Note: Descriptions are shown in the official language in which they were submitted.
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ASSAY FOR PREOPERATIVE PREDICTION OF ORGAN FUNCTION RECOVERY
[0001] This application claims benefit of United States provisional patent
application
numbers 62/528,748, filed July 5, 2017, and 62/595,383, filed December 6,
2017, the entire
contents of each of which are incorporated by reference into this application.
REFERENCE TO A SEQUENCE LISTING SUBMITTED VIA EFS-WEB
[0002] The content of the ASCII text file of the sequence listing named
"UCLA253_seq"
which is 3 kb in size was created on July 4, 2018, and electronically
submitted via EFS-Web
herewith the application is incorporated herein by reference in its entirety.
ACKNOWLEDGEMENT OF GOVERNMENT SUPPORT
[0003] This invention was made with Government support under HL120040, awarded
by the
National Institutes of Health. The Government has certain rights in the
invention.
BACKGROUND OF THE INVENTION
[0004] In the United States, heart failure (HF) affects 6 million persons
(Yancy 2013). HF
with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction
(HFpEF) each
affect 3 million people. The lifetime risk of developing HF is 1 in 5 for men
and women older
than 40 years of age. The death rate remains unacceptably high at
approximately 50%
within 5 years from the time of index diagnosis. In the US, an annually
estimated 300,000
persons are diagnosed with Stage D heart failure, also classified as advanced
heart failure
(AdHF) (Hunt 2009).
SUMMARY OF THE INVENTION
[0005] Described herein are methods and systems for treating a cardiovascular
disease. In
some embodiments, described herein are methods and systems for predicting a
prognosis of
an individual with a cardiovascular disease following the provision of a
treatment to that
individual. In some embodiments, a prognosis of an individual following the
provision of a
treatment to the individual is provided a score. In some embodiments, a
treatment modality
for an individual is selected based on the score provided to the prognosis of
the individual.
[0006] A large subgroup of patients with cardiovascular disease are patients
with heart
failure (HF). Heart failure (HF) is a complex clinical syndrome that causes
systemic hypo-
perfusion and failure to meet the body's metabolic demands. In an attempt to
compensate,
chronic upregulation of the sympathetic nervous system and renin-angiotensin-
aldosterone
leads to further myocardial injury, HF progression and reduced 02¨delivery.
This triggers
progressive organ dysfunction, immune system activation and profound metabolic
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derangements, creating a milieu similar to other chronic systemic diseases and
presenting
as advanced HF (AdHF) with severely limited prognosis.
[0007] In general, patients with AdHF may benefit from various
surgical/interventional
therapies such as mechanical circulatory support (MOS) surgery, heart
transplant (HTx)
surgery, coronary artery bypass graft (CABG) surgery, percutaneous coronary
interventions
(PCI), aortic valve replacement (AVR) surgery, mitral valve replacement (MVR)
surgery,
trans-catheter aortic valve replacement (TAVR), transcatheter mitral clip,
ventricular
tachycardia ablation, or stellate gangliectomy in lieu of optimal medical
management (OMM)
or palliative/hospice care (PC). While the Stage C HF guideline-based medical
therapy is
well established, the survival benefit of these surgical/interventional
therapeutic interventions
is not as well established.
[0008] We hypothesize that 1-year survival in AdHF after these
surgical/interventional
therapies is linked to Functional Recovery Potential (FRP), a novel clinical
composite
parameter that includes HF severity, secondary organ dysfunction,
comorbidities, frailty, and
disabilities as well as chronological age and that can be diagnosed by a
molecular
immunological biomarker.
[0009] HF is a major public health concern due to its tremendous societal and
economic
burden, with estimated direct and indirect cost in the U.S. of $37.2 billion
in 2009, which is
expected to increase to $97 billion by 2030 (Roger 2012). While 25% of all
spending occurs
during the last year of life (Orszag 2008, Zhang 2009) in patients
hospitalized with HF, more
resource spending is associated with lower mortality rates (Ong 2009). A key
consideration
is: Which of these AdHF surgical/interventional therapies does a healthcare
provider
recommend to the individual AdHF-patient in order to tailor personal benefits
in the most
cost-effective way?
[0010] Across the different AdHF-interventions, the 1-year mortality rate is
in the range of
10-30% (Deng 2018). This ambiguity suggests unpredictability of clinical
trajectories, even
with current clinical prediction tools tailored to the progressive clinical
trajectory of HF
severity and HF-related organ dysfunction (OD). Such models include Brain
Natriuretic
Peptide (BNP) measurements (Troughton 2000, Gardner 2003, Doust 2003), the
Heart
Failure Survival Score (HFSS) (Aaronson 1997), Seattle Heart Failure Model
(Levy 2006,
Ketchum 2010), MAGGIO score (Sartipy 2014), Frailty Scores (Martinez-Selles
2009, Flint
2012), INTERMACS Score (Smits 2013, Kirklin 2014), UCLA score (Chyu 2014),
Sequential
Organ Failure Assessment (SOFA) Score (Vincent 1996), HeartMate II risk score
(Cowger
2013), Model of End-stage Liver Disease (Matthews 2010), Model of End-stage
Liver
Disease Except 1NR (MELD-X1) Score (Abe 2014) and right ventricular failure
score (Kormos
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2010). However, most validated prediction tools have the tendency to
underestimate risk
among the most severely ill patients. (Sartipy 2014). The uncertainty of
predicting Stage D
HF or AdHF-progression has an impact on individual patients' health and
healthcare costs.
[0011] There remains a need for an improved prediction of risk associated with
each of the
above treatment options for heart failure, and ultimately an improved
prediction of risk
reduction when choosing one treatment option over another treatment option,
i.e. an
improved prediction of comparative survival benefit from the above
interventions in AdHF-
patients.
[0012] In some embodiments, the materials and methods described herein address
these
needs and more by using gene expression profiles to predict organ function
recovery.
Described herein is, in a representative embodiment, a method of measuring
gene
expression in a sample of peripheral blood mononuclear cells (PBMCs) obtained
from a
subject. The method can also be implemented as a method of predicting
treatment outcome
for advanced organ failure, such as advanced heart failure, and as a method of
treating
and/or a method of optimizing treatment of such organ failure. In some
embodiments, the
method comprises (a) measuring the expression level of a set of at least 8
genes in the
sample, wherein the at least 8 genes are selected from those listed in Tables
2 and Table 3;
(b) assigning a Function Recovery Potential (FRP) score to the sample that
reflects the
measured expression level of the genes in a direction associated with recovery
from organ
failure, wherein the FRP score corresponds to the measured expression level of
the set of
genes relative to a reference value. In some embodiments, the subject is
suffering from
heart failure. Optionally, the method further comprises (c) treating the
subject with optimal
medical management (OMM) and/or palliative care (PC) when the FRP score is
lower than
the reference value, and referring the subject for treatments including ¨ but
not limited to -
mechanical circulatory support (MCS) surgery, heart transplant (HTx) surgery,
coronary
artery bypass graft (CABG) surgery, percutaneous coronary interventions (PCI),
aortic valve
replacement (AVR) surgery, mitral valve replacement (MVR) surgery, trans-
catheter aortic
valve replacement (TAVR), transcatheter mitral clip, ventricular tachycardia
ablation, or
stellate gangliectomy when the FRP score is greater than the reference value.
In some
.. embodiments, the expression level of 10-75 genes is measured.
Alternatively, the
expression level of 10-30 genes is measured. In other embodiments, the
expression level of
10-15 genes is measured. In some embodiments, the set of genes is at least 10
of the genes
listed in Table 2, at least 10 of the genes listed in Table 3 or at least 10
of the genes listed in
Table 4, or comprises one gene selected from each of Tables 1A-11. Optionally,
the method
further comprises measuring one or more control genes.
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[0013] In some embodiments, the reference value corresponds to expression
levels of the
set of genes observed in subjects who recover from heart failure and/or major
organ
dysfunction. In one embodiment (see Bondar 2017), the reference value is
constituted by
averaging the GEP values across the 28 genes identified after 1) creating a
dichotomous
phenotype framework defined as Group I (High FRP=17 HF-patients who had a good
functional recovery, as defined by improvement of Sequential Organ Failure
Assessment
(SOFA) score and Model of Endstage Liver Disease except INR (MELD-XI) score on
day 8
after MCS surgery in comparison to day -1 before MCS-surgery) versus Group II
(Low
FRP=12 patients how did not fulfill this clinical criterion), 2) filtering the
entire set of mR NA
transcripts (36,938) (20th-100th percentile), retaining of the resulting
26,571 entities only
those with a fold change of at least 2.0 (123 transcripts) for statistical
analysis with the
unpaired Mann-Whitney test and Benjamini-Hochberg correction analysis (FDR
=0.1),
identifying 28 genes as differentially expressed between GROUP I and GROUP II
on day -1
and 3) building a model using the support vector machine (SVM) algorithm by
randomly
selecting 20 samples out of 29 total, stratified by membership in Group I
versus Group II. To
test the model, the remaining 9 samples were stratified by membership in Group
I or Group
II. An average prediction accuracy of 93% (range: 78-100%) was achieved after
re-running
the stratified random selection model building process 25 times. Thus, for any
new HF-
patient's blood sample with an unknown level of gene expression in the 28
genes listed in
Table 3, the imputation of expression value of the 28 genes yields a
dichotomous decision
whether this sample is to be allocated to Group I or Group II with an accuracy
of 93% and
therefore allows with an accuracy of 93% to predict before the scheduled HF-
=intervention
whether this new HF-patient has a high FRP (GROUP I) or low FRP (GROUP II)
(see two
patient examples in Figure 8). With this information added to the other
clinical data available
to this patients doctor, the doctor can make a more precisely informed
recommendation to
the patient about whether or not to undergo the scheduled HF-intervention. In
some
embodiments such as the example above, the subject is suffering from HF and
the method
comprises (c) treating the subject with optimal medical management (OMM)
and/or palliative
care (PC) when the FRP score is low and referring the subject for treatments
including ¨ but
not limited to - mechanical circulatory support (MCS) surgery, heart
transplant (HTx) surgery,
coronary artery bypass graft (CABG) surgery, percutaneous coronary
interventions (PCI),
aortic valve replacement (AVR) surgery, mitral valve replacement (MVR)
surgery, trans-
catheter aortic valve replacement (TAVR), transcatheter mitral clip,
ventricular tachycardia
ablation, or stellate gangliectomy when the FRP is high. In other embodiments,
the
expression level of 10-30 genes is measured. In some embodiments, the set of
genes is at
least 10 of the genes listed in Table 2, at least 10 of the genes listed in
Table 3 or at least 10
of the genes listed in Table 4A and 4B, or comprises one gene selected from
each of Tables
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1A-11. In some embodiments, the reference value corresponds to expression
levels of the
set of genes observed in subjects who recover from heart failure and/or major
organ
dysfunction. Optionally, the method further comprises measuring one or more
control genes.
[0014] In one representative embodiment, the FRP score is between 1 (lowest)
and 10
(highest), and the reference value is 5.5. The treating of step (c) comprises
treating the
subject with optimal medical management (OMM) and/or palliative care (PC) when
the FRP
score is 5 or less, and treating the subject with mechanical circulatory
support (MCS)
surgery, heart transplant (HTx) surgery, coronary artery bypass graft (CABG)
surgery,
percutaneous coronary interventions (PC1), aortic valve replacement (AVR)
surgery, mitral
valve replacement (MVR) surgery, trans-catheter aortic valve replacement
(TAVR),
transcatheter mitral clip, ventricular tachycardia ablation, or stellate
gangliectomy when the
FRP score is 6 to 10. In some embodiments, the lower scores are assigned to
"Group II",
while the higher scores are assigned to -Group I". In some embodiments, scores
of 1-4 are
assigned to Group II, while scores of 7-10 are assigned to Group I, and scores
of 5-6 are
considered an indeterminate zone, to be evaluated with additional
circumstances taken into
account, such as factors that weigh in favor of palliative care or factors
that favor aggressive
treatment, notwithstanding the less convincing FRP score.
[0015] In some embodiments, the measuring comprises polymerase chain reaction
(FOR) or
next generation sequencing (NGS). In some embodiments, the PCR is performed
using one
or more primers selected from GAPDH-f: CCACTCCTCCACCTTTGAC (SEQ ID NO: 1);
GAPDH-r: ACCCTGTTGCTGTAGCCA (SEQ ID NO: 2); K1R2DL4-f:
ACCCACTGCCTGTTTCTGTC (SEQ ID NO: 3); K1R20L4-r: ATCACAGCATGCAGGTGTCT
(SEQ ID NO: 4); NAPSA-f: CAGGACACCTGGGTTCACAC (SEQ ID NO: 5); NAPSA-r:
GGTTGGACTCGATGAAGAGG (SEQ ID NO: 6); BATF2-f AAAGGCAGCTGAAGAAGCAG
(SEQ ID NO: 7); BATF2-r: TCTTTTTCCAGAGACTCGTGC (SEQ ID NO: 8); ANKRD22-f:
CTCAGCCAGGAAGGATTTTG (SEQ ID NO: 9); ANKRD22-r:
TGATAGGCTGCTTGGCAGAT (SEQ ID NO: 10).
[0016] In some embodiments, the measuring is performed one to three days, or
72 hours,
prior to treatment with an AdHF intervention. In one embodiment, the measuring
is
performed one day prior to treatment. In some embodiments, the subject is
suffering from
heart failure with reduced ejection fraction or preserved ejection fraction.
[0017] Also provided is a method of predicting outcome of AdHF intervention in
a patient
suffering from heart failure. The method typically comprises performing the
method of
measuring gene expression in a sample as described herein, wherein a poor
outcome is
predicted when the FRP score is greater than the reference value. Optionally,
the method
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further comprises treating the subject with OMM, PC, MOS, HTx, or other AdHF
intervention
when the FRP score is less than the reference value.
[0018] Additionally provided is a method of monitoring progression of heart
failure in a
subject. In one embodiment, the method comprises performing the method of
measuring
gene expression in a sample as described herein. In a typical embodiment,
progression is
detected when the FRP score is reduced relative to a prior measurement
obtained from the
subject. In one embodiment, progression is detected when the FRP score is
reduced by 2
(on a scale of 1-10) relative to a prior measurement.
[0019] In some embodiments, the FRP score is determined on the basis of a
linear
discriminant analysis (LDA) of at least 10 of the 28 genes listed in Table 3
using
preoperative and postoperative expression levels of the at least 10 genes
observed in a
population of patients treated with AdHF intervention, wherein the FRP score
is adjusted by
weighting the contribution of each of the genes in accordance with the linear
discriminant
analysis. In some embodiments, the FRP score is determined on the basis of a
linear
discriminant analysis (LDA) of at least 10 of the genes listed in Table 4A and
4B. The linear
discriminant analysis can be based on expression levels of fewer than 10, or
up to all 28 of
the genes listed in Table 3. In some embodiments, the linear discriminant
analysis is based
on expression levels of fewer than 10, or up to all of the genes listed in
Table 4A and 48. In
some embodiments, the analysis takes into account additional genes, such as
those listed in
Table 2, or identified elsewhere. Those skilled in the art will recognize that
the analysis can
be performed using additional data from a larger patient population. In some
embodiments,
the preoperative expression levels are obtained one to three days prior to
treatment. In some
embodiments, the postoperative expression levels are obtained 7-10 days, and
typically 5-40
days, after treatment. Examples of treatment include, but are not limited to,
mechanical
circulatory support (MCS) surgery, heart transplant (HTx) surgery, coronary
artery bypass
graft (CABG) surgery, percutaneous coronary interventions (PCI), aortic valve
replacement
(AVR) surgery, mitral valve replacement (MVR) surgery, trans-catheter aortic
valve
replacement (TAVR), transcatheter mitral clip, ventricular tachycardia
ablation, or stellate
gangliectomy.
[0020] Also provided is a non-transitory computer-readable medium encoded with
computer-executable instructions for performing the methods described herein
(Figure 2). In
another embodiment, the invention provides a non-transitory computer-readable
medium
embodying at least one program that, when executed by a computing device
comprising at
least one processor, causes the computing device to perform one or more of the
methods
described herein. In some embodiments, the at least one program contains
algorithms,
instructions or codes for causing the at least one processor to perform the
method(s).
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Likewise, the invention provides a non-transitory computer-readable storage
medium storing
computer-readable algorithms, instructions or codes that, when executed by a
computing
device comprising at least one processor, cause or instruct the at least one
processor to
perform a method described herein.
[0021] The invention also provides a method of treating a subject suffering
from heart
failure. In one embodiment, the method comprises (a) measuring the expression
level of a
set of at least 8 genes in the sample, wherein the at least 8 genes are
selected from those
listed in Tables 2 and Table 3; (b) assigning a Function Recovery Potential
(FRP) score
between 1 (lowest) and 10 (highest) to the sample that reflects the measured
expression
level of the genes in a direction associated with recovery from organ failure;
and (c) treating
the subject with optimal medical management (OMM) and/or palliative care (PC)
when the
FRP score 5 or less, and referring the subject for treatment with mechanical
circulatory
support (MCS) surgery, heart transplant (HTx) surgery, coronary artery bypass
graft (CABG)
surgery, percutaneous coronary interventions (PCI), aortic valve replacement
(AVR) surgery,
mitral valve replacement (MVR) surgery, trans-catheter aortic valve
replacement (TAVR),
transcatheter mitral clip, ventricular tachycardia ablation, or stellate
gangliectomy when the
FRP score is 6 to 10. In one embodiment, the set of genes is BATF2, AGRN,
ANKRD22,
DNM1P46, FRMD6, KIR2DL4, BCORP1 SAP25; NAPSA, HEXA-AS1, TIMP3, and
RHBDD3.
.. [0022] Also described herein is a method for developing a function recovery
potential (FRP)
scoring algorithm that predicts a subject's ability to recover from medical
intervention for
organ failure. In one embodiment, the method comprises (a) obtaining the
expression levels
of at least 10 of the 28 genes listed in Table 3 or at least 10 of the genes
listed in Table 4A
and 4B using pre-intervention and post-intervention expression levels of the
at least 10
.. genes observed in PBMC samples obtained from a population of patients
treated with
medical intervention for organ failure; (b) performing linear discriminant
analysis of the
expression levels obtained in (a) to classify the PBMC samples into Group I
(post-
intervention improvement) or Group II (non-improvement); (c) estimating the
effect size of
each of the gene expression levels on the classification of a sample into
Group I or Group II;
and (d) adjusting the FRP scoring algorithm by weighting the contribution of
each of the
genes in accordance with the effect size. In one embodiment, the medical
intervention is
surgery. In some embodiments, the surgery is an organ transplant, or provision
of
mechanical support for the organ, such as circulatory support or dialysis. In
some
embodiments, the AdHF intervention includes treatments¨ but is not limited to -
mechanical
circulator/ support (MCS) surgery, heart transplant (HTx) surgery, coronary
artery bypass
graft (CABG) surgery, percutaneous coronary interventions (PCI); aortic valve
replacement
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(AVR) surgery, mital valve replacement (MVR) surgery, trans-catheter aortic
valve
replacement (TAVR), transcatheter mitral clip, ventricular tachycardia
ablation, or stellate
gangliectomy.
[0023] Also provided herein are methods for treating an individual,
comprising: (i) receiving
a sample from the individual; (ii) determining a gene expression level in the
sample for at
least one gene comprising RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1,
ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46,
KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN,
CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZK1 IP1, NEGRI, KCNH8, CCR8, MME,
ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, 07orf50, NEFL, CDCA2õALDH1A1,
OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1,
0D209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI, PRRG1, XIST, RPS4Y1, ZFY,
PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDIVI5D, ElF1AY, or FITM1; and (iii)
providing a
treatment to the individual based on the gene expression level. In some
embodiments, the
sample comprises blood, urine, sputum, hair, or skin. In some embodiments, the
gene
expression level is either an increase or a decrease in expression of the at
least one gene
relative to an expected expression level value. In some embodiments, the gene
expression
level in the sample that is determined is for two genes comprising RSG1,
TPRA1, SAP25,
MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3,
FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1,
GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZK1IP1,
NEGRI, KCNH8, OCRS, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164,
C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,
ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI,
PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElF1AY,
or FITM1. In some embodiments, the gene expression level is assigned a score,
and
wherein the treatment is determined based on the score. In some embodiments,
the score
comprises a Function Recovery Potential (FRP) score. In some embodiments, the
score is
determined based on a linear discriminant analysis of data comprising known
gene
expression levels and known FRP scores of a plurality of individuals. In some
embodiments,
the treatment is selected from - but not limited to - mechanical circulatory
support (MCS)
surgery, heart transplant (HTx) surgery, coronary artery bypass graft (CABG)
surgery,
percutaneous coronary interventions (PCI), aortic valve replacement (AVR)
surgery, mitral
valve replacement (MVR) surgery, trans-catheter aortic valve replacement
(TAVR),
transcatheter mitral clip, ventricular tachycardia ablation, or stellate
gangliectomy. In some
embodiments, the gene expression level is a level determined by polymerase
chain reaction
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(PCR), next generation sequencing (NGS), or other gene expression profiling
assay platform
such as Nanostring's NCounter hybridization platform. In some embodiments, the
PCR is
performed using at least one primer selected from GAPDH-f: CCACTCCTCCACCTTTGAC
(SEQ ID NO: 1); GAPDH-r: ACCCTGTTGCTGTAGCCA (SEQ ID NO: 2); KIR2DL4-f:
ACCCACTGCCTGTTTCTGTC (SEQ ID NO: 3); KIR2DL4-r: ATCACAGCATGCAGGTGTCT
(SEQ ID NO: 4); NAPSA-f: CAGGACACCTGGGTTCACAC (SEQ ID NO: 5); NAPSA-r:
GGTTGGACTCGATGAAGAGG (SEQ ID NO: 6); BATF2-f: AAAGGCAGCTGAAGAAGCAG
(SEQ ID NO: 7); BATF2-r: TCTTTTTCCAGAGACTCGTGC (SEQ ID NO: 8); ANKRD22-f:
CTCAGCCAGGAAGGATTTTG (SEQ ID NO: 9); ANKRD22-r:
TGATAGGCTGCTTGGCAGAT (SEQ ID NO: 10).
