Fab2 fragments, ScFv domains, diabodies, non-
antibody ligands,
small molecules, peptides, polypeptides, proteins, nanoparticles, affibodies
or the like.
[00128] The disclosure provides a method for diagnosing or confirming a
presence of
preeclampsia in a subject comprising: performing at least two different assays
that determine
a level of fibronectin in a sample derived from the subject; and evaluating
the sample and
using the levels from the plurality of assays to diagnose or confirm the
presence of
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preeclampsia and calculate an index. In some cases, the method further
comprises, based
upon the index, suggesting a treatment for preeclampsia, the treatment
involving aspirin,
preterm labor or bedrest.
[00129] The disclosure provides a method for diagnosing or confirming an
existence of
preeclampsia in a subject comprising: measuring a level of a ratio of sFlt-1
and P1GF and a
level of a plurality of biomarkers in a sample derived from the subject,
wherein none of the
biomarkers is ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC),
haptoglobin (HP),
alpha-2-macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III
(Apo-C3),
apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4), hemoglobin (HB),
fibrinogen
alpha (FGA), pikachurin (EGFLAM), free human chorionic gonadotropin (free beta
hCG) or
heme; and evaluating the sample and using the levels from the first step to
determine an index
to diagnose or confirm the presence of preeclampsia and calculate an index. In
some cases,
the method further comprises step, based upon the index, suggesting a
treatment for
preeclampsia, the treatment involving aspirin, preterm labor or bedrest. In
some cases, the
method further comprises performing replicates of identical assays for each
biomarker using
the sample. In some cases, the method further comprises determining a mean
level for each of
the biomarkers using levels derived from each of the multiple identical
assays. In some cases,
the method further comprises performing a 10g2, loge or logio transformation
of the mean
levels. In some cases, the method further comprises comparing the levels of
each biomarker
to a standard curve for that biomarker. In some cases, the method further
comprises
weighting each of the biomarkers, wherein the weighting includes providing
numbers into a
polynomial such that each marker has a distinct weight. In some cases, the
calculating
comprises determining a ratio of adjusted levels of sFlt-1 and P1GF. In some
cases, the
calculating comprises determining a ratio of normalized levels of sFlt-1 and
P1GF. In some
cases, the calculating comprises determining a ratio of raw levels of sFlt-1
and P1GF. In
some cases, the calculating comprises determining a ratio of adjusted levels
of sFlt-1 and
P1GF compared to a control. In some cases, the calculating comprises
determining a ratio of
normalized levels of sFlt-1 and P1GF compared to a control. In some cases, the
calculating
comprises determining a ratio of raw levels of sFlt-1 and P1GF compared to a
control.
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[00130] The disclosure provides a method for diagnosing or confirming a
presence of
preeclampsia in a subject comprising: measuring a level of a ratio of sFlt-1
and P1GF and a
level of a plurality of biomarkers in a sample derived from the subject,
wherein none of the
biomarkers is ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC),
haptoglobin (HP),
alpha-2-macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III
(Apo-C3),
apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4), hemoglobin (HB),
fibrinogen
alpha (FGA), pikachurin (EGFLAM), free human chorionic gonadotropin (free beta
hCG) or
heme; and evaluating the sample and using the levels from the first step to
determine an
index to diagnose or confirm the existence of preeclampsia and calculate an
index. In some
cases, the plurality of biomarkers is selected from the group consisting of
sFlt-1,P1GF, FN
and PAPP-A; sFlt-1, P1GF, FN, ADAM12 and PAPP-A; sFlt-1, P1GF, PAPP-A and FN;
sFlt-1, P1GF, HPX, FN and PAPP-A; P1GF, ADAM12, FN and PAPP-A; P1GF, FN and
PAPP-A; sFlt-1, P1GF and FN; P1GF, FN and PAPP-A; sFlt-1, P1GF, FN and ADAM12;
sFlt-1, P1GF and FN; P1GF, sFlt-1 and FN; sFlt-1, FN and ADAM12; P1GF, FN and
PAPP-
A. . In some cases, the method further comprises, based upon the index,
suggesting a
treatment for preeclampsia, the treatment involving aspirin, preterm labor or
bedrest. In some
cases, the method further comprises performing replicates of identical assays
for each
biomarker using the sample. In some cases, the method further comprises
determining a mean
level for each of the biomarkers using levels derived from each of the
multiple identical
assays. In some cases, the method further comprises performing a 10g2, loge or
logio
transformation of the mean levels. In some cases, the method further comprises
comparing
the levels of each biomarker to a standard curve for that biomarker. In some
cases, the
method further comprises weighting each of the biomarkers, wherein the
weighting includes
providing numbers into a polynomial such that each marker has a distinct
weight. In some
cases, the calculating consisting of determining a ratio of adjusted levels of
sFlt-1 and P1GF.
In some cases, the calculating consisting of determining a ratio of normalized
levels of sFlt-1
and P1GF. In some cases, the calculating consisting of determining a ratio of
raw levels of
sFlt-1 and P1GF. In some cases, the calculating consisting of determining a
ratio of adjusted
levels of sFlt-1 and P1GF compared to a control. In some cases, the
calculating consisting of
determining a ratio of normalized levels of sFlt-1 and P1GF compared to a
control. In some
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cases, the calculating consisting of determining a ratio of raw levels of sFlt-
1 and P1GF
compared to a control.
Methods of Measurement
1001311 Means for assaying protein or peptide levels include. but are not
limited to, enzyme
immunoassays (EIA) such as enzyme multiplied immunoassay technique (EMIT),
enzyme-
linked immunosorbent assay (ELISA), sandwich ELISA, competitive ELISA, IgM
antibody
capture ELISA (MAC ELISA), and microparticle enzyme immunoassay (MEIA);
capillary
electrophoresis immunoassays (CEIA), radioimmunoassays (RIA);
immunoradiometric
assays (IRMA); fluorescence polarization immunoassays (FPIA), or
chemiluminescence
assays (CL). Such assays can be automated. Immunoassays can also be used in
conjunction
with laser induced fluorescence. Liposome immunoassays, such as flow-injection
liposome
immunoassays and liposome immunosensors, are also suitable for use in the
present
disclosure. In addition, nephelometry assays, in which the formation of
protein/antibody
complexes results in increased light scatter that is converted to a peak rate
signal as a function
of the marker concentration, are suitable for use in the methods of the
present disclosure.
[00132] One example for ELISA assay methodology is competitive ELISA. In this
methodology an antibody to the target is first exposed with a labeled target
(e.g., biotinylated
hemopexin). The antibody is subsequently exposed to the unlabeled target
(e.g., hemopexin).
When introducing both the labeled target and unmodified target to the
antibodies, both sets of
targets compete for binding sites on the antibody. The more targets that are
available, the
fewer the amount of labeled targets that bind to the antibodies. Subsequently,
the detectable
signal from the labeled target will be detected. The labels can include, among
others,
radioisotopes (for example 14C5 3H5 32P5 33p5 35s5 1251 and ''I),
a
I), fluorescers, phosphoroscers,
chemiluminescers, chromogenic dyes, enzymes, antibodies, particles such as
magnetic
particles, quantum dots, heavy elements, nuclear magnetic resonance (NMR)
detectable
isotopes, molecules that can be detected by mass spectroscopy, or specific
binding molecules
including conjugates. The label may be directly connected to the target, or
connected though
a spacer arm (e.g., polyethylene glycol or hydrocarbon). Examples of
conjugates include, but
are not limited to calmodulin binding protein (CBP) and calmodulin, a
combination of biotin
and avidin, a combination of biotin and streptavidin, a combination of biotin
and
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NeutrAvidinO, a combination of biotin and human-derived biotin-binding
molecules, a
combination of biotin and Strep-TactinO, a combination of Strep-Tag and Strep-
TactinO, a
combination of Strep-TagII0 and Strep-Tactin 0, a combination of S-Tag and S-
protein, a
combination of Halo Ligand0 and HalotagO, a combination of glutathione and
glutathione S-
transferase, a combination of amylose and a maltose-binding protein, a
combination of
appropriately designed epitope and a humanized monoclonal antibody for the
epitope, and a
combination of appropriately designed sugar chains and relevant sugar chain-
recognizing
molecules including lectin and humanized monoclonal antibodies. Antigens-
antibodies
conjugates include for example, digoxigenin/anti-digoxigenin, dinitrophenyl
(DNP) and anti-
DNP, dansyl-X-anti-dansyl, Fluorescein and anti-fluorescein, lucifer yellow
and anti-lucifer
yellow, rhodamine and anti-rhodamine, and other conjugates known in the art.
Other suitable
binding pairs may include polypeptides such as the FLAG-peptide [Hopp et al.,
BioTechnology, 6:1204-1210 (1988)]; the KT3 epitope peptide [Martin et al.,
Science, 255:
192-194 (1992)]; tubulin epitope peptide [Skinner et al., J. Biol. Chem.,
266:15163-15166
(1991)]; and the T7 gene 10 protein peptide tag [Lutz-Freyermuth et al., Proc.
Natl. Acad.
Sci. USA, 87:6393-6397 (1990)] and the antibodies each thereto.
[00133] In the ELISA and ELISA-based assays and/or protocols, one or more
antibodies
specific for the proteins of interest may be immobilized onto a selected solid
surface,
preferably a surface exhibiting a protein affinity such as the wells of a
polystyrene microtiter
plate. After washing to remove incompletely adsorbed material, the assay plate
wells are
coated with a non-specific "blocking" protein that is known to be
antigenically neutral with
regard to the test sample such as bovine serum albumin (BSA), casein or
solutions of
powdered milk. This allows for blocking of non-specific adsorption sites on
the
immobilizing surface, thereby reducing the background caused by non-specific
binding of
antigen onto the surface. After washing to remove unbound blocking protein,
the
immobilizing surface is contacted with the sample to be tested under
conditions that are
conducive to immune complex (antigen/antibody) formation. Such conditions
include
diluting the sample with diluents such as BSA or bovine gamma globulin (BGG)
in
phosphate buffered saline (PBS)/Tween or PBS/Triton-X 100, which also tend to
assist in the
reduction of nonspecific background, and allowing the sample to incubate for
about 2-4 hours
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at temperatures on the order of about 25 -27 C (although other temperatures
may be used).
Following incubation, the antisera-contacted surface is washed so as to remove
non
immunocomplexed material. An exemplary washing procedure includes washing with
a
solution such as PBS/Tween, PBS/Triton-X 100 or borate buffer. The occurrence
and
amount of immunocomplex formation may then be determined by subjecting the
bound
immunocomplexes to a second antibody having specificity for the target that
differs from the
first antibody and detecting binding of the second antibody. In certain cases,
the second
antibody will have an associated enzyme, e.g., urease, peroxidase or alkaline
phosphatase,
which will generate a color precipitate upon incubating with an appropriate
chromogenic
label. For example, a urease or peroxidase-conjugated anti-human IgG may be
employed, for
a period of time and under conditions which favor the development of
immunocomplex
formation (e.g., incubation for 2 hours at room temperature in a PBS-
containing solution such
as PBS/Tween). After such incubation with the second antibody and washing to
remove
unbound material, the amount of label is quantified, for example by incubation
with a
chromogenic label such as urea and bromocresol purple in the case of a urease
label or 2,2'-
azino-di-(3-ethyl-benzthiazoline)-6-sulfonic acid (ABTS) and H202, in the case
of a
peroxidase label. Quantitation is then achieved by measuring the degree of
color generation,
e.g., using a visible spectrum spectrophotometer.
[00134] Following quantitation, the data may be expressed in optical units,
for example, as
optical density or OD values if data are quantified using a visible spectrum
spectrophotometer. In some cases, data may be expressed as absorbance,
emission,
radioactivity counts or the like depending on the reactive substrate used with
the second
antibody as described above. In any case, the magnitude of optical units
quantified from any
well containing no detectable substrate, for example a blank, may be
subtracted from the
optical units quantified from any well containing detectable substrate. This
value may be an
adjusted optical unit value, or an adjusted OD and included in further
calculations described
herein. In some cases, the magnitude of optical units quantified from any well
containing no
detectable substrate, for example a blank, may not be subtracted from the
optical units
quantified from any well containing detectable substrate. This value may be a
raw optical unit
value or a raw OD and included in further calculations described herein.
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[00135] Alternatively, non-ELISA based-methods for measuring the levels of one
or more
proteins in a sample may be employed. Representative examples include but are
not limited
to mass spectrometry, chromatography, proteomic arrays, xMAPTm microsphere
technology,
flow cytometry, western blotting, spectroscopy, nephelometry, radial
immunodiffusion
techniques, single radial immodiffiLsion assay, protein digestion and peptide
analysis (e.g.,
the methods and systems described by Applied Proteomies) and
immunohistochemistry.
[00136] Mass spectroscopy method may include any mass spectrometric (MS)
techniques
that can obtain precise information on the mass of peptides, and preferably
also on
fragmentation and/or (partial) amino acid sequence of selected peptides (e.g.,
in tandem mass
spectrometry, MS/MS; or in post source decay, TOF MS), are useful herein.
Suitable peptide
MS and MS/MS techniques and systems are well-known per se and may be used
herein, as
well as liquid chromatography coupled to mass spectroscopy (LC-MS) and two-
dimensional
liquid chromatography coupled to tandem mass spectroscopy (2D-LC-MS/MS). MS
arrangements, instruments and systems suitable for biomarker peptide analysis
may include,
without limitation, matrix-assisted laser desorption/ionisation time-of-flight
(MALDI-TOF)
MS; MALDI-TOF post-source-decay (PSD); MALDI-TOF/TOF; surface-enhanced laser
desorption/ionization time-of-flight mass spectrometry (SELDI-TOF) MS;
electrospray
ionization mass spectrometry (ESI-MS); ESI-MS/MS; ESI-MS/(M5 1 (n is an
integer greater
than zero); ESI 3D or linear (2D) ion trap MS; ESI triple quadrupole MS; ESI
quadrupole
orthogonal TOF (Q-TOF); ESI Fourier transform MS systems;
desorption/ionization on
silicon (DIOS); secondary ion mass spectrometry (SIMS); atmospheric pressure
chemical
ionization mass spectrometry (APCI-MS); APCI-MS/MS; APCI-(M5 1; atmospheric
pressure
photoionization mass spectrometry (APPI-MS); APPI-MS/MS; and APPI-(M5)11.
Peptide ion
fragmentation in tandem MS (MS/MS) arrangements may be achieved using manners
established in the art, such as, e.g., collision induced dissociation (CID).
Detection and
quantification of biomarkers by mass spectrometry may involve multiple
reaction monitoring
(MRM). MS peptide analysis methods may be advantageously combined with
upstream
peptide or protein separation or fractionation methods, such as for example
with the
chromatographic and other methods.
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[00137] In some cases, biomarker proteins can be derivatized or modified prior
to analysis,
measurement, quantification or the like. Methods known to those of ordinary
skill in the art
may be employed to derivatize or modify proteins. Polymorphisms or
modifications to
protein biomarkers listed herein may be identified using the methods described
herein, using
the analytical, measurement or quantification methods described herein.
[00138] In other cases, the level of at least one PE biomarker may be
evaluated by detecting
in a patient sample the amount or level of one or more RNA transcripts or a
fragment thereof,
encoded by the gene of interest to arrive at a nucleic acid marker
representation. The level of
nucleic acids in the sample may be detected using any convenient protocol.
While a variety
of different manners of detecting nucleic acids are known, such as those
employed in the field
of differential gene expression analysis, one representative and convenient
type of protocol
for generating marker representations is array-based gene expression profiling
protocols.
Such applications are hybridization assays in which a nucleic acid that
displays "probe"
nucleic acids for each of the genes to be assayed/profiled in the marker
representation to be
generated is employed. In these assays, a sample of target nucleic acids is
first prepared from
the initial nucleic acid sample being assayed, where preparation may include
labeling of the
target nucleic acids with a label, e.g., a member of signal producing system.
Following target
nucleic acid sample preparation, the sample is contacted with the array under
hybridization
conditions, whereby complexes are formed between target nucleic acids that are
complementary to probe sequences attached to the array surface. The presence
of hybridized
complexes is then detected, either qualitatively or quantitatively.
[00139] Specific hybridization technology which may be practiced to generate
the marker
representations employed in the subject methods includes the technology
described in U.S.
Patent Nos.: 5,143,854; 5,288,644; 5,324,633; 5,432,049; 5,470,710; 5,492,806;
5,503,980;
5,510,270; 5,525,464; 5,547,839; 5,580,732; 5,661,028; 5,800,992; the
disclosures of which
are herein incorporated by reference; as well as WO 95/21265; WO 96/31622; WO
97/10365;
WO 97/27317; EP 373 203; and EP 785 280. Arrays of "probe" nucleic acids that
include a
probe for each of the phenotype determinative genes whose expression is being
assayed can
be contacted with target nucleic acids from a subject sample. Contact is
carried out under
hybridization conditions, e.g., stringent hybridization conditions, and
unbound nucleic acid is
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then removed. The term "stringent assay conditions" as used herein refers to
conditions that
are compatible to produce binding pairs of nucleic acids, e.g., surface bound
and solution
phase nucleic acids, of sufficient complementarity to provide for the desired
level of
specificity in the assay while being less compatible to the formation of
binding pairs between
binding members of insufficient complementarity to provide for the desired
specificity.
Stringent assay conditions are the summation or combination (totality) of both
hybridization
and wash conditions.
[00140] The resultant pattern of hybridized nucleic acid provides information
regarding
expression for each of the genes that have been probed, where the expression
information is
in terms of whether or not the gene is expressed and, typically, at what
level, where the
expression data, e.g.õ marker representation (e.g., in the form of a
transcriptosome), may be
both qualitative and quantitative.
[00141] Alternatively, non-array based methods for quantitating the level of
one or more
nucleic acids in a sample may be employed, including those based on
amplification protocols,
e.g., Polymerase Chain Reaction (PCR)-based assays, including quantitative
PCR, reverse-
transcription PCR (RT-PCR), real-time PCR, loop mediated isothermal
amplification of DNA
(LAMP), strand displacement amplification (SDA), sequence based amplification
(NASBA),
self-sustained sequence replication (3SR), linear amplification, and the like.
[00142] Alternatively, functional assays, methods and protocols for
determining the function
of a protein hypothesized to be in a sample may be employed, including any
standard
functional assays known to one of ordinary skill in the art which would
confirm or deny the
presence of a hypothesized protein in a sample. In some cases, functional
assay may include
enzymatic assays, substrate assays, cleavage assays, colorimetric assays, pH
assays and the
like. Lysosome assays may also be performed.
