Note: Descriptions are shown in the official language in which they were submitted.
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MIRNA EXPRESSION SIGNATURE IN THE CLASSIFICATION OF
THYROID TUMORS
FIELD OF THE INVENTION
The present invention relates to methods for classification of thyroid tumors.
Specifically
the invention relates to microRNA molecules associated with specific thyroid
tumors. 5
BACKGROUND OF THE INVENTION
The accurate diagnosis of thyroid nodules continues to challenge physicians
managing
patients with thyroid disease. Patients with cytologically indeterminate
nodules are often
referred for diagnostic surgery, though most of these nodules prove post-
surgery to be benign.
This limitation of FNA cytology in the pre-operative diagnosis leads to a
clinical need for 10
reliable pre-operative molecular markers to distinguish benign from malignant
thyroid nodules.
MicroRNAs (miRs) are an important class of regulatory RNAs, which have a
profound impact
on a wide array of biological processes. These small (typically 18-24
nucleotides long) non-
coding RNA molecules can modulate protein expression pattern by promoting RNA
degradation, inhibiting mRNA translation, and also by affecting gene
transcription. miRs play 15
pivotal roles in diverse processes such as development and differentiation,
control of cell
proliferation, stress response and metabolism. The expression of many miRs was
found to be
altered in numerous types of human cancer, and in some cases suggesting that
such alterations
may play a causative role in tumor progression.
'The thyroid gland is foimed of two main types of cells: the follicular cells
and the C or 20
parafollicular cells. Follicular cells produce thyroid hormones, which are
regulators of human
metabolism. Overproduction of thyroid hormone (hyperthyroidism) causes rapid
or irregular
heartbeat, trouble sleeping, nervousness, hunger, weight loss, and a feeling
of being too warm. In
counterpart, hypothyroidism causes metabolism slowdown, tiredness, and weight
gain. Thyroid
hormone release is regulated by the thyroid-stimulating hormone (TSH),
produced by the 25
pituitary gland. The C cells produce calcitonin, a hormone responsible for use
of calcium.
Lymphocytes and stromal cells are also found in the thyroid.
Thyroid cancer is the eighth most common cancer in the United States, and the
most
rapidly increasing cancer in the US, with more than 60,000 new cases diagnosed
every year, and
being the cause of about 1,800 deaths in 2014. Thyroid cancer usually presents
itself as a 30
palpable thyroid nodule. Different types of thyroid tumors develop from
different cell types,
which is a determinant for the gravity and the optimal treatment administered.
Most of the
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growths and tumors in the thyroid gland are benign (non-cancerous) but others
are malignant
(cancerous).
Approximately 95% of thyroid cancers are differentiated thyroid carcinomas
(DTC) that
arise from thyroid follicular cells. There are two histological subtypes of
DTC: papillary thyroid
carcinoma (PTC) type (90-95%) and follicular thyroid carcinoma (FTC) type (5-
10%). 5
The most commonly used method for thyroid cancer diagnosis is biopsy by fine-
needle
aspiration (FNA). FNA samples are routinely examined for cytology to determine
whether the
nodules are benign or cancerous. The sensitivity and specificity of the
cytological examination
of an FNA sample range from 68% to 98%, and 72% to 100%, respectively,
depending on
institutions and doctors. Unfortunately, in at least 25% of the cases the FNA
specimens collected 10
are either inadequate for diagnosis or indeterminable by cytology. In current
medical practice,
most patients with indeterminate results undergo surgery, and are subject to
all risks and
consequences of the surgical procedure. Follow-up results show that only 25%
of the patients
operated on are diagnosed with cancer, meaning that 75% of the patients
underwent an
unnecessary surgical procedure. 15
When examining cytochemical or genetic markers, there is no unique marker that
on its
own is able to provide reliable results in order to replace the morphologic
diagnosis of thyroid
lesions. US 7,319,011 describes the measuring the expression of any one of the
genes DDIT3,
ARG2, ITM1, Clorf24, TARSH, and AC01 in a test follicular thyroid specimen for
distinguishing between follicular adenoma (FA) from follicular carcinoma (FC).