[0024] Further provided herein are computer implemented systems, comprising:
(a) a
sample receiver for receiving a sample provided by an individual; (b) a
digital processing
device comprising an operating system configured to perform executable
instructions and a
memory; (c) a computer program including instructions executable by the
digital processing
device to provide a treatment to a healthcare provider based on the sample,
the computer
program comprising: (i) an gene analysis module configured to determine a gene
expression level in the sample for at least one gene comprising RSG1, TPRA1,
SAP25,
MFSD3, FITM1, SPTBN5, CEMP1õASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3,
FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1,
GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZK1IP1,
NEGRI, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164,
C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,
ST6GALNAC1, CHMP6, SKA1, 0D209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI,
PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElF1AY,
or F1TM1; (ii) a treatment determination module configured to determine the
treatment based
on the gene expression level; and (iii) a display module configured to provide
the treatment
to the healthcare provider. In some embodiments, the sample comprises blood,
urine,
sputum, hair, or skin. In some embodiments, the gene expression level is
either an increase
or a decrease in expression of the at least one gene relative to an expected
expression level
value. In some embodiments, the gene expression level in the sample that is
determined is
for two genes comprising RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1,
ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46,
KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN,
CKAP2L,IGSF10, HEXA-AS1, L00728431, PDZK1IP1, NEGRI, KCNH8, CCR8, MME,
ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2õALDH1A1,
OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1,
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00209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI, PRRG1, XIST, RPS4Y1, ZFY,
PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElF1AY, or FITM1. In some
embodiments, the gene expression level is assigned a score. and wherein the
treatment is
determined based on the score. In some embodiments, the score comprises a
Function
Recovery Potential (FRP) score. In some embodiments, the score is determined
based on a
linear discriminant analysis of data comprising known gene expression levels
and known
FRP scores of a plurality of individuals. In some embodiments, the treatment
is selected
from mechanical circulatory support (MCS) surgery, heart transplant (HTx)
surgery, coronary
artery bypass graft (CABG) surgery, percutaneous coronary interventions (PCI),
aortic valve
replacement (AVR) surgery, mitral valve replacement (MVR) surgery, trans-
catheter aortic
valve replacement (TAVR), transcatheter mitral clip, ventricular tachycardia
ablation, or
stellate gangliectomy. In some embodiments, the gene expression level is a
level
determined by polymerase chain reaction (PCR), next generation sequencing
(NGS), or
other gene expression profiling assay platform such as Nanostring's NICounter
hybridization
platform. In some embodiments, the PCR is performed using at least one primer
selected
from GAPDH-f: CCACTCCTCCACCTTTGAC (SEQ ID NO: 1); GAPDH-r:
ACCCTGTTGCTGTAGCCA (SEQ ID NO: 2); KIR2DL4-f: ACCCACTGCCTGTTTCTGTC
(SEQ ID NO: 3); KIR2DL4-r: ATCACAGCATGCAGGTGTCT (SEQ ID NO: 4); NAPSA-f:
CAGGACACCTGGGTTCACAC (SEQ ID NO: 5); NAPSA-r: GGTTGGACTCGATGAAGAGG
(SEQ ID NO: 6); BATF2-f: AAAGGCAGCTGAAGAAGCAG (SEC) ID NO: 7); BATF2-r:
TCTTTTTCCAGAGACTCGTGC (SEQ ID NO: 8); ANKRD22-f:
CTCAGCCAGGAAGGATTTTG (SEQ ID NO: 9); ANKRD22-r:
TGATAGGCTGCTTGGCAGAT (SEQ ID NO: 10).
[0025] Described herein is a Non-transitory computer-readable storage media
encoded with
a computer program including instructions executable by a processor to cause
the processor
to determine a gene expression level in a sample for at least one gene
comprising RSG1,
TPRA1 SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2,
RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1,
HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431,
PDZK1IP1, NEGRI, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43,
C6orf164, C7or150, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2,
C15orf38, ST6GALNAC1, CHMP6, SKA1, 0D209, SNAPC2, AXL, KIR2DL1, NTSR1,
SEPT5, KALI, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P,
KDM5D, ElFlAY, or FITM1; and provide a suggestion for a treatment to the
individual based
on the gene expression level. In some embodiments, the gene expression level
is either an
increase or a decrease in expression of the at least one gene relative to an
expected
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expression level value. In some embodiments, the gene expression level in the
sample that
is determined is for two genes comprising RSG1 TPRA1, SAP25, MFSD3, FITM1,
SPTBN5,
CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C,
DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1,
AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZK1IP1, NEGRI, KCNH8; CCR8,
MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, 06orf164, C7orf50, NEFL, CDCA2,
ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, 015orf38, ST6GALNAC1, CHMP6,
SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI, PRRG1, XIST, RPS4Y1,
ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElFlAY, or FITM1. In some
embodiments, the gene expression level is assigned a score, and wherein the
treatment is
determined based on the score. In some embodiments. the score comprises a
Function
Recovery Potential (FRP) score. In some embodiments. the score is determined
based on a
linear discriminant analysis of data comprising known gene expression levels
and known
FRP scores of a plurality of individuals. In some embodiments, the treatment
is selected
from mechanical circulatory support (MCS) surgery, heart transplant (HTx)
surgery, coronary
artery bypass graft (CABG) surgery, percutaneous coronary interventions (PCI),
aortic valve
replacement (AVR) surgery, mitral valve replacement (MVR) surgery, trans-
catheter aortic
valve replacement (TAVR), transcatheter mitral clip, ventricular tachycardia
ablation, or
stellate aangliectomy. In some embodiments, the gene expression level is a
level
determined by polymerase chain reaction (PCP.), next generation sequencing
(NGS) or other
platform such as Nanostring's NCounter hybridization platform. In some
embodiments, the
FOR is performed using at least one primer selected from GAPDH-f:
CCACTCCTCCACCTTTGAC (SEQ ID NO: 1); GAPDH-r: ACCCTGTTGCTGTAGCCA (SEQ
ID NO: 2); KIR2DL4-f: ACCCACTGCCTGTTTCTGTC (SEQ ID NO: 3); KIR2DL4-r:
ATCACAGCATGCAGGTGTCT (SEQ ID NO: 4); NAPSA-f CAGGACACCTGGGTTCACAC
(SEQ ID NO: 5); NAPSA-r: GGTTGGACTCGATGAAGAGG (SEQ ID NO: 6); BATF2-f:
AAAGGCAGCTGAAGAAGCAG (SEQ ID NO: 7); BATF2-r: TCTTTTTCCAGAGACTCGTGC
(SEQ ID NO: 8); ANKRD22-f: CTCAGCCAGGAAGGATTTTG (SEQ ID NO: 9); ANKRD22-
r: TGATAGGCTGCTTGGCAGAT (SEC) ID NO: 10).
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figures 1A-1B show schematic illustrations of exemplary methods and
frameworks
described herein. Figure 1A shows a schematic of an exemplary predictive model
and
theoretical framework. Figure 1B shows an exemplary algorithm for determining
an FRP
score from gene expression values of selected genes.
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[0027] Figure 2 is a block diagram of an embodiment of a computer system that
can be
used to implement a method as described herein.
[0028] Figures 3A-3B illustrate organ function and outcomes. Figure 3A shows
organ
function and outcomes of 29 patients across five time points. Figure 3B shows
that, out of
29 AdHF-patients undergoing MCS-surgery, 17 patients improved (Group I, upper
right
quadrant) and 12 patients did not improve (Group II, remaining 3 quadrants)
from day -1
(TP1) to day 8 (TP5). Each large dark bullet represents one patient who died
within one
year.
[0029] Figure 4 shows the Kaplan-Meier 1-year survival in Group I vs. Group
II.
[0030] Figures 5A-5C show overlap of significant genes associated with organ
function
improvement and survival benefit. The indicated color range in Figures 5A and
58
corresponds to the differential expression, ranging from blue (-2) to gray, to
yellow (0), to
orange, to red (1.1). Fig. 5A shows hierarchical clustering of significant
genes day -1 (TP1).
Left: The Volcano plot of 28 genes, which are differentially expressed between
Group I and
Group II. Right: Hierarchical clustering of the 28 candidate genes for the
prediction test
demonstrates the differential gene expression between Group I and Group II.
Fig. 5B shows
hierarchical clustering of genes associated with survival benefit. Left: The
Volcano plot of
105 genes, which are differentially expressed between Group I and Group II.
Right:
Hierarchical clustering 17 of the 105 candidate genes for the prediction test
demonstrates
the differential gene expression between Group I=Survival, Group II=Non-
survival. Fig. 5C
shows overlap genes from both improvement group and 1-year survival outcome.
Left:
Venn-Diagram shows the 28 DEGs identified in the comparison by Improvement
Score (red;
left circle) and the Right shows the 105 DEGs identified by comparing 1-Year
Survival (blue:
right circle). 12 DEGs were shared across the two comparisons. Right: The 12
overlap
genes.
[0031] Figure 6 shows an exemplary prediction biomarker development rationale.
[0032] Figure 7 illustrates the concept of FRP, a clinical composite parameter
that can
include chronological age as well as personal biological age (measurable by
established and
validated HF-. OD-, cornorbidity-, frailty- and disability-instruments), that
represents the
instantaneous potential of the person with AdHF to cope with and survive
stressors such as
AdHF-surgicallinterventional therapies, and can be diagnosed by a molecular
biomarker.
[0033] Figure 8 shows two case studies out of the 29 AdHF-patients in the
Proof-Of-
Concept Study that illustrate the clinical utility of FRP scoring. The
indicated color range
corresponds to the differential expression, ranging from blue (-2) to gray, to
yellow (0), to
orange, to red (1.1).
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DETAILED DESCRIPTION OF THE INVENTION
[0034] Described herein are methods and systems for providing a treatment to
an individual
based on a gene expression profile assay and classification system for
predicting whether
an individual has the potential to recover organ function after AdHF-
surgical/interventional
therapies, particularly for an individual suffering from heart failure and/or
multiorgan
dysfunction syndrome.
Definitions
[0035] All scientific and technical terms used in this application have
meanings commonly
used in the art unless otherwise specified. As used in this application, the
following words or
phrases have the meanings specified.
[0036] As used herein, "AdHF intervention" refers to treatments for advanced
heart failure.
Representative examples of AdHF surgical/interventional therapies include, but
are not
limited to: mechanical circulatory support (MCS), heart transplantation (HTx),
coronary artery
bypass graft (CABG) surgery, percutaneous coronary interventions (PCI), aortic
valve
replacement (AVR) surgery, mitral valve replacement (MVR) surgery, trans-
catheter aortic
valve replacement (TAVR), transcatheter Mitra-Clip, ventricular tachycardia
ablation and
stellate gangliectomy.
[0037] As used herein, "reference" in the context of gene expression levels
refers to that
observed in healthy volunteers, or in a subject who recovers from heart
failure and/or major
organ dysfunction. In some embodiments, the reference group is a set of
normalization or
control genes, as described herein.
[0038] As used herein, a "normalization gene" refers to a gene whose measured
expression
level does not discriminate between subjects who improve and those who do not
improve
with small standard deviations across samples.
[0039] As used herein, "a- or "an" means at least one, unless clearly
indicated otherwise.
[0040] As used herein, to "prevent" or "protect against" a condition or
disease means to
hinder, reduce or delay the onset or progression of the condition or disease.
[0041] "About" a number, as used herein, refers to range including the number
and ranging
from 10% below that number to 10% above that number. "About" a range refers to
10%
below the lower limit of the range, spanning to 10% above the upper limit of
the range.
[0042] As used herein, the terms "treatment," "treating," "ameliorating a
symptom," and the
like, in some cases, refer to administering an agent, or carrying out a
procedure, for the
purposes of obtaining an effect. The effect may be prophylactic in terms of
completely or
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partially preventing a disease or symptom thereof and/or may be therapeutic in
terms of
effecting a partial or complete cure for a disease and/or symptoms of the
disease.
"Treatment," as used herein, may include treatment of a heart condition, such
as heart
failure, in a mammal, particularly in a human, and includes: (a) preventing
the disease or a
symptom of a disease from occurring in a subject which may be predisposed to
the disease
but has not yet been diagnosed as having it (e.g., including diseases that may
be associated
with or caused by a primary disease; (b) inhibiting the disease, i.e.,
arresting its
development; and (c) relieving the disease, i.e., causing regression of the
disease. Treating
may refer to any indicia of success in the treatment or amelioration or
prevention of an heart
condition, including any objective or subjective parameter such as abatement;
remission;
diminishing of symptoms or making the disease condition more tolerable to the
patient;
slowing in the rate of degeneration or decline; or making the final point of
degeneration less
debilitating. The treatment or amelioration of symptoms is based on one or
more objective
or subjective parameters; including the results of an examination by a
physician.
Accordingly, the term "treating" includes the administration of the compounds
or agents of
the present invention to prevent or delay, to alleviate, or to arrest or
inhibit development of
the symptoms or conditions associated with heart disease or other diseases.
The term
"therapeutic effect" refers to the reduction, elimination, or prevention of
the disease,
symptoms of the disease, or side effects of the disease in the subject. A
treatment, in some
embodiments, comprises taking a course of action with respect to an
individual. In some
embodiments, a course of action taken with respect to an individual comprises
no medical
procedure being performed on the individual. In, some embodiments, a course of
action
taken with respect to an individual comprises conservative care or no
additional care being
provided.
Methods of Treatment
[0043] In some embodiments, there are provided methods of treating an
individual in need
thereof, such as methods of treating an individual suffering from a heart
disease, such as
heart failure. In some embodiments, there are provided methods of predicting
treatment
outcomes in an individual in need of heart failure interventional/surgical
therapies. Some
such methods comprise obtaining a gene expression value from a biological
sample from the
individual. In some embodiments, methods of treatment herein comprise (i)
receiving a
sample from an individual; and (ii) determining a gene expression level in the
sample for at
least one gene. In some embodiments, methods of treatment herein comprise
providing a
treatment to the individual based on the gene expression level. In some
embodiments,
methods herein comprise recommending a treatment to the individual based on
the gene
expression level. In some embodiments, the gene comprises at least one of
RSG1, TPRA1,
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SAP25, MFSD3, FITM1, SPTBN5, CEMP1 ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3,
FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1,
GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZK1IP1,
NEGRI, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164,
C7orf50, NEFL, 000A2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,
ST6GALNAC1, CHMP6, SKA1, 0D209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI,
PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElFlAY,
or FITM1. In some embodiments, the gene expression level in the sample is
determined for
at least two genes of RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1,
ASPSCR1,
NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DN1M1P46, KIR2DL4,
USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L,
IGSF10, HEXA-AS1, L00728431, PDZK1IP1, NEGRI, KCNH8, CCR8, MME, ETV5,
CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1,
OLFM1 FADS3, SAC3D1, FZD4, RBPMS2, C15or138, ST6GALNAC1, CHMP6, SKA1,
0D209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI, PRRG1, X1ST, RPS4Y1, ZFY,
PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElFlAY, or FITM1.
[0044] In some embodiments, methods of treatment provided herein further
comprise
determining a function recovery potential (FRP) score based on the gene
expression level.
Function Recovery Potential (FRP=Resilience) score between 1 (lowest) and 10
(highest) to
the sample that reflects the measured expression level of the genes. In one
embodiment, as
exemplified in the proof-of-concept study (Example 1), the FRP is defined
using the
Sequential Organ Failure Assessment score and Model of End-stage Liver Disease
Except
1NR score (measured one day before and eight days after surgery): Group
1=improving
(both scores improved from day -1 to day 8) and Group II=not improving (either
one or both
scores did not improve from day -1 to day 8). The FRP correlates with 1-year
survival. In
some embodiments, the method further comprises (c) selecting a treatment of
optimal
medical management (OMM) and/or palliative care (PC) for the individual when
the FRP
score is 5 or less, and selecting a treatment of an AdHF intervention for the
individual when
the FRP score is 6 to 10. Examples of treatments comprising AdHF intervention
include, but
are not limited to, mechanical circulatory support (MOS) surgery, heart
transplant (HTx)
surgery, coronary artery bypass graft (CABG) surgery, percutaneous coronary
interventions
(PCI), aortic valve replacement (AVR) surgery, mitral valve replacement (MVR)
surgery,
trans-catheter aortic valve replacement (TAVR), transcatheter rnitral clip,
ventricular
tachycardia ablation, and stellate gangliectomy.
[0045] Figure 1A illustrates an exemplary predictive model and theoretical
framework.
Worsening heart failure with reduced ejection fraction (HFrEF; left panel) is
linked to
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progressive frailty/organ dysfunction (OD) via neuro-endocrine-immune
activation mediated
by complex interactions between diseased myocyte, peripheral blood mononuclear
cells
(PBMCs), endothelial cells (EC), and platelets (PLT) (middle panel). Outcome
and
comparative survival benefit prediction is improved by adding the molecular
immunology
.. biomarker or multidimensional molecular biamarker (MMB) to clinical
predictors (upward
arrow in bottom right ROC curve; right panel). Figure 1B shows an exemplary
algorithm for
determining an FRP score from gene expression values of selected genes as
described
herein.
Gene Expression Profiling
[0046] In one embodiment, the invention provides a method of measuring gene
expression
in a biological sample obtained from a subject suffering from heart failure.
In a related
embodiment; the invention provides a method of reducing risk and optimizing
treatment
outcome for a subject suffering from organ failure. In one embodiment, the
method
comprises profiling gene expression in a sample of peripheral blood
mononuclear cells
.. (PBMCs). Typical steps of the method comprise: (a) measuring the expression
level of a set
of genes in the sample, wherein the set of genes are selected from those
listed Tables 1A-
11, Table 2, Table 3, or Table 4A and 4B; and, based on this gene expression
test result,
(b) assigning a ¨ clinical - Function Recovery Potential (FRP=Resilience)
score between 1
(lowest) and 10 (highest) to the sample that reflects the measured expression
level of the
genes. The FRP is defined using the Sequential Organ Failure Assessment score
and Model
of End-stage Liver Disease Except 1NR score (measured one day before and eight
days
after surgery): Group 1=irnproving (both scores improved from day -1 to day 8)
and Group
11=not improving (either one or both scores did not improve from day -1 to day
8). The FRP
correlates with 1-year survival. In some embodiments, therefore, the method
further
comprises (c) referring the subject for treatment with optimal medical
management (OMM)
and/or palliative care (PC) if the FRP score is 5 or less, and referring the
subject for
treatment with an AdHF intervention if the FRP score is 6 to 10. Examples of
AdHF
intervention include, but are not limited to, mechanical circulatory support
(MCS) surgery,
heart transplant (HTx) surgery; coronary artery bypass graft (CABG) surgery,
percutaneous
coronary interventions (PCI), aortic valve replacement (AVR) surgery, mitral
valve
replacement (MVR) surgery, trans-catheter aortic valve replacement (TAVR),
transcatheter
mitral clip, ventricular tachycardia ablation, and stellate gangliectomy.
[0047] In some embodiments, the gene expression level varies from an expected
expression
level value. In some embodiments, the gene expression level is determined
relative to
another gene, such as a housekeeping gene or other appropriate gene
exemplified herein.
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In some embodiments, the gene expression level is increased relative to an
expected
expression level value. In some embodiments, the gene expression level is
decreased
relative to an expected expression level value. In some cases, the gene
expression level is
assigned a score, such as an FRP score. In some embodiments, methods of
treatment
herein are determined based on the score. In some embodiments, the score is
determined
based on a linear discriminant analysis of data comprising known gene
expression levels
and known FRP scores of a plurality of individuals.
[0048] The number of genes included in the set of genes whose expression level
is
measured can range, during iterations of test development, from 5 to 100. In a
typical
embodiment, the expression level of at least 8 genes is measured. In some
embodiments,
the expression level of 10-75 genes is measured. In other embodiments, the
expression
level of 10-20, or 10-30 genes is measured. In one embodiment, the expression
level of 10-
genes is measured. In some illustrative specific embodiments, the set of genes
is at least
10 of the genes listed in Table 2, at least 10 of the genes listed in Table 3
or at least 10 of
15 the genes listed in Table 4A or 413, or comprises one gene selected from
each of Tables
1A-11. In some embodiments, all of the genes listed in Tables 1, 2, 3, and/or
4 are
measured. In one embodiment, the expression level of at least one gene in each
of Tables
1A-11 is measured.
[0049] In a typical embodiment, the measuring comprises, for example, any one
or a
combination of RNA quantification, such as by next generation sequencing
(NGS),
polymerase chain reaction (PCR), gene array technology, or a hybridization
platform (such
as, for example, the NanoString nCounter system). In one embodiment, the
measuring
employs NanoString NCounter Hybridization. Those skilled in the art will
appreciate
alternative methods of measuring gene expression that can be employed.
Likewise, where
amplification methods require the use of primers, those skilled in the art can
obtain
appropriate primers either by referring to the exemplary primers described
herein, or through
publicly accessible databases. The methods can be performed using, for
example,
techniques for detecting gene expression, such as PCR, including RT-PCR, RNA-
Seq, DNA
microarrays, etc. Other assays can be employed, as will be understood to those
skilled in the
art.
[0050] For use in the methods described herein, representative examples of the
sample
include, but are not limited to, blood, plasma, serum, urine, or sputum, and
other bodily
fluids, particularly those containing PBMCs. PBMCs can be isolated, for
example, from
venous blood obtained via phlebotomy. RNA can be isolated from the PBMCs for
use in the
assays described herein. In some embodiments, the sample comprises blood,
urine,
sputum, hair, or skin.
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[0051] In one embodiment, the method further comprises measuring normalization
genes
that will be empirically selected using PCR data from the training samples.
Genes that do not
discriminate between subjects who improve and those who do not improve with
small
standard deviations across all samples are considered as normalization genes.
Specifically,
normalization genes are chosen from a set of over 200 gene assays that include
at least 10
genes described in the literature as housekeeping genes. The final control
genes are
selected using the following criteria: First, the amplicon assays need to show
very low
variance. Second, they must not show discrimination between Group I and Group
II
samples. Third, we identify amplicons that cover a range of CT values in a
typical CP tube
sample so as to control for CT dependent efficiency changes. The use of a set
of control
genes to normalize the amount of RNA present is based on the premise that an
average
from several measurements would be more robust than any single measurement and
would
also take into account any RNA abundance dependent effects.
[0052] In a typical embodiment, the measuring is performed one day prior to
MCS or HTx
surgery or other AdHF intervention. In some embodiments, the measuring is
performed
within 72 hours prior to surgery or other intervention. This approach
facilitates tailoring the
treatment of the subject to the subject's condition and prospects for
improvement and
recovery at the relevant point in time. A subject whose FRP score leads to
recommending
treatment with OrkvIM or PC at one point in time can be evaluated again at a
later point in time
and subsequently be recommended for treatment with MCS or HTx or other AdHF
intervention.
[0053] In some embodiments, the subject is suffering from heart failure with
reduced
ejection fraction. In some embodiments, the subject is suffering from heart
failure with
preserved ejection fraction.
[0054] The invention provides a method of predicting outcome of MCS or HTx or
other AdHF
intervention in a patient suffering from heart failure, comprising performing
the method
described above, wherein a poor outcome is predicted if the FRP score is 5 or
less. In some
embodiments, the methods of the invention further comprise treating the
subject with OMM,
PC, MCS, or HTx or other AdHF intervention, in accordance with the FRP score.