[00143] When protein or peptide levels are to be detected, prior to performing
a protocol, the
total amount protein or a portion of the total amount of protein in a sample
may be
determined. This can be accomplished using a colorimetric assay, such as BCA,
Lowry,
Bradford, Coomassie, 660nm or the like.
[00144] Any of the assays herein may involve the use of a standard curve and
or quality
control, e.g., a high quality control, a low quality control and at least one
blank.
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[00145] A PE biomarker standard may be a purified recombinant protein, a
purified protein, a
synthetic protein, an engineered protein or the like that is similar to or the
same as a
biomarker measured by the methods described herein. In an exemplary case, a
biomarker
standard is a purified recombinant protein that is similar to or the same as a
biomarker
measured by the methods described herein. A blank may be water, a buffer, more
than one
buffer, a chemical, more than one chemical, a reagent, more than one reagent,
an antibody,
more than one antibody or any component of the methods described herein. A low
quality
control may be a previously analyzed sample, be a purified recombinant
protein, a purified
protein, a synthetic protein, an engineered protein or the like which has a
low level of the
biomarker analyzed by the method described herein. A high quality control may
be a
previously analyzed sample, be a purified recombinant protein, a purified
protein, a synthetic
protein, an engineered protein or the like which has a high level of the
biomarker analyzed by
the method described herein. A curve standard may be a concentrated, diluted
or purified
protein supplied by the user or the manufacturer of an ELISA which may be used
to analyze a
level of total protein, or total biomarker levels, in order to calculate a
standard curve for the
method described herein, often an ELISA method.
[00146] The wells of a single plate may contain, but are not limited to, at
least one sample, at
least one biomarker standard, at least one curve standard, a high quality
control, a low quality
control and at least one blank. In some cases, the wells of a single plate may
contain, but are
not limited to, more than one sample, more than one biomarker standard, more
than one curve
standard, a high quality control, a low quality control and more than one
blank. For example,
the wells of a single plate may contain at least 1, 2, 3, 4, 5, 5, 6, 7, 8, 9,
or 10 samples; at least
1, 2, 3, 4, 5, 5, 6, 7, 8, 9, or 10 biomarker standards; at least 1, 2, 3, 4,
5, 5, 6, 7, 8, 9, or 10
curve standards; at least 1, 2, 3, 4, 5, 5, 6, 7, 8, 9, or 10 high quality
controls; at least 1, 2, 3,
4, 5, 5, 6, 7, 8, 9, or 10 low quality controls; and/or at least 1, 2, 3, 4,
5, 5, 6, 7, 8, 9, or 10
blanks. The samples may be singles, duplicates, triplicates, quadruplicates,
or any further
extension of replicates.
[00147] In one example, a single ELISA assay plate contains eight standard
curve samples in
triplicate, six high quality control samples, six low quality control samples,
and three samples
in triplicate arrayed across the plate to avoid variation. Often, controls may
be used in place
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of high quality control and/or low quality control. In some cases, controls
may be used as an
internal calibrator. In other cases, controls may be used in place of
biomarker standards. In
other cases, controls may be used in place of standards. For example, controls
may be used
to generate the standard curve described herein.
[00148] Monoclonal Antibodies
[00149] The disclosure further provides a method for confirming the presence,
absence or
severity of preeclampsia in a subject comprising: utilizing a monoclonal
antibody that
selectively binds fibronectin to determine the levels of fibronectin in a
sample derived from
the subject, generating a report indicating the presence, absence or severity
of preeclampsia
based on the levels and containing an index; and evaluating the sample and
based upon the
index, suggesting a treatment for preeclampsia, the treatment selected
involving aspirin,
preterm labor or bedrest.
[00150] In some cases, a method of confirming, diagnosing, prognosing,
monitoring,
characterizing or evaluating the severity of preeclampsia in a subject
comprises: deriving a
biological sample from the subject; diluting the biotenylated hemopexin two
fold, mixing the
biotenylated hemopexin with the subject's biological sample, adding this
mixture to an
immunoassay plate; performing an analysis of the biological sample for the
presence and
amount of hemopexin (HPX) in an immunoassay test, and employing the biomarker
level to
provide a preeclampsia diagnosis, prognosis, confirmation, monitoring,
characterization or
evaluation of its severity. In such cases, the assay methodology exemplifies a
competitive
ELISA assay. The biotinylated hemopexin competes with the sample-hemopexin for
antibody binding sites. The amount of biotinylated hemopexin is subsequently
detected
spectroscopically following the introduction of appropriate conjugate forming
partners such
as avidin, strepavidin, NeutrAvidinO, human-derived biotin-binding molecules,
and the like.
Such conjugate results in turn in a detectable signal.
[00151] ELISA kits may be used for performing ELISA assays. Those may be
purchased
from standard commercial manufacturers such as for example, disintegrin and
metalloproteinase domain-containing protein 12 (ADAM12) from Mybiosource (SD,
US);
hemopexin (HPX) from Abcam Inc. (MA, US); placental growth factor (P1GF) from
R&D
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system Inc. (MN, US); and soluble fms- like tyrosine kinase (sFlt)-1 from R&D
system Inc.
(MN, US).
[00152] Any commercially available kit known to one of ordinary skill in the
art may be used
to detect a biomarker listed herein by the ELISA method and may be used in
combination
with the subject methods. For example, commercially available kits can
include, but are not
limited to, anti-P1GF (distributed by USCN Life Science Inc., Roche and R&D
Systems),
anti-sFlt-1 (distributed by Boster Bio., R&D systems, MyBioSource.com,
antibodies-online,
Biotrend Chemikalien GmbH, and Enzo Life Sciences), anti- PAPP-A (distributed
by R&D
systems, RayBioTech, IBL Japan, DRG International, Abnova, USCN Life Science,
Novus
Bio, Rapid Test, MyBioSource, antibodies-online.com, Fisher Scientific,
elabscience, Sigma
Aldrich, C USA Bio, ANSH Labs, Demeditec, Alpco, AMS Bio, NovaTeinBio,
Creative
Biomart, Biorbyt, Biomatic Corporation), anti-VEGF (distributed by AMS Bio,
Mybiosource,
Abnova, antibodies-online.com, United States Biological, Biomatik Corporation,
Cloud-
Clone Corp, Biovendor, Boster Immunoleader, Enzo Life Sciences, Fitzgerald,
Abnova,
Aviva Systems Biology and Creative Biomart), anti-fibronectin (distributed by
Biovendor,
Boster Immunoleader, QED Bioscience, eBioscience, Biorbyt, Fitzgerald, Amsbio,
MyBioSource, Nova TeinBio, Abnova, Aviva Systems Biology, Creative Biomart,
antibodies-online.com, Abcam, Novus Biologicals, United States Biological,
EIAAB (Hong
Kong) Company Limited, Biomatik Corporation, Cloud-Clone Corp), anti-
fibrinogen
(distributed by Molecular Innovations, Fitzgerald, Ams Bio, Biorbyt,
MyBioSource,
NovaTein Bio, Abnova, Creative BioMart, Aviva Systems Biology, antibodies-
online.com,
Abcam, Novus Biologicals, United States Biological, EIAAB (Hong Kong) Company
Limited, Biomatik Corporation, Cloud-Clone Corp) and anti-ADAM12 (distributed
by Boster
Immunoleader, AMS Bio, MyBioSource, Abnova, Creative Biomart, antibodies-
online.com,
GeneTex, Biorbyt, United States Biological, EIAAB (Hong Kong) Company Limited,
R&D
Systems and Cloud-Clone Corp).
[00153] Standard curve samples may be analyzed for concentration of biomarkers
using the
methods described herein, for example, using ELISA methods, protocols, assays
and the like.
Each commercially available or in-house designed ELISA method, protocol and/or
assay may
provide instructions containing recommended dilutions of standard curve
samples prior to
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performing the method, protocol and/or assay on a sample or a set of standard
curve samples.
In some cases, the instructions for the commercially available or in-house
designed ELISA
method, protocol and/or assay may be followed and standard curve samples
diluted according
to the instructions. In some cases, standard curve samples may be diluted to a
ratio of 1:1,
1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:20, 1:30, 1:40, 1:50, 1:60,
1:70, 1:80, 1:90, 1:100,
1:150, 1:200, 1:250, 1:300, 1:350, 1:400, 1:450, 1:500, 1:550, 1:600, 1:650,
1:700, 1:750,
1:800, 1:850, 1:900, 1:950, 1:1000, 1:1500, 1:2000, 1:2500, 1:3000, 1:3500,
1:4000, 1:10
000, 1:15 000, 1:20 000, 1:25 000, 1:30 000, 1:35 000, 1:40 000, 1:45 000,
1:50 000, 1:55
000, 1:60 000, 1:65 000, 1:70 000, 1:75 000, 1:80 000, 1:85 000, 1:90 000,
1:95 000 or 1:100
000., 1:4500, 1:5000, 1:5500, 1:6000, 1:6500, 1:7000, 1:7500, 1:8000, 1:8500,
1:9000,
1:9500 or 1:100 000. In some cases, standard curve samples may be diluted to a
ratio of
about 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:20, 1:30, 1:40,
1:50, 1:60,
1:70, 1:80, 1:90, 1:100, 1:150, 1:200, 1:250, 1:300, 1:350, 1:400, 1:450,
1:500, 1:550,
1:600, 1:650, 1:700, 1:750, 1:800, 1:850, 1:900, 1:950, 1:1000, 1:1500,
1:2000,
1:2500, 1:3000, 1:3500, 1:4000, 1:10 000, 1:15 000, 1:20 000, 1:25 000, 1:30
000, 1:35
000, 1:40 000, 1:45 000, 1:50 000, 1:55 000, 1:60 000, 1:65 000, 1:70 000,
1:75 000,
1:80 000, 1:85 000, 1:90 000, 1:95 000 or 1:100 000., 1:4500, 1:5000, 1:5500,
1:6000,
1:6500, 1:7000, 1:7500, 1:8000, 1:8500, 1:9000, 1:9500 or about 1:100 000. In
some
cases, standard curve samples may be diluted to a ratio of less than 1:1, 1:2,
1:3, 1:4, 1:5,
1:6, 1:7, 1:8, 1:9, 1:10, 1:20, 1:30, 1:40, 1:50, 1:60, 1:70, 1:80, 1:90,
1:100, 1:150,
1:200, 1:250, 1:300, 1:350, 1:400, 1:450, 1:500, 1:550, 1:600, 1:650, 1:700,
1:750,
1:800, 1:850, 1:900, 1:950, 1:1000, 1:1500, 1:2000, 1:2500, 1:3000, 1:3500,
1:4000,
1:10 000, 1:15 000, 1:20 000, 1:25 000, 1:30 000, 1:35 000, 1:40 000, 1:45
000, 1:50
000, 1:55 000, 1:60 000, 1:65 000, 1:70 000, 1:75 000, 1:80 000, 1:85 000,
1:90 000,
1:95 000 or 1:100 000., 1:4500, 1:5000, 1:5500, 1:6000, 1:6500, 1:7000,
1:7500, 1:8000,
1:8500, 1:9000, 1:9500 or less than 1:100 000. In some cases, standard curve
samples may
be diluted to a ratio of greater than 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8,
1:9, 1:10, 1:20,
1:30, 1:40, 1:50, 1:60, 1:70, 1:80, 1:90, 1:100, 1:150, 1:200, 1:250, 1:300,
1:350,
1:400, 1:450, 1:500, 1:550, 1:600, 1:650, 1:700, 1:750, 1:800, 1:850, 1:900,
1:950,
1:1000, 1:1500, 1:2000, 1:2500, 1:3000, 1:3500, 1:4000, 1:10 000, 1:15 000,
1:20 000,
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1:25 000, 1:30 000, 1:35 000, 1:40 000, 1:45 000, 1:50 000, 1:55 000, 1:60
000, 1:65
000, 1:70 000, 1:75 000, 1:80 000, 1:85 000, 1:90 000, 1:95 000 or 1:100 000.,
1:4500,
1:5000, 1:5500, 1:6000, 1:6500, 1:7000, 1:7500, 1:8000, 1:8500, 1:9000, 1:9500
or
greater than 1:100 000. For the ratios listed above, 1 is the portion of the
sample and the
other value represents the diluent, wherein the diluent may be provided by the
commercial
manufacturer or the diluent may be provided by the user.
Data Processing and Data Use
[00154] The resultant data provides information regarding levels in the sample
for each of the
markers that have been probed, wherein the information is in terms of whether
or not the
marker is present and, typically, at what level, and wherein the data may be
both qualitative
and quantitative. As such, where detection is qualitative, the methods provide
a reading or
evaluation, e.g., assessment, of whether or not the target marker, e.g.,
nucleic acid or protein,
is present in the sample being assayed. In some cases, the methods provide a
quantitative
detection of whether the target marker is present in the sample being assayed,
e.g., an
evaluation or assessment of the actual amount or relative abundance of the
target analyte,
e.g., nucleic acid or protein in the sample being assayed. In such cases, the
quantitative
detection may be absolute or, if the method is a method of detecting plurality
of different
analytes, e.g., target nucleic acids or protein, in a sample, relative.
[00155] Once the level of the one or more preeclampsia markers has been
determined, the
measurement(s) may be analyzed in any of a number of ways to obtain a
preeclampsia
marker level representation. By a "biomarker level representation" or "gene
representation"
it is meant a representation of the levels, e.g.,. RNA, DNA or protein levels,
of one or more
preeclampsia markers and/or protein cofactors of interest.
[00156] For example, the preeclampsia marker measurements may be analyzed to
produce a
preeclampsia score or index which may be calculated using a logistic
regression analysis. In
some cases, a ratio of at least two biomarkers may be calculated and combined
with
additional explanatory variables for use in a model. Often the ratio of
biomarkers may be the
ratio of sFlt-1 and P1GF. In some cases, the model generated may be a
penalized model. In
other cases, the model generated may be an un-penalized model. A sample may
contribute to
an inaccurate model and as such, the out-of-sample performance may be
evaluated to
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determine if a sample contributes to an inaccurate model. In some cases, the
out-of-sample
performance may be evaluated using any number of approaches, including but not
limited to,
a cross-validation approach. In some instances, the accuracy of the model
depends on a
number of parameters including, but not limited to, how representative the
training sample is
of the target population, the number of variables included in the model,
biomarker
measurement uncertainty, etc. The out-of-sample performance of the model is
often evaluated
to estimate the error of the model in a sample set that has not been used for
training.
[00157] As an example, the preeclampsia marker measurements may be analyzed as
a
biomarker panel. Predictive members of the biomarker panel may be selected by
statistical
feature selection process. For example, the panel of analytes may be selected
by combining
genetic algorithm (GA) and all paired (AP) support vector machine (SVM)
methods for
preeclampsia classification analysis. Predictive features are automatically
determined, e.g.
through iterative GA/SVM, leading to very compact sets of non-redundant
preeclampsia-
relevant analytes with the optimal classification performance. It is possible
that these
different classifier sets harbor only modest overlapping gene, protein
fragment or protein
features, but have similar levels of accuracy.
[00158] As an example, the preeclampsia marker measurements may be analyzed to
generate
a preeclampsia signature. A preeclampsia signature for a patient sample may be
calculated
by any of a number of methods known in the art for calculating biomarker
signatures. For
example, the levels of each of the one or more preeclampsia markers in a
patient sample may
be 10g2, loge or logio transformed, and normalized, e.g., as described above
for generating a
preeclampsia marker profile. The normalized expression levels for each marker
is then
weighted by multiplying the normalized level to a weighting factor, or weight,
to arrive at
weighted expression levels for each of the one or more markers. The weighted
levels are
then totaled and in some cases averaged to arrive at a single weighted level
for the one or
more preeclampsia markers analyzed.
[00159] In some cases, a PE test can confirm whether or not a subject
suspected of having PE
actually does have PE. The single weighted level for the one or more
preeclampsia markers
analyzed can confirm the severity of PE. For example, the single weighted
level can yield
high, medium or low weighted scores which can confirm high, medium or low
severity of PE
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in a subject. In some instances, a high single weighted level confirms a high
severity of PE in
a subject. In some instances, a medium single weighted level confirms a high
severity of PE.
In some instances, a low single weighted level confirms a high severity of PE.
In some
instances, a high single weighted level confirms a medium severity of PE. In
some instances,
a medium single weighted level confirms a medium severity of PE. In some
instances, a low
single weighted level confirms a medium severity of PE. In some instances, a
high single
weighted level confirms a low severity of PE. In some instances, a medium
single weighted
level confirms a low severity of PE. In some instances, a low single weighted
level confirms
a low severity of PE in a subject.
[00160] The single weighted level for the one or more preeclampsia markers
analyzed can
indicate the likelihood that a subject suspected of having PE has or doesn't
have PE. For
example, the single weighted level can yield high, medium, and low weighted
scores which
can indicate the likelihood of a subject having or not having PE. In some
instances, a high
single weighted level indicates a high likelihood of a subject having or not
having PE. In
some instances, a medium single weighted level indicates a high likelihood of
a subject
having or not having PE. In some instances, a low single weighted level
indicates a high
likelihood of a subject having or not having PE. In some instances, a high
single weighted
level indicates a medium likelihood of a subject having or not having PE. In
some instances,
a medium single weighted level indicates a medium likelihood of a subject
having or not
having of PE. In some instances, a low single weighted level indicates a
medium likelihood
of a subject having or not having PE. In some instances, a high single
weighted level
indicates a low likelihood of a subject having or not having PE. In some
instances, a medium
single weighted level indicates a low likelihood of a subject having or not
having PE. In
some instances, a low single weighted level indicates a low likelihood of a
subject having or
not having PE.
[00161] The single weighted level for the one or more preeclampsia markers
analyzed can
indicate the likelihood that a subject will develop preeclampsia. For example,
the single
weighted level can yield high, medium, and low weighted scores which can
indicate the
likelihood that a subject will develop preeclampsia prior to having any
symptoms. In some
instances, a high single weighted level indicates a high likelihood that a
subject will develop
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preeclampsia. In some instances, a medium single weighted level indicates a
high likelihood
that a subject will develop preeclampsia. In some instances, a low single
weighted level
indicates a high likelihood that a subject will develop preeclampsia. In some
instances, a
high single weighted level indicates a medium likelihood that a subject will
develop
preeclampsia. In some instances, a medium single weighted level indicates a
medium
likelihood that a subject will develop preeclampsia. In some instances, a low
single weighted
level indicates a medium likelihood that a subject will develop preeclampsia.