US 7,670,775 20
describes the analysis of the expression of CCND2, PCSK2, and PLAB for
identifying
malignant thyroid tissue. US 6,723,506 describes the molecular
characterization of PAX8-
PPAR1 molecules in connection with diagnosis and treatment of thyroid
follicular carcinomas.
US 7,378,233 describes the occurrence of the T1796A mutation of the BRAF gene
in 24 (69%)
of papillary thyroid carcinomas. 25
Accumulated efforts have been invested in finding a molecular diagnostic test
which will
overcome the uncertainty of indeterminate cytology, and ultimately eliminate
unnecessary
surgery for non-cancer patients [Chen, Y. T. et.al. (2008) Mod. Pathol. 21,
1139-1146; He, H. et
al. (2005) Proc. Natl Acad. Sci. IJSA 102, 19075-19080; Nikiforova, M. N. et
al. (2009) Endocr.
Pathol. 20, 85-91; Pallante, P. et al. (2006) Endocr. Relat. Cancer 13, 497-
508; Nikiforova, M. 30
N. et al. (2008) J. Clin. Endocrinol. Metab. 93, 1600-1608; Visone, R. et al.
(2007) Endocr.
Relat. Cancer 14(3):791-8; US 2014/0030714 Al; US 8,541,170; US 2012/0220474
Al; US
8.465,914; US 7,598,052; US 8,202.692; WO 2013/066678; WO 2012/129378; US
I
CA 2945531 2017-04-18
2013/0237590; EP 2772 550 Al; Pallante etal. (2010) Endocrine-Related Cancer
17 F91-F104;
Dettmer et al. (2014) .1 Mol Endocrinol. Mar 6; 52(2):181-91.
Nonetheless, numerous are the challenges that remain. It is of great necessity
to develop
a molecular assay with not only high sensitivity and specificity, but also
that is able to deal with
samples that failed the cytology analysis and that fall under the category of
indeterminate
samples. The present invention provides solutions for this challenge.
SUMMARY
A novel integrated technology platform was developed by the inventors for
profiling
and characterizing microRNAs in thyroid clinical samples, including biopsies,
generally
surgically-obtained resections, and cytological specimens, generally obtained
by fine-needle
aspiration (FNA), and was used applied to classify thyroid lesions as benign
or malignant
neoplasms, as well as its sub-types. Novel microRNAs are disclosed as
potential biomarkers.
Certain exemplary embodiments provide a method for classifying a thyroid
lesion in a
subject, the method comprising: a. measuring the expressions of the nucleic
acids of SEQ ID
NOS: 1-37, or a sequence having at least 90% identity thereto, in a thyroid
lesion sample
collected by fine needle aspiration (FNA) previously obtained from said
subject;
b. determining a nucleic acid expression profile; c. applying a classifier
algorithm to the
nucleic acid expression profile, wherein the classifier algorithm compares the
expression
profile to a reference value; and d. classifying said thyroid lesion as benign
or malignant
based on the result from the algorithm applied to the nucleic acid expression
profile of said
sample.
Other exemplary embodiments provide a method for classifying a thyroid lesion
in a
subject, the method comprising: a, measuring the expressions of the nucleic
acids SEQ ID
NOS: 1-13, 17-22, 25-26, and 36-37, or a sequence having at least 90% identity
thereto, in a
thyroid lesion sample collected by fine needle aspiration (FNA) previously
obtained from
said subject; b. determining a nucleic acid expression profile; c. applying a
classifier
algorithm to the nucleic acid expression profile, wherein the classifier
algorithm compares
the expression profile to a reference value; and d. classifying said thyroid
lesion as benign or
malignant based on the result from the algorithm applied to the nucleic acid
expression
profile of said sample.