In some
embodiments, a poor outcome is predicted if the FRP score is 1-4, a score of 7-
10 is
predictive of recovery following advanced heart failure intervention, and a
score of 5-6 is
considered intermediate. For intermediate cases, other factors may be brought
into the
decision-making for treatment. In some embodiments, such other factors will
include a
subject's willingness to accept greater risk, or a preference for less
aggressive treatment.
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[0055] Further provided herein are methods of treating an individual. In some
embodiments,
methods of treatment comprise treatment for heart disease, such as heart
failure or
congestive heart failure. Methods of treatment provided herein comprise (i)
receiving a
sample from an individual; (ii) determining a gene expression level in the
sample for at least
one gene; and providing a treatment to the individual based on the gene
expression level. In
some embodiments, the gene comprises at least one of RSG1, TPRA1, SAP25,
MFSD3,
FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3,
ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2,
SL022A1 õAGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZKlIP1, NEGRI,
KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50,
NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,
ST6GALNAC1, CHMP6, SKA1, 0D209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI,
PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElF1AY,
or FITM1. In some embodiments, the gene expression level in the sample is
determined for
at least two genes of RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1,
ASPSCR1,
NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4,
USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L,
IGSF10, HEXA-AS1, L00728431, PDZK1IP1, NEGRI, KCNH8, CCR8, MME, ETV5,
CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1,
OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1,
0D209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI, PRRG1, XIST, RPS4Y1, ZFY,
PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDIV15D, ElF1AY, or FITM1.
[0056] Also provided is a method of monitoring progression of heart failure in
a subject. In
one embodiment, the method comprises performing the method described above,
wherein
progression is detected if the FRP score is reduced by 2 relative to a prior
measurement and
wherein improvement is detected if the FRP score is increased by 2 relative to
a prior
measurement.
[0057] In some embodiments, the FRP corresponds to the measured expression
level of the
set of genes relative to a reference group of expression levels. The reference
group may
correspond to expression levels of the set of genes observed in healthy
volunteers, or in a
subject who recovers from heart failure and/or major organ dysfunction. In
some
embodiments, the reference group is a set of normalization or control genes,
as described
above.
[0058] Provided below is a list of genes whose expression levels can be
measured for this
assay. The 28 predictive genes have been grouped by \A/GC NA-derived modules
representing integrated systems biological roles. Tables 1A-II (or referred to
collectively as
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"Table 1") list the 28 genes identified using a Mann-Whitney test based
evaluation of data
predicting day 8 organ function recovery. The full list of 28 genes appears as
one group in
Table 3 (known gene function summary in Table 5). Table 2 lists the 71 genes
whose
expression is predictive of day 8 organ function recovery based on t-test
evaluation of data.
Table 4A lists 12 genes that overlap between the genes listed in Table 3,
predictive of day 8
organ function recovery, and genes whose expression is predictive of one-year
survival.
Table 4B lists genes whose expression is predictive of one-year survival.
[0059] Gene Test Combination Options: In one embodiment, the gene is at least
one gene
selected from Table 1-4. In one embodiment, at least two or more genes of
Table 1, 2 and/or
3 is used in combination. In one embodiment, at least three or more genes of
Table 1, 2
and/or 3 is used in combination. In one embodiment, at least four or more
genes of Table 1,
2 and/or 3 is used in combination. In one embodiment, at least five or more
genes of Table
1, 2 and/or 3 is used in combination. In one embodiment, at least six or more
genes of Table
1, 2 and/or 3 is used in combination. In one embodiment, at least seven or
more genes of
Table 1, 2 and/or 3 is used in combination. In one embodiment, at least eight
or more genes
of Table 1, 2 and/or 3 is used in combination. In one embodiment, at least
nine or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least ten or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 11 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 12 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 13 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 14 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 15 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 16 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 17 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 18 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 19 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 20 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 21 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 22 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 23 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 24 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 25 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 26 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 27 or more
genes of Table 1, 2 and/or 3 is used in combination. In one embodiment, at
least 28 or more
genes of Table 1, 2 and/or 3 is used in combination. Examples of combinations
of genes
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include: BATF2 and an additional gene selected from Table 1, 2, or 3; AGRN and
an
additional gene selected from Table 1, 2, or 3; ANKRD22 and an additional gene
selected
from Table 1, 2, or 3; DNM1P46 and an additional gene selected from Table 1,
2, or 3;
FRMD6 and an additional gene selected from Table 1, 2, or 3; 1L-17A and an
additional gene
selected from Table 1, 2, or 3; KIR2DL4 and an additional gene selected from
Table 1, 2, or
3; BCORP1 and an additional gene selected from Table 1, 2, or 3; SAP25 and an
additional
gene selected from Table 1, 2, or 3; NAPSA and an additional gene selected
from Table 1,
2, or 3; HEXA-AS1 and an additional gene selected from Table 1, 2, or 3; TIMP3
and an
additional gene selected from Table 1, 2, or 3; RHBDD3 and an additional gene
selected
from Table 1, 2, or 3; any combination of 3 or more genes selected from Tables
1, 2 and 3;
any combination of 4, 5, 6, 7, 8, 9, 10, 11, or 12 genes of Tables 1, 2 and 3.
In one
embodiment, the combination of genes is 2, 3, 4, or 5 genes selected from
Table 1. In
another embodiment, the combination of genes is at least one gene from Table
1, and at
least one gene from Table 2. In another embodiment, the combination of genes
is at least
one gene from Table 1, and at least one gene from Table 3. In another
embodiment, the
combination of genes is at least one gene from Table 2, and at least one gene
from Table 3.
In another embodiment, the combination of genes is at least one gene from each
of Tables
1, 2, and 3. In one embodiment, the combination of genes is all or a subset of
the genes
listed in one or more of Tables 1-4.
[0060] In some embodiments, the combination of genes comprises at least one
gene
selected from Table 1A. In some embodiments, the combination of genes
comprises at least
one gene selected from Table 1B. In some embodiments, the combination of genes
comprises at least one gene selected from Table 1C. In some embodiments, the
combination of genes comprises at least one gene selected from Table 1D. In
some
embodiments, the combination of genes comprises at least one gene selected
from Table
1E. In some embodiments, the combination of genes comprises at least one gene
selected
from Table 1F. In some embodiments, the combination of genes comprises at
least one
gene selected from Table 1G. In some embodiments, the combination of genes
comprises
at least one gene selected from Table 1H. In some embodiments, the combination
of genes
comprises at least one gene selected from Table 11. In some embodiments, the
combination
of genes comprises at least two genes selected from Table 1A. In some
embodiments, the
combination of genes comprises at least two genes selected from Table 1B. In
some
embodiments, the combination of genes comprises at least two genes selected
from Table
1C. In some embodiments, the combination of genes comprises at least two genes
selected
from Table 1D. In some embodiments, the combination of genes comprises at
least two
genes selected from Table 1E. In some embodiments, the combination of genes
comprises
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at least two genes selected from Table 1F. In some embodiments, the
combination of genes
comprises at least two genes selected from Table 1G. In some embodiments, the
combination of genes comprises at least two genes selected from Table 1H. In
some
embodiments, the combination of genes comprises at least one gene selected
from Table
.. 1A, at least one gene selected from Table 1B, at least one gene selected
from Table 10, at
least one gene selected from Table 1D, at least one gene selected from Table
1E, at least
one gene selected from Table 1F, at least one gene selected from Table 1G, at
least one
gene selected from Table 1H, and at least one gene selected from Table 11. In
some
embodiments, the combination of genes comprises at least one gene selected
from Table
1A, at least one gene selected from Table 1B, at least one gene selected from
Table 1C, at
least one gene selected from Table 1D, at least one gene selected from Table
1E, at least
one gene selected from Table 1 F, at least one gene selected from Table 1G, at
least one
gene selected from Table 1H, and/or at least one gene selected from Table 11.
[0061] In some embodiments, the combination of genes comprises at least one
gene
selected from Table 2. In some embodiments, the combination of genes comprises
at least
two genes selected from Table 2. In some embodiments, the combination of genes
comprises at least three genes selected from Table 2. In some embodiments, the
combination of genes comprises at least four genes selected from Table 2. In
some
embodiments, the combination of genes comprises at least five genes selected
from Table
2. In some embodiments, the combination of genes comprises at least six genes
selected
from Table 2. In some embodiments, the combination of genes comprises at least
seven
genes selected from Table 2. In some embodiments, the combination of genes
comprises at
least eight genes selected from Table 2. In some embodiments, the combination
of genes
comprises at least nine genes selected from Table 2. In some embodiments, the
combination of genes comprises at least ten genes selected from Table 2. In
some
embodiments, the combination of genes comprises at least 11 genes selected
from Table 2.
In some embodiments, the combination of genes comprises at least 12 genes
selected from
Table 2. In some embodiments, the combination of genes comprises at least 13
genes
selected from Table 2. In some embodiments, the combination of genes comprises
at least
14 genes selected from Table 2. In some embodiments, the combination of genes
comprises at least 15 genes selected from Table 2. In some embodiments, the
combination
of genes comprises at least 16 genes selected from Table 2. In some
embodiments, the
combination of genes comprises at least 17 genes selected from Table 2. In
some
embodiments, the combination of genes comprises at least 18 genes selected
from Table 2.
In some embodiments, the combination of genes comprises at least 19 genes
selected from
Table 2. In some embodiments, the combination of genes comprises at least 20
genes
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selected from Table 2. In some embodiments, the combination of genes comprises
at least
21 genes selected from Table 2. In some embodiments, the combination of genes
comprises at least 22 genes selected from Table 2. In some embodiments, the
combination
of genes comprises at least 23 genes selected from Table 2. In some
embodiments, the
combination of genes comprises at least 24 genes selected from Table 2. In
some
embodiments, the combination of genes comprises at least 25 genes selected
from Table 2.
In some embodiments, the combination of genes comprises at least 26 genes
selected from
Table 2. In some embodiments, the combination of genes comprises at least 27
genes
selected from Table 2. In some embodiments, the combination of genes comprises
at least
28 genes selected from Table 2. In some embodiments, the combination of genes
comprises AGRN and at least one additional gene from Table 2. In some
embodiments, the
combination of genes comprises RSG1 and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises L00728431 and at least
one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
PDZKlIP1 and at least one additional gene from Table 2. In some embodiments,
the
combination of genes comprises NEGRI and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises HMCN1 and at least one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
CKAP2L and at least one additional gene from Table 2. In some embodiments, the
combination of genes comprises ACVR1C and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises KCNH8 and at least one
additional
gene from Table 2. In some embodiments, the combination of genes comprises
CCR8 and
at least one additional gene from Table 2. In some embodiments, the
combination of genes
comprises TPRA1 and at least one additional gene from Table 2. In some
embodiments, the
combination of genes comprises IGSF10 and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises MME and at least one
additional
gene from Table 2. In some embodiments, the combination of genes comprises
ETV5 and
at least one additional gene from Table 2. In some embodiments, the
combination of genes
comprises CXCL9 and at least one additional gene from Table 2. In some
embodiments, the
.. combination of genes comprises HBEGF and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises RANBP17 and at least one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
DDX43 and at least one additional gene from Table 2. In some embodiments, the
combination of genes comprises C6orf164 and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises GPR63 and at least one
additional
gene from Table 2. In some embodiments, the combination of genes comprises
SLC22A1
and at least one additional gene from Table 2. In some embodiments, the
combination of
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genes comprises C7orf50 and at least one additional gene from Table 2. In some
embodiments, the combination of genes comprises SAP25 and at least one
additional gene
from Table 2. In some embodiments, the combination of genes comprises NEFL and
at
least one additional gene from Table 2. In some embodiments, the combination
of genes
comprises CDCA2 and at least one additional gene from Table 2. In some
embodiments,
the combination of genes comprises MFSD3 and at least one additional gene from
Table 2.
In some embodiments, the combination of genes comprises ALDH1A1 and at least
one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
OLFM1 and at least one additional gene from Table 2. In some embodiments, the
combination of genes comprises ANKRD22 and at least one additional gene from
Table 2.
In some embodiments, the combination of genes comprises FADS3 and at least one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
BATF2 and at least one additional gene from Table 2. In some embodiments, the
combination of genes comprises SAC3D1 and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises FZD4 and at least one
additional
gene from Table 2. In some embodiments, the combination of genes comprises
FITM1 and
at least one additional gene from Table 2. In some embodiments, the
combination of genes
comprises FRMD6 and at least one additional gene from Table 2. In some
embodiments,
the combination of genes comprises SPTBN5 and at least one additional gene
from Table 2.
In some embodiments, the combination of genes comprises RBPMS2 and at least
one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
HEXA-AS1 and at least one additional gene from Table 2. In some embodiments,
the
combination of genes comprises 015or138 and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises DNM1P46 and at least one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
OEM P1 and at least one additional gene from Table 2. In some embodiments, the
combination of genes comprises ST6GALNAC1 and at least one additional gene
from Table
2. In some embodiments, the combination of genes comprises CHMP6 and at least
one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
ASPSCR1 and at least one additional gene from Table 2. In some embodiments,
the
combination of genes comprises SKA1 and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises 0D209 and at least one
additional
gene from Table 2. In some embodiments, the combination of genes comprises
SNAPC2
and at least one additional gene from Table 2. In some embodiments, the
combination of
genes comprises AXL and at least one additional gene from Table 2. In some
embodiments,
the combination of genes comprises NAPSB and at least one additional gene from
Table 2.
In some embodiments, the combination of genes comprises NAPSA and at least one
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additional gene from Table 2. In some embodiments, the combination of genes
comprises
KIR2DL1 and at least one additional gene from Table 2. In some embodiments,
the
combination of genes comprises KIR2DL4 and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises NLRP2 and at least one
additional
gene from Table 2. In some embodiments, the combination of genes comprises
NTSR1 and
at least one additional gene from Table 2. In some embodiments, the
combination of genes
comprises SEPT5 and at least one additional gene from Table 2. In some
embodiments, the
combination of genes comprises RHBDD3 and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises TIM P3 and at least one
additional
gene from Table 2. In some embodiments, the combination of genes comprises
KAL1 and
at least one additional gene from Table 2. In some embodiments, the
combination of genes
comprises PRRG1 and at least one additional gene from Table 2. In some
embodiments,
the combination of genes comprises XIST and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises RPS4Y1 and at least one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
ZFY and at least one additional gene from Table 2. In some embodiments, the
combination
of genes comprises PRKY and at least one additional gene from Table 2. In some
embodiments, the combination of genes comprises TTTY15 and at least one
additional gene
from Table 2. In some embodiments, the combination of genes comprises USP9Y
and at
least one additional gene from Table 2. In some embodiments, the combination
of genes
comprises DDX3Y and at least one additional gene from Table 2. In some
embodiments,
the combination of genes comprises UTY and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises BCORP1 and at least one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
TXLNG2P and at least one additional gene from Table 2. In some embodiments,
the
combination of genes comprises KDM5D and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises El FlAY and at least one
additional
gene from Table 2.
[0062] In some embodiments, the combination of genes comprises at least one
gene
selected from Table 3. In some embodiments, the combination of genes comprises
at least
two genes selected from Table 3. In some embodiments, the combination of genes
comprises at least three genes selected from Table 3. In some embodiments, the
combination of genes comprises at least four genes selected from Table 3. In
some
embodiments, the combination of genes comprises at least five genes selected
from Table
3. In some embodiments, the combination of genes comprises at least six genes
selected
from Table 3. In some embodiments, the combination of genes comprises at least
seven
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genes selected from Table 3. In some embodiments, the combination of genes
comprises at
least eight genes selected from Table 3. In some embodiments, the combination
of genes
comprises at least nine genes selected from Table 3. In some embodiments, the
combination of genes comprises at least ten genes selected from Table 3. In
some
embodiments, the combination of genes comprises at least 11 genes selected
from Table 3.
In some embodiments, the combination of genes comprises at least 12 genes
selected from
Table 3. In some embodiments, the combination of genes comprises at least 13
genes
selected from Table 3. In some embodiments, the combination of genes comprises
at least
14 genes selected from Table 3. In some embodiments, the combination of genes
comprises at least 15 genes selected from Table 3. In some embodiments, the
combination
of genes comprises at least 16 genes selected from Table 3. In some
embodiments, the
combination of genes comprises at least 17 genes selected from Table 3. In
some
embodiments, the combination of genes comprises at least 18 genes selected
from Table 3.
In some embodiments, the combination of genes comprises at least 19 genes
selected from
Table 3. In some embodiments, the combination of genes comprises at least 20
genes
selected from Table 3. In some embodiments, the combination of genes comprises
at least
21 genes selected from Table 3. In some embodiments, the combination of genes
comprises at least 22 genes selected from Table 3. In some embodiments, the
combination
of genes comprises at least 23 genes selected from Table 3. In some
embodiments, the
combination of genes comprises at least 24 genes selected from Table 3. In
some
embodiments, the combination of genes comprises at least 25 genes selected
from Table 3.
In some embodiments, the combination of genes comprises at least 26 genes
selected from
Table 3. In some embodiments, the combination of genes comprises at least 27
genes
selected from Table 3. In some embodiments, the combination of genes comprises
LiSP9Y
and at least one additional gene from Table 3. In some embodiments, the
combination of
genes comprises BATF2 and at least one additional gene from Table 3. In some
embodiments, the combination of genes comprises AGRN and at least one
additional gene
from Table 3. In some embodiments, the combination of genes comprises ANKRD22
and at
least one additional gene from Table 3. In some embodiments, the combination
of genes
comprises HMCN1 and at least one additional gene from Table 3. In some
embodiments,
the combination of genes comprises ACVR1C and at least one additional gene
from Table 3.
In some embodiments, the combination of genes comprises GPR63 and at least one
additional gene from Table 3. In some embodiments, the combination of genes
comprises
DNM1P46 and at least one additional gene from Table 3. In some embodiments,
the
combination of genes comprises CKAP2L and at least one additional gene from
Table 3. In
some embodiments, the combination of genes comprises FRMD6 and at least one
additional
gene from Table 3. In some embodiments, the combination of genes comprises
KIR2DL4
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and at least one additional gene from Table 3. In some embodiments, the
combination of
genes comprises IGSF10 and at least one additional gene from Table 3. In some
embodiments, the combination of genes comprises BCOR P1 and at least one
additional
gene from Table 3. In some embodiments, the combination of genes comprises
SAP25 and
at least one additional gene from Table 3. In some embodiments, the
combination of genes
comprises NAPSA and at least one additional gene from Table 3. In some
embodiments,
the combination of genes comprises FITM1 and at least one additional gene from
Table 3.
In some embodiments, the combination of genes comprises SPTBN5 and at least
one
additional gene from Table 3. In some embodiments, the combination of genes
comprises
HEXA-AS1 and at least one additional gene from Table 3. In some embodiments,
the
combination of genes comprises SLC22A1 and at least one additional gene from
Table 3. In
some embodiments, the combination of genes comprises RSG1 and at least one
additional
gene from Table 3. In some embodiments, the combination of genes comprises TIM
P3 and
at least one additional gene from Table 3. In some embodiments, the
combination of genes
comprises TPRA1 and at least one additional gene from Table 3. In some
embodiments, the
combination of genes comprises OEM P1 and at least one additional gene from
Table 3. In
some embodiments, the combination of genes comprises ASPSCR1 and at least one
additional gene from Table 3. In some embodiments, the combination of genes
comprises
MFSD3 and at least one additional gene from Table 3. In some embodiments, the
combination of genes comprises NAPSB and at least one additional gene from
Table 3. In
some embodiments, the combination of genes comprises NLRP2 and at least one
additional
gene from Table 3. In some embodiments, the combination of genes comprises
RHBDD3
and at least one additional gene from Table 3.
[0063] In some embodiments, the combination of genes comprises at least one
gene
selected from Table 4A. In some embodiments, the combination of genes
comprises at least
two genes selected from Table 4A. In some embodiments, the combination of
genes
comprises at least three genes selected from Table 4A. In some embodiments,
the
combination of genes comprises at least four genes selected from Table 4A. In
some
embodiments, the combination of genes comprises at least five genes selected
from Table
4A. In some embodiments, the combination of genes comprises at least six genes
selected
from Table 4A. In some embodiments, the combination of genes comprises at
least seven
genes selected from Table 4A. In some embodiments, the combination of genes
comprises
at least eight genes selected from Table 4A. In some embodiments, the
combination of
genes comprises at least nine genes selected from Table 4A. In some
embodiments, the
combination of genes comprises at least ten genes selected from Table 4A. In
some
embodiments, the combination of genes comprises at least 11 genes selected
from Table
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4A. In some embodiments, the combination of genes comprises BATF2 and at least
one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
AGRN and at least one additional gene from Table 2. In some embodiments, the
combination of genes comprises ANKRD22 and at least one additional gene from
Table 2.
In some embodiments, the combination of genes comprises DNM1 P46 and at least
one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
FRMD6 and at least one additional gene from Table 2. In some embodiments, the
combination of genes comprises KIR2DL4 and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises BCORP1 and at least one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
SAP25 and at least one additional gene from Table 2. In some embodiments, the
combination of genes comprises NAPSA and at least one additional gene from
Table 2. In
some embodiments, the combination of genes comprises HEXA-AS1 and at least one
additional gene from Table 2. In some embodiments, the combination of genes
comprises
TIMP3 and at least one additional gene from Table 2. In some embodiments, the
combination of genes comprises RHBDD3 and at least one additional gene from
Table 2.
[0064] In some embodiments, the combination of genes comprises at least one
gene
selected from Table 4B. In some embodiments, the combination of genes
comprises at least
two genes selected from Table 4B. In some embodiments, the combination of
genes
comprises at least three genes selected from Table 4B. In some embodiments,
the
combination of genes comprises at least four genes selected from Table 4B. In
some
embodiments, the combination of genes comprises at least five genes selected
from Table
4B. In some embodiments, the combination of genes comprises at least six genes
selected
from Table 4B. In some embodiments, the combination of genes comprises at
least seven
genes selected from Table 4B. In some embodiments, the combination of genes
comprises
at least eight genes selected from Table 4B. In some embodiments, the
combination of
genes comprises at least nine genes selected from Table 4B. In some
embodiments, the
combination of genes comprises at least ten genes selected from Table 4B. In
some
embodiments, the combination of genes comprises at least 11 genes selected
from Table
4B. In some embodiments, the combination of genes comprises at least 12 genes
selected
from Table 4B. In some embodiments, the combination of genes comprises at
least 13
genes selected from Table 4B. In some embodiments, the combination of genes
comprises
at least 14 genes selected from Table 4B. In some embodiments, the combination
of genes
comprises at least 15 genes selected from Table 48. In some embodiments, the
combination of genes comprises at least 16 genes selected from Table 4B. In
some
embodiments, the combination of genes comprises at least 17 genes selected
from Table
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4B. In some embodiments, the combination of genes comprises at least 18 genes
selected
from Table 4B. In some embodiments, the combination of genes comprises at
least 19
genes selected from Table 4B. In some embodiments, the combination of genes
comprises
at least 20 genes selected from Table 4B. In some embodiments, the combination
of genes
comprises at least 21 genes selected from Table 4B. In some embodiments, the
combination of genes comprises at least 22 genes selected from Table 4B. In
some
embodiments, the combination of genes comprises at least 23 genes selected
from Table
4B. In some embodiments, the combination of genes comprises at least 24 genes
selected
from Table 4B. In some embodiments, the combination of genes comprises at
least 25
genes selected from Table 4B. In some embodiments, the combination of genes
comprises
at least 26 genes selected from Table 4B. In some embodiments, the combination
of genes
comprises at least 27 genes selected from Table 4B. In some embodiments, the
combination of genes comprises at least 28 genes selected from Table 4B.