In some
instances, a high single weighted level indicates a low likelihood that a
subject will develop
preeclampsia. In some instances, a medium single weighted level indicates a
low likelihood
that a subject will develop preeclampsia. In some instances, a low single
weighted level
indicates a low likelihood that a subject will develop preeclampsia.
[00162] The weighting factor, or weight, may be determined by any statistical
machine
learning methodology, for example, Principle Component Analysis (PCA), linear
regression,
support vector machines (SVMs), and/or random forests of the dataset from
which the sample
was obtained may be used. For example, the analyte level of each preeclampsia
marker may
be 10g2, loge or logio transformed and weighted either as 1 (for those markers
that are
increased in level in preeclampsia) or -1 (for those markers that are
decreased in level in
preeclampsia), and the ratio between the sum of increased markers as compared
to decreased
markers determined to arrive at a preeclampsia signature. A preeclampsia
signature is an
example of a preeclampsia marker level representation.
[00163] In certain cases the relative weight of plurality of markers is used
to determine a
biomarker signature, biomarker score or biomarker index. Markers whose weight
may be
compared include any of the markers described herein or those selected from a
group
comprising HPX, sFlt-1, PAPP-A, FN, FG, VEGF (excluding VEGF-R1), P1GF and
ADAM12. For example, in some instances a PE signature, PE score or PE index
involves
comparing the levels of sFlt-1 and P1GF in combination with at least one other
biomarker
and determining if their relative weight is at least 1.5:1, 2:1, 2.5:1, 3:1 or
3.5:1 respectively,
wherein such a determination is indicative of PE or likelihood of PE.
[00164] The PE score or PE index can be further categorized into values that
confirms the
severity of PE in a subject. For example, the PE score or PE index can yield
high, medium or
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low values which can confirms the severity of PE in a subject. In some
instances, a high PE
score or PE index can confirms a high severity of PE in a subject. In some
instances, a
medium PE score or PE index confirms a high severity of PE. In some instances,
a low PE
score or PE index confirms a high severity of PE. In some instances, a high PE
score or PE
index confirms a medium severity of PE. In some instances, a medium PE score
or PE index
confirms a medium severity of PE. In some instances, a low PE score or PE
index confirms a
medium severity of PE. In some instances, a high PE score or PE index confirms
a low
severity of PE. In some instances, a medium PE score or PE index confirms a
low severity of
PE. In some instances, a low PE score or PE index confirms a low severity of
PE in a
subject.
[00165] The PE score or PE index can be further categorized into values that
indicate the
likelihood that a subject suspected of having PE has or does not have PE. For
example, PE
score or PE index can yield high, medium, and low values which can indicate
the likelihood
of a subject having or not having PE. In some instances, a high PE score or PE
index
indicates a high likelihood of a subject having or not having PE. In some
instances, a
medium PE score or PE index indicates a high likelihood of a subject having or
not having
PE. In some instances, a low PE score or PE index indicates a high likelihood
of a subject
having or not having PE. In some instances, a high PE score or PE index
indicates a medium
likelihood of a subject having or not having PE. In some instances, a medium
PE score or PE
index indicates a medium likelihood of a subject having or not having of PE.
In some
instances, a low PE score or PE index indicates a medium likelihood of a
subject having or
not having PE. In some instances, a high PE score or PE index indicates a low
likelihood of a
subject having or not having PE. In some instances, a medium PE score or PE
index
indicates a low likelihood of a subject having or not having PE. In some
instances, a low PE
score or PE index indicates a low likelihood of a subject having or not having
PE.
[00166] The PE score or PE index can be further categorized into values that
can predict the
likelihood that a subject will develop preeclampsia prior to the onset of
symptoms (e.g., high,
medium or low likelihood). In some instances, a high PE score or PE index
indicates a high
likelihood that a subject will develop preeclampsia. In some instances, a
medium PE score or
PE index indicates a high likelihood that a subject will develop preeclampsia.
In some
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instances, a low PE score or PE index indicates a high likelihood that a
subject will develop
preeclampsia. In some instances, a high PE score or PE index indicates a
medium likelihood
that a subject will develop preeclampsia. In some instances, a medium PE score
or PE index
indicates a medium likelihood that a subject will develop preeclampsia. In
some instances, a
low PE score or PE index indicates a medium likelihood that a subject will
develop
preeclampsia. In some instances, a high PE score or PE index indicates a low
likelihood that
a subject will develop preeclampsia. In some instances, a medium PE score or
PE index
indicates a low likelihood that a subject will develop preeclampsia. In some
instances, a low
PE score or PE index indicates a low likelihood that a subject will develop
preeclampsia.
[00167] In some cases, the data may be processed and/or further analyzed using
any number
of methods, algorithms and calculations in order to prioritize the individual
data points, data
sets and prevent over-fitting of false positive or falsely correlated results.
As described and
shown herein, some data may not be corrected, for example training data, which
may be at
times an optimistic estimate of the data. In addition, some data may be
corrected, for
example, corrected data, which may be at times a conservative estimate of the
data. In some
cases, the corrected data are a cross-validated estimate of a value, for
example the value may
be the area under the curve (AUC). Sometimes, the performance of the model is
evaluated on
the training data providing an optimistic estimate. Occasionally, a correction
for optimism
estimate of performance is calculated for different performance metrics. In
some cases, the
corrected performance estimate is calculated using cross-validation. In some
examples, the
performance metric is the area under the curve (AUC) or ROC value.
[00168] Data may be used to prepare models. In some cases, the models are
penalized or
optimized. Models derived from using penalized data treat biomarkers
individually and
prevents the data acquired for each individual biomarker, or replicate data
measurement for
each biomarker, from controlling the preeclampsia index, preeclampsia score or
preeclampsia
profile. In some cases, the data used in penalized models controls the
preeclampsia index,
preeclampsia score or preeclampsia profile by improperly weighting the
preeclampsia index,
preeclampsia score or preeclampsia profile to one direction or another. In
some cases, the
data is used in a penalized model such that the preeclampsia index,
preeclampsia score or
preeclampsia profile is not improperly weighted and prevents the preeclampsia
index,
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preeclampsia score or preeclampsia profile from being determined on an
unrelated factor,
such as a damaged sample, a sample not reflective of the subject, etc. In some
cases,
optimized models are derived from data such that correction may prevent the
acquired data
value for an individual marker from falsely contributing to the equation. In
some cases,
corrected and/or optimized models are derived from data that may be more
predictive of a
preeclampsia index, preeclampsia score or preeclampsia profile compared to the
training
numbers. In an exemplary case, a model is constructed using a penalized
logistic regression
approach, wherein the penalized approach fits a regression model while adding
a constraint
on the sum of the absolute values of the coefficients of the model described
herein. The
constraint may prevent variables that might be highly associated with the
outcome in this
sample set from falsely affecting the model.
[00169] In some cases, the data calculation to determine the preeclampsia
index,
preeclampsia score or preeclampsia profile includes the ratio of sFlt-1 and
P1GF. This ratio
may or may not be normalized along with the assay result. As described herein,
the
preeclampsia index, preeclampsia score or preeclampsia profile is a
combination of
biomarkers that may be determined using the analysis methods described herein.
[00170] The data may fit into three separate categories, Tier I, Tier II and
Tier III. Tier I
corresponds to ROC values of at least 0.98 or more. Tier II corresponds to ROC
values
between 0.92 and 0.98, and Tier III corresponds to ROC values of 0.92 or less.
In some cases,
ROC values greater than 0.850 may be clinically valuable. In some cases, ROC
values
greater than 0.90 or 0.950 may be clinically valuable. In some instances,
there is no
statistically significant difference between Tier I and Tier II. In some
examples, there is
statistically significant difference between Tier I and Tier II. In some
cases, the tier values
may be ROC values which may indicate the sensitivity and/or specificity of a
method and or
a data point for a particular biomarker or set of biomarkers. In some cases,
the data outputs
may be classified using at least one algorithm, at least one threshold value,
at least directional
change over time, comparisons within a single subject, comparisons within a
group of
subjects or comparisons to a reference standard. Often, the at least one
algorithm may be an
algorithm described herein or at least one known to one of ordinary skill in
the art. Often the
at least one threshold value may be described herein or known to one of
ordinary skill in the
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art. In some cases, the threshold value may be set based on a single parameter
or a set of
parameters; either the single or the set may be defined by the user. Often the
directional
change may be described herein or known to one of ordinary skill in the art.
In some cases,
the directional change may be based on a single parameter or a set of
parameters; either the
single or the set may be defined by the user. Often the comparisons within a
single subject
may be described herein or known to one of ordinary skill in the art. In some
cases, the
comparisons within a single subject (which may be the same or different from
the tested
subject) may be based on a single parameter or a set of parameters; either the
single or the set
may be defined by the user. Often the comparisons within a group of subjects
may be
described herein or known to one of ordinary skill in the art. In some cases,
the comparisons
within a group of subjects may be based on a single parameter or a set of
parameters; either
the single or the set may be defined by the user. Often the comparisons to a
reference
standard may be described herein or known to one of ordinary skill in the art.
In some cases,
the comparisons to a reference standard may be based on a single parameter or
a set of
parameters; either the single or the set may be defined by the user. The
reference standard
may be based on actual test subject or subjects, or other experimental values,
or a theoretical
value derived from a model, or on any combination thereof
[00171] In some cases the disclosure provides for a method for confirming the
presence or
the absence of preeclampsia in a subject comprising: evaluating a plurality of
biomarkers in a
sample derived from the subject to confirm if the subject has or does not have
preeclampsia
wherein the confirmation has a specificity of at least 90, 91, 92, 93, 94, 95,
96, 97, 98, 99% or
more, which is used to calculate an index. In some instances, the confirmation
has an AUC of
at least 0.9, 0.91, 0.92, 0.93, 0.95, 0.96, 0.97, 0.980, 0.985, 0.988, 0.990,
0.995, 0.999 or
more, which is used to calculate an index. For example, a sample derived from
more than
subject is evaluated to confirm the presence or the absence of PE, at a
specificity described
herein, for examples samples from more than 5, 10, 50, 100, 130, 135, 138,
150, 200, 220,
247, 250, 300, 350, 400, 450, 500, or more than 1000 subjects are evaluated to
confirm the
presence or the absence of PE at a specificity described herein. In some
instances, the subject
is evaluated as having PE using traditional methods. In some examples, the
subject actually
experiences PE. Traditional methods involve measuring proteinuria, blood
pressure, weight
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gain, blood glucose, platelet count and any other method traditionally used to
evaluate PE
known in the art.
[00172] The disclosure further provides a method for distinguishing a subject
having
preeclampsia from a subject having symptoms suggestive of preeclampsia but who
does not
have preeclampsia, the method distinguishing preeclampsia from complication of
pregnancy
symptoms, chronic hypertension, gestational hypertension, autoimmune disorders
and/or
gestational diabetes, wherein the method has a specificity of at least 95%, or
has an AUC of
at least 0.9, comprising: evaluating the level of a plurality of different
biomarkers from a
sample derived from the subject, generating an index indicative of the
presence of
preeclampsia, absence of preeclampsia, severity of preeclampsia. In some
cases, the method
further comprises based upon the index, suggesting a treatment for
preeclampsia, the
treatment involving aspirin, preterm labor or bedrest. In some cases, the
autoimmune disorder
is SLE or lupus. In some cases, the method further comprises weighting each of
the plurality
of biomarkers, wherein the weighting includes providing numbers into a
polynomial such that
each marker has a distinct weight.
[00173] The disclosure provides a method for confirming the presence or the
absence of
preeclampsia in a subject comprising: evaluating a plurality of biomarkers in
a sample
derived from the subject to confirm if the subject has preeclampsia wherein
the confirmation
has a specificity of greater than 95%, or has an AUC greater than 0.9 and is
used to calculate
an index.
[00174] The disclosure further provides a method for diagnosing or confirming
a presence of
preeclampsia in a subject comprising: (a) performing at least two different
assays that
determine a level of fibronectin in a sample derived from the subject; and (b)
evaluating the
sample and using the levels from the two different assays to diagnose or
confirm the presence
of preeclampsia and calculate an index.
[00175] In some cases, the disclosure provides a method for diagnosing or
confirming a
presence of preeclampsia in a subject comprising: (a) performing at least one
assay which
utilizes an antibody which binds fibronectin or an antibody that selectively
binds a same
antigen of fibronectin as the antibody, wherein the binding of the antibody
determines a level
of fibronectin in a sample derived from the subject; and (b) evaluating the
sample and using
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the level of fibronectin from the at least one assay to diagnose or confirm
the presence of
preeclampsia and calculating an index.
[00176] The disclosure also describes a method for diagnosing or confirming a
presence of
preeclampsia or the absence of preeclampsia in a subject comprising: (a)
evaluating a level of
a ratio of sFlt-1 and P1GF and a level a plurality of different biomarkers in
a sample derived
from the subject, wherein the different biomarker is not ferritin (FT),
cathepsin B (CTSB),
cathepsin C (CTSC), haptoglobin (HP), alpha-2-macroglobulin (A2M),
apolipoprotein E
(ApoE), apolipoprotein C-III (Apo-C3), apolipoprotein A-1 (ApoA1), retinol
binding protein
4 (RBP4), hemoglobin (HB), fibrinogen alpha (FGA), pikachurin (EGFLAM), free
human
chorionic gonadotropin (free beta hCG) or heme; and (b) evaluating the sample
and using the
levels from step (a) to determine an index to diagnose or confirm the
existence of
preeclampsia and calculate an index.
[00177] The disclosure further provides a method for distinguishing a subject
having
preeclampsia from a subject having symptoms suggestive of preeclampsia but who
does not
have preeclampsia, the method distinguishing preeclampsisa from complication
of pregnancy
symptoms, chronic hypertension, gestational hypertension, autoimmune disorders
and/or
gestational diabetes, wherein the method has a specificity of at least 90% or
AUC of at least
0.9 comprising: evaluating the level of a plurality of different biomarkers
from a sample
derived from the subject, generating an index indicative of the presence of
PE, absence of
PE, characteristics of PE, severity of PE, diagnosis of PE or prognosis of PE.
[00178] In some cases, the disclosure further describes a method for analyzing
the diagnosis,
prognosis, characteristics, presence, absence or severity of preeclampsia in a
subject
comprising: (a) utilizing a monoclonal antibody that selectively binds
fibronectin to
determine the levels of fibronectin in a sample derived from the subject, (b)
generating a
report indicating the presence, absence or severity of preeclampsia based on
the levels and
containing an index; and (c) evaluating the sample and based upon the index,
suggesting a
treatment for preeclampsia, the treatment involving aspirin, preterm labor or
bedrest.
[00179] The disclosure also describes a method for analyzing the diagnosis,
prognosis,
characteristics, presence, absence or severity of preeclampsia in a sample
derived from a
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subject comprising: utilizing an antibody directed to the antigen of the
fibronectin antibody in
at least one fibronectin ELISA kit to analyze and evaluate a sample from the
subject.
[00180] The disclosure further provides a method for diagnosing or confirming
a presence of
preeclampsia in a subject comprising: (a) performing at least one assay which
utilizes an
antibody that selectively binds fibronectin, a portion of fibronectin, a part
of fibronectin or a
fragment of fibronectin, wherein the binding of the antibody determines the
level of
fibronectin in a sample derived from the subject; and (b) evaluating the
sample and using the
level of fibronectin from the one assay to diagnose or confirm the existence
of preeclampsia
and calculate an index.
[00181] In some cases, the disclosure describes a method for confirming that a
subject does
not have preeclampsia comprising: evaluating a plurality of biomarkers in a
sample derived
from the subject to confirm the subject does not have preeclampsia wherein the
confirmation
has a specificity of greater than 95% or has an 0.9 AUC and is used to
calculate an index.
[00182] In some cases a PE signature, PE score or PE index involves comparing
the levels of
FN, FG, and another biomarkers selected from the following: HPX, sFlt-1, PAPP-
A, VEGF
(excluding VEGF-R1), P1GF and ADAM12; and determining if their relative weight
is at
least 3:1, 5:1, 10:1, 20:1, 30:1, 50:1, 50:1, 100:1, respectively, wherein
such a determination
is indicative of PE or likelihood of PE.
[00183] Other combinations of interest include comparing sFlt-1 and another
biomarkers
selected from the following: HPX, FN, FG, PAPP-A, VEGF (excluding VEGF-R1),
P1GF
and ADAM12 and determining if their relative weight is at least 2:1, 2.5:1
3:1, 5:1, 10:1,
20:1, 30:1, 50:1, 50:1, 100:1, respectively, wherein such a determination is
indicative of PE
or likelihood of PE.
[00184] Other combinations of interest include comparing P1GF and another
biomarkers
selected from the following: HPX, sFlt-1, PAPP-A, VEGF (excluding VEGF-R1),
FN, FG,
and ADAM12 and determining if their relative weight is at least 2:1, 2.5:1
3:1, 5:1, 10:1,
20:1, 30:1, 50:1, 50:1, 100:1, respectively, wherein such a determination is
indicative of PE
or likelihood of PE.
[00185] Other combinations of interest include comparing VEGF (excluding VEGF-
R1) and
another biomarkers selected from the following: HPX, sFlt-1, PAPP-A, P1GF, FN,
FG, and
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ADAM12; and determining if their relative weight is at least 2:1, 2.5:1
3:1,5:1, 10:1,20:1,
30:1, 50:1, 50:1, 100:1, respectively, wherein such a determination is
indicative of PE or
likelihood of PE.
[00186] As an example, the preeclampsia marker measurements may be analyzed to
produce
a preeclampsia score. Like a preeclampsia signature, a preeclampsia score is a
single metric
value that represents the sum of the weighted levels of one or more
preeclampsia markers in a
patient sample. A preeclampsia score may be determined by methods very similar
to those
described above for a preeclampsia signature, e.g. the levels of each of the
one or more
preeclampsia markers in a patient sample may be 10g2, loge or logio
transformed and
normalized, e.g., as described above for generating a preeclampsia profile;
the normalized
expression levels for each marker is then weighted by multiplying the
normalized level to a
weighting factor, or weight, to arrive at weighted levels for each of the one
or more markers;
and the weighted levels are then totaled and in some cases averaged to arrive
at a single
weighted level for the one or more preeclampsia markers analyzed.