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Thus, in a first aspect, selected embodiments provide a method for classifying
a thyroid
lesion sample, the method comprising the steps of:
a. obtaining a thyroid lesion sample from a subject in need thereof:
b. measuring the expression level of at least four nucleic acids in the
sample, said
-- nucleic acid comprising a sequence of SEQ ID NOS: 1-308, variants thereof
or a sequence having
at least about 80% identity thereto;
c. determining a nucleic acid expression profile;
d. applying a classifier algorithm to the nucleic acid expression profile;
and
e. classifying said thyroid lesion as benign, malignant or of a sub-type of
benign or
-- malignant tumor based on the result from the algorithm applied to the
nucleic acid expression
profile of said sample.
In one embodiment, following step (b) or (c) there further comprises a step of
obtaining the
ratio between the expression levels of at least one pair of nucleic acids; and
wherein in step (d) said
classifier algorithm may be applied to any one of the nucleic acid expression
profile, said ratio of at
-- least one pair of nucleic acids, or to a combination thereof.
In one embodiment of the method, said nucleic acid sequence comprises a
sequence of any
one of SEQ ID NOs. 1-37, variants thereof or a sequence having at least about
80% identity thereto.
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In a further embodiment of the method, said nucleic acid sequence comprises a
sequence
of any one of SEQ ID NOs. 1-25, variants thereof or a sequence having at least
about 80%
identity thereto.
In a further embodiment of the method, said thyroid lesion sample is obtained
by fine
needle aspiration (FNA) biopsy. In one particular embodiment, said sample is a
smear from a
FNA biopsy.
In another further embodiment of the method, said thyroid lesion is a nodule
of less than
1 cm.
In another further embodiment of the method, algorithm is a machine-learning
algorithm.
In one particular embodiment of said method, said algorithm further combines
the nucleic acid
expression profile with clinical or genetic data from said sample.
In another further embodiment of the method, following step (b) if at least
one of said
nucleic acid expression level is below or above a threshold for thyroid cells,
said sample is
discarded based on the expression level of said nucleic acid.
In another further embodiment of the method, said sample has less than 50
thyroid cells.
In another further embodiment of the method, said measuring is performed by
hybridization, amplification or next generation sequencing method.
In one particular embodiment of the method, said hybridization comprises
contacting the
sample with probes, wherein the probes comprise (i) DNA equivalents of the
microRNAs, (ii) the
complements thereof, (iii) sequences at least 80% identical to (i) or (ii) or
(iv) a nucleic acid
sequence that hybridizes with at least eight contiguous nucleotides of any one
of SEQ ID
NOs. 1-25. In another particular embodiment, said probes are attached to a
solid substrate.
In another further particular embodiment of the method, amplification is real-
time
polymerase chain reaction (RT-PCR), said RT-PCR amplification method
comprising forward
and reverse primers, and optionally further comprising hybridization with a
probe.
In another further embodiment, said method further comprises the step of
administering a
differential treatment to said subject if said thyroid lesion is benign or
malignant.
In another further particular embodiment of the method, said lesion is
malignant and said
treatment is any one of surgery, chemotherapy, radiotherapy, hormone therapy,
or any other
recommended treatment.
In another aspect, selected embodiments provide a protocol for classifying a
thyroid lesion
sample comprising the steps of:
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a. obtaining a thyroid lesion sample from a subject in need thereof;
b. measuring the level of at least four nucleic acid in the sample, said
nucleic acid
comprising a sequence of SEQ ID NOS: 1-308, variants thereof or a sequence
having at least
about 80% identity thereto;
c. determining the expression of nucleic acids in said sample that
associate with
specific cell types;
d. wherein (i) the expression level of at least one nucleic acid that is a
non-thyroid
cell marker above a threshold determines that the sample is discarded; or (ii)
expression levels of
non-thyroid cell markers below a threshold determines that the sample proceeds
to step (e) for
further analysis;
e. if the sample is not discarded in step (d), determining a nucleic acid
expression
profile;
f. applying a classifier algorithm to the microRNA expression profile;
g= classifying said thyroid lesion as benign, malignant or of a
sub-type of benign or
malignant tumor based on the result of the algorithm applied to the nucleic
acid expression profile
of said sample.
In one embodiment of the protocol, following step (b) there further comprises
a step of
obtaining the ratio between the expression levels of at least one pair of
nucleic acids; and wherein
in step (0 said classifier algorithm may be applied to any one of the nucleic
acid expression
profile, said ratio of at least one pair of nucleic acids, or to a combination
thereof.