[0065] Table 1A (12 members of module blue (Metabolism); right two columns
indicate
direction of regulation):
Gene ID Gene Symbol Recovery Group OMM/PC Group
79363 RSG1 Up Down
131601 TPRA1 Up Down
100316904 SAP25 Up Down
113655 MFSD3 Up Down
161247 FITM1 Up Down
51332 SPTBN5 Up Down
752014 CEMP1 Up Down
79058 ASPSCR1 Up Down
256236 NAPSB Up Down
9476 NAPSA Up Down
55655 NLRP2 Up Down
25807 RHBDD3 Up Down
[0066] Table 18 (2 members of module black (Catabolic Metabolism); right two
columns
indicate direction of regulation):
Gene ID Gene Symbol Recovery Group OMM/PC Group
122786 FRMD6 Down Up
7078 TIMP3 Up Down --
[0067] Table 1C (2 members of module green (T Cell Regulation); right two
columns
indicate direction of regulation):
Gene ID Gene Symbol Recovery Group OMM/PC Group
130399 ACVR1C Down Up
196968 DNM1P46 Down Up
[0068] Table 1D (2 members of module pink (Immune System Development); right
two
columns indicate direction of regulation):
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Gene ID Gene Symbol Recovery Group OMM/PC Group
3805 KIR2DL4 Down Up
8287 USP9Y Down Up
[0069] Table 1E (2 members of module turquoise (RNA Metabolism); right two
columns
indicate direction of regulation):
Gene ID Gene Symbol Recovery Group OMM/PC Group
118932 ANKRD22 Down Up
286554 BCORP1 Down Up
[0070] Table IF (2 members of module lightgreen (* GO Biological Process Not
Classified);
right two columns indicate direction of regulation):
Gene ID Gene Symbol Recovery Group OMM/PC Group
83872 HMCN1 Down Up
81491 GPR63 Down Up
[0071] Table 1G (1 member of module cyan (Innate Immunity); right two columns
indicate
direction of regulation):
Gene ID Gene Symbol Recovery Group OMM/PC Group
116071 BATF2 Down Up
[0072] Table 1H (1 member of module darkred (Immune Process); right two
columns
indicate direction of regulation):
Gene ID Gene Symbol Recovery Group OMM/PC Group
6580 SLC22A1 Up Down
[0073] Table 11(4 members of module grey (Unclustered); right two columns
indicate
direction of regulation):
Gene ID Gene Symbol Recovery Group OMM/PC Group
375790 AGRN Down Up
150468 CKAP2L Down Up
285313 IGSF10 Down Up
80072 HEXA-AS1 Up Down
[0074] Table 2 (UCLA t-test based list of 71 genes predicting day 8 organ
function recovery)
Gene ID Gene Symbol Recovery Group OMM/PC Group
375790 AGRN Down Up
79363 RSG1 Up Down
728431 L00728431 Up Down
10158 PDZK1 !Pi Up Down
257194 NEGRI Down Up
83872 HMCN1 Down Up
150468 CKAP2L Down Up
130399 ACVR1C Down Up
131096 KCNH8 Down Up
1237 CCR8 Down Up
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131601 TPRA1 Up Down
285313 IGSF10 Down Up
4311 MME Down Up
2119 ETV5 Up Down
4283 CXC L9 Down Up
1839 HBEGF Down Up
64901 RANBP17 Up Down
55510 DDX43 Down Up
63914 C6orf164 Up Down .
81491 GPR63 Down Up .
6580 SLC22A1 Up Down .
84310 C7orf50 Up Down
1E+08 SAP25 Up Down
4747 N EFL Down Up
157313 CDCA2 Down Up
113655 MFSD3 Up Down
216 ALDH1A1 Down Up
10439 OLFM 1 Down Up
118932 ANKRD22 Down Up
3995 FADS3 Up Down
116071 BATF2 Down Up
29901 SAC3D1 Up Down
8322 FZ D4 Down Up
161247 FITM1 Up Down
122786 FRM D6 Down Up
51332 SPTBN5 Up Down .
348093 RBPMS2 Up Down .
80072 11 EXA-AS1 Up Down
=
348110 C 1 5orf38 Down Up
=
196968 DN M 1P46 Down Up
'
752014 CEMP1 Up Down
'
55808 ST6GALNAC1 Down Up
'
79643 CHM P6 Up Down
79058 ASPSCR1 Up Down
220134 SKA1 Down Up
30835 C D209 Down Up
6618 SNAPC2 Up Down
558 AXL Down Up
256236 NAPSB Up Down
9476 NAPSA Up Down
3802 KIR2D1.1 Down Up
3805 KIR2DL4 Down Up
55655 N LRP2 Up Down
4923 NTSR 1 Up Down .
5413 SEPT5 Up Down
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25807 RHBDD3 Up Down
7078 TIMP3 Up Down
3730 KALI Down Up
5638 PRRG1 Down Up
7503 XIST Up Down
6192 RPS4Y1 Down Up
7544 ZFY Down Up
5616 PRKY Down Up
64595 TTTY15 Down Up
8287 USP9Y Down Up
8653 DDX3Y Down Up
7404 UTY Down Up
286554 BCORP1 Down Up
246126 TXLNG2P Down Up
8284 KDM5D Down Up
9086 ElFlAY Down Up
[0075] Table 3 (UCLA Mann-Whitney-test based list of 28 genes predicting day 8
organ
function recovery)
Gene ID Gene Symbol Recovery Group OMM/PC Group
8287 USP9Y Down Up
116071 BATF2 Down Up
375790 AGRN Down Up
118932 ANKRD22 Down Up
83872 HMCN1 Down Up
130399 ACVR1C Down Up
81491 GPR63 Down Up
196968 DN M 1P46 Down Up
150468 CKAP2L Down Up
122786 FRMD6 Down Up
3805 KIR2DL4 Down Up
2855313 IGSF10 Down Up
286554 BCORP1 Down Up
100316904 SAP25 Up Down
9476 NAPSA Up Down
161247 FITM1 Up Down
51332 SPTBN5 Up Down
80072 HEXA-AS1 Up Down
6580 SLC22A1 Up Down
79363 RSG1 Up Down
7078 TIMP3 Up Down
131601 TPRA1 Up Down
752014 CEMP1 Up Down
79058 ASPSCR1 Up Down
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113655 MFSD3 Up Down
256236 NAPSB Up Down
55655 NLRP2 Up Down
25807 RHBDD3 Up Down
[0076] Table 4A (12 gene overlap list of UCLA Mann-Whitney-test based list of
28 genes
predicting day 8 organ function recovery and predicting 1-year survival)
Recovery Group OMM/PC Group
Gene ID Gene Symbol .
116071 BATF2 Down Up
375790 AGRN Down Up
118932 ANKRD22 Down Up
196968 DNM 1P46 Down Up
122786 FRMD6 Down Up
3805 KIR2DL4 Down Up
286554 BCORP1 Down Up
100316904 SAP25 Up Down
9476 NAPSA Up Down
80072 HEXA-AS1 Up Down
7078 TIMP3 Up Down
25807 RHBDD3 Up Down
Table 48 (105 genes predicting 1-year survival) *indicates overlap with genes
listed in
Table 3
Gene ID Gene Symbol Regulation in 1-year survival group
375790 AGRN* down
10911 UTS2 down
90853 SPOCD1 up
728431 L00728431 up
26027 ACOT11 up
257194 NEGRI down
=
388646 GBP7 down
=
163351 GBP6 down
1952 CELSR2 down
644591 PPIAL4G up
913 CD1E Down
116123 FM09P down
8497 PPFIA4 up
400950 C2orf91 down
84279 PRADC1 up
3625 INHBB down
5270 SERPINE2 up
643387 L00643387 down
79750 ZNF385D up
115560 ZNF501 down
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2815 GP9 up
55214 LEPREL1 down
151963 MB21D2 down
200958 MUC20 up
401115 C4orf48 up
84740 AFAP1-AS1 down
152831 KLB down
677810 SNORA26 down
8492 PRSS12 down
79931 TNIP3 down
7098 TLR3 down
3003 GZMK down
140947 C5or120 down
9832 JAKMI P2 down
9509 ADAMTS2 up
51149 . C5orf45 up
10471 PFDN6 down
594839 SNORA33 up
84310 C7orf50 up
2791 GNG11 up
100316904 SA P25* up
4747 NEFL down
1135 CHRNA2 up
6129 RPL7 down
157638 FAM84B down
26149 ZNF658 down
216 ALDH1A1 down
10439 OLFM1 down
1959 EGR2 down
118881 COMTD1 up
118932 ANKRD22* down
619562 SNORA3 down
79080 CCDC86 down
11251 PTGDR2 down
116071 BATF2* down
55359 STYK1 down
6297 SALL2 down
122786 FRMD6* down
161291 TMEM3OB down
100750247 HIF1A-AS2 up
8747 ADAM21 up
440278 CATSPER2P1 down
348093 RBPMS2 up
595097 SNORD16 down
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80072 H EXA-AS1* up
348110 C15orf38 down
196968 DNM1P46* down
645811 CCDC154 up
5376 PM P22 down
400617 KCNJ2-AS1 up
645158 CBX3P2 down
220134 SKA1 down
79839 000C102B down
284451 ODF3L2 down
79187 FSD1 up
30835 CD209 down
4066 LYL1 up
773 CACNA1A down
26659 0R7A5 down
126248 W0R88 up
3743 KCNA7 down
9476 NAPSA* Up
79986 ZNF702P down
94059 LENG9 up
3805 KIR2DL4* down
282566 LI NC00515 up
9510 ADAMTS1 down
11274 USP18 down
5413 SEPT5 up
100526833 SEPT5-GP1BB up
2812 GP1BB up
1415 CRYBB2 down
91353 I GLL3P down
23544 SEZ6L down
25807 R H BDD3* up
7078 TIMP3* up
79924 ADM2 down
284942 RPL23AP82 down
5638 PRRG 1 down
27238 GPKOW down
139189 DGKK down
1741 DLG3 down
56000 NXF3 up
8653 DDX3Y down
286554 BCORP1* down
_
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FRP Scoring
[0077] In certain embodiments of methods provided herein, a functional
recovery potential
(FRP) score is based on a linear discriminant analysis of the gene expression
profiles on day
-1 (or up to 72 hours) before the AdHF intervention that are predictive of
improvement in
organ function recovery, or "functional recovery", after the AdHF
intervention, such as can be
obtained from the information described in Tables 1-4 herein. One can perform
this linear
discriminant analysis using all 28 of the genes listed in Table 3, a select
subset, for example,
of 10-20 genes shown to be predictive of FRP. The linear discriminant analysis
is adapted
from the development of the Allomap test (Deng et al. AJT 2006:6:150), the
first in history
FDA-cleared cardiovascular in-vitro-diagnostic multivariate index assay
(IVDMIA) test to
assist the clinician in ruling out heart transplantation rejection, to select
genes and/or
nietacienes that, in combination, optimally predict functional recovery. As
more data are
gathered, for example, following completion of a planned =a1000 patient FDA-
Pivotal Trial,
the functional recovery potential (FRP) can be refined further according to
the rationale
described in Deng, MC, A peripheral blood transcriptome biomarker test to
diagnose
functional recovery potential in advanced heart failure. Biomark Med. 2018 May
8. dois
10.2217/bmm-2018-0097. Such further refinement includes, for example,
weighting the
contribution of individual genes to the FRP score as the analysis reveals
which genes have
greater predictive value.
[0078] Thus, the invention provides a method for developing a function
recovery potential
(FRP) scoring algorithm that predicts a subject's ability to recover from
medical intervention
for organ failure. In one embodiment, the method comprises (a) obtaining the
expression
levels of at least 10 of the 28 genes listed in Table 3 using pre-intervention
and post-
intervention expression levels of the at least 10 genes observed in PBMC
samples obtained
from a population of patients treated with medical intervention for organ
failure; (b)
performing linear discriminant analysis of the expression levels obtained in
(a) to classify the
PBMC samples into Group I (post-intervention improvement) or Group II (non-
improvement);
(c) estimating the effect size of each of the gene expression levels on the
classification of a
sample into Group I or Group II; and (d) adjusting the FRP scoring algorithm
by weighting
the contribution of each of the genes in accordance with the effect size.
Estimating the effect
size can comprise, for example, determining the eigenvalue for each gene, or
it can be
based on the canonical correlation.
[0079] As described in the Examples below, and now published as Bonder, G. et
al., PLoS
One 2017 Dec 13; 12(12), one can construct a PBMC-GEP-prediction model using
preoperative day -1 patient data to predict and classify postoperative Group I
(functional
recovery; low risk; high FRP score) vs. Group II (no recovery of organ
function; high risk; low
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FRP score). In the proof of concept study (Bondar. G. et al. 2017 cited
above), to achieve
a prediction model with highest accuracy for classification of patients into
Group I vs.
Group II, Strand NGS v2.9 was used for the alignment and analysis of the RNA-
Seg data.
After alignment, DESeg normalization, filtering and fold change analysis of
genes
expressed above noise levels resulted in 28 genes.
[0080] The 28 PBMC-genes that are differentially expressed between Group I and
Group II
were identified by non-parametric statistics (Mann-Whitney test with Benjamini-
Hochberg
correction). Since the original False Discovery Rate (FDR) methodology is too
conservative
for genomics applications and results in a substantial loss of power, we used
a more relaxed
criteria (FDR 5 0.1). Only those genes with fold change of at least 2.0 were
included in the
analysis. Biological significance was assessed using gene ontology, pathway
analysis and
via GeneCards database. The list of 28 genes was then used to build the model
to classify
postoperative Group I vs. Group II. We constructed this prediction model on
preoperative
day -1 gene expression data using the support vector machine (SVM) algorithm.
Out of 29
samples, 20 were randomly selected to build the model and the remaining 9
samples,
stratified by membership in Group I or Group II, were used to test the model.
The
prediction model was tested on 25 repetitions with random sampling. Hence, the
model
was built on a 20X28 matrix. Testing of the model showed prediction of Group I
versus
Group II membership with 93% accuracy. One-year survival in Group I was 15/17
and in
Group II 3/11, indicating lower risk in Group I (Fisher's Exact Test p<0.005).
Importantly,
the time-to-event Kaplan-Meier survival analysis suggested that the
significantly elevated
risk of death in Group II vs. Group I continued over the 1-year period
following MCS-
surgery (log rank p=0.00182).
[0081] In one illustrative example, a subject's blood sample is assayed for
expression levels
of the 12 genes listed in Table 4. The amount of gene expression is determined
relative to a
reference value. The reference value is the level of a normalization gene (one
known NOT to
be related to FRP) and/or it can be a level known to be representative of
healthy individuals
and/or individuals known to recover from heart failure. A neutral score on the
10-point FRP
scale would be 5.5. The average fold-change in gene expression would
contribute to an
increase or decrease from 5.5, depending on whether it was in the direction of
change
associated with recovery, to arrive at the FRP score for that subject. As
shown in Table 4,
down-regulation of the first seven genes is associated with recovery from
heart failure, while
up-regulation of the last five genes is associated with recovery. Optionally,
the contribution of
each gene's expression level is weighted based on the linear discriminant
analysis to adjust
for differences amongst genes in their predictive value.
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[0082] Those skilled in the art will recognize that the FRP scale can be
expressed on the
basis of other numerical ranges and still operate in the same manner as the 10-
point scale
described herein. For example, the FRP scale can be a 0-5 point range, a 0-50
point range,
or a 0-100 point range. Deviation relative to a neutral midpoint can still be
calculated in a
manner that is based on the relative expression levels of the genes listed in
Tables 1-4, and
adjusted to take into account appropriate weighting and other parameters
considered
predictive of functional recovery.
Kits and Assay Standards
[0083] In some embodiments the invention provides kits for measuring gene
expression for
one or more of the genes provided in Tables 1-4. Some such kits comprise a set
of reagents
as described herein that specifically bind one or more genes of the invention,
and optionally,
one or more suitable containers containing reagents of the invention. In some
embodiments,
reagents herein specifically bind to at least one gene comprising RSG1, TPRA1,
SAP25,
MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3,
FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1,
GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZK1IP1,
NEGRI, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6or1164,
C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,
ST6GALNAC1, CHMP6, SKA1, 0D209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KAL1,
PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElF1AY,
or FITM1. Reagents include molecules that specifically bind one or more genes
or gene
products of the invention, including primers and probes. Reagents can
optionally include a
detectable label. Labels can be fluorescent, luminescent, enzymatic,
chromogenic, or
radioactive.
[0084] Kits of the invention optionally comprise an assay standard or a set of
assay
standards, either separately or together with other reagents. An assay
standard can serve as
a normal control by providing a reference level of normal expression for a
given marker that
is representative of a healthy individual.
[0085] Kits can include probes for detection of alternative gene expression
products The kit
can optionally include a buffer. While some embodiments use the NGS-platform,
other
embodiments use qPCR/Nanostring/Nanopore technology.
[0086] In some embodiments the invention provides for the establishment of one
or more
central laboratories to which patient blood samples can be shipped for assay
using
polymerase chain reaction (PCR), next generation sequencing (NGS), or other
gene
expression profiling assay for one or more of the genes provided in Tables 1-
4.
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Computer Implementations
[0087] Provided herein; in certain aspects, are computer implemented systems
for use in
methods herein, such as methods of treatment, methods of gene expression
profiling, and
methods of recommending a treatment (Figure 2). In some embodiments, computer
implemented systems herein comprise: (a) a sample receiver for receiving a
sample
provided by an individual; (b) a digital processing device comprising an
operating system
configured to perform executable instructions and a memory: and (c) a computer
program
including instructions executable by the digital processing device to provide
a treatment to a
healthcare provider based on the sample. In some embodiments, the computer
program
comprises: (i) an gene analysis module configured to determine a gene
expression level in
the sample for at least one gene comprising RSG1, TPRA1, SAP25, MFSD3, FITM1,
SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3,
ACVR1C, DNIV11 P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCNt GPR63, BATF2,
SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZK1IP1, NEGR1;
KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6or1164, C7orf50,
NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,
ST6GALNAC1, CHMP6, SKA1, 0D209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KAL1,
PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15; DDX3Y, UTY, TXLNG2P, KDM5D, ElF1AY,
or FITM1: (ii) a treatment determination module configured to determine the
treatment based
on the gene expression level; and (iii) a display module configured to provide
the treatment
to the healthcare provider.
[0088] The invention provides a non-transitory computer-readable medium
encoded with
computer-executable instructions for performing the methods described herein.
In another
embodiment, the invention provides a non-transitory computer-readable medium
embodying
at least one program that, when executed by a computing device comprising at
least one
processor, causes the computing device to perform one or more of the methods
described
herein. In some embodiments; the at least one program contains algorithms,
instructions or
codes for causing the at least one processor to perform the method(s).
Likewise, the
invention provides a non-transitory computer-readable storage medium storing
computer-
readable algorithms, instructions or codes that, when executed by a computing
device
comprising at least one processor, cause or instruct the at least one
processor to perform a
method described herein.
[0089] Those of ordinary skill in the art would understand that the various
embodiments of
the method described herein, including analysis of gene expression profiles,
generation of
FRP scores, and prediction of outcomes, for example, can be implemented in
electronic
hardware, computer software, or a combination of both (e.g., firmware).
Whether the
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present method is implemented in hardware and/or software may depend on, e.g.,
the
particular application and design constraints imposed on the overall system.
Ordinary
artisans can implement the present method in varying ways depending on, e.g.,
particular
application and design constraints, but such implementation decisions do not
depart from the
scope of the present disclosure.
[0090] The computer programs/algorithms for performing the present method can
be
implemented with, e.g., a general-purpose processor, a digital signal
processor (DSP), an
application-specific integrated circuit (ASIC), a field-programmable gate
array (FPGA), or
other programmable logic device, discrete gate or transistor logic, discrete
hardware
components, or any combination thereof designed to perform the functions and
steps
described herein. A general-purpose processor can be a microprocessor, but
alternatively
the processor can be any conventional processor, controller, microcontroller
or state
machine. A processor can also be implemented as a combination of computing
devices,
e.g., a combination of a DSP and a microprocessor, a plurality of
microprocessors, one or
more microprocessors in conjunction with a DSP core, or any other such
configuration.
[0091] The steps of the present method, or the computer programs/algorithms
for
performing the method, can be embodied directly in hardware, in a software
module
executed by a processor, or in a combination of hardware and software (e.g.,
firmware). A
software module can reside in, e.g.. RAM memory, flash memory, ROM memory,
EPROM
.. memory, EEPROM memory, registers, a hard drive, a solid-state drive, a
removable disk or
disc, a CD-ROM, or any other form of storage medium known in the art. An
exemplary
storage medium is coupled to the processor such that the processor can read
information
from, and write information to, the storage medium. Alternatively, the storage
medium can
be integral to the processor. The processor and the storage medium can reside
in, e.g., an
ASIC, which in turn can reside in, e.g., a user terminal. In the alternative,
the processor and
the storage medium can reside as discrete components in, e.g., a user
terminal.