Occasionally, the
weighted levels for the one or more preeclampsia markers may be subsequently
transformed,
for example using a logarithm-like inverse functions such as double logarithm
ln(ln(x)),
super-4-logarithm (i.e. tetra logarithm), hyper-4-logarithm (i.e. tetration),
iterated logarithm,
Lambert W function, or logit.
[00187] In contrast to a preeclampsia signature, the weighted levels are
defined by a
reference dataset, or training dataset. Thus, the preeclampsia score is
defined by a reference
dataset.
[00188] A preeclampsia index is an example of a preeclampsia marker level
representation.
A PE index is a metric system that indicates severity of PE or the degree of
likelihood of
developing PE. It is used to determine in what class the female subject is in.
The PE index is
calculated from the PE score, using a classification algorithm. Examples for
classification
algorithms are well known in the art. These algorithms can be formed using any
suitable
statistical classification (or "learning") method that attempts to segregate
bodies of data into
classes based on objective parameters present in the data. Classification
methods may be
either supervised or unsupervised. Examples of supervised and unsupervised
classification
processes are described in Jain, "Statistical Pattern Recognition: A Review,"
IEEE
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Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1,
January 2000. In
supervised classification, training data containing examples of known
categories are
presented to a learning mechanism, which learns one or more sets of
relationships that define
each of the known classes. New data may then be applied to the learning
mechanism, which
then classifies the new data using the learned relationships. Examples of
supervised
classification processes include linear regression processes (e.g., multiple
linear regression
(MLR), partial least squares (PLS) regression and principal components
regression (PCR)),
binary decision trees (e.g., recursive partitioning processes such as CART-
classification and
regression trees), artificial neural networks such as back propagation
networks, discriminant
analyses (e.g., Bayesian classifier or Fischer analysis), logistic
classifiers, and support vector
classifiers (support vector machines). An additional classification method is
a recursive
partitioning process. Recursive partitioning processes use recursive
partitioning trees to
classify spectra derived from unknown samples. Further details about recursive
partitioning
processes are provided in U.S. Patent Application No. 2002/0138208 Al to
Paulse et al.,
"Method for Analyzing Mass Spectra."
[00189] In some embodiments, the classification of PE that is used to provide
the
preeclampsia index described herein is not based on at least one of the
factors in the group
comprising blood pressure, weight gain, water retention, hereditary factors,
proteinurea,
headache, edema, protein/creatinine ratio, platelet count, stress, PE in prior
pregnancies,
nulliparity, age, age less than 20 years, age greater than 35, race, African-
American and
Filipino decent, serotype, Papanicolaou test results (Pap smear), prior
preeclampsia episodes,
familial history, number of pregnancies, number of miscarriages, body mass
index (BMI),
gestational diabetes, type I diabetes, obesity, glucose level, current and
past medications,
stress, PE in prior pregnancies (of the subject or her family members),
chronic hypertension,
renal disease or thrombophilia. In some embodiments, the classification of PE
that is used to
provide the preeclampsia index described herein is not based on any of the
characteristics just
delineated in this paragraph.
[00190] In some examples, the classification of PE that is used to provide the
preeclampsia
index described herein is based on at least one of the factors in the group
comprising blood
pressure, weight gain, water retention, hereditary factors, proteinurea,
headache, edema,
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proteinicreatinine ratio, platelet count, stress, PE in prior pregnancies,
nulliparity, age, age
less than 20 years, age greater than 35, race, African-American and Filipino
decent, serotype,
Papanicolaou test results (Pap smear), prior preeclampsia episodes, familial
history, number
of pregnancies, number of miscarriages, body mass index (BMI), gestational
diabetes, type I
diabetes, obesity, glucose level, current and past medications, stress, PE in
prior pregnancies
(of the subject or her family members), chronic hypertension, renal disease
and
thrombophilia.
Analysis of Data and Determination of Phenotype
[00191] In certain cases, the expression (e.g., polypeptide level) of only one
marker is
evaluated to produce a marker level representation. In some cases, the
expression of plurality
of markers (e.g., at least 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, or
more markers) is
evaluated. Accordingly, in the subject methods, the expression of at least one
marker in a
sample is evaluated. In certain cases, the evaluation that is made may be
viewed as an
evaluation of the proteome, as that term is employed in the art.
[00192] The marker level representation arrived at in this manner finds many
uses in
diagnosing, prognosing, characterizing, evaluating the severity of
preeclampsia, or in
confirming the presence or the absence of preeclampsia. For example, the
marker level
representation may be employed to predict if a subject will develop
preeclampsia, to diagnose
preeclampsia in a subject, to characterize a diagnosed preeclampsia, or to
monitor the
responsiveness of the subject to treatment for preeclampsia. In some
instances, the
measurement of particular combinations of preeclampsia markers disclosed
herein provides
for a preeclampsia prognosis that has an improved accuracy over a preeclampsia
prognosis
made using standard methods known in the art, e.g. VEGF-R1 (e.g., sFLT-1) and
P1GF.
[00193] In one case, the marker level representation may be employed in a
method for
diagnosing, prognosing, monitoring, characterizing or evaluating the severity
of
preeclampsia, or for confirming the presence or the absence of preeclamsia in
a subject based
on relative weights of biomarkers. Such method comprises: deriving a
biological sample
from a subject; performing an analysis of the subject's biological sample for
the presence and
amount of P1GF , HPX, sFlt-1 (i.e. VEGF-R1), PAPP-A, VEGF (excluding VEGF-R1),
FN,
FG, and ADAM12 employing the biomarker level to provide a preeclampsia
diagnosis or
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prognosis; wherein the relative weight of FN, to the biomarker is at least
1.5, 2, 2.5, 3, 3.5,
4, 4.5, or at least 5, and employing the biomarker level to provide a
preeclampsia diagnosis
or prognosis; wherein the weight comprises: generating a biomarker profile,
and determining
a single weighted level of the biomarker; wherein the profile comprises
expression 10g2, loge
or logio transformation and normalization; wherein weight level comprises
multiplying the
profile with a weighting factor, and wherein the weighting factor is
calculated by a method
comprising statistical machine learning method which may include, for example,
any of the
following: Principle Component Analysis (PCA), linear regression, support
vector machines
(SVMs), and random forests analysis.
[00194] In some cases, the marker level representation may be employed in a
method
diagnosing, prognosing, monitoring, characterizing, evaluating the severity of
preeclampsia,
or confirming the presence or the absence of preeclampsia in a subject based
on relative
weights of biomarkers. Such method comprises: deriving a biological sample
from the
subject; performing an analysis of her biological sample for the presence and
amount P1GF,
HPX, sFlt-1, PAPP-A, VEGF (excluding VEGF-R1), and ADAM12; employing the
biomarker level to provide a preeclampsia diagnosis or prognosis; wherein the
relative level
of FN, FG, to the number of biomarkers is at least 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14,
15, 20, 25, 30, 35, 40, 45, 50 or more.
[00195] The disclosure provides a method for confirming if a subject does not
have
preeclampsia comprising: evaluating a sample derived from the subject to
determine level of
plurality of biomarkers in the sample, using the levels of the plurality of
biomarkers to
calculate an index representative of a likelihood that the subject does not
have preeclampsia;
and based upon the index, confirming if the subject does not have
preeclampsia. In some
cases, the evaluating does not comprise comparing a sample derived from the
subject at a first
time point and a sample derived from the same subject at a second time point.
In some
instances, the evaluating does comprise comparing a sample derived from the
subject at a
first time point and a sample derived from the same subject at a second time
point. In some
cases, the method further comprises, based upon the index, suggesting a
treatment for
preeclampsia, the treatment involving aspirin, preterm labor or bedrest.
Sometimes, the
plurality of biomarkers are selected from the group comprising sFlt-1,P1GF, FN
and PAPP-
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A; sFlt-1, P1GF, FN, ADAM12 and PAPP-A; sFlt-1, P1GF, PAPP-A and FN; sFlt-1,
P1GF,
HPX, FN and PAPP-A; P1GF, ADAM12, FN and PAPP-A; P1GF, FN and PAPP-A; sFlt-1,
P1GF and FN; P1GF, FN and PAPP-A; sFlt-1, P1GF, FN and ADAM12; sFlt-1, P1GF
and
FN; P1GF, sFlt-1 and FN; sFlt-1, FN and ADAM12; or P1GF, FN and PAPP-A. In
some
cases, the calculating further comprises determining a ratio of levels of sFlt-
1 and P1GF. In
some cases, the levels are adjusted, normalized or raw levels, or any
combination thereof
[00196] In some cases, the marker level representation is employed by
comparing it to a
phenotype determination element, e.g., a preeclampsia phenotype determination
element, to
identify similarities or differences with the phenotype determination element,
where the
similarities or differences that are identified are then employed to predict
if a subject will
develop preeclampsia, to diagnose preeclampsia in a subject , to characterize
a diagnosed
preeclampsia, to monitor the responsiveness of the subject to treatment for
preeclampsia, to
evaluate the severity of preeclampsia, etc. For example, a preeclampsia
phenotype
determination element may be a sample derived from an individual that has or
does not have
preeclampsia. Such sample may be used, for example, as a reference or control
in the
experimental determination of the marker representation for a given subject.
As an example,
a preeclampsia phenotype determination element may be a marker level
representation (e.g.,
marker profile, signature, score or index) that is representative of a
preeclampsia state and
may be used as a reference or control to interpret the marker level
representation of a given
subject. The phenotype determination element may be a positive reference or
control. The
positive reference or control may be a sample or marker level representation
thereof from a
subject that has preeclampsia, or that will develop preeclampsia, or that has
preeclampsia that
is manageable by known treatments, or that has preeclampsia that has been
determined to be
responsive only to the delivery of the baby. Alternatively, the phenotype
determination
element may be a negative reference or control. The negative reference or
control may be a
sample or marker level representation thereof from a subject that has not
developed
preeclampsia, or a subject that is not pregnant. In some instances, the marker
representations
are obtained from the same type of sample as the sample that was employed to
generate the
marker representation for the individual being monitored. In such instances,
the phenotype
determination elements are obtained from the same type of sample. For example,
if the
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serum of an individual is being evaluated, the reference or control would
preferably be of
serum.
[00197] In certain cases, the obtained marker level representation is compared
to a single
phenotype determination element to obtain information regarding the individual
being tested
for preeclampsia. In certain cases, the obtained marker level representation
is compared to
plurality of phenotype determination elements. For example, the obtained
marker level
representation may be compared to a negative reference and a positive
reference to obtain
confirmed information regarding if the individual will develop preeclampsia.
In some
examples, the obtained marker level representation may be compared to a
reference that is
representative of a preeclampsia which is responsive to treatment, and a
reference that is
representative of a preeclampsia that is not responsive to treatment, in order
to obtain
information as to whether or not the patient will be responsive to treatment.
[00198] The comparison of the obtained marker level representation and the one
or more
phenotype determination elements may be performed using any convenient
methodology
known to those of skill in the art. For example, those of skill in the art of
arrays will know
that array profiles may be compared by, e.g., comparing digital images of the
expression
profiles, by comparing databases of expression data, etc. Patents describing
ways of
comparing expression profiles include, but are not limited to, U.S. Patent
Nos. 6,308,170 and
6,228,575, the disclosures of which are herein incorporated by reference.
Methods of
comparing marker level profiles are also described above. Similarly, those of
skill in the art
of ELISAs will know that ELISA data may be compared by, e.g. normalizing to
standard
curves, comparing normalized values, etc. The comparison step results in
information
regarding how similar or dissimilar the obtained marker level profile is to
the control or
reference profile(s), and which similarity or dissimilarity information is
employed to
diagnose, prognose, monitor, characterize or evaluate the severity of
preeclampsia, or
confirm the presence or absence of preeclampsia in order to predict the onset
of a
preeclampsia, diagnose preeclampsia, monitor a preeclampsia patient, evaluate
the severity of
PE, characterize PE, confirm the presence of PE or confirm the absence of PE.
Similarity
may be based on relative marker levels, absolute marker levels or a
combination of both. In
certain cases, a similarity determination is made using a computer having a
program stored
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thereon that is designed to receive input for a marker level result obtained
from a subject,
e.g., from a user, determine similarity to one or more reference profile, and
return a
preeclampsia prognosis, e.g., to a user (e.g., lab technician, physician, lay
person, pregnant
female, etc.). Further descriptions of computer-implemented cases of the
disclosure are
described below.
[00199] Depending on the type and nature of the reference or control
profile(s) to which the
obtained marker level profile is compared, the above comparison step yields a
variety of
different types of information regarding the cell or bodily fluid that is
assayed. As such, the
above comparison step can yield a positive or negative prediction of the onset
of
preeclampsia. Alternatively, such a comparison step can yield a positive or
negative
diagnosis of preeclampsia. Alternatively, such a comparison step can provide a
characterization or an evaluation of the severity of a preeclampsia.
[00200] In some cases, the PE marker level representation may be based on a
threshold value.
The method may also involve obtaining levels of one or more biomarkers and
comparing the
levels to a pre-determined threshold (e.g. a standard value). Such threshold
may be
determined according to the concentration of a biomarker.
[00201] In an example, the threshold for prediction and/or confirmation of PE
may be
determined according to the relative concentration of a biomarker in a subject
tested for PE or
having PE as compared to a control (e.g., the same subject pre-pregnancy or at
an earlier
stage in pregnancy or another female in the same or another gestation period
without PE).
For example, an indication of PE or likelihood of PE, or severity of PE may be
a FN,
concentration that is increased by a factor of at least, 100, at least 500, at
least 1,000, at least
2,000, at least 3,000, at least 4,000, at least 5,000, at least 10,000, at
least 12,000, at least
15,000, at least 20,000, at least 30,000, at least 40,000, at least 50,000, at
least 100,000 or at
least 20,000 relative to control; and/or VEGF concentration increased a factor
of at least 2, at
least 4, at least, at least 8, at least 10, at least 15, at least 20, at least
30, at least 40 or at least
50 relative to control; and/or concentration decreased by a factor of at least
20, at least 30, at
least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at
least 100, at least 110, at
least 120, at least 130, at least 140 or at least 150 relative to a control;
and/or Fms-like
tyrosine kinase 1 (sFlt1) concentration increased by a factor of at least 5,
at least 10, at least
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20, at least 30, at least 40, at least 40, at least 50, at least 60, at least
70 or at least 80 relative
to control; and/or Placental growth factor (P1GF) concentration decreased by a
factor of at
least 100, at least 200, at least 300, at least 400, at least 500, at least
600, at least 700, at
least 800, at least 900, at least 100, at least 1100, at least 1200, at least
1300, at least
1400 or at least 1500 relative to control; and/or ADAM metalloproteinase
domain 12
(ADAM12) concentration increased by a factor of at least 2, at least 5, at
least 10, at least 15,
at least 20, at least 30, at least 40, at least 50, at least 60, at least 70,
at least 80, at least 90 or
at least 100 relative to control.
[00202] In certain cases, no physical characteristics aside from biomarker
level is taken into
account when diagnosing, prognosing, monitoring, characterizing or evaluating
the severity
of preeclampsia, or when confirming the presence or absence of PE in a
subject. In some
instances, at least one of gestation period, blood pressure, weight gain,
water retention,
hereditary factors, proteinurea, headache, edema, protein/creatinine ratio,
platelet count,
stress, PE in prior pregnancies (of the subject or her family members),
nulliparity, age, age
less than 20 years, age greater than 35, race, African-American and Filipino
decent, serotype,
Papanicolaou test results (Pap smear), prior preeclampsia episodes, familial
history, number
of pregnancies, number of miscarriages, body mass index (BMI), gestational
diabetes, type I
diabetes, obesity, glucose level, current and past medications, chronic
hypertension, renal
disease and thrombophilia, are not taken into account when confirming,
diagnosing,
prognosing, characterizing or evaluating the severity of PE in a subject.
[00203] In some instances, at least one of gestation period, blood pressure,
weight gain, water
retention, hereditary factors, proteinurea, headache, edema,
protein/creatinine ratio, platelet
count, stress, PE in prior pregnancies (of the subject or her family members),
nulliparity, age,
age less than 20 years, age greater than 35, race, African-American and
Filipino decent,
serotype, Papanicolaou test results (Pap smear), prior preeclampsia episodes,
familial history,
number of pregnancies, number of miscarriages, body mass index (BMI),
gestational
diabetes, type I diabetes, obesity, glucose level, current and past
medications, chronic
hypertension, renal disease and thrombophilia, are taken into account when
confirming,
diagnosing, prognosing, characterizing or evaluating the severity of PE in a
subject.
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[00204] In some cases, gestation period is taken into account when confirming,
diagnosing,
prognosing, monitoring, characterizing or evaluating the severity of
preeclampsia. In some
cases, gestational period may be divided into early and late gestational
period. In some cases,
in addition to the patient's gestation period, other elements may be taken to
account
comprising the subject's blood pressure, familial history and urine protein
index.
[00205] In some cases, other analysis may be employed in conjunction with the
aforementioned marker level representation to provide a preeclampsia prognosis
for the
individual. Such analyses are well known in the art, and take into account,
for example,
blood pressure, weight gain, water retention, hereditary factors, proteinurea,
headache,
edema, protein/creatinine ratio, platelet count, stress, PE in prior
pregnancies (of the subject
or her family members), nulliparity, age, age less than 20 years, age greater
than 35, race,
African-American and Filipino decent, serotype, Papanicolaou test results (Pap
smear), prior
preeclampsia episodes, familial history, number of pregnancies, number of
miscarriages,
body mass index (BMI), gestational diabetes, type I diabetes, obesity, glucose
level, current
and past medications, chronic hypertension, renal disease, thrombophilia well
as other
characteristics of the pregnancy.
[00206] A test for PE measuring biomarkers from a subject's biological sample,
may provide
predictive performance of each biomarker panel analysis, as evaluated by ROC
curve
analysis (Zweig et al. Receiver-operating characteristic (ROC) plots: a
fundamental
evaluation tool in clinical medicine. Clinical chemistry 1993;39:561-77; Sing
et al. ROCR:
visualizing classifier performance in R. Bioinformatics 2005;21:3940-1). In
certain cases, the
PE signature, score or index may have a cumulative ROC value of at least 0.8,
0.85, 0.9, 0.95,
0.96, 0.97, 0.980, 0.985, 0.988, 0.990, 0.995, 0.998 or more. In certain
cases, the PE
signature, score or index may have a cumulative ROC value of at most 0.9,
0.95, 0.96, 0.97,
0.980, 0.985, 0.988, 0.990, 0.995, 0.998 or less. Alternatively or
additionally, the PE
threshold, signature, score or index can have a sensitivity of at least 60%,
65%, 70%, 75%,
80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99%; and/or a
specificity of
at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,
98%
or 99%. Alternatively or additionally, the PE threshold, signature, score or
index can have a
sensitivity of at most 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%,
95%,
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96%, 97%, 98% or 99%; and/or a specificity of at most 60%, 65%, 70%, 75%, 80%,
85%,
90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99%. Such PE signature, score
or
index can be for prognosis, diagnosis, monitoring, characterization or
evaluating the severity
of PE, confirming the absence of PE, or confirming the presence of PE, early
PE, or late PE.