[0092] In one or more exemplary designs, the functions for carrying out the
method
described herein can be implemented in hardware, software, firmware or any
combination
thereof. If implemented in software, the functions can be stored on or
transmitted over a
.. computer-readable medium as instructions or codes. Computer-readable media
include
without limitation computer storage media and communication media, including
any medium
that facilitates transfer of a computer program/algorithm from one place to
another. A
storage medium can be any available medium that can be accessed by a general-
purpose or
special-purpose computer or processor. As a non-limiting example, computer-
readable
media can comprise RAM, ROM; EEPROM, CD-ROM or other optical disc storage,
magnetic disk storage or other magnetic storage devices, or any other medium
that can be
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used to carry or store a computer program/algorithm in the form of
instructions/codes and/or
data structures and that can be accessed by a general-purpose or special-
purpose computer
or processor. In addition, any connection is deemed a computer-readable
medium. For
example, if the software is transmitted from a website, a server or other
remote source using
a coaxial cable, fiber optic cable, twisted pair, digital subscriber line
(DSL), or a wireless
technology such as infrared, radio wave or microwave, then the coaxial cable,
fiber optic
cable, twisted pair. DSL, or wireless technology such as infrared, radio wave
or microwave
are computer-readable media. Discs and disks include without limitation
compact disc (CD),
laser disc, optical disc, digital versatile disc (DVD), blu-ray disc, hard
disk and floppy disk,
where discs normally reproduce data optically using a laser, while disks
normally reproduce
data magnetically. Combinations of the above are also included within the
scope of
computer-readable media.
[0093] The methods described herein can be automated. Accordingly, in some
embodiments the method is implemented with a computer system (e.g., a server,
a desktop
computer, a laptop, a tablet or a smartphone) comprising at least one
processor. The
computer system can be configured or provided with algorithms, instructions or
codes for
performing the method which are executable by the at least one processor. The
computer
system can generate a report containing information on any or all aspects
relating to the
method, including results of the analysis of the biological sample from the
subject. The
disclosure further provides a non-transitory computer-readable medium encoded
with
computer-executable instructions for performing the present method.
[0094] Figure 2 is a block diagram of an embodiment of a computer system 200
that can be
used to implement a method as described herein. System 200 includes a bus 208
that
interconnects major subsystems such as one or more processors 210, a memory
subsystem
212, a data storage subsystem 214, an input device interface 216, an output
device interface
218, and a network interface 220. Processor(s) 210 perform many of the
processing
functions for system 200 and communicate with a number of peripheral devices
via bus 208.
[0095] Memory subsystem 212 can include, e.g., a RAM 232 and a ROM 234 used to
store
codes/instructions/algorithms and data that implement various aspects of the
present
method. Data storage subsystem 214 provides non-volatile storage for program
codes/instructions/ algorithms and data that implement various aspects of the
present
method, and can include, e.g., a hard disk drive 242, a solid-state drive 244,
and other
storage devices 246 (e.g., a CD-ROM drive, an optical drive, a removable-media
drive, and
so on). Memory subsystem 212 and/or data storage subsystem 214 can be used to
store,
e.g., the gene expression profile and the FRP score of subjects, and the
therapeutic
outcome of treatment of those subjects with a particular AdHF or other
intervention. The
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codes/instructions/algorithms for implementing certain aspects of the present
method can be
operatively disposed in memory subsystem 212 or stored in data storage
subsystem 214.
[0096] Input device interface 216 provides interface with various input
devices, such as a
keyboard 252, a pointing device 254 (e.g., a mouse, a trackball, a scanner, a
pen, a tablet, a
touch pad or a touch screen), and other input device(s) 256. Output device
interface 218
provides an interface with various output devices, such as a display 262
(e.g., a CRT or an
LCD) and other output device(s) 264. Network interface 220 provides an
interface for
computer system 200 to communicate with other computer systems coupled to a
network to
which system 200 is coupled.
[0097] Other devices and/or subsystems can also be coupled to computer system
200. In
addition, it is not necessary for all of the devices and subsystems shown in
Figure 2 to be
present to practice the method described herein. Furthermore, the devices and
subsystems
can be interconnected in configurations different from that shown in Figure 2.
EXAMPLES
[0098] The following examples are presented to illustrate the present
invention and to assist
one of ordinary skill in making and using the same. The examples are not
intended in any
way to otherwise limit the scope of the invention.
Example 1: Association Between Preoperative PBMC Gene Expression Profiles,
Early
Postoperative Organ Function Recovery Potential, and Long-Term Survival in
Advanced
Hearth Failure Patients Undergoing Mechanical Circulatory Support
[0099] This Example demonstrates that preoperative PBMC-GEP predicts early
changes in
organ function scores and correlates with long-term outcomes in AdHF patients
following
MCS implantation. Therefore, gene expression lends itself to outcome
prediction and
warrants further studies in larger longitudinal cohorts.
[0100] Heart failure (HF) is a complex clinical syndrome that results from any
structural or
functional cardiovascular disorder causing a mismatch between demand and
supply of
oxygenated blood and consecutive failure of the body's organs. In the United
States, HF
affects about 6 million persons [1]. HF with reduced ejection fraction (HFrEF)
affects 3
million people [2]. The lifetime risk of developing HF for men and women older
than 40 years
.. of age is 1 in 5. The death rate remains unacceptably high at approximately
50% within 5
years from time of initial diagnosis. Stage D, or advanced heart failure
(AdHF), designates
patients with truly refractory HF (estimated at 300,000 persons in the US
annually) [2].
[0101] AdHF patients may benefit from the following therapeutic options:
optimal medical
management (OMM) or palliative/hospice care (PC, n = 300,000), mechanical
circulator/
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support (MCS, n = 30,000) or heart transplantation (HTx, n = 3,000) [3]. MCS
devices,
originally used for patients with AdHF as a bridge-to-transplant or bridge-to-
recovery, are
now increasingly used as destination (lifelong) therapy and have the potential
to outnumber
HTx by a factor of 1:10, currently showing an improved survival rate of
approximately 80% at
1 year [4].
[0102] Because of this success, destination MCS is increasingly being offered
to patients
with challenging clinical profiles. There is significant patient-to-patient
variability for risk of
adverse events, including death, after MCS-surgery. The ability to
preoperatively predict this
risk for the individual AdHF-patient before surgery and the impact of this
risk on the
associated long-term survival prognosis would be a very important component of
clinical
decision-making and management. Currently, we have our clinical expertise and
validated
clinical tools [4-18] for risk prediction.
[0103] However, despite our clinical expertise and validated tools, it is not
easy to assess
this risk and, therefore, make recommendations about which therapy benefits
the individual
patient most with respect to long-term survival. Often, elderly and frail AdHF
patients, if not
doing well on OMM, are also at increased risk for organ dysfunction (OD) and
death after
MCS-surgery. One of the reasons for the current challenges of risk prediction
is the difficulty
in assessing the degree of frailty and OD in the individual AdHF patient who
often suffers
from malnutrition, immune dysfunction, and poor infection coping potential.
[0104] Preoperative HF-related immunologic impairment is a component of poor
outcomes
after MCS and HTx, owing to the known associations between increased age. T
cell and
innate immune cell dysfunction, frailty, increased numbers of terminally
differentiated T cells,
immune senescence (deficient replicative ability), and immune exhaustion
(impaired antigen
response) [19-23]. Multi-organ dysfunction syndrome (MOD) is one of the
leading causes of
morbidity and mortality. It is associated with grossly aberrant immune
activation [4-18, 24-
26].
[0105] None of the current established clinical scoring and prediction tools
integrate immune
function parameters. They have the tendency to be imprecise in estimating risk
among
severely ill patients [11, 12], making the therapeutic recommendation with the
best survival
estimate for the individual patient very difficult. Our central postulate is
that OD and patient
death after MCS- or HTx-surgery results from innate and adaptive immune cell
dysfunction.
Therefore, our goal is to use leukocyte immune-biology information to develop
a
preoperative test, which would precisely predict postoperative outcomes in the
individual
AdHF patient. We utilized the widely accepted Sequential Organ Failure
Assessment
(SOFA) [27] and Model of End Stage Liver Disease without INR (MELD-XI) [24,
28, 29]
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scores as quantitative assessment tools to interpret the PBMC data and to
develop a
predictive leukocyte biomarker.
[0106] In order to achieve this goal, we hypothesize that in AdHF patients
undergoing MCS-
surgery, HF-related preoperative peripheral blood mononuclear cell (PBMC) gene
expression profiles (GEP) correlate with and predict changes of early
postoperative organ
function status as surrogates for 1 year survival.
[0107] In our prior studies, we reported on PBMC GEP time course analyses
after MCS-
surgery [30-32]. Here, we present data to support our hypotheses that, in AdHF
patients
undergoing MCS implantation, preoperative differential PBMC-GEP are associated
with and
.. are predictive of early postoperative SOFA and MELD-XI score changes,
defined as score
difference between immediately before surgery to 8 days after surgery as a
surrogate
marker for long term mortality risk.
[0108] The findings support the concept of developing a Functional Recovery
Potential
(FRP), seen as a person's quantifiable potential to improve after being
exposed to a
stressor, such as MCS-surgery.
Methods & design
[0109] Study design
[0110] To address the most pressing clinical problem of MCS-related
perioperative MOD [4,
33,34], we chose to base this analysis on a control population of AdHF-
patients undergoing
MCS-surgery alone. We conducted a study with 29 AdHF patients undergoing MCS-
surgery
at UCLA Medical Center between August 2012-2014 under UCLA Medical
Institutional
Review Board lapproved Protocol Number 12-000351. Written informed consent was
obtained from each participant.
[0111] Clinical management.
[0112] All study participants were referred to the UCLA Integrated AdHF
Program and
evaluated for the various therapeutic options, including OMM. MCS, and HTx.
All patients
were optimized regarding HF therapy, consented to and underwent MCS-therapy
according
to established guidelines [35,36], based on the recommendations of the
multidisciplinary
heart transplant selection committee.
[0113] After anesthesia induction, patients were intubated and placed on
cardiopulmonary
bypass. The type of MCS-device selected depended on the acuity and severity of
the heart
failure syndrome, as well as patient characteristics [37]. For left
ventricular support, patients
underwent either Heartmate II (HeartMate IIC) pumps are valveless, rotary,
continuous flow
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pumps) or HVAD (HeartWare HVAD pumps are valveless, centrifugal, continuous
flow
pumps). For biventricular support, patients underwent either Centrimag-BVAD
(Centrimag
pumps are valveless, centrifugal, continuous flow pumps that are external to
the body),
PVAD biventricular assist device (BVAD) (Thoratec Paracorporeal Ventricular
Assist
Device (PVAD) pumps each contain two mechanical tilting disk valves) or the t-
TAH (the
Temporary Total Artificial Heart consists of two artificial ventricles that
are used to replace
the failing heart).
[0114] Various combinations of cardiovascular inotropic and vasoactive drugs
were used to
support patient's hemodynamics postoperatively, tailored to the individual
requirements. In
addition, other temporary organ system support was administered as required
(e.g.
mechanical ventilation, hemodialysis, blood transfusions, and antibiotic
therapy).
[0115] Clinical phenotyping.
[0116] Demographic variables were obtained for all patients. Twelve distinct
parameters
were collected on a daily basis for time-dependent clinical phenotypina of the
patient cohort,
which included serum bilirubin, serum creatinine, leukocyte count, platelet
count, alveolar
oxygen pressure, fraction of inspired oxygen (Fi02), mean systemic arterial
pressure (MAP),
1NR (International Normalized Ratio, for prothrombin time), blood glucose,
heart rate,
respiratory rate, temperature, and the Glasgow Coma Scale (GCS).
[0117] Using combinations of these parameters, we also calculated two
validated and
commonly used composite OD scores, SOFA [27] and MELD-XI [24]. The SOFA score
is a
validated and widely accepted measure that rates degree of organ failure
across six major
organ systems (cardiovascular, respiratory, neurological, renal, hepatic, and
coagulation).
The MELD-XI score is a variation of the MELD score that uses only the
bilirubin and
creatinine levels, and eliminates the INR, which is typically not
interpretable in these patients
given the need of anticoagulation.
[0118] We also used the Interagency Registry for Mechanically Assisted
Circulatory Support
(INTERMACS) scoring system, which has been developed to improve patient
selection and
timing of MCS therapy [4] for preoperative HF-severity assessment. Higher
INTERMACS
risk categories are considered predictors of worse survival. While INTERMACS
identifies
.. clinical outcomes and risk of MOD, it does not provide insights into the
underlying
immunological mechanisms of disease.
[0119] Clinical outcome parameter.
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[0120] One of the most significant clinical outcome parameters for AdHF
patients
undergoing MCS is the probability of organ function improvement from one day
before to
eight days after surgery.
[0121] From a clinical utility perspective, we aim to provide AdHF-patients
with the most
precise prediction of short- and long-term outcome [38, 39] on either OMM or
MCS. Since
many AdHF patients have varying recovery potential, we chose a short-term
improvement
criteria, i.e. 8 days postoperatively, as a surrogate outcome parameter for
long-term survival.
For these reasons, we chose not to use a static preoperative organ function
severity score to
develop our biomarker. The most logical clinical parameter is the potential
for organ function
improvement, which we named the short-term functional recovery potential
(FRP). This
parameter may identify patients who benefit from aggressive therapies, such as
MCS, even
if they are very ill.
[0122] Therefore, patients were grouped into two organ failure risk strata:
Group I =
improving (both SOFA and MELD-Xl scores improve from day -1 to day 8) and
Group II =
not improving (SOFA and/or MELD-Xl score(s) do not improve from day -1 to day
8). In other
words, if the MCS-surgery improves the hemodynamic situation without
complications, then
the patient's organ function is expected to recover by postoperative day 5 and
clearly by
postoperative day 8, which should be reflected in a concordant improvement of
SOFA and
MELD-XI score, from day -1 to day 8. On the other hand, if SOFA or MELD-Xl, or
both,
scores do not improve from day -Ito day 8, we hypothesize that this problem
may
potentially impact long-term survival.
[0123] PBMC sample processing & GEP protocol.
[0124] PBMC samples were collected one day before surgery (day -1). Clinical
data was
collected on day -1 and day 8 postoperatively. We chose, based on our
successful
Allomap TM biomarker test development experience [40-43], to focus on the
mixed PBMC
population.
[0125] Eight ml of blood was drawn into a CPT tube (Becton Dickinson, Franklin
Lakes, NJ).
Peripheral Blood Mononuclear cells (PBMC) from each sample were purified
within 2h of
phlebotomy. The collected blood was mixed and centrifuged at room temperature
(22 C) for
20min at 3000RPM. Two ml of plasma was separated without disturbing the cell
layer into an
eppendorf tube (Sigma-Aldrich, St. Louis, MO) and stored at -80*C for future
experiments.
The cell layer was collected, transferred to 15m1 conical tubes and re-
suspended in cold
Phosphate Buffer Saline (PBS) (Sigma-Aldrich, St. Louis, MO) and centrifuged
for 20min at
1135RPM at 4 C. The supernatant was aspirated and discharged. The cell pellet
was re-
suspended in cold PBS, transferred into an eppendorf tube and centrifuged for
20min at 5.6
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RPM at 4 C. The supernatant was discharged. The pellet was re-suspended in 0.5
ml RNA
Protect Cell Reagent (Qiagen, Valencia, CA) and frozen at -80 C.
[0126] PBMC transcriptome RNA sequencing.
[0127] All samples were processed using next-generation RNA sequencing
transcriptome
analysis at the UCLA Technology Center for Genomics & Bioinformatics. Briefly,
the RNA
was isolated from the PBMC using RNeasy Mini Kit (Oiagen, Valencia, CA). The
quality of
the total RNA was assessed using NanoDrop() ND-1000 spectrophotometer
(NanoDrop
Technologies, Wilmington, DE) and Agilent 2100 Bioanalyzer (Agilent
Technologies, Palo
Alto, CA) concentration above 50 ng/pla purity-260/280 ¨ 2Ø, integrity¨RIN >
9.0 and
average > 9.5. Then; mRNA library was prepared with Illumina TruSeg RNA kit
according to
the manufacturer's instructions (Illumina, San Diego, CA). Library
construction consists of
random fragmentation of the polyA mRNA, followed by cDNA production using
random
polymers. The cDNA libraries were quantitated using Qubit and size
distribution was
checked on Bioanalyzer 2100 (Agilent Technologies, Palo Alto, CA). The library
was
.. sequenced on HiSeq 2500. Clusters were generated to yield approximately
725K-825K
clusters/mm2. Cluster density and quality was determined during the run after
the first base
addition parameters were assessed. We performed single end sequencing runs to
align the
cDNA sequences to the reference genome. Generated FASTQ files were transferred
to the
AdHF Research Data Center where Avadis NGS 1.5 (Agilent, Palo Alto, CA and
Strand
.. Scientific, CA) was used to align the raw RNA-Sal FASTQ reads to the
reference genome.
After RNA extraction, quantification and quality assessment, total mRNA was
amplified and
sequenced on the whole-genome Illumina HiSeq 2500. Data was then subjected to
DeSeg
normalization using NGS Strand/Avadis (v2.1 Oct 10, 2014). Batch effects were
removed
using the ComBat algorithm in R [44].
[0128] Statistical analysis
[0129] Transcriptome analysis.
[0130] We were interested in finding the preoperatively differentially
expressed genes (DEG)
in the GEP of 29 patients, as they correlate to early postoperative organ
function
improvement as markers for long-term survival outcome. PBMC-genes
differentially
.. expressed between Group I and Group II were identified by non-parametric
statistics (Mann-
Whitney test with Benjamini-Hochberg correction). Since the original False
Discovery Rate
(FDR) methodology [45] is too conservative for genomics applications and
results in a
substantial loss of power [46], we used a more relaxed criteria (FDR 1; 0.1)
values as an
exploratory guide for which entities to investigate further. Only those genes
with fold change
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of at least 2.0 were included in the analysis. Biological significance was
assessed using
gene ontology, pathway analysis and via GeneCards database.
[0131] Prediction model building and testing.
[0132] To classify postoperative Group I vs. Group II, we constructed a PBMC-
GEP
prediction model on preoperative day -1 gene expression data using the support
vector
machine (SVM) algorithm. Out of 29 samples, 20 were randomly selected to build
the model
and the remaining 9 samples, stratified by membership in Group I or Group II,
were used to
test the model. The prediction model was tested on 25 repetitions with random
sampling.
[0133] Quantitative Real-time polymerase chain reaction (RT-gPCR) validation.
[0134] NGS data were validated by Quantitative PCR obtained from PBMC of 6
samples
taken across Group I (n = 3) and Group II (n = 3). Total RNA from PBMC were
purified using
RNeasy Mini Kit (Qiagen, Valencia, CA). CDNA was synthesized with iScript
supermix for
RT-qPCR (Bic'Rad, Hercules, CA). RT-gPCR analysis was carried out with iTaq
SYBR green
supermix (BioRad, Hercules, CA) on the 7500 Fast Real-time PCR system (Applied
Biosystems, Foster City, CA). GAPDH levels were used as an internal control
for RT-gPCR.
Sequences of the primer pairs used were as follows: GAPDH-f:
CCACTCCTCCACCTTTGAC (SEQ ID NO: 1); GAPDH-r: ACCCTGTTGCTGTAGCCA (SEQ
ID NO: 2); KIR2DL4-f: ACCCACTGCCTGTTTCTGTC (SEQ ID NO: 3); KIR2DL4-r:
ATCACAGCATGCAGGTGTCT (SEQ ID NO: 4); NAPSA-f: CAGGACACCTGGGTTCACAC
(SEQ ID NO: 5); NAPSA-r: GGTTGGACTCGATGAAGAGG (SEQ ID NO: 6); BATF2-f:
AAAGGCAGCTGAAGAAGCAG (SEQ ID NO: 7); BATF2-r: TCTTTTTCCAGAGACTCGTGC
(SEQ ID NO: 8); ANKRD22-f: CTCAGCCAGGAAGGATTTTG (SEQ ID NO: 9); ANKRD22-r:
TGATAGGCTGCTTGGCAGAT (SEQ ID NO: 10).
Results
[0135] Clinical profiles and outcomes
[0136] Pre-, intra- and postoperative clinical profiles and long-term
survival.
[0137] Out of 29 patients, 17 were preoperatively in INTERMACS class 1-2 (a
state of
critical cardiogenic shock or progressively declining on inotropic support),
while the
remaining 12 patients were in 1NTERMACS class 3-4 (inotrope dependent or
resting
symptoms) [4]. Characteristics of the patients are shown in Table 1 of Bondar,
G. et al.,
2017, PLoS ONE 12(12): e0189420. The SOFA and MELD-XI OD trajectory for each
group
is summarized in Fig 3A. The same data in terms of amount of improvement is
shown in Fig
3B. One-year survival in Group I was 15/17 and in Group II 3/11, indicating
lower risk in
Group I (Fisher's Exact Test p<0.005). Importantly, the time-to-event Kaplan-
Meier survival
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analysis suggested that the significantly elevated risk of death in Group II
vs. Group I
continued over the 1-year period following MCS-surgery (log rank p = 0.00182;
Fig 4).
[0138] Neither correlation between preoperative clinical variables (i.e.
INTERMACS class,
SOFA median score, MELD-XI median score, and Seattle Heart Failure Model,
excluding
respiratory rate) nor intra/postoperative clinical variables predict Group I
versus Group II
membership or year 1 survival status. We grouped preoperative right
ventricular function,
defined by echocardiographic criteria, into two groups: normal to mildly
reduced right
ventricular function (n = 12) and moderately to severely reduced right
ventricular function (n
= 17). The chi-square p-value for postoperative Group I versus II membership
was non-
significant (p = 0.42). We grouped preoperative inotrope support levels into
the following
categories: no inotrope (n = 7), 1 inotrope (n = 3), 2 inotropes (n = 11),
inotropes or MCS
(e.g. VA ECMO) (n = 8). The chi-square p-value for postoperative Group I
versus II
membership was non-significant (p = 0.61). Additional preoperative clinical
information data
(i.e. bilirubin, creatinine, international normalized ratio, white blood
cells, heart rate, and
glucose level, all non-significant chi-square p-value) (respiratory rate, p =
0.03) are also
summarized in Table 1. None of the 29 patients had a clinical infection
episode on the day
prior to MCS surgery.
[0139] The intraoperative median cardiopulmonary bypass (CPB) time was 107min
(25%/75%: 75min1145min). We categorized patient CPB time into two groups:
patients with
no CPB (e.g. minimally invasive LVAD-placement) or CPB time shorter than the
median time
(n = 15) and patients with CPB time equal to or longer than the median time (n
= 14). The
chi-square p-value for Group I versus II membership was non-significant (p =
0.51).
Additionally, the group without major intraoperative bleeding (n = 20) was
compared to those
patients with major bleeding (n = 9). Bleeding severity was defined per
INTERMACS criteria
as greater than or equal to 4 RBC per any 24h period during the first 8
postoperative days.
The chi-square p-value for postoperative Group I versus II membership was non-
significant
(p = 0.06).
[0140] Out of 11 patients who died postoperatively, 9 patients died from MOD,
1 patient from
gastro-intestinal hemorrhage and 1 patient from sepsis.
[0141] Correlation between preoperative PBMC-transcriptome and clinical
outcomes
[0142] PBMC-transcriptome and clinical course.