Such PE signature, score or index preferably comprises up to 12, 11, 10, 9, 8,
7, 6, 5, 4, 3, 2
or 1 biomarkers. ROC values may be applicable to biomarker signature, score,
threshold or
index.
[00207] In some cases, the prediction, diagnosis, prognosis, monitoring,
characterization,
evaluating the severity of PE, or confirming the presence or absence of PE may
be provided
by providing, e.g., generating, a written report that includes the artisan's
monitoring
assessment, e.g., the artisan's prediction of the onset of preeclampsia (a
"preeclampsia
prediction"), the artisan's diagnosis of preeclampsia (a "preeclampsia
diagnosis"), the
artisan's confirmation of the presence of preeclampsia (a "preeclampsia
positive
confirmation"), the artisan's confirmation of the absence of preeclampsia (a
"preeclampsia
negative confirmation"), the artisan's monitoring of the subject's
preeclampsia (a
"preeclampsia monitor"), the artisan's characterization of the subject's
preeclampsia (a
"preeclampsia characterization"), or the artisan's determination of the
severity of the
subject's preeclampsia (a "evaluating the severity of preeclampsia"). Thus, a
subject method
may further include a step of generating or outputting a report providing the
results of an
assessment, which report can be provided in the form of an electronic medium
(e.g., an
electronic display on a computer monitor or an electronic file which may be
transferable), or
in the form of a tangible medium (e.g., a report printed on paper or other
tangible medium).
Computer Systems and Software
[00208] These methods of analysis may be readily performed by one of ordinary
skill in the
art by employing a computer-based system, e.g., using any hardware, software
and data
storage medium as is known in the art, and employing any algorithms convenient
for such
analysis. For example, data mining algorithms can be applied through "cloud
computing",
smartphone based or client-server based platforms, and the like.
[00209] The present disclosure contemplates the use of a computer system,
computer
readable medium, or software, with an input module for collecting input on
levels of a
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plurality of biomarkers; a processor for performing an algorithm for
performing a 10g2, loge
or logo transformation of the biomarker levels, thus obtaining log transformed
levels; a
processor for performing an algorithm for normalizing each of the log
transformed levels to
normalized levels; a processor for performing an algorithm for adjusting each
of the
normalized levels to a weighted normalized level; a processor for an algorithm
for totaling
and optionally averaging each of the adjusted levels; a processor for an
algorithm for
providing a PE score based on the total amount, and a processor for optionally
performing an
algorithm for providing a PE index based on the preeclampsia score. The
computer
preferably generates a report that can be provided to the caregiver and/or the
subject female
(e.g., pregnant female). The report can include in it none, any or all of the
following: name
of patient or subject, gestation period at time of testing, list of markers
analyzed, levels of
each marker as measured in the sample, direct comparison of level of
biomarkers to those in
the training set, log transformed and normalized level of biomarkers as
compared to log
transformed and normalized levels in the training set, log transformed,
normalized and
weighted numbers of biomarkers, a PE score, a PE index, and recommended course
of action
for the subject.
[00210] In some cases, the disclosure includes a system for diagnosing,
prognosing,
monitoring, characterizing, or evaluating the severity of preeclampsia,
confirming the
presence or the absence of PE in a female subject comprising: (a) an input
module for
receiving as input levels of one or more biomarkers, such as sFLT-1, P1GF and
at least two
other different biomarkers, (b) a processor optionally configured to perform
algorithms such
as (i) a 10g2, loge or logio transformation of the levels to obtain log
transformed levels, (ii)
normalizing each of the log transformed levels to normalized levels, (iii)
adjusting each of
said normalized levels to a weighted normalized level, (iv) totaling each of
the adjusted
levels, (v) averaging each of the adjusted levels; and (c) an output module
for outputting a
preeclampsia index based on a score wherein the index score comprises sFLT-
1/P1GF and an
addition of two other different biomarkers. In some cases, the processor may
perform an
algorithm adjusting the levels of one or more biomarkers to a training set or
a control value,
thereby providing one or more adjusted biomarker levels. The processor may
further perform
another algorithm that applies at least one binary operation using the
adjusted biomarker
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levels, adds or subtracts the one or more adjusted biomarker level, calculates
a ratio between
two adjusted biomarker levels, and/or manipulates the one or more adjusted
biomarker levels
by multiplying one or more variables by one or more corresponding weight
factors, wherein
the level of each of the one or more adjusted biomarker levels is input into a
specific variable,
wherein the corresponding weight factor is unique for each specific variable,
wherein at least
one of the corresponding weight factors is not one. In some cases, the
algorithm is a real
function.
1002111 In some cases, the disclosure includes a computer readable medium
containing
instructions which, when executed by a computer system, cause the computer
system to
receive a first data set pertaining to levels of PE biomarkers in a biological
sample derived
from a subject, and perform an analysis on those levels to obtain an
assessment of PE in the
subject. In some cases, the instructions, when executed by a computer system,
can cause the
computer system to perform those steps a second time (e.g., receive a second
data set
pertaining to levels of PE biomarkers, and perform a second analysis to obtain
a second
assessment). In some cases, those steps may be performed at different points
in time. In
some cases, the instructions cause the computer system to compare the first
assessment with
the second assessment and confirm PE or the lack thereof based on the
comparison.
Reports
[00212] The report may include information about the testing facility, which
information is
relevant to the hospital, clinic, or laboratory in which sample gathering
and/or data generation
was conducted. Sample gathering can include deriving a fluid sample, e.g.
blood, saliva,
urine etc.; a tissue sample, e.g. a tissue biopsy, etc. from a subject. Data
generation can
include measuring the level of polypeptide concentration for one or more genes
that are
differentially expressed or present at different levels in preeclampsia
patients versus healthy
individuals, e.g., individuals that do not have and/or do not develop
preeclampsia. This
information can include one or more details relating to, for example, the name
and location of
the testing facility, the identity of the lab technician who conducted the
assay and/or who
entered the input data, the date and time the assay was conducted and/or
analyzed, the
location where the sample and/or result data is stored, or the lot number of
the reagents (e.g.,
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kit, etc.) used in the assay. Report fields with this information can be
populated using
information provided by the user.
[00213] The report may include information about the service provider, which
may be located
outside the healthcare facility at which the user is located, or within the
healthcare facility.
Examples of such information can include the name and location of the service
provider, the
name of the reviewer, and where necessary or desired the name of the
individual who
conducted sample gathering and/or data generation. Report fields with this
information can
be populated using data entered by the user, which can be selected from among
pre-scripted
selections (e.g., using a drop-down menu). Other service provider information
in the report
can include contact information for technical information about the result
and/or about the
interpretive report.
[00214] The report may include a patient data section. The patient data
section may include
one or more items of the list consisting of patient medical history and
symptoms (which can
include, e.g., gestational period, blood pressure, proteinurea, diabetes,
glucose level, body
mass index, age, race, serotype, Papanicolaou test results (Pap smear), prior
preeclampsia
episodes, familial history, number of pregnancies, number of miscarriages,
weight gain, water
retention, hereditary factors, headache, edema, protein/creatinine ratio,
current medications or
past medications, stress, PE in prior pregnancies (of the subject or her
family members),
nulliparity, chronic hypertension, renal disease or thrombophilia, and any
other
characteristics of the pregnancy), administrative patient data such as
information to identify
the patient (e.g., name, patient date of birth (DOB), gender, mailing and/or
residence address,
medical record number (MRN), room and/or bed number in a healthcare facility,
insurance
information, and the like), the name of the patient's physician or other
health professional
who ordered the monitoring assessment, and (if different from the ordering
physician) the
name of a staff physician who is responsible for the patient's care (e.g.,
primary care
physician).
[00215] The report may include a sample data section, which may provide
information about
the biological sample analyzed in the monitoring assessment, such as the
source of biological
sample derived from the patient (e.g., blood, saliva, or type of tissue,
etc.), how the sample
was handled (e.g., storage temperature, preparatory protocols) or the date and
time collected.
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Report fields with this information can generally be populated using data
entered by the user,
some of which may be provided as pre-scripted selections (e.g., using a drop-
down menu).
[00216] The report may include an assessment report section, which may include
information
generated after processing of the data as described herein. The interpretive
report can include
a prediction of the likelihood that the subject will develop PE, diagnosis of
PE, a
confirmation of that the subject has PE, a confirmation that the subject does
not have PE,
monitoring of the subject's PE (e.g., whether the level of biomarkers remains
stable or is
altered), the characteristics of PE, the level of severity of PE, or any
combination thereof If
the biomarker level is altered, the report may include the degree of such
alteration, and the
alteration's significance in terms of PE occurrence, forecast or severity. The
interpretive
report can include, for example, the results of a protein level determination
assay (e.g., "1.5
nmol/liter ADAM12 in serum"); and interpretation of that biomarker level,
e.g., prediction,
diagnosis, monitoring, characterization, evaluation of the severity of PE, or
confirmation of
the presence or absence of PE. In some examples, the assessment portion of the
report
includes a recommendation(s). For example, where the results indicate that
preeclampsia is
likely, the recommendation includes a recommendation that diet be altered,
blood pressure
medicines administered, bed-rest is recommended, pre-term labor recommended,
diabetes
medicines administered, etc., as recommended in the art.
[00217] The report may include at least one of diagnosis, prognosis,
characteristics, monitor,
severity of PE, or confirmation of the presence or absence of PE; a biomarker
index value
based on the analysis of one or more biomarkers detected in a biological
sample from the
pregnant subject. The report may include the predictive performance of each
biomarker
panel analysis. In some instances, the predictive performance is evaluated by
ROC curve
analysis.
[00218] It will also be readily appreciated that the reports can include
additional elements or
modified elements. For example, where electronic, the report can contain
hyperlinks which
point to internal or external databases which provide more detailed
information about
selected elements of the report. For example, the patient data element of the
report can
include a hyperlink to an electronic patient record, or a site for accessing
such a patient
record, which patient record is maintained in a confidential database. In some
instances, the
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inclusion of a hyperlink may be of interest in an in-hospital system or in-
clinic setting. In
some instances, the inclusion of a hyperlink may be of interest in a home or
work setting.
When in electronic format, the report is recorded on a suitable physical
medium, such as a
computer readable medium, e.g., in a computer memory, zip drive, floppy disc,
USB chip,
CD, DVD, or any other storage media capable of storing magnetic or electronic
information
that is retrievable.
[00219] It will be readily appreciated that the report can include all or some
of the elements
above, with the proviso that the report generally includes at least the
elements sufficient to
provide the analysis requested by the user (e.g., prediction, diagnosis,
monitoring,
characterization, evaluation of the severity of preeclampsia, or confirmation
of the absence or
presence of preeclampsia).
[00220] The disclosure further provides a business method comprising the step
of
determining presence, absence, forecast, severity, characteristics, of
preeclampsia in a
subject, or confirming the absence or presence of preeclamsia in a subject .
The method
comprises the steps of: evaluating levels of sFLT-1, P1GF and a plurality of
biomarkers in a
sample derived from the subject, wherein the plurality of biomarkers are not
ferritin (FT),
cathepsin B (CTSB), cathepsin C (CTSC), haptoglobin (HP), alpha-2-
macroglobulin (A2M),
apolipoprotein E (ApoE), apolipoprotein C-III (Apo-C3), apolipoprotein A-1
(ApoA1),
retinol binding protein 4 (RBP4), hemoglobin (HB), fibrinogen alpha (FGA),
pikachurin
(EGFLAM), free human chorionic gonadotropin (free beta hCG) or heme,
determining a
biomarker index value, the index comprises sFLT-1/P1GF and the addition of the
a plurality
of biomarkers, employing the biomarker index to provide a preeclampsia
determination,
confirmation, absence, diagnosis, prognosis, severity or characteristics, and
providing a
report in exchange for a fee, wherein the report indicates an index value
based on the analysis
of the biomarkers, and an indication of a range specifying whether the subject
is at low risk of
PE, high risk of preeclampsia, or experiencing preeclampsia. In some cases,
the business
method further comprises transmitting a report. In some cases, the report
contains
information about the subject including blood pressure, age, weight,
gestational age,
ethnicity, diabetes, kidney disease, autoimmune disease, maternal history,
proteinurea, body
mass index, swelling, abdominal pressure, uterine pulsatility index,
thrombocytopenia,
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previous history of preeclampsia, previous history of eclampsia, first birth,
multiple births,
circulating free DNA, circulating fetal DNA, free DNA, fetal DNA, history of
smoking or a
family history of a related complicating factor. In some cases, the a
plurality of biomarkers
excludes endoglin, fibrinopeptide A, antithrombin III, IGFALS, FLT4, IGFBP-5,
TGF-B1,
fibrinopeptideA:D-dimer, ficolin-2, ficolin-3, creatinine, clusterin, H2
relaxin, P1GF-2,
P1GF-3 and human chorionic gonadotropin. In some cases, the report is
electronic. In some
cases, the index is unaffected by at least one of blood pressure, age, weight,
gestational age,
ethnicity, diabetes, kidney disease, autoimmune disease, maternal history and
a family
history. In some cases, the index is unaffected by all of blood pressure,
weight, ethnicity,
diabetes, kidney disease, autoimmune disease, maternal history and a family
history. In some
cases, the index is unaffected by age. Sometimes, the index is unaffected by
gestational age.
[00221] In some cases, the disclosure further provides a business method
comprising the step
of determining the presence, absence, forecast, severity, or characteristics,
of preeclampsia in
a subject, or confirming the absence or presence of preeclamsia in a subject.
The method can
comprise the steps of: (a) evaluating levels of sFLT-1, P1GF and at least two
other different
biomarkers in a sample derived from the subject, wherein the at least two
other biomarkers
are not ferritin (FT), cathepsin B (CTSB), cathepsin C (CTSC), haptoglobin
(HP), alpha-2-
macroglobulin (A2M), apolipoprotein E (ApoE), apolipoprotein C-III (Apo-C3),
apolipoprotein A-1 (ApoA1), retinol binding protein 4 (RBP4), hemoglobin (HB),
fibrinogen
alpha (FGA), pikachurin (EGFLAM), free human chorionic gonadotropin (free beta
hCG) or
heme, (b) determining a biomarker index value, the index comprising sFLT-
1/P1GF and the
addition of the at least two other different biomarkers, (c) employing the
biomarker index to
provide a preeclampsia determination, confirmation, absence, diagnosis,
prognosis, severity
or characteristic, and (d) providing a report in exchange for a fee, wherein
the report indicates
an index value based on the analysis of the biomarkers, and at least one of:
specifying
whether the subject is at low risk of preeclampsia, specifying whether the
subject is at high
risk of preeclampsia, specifying whether the subject has preeclampsia,
specifying whether the
subject does not have preeclampsia, characterizing preeclampsia, and
indicating the severity
of preeclampsia.
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Reagents, Systems and Kits
[00222] Also provided are reagents, systems and kits thereof for practicing
one or more of the
above-described methods. The subject reagents, systems and kits thereof may
vary greatly.
Variation may include alterations in incubation time and temperature. Reagents
of interest
include reagents specifically designed for use in producing the above-
described marker level
representations of preeclampsia markers from a sample, for example, one or
more detection
elements. The detection elements may be antibodies or peptides for the
detection of protein,
protein fragments. The detection elements can be oligonucleotides for the
detection of nucleic
acids. In some instances, the detection element comprises a reagent to detect
the expression
of a single preeclampsia marker, for example, the detection element may be a
dipstick, a
plate, an array, or cocktail that comprises one or more detection elements
(e.g. one or more
antibodies, one or more oligonucleotides, one or more sets of PCR primers, one
or more sets
of or isothermal polynucleotide amplification primers, etc.) which may be used
to detect the
expression of at least two preeclampsia markers simultaneously.
[00223] In one case, a kit for detecting the presence, absence, forecast,
severity or character
of preeclampsia in subject is provided. Such kit includes a plurality of
detection elements
(analytes) used for measuring the plurality of biomarkers selected from the
group consisting
of P1GF, HPX, sFlt-1, PAPP-A, VEGF (excluding VEGF-R1), FN, FG, and ADAM12. In
other instances, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14 (or
more) different reagents
are utilized to measure at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14
(or more) different
biomarkers. When referring to a biomarker, for example P1GF, unless explicitly
stated, it is
understood that all isoforms of the biomarker, for example, P1GF, which are
known or to be
discovered are referred to by the biomarker term, for example, P1GF (e.g.,
hPA18788.6 [152
aa], hPA18788.1 [1017 aa], hPA18788.2 [1009 aa], hPA18788.3 [775 aa],
hPA18788.4 [403
aa], hPA18788.5 [375 aa] or hPA18788.9 [463 aa]). (P1GF as used herein is
equivalent to
P1GF or PLGF). When referring to hemopexin (HPX), it is understood that both
heme bound
and heme unbound forms of hemopexin are referred to.
[00224] In one embodiment, one type of reagent specifically tailored for
generating marker
level representations (e.g., preeclampsia markers) is a collection of
antibodies that bind
specifically to the protein markers. In some examples, such antibodies may be
employed in
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an ELISA (such as competitive or sandwich ELISA format). Other analytic
methodologies
that may be employed include xMAPTm microsphere format, a proteomic array,
suspension
for analysis by flow cytometry, western blotting, dot blotting or
immunohistochemistry.
Methods for using the same are well understood in the art. These antibodies
can be provided
in solution. Alternatively, they may be provided pre-bound to a solid matrix,
for example,
the wells of a multi-well dish or the surfaces of xMAP microspheres.
[00225] In some embodiments, an array of probe nucleic acids in which the
genes of interest
are represented, may be employed as reagents. A variety of different array
formats are
known in the art, with a wide variety of different probe structures, substrate
compositions and
attachment technologies (e.g., dot blot arrays, microarrays, etc.).