[0143] Out of 29 patients undergoing MCS-surgery, 17 were in Group I and 12 in
Group II.
Twenty-eight MCS-surgery patients were alive 8 days postoperatively. Since our
study
explored how the preoperative PBMC-transcriptome can predict postoperative
clinical
outcomes, we restricted our analysis to the relationship between preoperative
day -1 PBMC
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data and change of clinical data from preoperative day -Ito postoperative day
8. This
project is based on our previously published studies that characterized the
postoperative
correlation between PBMC GEP and clinical parameters [30, 31], as well as our
time-course
analysis of the correlation between PBMC GEP module eigengenome and clinical
parameters [32].
[0144] Preoperative PBMC-transcriptome and early postoperative organ function
changes.
[0145] In order to identify day -1 transcripts related to organ function
change, the entire set
of mRNA transcripts (36,938) was filtered (20th-100th percentile) [44]. Of the
resulting
26,571 entities, only those with a fold change of at least 2.0 (123
transcripts) were retained
for statistical analysis with the unpaired Mann-Whitney test and Benjamini-
Hochberg
correction analysis (FDR = 0.1). After these filtering steps, 28 genes were
identified as
differentially expressed between the two groups on day -1 (Fig 5A, Table 5).
[0146] Preoperative PBMC-transcriptome and 1-year outcome.
[0147] Eighteen out of 29 patients were alive after 1 year while 11/29 died
during year 1.
The causes of death are summarized in Table 1 of Bondar, G. et al. 2017. The
preoperative
GEP was different in year 1 survivors and non-survivors. The filtered 25,319
entities were
analyzed using 2.0 fold change criteria. The 177 differentially expressed
genes were
analyzed by unpaired Mann-Whitney test with Benjamini-Hochberg correction,
resulting in
105 transcripts (FDR = 0.1). Hierarchical clustering was used on the 105
differentially
expressed genes (Corresponding 105 genes in Table 48 and publication Bondar et
al 2017)
for the year 1 survival patients (Fig 5B). Out of these genes, 12 overlap with
the 28 genes
that correlated with day 8 organ function improvement (Fig 5C, Table 5).
[0148] Out of the 28 genes that were differentially expressed between the two
groups
(Group I vs Group II membership) on postoperative day 8, 12 genes overlapped
with 1-year
survival status (blue rows).
[0149] Table 5: Known Function of 28 Gene Classifier
w -5 Fold = p-value p (corr) Gene Summary
0) Change with FDR
E
2 0
11.1
r=-= >- -6.442405 z c 0.0179204 0.0917301 USP9Y
is associated to Ubiquitin-
oo
1 2 Proteasome Dependent
Proteolysis,
oo 0
and essential component of IGF-
beta/BWIP signaling cascade. Within
nondiabetic heart failure-associated
genes with ischernic
cardiomyopathy, it was shown to
have a high degree of upregulation
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r-f r\I -2.2702124 z a. 0.0160600 0.0917301
BATF2 controls the differentiation of
0 8 2 lineage-specific cells in the
immune
system. Following infection,
participates in the differentiation of
CD8(+) thymic conventional
dendritic cells in the immune
system. Selectively suppresses
0/1161/CCN1 transcription and
hence blocks the downstream cell
proliferation signals produced by
0/1161 and inhibits CYR61-induced
anchorage-independent growth and
invasion in several cancer types;
IFNs, apart from their function as
antiviral infection agents, exert a
variety of inhibitory effects on cell
growth, apoptosis, and
angiogenesis. IFNs induce growth
inhibition by a variety of pathways
that involve many IFN-stimulated
genes BATF2 is one of these genes
and can be induced by IFNb, which
indicates that BATF2 may be a key
component involved in IFN
signaling.
o z -2.2644913 z a. 0.0173365 0.0917301 AGRN is
responsible for the
a)
2 maintenance of neuromuscular
1.11
junction (NW) and directs key
events in postsynaptic
differentiation
(N1 -2.685126 z 0.. 0.0201358 0.0917301 ANKR.D22 shows the
highest
r.%
8 2 upregulation with a value of
3.06 in
oo 0
the RI-gPCR analysis in finding
diagnostic biomarkers in Pancreatic
Adenocarcinoma Patients. The
function of ANKRD22 remains
unknown, but it has been patented
by Rosenthal et al as a possible
biomarker for several types of
cancer and by Brichard et al. for
identification of the patient
response to cancer immunotherapy
r-i -2.608948 z a 0.0081984 0.0917301 HMCN1 encodes a large
6
00 2 extracellular member of the
0
Co 2 0 immunoglobulin superfamily it
is
associated with Age-Related and
Postpartum Depression
c.) -2.2228224 z a. 0.0053835 0.0917301 ACVR1C is a type
I receptor for the
a%
cc% 3 2 TGFB, Plays a role in cell
0 0
0 differentiation, growth arrest and
apoptosis.
51
CA 03068981 2020-01-03
WO 2019/010339 PCT/US2018/040961
1--; (Y1 - z a. 0.0026020 0.0917301 GPR63
is a G-protein coupled
O l
22 2.2556078 3 2 receptor activity and plays a role
in
0
oo c.9 c brain function.
00 to -2.2071562 z a. 0.0073140 0.0917301 DNM1P46 is a
pseudogene.
a% a. 7 2 Although not fully functional,
cr% 2 c pseudogenes may be
functional,
c similar to other kinds of noncoding
DNA, which can perform regulatory
functions.
00 -.J -2.7842844 z a. 0.0041291 0.0917301 CKAP21 is a
microtubule-associated
to rq
7 2 protein.
o a 0
LO if) -2.4180312 z a. 0.0028533 0.0917301 FRMD6
is a Protein Coding gene.
00 o 5 N 2 2 Among its related
pathways are
(.: 0
cNi = 0 Cytoskeletal Signaling and
Hippo
signaling pathway
tr) .i- -3.4912138 z 0... 0.0036256 0.0917301
KIR2D14 is part of the killer cell
o
00 EDI 3 2 immunoglobulin-like receptors
m NI 0
cc 0 (Kiiis) which are transmembrane
--
glycoproteins expressed by natural
killer cells and subsets of T cells.
Inhibits the activity of NK cells thus
preventing cell lysis. Unlike classic
HLA class I molecules, HLA-G does
not seem to possess significant
immune stimulatory functions, and
even responses directed against
allogeneic HLA-G have not been
reported. HLA-G, however,
possesses the capability common to
HLA class I molecules, to bind
inhibitory receptors (Figure 1C).
Three HIA-G receptors have been
described: 1132/CD85j/LILRB1 (ILT2),
I1T4/ CD85d/1I1RB2 (I1T4), and
K1R201.41CD158d (10122D1.4)
m o -3.154924 z a. 0.0099618 0.0917301 IGSF10 (Immunoglobulin
m LL. 6 2 Superfamily Member 10) is a
Protein
co 0 0 Coding gene
=::t ,=-i -3.629981 z 0.. 0.0200527 0.0917301
BCORP1 is a pseudogene. Although
u-) a.
t.n cc 2 not fully functional,
pseudogenes
1.0 0 0
0 may be functional, similar to other
kinds of noncoding DNA, which can
_ _____________________________________________ perform regulatory functions.
52
CA 03068981 2020-01-03
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.4. in 2.371788 a. z 0.0069456 0.0917301 SAP25 is a new member
of the
0 r=J
01 O. 7
2 growing family of
nucleocytoplasmic
ko < 0
shuttling proteins that are located in
rn
o
0 PML nuclear bodies. PML nuclear
,--i
bodies are implicated in diverse
cellular functions such as gene
regulation, apoptosis, senescence,
DNA repair, and antiviral response.
Involved in the transcriptional
repression
Lo < 2.1895149 a_ z 0.0159894 0.0917301 NAPSA is a pronapsin
A which may
r- vs
2 2 have considerable diagnostic
value
c:i= < 0
Z o as a marker for primary lung
cancer.
In contrast, the pronapsin B gene,
which lacks an in-frame stop codon
and so may be a transcribed
pseudogene, is expressed at
comparable levels in normal human
spleen, thymus, cytotoxic and
helper Tlymphocytes, natural killer
(NK) cells and Blymphocytes; may
also function in protein catabolism .
N ,--1 2.272873 0- z 0.0093850 0.0917301
Fill in skeletal muscle and FIT2 in
vt I¨ 6
hi 7_ 2 adipose, it is interesting to
speculate
r-f 0
lf) 0 that Fill. might be essential for
the
e-i
rapid oxidation of FAs stored as TG
in I.Ds while F1T2 is required for the
longterm storage of TG in
adipocytes. Plays an important role
in lipid droplet accumulation.
tr, 2.3029516 a. z 0.0143412 0.0917301 SPTBN5 is related to pathways
of
m z = m > 7 2 Interleukin-3, 5 and GM-CSF
,--i 1--- 0
signaling and Signaling by GPCR
kn
rsi ¨1 2.1215222 0. z 0.0186288 0.0917301 SPTBN5 (Spectrin
Beta, Non-
o 1 4 2 Erythrocytic 5) is a
Protein Coding
o 0
oo <
x 0 gene. HEXA-A51 (HEXA Antisense
Lt.!
X RNA 1) is an RNA Gene, and is
affiliated with the antise.nse RNA
class.
,
o ri 2.033342 0.. z 0.0173365 0.0917301 51C22A1 (Solute
Carrier Family 22
00 < =
in CV 2 Member 1) is a Protein Coding
gene.
14) CV 0
Plays a critical for elimination of
...,
kn
many endogenous small organic
cations as well as a wide array of
drugs and environmental toxins
53
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<-1 2.0549338 a. z 0.0073556 0.0917301 Differential expression
of ABCA1,
2 RSG1 and ADBR2 was replicated
in
cr$ 0
monocyte gene expression in
patients with early onset coronary
artery disease (CAD). These three
genes identified expressed
differently in CAD cases which might
play a role in the pathogenesis of
atherosclerotic vascular disease.
Potential effector of the planar cell
polarity signaling path,,ivay.
00 m 2.2165775 a. z 0.0200527 0.0917301 TIMP3 blocks the binding
of VEGF to
0 2 2 VEGF receptor-2 and inhibits
r- 0
0 downstream signaling and
angiogenesis. This property seems
to be independent of its MMP-
inhibitory activity, indicating a new
function for this molecule.
Complexes with metalloproteinases
(such as collagenases) and
irreversibly inactivates them by
binding to their catalytic zinc
cofactor. Diseases associated with
TIM P3 include Sorsby Fundus
Dystrophy and Pseudoinflammatory
= ¨1 2.0833867 0. z 0.0127678 0.0917301 TPRA1 whose
physiological
t.0
2 functions are unknown, was
first
O.. 0
rn 0 cloned as a GLP4 receptor
homolog
in 3T3-1.1 adipocytes and is also
expressed in tissues whose
development requires Hh signaling,
including heart, brain, lung,
pancreas, and muscle
2.0399396 a. z 0.0143521 0.0917301 CEMP1 (Cementum Protein 1) is
a
= a.
2 1 2 Protein Coding gene. Diseases
rsi 0
Lt.!
r- 0 associated with CEMP1 include
Coccidiosis.
co 2.0533528 a. z 0.0917301 ASPSCR1 encodes a protein
that
0 t.) 0.0143521 2 contains a UBX domain and
a) 0
N ci 1 interacts with glucose
transporter
type 4 (GLUT4). This protein is a
tether, which sequesters the GLUT4
in intracellular vesicles in muscle
and fat cells in the absence of
insulin, and redistributes the GLUT4
to the plasma membrane within
minutes of insulin stimulation
54
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Le) m 2.3885236 a. z 0.0067281 0.0917301 Membrane-bound solute
carriers
LA 0
2
2 (SL.Cs) are essential
as they maintain
*"
2
several physiological functions, such
as nutrient uptake, ion transport
and waste removal The SIC family
comprise about 400 transporters,
and two new putative family
members were identified, major
facilitator superfarnily domain
containing 1 (MFSD1) and 3
(MFSD3)
1.0 co 2.6573431 a. z 0.0917301 NAPSB is a
pseudogene. Although
m (..e)
0.0161072 2 not fully functional,
pseudogenes
< 0
1-n z ci 7 may be functional,
similar to other
fNi
kinds of noncoding DNA, which can
perform regulatory functions.
Lii iN1 2.3330774 0. z 0.0143412 0.0917301 NIRP2 suppresses TNF-
and CD40-
tfl CI.
7
2 induced NFKB1 activity
at the level
Er) 0
it) z of the IKK complex, by
inhibiting
NFKBIA degradation induced by
TNF. When associated with PYCARD,
activates CASP1, leading to the
secretion of mature
proinflammatory cytokine1118. May
be a component of the
inflammasome, a protein complex
which also includes PYCARD, CARD8
and CASP1 and whose function
would be the activation of pro-
inflammatory caspases.
m 2.2666128 0. z 0.0224031 0.0984139 Rhbdd3, a member of the
rhomboid
c)
00 8 7 family of proteases,
suppressed the
tn co 0
activation of DCs and production of
interleukin 6 (IL-6) triggered by Toll-
like receptors (TLRs). Rhbdd3-
deficient mice spontaneously
developed autoimmune diseases
characterized by an increased
abundance of the TH17 subset of
helper T cells and decreased
number of regulatory T cells due to
the increase in IL-6 from DCs"
[0150] PBMC-GEP prediction model development
[0151] Clinical profiles and outcome correlation.
[0152] Neither preoperative clinical variables including I NTERMACS [4] class,
SOFA median
score. MELD-XI median score, and Seattle Heart Failure Model (SHFM) nor
intralpostoperative clinical variables (except for respiratory rate) predict
Group 1 versus
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Group H membership nor year 1 survival status. On day 8, 17 patients had organ
function
improvement (Group I) and 12 patients had no organ function improvement (Group
II), with
one died on postoperative day 3. Nine patients in INTERMACS class 1-2
preoperatively
improved at day 8, while 8 patients did not improve (Fisher's Exact Test
p<0.005). Eight
patients in INTERMACS class 3-4 improved while 4 did not improve (Fig 6). The
inefficiency
of clinical scores in correlating with OD in critically ill AdHF patients [10]
supports our
rationale in developing a preoperative biomarker prediction test.
[0153] Prediction of early postoperative organ function changes.
[0154] We built a model using the SVM algorithm by randomly selecting 20
samples out of
29 total, stratified by membership in Group I versus Group II. To test the
model, the
remaining 9 samples were stratified by membership in Group I or Group II. An
average
prediction accuracy of 93% (range: 78-100%) was achieved after running the
model building
process 25 times (Table 6).
[0155] Table 6: Prediction of Organ Function Improvement Group I vs II.
Run Accuracy % Run Accuracy %
PM1 100 PM14 100
PM2 100 PM15 89
PM3 100 PM16 100
=
PM4 89 PM17 89
PM5 89 PM18 100
PM6 78 PM19 100
PM7 100 PM20 89
PM8 89 PM21 89
PM9 89 PM22 100
PM10 89 PM23 100
PM11 100 PM24 89
PM12 100 PM25 89
PM13 100 Average 94
[0156] Out of 29 samples, 20 were randomly selected, stratified by membership
in Group I
or Group II, were used to build the model and the remaining 9 samples were
used to test the
model.
[0157] RT-ciPCR validation.
[0158] To validate the NGS results in this study, we performed a limited RT-
ciPCR
experiment to assay the 4 highest ranked genes (by statistical significance
and correlation
between Group I and Group II expression levels). Results show that 2 out of 4
genes
(KIR2OL4, BATF2) concordantly correlated between NGS and RT-ciPCR expression
levels,
showing downregulation in Group I and upregulation in Group II. Those 2 genes
therefore
might become candidates for the prognostic test development. The RT-dPCR
results of
56
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ANKR022 and NAPSA expression level showed an equivocal relationship to the NGS
results. We attributed this discrepancy to the difference in method. This
result is in
agreement with the internal validation during the AlIomapTM test development,
in which 68
out of 252 candidate genes discovered by high-throughput technology were
confirmed by
concordant expression changes using RT-gPCR. Therefore, these 68 genes were
retained
for further AllomapIm test development.
[00159] Figures. 3A-3B. illustrate organ function and outcomes. Figure 3A
shows organ
function and outcomes of 29 patients across five time points. Out of 29 AdHF-
patients
undergoing MCS-surgery, 17 patients had organ function improvement from
preoperative
day -1 (TP1) to day 8 (TP5) (Group I) and 12 patients had no organ function
improvement
(Group II). Each black line represents one 1-year survivor while each red line
represents one
1-year non-survivor. In each group, non-survivors are shown in red. Figure 3B
shows that,
out of 29 AdHF-patients undergoing MOS-surgery, 17 patients improved (Group I,
upper
right quadrant) and 12 patients did not improve (Group II, remaining 3
quadrants) from day -
1 (TP1) to day 8 (TP5). Each large dark bullet represents one patient who died
within one
year. Absence of improvement of either score was associated with reduced 1-
year survival.
[00160] Figure 4 show the Kaplan-Meier 1-year survival in Group I vs. Group
II. In the 17
patients who improved (Group I=Functional recovery=Organ function
improving=Low Risk)
vs. the 11 patients who did not improve (Group II=No functional recovery=Organ
function not
.. improving=High Risk), the time-to-event Kaplan-Meier survival analysis
suggested that the
significantly elevated risk (log rank test p=0.00182) of death in Group II
continued over the 1-
year period following MCS-surgery.
[00161] Figures 5A-5C show overlap of significant genes associated with organ
function
improvement and survival benefit. Figure 5A shows hierarchical clustering of
significant
genes day -1 (TP1). Left: The Volcano plot of 28 genes which are
differentially expressed
between Group I and Group II. Right: Hierarchical clustering of the 28
candidate genes for
the prediction test demonstrates the differential gene expression between
Group I and
Group II. Figure 58 shows hierarchical clustering of genes associated with
survival benefit.
Left: The Volcano plot of 105 genes, which are differentially expressed
between Group I and
Group II. Right: Hierarchical clustering 17 of the 105 candidate genes for the
prediction test
demonstrates the differential gene expression between Group I=Survival, Group
II=Non-
survival. Figure 5C shows overlap genes from both improvement group and 1-year
survival
outcome. Left: Venn-Diagram shows the 28 DEGs identified in the comparison by
Improvement Score (red) and the Right shows the 105 DEGs identified by
comparing 1-Year
Survival (blue). 12 DEGs were shared across the two comparisons. Right: The 12
overlap
genes.
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[00162] Figure 6 shows an exemplary prediction biomarker development
rationale.
Preoperative clinical heart failure/organ function severity scores (I NTERMACS
class, SOFA
median score, MELD-XI median score) and demographics (age, gender) did not
reliably
discriminate postoperative organ function improvement (ROC, 95% confidence
interval) and
long term survival (Cox Proportional Hazard Model, 95% confidence interval).
In contrast,
the PBMC-GEP correlates well with postoperative organ function improvement and
long
term survival.
Discussion
[0163] We present data to support our hypotheses that in AdHF patients
undergoing MCS
.. implantation, preoperative differential PBMC-GEP are associated with and
are predictive of
early postoperative SOFA and MELD-XI score changes. We defined these clinical
parameters as the difference in score between one day before surgery and 8
days after
surgery as a surrogate marker for long-term mortality risk. Our studies show
the set of 28
genes derived from preoperative PBMC GEP is predictive of early postoperative
.. improvement or non-improvement of SOFA and MELD-XI scores. Out of the 28
preoperative
genes, the following 12 genes are of specific biological interest due to their
overlap in
differentiating early postoperative organ function improvement and year 1
survivor status.
[0164] Potential biological implications of overlapping genes
[0165] Hypothetical mechanisms of up-regulated genes in non-improvement of
SOFA score
and MELD-XI score and year 1 non-survivors.
[0166] BATF2 belongs to a class of transcription factors that regulate various
immunological
functions and control the development and differentiation of immune cells.
Functional studies
demonstrated a predominant role for BATF2 in controlling Th2 cell functions
and lineage
development of T lymphocytes. Following infection, BATF2 participates in the
development
of and differentiation of CD8 (+) thymic conventional dendritic cells in the
immune system
[47]. BATF2 plays a key component involved in IFN signaling and positive
regulation of
immune responses by altering expression of cytokines and chemokines.
Therefore, it
possibly maintains the balance in inflammatory processes. BATF2 is an
essential
transcription factor for gene regulation and effector functions in classical
macrophage
activation [48]. AGRI N is a gene with a ubiquitous role and is evolutionarily
conserved in the
extracellular matrix (ECM) [49], Its intracellular processes include
proliferation, apoptosis,
migration, motility, autophagy, angiogenesis, tumorigenesis, and immunological
responses
[50, 51]. AGRI N interacts with the a'r3-dystroglycan receptor in the
formation of
immunological synapses with lymphocytes and aids in activation [52] as well as
maintaining
monocyte cell survival downstream in an a-dystroglycan dependent manner [53].
The
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AGRIN LG3 domain has been used as a biomarker for detection of prematurely
ruptured
fetal membranes [54]. ANKR22, involved in the lipid modification of proteins
[55], has been
patented as a possible biomarker for several types of cancers [56, 57] to
identify patient
responses to cancer immunotherapy. FRMD6 has been linked to various complex
diseases,
such as asthma, Alzheimer's disease, and lung cancer. It plays a critical role
in regulating
both cell proliferation and apoptosis, where it is thought to have tumor
suppressor properties.
FRMD6 may help mediate the process by which Vitamin D inhibits the
proliferation of
immune cells [58, 59]. Upregulation of FRMD6 has been suggested as a
prognostic marker
in colorectal cancer [59]. KIR2DL4 codes for transmembrane glycoproteins
expressed by
natural killer (NK) cells and subsets of T cells. KIR2DL4 inhibits the
activity of NK cells and
may reduce activation induced cell death in these T cells in Sezary syndrome
[60], [61, 62].
KIR2DL4 is an unusual member of the KIR family that recognizes human leukocyte
antigen
G and mediates NK-cell activation [63] and has been suggested as a useful
diagnostic
biomarker of neoplastic NK-cell proliferations [64].
[0167] Hypothetical mechanisms of down-regulated genes in non-improvement of
SOFA
score and MELD-XI score and year 1 non-survivors.