Representative array
structures of interest include those described in U.S. Patent Nos.: 5,143,854;
5,288,644;
5,324,633; 5,432,049; 5,470,710; 5,492,806; 5,503,980; 5,510,270; 5,525,464;
5,547,839;
5,580,732; 5,661,028; 5,800,992; the disclosures of which are herein
incorporated by
reference; as well as WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP
373
203; and EP 785 280.
[00226] A type of reagent that is specifically tailored for generating marker
level
representations of genes, e.g. preeclampsia genes, is a collection of gene
specific primers that
is designed to selectively amplify such genes (e.g., using a PCR-based
technique, e.g., real-
time RT-PCR, or isothermal amplification techniques such as loop mediated
isothermal
amplification of DNA (LAMP), strand displacement amplification (SDA), sequence
based
amplification (NASBA), self-sustained sequence replication (3 SR) and the
like). Gene
specific primers and methods for using the same are described in U.S. Patent
No. 5,994,076,
the disclosure of which is herein incorporated by reference.
[00227] In some cases, the kit may include polynucleotide primers that
selectively hybridize
polynucleotide sequences encoding selected biomarkers. In certain cases, the
primers
selectively hybridize at least two polynucleotide sequences encoding proteins
that are
selected from the group consisting of P1GF, HPX, sFlt-1, PAPP-A, VEGF
(excluding VEGF-
R1), FN, FG, and ADAM12. Such primers may be DNA or RNA primers.
[00228] Of interest are also arrays of probes, collections of primers, or
collections of
antibodies that include probes, primers or antibodies (also called reagents)
that are specific
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for at least one gene or protein selected from the group consisting P1GF, HPX,
sFlt-1, PAPP-
A, VEGF (excluding VEGF-R1), FN, FG, and ADAM12. In some instances for a
plurality of
these genes, e.g., at least 2, 3, 4, 8 or more may be selected.
[00229] In certain cases, the collection of probes, primers or antibodies
include reagents
specific for one or more of P1GF, HPX, sFlt-1, PAPP-A, VEGF (excluding VEGF-
R1), FN,
FG, and ADAM12. The subject probe, primer, or antibody collections or reagents
may
include reagents that are specific only for the genes, proteins or cofactors
that are listed
above, or they may include reagents specific for additional genes, proteins or
cofactors that
are not listed above, such as probes, primers, or antibodies specific for
genes, proteins or
cofactors whose expression pattern are known in the art to be associated with
preeclampsia,
e.g. sFLT-1 (VEGF-R1) and P1GF.
[00230] The systems and kits of the subject disclosure may include the above-
described
arrays, gene-specific primer collections, or protein-specific antibody
collections. In the case
of protein-specific antibody kit, the systems and kits may further include one
or more
additional reagents, such as bovine serum albumin (BSA), casein, solutions of
powdered
milk, bovine gamma globulin (BGG) in phosphate buffered saline (PBS)/Tween or
PBS/Triton-X 100, PBS/Tween, PBS/Triton-X 100, or borate buffer; selected
solid surface,
preferably a surface exhibiting a protein affinity such as the wells of a
polystyrene microtiter
plate; second antibody will have an associated enzyme, e.g. urease,
peroxidase, or alkaline
phosphatase, and an appropriate chromogenic substrate. For example, a urease
or
peroxidase-conjugated anti-human IgG may be employed, PBS-containing solution
such as
PBS/Tween), a chromogenic substrate such as urea and bromocresol purple in the
case of a
urease label or 2,2'-azino-di-(3-ethyl-benzthiazoline)-6-sulfonic acid (ABTS)
and H202, in
the case of a peroxidase label. Quantitation is then achieved by measuring the
degree of
color generation, e.g., using a visible spectrum spectrophotometer.
[00231] The systems and kits may further include one or more additional
reagents employed
in the various methods, such as primers for generating target nucleic acids,
dNTPs and/or
rNTPs, which may be either premixed or separate, one or more uniquely labeled
dNTPs
and/or rNTPs, such as biotinylated or Cy3 or Cy5 tagged dNTPs, gold or silver
particles with
different scattering spectra, or other post synthesis labeling reagent, such
as chemically active
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derivatives of fluorescent dyes, enzymes, such as reverse transcriptases, DNA
polymerases,
RNA polymerases, and the like, various buffer mediums, e.g. hybridization and
washing
buffers, prefabricated probe arrays, labeled probe purification reagents and
components, like
spin columns, etc., signal generation and detection reagents, e.g. labeled
secondary
antibodies, streptavidin-alkaline phosphatase conjugate, chemifluorescent or
chemiluminescent substrate, and the like.
[00232] The subject systems and kits may also include a preeclampsia phenotype
determination element, which element is, in many cases, a reference or control
sample or
marker representation that can be employed, e.g., by a suitable experimental
or computing
means, to make a preeclampsia prognosis based on an "input" marker level
profile, e.g., that
has been determined with the above described marker determination element.
Representative
preeclampsia phenotype determination elements include samples from an
individual known
to have or not have preeclampsia, databases of marker level representations,
e.g., reference or
control profiles, and the like, as described above.
[00233] In addition to the above components, the subject kits will further
include instructions
for practicing the subject methods. These instructions may be present in the
subject kits in a
variety of forms, one or more of which may be present in the kit. One form in
which these
instructions may be present is as printed information on a suitable medium or
substrate, e.g.,
a piece or pieces of paper on which the information is printed, in the
packaging of the kit, in a
package insert, etc. A means would be a computer readable medium, e.g.,
diskette, floppy
disc, USB device, CD, etc., on which the information has been recorded. Some
means may
be present is a website address which may be used via the intern& (i.e. via
the cloud) to
access the information at a removed site. Any convenient means may be present
in the kits.
[00234] In certain cases, the present disclosure provides a business method
for determining
presence, absence, forecast, severity, monitor or character of PE in a
subject. Such method
includes: performing an analysis of the subject's biological sample for the
presence and
amount of one or more biomarkers selected from a group consisting of P1GF,
HPX, sFlt-1,
PAPP-A, VEGF (excluding VEGF-R1), FN, FG, and ADAM12; determining a weight,
signature, score or index value of the biomarkers; employing the biomarker
weight, signature,
score or index value to provide a diagnosis, prognosis, severity, confirmation
of presence,
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confirmation of absence. or characteristics of PE, and providing a report in
exchange for a
fee.
[00235] In other cases, the present disclosure provides a business method for
determining
presence, determining absence, determining forecast, monitoring, determining
severity,
confirming presence of, confirming absence of or characterizing PE in a
subject. Such
method includes: performing an analysis of the subject's biological sample for
the presence
and amount of one or more biomarkers selected from a group consisting of P1GF,
HPX, sFlt-
1, PAPP-A, VEGF (excluding VEGF-R1), FN, FG, and ADAM12. In certain cases, the
collection of probes, primers, or antibodies includes reagents specific for
P1GF, HPX, sFlt-1,
PAPP-A, VEGF (excluding VEGF-R1), FN, FG, and ADAM12; determining a weight,
signature, score or index value of the biomarkers; employing the biomarker
weight, signature,
score or index value to provide a diagnosis, prognosis, characteristic,
confirmation presence,
confirmation absence , or determination of the severity of PE and providing a
report in
exchange for a fee.
[00236] In certain cases, the index value is based on the analysis of
biomarkers. In some
examples, an indication of a range or threshold value is provided specifying
whether the
subject is at low risk of PE, high risk of PE, or experiencing PE. In other
cases, the index
value is based on the analysis of the biomarkers, and an indication of a range
specifying
whether the subject has mild PE, moderate PE, or severe PE. In some cases an
indication of
the reliability or certainty of the confirmation (whether the female subject
has or does not
have PE) is provided.
[00237] Any method, kit, composition, business method, computer system
disclosed herein
may be used with a sample wherein the sample is a serum sample. In some cases,
the sample
is derived from blood, plasma, serum, urine, cells or body fluids. In some
cases, the sample is
derived from the mother or the fetus. In some cases, the sample is a vaginal
swab. In some
cases, the sample is not a urine sample. In some cases, the biomarker is a
peptide. In some
cases, the biomarker in a sample is a peptide, a portion of the peptide, a
fragment of the
peptide, a peptide containing an antigen, a portion of the peptide wherein the
portion of the
peptide contains an antigen, a fragment of the peptide wherein the fragment of
the peptide
contains an antigen. In some cases, evaluating comprises measuring a level of
at least one
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RNA molecule. In some cases, evaluating comprises performing at least one
sequencing
reaction. In some cases, the index is unaffected by blood pressure, age,
weight, gestational
age, ethnicity, diabetes, kidney disease, autoimmune disease, maternal history
or a family
history. In some cases, evaluating does not comprise comparing a sample
derived from a
subject at a first time point and a sample derived from the same subject at a
second time
point. In some cases, evaluating comprises comparing a sample derived from a
subject at a
first time point and a sample derived from the same subject at a second time
point. In some
cases, the evaluating step comprises determining the levels of a biomarker
selected from the
group consisting of sFlt-1, P1GF, VEGF (excluding VEGF-R1), ADAM12, HPX, PAPP-
A
and FN. In some cases, the plurality of biomarkers excludes endoglin,
fibrinopeptide A,
antithrombin III, IGFALS, FLT4, IGFBP-5, TGF-B1, fibrinopeptideA:D-dimer,
ficolin-2,
ficolin-3, creatinine, clusterin, H2 relaxin, P1GF-2, P1GF-3 and human
chorionic
gonadotropin. In some cases, the measuring is conducted using at least one
antibody which
recognizes a biomarker selected from the group consisting of sFlt-1, P1GF,
VEGF (excluding
VEGF-R1), ADAM12, HPX, PAPP-A and FN. In some cases, the method does not
include
predicting preeclampsia in a subject that is asymptomatic of preeclampsia. In
some cases, the
evaluating does not comprise Doppler screening. In some cases, the method does
not include
detecting the presence of microvesicles or exosomes in the sample. In some
cases, the method
is greater than 85% accurate. In some cases, the method is greater than 85%
sensitive. In
some cases, the method is greater than 85% specific. In some cases, the method
is greater
than 85% accurate. In some cases, the method has a positive predictive value
greater than
85%. In some cases, the method has a negative predictive value greater than
85%. In some
cases, the biomarker correlates with blood pressure, age, weight, gestational
period, ethnicity,
diabetes, kidney disease, autoimmune disease, maternal history, proteinurea,
body mass
index, swelling, abdominal pressure, uterine pulsatility index,
thrombocytopenia, previous
history of preeclampsia, previous history of eclampsia, the number of previous
births given
by the subject, circulating free DNA, circulating fetal DNA, free DNA, fetal
DNA, history of
smoking or a family history of a related complicating factor. In some cases,
the biomarker
does not correlate with blood pressure, age, weight, gestational age,
ethnicity, diabetes,
kidney disease, autoimmune disease, maternal history, proteinurea, body mass
index,
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swelling, abdominal pressure, uterine pulsatility index, thrombocytopenia,
previous history of
preeclampsia, previous history of eclampsia, the number of previous births
given by the
subject, circulating free DNA, circulating fetal DNA, free DNA, fetal DNA,
history of
smoking or a family history of a related complicating factor.
[00238] The disclosure further provides a test for excluding diagnosis of
preeclampsia,
wherein said test measures one or more biomarkers from a sample derived from a
subject and
has an overall ROC value of at least 0.8, 0.85, 0.9, 0.95, 0.980, 0.981,
0.982, 0.983, 0.984,
0.985, 0.986, 0.987, 0.989, 0.990, 0.991, 0.992, 0.993, 0.994, 0.995, 0.996,
0.997, 0.998,
0.999 or more. In some cases, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
or 14 biomarkers are
assayed. In some cases, at most 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14
biomarkers are
assayed. In some cases, the overall ROC value is unaffected by blood pressure,
age, weight,
gestational period, ethnicity, diabetes, kidney disease, autoimmune disease,
maternal history
or a family history. In some cases, the sample is a serum sample. In some
cases, the sample is
derived from blood, plasma, serum, urine, cells or body fluids. In some cases,
the sample is
derived from the mother or the fetus. In some cases, the sample is a vaginal
swab. In some
cases, the sample is not a urine sample. In some cases, the biomarker is a
peptide. In some
cases, the biomarker in a sample is a peptide, a portion of the peptide, a
fragment of the
peptide, a peptide containing an antigen, a portion of the peptide wherein the
portion of the
peptide contains an antigen, a fragment of the peptide wherein the fragment of
the peptide
contains an antigen. In some cases, the result of the test is unaffected by
blood pressure, age,
weight, gestational period, ethnicity, diabetes, kidney disease, autoimmune
disease, maternal
history or family history. In some cases, the test does not comprise comparing
a sample
derived from a subject at a first point in time and a sample derived from the
same subject at a
second point in time. In some cases, the test consists of comparing a sample
derived from a
subject at a first point in time and a sample derived from the same subject at
a second point in
time. In some cases, the test comprises determining the levels of a biomarker
selected from
the group consisting of sFlt-1, P1GF, VEGF (excluding VEGF-R1), ADAM12, HPX,
PAPP-
A and FN. In some cases, the biomarkers excludes endoglin, fibrinopeptide A,
antithrombin
III, IGFALS, FLT4, IGFBP-5, TGF-B1, fibrinopeptideA:D-dimer, ficolin-2,
ficolin-3,
creatinine, clusterin, H2 relaxin, P1GF-2, P1GF-3 and human chorionic
gonadotropin. In
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some cases, the test is conducted using at least one antibody which recognizes
a biomarker
selected from the group consisting of sFlt-1, P1GF, VEGF (excluding VEGF-R1),
ADAM12,
HPX, PAPP-A and FN. In some cases, the test does not include predicting
preeclampsia in a
subject that is asymptomatic. In some cases, the test does not consist of
Doppler screening. In
some cases, the test does not include detecting the presence of microvesicles
or exosomes in
the sample. In some cases, the test is at least 85, 90, 95% or more accurate.
In some cases, the
test is at least 85, 90, 95% or more sensitive. In some cases, the test is at
least 85, 90, 95% or
more specific. In some cases, the test has a positive predictive value of at
least 85, 90, 95%.
In some cases, the test has a negative predictive value of at least 85, 90,
95% or more . In
some cases, the biomarker correlates with blood pressure, age, weight,
gestational period,
ethnicity, diabetes, kidney disease, autoimmune disease, maternal history,
proteinurea, body
mass index, swelling, abdominal pressure, uterine pulsatility index,
thrombocytopenia,
previous history of preeclampsia, previous history of eclampsia, the number of
previous
births given by the subject, circulating free DNA, circulating fetal DNA, free
DNA, fetal
DNA, history of smoking or a family history of a related complicating factor.
In some cases,
the biomarker does not correlate with blood pressure, age, weight, gestational
age, ethnicity,
diabetes, kidney disease, autoimmune disease, maternal history, proteinurea,
body mass
index, swelling, abdominal pressure, uterine pulsatility index,
thrombocytopenia, previous
history of preeclampsia, previous history of eclampsia, the number of previous
births given
by the subject, circulating free DNA, circulating fetal DNA, free DNA, fetal
DNA, history of
smoking or a family history of a related complicating factor. In some cases,
the test includes
performing a biological assay, a functional assay, an ELISA, an immunological
assay, mass
spectrometry, chromatography, mephelometry, radial immunodiffusion or single
radial
immunodiffusion. In some cases, the test includes digesting and measuring a
biomarker. In
some cases, the test includes derivatizing and measuring a biomarker. In some
cases, the test
includes isolating peptides from at least one cell in the sample. In some
cases, the test further
includes detecting polymorphisms or modifications to the biomarker. In some
cases, the test
further includes detecting RNA and/or DNA. In some cases, the test further
includes
detecting RNA and/or DNA associated with the biomarker. In some cases, the
test further
includes detecting transcription factors and/or transcription factor co-
factors. In some cases,
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the test further includes detecting transcription factors and/or transcription
factor co-factors
associated with the biomarker. In some cases, the test further includes use of
an algorithm, a
threshold value, a directional change over time, comparing the index to a
single patient,
comparing the index to a control group or comparing the index to a reference
standard. In
some cases, the test is used to confirm the presence of preeclampsia in a
subject wherein the
subject has at least one symptom associated with preeclampsia. In some cases,
the test is used
to provide a diagnosis of preeclampsia in a subject wherein the subject has
hypertension and
proteinurea. In some instances, the test is used to provide a diagnosis of
preeclampsia in a
subject wherein the subject has hypertension.
[00239] In some cases, the test is used to provide a diagnosis of preeclampsia
in a subject
wherein the subject has one risk factor associated with preeclampsia. In some
cases, the test
is used to determine if a subject is at risk for preeclampsia. In some cases,
the test is used to
quantify a risk that a subject will develop preeclampsia. In some cases, the
test is used to
predict a time at which a subject will develop preeclampsia. In some cases,
the test is used to
predict maternal and/or fetal outcomes of preeclampsia. In some cases, the
test is used to
distinguish between mild, moderate and severe preeclampsia. In some cases, the
test is used
to determine whether HELLP syndrome, preterm birth, interuterine growth
restriction,
placental abruption, placental accrete, low fetal birth weight, low size for
gestational period,
gestational hypertension, chronic hypertension, gestational diabetes, Type I
diabetes, Type II
diabetes or risk of spontaneous abortion are a result of the subject having
preeclampsia. In
some cases, the test further includes creating an electronic on non-electronic
report.
[00240] In some embodiments, the disclosure includes a test for excluding
diagnosis of
preeclampsia, wherein said test measures one or more biomarkers from a sample
taken from a
subject and has an overall ROC value of at least 0.8. In some examples the
test has a ROC
value of at least 0.8, 0.85, 0.9, 0.95, 0.980, 0.981, 0.982, 0.983, 0.984,
0.985, 0.986, 0.987,
0.989, 0.990, 0.991, 0.992, 0.993, 0.994, 0.995, 0.996, 0.997, 0.998, 0.999 or
more.
[00241] The disclosure further provides a kit for confirming the presence of
PE, confirming
the absence of PE, diagnosing, prognosing, monitoring, characterizing, or
determining the
severity of preeclampsia in a subject, said kit comprising at least two
reagents that are
specific for determining level of fibronectin in a sample from the subject. In
some cases, the
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at least two reagents comprise two different antibodies that selectively bind
fibronectin. In
some cases, the kit further comprises reagents that are specific for
determining levels of sFlt-
1 and P1GF in the sample. In some cases, the sample is a serum sample. In some
cases, the
sample is a blood sample. In some cases, the sample is not a urine sample. In
some cases, the
kit does not include a reagent to detect IGFALS, FLT4, P1GF, P1GF-2, P1GF-3 or
sFlt-1.
[00242] The disclosure further provides a kit for diagnosing, prognosing,
monitoring,
characterizing, determining the severity of preeclampsia, or confirming the
presence or
absence of preeclampsia in a subject, said kit comprising a first reagent
specific for
determining level of PAPP-A and a second reagent specific for determining
ADAM12. In
some cases, the kit does not include a reagent to detect IGFALS, FLT4, P1GF,
P1GF-2,
P1GF-3 or sFlt-1. In some cases, the kit cannot be used to prognose, diagnose,
screen for,
determine or confirm Down's syndrome. In some cases, the kit cannot be used to
prognose,
diagnose, screen for, determine or confirm cardiovascular disease. In some
cases, the
biomarker is not P1GF-2 or P1GF-3.
[00243] Often, the disclosure further provides a kit for confirming the
presence of PE,
confirming the absence of PE, diagnosing, prognosing, monitoring,
characterizing or
determining the severity of PE in a subject, said kit comprising at least two
reagents that are
specific for determining level of fibronectin in a sample from the pregnant
woman.
[00244] In some cases, the evaluating includes performing a biological assay,
a functional
assay, an ELISA, an immunological assay, mass spectrometry, chromatography,
nephelometry, radial immunodiffusion or single radial immunodiffusion. In some
cases, the
evaluating includes digesting and measuring a biomarker. In some cases, the
evaluating
includes derivatizing and measuring a biomarker. In some cases, the evaluating
includes
isolating peptides from at least one cell in the sample. In some cases, the
method further
includes detecting polymorphisms or modifications to the biomarker. In some
cases, the
method further includes detecting RNA and/or DNA. In some cases, the method
further
includes detecting RNA and/or DNA associated with the biomarker. In some
cases, the
method further includes detecting transcription factors and/or transcription
factor co-factors.
In some cases, the method further includes detecting transcription factors
and/or transcription
factor co-factors associated with the biomarker. In some cases, the method
further includes
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use of an algorithm, a threshold value, a threshold range, a directional
change over time,
comparing the index to a single patient, comparing the index to a control
group or comparing
the index to a reference standard. In some cases, the method is used to
confirm a diagnosis of
preeclampsia in a subject wherein the subject has at least one symptom
associated with
preeclampsia. In some cases, the method is used to provide a diagnosis of
preeclampsia in a
subject wherein the subject has hypertension and proteinurea. In some cases,
the method is
used to provide a diagnosis of preeclampsia in a subject wherein the subject
has hypertension.
In some cases, the method is used to provide a diagnosis of preeclampsia in a
subject wherein
the subject has one risk factor associated with preeclampsia. In some cases,
the method is
used to determine if a subject is at risk for preeclampsia. In some cases, the
method is used to
quantify a risk that a subject will develop preeclampsia. In some cases, the
method is used to
predict a time at which a subject will develop preeclampsia. In some cases,
the method is
used to predict maternal and/or fetal outcomes of preeclampsia. In some cases,
the method is
used to distinguish between mild, moderate and severe preeclampsia. In some
cases, the
method is used to determine whether HELLP syndrome, preterm birth,
interuterine growth
restriction, placental abruption, placental accrete, low fetal birth weight,
low size for
gestational age, gestational hypertension, chronic hypertension, gestational
diabetes, Type I
diabetes, Type II diabetes or risk of spontaneous abortion are a result from a
subject having
preeclampsia. In some cases, the method further includes creating an
electronic report.
[00245] The disclosure further provides a kit for confirming the presence of
PE, confirming
the absence of PE, diagnosing, prognosing, monitoring , characterizing, or
determining the
severity of preeclampsia in a subject, said kit comprising a first reagent
specific for
determining level of PAPP-A and a second reagent specific for determining
level of
ADAM12. In some cases, the kit does not include a reagent to detect IGFALS,
FLT4, P1GF,
P1GF-2, P1GF-3 or sFlt-1. In some cases, the kit cannot be used to prognose,
diagnose,
screen for, determine or confirm Down's syndrome. In some cases, the kit
cannot be used to
prognose, diagnose, screen for, determine or confirm cardiovascular disease.
In some cases,
the biomarker is not P1GF-2 or P1GF-3.
Applications
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[00246] The methods, compositions and reagents provided herein may be used for
diagnosing, analyzing, distinguishing or confirming the presence or absence of
preeclampsia
(PE) in a female subject. Additionally, the methods, compositions and reagents
provided
herein may be used for diagnosing, analyzing, distinguishing or confirming
preeclampsia
(PE) in a female subject wherein the female subject also has other symptoms as
described
herein. The methods, compositions and reagents find use in a number of
applications,
including, for example, predicting if an individual will develop preeclampsia,
diagnosing
preeclampsia, confirming the presence, absence or severity of preeclampsia,
and monitoring
an individual with preeclampsia.
[00247] Additionally, the methods, compositions and reagents find use in a
number of
applications, including, for example, predicting if an individual will develop
preeclampsia
who has other symptoms as described herein, diagnosing preeclampsia in an
individual who
has other symptoms as described herein, confirming the presence, absence or
severity of
preeclampsia in an individual who has other symptoms as described herein, and
monitoring
an individual with preeclampsia in an individual who has other symptoms as
described
herein. In some cases, the other symptoms may include hypertension and
proteinurea. In
other cases, the other symptoms may include hypertension.
[00248] The methods, compositions and reagents may also find use in
identifying an
individual at risk for preeclampsia and quantifying the risk of preeclampsia
in an individual.
Often, the methods, compositions and reagents find use in predicting the time
of the onset of
preeclampsia. In some cases, the methods, compositions and reagents find use
in predicting
the timeline of progression of preeclampsia. The methods, compositions and
reagents
provided herein may find use to predict an outcome of preeclampsia, wherein
the outcome
affects the fetus or wherein the outcome affects the subject, often the
mother. In some cases,
the methods, compositions and reagents provided herein may find use to
distinguish a stage
of preeclampsia from another stage of preeclampsia. For example, a stage of
preeclampsia
may be mild or severe. In some cases, the methods, compositions and reagents
provided
herein may find use to distinguish preeclampsia from eclampsia. Often, the
methods,
compositions and reagents provided herein may find use to determine whether
HELLP
syndrome, risk of or ongoing process of preterm birth, risk of or ongoing
interuterine growth
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restriction, risk of spontaneous abortion, risk of or ongoing placental
abruption, risk of or
ongoing placental accrete, risk of low or high fetal birth weight and risk of
small or large size
relative to gestational age are a result of the subject having preeclampsia.
The methods,
compositions and reagents provided herein may further find use to distinguish
patients having
preeclampsia from patients not having preeclampsia but having symptoms
associated with
preeclampsia, such symptoms including complication of pregnancy symptoms,
gestational
hypertension, chronic hypertension, gestational diabetes, Type I diabetes
and/or Type II
diabetes.
[00249] The methods, compositions and reagents provided herein also find use
in creating a
report, often an electronic report, to communication the outcomes of the
methods,
compositions and reagents described herein. In some cases, the report may
communicate
preeclampsia score, preeclampsia index and/or preeclampsia profile of a
subject.
[00250] The following examples are offered by way of illustration and not by
way of
limitation.
EXAMPLES
[00251] The following examples are put forth so as to provide those of
ordinary skill in the
art with a complete disclosure and description of how to make and use the
present disclosure,
and are not intended to limit the scope of the disclosure nor are they
intended to represent that
the experiments below are all or the only experiments performed. Efforts have
been made to
ensure accuracy with respect to numbers used (e.g. amounts, temperature, etc.)
but some
experimental errors and deviations should be accounted for. Unless indicated
otherwise,
parts are parts by weight, molecular weight is weight average molecular
weight, temperature
is in degrees Centigrade, and pressure is at or near atmospheric.
EXAMPLE 1- Analysis of Samples from Normal Subjects and
Subjects with Preeclampsia by ELISA
[00252] This example demonstrates use of the disclosure described herein for
performing an
analysis of a set of samples derived from pregnant females, some of which did
not have
preeclampsia and some of which had preeclampsia using an ELISA method. Use of
the
biomarkers demonstrated herein is a practical advancement for diagnosing,
prognosing,
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monitoring, characterizing, predicting preeclampsia, or confirming the
presence or absence of
preeclampsia in a female subject.
Equipment
[00253] The equipment used to perform ELISA assays included Eppendorf Research
plus
calibrated single and multi-channel pipettes, Accu-Jet pro pipettor, 2 -8 C
deli refrigerator
or cold room, -80 C freezer, non-humidified laboratory incubator set to 37 C,
orbital
microplate shaker, automated microplate washer (TitretekTm, M384 Washer with
Stacker),
microplate spectrophometer (Molecular Devices, SpectraMax 340PC), SoftMax0
Pro
software and an Alarm timer. Other equivalent equipment may be substituted for
the above
and achieve the same quality results.
Materials
[00254] Materials and reagents needed to perform the method include 0.5 mL and
2 mL deep
well polypropylene dilution blocks, 15 mL and 50 mL polypropylene tubes, 5 mL,
10 mL and
25 mL disposable pipettes, and reagent troughs. 10X PBST (i.e. Phosphate
Buffered Saline
with Tween0) (1 L) was used for washing and was prepared by combining 80 g
NaC1, 2 g
KC1, 11.5 g Na2HPO4, 2 g KH2PO4, 5 ml Tween0-20 to 1 L in distilled water and
1 N
sulfuric acid (0.1% Proclin 300 may be added to wash buffer as a
preservative). Calibrator
and assay diluent includes either 1% BSA or animal serum in phosphate buffered
saline. As
known by those skilled in the art, diluents generally need to be optimized to
maximize the
detection of the marker of interest in complex matrices like serum and were
optimized as
such for the experiments described herein.
Clinical Samples
[00255] Serum samples derived from pregnant patient with or without
preeclampsia and/or
with a variety of comorbidities were obtained from typical sources including
ProMedDx0,
Discovery Life Sciences, Ortho Clinical Diagnostics, SeraCare0, etc. Samples
were stored at
-80 C, thawed at 4 C before use and snap frozen in LN2 after use. Stability of
some markers
tested was confirmed by accelerated freeze/thaw experiments. To reduce
biological
variability of clinical samples, templates of 20 to 32 serum samples (and
quality control (QC)
samples) were thawed at 4 C the night before an assay was run and tested in
all assays on the
same day.
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Templates
[00256] Multiple different 96-well plate templates containing clinical
samples, QC controls
and standards can be used (see FIG. 3 and FIG. 4). In all cases, columns 1 and
2 contained
duplicate, 8-point standard curves with 2-fold dilutions. Column 12 contained
an inverted 8-
point standard curve or an inverted duplicate 4 point standard curve to
address right to left
and top to bottom variation. 20 (triplicates) or 32 (duplicates) clinical
serum samples were
arrayed on the plate in a randomized fashion. For triplicates, samples were
interleaved by
column across the plate and for duplicates, samples were interleaved by row
down the plate.
Each plate contained 4 or 6 replicates of Hi QC and Lo QC control samples
arrayed
strategically across the plate to assess top to bottom and left to right
variation.
Experimental Procedures
[00257] Preparation of Master Blocks. The front side of 0.5 ml or 2 mL deep
well block was
labeled with template number (e.g. "T1 Master"). Each thawed clinical sample
was mixed
with a pipette and sufficient sample for all the assays being run (typically
30-500 microliter
(g1) including 20% average) was added to the master block. Hi and Lo QC are
not added to
the master block (only the dilution blocks) so these wells remained empty in
Master Block.
The master block was sealed with the plate sealer and stored at 4 C when not
being used to
make dilution blocks.
[00258] Preparation of Dilution Blocks. All necessary reagents and samples
were warmed for
30 minutes at room temperature before use. The front of a 2 mL deep well block
was labeled
as "TX-Marker" as a human-readable barcode for tracking where "X" was the
Template
being prepared and "Marker" was the protein marker being assayed. Lyophilized
marker
standards were reconstituted with 1-2 ml of deionized water or calibrator
diluent, mixed
gently by agitation, and allowed to reconstitute for at least 5 minutes but up
to 15 minutes to
make 10X or 1X concentrated stock solutions. Serum samples were used undiluted
or diluted
between 1:2 and 1:1,000 (typically 1:4, 1:10, 1:15, 1:20, or 1:1000) with
assay diluent
depending on the concentration range of the biomarker of interest in the
samples (based on
historical results). For example, for a 1:2 dilution, 120 t1 of serum from the
master block
was added to 120 t1 of assay diluent in the dilution block. Standard solutions
were diluted
1:10 if necessary for the first concentration and then a standard curve was
made by making
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1:2 serial dilutions into calibrator diluent. Typically, standard curves
started at 50, 20, 10, 5,
2 or 1 nanograms per milliliter (ng/ml) for low concentration markers, but
could start as high
as 0.8, 2.5, or 5 microcrams per milliliter (ig/m1) if the marker is at a high
concentration if
serum. Hi and Lo QC controls were diluted similarly to serum samples or,
sometimes,
received a lower dilution due to the stock concentration of the QC controls.
QC controls
were set up by spiking purified protein into normal or synthetic serum so that
the OD of the
QC control was at or near the second or fifth point on the standard curve.
[00259] ELISA protocol. Assay plates pre-coated with capture antibody against
the marker of
interest were labeled with template number and marker as "TX-Marker" for
tracking. Assay
plates were filled with 50 or 100 ill of assay diluent prior to adding sample.
Samples and
standard curve samples were added to the plate as shown in the templates
above. Plates were
covered with an adhesive strip and incubated for 1, 2 or 3 hours at room
temperature or 37 C
(depending on the marker being tested). Plates were then washed on a plate
washer with 3-6
washes of 300-400 ill of wash buffer each then blotted on a paper towel. 50 to
200 ill of an
HRP-conjugated secondary antibody against the antigen of interest in assay
diluent was
added to the whole plate, covered with an adhesive strip, and incubated for 30
minutes to 2
hours at room temperature or 37 C depending on the marker being tested. The
plate was
washed again with 3-6 washes of 300-400 ill of wash buffer each then blotted
on a paper
towel. While washing, a two-component TMB reagent (i.e. 3,3',5,5'-
Tetramethylbenzidine)
was mixed together (equal volumes of both reagents) and 100 to 200 ill of this
reagent was
added to the plate. Plates were incubated in the dark for 5 to 28 minutes
depending on the
marker being tested. At the end of this incubation, 50 ill of 1N sulfuric acid
was added to the
whole plate to stop the HRP (i.e. horseradish peroxidase) enzymatic reaction.
Plates were
read on a spectrophotometer at 450 nm (subtracting 570 nm reading).
Data Calculation, Acceptance and Retesting
[00260] Calculation of marker concentrations. All data calculations to convert
optical density
(OD) to concentration were carried out in SoftMax0 pro using preformatted
protocols. These
protocols fit the standard curves and then the final concentrations calculated
taking into
account dilutions to 4 significant digits. Analyte concentrations were flagged
with "R" if the
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measured OD values are outside range of standard curve. Data were exported to
*.TXT
format using SoftMax Pro for further analysis.
[00261] Assay acceptance criteria. The coefficient of determination of the
standard curve can
be R2 > 0.95. Hi and Lo QC control values were tracked over time with a 3s
control chart.
QC control results may be used to alert the technical team that additional
scrutiny may be
appropriate. Any other variability in standard curve or QC controls (edge
effects, left to right
variation, etc.) are assessed by a statistician and reported in the study
report.
[00262] Sample acceptance criteria. A logarithmic transformation was applied
to all the
standard curve measurements prior to their use for calculating the overall
standard deviation
of replicates. Any individual measurement that exceeded 4 standard deviations
(SDs) was
removed and the mean at each concentration calculated using the non-excluded
points.
[00263] Sample retesting. If sample mean concentration is not in the
quantifiable range of
the standard curve, samples were re-tested with necessary dilution changes.
See FIG. 1 and
FIG. 2 for exemplary data.
[00264] The preceding merely illustrates the principles of the disclosure. It
will be
appreciated that those skilled in the art will be able to devise various
arrangements which,
although not explicitly described or shown herein, embody the principles of
the disclosure
and are included within its spirit and scope. Furthermore, all examples and
conditional
language recited herein are principally intended to aid the reader in
understanding the
principles of the disclosure, and are to be construed as being without
limitation to such
specifically recited examples and conditions. Moreover, all statements herein
reciting
principles, cases, and cases of the disclosure as well as specific examples
thereof, are
intended to encompass both structural and functional equivalents thereof.
Additionally, it is
intended that such equivalents include both currently known equivalents and
equivalents
developed in the future, i.e., any elements developed that perform the same
function,
regardless of structure. The scope of the present disclosure, therefore, is
not intended to be
limited to the exemplary cases shown and described herein. Rather, the scope
and spirit of
the present disclosure is embodied by the appended claims.
[00265] While preferred cases of the present disclosure have been shown and
described
herein, it will be obvious to those skilled in the art that such cases are
provided by way of
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example only. Numerous variations, changes, and substitutions will now occur
to those
skilled in the art without departing from the disclosure. It should be
understood that various
alternatives to the cases of the disclosure described herein may be employed
in practicing the
disclosure. It is intended that the following claims define the scope of the
disclosure and that
methods and structures within the scope of these claims and their equivalents
be covered
thereby.
[00266] Table 2: A listing of various PE biomarkers.