[0168] SAP25 is a member of the nucleocytoplasmic shuttling proteins that are
located in
promyelocytic leukemia (PML) nuclear bodies. PML nuclear bodies are implicated
in diverse
cellular functions, such as gene regulation, apoptosis, senescence, DNA
repair, and antiviral
response [65], [66, 67]. NAPSA is a pronapsin gene, which may have a
considerable
diagnostic value as a marker for primary lung cancer. NAPSA was detected in a
subset of
poorly differentiated papillary thyroid carcinomas and anaplastic carcinomas
[68]. TIM P3 is
an extracellular matrix-bound protein, which regulates matrix composition and
affects tumor
growth. TIM P3 suppresses tumor inactivation in cancer by mechanisms of
invasion and
angiogenesis [69]. TIMP-3 downregulation is associated with aggressive non-
small cell lung
cancer and hepatocarcinorna cells, as compared with less invasive and/or
normal lung and
liver cells [70]. It mediates vascular endothelial growth factor (VEGF) by
blocking the binding
of VEGF to VEGF receptor-2, inhibiting downstream signaling, and prevents
angiogenesis.
These inhibitive properties seem to be independent of its matrix
metalloproteinases (MMP)-
inhibitory activity, which indicates a new function for this molecule. RHBDD3
is a member of
the rhomboid family of proteases that suppresses the activation of dendritic
cells (DCs) and
production of interleukin 6 (IL-6) triggered by Toll-like receptors. The
rhomboid proteins are
involved in signaling via the receptor for epidermal growth factor,
mitochondrial homeostasis
and parasite invasion [71, 72]. RHBDD3 negatively controls the activation of
DCs and
maintains the balance of regulator/ T cells and TH17 cells by inhibiting the
production of IL-6
by DCs, thus contributing to the prevention of autoimmune diseases [72].
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[0169] In summary, our central postulate is that OD and death after MCS- or
HTx-surgery
results from innate and adaptive immune cell dysfunction. Therefore, leukocyte
immune-
biology information may be used to develop a preoperative test, which more
precisely
predicts postoperative outcomes in the individual AdHF-patient. To meet this
clinical goal,
.. we have developed a novel concept of FRP, which is based on our assessment
that the key
prognostic information is the preoperative potential to postoperatively
restore an equilibrium
rather than the absolute magnitude of preoperative OD. In this clinical
context, we interpret
the potential biological role of the 12 overlap genes as follows: we
hypothesize BATF2 is
chronically more activated in GROUP II AdHF-patients in comparison to GROUP I
patients.
BATF2 activation is due to its attempts to repair the cell necrosis-mediated
damage caused
by OD. This hyper-activation leads to exhaustion of adaptive immunity cells,
which may
explain the protracted time-course-to-death in Group II patients. (Fig 4). To
garner support
for this hypothesis, we have initiated a study that incorporated multiplex
flow cytometry
markers, cell free methylated DNA, and mitochondrial DNA into the study
protocol. For
RHBDD3, its downregulation in patients with rheumatoid arthritis, ulcerative
colitis and
Crohn's disease [72] may be beneficial in preventing auto-immune aggression.
However, its
down-regulation in AdHF-patients undergoing MCS-surgery might exacerbate an
inappropriate innate inflammatory response and inappropriate adaptive immune-
incompetence via a less inhibitory effect on the 1L6-pathway [73].
Furthermore, it is
.. interesting to note that upregulation of genes, such as ANKRD22. FRMD6, and
K1R3DL2,
and down-regulation of genes, such as TIMP3, SAP25, NAPSA, and TIMP are
associated
with a worse prognosis in cancer, are also associated with a worse prognosis
in AdHF. This
raises the question about common pathways in both clinical syndromes.
[0170] Health system implication perspectives.
[0171] Our data suggest that the preoperative dynamic recovery potential,
rather than the
static severity of OD, is the key prognostic property to restoring equilibrium
after surgery.
This also presents the possibility of using a preoperative blood sample to
identify AdHF-
patients who may have a high chance of early postoperative recovery and a
potentially good
long-term prognosis. If the preoperative blood test result predicts a high FRP
(Group I), this
data might lead to the recommendation to undergo surgery. If the preoperative
blood test
suggests a low FRP (Group II), the healthcare team may avoid a potentially
harmful
recommendation of surgery at that time. In the US, we estimate that out of
30,000-60,000
individuals per year with AdHF and potential candidates for MCS, at least
7,500-15,000
might not benefit from undergoing surgery based on the test results if they
are too sick to
benefit from MCS surgery. Since HF is a major public health concern due to its
tremendous
societal and economic burden, with estimated costs in the U.S. of $37.2
billion in 2009 and
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with expectations to increase to $97.0 billion by 2030, our proposed
prediction test would
simultaneously allow to tailor the individual patient's personal benefits and
also enhance
cost-effectiveness in U.S. healthcare.
[0172] The clinical decision-making challenge at the time of AdHF evaluation
often
culminates in the choice between modern medicine and compassionate end of life
care. This
ultimate scenario is demanding medically, ethically and economically. It
deserves the best
evidence-based decision making support that personalized precision medicine
research has
to offer that lives up to the highest humanistic expectations that society
entrusts us with.
[0173] Limitations.
[0174] First, our outcome parameter in this proof-of-principle study used a
dichotomous
endpoint (Improvement versus No Improvement of organ function on day 8
postoperatively).
In a planned expansion of the study to include a larger cohort, we will treat
the outcome
parameter as a quantitative continuous variable. Second, we have not
incorporated
multisystem level protein markers into our analysis. In a planned extension of
the project, we
will include multiplex flow cytometry and cytokine parameters. Third, the
study had a small
sample size. This poses inherent limitations on Group I vs Group II
comparisons. The logistic
regression/Cox-PH models were constructed with only one predictor variable
each due to
sample size constraints. We also reported the coefficients/accuracy measures
from these
models with 95% confidence intervals, which properly reflect our uncertainty
about the
parameter estimates as a function of sample size. Fourth, the RT-qPCR
validation was
limited by a lack of biological material necessary to complete the test. We
will expand this
validation to include all candidate genes in a follow-up study. Fifth, as in
translational
biomarker development in general, many results were a consequence of
operator/researcher-dependent decisions. Sixth, while we chose to base our
analysis on
AdHF-patients undergoing MCS-surgery alone to address the problem of MCS-
related
perioperative MOD [4, 33, 34], we acknowledge that we have not addressed
aspects of the
PBMC-biology related to MCS-surgery intervention versus general heart surgery.
In order to
address this question, we have initiated a follow-up project examining AdHF-
cohorts
undergoing OMM, HTx, coronary artery bypass surgery, percutaneous coronary
interventions, valve replacement, valve repair, arrhythmia interventions and
healthy
volunteers, utilizing the same study protocol. These results will be reported
separately.
[0175] Conclusions
[0176] In AdHF patients undergoing MCS implantation, the postoperative
clinical
improvement of OD within one week of surgery is associated with reduced long-
term
mortality and a PBMC GEP that differs from that of patients who do not
improve, is already
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present preoperatively and may lend itself to outcome prediction. The
underlying
mechanisms and prognostic implications to improve patient outcomes warrant
further study
in larger longitudinal cohorts.
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Example 2: Peripheral Blood Transcriptome Biomarker Test to Diagnose
Functional
Recovery Potential in Advanced Heart Failure
[0250] This Example illustrates the outcome prediction obtained by use of the
Functional
Recovery Potential (FR F), which refers to the potential to recover from
stressors based on
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chronological age and multiple other factors, including primary and secondary
organ failure,
comorbidities, frailty, disabilities.
[0251] Heart failure (HF) is a complex clinical syndrome that results from any
structural or
functional cardiovascular disorder that causes systemic hypoperfusion and
failure to meet
the body's metabolic demands. HF is initiated by various myocardial injury
mechanisms.
Despite chronic neurohormonal upregulation in order to maintain a compensated
state,
further myocardial injury leads to HF progression, resulting in overall
catabolic/anabolic
imbalance, secondary organ dysfunction, cardiac cachexia, iron deficiency
anemia, and
frailty[3]. This triggers immune system activation which coincides with
progressive
dysfunction of the kidneys, liver, bone marrow, brain and metabolism, creating
a milieu
similar to systemic diseases[4-6], clinically presenting as advanced HF (AdHF)
with severely
limited prognosis. Outcomes are dependent on HF-severity, but also on
chronological age
(CA) and multiple other factors jointly called the "Personal Biological Age
(PBA)". At any
given CA, there are great biological disparities and great heterogeneity in
health
outcomes[7,8]. This discrepancy relates to a difference in the potential of
individual persons
to recover from stressors termed the Functional Recovery Potential (FRP), or
in equivalent
term, the probability of Functional Recovery (FR). Our central postulate is
that FRP
integrates the clinical composite including CA as well as PBA (primary and
secondary organ
failure, comorbidities, frailty, disabilities) (Figure 7).
[0252] AdHF patients with low FRP may be at increased risk for death after
AdHF-therapies
such as mechanical circulatory support (MCS) or heart transplantation (HTx).
As described
in Example 1, preoperative differential gene expression profiles (GEP) of
peripheral blood
mononuclear cell (PBMC) are predictive of early postoperative outcomes in AdHF
patients
undergoing MCS. We defined FRP outcomes as changes of Sequential Organ Failure
Assessment (SOFA) and Model of Endstage Liver Disease except NR (MELD-XI)
score
from preoperatively to 8 days postoperatively, which correlates with long term
mortality[9].
[0253] FRP is a generalizable and clinically useful concept and can be: 1)
defined as a
person's potential to return to a functional life, after stressor exposure; 2)
modulated by long-
term bio-psycho-social interventions; 3) characterized within a general
clinical framework
integrating CA and PBA data; 4) quantitatively described: 5) used as a
surrogate for long-
term outcome prediction; and 6) diagnosed from pre-stressor molecular data.
Incorporating
these molecular data in the clinical encounter can improve the quality of the
decision-making
in a shared decision-making process and help achieve the value-based
healthcare goals of
optimizing patient experience, minimizing morbidity and mortality outcomes and
maximizing
health system cost-effectiveness. In this Example, we discuss the biomedical
foundation of
FRP and potential clinical utility in HF medicine.
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AdHF outcomes in high-risk AdHF patients
[0254] During the last five decades, HF outcomes have improved with medical
management[1,2,10]. However, patients with stage D, or AdHF, often cannot
tolerate optimal
medical management (OMM) by guideline directed medical therapy (GDMT) and do
not
derive the same benefit as patients with less advanced disease[l 1]. Older
patients may not
derive the same benefit as younger patients[12].
[0255] It has been suggested that biomarker-guided therapy could improve
outcomes over
solely GDMT[13]. We describe a biomarker to assist the clinician in predicting
long-term
outcomes after AdHF-surgical/interventional therapies.
Outcomes in AdHF patients undergoing revascularization
[0256] For AdHF patients undergoing high risk percutaneous coronary
intervention (PCI),
the benefits or harms of PCI in HF populations are unknown because of a lack
of
randomized trials [14].
[0257] For AdHF patients undergoing high risk coronary artery bypass surgery
(CABG),
there is limited information regarding efficacy in different age groups. In
the Surgical
Treatment for Ischemic Heart Failure (STICH) Study trial, a total 1,212
patients with an left
ventricular ejection fraction (LVEF) of <35% were randomly assigned to undergo
CABG plus
medical therapy or medical therapy alone[15,16]. In the Surgical Treatment for
Ischemic
Heart Failure Extended Study (STICHES) trial, the median duration of follow-up
was 9.8
years. There was a trend towards a smaller reduction in all-cause mortality
with CABG
compared to GDMT in older compared with younger patients, implying that an
improved
understanding of the efficacy of CABG in different age groups is
needed[14,17]. This result
is consistent with recent HF trials[12]. Since there were few patients in the
older age groups,
the true long-term benefit may be even lower. i.e. there is equipoise between
GDMT and
CABG in patients > 67 years with Heart failure with reduced ejection fraction
(HFrEF)[14]
(Table 7).
Outcomes in AdHF patients undergoing valve interventions
[0258] AdHF patients undergoing Transcatheter Aortic Valve Replacement (TAVR).
Following the initial TAVR experience[18,19] (Table 7), mortality in US
clinical practice at 1-
year follow-up was 23.7%. It is "imperative to focus on better prediction of
the overall risks
and benefits of the procedure, particularly given the existing comorbidities
of the group of
patients being considered for TAVR."[20] (Table 7). In a systematic review on
TAVR
outcomes, 46.4% and 51.6% of deaths were related to non-cardiovascular causes
within and
after the first 30 days; respectively[21]. In the Intermediate Risk TAVR
trial[22] (Table 7); the
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guideline for patient inclusion was an Society of Thoracic Surgeons (STS) risk
score [102]
and EuroSCORE [103], based on the presence of coexisting illnesses to predict
mortality at
30 days, between 4-8%[23]. The main results showed that TAVR was not inferior
to surgery
with respect to outcomes at 2 years (death from any cause or disabling
stroke).
[0259] Per 2017 recommendations, the risks of death and morbidity associated
with the
natural history of severe aortic valve stenosis need to be weighed against the
risk related to
aortic valve replacement as a basis for recommendation of treatment[24,25].
TAVR is not
recommended in patients in whom existing cornorbidities would preclude the
expected
benefit from correction of aortic stenosis (AS)[26].
[0260] AdHF patients undergoing MitraClip. The overall mortality rate after
surgical repair of
functional mitral regurgitation (FMR) ranges from 20% to 50%[27-29]. Mitra-
Clip therapy is
an emerging option for selected high-risk patients with FMR[30,31]. The High
Risk Study, an
arm of the EVEREST II trial, enrolled symptomatic patients with 3+ to 4+ MR
for whom
surgical risk for perioperative mortality rate was estimated to be ?..12%,
using the STS
calculator[32,33]. Potentially qualifying criteria included high-risk patients
with porcelain
aorta, mobile ascending aorta atheroma, post-mediastinal radiation, functional
MR with left
ventricular ejection fraction (LVEF) <40%, age older than 75 years with LVEF
<40%,
previous median sternotomy with patent bypass graft(s), >2 previous chest
surgeries,
hepatic cirrhosis, or of the following STS high-risk criteria: creatinine
level >2.5 mg/di,
previous chest surgery, age older than 75 years, or LVEF <35%[34] (Table 7). A
significant
number of patients with symptomatic MR have extensive comorbidities or
uncertain
indications for surgery and are defined as high surgical risk, inoperable or
not indicated for
surgery, and approximately one-half of patients with symptomatic severe MR may
not
undergo surgery. In a recent Mitra-Clip-meta-analysis, one-year mortality rate
was 16%
(408/2498) and similar among groups in patients with FMR vs degenerative
mitral
regurgitation (DMR). The authors conclude that better patient selection and
performing
percutaneous edge-to-edge repair at earlier stage could avoid treatment of
those patients
with advanced LV remodeling, more than severe MR, and many comorbidities, who
benefit
less from the procedure[35] (Table 7).
[0261] Per 2017 American College of Cardiology (ACC)/American Heart
Association (AHA)
recommendations, transcatheter mitral valve repair may be considered for
severely
symptomatic patients (New York Heart Association (NYHA) class III to IV) with
chronic
severe primary MR (stage 0) who have favorable anatomy for the repair
procedure and a
reasonable life expectancy but who have a prohibitive surgical risk because of
severe
comorbidities and remain severely symptomatic despite optimal GDMT for HF[24].
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Outcomes in AdHF patients undergoing ventricular tachyarrhythrnia (VT)
interventions
[0262] AdHF patients undergoing internal cardioverter defibrillator (1CD)
device therapy.
Patients with stage D heart failure are at increased risk of sudden cardiac
death (SCD) from
ventricular tachyarrhythmia, thus anti-arrhythmia device therapy is an
integral part of their
management. Introduction of 1CD for primary prevention of sudden cardiac death
was
proven to be of great benefit with reduction in mortality of 31% in 20 months
in patients with
history of myocardial infarction (MI) and EF<309/0[36]. Furthermore, in
patients with EF<35%
regardless of etiology and mild to moderate symptoms, 1CD implantation
decreases mortality
by 23% over 5 years [40] [Bardy 2005].
[0263] ACC/AHA heart failure guidelines recommend ICD implantation in all
patients with
ejection fraction of <30% and NYHA classl symptoms and in those with EF <35%
with
NYHA Class II and III syrnptoms[2]. However, this therapy is reserved for
patients with
projected survival of more than one year, which precludes some of the patients
with very
advanced disease from receiving an ICD. In octogenarians who are due for an
ICD, careful
thought should be given to the current clinical status, comorbidities, and
general frailty prior
to considering them for the procedure[38]. Goldenberg et al. highlighted a U-
shaped
relationship between the severity of heart failure and mortality benefit from
ICD therapy[39].
[0264] AdHF patients undergoing BVPM-device therapy. Cardiac resynchronization
therapy
(CRT) in patients with wide QRS complex and Left Bundle Branch Block (LBBB)
pattern has
led to improvement of ventricular contractility and EF, reduction in secondary
mitral
regurgitation, reversal of remodeling and decrease in mortality. However,
around 30% of
individuals receiving this therapy derive no benefit or experience worsening
of their
symptoms[40]. Similar to ICD, patients with stage D HF are often considered to
be too sick
to benefit from CRT and therefore their treatment is limited to advanced
therapies (MCS and
Htx) or palliative care[2].
[0265] AdHF patients undergoing VT-ablation therapy. Ventricular tachycardia
(VT)-ablation
therapy has increased in the US, specifically in patients worsening clinical
risk profile
including age and comorbidity burden[41]. In a contemporary registry, catheter
ablation of
VT in patients with structural heart disease results in 70% freedom from VT
recurrence, with
an overall transplant and/or mortality rate of 15% at 1 year. Patients who
died or underwent
transplant were older and had higher rates of hyperlipidemia, diabetes
mellitus, atrial
fibrillation, chronic kidney disease, advanced heart failure, ICD, CRT, lower
EF, electrical
storm (ES), shocks, amiodarone, and antiarrhythmic drugs. In the Cox
multiple regression
frailty analysis, transplant or death was associated with older age, NYHA
class III and IV,
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chronic kidney disease, electrical storm, and use of hemodynamic support
devices[42]
(Table 7). The International Ventricular Tachycardia Center Collaborative
Study Group
registry of 2,061 patients who underwent VT ablation analyzed survival of
patients L-70 years
with and without VT recurrence. Of 681 patients, 92% were men, 71% had
ischemic VT, and
42% had VT storm at presentation. LVEF was 30 11%. Compared with patients <70
years,
patients .?_70 years had higher 1-year mortality (15% versus 11%; P=0.002)[43]
(Table 7).
Patients with electrical storm are among the highest risk VT populations
because they are
frailer, older, with a lower LVEF, more advanced heart failure status, and
more
comorbidities. A comprehensive approach needs to include not only the
arrhythmia ablation
but also careful treatment of the comorbidities, such as advanced heart
failure, hypertension,
hyperlipidemia, atrial fibrillation, diabetes, and chronic kidney disease[44].
A major challenge
of VT ablation is hemodynamic intolerance of the induced arrhythmia, with as
few as 10% of
induced arrhythmias being stable[45]. Extracorporeal membrane oxygenation
(ECMO) will
be increasingly used in this scenario[46]. The challenge is to predict a
prohibitively high risk
of not being able to wean the patient from VA-ECMO post-interventionally.
Outcomes in AdHF patients undergoing MCS/HTx
[0266] AdHF patients undergoing MCS. MCS devices, originally used for patients
with AdHF
as a bridge-to-transplant or bridge-to-recovery, now increasingly used as
destination
(lifelong) therapy, have the potential to outnumber HTx by a factor of
1:10[47]. Because of
this success, destination MCS is increasingly being offered to patients with
challenging
clinical profiles. There is significant patient-to-patient variability for
risk of adverse events.
Overall survival continues to remain >80% at 1 year and 70% at 2 years[48]
(Table 7).
[0267] AdHF patients undergoing heart transplantation. Since its first
introduction in 1967,
heart transplantation (HTx) offers an unparalleled survival benefit in select
patients with
stage D HF, and remains the gold standard of treatment. Stage D HF is defined
as refractory
HF and often accompanied by the following parameters: repeated (>2)
hospitalizations or
emergency department visits for HF in the past year, worsening renal function,
unintentional
weight loss >10% (cardiac cachexia), intolerance to medical therapy due to
hypotension
and/or worsening renal function, persistent dyspnea/fatigue, hyponatremia and
escalating
use of diuretics (>160 mg/d and/or use of supplemental metolazone therapy) and
frequent
1CD shocks.
[0268] Annually, there are approximately 3,000 HTx performed in the U.S. and
the number
of donors have remained steady for decades. Current graft survival rates with
advances in
immunosuppressive therapy are 85-90%, 75-80%, and 70-75% in adults at 1-, 3-,
5-year
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respectively, and a median survival of 11-13 years. Internationally,
contemporary median
survival after adult heart transplantation is 10.7 years[49] (Table 7).
[0269] ACC/AHA guidelines designates a class I indication for heart
transplantation only in
carefully selected patients with stage D HF despite GDMT, device, and surgical
management. The leading cumulative causes of death are graft failure,
infection, cancer,
and multiple organ failure.
[0270] Table 7: Summary of AdHF-intervention studies with inclusion criteria,
sample size
and major outcomes: Across the different interventions, the 1-year mortality
rate is in the
range of 10-30%. Abbreviations: CABG - coronary artery bypass surgery; GDMT -
Guideline
directed medical therapy; LVEF - left ventricular ejection fraction; HTx -
Heart
transplantation; MCS - Mechanical circulatory support; STS - Society of
Thoracic Surgeons;
TAVR - Transcatheter Aortic Valve Replacement; VT - Ventricular tachycardia;
Y¨ year
Author (Year) Inclusion Patients Intervention
Outcome/Comments
Petrie 2016 LVEF<35% 1,212 CABG >67y equipoise
GDMT vs
CABG at 10 y
Holmes 2015 STS7% 12,182 TAVR 1y mortality 23%
Leon 2010 STS>15-50% 2= ,032 TAVR 1y mortality 30%
Smith 2011 STS>10-15% 699 TAVR 1y mortality 24%
Leon 2016 STS>4-8% 2,032 TAVR ly mortality 12%
Whitlow 2012 STS?_12 78 Mitra-Clip 1y mortality 24%
Chiarito 2018 LVEF39-59% 2,615 Mitra-Clip 1y mortality 16%
Tung 2015 LVEF31% 2= ,061 VT-ablation 1y mortality
12%
Vakil 2017 LVEF30%, >70y 681 VT-ablation 1y mortality
15%
Vergara 2017 LVEF34%, ES 677 VT-ablation 1y mortality
20%
Kirklin 2017 LVEF low 17,633 MCS 1y mortality 20%
Lund 2017 LVEF low 2= 1,614 HTx 1y mortality 10-
15%
Outcome prediction biomarker prototype
[0271] In our proof-of-principle outcome prediction biomarker prototype study
described in
Example 1, our central postulate is that OD and patient death after MCS- or
HTx-surgery
results from innate and adaptive immune cell dysfunction. Therefore, our goal
was to use
leukocyte immune-biology information to develop a preoperative test, which
would precisely
predict postoperative outcomes in the individual AdHF patient. We utilized the
widely
accepted SOFA[72] and MELD-XI [67,73,74] scores as quantitative assessment
tools to
interpret the PBMC data and to develop a predictive leukocyte biomarker. We
specifically
hypothesized that one of the most significant clinical outcome parameters for
AdHF patients
undergoing MCS is the probability of organ function improvement from one day
before to
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eight days after surgery. Therefore, patients were grouped into two organ
failure risk strata:
Group I= improving (both SOFA and MELD-Xl scores improve from day -1 to day 8)
and
Group II= not improving (SOFA and/or MELD-XI score(s) do not improve from day -
1 to day
8). In other words, if the MCS-surgery improves the hemodynamic situation
without
complications, then the patient's organ function is expected to recover by
postoperative day
5 and clearly by postoperative day 8, which should be reflected in a
concordant improvement
of SOFA and MELD-XI score, from day -1 to day 8. On the other hand, if SOFA or
MELD-Xl,
or both, scores do not improve from day -1 to day 8, we hypothesize that this
problem may
potentially impact long-term survival. We hypothesized that in AdHF patients
undergoing
MCS-surgery, HF-related preoperative PBMC GEP correlate with and predict
changes of
early postoperative organ function status as surrogates for 1-year survival.