Renin (REN), human chorionic gonadotropin (HCG), alpha fetoprotein (AFP),
inhibin
A (INHA), activin A (INHBA), sex hormone-binding globulin (SHBG), adiponectin
(ADIPOQ), antithrombin III (SERPINC1), plasminogen activator inhibitor-1 (PAI-
1/SERPINE1), plasminogen activator inhibitor-2 (PAI-2/SERPINB2),
apolipoprotein
A-I (AP0A1), apolipoprotein B-100 (APO), apolipoprotein C-II (APOC2),
apolipoprotein CIII (APOC3), apolipoprotein E (APOE), endothelin (EDN),
prostacyclin, thromboxane, placenta growth factor-1 (P1GF-1), placenta growth
factor-2 (P1GF-2), placenta growth factor-3 (P1GF-3), vascular endothelial
growth
factor (VEGF), FMS-like tyrosine kinase (F1t1), soluble FMS-like tyrosine
kinase
(sFlt1), vascular endothelial growth factor receptor 3 (F1t4), endoglin (ENG),
soluble
endoglin (sENG), endothelial PAS domain-containing protein 1 (EPAS1),
neurokinin
B, metallopeptidase inhibitor 1 (TIMP1), metallopeptidase inhibitor (TIMP-2),
metallopeptidase inhibitor 3 (TIMP3), angiopoietin 2 (ANGPT2), decorin (DCN),
proheparin-binding EGF-like growth factor (HBEGF), amiloride-binding protein-1
(ABP1), solute carrier family 21 (prostaglandin transporter) member 2
(SLC21A2),
palladin (KIAA0992), lipoprotein lipase (LPL), insulin-like growth factor-
binding
protein complex acid labile subunit (IGFALS), selenoprotein P (SEPP1),
sulfhydryl
oxidase 1 (QS0X1), peroxiredoxin-1 (PRDX1), peroxiredoxin-2 (PRDX2), lysosomal
pro-X carboxypeptidase (PRCP), leucyl-cystinyl aminopeptidase (LNPEP),
tenascin-
X (TNXB), basement membrane-specific heparan sulfate proteoglycan core protein
(HSPG2), cell surface glycoprotein MUC18 (MCAM), phosphatidylinositol-glycan-
specific phospholipase D (GPLD1), Kunitz-type serine protease inhibitor 1
(SPINT1),
G-protein-coupled receptor 126 (GPR126), C-reactive protein (CRP),
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phosphatidylcholine-sterol acyltransferase (LCAT), roundabout homolog 4
(ROB04),
ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2),
protein S100-A9 (S100-A9), fatty acid-binding protein 4 (FABP4), enoyl-CoA
hydratase (ECHS1), 43,5-42,4-dienoyl-CoA isomerase (ECH1), peroxidase 6
(PER6),
heat shock protein beta-1 (HSP27), stathmin (STMN), annexin Al (ANXA), annexin
A2 (ANXA2), annexin A4 (ANXA4), prostaglandin dehydrogenase 1 (HPGD),
proliferation-associated protein 2G4 (PA2G4), estradiol 17-beta-dehydrogenase
(HSD17), macrophage-capping protein (CAPG), hypoxia-inducible factor 1-alpha
(HIF1A), chaperonin (CPN), ER-60 protease, isocitrate dehydrogenase 1 (IDH1),
aldehyde reductase 1 (AKR1B1), fidaresta chain B bonded to human aldose
reductase,
voltage-dependent anion-selective channel protein 1 (VDAC1), nuclear choloride
channel, phosphoglycerate mutase 1 (PGAM1), endoplasmic reticulum protein,
proteasome subunit alpha type-2 (PSMA2), glutathione-S-transferase (GST), Ig
heavy-chain V region, smooth muscle myosin alkali light chain, tumor necrosis
factor
alpha (TNF), macrophage colony-stimulating factor (M-CSF), granulocyte colony-
stimulating factor (G-CSF), granulocyte-macrophage colony-stimulating factor
(GM-
CSF), fibroblast growth factor (FGF), relaxin H2 (RLN2), FERM and PDZ domain-
containing protein 4 (FRMPD4), somatostatin (SST), interphotoreceptor matrix
proteoglycan 1 (IMPG1), C-X-C motif chemokine 9 (CXCL9), C-X-C motif
chemokine 11 (CXCL11), hydroxy-delta-5-steroid dehydrogenase 3-beta and
steroid
delta-isomerase 2 (HSD3B2), partitioning defective 6 homolog beta (PARD6B),
Bile
salt export pump (ABCB11), membrane-spanning 4-domains subfamily A member 3
(M54A3), LOC196993, bestrophin (BEST1), glycosyl-phosphatidylinositol-anchored
molecule-like protein (GML), cell division cycle 2-like 5 (CDC2L5), glycine
receptor
alpha 1 (GLRA1), dihydropyrimidinase-related protein 4 (DPYSL4), kappa-type
opioid receptor (OPRK1), dimethylarginine dimethylaminohydrolase 1 (DDAH1),
homeobox protein Hox-A4 (HOXA4), Homeobox protein Hox-A7 (HOXA7),
Homeobox protein Hox-B5 (HOXB5), thyrotropin-releasing hormone receptor
(TRHR), nuclear transition protein 2 (TNP2), vasopressin, placental protein 13
(PP13),
neutrophil gelatinase-associated lipocalin (LCN2), interferon gamma-inducible
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protein-10 (IP-10), monocyte chemotactic protein-1 (MCP-1), intracellular
adhesion
molecule-1 (ICAM-1), intracellular adhesion molecule-3 (ICAM-3), vascular cell
adhesion molecule-1 (VCAM-1), interleukin-1 (IL-1), interleukin-2 (IL-2),
interleukin-3 (IL-3), interleukin-4 (IL-4), interleukin-5 (IL-5), interleukin-
6 (IL-6),
interleukin-7 (IL-7), interleukin 8 (IL-8), interleukin-9 (IL-9), interleukin
10 (IL-10),
interleukin-11 (IL-11), interleukin-12 (IL-12), interleukin 13 (IL-13),
interleukin-27
subunit beta (EBI3), lectin, platelet-derived growth factor (PDGF), matrix
metalloprotease-2 (MMP-2), matrix metalloprotease-9 (MMP-9), matrix
metalloprotease-12 (MMP12), matrix metalloprotease-23A (MIFR), matrix
metalloprotease-23B (MIFR-2), fibrinogen, fibrinogen alpha (FGA), fibronectin-
1
(FN1), protein S (PROS1), protein C (PROC), pikachurin (EGFLAM), hemopexin
(HPX), ADAM metallopeptidase domain 2 (ADAM2), ADAMTS3, ADAM
metallopeptidase domain 12 (ADAM12), ADAM metallopeptidase domain 12 short
isoform (ADAM12-S), ADAM metallopeptidase domain 12 long isoform (ADAM12-
L), haptoglobin (HP), serum-alpha2-macroglobulin (A2M), retinol-binding
protein 4,
small inducible cytokine A2 (CCL2), C-C motif chemokine 5 (CCL5), cathepsin B
(CTSB), cathepsin C (CTSC), cathepsin D chain H (CTSD), heme oxygenase-1
(HMOX1),insulin-like growth factor-binding protein 1 (IGFBP1), insulin-like
growth
factor-binding protein 2 (IGFBP2), insulin-like growth factor-binding protein-
3
(IGFBP3), insulin-like growth factor-binding protein-5 (IGFBP5), insulin-like
growth
factor-binding protein-7 (IGFBP7), insulin-like growth factor-1 (IGF-1),
keratin 4
(KRT4), keratin 16 (KRT16), keratin 19 (KRT19), keratin 33A (KRT33A), keratin
40
(KRT40), pro-platelet basic protein (PPBP), perilipin 2 (PLIN2), kininogen-1
(KNG1),
choriogonadotropin-subunit beta (CGB), cystatin C (CST3), pappalysin-1
(PAPPA1),
pappalysin-2 (PAPPA2), alpha-1 B-glycoprotein (A1BG), actin (ACTB), C4b-
binding
protein beta chain (C4BP), cholinesterase (BCHE), chorionic somatomammotropin
hormone (CSH1), coagulation factor VII (F7), coagulation factor XI (F11),
filamin A
(FLNA), filamin B (FLNB), heparin cofactor 2 (HCII), hepatocyte growth factor-
like
protein (MST1), histidine-rich glycoprotein (HRG), laminin subunit beta-1
(LAMB1),
lipopolysaccharide-binding protein (LBP), plastin-2 (LCP1), profilin-1 (PFN1),
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pregnancy-specific beta-l-glycoprotein (PSG1), pregnancy-specific beta-4-
glycoprotein (PSG4), pregnancy-specific beta-11-glycoprotein (PSG11), receptor-
type tyrosine-protein phosphatase gamma precursor (PTPRG), pregnancy-zone
protein (PZP), SH3 domain-binding glutamic acid-rich-like protein 3
(SH3BGRL3),
transgelin-2 (TAGLN2), talin-1 (TLN-1), tropomyosin alpha-4 chain (TPM4),
vasorin
(VSN), vinculin (VCL), von Willebrand factor (VWF), ferritin (FT), ferritin
light
chain, hemoglobin (HB), heme, podocin (NPHS2), nephrin (NPHS1), podocalyxin
(PODXL), synaptopodin (SYNPO), leptin (LEP), follistatin-like 3 protein
(FSTL3),
beta fertilin (FTNB), CD33L, neutrotrophic tyrosine kinase receptor 2 (TRKB),
beta
glucosidase (BGL), angiogenin (ANG), leukocyte-associated Ig-like receptor
secreted
protein (LAIR), erythroid differentiation protein, adipogenesis inhibitory
factor (IL-
11), corticotropin-releasing factor-binding protein (CRHBP), alpha 1-
antichymotrypsin (SERPINA3), cytokine receptor-like factor 1 (CRLF1), lysyl
hydroxylase isoform 2 (LH2), stanniocalcin precursor (STC), secreted frizzled
related-protein (SFRP), galectin-3 (LGALS3), alpha neutrophil defensin 1
(DEFA1),
cholecystokinin precursor (CCK), interferon-stimulated T-cell alpha
chemoattractant
(I-TAC), azurocidin (HBP), spermine oxidase (SMOX), UDP glycosyltransferase 2
family polypeptide B28 (UGT2B28), neutral endopeptidase (NEP), CDC28 protein
kinase regulatory subunit 2 (CKSHS2), lanosterol synthase (LSS),
calcium/calmodulin-dependent serine protein kinase (CASK), chemokine (CX3C
motif) receptor 1 (CX3CR1), tyrosinase-related protein 1 (TYRP1), hydoxy-delta-
5-
steroid dehydrogenase (HSD3), cytochrome P450-family 11 (CYP11), cytochrome
P450-family 11 subfamily A polypeptide 1 (CYP11A1), cytochrome P450-family 11
subfamily B polypeptide 1 (CYP11B1), cytochrome P450 1A1 (CYP1A1), coronin-
2A (CORO2A), cytochrome P450 2J2 (CYP2J2), paralemmin (PALM),
glyceraldehyde-3-phophase dehydrogenase (GAPD), ATP-binding cassette sub-
family A member 12 (ABCA12), transcription factor Eb (TFEB), transcription
factor
IIE (TFIIE), syntaxin binding protein 5-like (STXBP5L), guanylin (GUCA2A),
ribosomal protein S6 kinase alpha-2 (RPS6KA2), protein phosphatase 1
regulatory
subunit 16B (PPP1R16B), class B basic helix-loop-helix protein 2 (BHLHB2),
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glyocophorin E (GYPE), nebulette (NEBL), leucine-rich repeats and
immunoglobulin-like domains protein 1 (LRIG1), glucose transporter 3 (GLUT3),
UDP-glucuronosyltransferase 2B28 (UGT2B28), nuclear receptor subfamily 5 group
A member 2 (NR5A2), neuronatin (NNAT), sodium- and chloride-dependent creatine
transporter 1 (SLC6A8), receptor tyrosine-protein kinase erbB-2 (ERBB2),
receptor
tyrosine-protein kinase erbB-3 (ERBB3), sialic acid-binding Ig-like lectin 6
(SIGLEC6), SHC-transforming protein 3 (SHC3), neurexophilin 4 (NXPH4),
lymphocyte antigen 6D (LY6D), prostacyclin synthase (PTGIS), ATP-dependent
RNA helicase DDX51 (DDX51), TRAF3-interacting protein 1 (TRAF3IP1),
trophoblast glycoprotein (TPBG), transforming growth factor beta-3 (TGFB3),
cyclin
B1 (CCNB1), kinesin family member 17 (KIF17), N-myc downstream mediated gene
1 (NDRG1), SWI/SNF-related matrix-associated actin-dependent regulator of
chromatin subfamily D member 3 (SMARCD3), serine/threonine-protein kinase Chk2
(CHEK2), amphiregulin (AREG), minor histocompatibility antigen HA-1 (HA-1),
POU domain, class 4, transcription factor 1 (POU4F1), prostate stem cell
antigen
(PSCA), collagen alpha-1(X) chain (COL10A1), collagen alpha-3(VI) chain
(COL6A3), collagen alpha-3 (IX) chain (COL9A3), paired box gene 2 (PAX2),
paired
box gene 4 (PAX4), paired box gene 7 (PAX7), latrophilin 3 (LPHN3), bile acid
receptor (NR1H4), empty spiracles homolog 1 (EMX1), desmoglein 3 (DSG3), DNA-
binding protein Ikaros (ZNFN 1A1), melanoma-associated antigen 5 (MAGEA5),
melanoma-associated antigen 3 (MAGEA3), afadin- and alpha-actinin-binding
protein
(SSX2IP), WD repeat-containing protein 21 (WDR21), orexin receptor type 2
(HCRTR2), NKG2-D type II integral membrane protein (KLRK1), HLA class II
histocompatibility antigen DP alpha 1 chain (HLA-DPA1), HLA class II
histocompatibility antigen DP beta 1 chain (HLA-DPB1), HLA class II
histocompatibility antigen DR alpha chain (HLA-DRA), HLA class I
histocompatibility antigen, alpha chain G (HLA-G), peripheral myelin protein 2
(PMP2), guanine nucleotide-binding protein G(o) subunit alpha (GNA01), voltage-
dependent L-type calcium channel subunit beta-2 (CACNB2), c-Jun-amino-terminal
kinase-interacting protein 2 (MAPK8IP2), P antigen family member 1 (PAGE1),
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GABA receptor subunit beta-1 (GABRB1), sodium- and chloride-dependent betaine
transporter (SLC6Al2), lactadherin (MFGE8), integrin alpha-L (ITGAL),
desmocollin 1A/1B (DSC1), villin 2 (VIL2), plectin 1 (PLEC), ankyrin 1 (ANK1),
vimentin (VIM), osteopontin (SPP1), dynamin 2 (DNM2), muscle cadherin (CDH15),
kinesin heavy chain, fatty acid synthase (FASN), alpha-adducin (ADD1), NADH-
cytochrome B5 reductase (CYB5R), dihydrofolate reductase (DHFR), ADP-
ribosylation factor-like protein 3 (ARL3), NADPH menadione oxidoreductase 1-
dioxin-inducible (NQ01), CD73, ubiquitin (UB), glutathione S-transferase Mu 3
(GSTM3), superoxide dismutase 1 (SOD1), cytochrome C oxidase subunit VIa
polypeptide 1 (COX6A1), glutathione reductase (GSR), myristoylated alanine-
rich C-
kinase substrate (MARCKS), protein disulfide-isomerase A2 (PDIA2), DNA
topoisomerase 3-alpha (TOP3A), forkhead (Drosophila)-like 7, LIM/homeobox
protein Lhx2 (LHX2), T-box transcription factor TBX3 (TBX3), CCAAT/enhancer-
binding protein alpha (CEBPA), CCAAT/enhancer-binding protein delta (CEBPD),
disrupted in schizophrenia 1 protein (DISCI), runt-related transcription
factor 1
(RUNX1), sterol regulatory element-binding protein 2 (SREBF2), interferon-
induced,
double-stranded RNA-activated protein kinase (EIF2AK2), zinc finger protein
208
(ZNF208), tonsoku-like protein (TONSL), signal transducer and activator of
transcription 2 (STAT2), myocyte-specific enhancer factor 2D (MEF2D), GA-
binding
protein alpha chain (GABPA), dual specificity mitogen-activated protein kinase
kinase 6 (MAP2K6), growth hormone variant (GH2), erythropoietin (EPO), ephrin
type-A receptor 3 (EPHA3), ephrin type-A receptor 4 (EPHA4), ephrin type-A
receptor 5 (EPHA5), granulin (GRN), granulocyte colony-stimulating factor
receptor
(CSF3R), macrophage colony-stimulating factor 1 receptor (CSF1R), receptor-
type
tyrosine-protein phosphatase F (PTPRF), bone morphogenetic protein 1 (BMP1),
epithelial discoidin domain-containing receptor 1 (DDR1), transferrin receptor
protein
1 (TFRC), angiopoietin 1 receptor (TEK), insulin receptor (INSR), 78 kDa
glucose-
regulated protein (HSPA5), S-phase kinase-associated protein 1 (SPK1),
Regulator of
chromosome condensation (RCC1), caspase 6 (CASP6), heat shock 90kDa protein A
(HSP90A), butanoic acid, hexanoic acid, octanoic acid, decanoic acid,
dodecanoic
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acid, tetradecanoic acid, hexadecanoic acid, octadecanoic acid, eicosanoic
acid,
docosanoic acid, tetracosanoic acid, hexacosanoic acid, pristanic acid,
phytanic acid,
dihydroxycholestanoic acid (DHCA), and trihydroxycholestanoic acid (THCA),
uric
acid.
Fibronectin ("FN") Hemopexin ("HPX") interferon-gamma
Fibrinogen ("FG") TNF-alpha
ADAM metallopeptidase placental growth factor beta-amyloid
domain 12 ("ADAM12") ("P1GF")
pregnancy-associated plasma soluble vascular endothelial IL-4
protein A ("PAPP-A") growth factor ("sFlt-1")
arginine vasopressin copeptin IL-10
arginine vasopressin;
copeptin;
interferon-gamma;
TNF-alpha;
IL-10;
IL-4;
beta-amyloid;
interferon-inducible protein 6-16;
albumin;
SERPINAl;
Ceruloplasmin; and
Immunoglobulin free light chains
Isoforms of the above biological entities are also contemplated as biomarkers.
Such isoforms include, for example, sFlt-2, sFlt-4 and sFlt-5.
Other isoforms include FN GenBank Accession No. NM 212474.1), FG GenBank
Accession
No. NM 000508.3 (FGA) and GenBank Accession No. NM 005141.4 (FGB), PAPP-A
e.g.,
GenBank Accession No. NM 002581.3), HPX GenBank Accession No. NM 000613.2);
ADAM12 Genbank Accession Nos. NM 003474.4 (isoform 1), NM 021641.3 (isoform
2);
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sFlt-1; e.g., Genbank Accession Nos. NM 001159920.1 (isoform 2), NM
001160030.1
(isoform 3), and NM 001160031.1 (isoform 4)); P1GF e.g., Genbank Accession
Nos.NM 002632.5 (isoform 1) and NM 001207012.1 (isoform 2)).
Fragments or portions of a PE biomarker which are recognized by a detection
reagent, e.g.,
an antibody, are also deemed PE biomarkers herein.
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