Our studies
showed the set of 28 identified genes [201] derived from preoperative PBMC GEP
is
predictive of early postoperative improvement or non-improvement of SOFA and
MELD-XI
scores. Out of the 28 preoperative genes, 12 genes were of specific biological
interest due to
their overlap in differentiating not only early postoperative organ function
improvement but
also year 1 survivor status[9].
[0272] Our data suggest that the pre-interventional dynamic recovery
potential, rather than
the static parameter of "severity of OD", is the key prognostic property to
restoring
equilibrium after surgery. This also presents the possibility of using a
preoperative blood
sample to identify AdHF-patients who may have a high chance of early
postoperative
recovery and a potentially good long-term prognosis. If the preoperative blood
test result
predicts a high FRP (Group 0, this data might lead to the recommendation to
undergo
surgery. If the preoperative blood test suggests a low FRP (Group II), the
healthcare team
may avoid a potentially harmful recommendation of surgery at that time. In the
US, we
estimate that out of 30,000-60,000 individuals per year with AdHF and
potential candidates
for MCS and other AdHF-surgical/interventional therapies, at least 7,500-
15,000 might not
benefit from undergoing the intervention based on the test results if they are
too sick at the
time of testing. Since HF is a major public health concern due to its
tremendous societal and
economic burden, with estimated costs in the U.S. of $37.2 billion in 2009 and
with
expectations to increase to $97.0 billion by 2030, our proposed prediction
test would
simultaneously allow to tailor high-tech modern medicine to the individual
patient's needs,
i.e. optimize personal morbidity and mortality benefits and personal
experience while also
enhancing cost-effectiveness in U.S. healthcare. This concept would contribute
to the
advancement of high value-healthcare and reduction of low-value-healthcare.
[0273] It is important for the patient to choose the therapeutic option with
the best short-,
medium- and long-term outcome. In order to do so, the doctor needs to be able
to predict,
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from pre-intervention data of the patient, what the consequences of the
different options are.
First and foremost, this means that all available pre-intervention data need
to be analyzed
for their long-term outcome prediction capacity. None of the current
established clinical
scoring and prediction tools integrate immune function parameters [53-59,61-
69,72-
74,162,163]. They have the tendency to be imprecisely calibrated in estimating
risk among
severely ill patients [60,61], making the therapeutic recommendation with the
best survival
estimate for the individual patient very difficult. Therefore, we intend to
develop a molecular
blood test that predicts; from pre-intervention data, recovery of organ
function and frailty
reversal, which, in turn, predict 1-year survival. This information will help
tackle the following
challenge for the individual patient and doctor: We describe a molecular blood
test, based on
a PBMC GEP sample taken 1-3(7) days before undergoing surgical/interventional
therapies
for AdHF, that can assist clinicians in more precisely diagnosing FRP, i.e.
predicting FR, as
a surrogate marker for 1-year survival and help the patient and clinician in
the shared-
decision making process to choose the most meaningful treatment option.
Clinical Validity Study
[0274] We plan to complete a FDA-clearance Pivotal Trial with -a1,000 AdHF
patients,
stratified for four primary HF-mechanisms (ischemic, overload, arrhythmia,
dyscontractility).
After completion of a clinical validity study of developing the test in a
framework of
diagnosing the potential of future organ function recovery and frailty
reversal, FDA-clearance
and clinical implementation, we plan to conduct a clinical utility trial,
testing the impact of
adding the test information to the best current clinical prediction tools of
net health outcomes
as we did with the AlloMAPTm test development[164-166]. We plan to make this
test
commercially available, likely using the Nanostring platform that has already
been used for
an FDA-cleared In-vitro-Diagnostic Multivariate Index Assay test[167].
Biomarkers in the practice of shared decision-makinq
[0275] It is critical to have a multidisciplinary heart team to provide
expertise to make the
best recommendation regarding the individual patient's anticipated benefit
[168]. It is
important for these teams to get comfortable with the decision to not pursue
the most
aggressive option available in patients for whom the anticipated benefits do
not outweigh the
risks. The decision not to offer specific AdHF-surgical/interventional
therapies should not be
equated with abandoning care [169]. Shared decision-making requires both the
patient and
the provider to share information, work toward a consensus, and reach
agreement on the
course of action[170] consistent with the patient's preferences[171-173]. As
we work on
technological innovations to improve the devices, we must also use it
responsibly within a
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framework of care that enables shared decision making and promotes patient
goals and
well-being [169].
Future Perspectives
[0276] We will tailor the molecular test precision medicine results to a high
quality Relational
Medicine [174] encounter to maximize its effectiveness. The clinical decision-
making
challenge at the time of AdHF evaluation often culminates in the choice
between everything
modern medicine has to offer and compassionate end of life care. This ultimate
scenario is
medically, ethically, and economically demanding. It deserves the best
evidence-based
decision making support that personalized precision medicine research has to
offer in order
to live up to the highest humanistic expectations that society entrusts us
with.
[0277] Over the next decade, this vision of a meaningful practice of modern
medicine will
increasingly incorporate the elements of molecular precision medicine with
Relational
Medicine, promoting high value healthcare over low value healthcare. The
monetary have all
been implemented in the US-healthcare system and are already taking effect. In
order to
.. achieve these goals, future generations of healthcare professionals will be
trained to pursue
a practice that allows them to achieve these goals.
[0278] References cited in this Example can be found in Deng, MC., 2018
Biomarkers in
Medicine Vol. 12(6).
Example 3: Case Studies Show Predictive Value of FRP Scoring
[0279] This Example demonstrates the advantages achieved using the predictive
value of
the FRP scoring. Two case studies out of the 29 AdHF-patients in the Proof-Of-
Concept
Study illustrate the clinical utility of FRP scoring. Case Study #1 (Figure
8): MH, a 69-
year-old woman, born in 1942, married, who was in the 1970's diagnosed with
Dilated
Cardiomyopathy, had a "heart attack" in the 1990's, underwent Implantable
Cardioverter-
Defibrillator (ICD) implantation and Biventricular Pacemaker Implantation
(BVPM) 1999,
had a history of Monoclonal Gammopathy of Unknown Significance (MGUS),
Diabetes
Mellitus (DM) and hypothyroidism. In 2012, she suffered a cardiac arrest,
developed renal
dysfunction and was hospitalized three times in 12 months for heart failure
decompensation. In July 2012, she was admitted to UCLA in cardiogenic shock
and
multiorgan dysfunction (liver, kidneys, lung, immune system). The AdHF-team
was
uncertain, but felt that she was likely approaching end-of-life, and had only
a very small
chance of reversing her organ dysfunction in order to be evaluated for
advanced heart
failure therapies such as MCS or Htx. In contrast to this assessment, the
patient
recovered, was eventually being evaluated, six weeks later underwent
destination
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Heartmate II Left Ventricular Assist Device (LVAD) implantation and lived a
very active life
with her husband thereafter for > 5 years. Her preoperative PBMC-GEP (left
arrow in
Figure) would have indicated - with an accuracy of 93% - a high FRP and
therefore high
long-term (1-year) survival probability and would have supported a proactive
strategy
recommending an earlier LVAD-surgery timepoint to the patient. However, this
patient's
test results were not available at the time of shared decision-making.
[0280] Case Study #2 (Figure 8): DB, an 80-year-old man, born in 1933,
married, 3 kids,
biomedical company Ex-CEO, in 1993 suffered a large myocardial infarction
(MI),
underwent LAD-PTCA 1993/1997, CABG 2001, 1CD 2002, and BVPM 2003. In 2013, his
cardiopulmonary exercise capacity was reduced to 10m1/kg/min, and he was
evaluated for
AdHF-therapy options. Since he was not a HTx candidate (frailty/age 80), he
was offered
destination-MCS at UCLA. The patient declined LVAD-surgery for fear of a 10%
stroke
risk. In July 2014, he was transferred to UCLA from an outside hospital on
intra-aortic
balloon pump (IABP), having become more cachectic with temporal wasting,
impending
renal and hepatic failure, as well as pneumonia. While the patient now
requested
destination-LVAD implantation, the AdHF-team was uncertain, but felt that the
patient was
possibly too sick for surgery. Ultimately, the team went ahead, implanted the
destination
Heartmate II LVAD, and the patient died 6 weeks later on the respirator and on
dialysis on
multiorgan failure in the Cardiothoracic Intensive Care Unit (CTICU). His
preoperative
PBMC-GEP (right arrow in Figure) would have indicated - with an accuracy of
93% - a
low FRP and therefore low long-term (1-year) survival probability, and would
have
supported a palliative strategy, recommending discharge home to allow a
dignified dying
process in the context of the patient's family. However, this patient's FRP
test results
were not available at the time of shared decision-making.
Example 4: Treatment of Heart Failure
[0281] An individual presents with clinical symptoms of heart failure
including shortness of
breath, excessive tiredness, and leg swelling. It is determined that the
individual has heart
failure via diagnostic tests including echocardiography, blood tests,
electrocardiography, and
chest radiography. A blood sample is obtained from the individual and PBMCs
are isolated
from the blood sample. RNA is isolated from the isolated PBMCs and subjected
to
Nanostring analysis to measure gene expression of RSG1, TPRA1, SAP25, MFSD3,
FITIVI1,
SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3,
ACVR1C, DNIVI1P46, K1R2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63.; BATF2,
SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZK11P1, NEGRI,
KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6or1164, C7orf50,
-7 g
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NEFL, 000A2, ALDH1A1, 0LFM1, FADS3, SAC3D1, FZD4, RBPMS2, 015orf38,
ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI,
PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElF1AY,
and FITM1. The gene expression levels are determined to be elevated or reduced
for each
.. of these genes and a FRP score is calculated based on these gene expression
levels. It is
determined that the individual as an FRP score of less than 5 and is therefore
referred for
optimal medical management (OMM) and/or palliative care (PC).
[0282] A second individual presents with clinical symptoms of heart failure
and diagnostic
tests confirm that the individual has heart failure. A blood sample is
obtained from the
individual and PBMCs are isolated from the blood sample. RNA is isolated from
the PBMCs
and subjected to Nanostring analysis to measure gene expression of RSG1,
TPRA1,
SAP25, MFSD3, FITM1, SPTBN5, CEMP1 ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3,
FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1,
GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZK1 IP1,
NEGRI, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164,
C7orf50, NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,
ST6GALNAC1, CHMP6, SKA1, 00209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI,
PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElFlAY,
and FITM1. The gene expression levels are determined to be elevated or reduced
for each
of these genes and a FRP score is calculated based on these gene expression
levels. It is
determined that the individual as an FRP score of 7 and is therefore referred
for treatment
with mechanical circulatory support (MCS) surgery. The individual survives the
surgery and
the symptoms of heart failure are reduced.
Example 5: Systems Biological Identification of an Age-Related Predictor of
Functional
Recovery Potential in Advanced Heart Failure
[0283] This Example demonstrates that FRP can be improved by additional
clinical and age-
related transcriptome data. The Example shows that, in AdHF patients, a model
obtained
from preoperative data that incorporates clinical and genomic parameters
including genes
related to chronological age has the ability to predict Group I/II outcomes
after MCS surgery.
This correlates with long-term outcomes lending itself to outcome prediction
beyond
recovery from surgery.
[0284] From the study with 29 patients undergoing mechanical circulatory
support (MCS)
surgery, FRP was defined by grouping patients into two clinically relevant
organ failure risk
strata: Group I=IMPROVING (SOFA and MELD-XI scores both improve from day -1 to
day
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8) and Group 11= NOT IMPROVING (SOFA and/or MELD-XI score(s) do not improve
from
day -1 to day 8). Peripheral blood mononuclear cell (PBMC) samples were
collected one
day before surgery (day -1). Clinical data was collected on day -1 and day 8
postoperatively.
Purified mRNA was subjected to whole-genome Next-Generation Sequencing (NGS)
analysis. Correlation analyses were performed using NGS Strand. Two groups
were created
by age (60 y): Age A (<60y, n=13), Age B (:ed60y, n=16). A model was built
using the
following strategy: Step 1: Clinical model using multivariate logistic
regression, Step 2:
Transcriptomics model using support vector machine (28 genes transcriptome
differentially
expressed between Group I/Group II (Step 2A) and 12 genes based on biological
age (Step
2B), and Step 3: Combined Model. This model prediction was proposed to
optimize the
clinical and transcriptome model.
[0285] Out of 29 AdHF-patients undergoing MCS-surgery, 17 patients improved
(Group I)
while 12 patients did not (Group II). Older patients were more likely in Group
II, i.e. Age
B=10/16 (62%) and Age A=2/13 (15%). One-year survival in Group Age I was 10/13
(77%)
and in Group Age II 8/15 (53%).
[0286] The Clinical model, using all clinical parameters as input, identified
respiratory rate,
chronological age and white blood cell count as the best clinical combination
(cross
validation accuracy 82%) to predict Group I vs Group II. The Transcriptomics
model,
consisting of the 28 previously identified genes (Step 2A) (accuracy 93%) and
adding 12
age-related genes (Step 2B) (derived from a sub-cohort analysis of older male
patients)
increased the accuracy of prediction model to 94%. To optimize the accuracy of
prediction,
the clinical and transcriptomic models were combined to create the
Combinatorial Model
(accuracy 96%).
[0287] Bandar et al., 2017, PLoS One Dec 13;12(12) (see Table 1 therein)
summarizes the
demographics and key clinical data, which can be sorted by age grouping. NICM
=
nonischemic dilated cardiomyopathy, PPCM = peripartum cardiomyopathy, ICM =
ischemic
cardiomyopathy, ChemoCM = chemotherapy-induced cardiomyopathy, HM II=Heartmate
II,
CMAG = Centrimag, LVAD = Left ventricular assist device, RVAD = right
ventricular assist
device; BVAD = biventricular assist device, HVAD = Heartware LVAD, TAH = Total
Artificial
Heart, ECM = extracorporeal membrane oxygenator, GROUP: Organ function
changes of
SOFA-score and MELD-XI score from preoperative day -1 (TP1) to postoperative
day 8
(TP5) (Group I=WHITE ROWS=Improvement vs. Group II=GREY ROWS=No improvement.
[0288] Figure 3A summarizes the individual patients' organ function
improvement and 1-
year survival trajectory (discussed further in Example 1 above). SOFA and MELD-
XI across
five time points (TP) grouped by age (Age A, <60y, Age B, ?.60y). Each black
line represents
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one 1-year survivor while each red line represents one 1-year non-survivor.
Figure 4 shows
Kaplan-Meier 1-year survival in Age B vs. Age A. The time-to-event Kaplan-
Meier survival
analysis suggested a trend of elevated risk of death (log rank test p=0.12) in
older patients
(Age B) continued over a 3-6 month period following MCS-surgery.
[0289] Table 8 summarizes role of clinical parameters in prediction of FRP. RR-
Respiratory
Rate; HR-Heart Rate; WBC-White Blood Cell; CB-Serum Creatinine (mg/d1); and
AIC-Akaike
information criterion. The total number of samples were 29. The multivariate
Regression
analysis model was built on 24 samples and tested on the remaining 5 samples.
[0290] Table 8: Clinical model building for Group I vs. II membership
prediction using
multivariate logistic regression
Clinical Variables Variable Removed P-value for
AlC
Variable Removed
RR, Age, Sofa Score, HR, Full model
21.42
WBC. CB, Glucose
RR, Age, Sofa Score, Glucose 0.8402
19.44
HR,\NBC,CB
RR, Age, Sofa Score, WBC, Heart Rate 0.33447
18.87
CB
RR, Age, Sofa Score, WBC Sofa Score 0.34133
18.29
RR, Age, WBC CB 0.9336
17.47
[0291] The prediction model was enhanced using the combinatorial model. To
optimize the
clinical and transcriptomics model, we combined respiratory rate,
chronological age, and
White Blood Cell, the 28 genes associated with Group I/11 (Bondar 2017) and
the 12 genes
associated with biological age. This model increased accuracy of Group MI
prediction to
96%.
Example 6: Centralized Testing and Assigning Treatment Regimen
[0292] A preserved blood sample is received from a clinician treating an
individual who has
been diagnosed with heart failure. RNA is isolated from the blood and
subjected to
Nanostring analysis to measure gene expression of RSG1, TPRAl; SAP25; MFSD3,
FITM1,
SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3,
ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2,
SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431, PDZK1 IP1, NEGRI,
KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50,
NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,
ST6GALNAC1, CHMP6, SKA1, CD209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI,
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PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElFlAY,
and FITM1. The gene expression levels are determined to be elevated or reduced
for each
of these genes and a FRP score is calculated based on these gene expression
levels. The
FRP score of less than 5 is reported to the clinician with a recommendation
for optimal
medical management (OMM) and/or palliative care (PC).
[0293] Another preserved blood sample is received from a clinician treating an
individual
who has been diagnosed with heart failure. RNA is isolated from the blood and
subjected to
NanoString analysis to measure gene expression of RSG1, TPRA1, SAP25, MFSD3,
FITM1,
SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3,
ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2,
SLC22A1, AGRN, CKAP2L,IGSF10, HEXA-AS1, L00728431, PDZK1 IP1, NEGRI,
KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50,
NEFL, CDCA2, ALDH1A1, OLFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38,
ST6GALNAC1, CHMP6, SKA1, 00209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI,
PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElF1AY,
and FITM1. The gene expression levels are determined to be elevated or reduced
for each
of these genes and a FRP score is calculated based on these gene expression
levels. The
FRP score of 7 is reported to the clinician with a recommendation for
treatment with
mechanical circulatory support (MCS) surgery.
Example 7: Kit for Determining Treatment Regimen for Heart Failure
[0294] An individual presents with clinical symptoms of heart failure
including shortness of
breath, excessive tiredness, and leg swelling. It is determined that the
individual has heart
failure via diagnostic tests including echocardiography, blood tests,
electrocardiography, and
chest radiography. A blood sample is obtained from the individual and PBMCs
are isolated
from the blood sample. A kit is obtained for isolating RNA is the isolated
PBMCs. The kit
also contains reagents for Nanostring analysis to measure gene expression of
RSG1,
TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1, ASPSCR1, NAPSB, NAPSA, NLRP2,
RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46, KIR2DL4, USP9Y, ANKRD22, BC0RP1,
HMCN1, GPR63, BATF2, SLC22A1, AGRN, CKAP2L, IGSF10, HEXA-AS1, L00728431,
PDZKlIP1, NEGRI, KCNH8, CCR8, MME, ETV5, CXCL9, HBEGF, RANBP17, 00X43,
C6orf164, C7orf50, NEFL, 000A2, ALDH1A1, 0LFM1, FADS3, SAC3D1, FZD4, RBPMS2,
C15orf38, ST6GALNAC1, CHMP6, SKA1, 00209, SNAPC2, AXL, KIR2DL1, NTSR1,
SEPT5, KALI, PRRG1, XIST, RPS4Y1, ZFY, PRKY, TTTY15, DDX3Y, UTY, TXLNG2P,
KDM5D, ElF1AY, and FITM1. The kit includes software to determine that the gene
expression levels are elevated or reduced for each of these genes and a FRP
score is
calculated by the software based on these gene expression levels. The software
assigns to
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the individual an FRP score of less than 5 and recommends optimal medical
management
(OMM) and/or palliative care (PC).
[0295] A second individual presents with clinical symptoms of heart failure
and diagnostic
tests confirm that the individual has heart failure. A blood sample is
obtained from the
individual and PBMCs are isolated from the blood sample. A kit is obtained for
isolating
RNA is the isolated PBMCs. The kit also contains reagents for Nanostring
analysis to
measure gene expression of RSG1, TPRA1, SAP25, MFSD3, FITM1, SPTBN5, CEMP1,
ASPSCR1, NAPSB, NAPSA, NLRP2, RHBDD3, FRMD6, TIMP3, ACVR1C, DNM1P46,
KIR2DL4, USP9Y, ANKRD22, BCORP1, HMCN1, GPR63, BATF2, SLC22A1, AGRN,
CKAP2L, IGSF10, HEXA-AS1, L00728431; PDZK1IP1, NEGRI, KCNH8, CCR8, MME,
ETV5; CXCL9, HBEGF, RANBP17, DDX43, C6orf164, C7orf50, NEFL, CDCA2, ALDH1A1,
0LFM1, FADS3, SAC3D1, FZD4, RBPMS2, C15orf38, ST6GALNAC1, CHMP6, SKA1,
0D209, SNAPC2, AXL, KIR2DL1, NTSR1, SEPT5, KALI, PRRG1, XIST, RPS4Y1, ZFY,
PRKY, TTTY15, DDX3Y, UTY, TXLNG2P, KDM5D, ElF1AY, and FITM1. The kit includes
software to determine that the gene expression levels are elevated or reduced
for each of
these genes and a FRP score is calculated by the software based on these gene
expression
levels. The software assigns to the individual an FRP score of 7 and
recommends treatment
with mechanical circulatory support (MCS) surgery. The individual survives the
surgery and
the symptoms of heart failure are reduced.
[0296] Throughout this application various publications are referenced. The
disclosures of
these publications in their entireties are hereby incorporated by reference
into this
application in order to describe more fully the state of the art to which this
invention pertains.
[0297] Those skilled in the art will appreciate that the conceptions and
specific embodiments
disclosed in the foregoing description may be readily utilized as a basis for
modifying or
designing other embodiments for carrying out the same purposes of the present
invention.
Those skilled in the art will also appreciate that such equivalent embodiments
do not depart
from the spirit and scope of the invention as set forth in the appended
claims.
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