In another embodiment of the protocol, said nucleic acid sequence comprises a
sequence
of any one of SEQ ID NOs. 1-37, variants thereof or a sequence having at least
about 80%
identity thereto. In another embodiment of the protocol, said nucleic acid
sequence comprises a
sequence of any one of SEQ ID NOs. 1-25, variants thereof or a sequence having
at least about
80% identity thereto.
In a further embodiment of the protocol, said thyroid lesion sample is
obtained by fine
needle aspiration (FNA) biopsy. In one particular embodiment, said sample is a
smear from a
FNA biopsy.
In a further embodiment of the protocol, said thyroid lesion is a nodule of
less than 1 cm.
In another further embodiment of the protocol, said sample has less than 50
thyroid cells.
In a further embodiment of the protocol, said algorithm is a machine-learning
algorithm.
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In a further embodiment of the protocol, the measuring is performed by
hybridization,
amplification or next generation sequencing method.
In another further aspect, selected embodiments provide a kit for thyroid
tumor
classification, said kit comprising:
a. probes for performing thyroid tumor classification, wherein said probes
comprise
any one of (i) DNA equivalents of microRNAs comprising at least one of SEQ ID
NOs 1-308,
(ii) the complements thereof, (iii) sequences at least 80% identical to (i) or
(ii), (iv) a nucleic acid
sequence that hybridizes with at least eight contiguous nucleotides of any one
of SEQ ID
NOs 1-182, or (v) a nucleic acid sequence that hybridizes with RT-PCR
products; and optionally
b. an instruction manual for using said probes.
In one embodiment, said kit further comprises forward and reverse PCR primers.
In another embodiment, the kit may comprise forward and reverse primers. In
another
embodiment, the kit may further comprise reagents for performing in situ
hybridization analysis.
In another further aspect, the kit for thyroid tumor classification comprises:
(a) at least one forward RT-PCR primer, such as for example at least one of
the
primers comprising SEQ ID NO. 270-293;
(b) a reverse primer;
(c) at least one probe that hybridizes with molecules amplified by the RT-
PCR, as for
example the probes presented in the Examples; and optionally
(d) any one of an instruction manual for performing said RT-PCR, or an
instruction
manual for thyroid tumor classification.
In one embodiment, said probe is a general probe. In another embodiment said
probe is a
microRNA sequence-specific probe.
In another further aspect, selected embodiments provide an isolated nucleic
acid, said
nucleic acid comprising at least 12 contiguous nucleotides at least 80%
identical to the sequence
of any one of SEQ ID NOs. 27-29, 33, 34, 139, 140, 307 and 308.
In another further aspect, selected embodiments provide a pharmaceutical
composition
comprising as active agent the isolated nucleic acids described herein, and
optionally adjuvants,
carriers, diluents and excipients. Thus, said nucleic acid molecules may be
comprised as an active
agent in a pharmaceutical composition, a formulation or a medicament.
In another further aspect, selected embodiments provide a vector comprising
the isolated
nucleic acid described herein.
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In another further aspect, selected embodiments provide a probe comprising the
isolated
nucleic acid described herein.
In another further aspect, selected embodiments provide a biochip comprising
the isolated
nucleic acid described herein.
In another further aspect, selected embodiments provide the use of an isolated
nucleic acid
as described herein in the preparation of a medicament.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1: Expression of microRNAs in Giemsa-stained papillary carcinoma (Pap-
care.)
and non-papillary carcinoma (N-Pap-care.) smears. The scatter plot shows
differential expression
of miRs from a Giemsa-stained papillary carcinoma smear (y-axis, n= 1) versus
a Giemsa-stained
non-papillary carcinoma smear (x-axis, n= 1). The data are shown in normalized
fluorescence
units, as measured by microarray. The parallel lines describe a 1.5-fold
change between the
samples in either direction. Gray crosses represent untested (NT) control
probes or median signal
<300 in both samples. Five microRNAs (hsa-miR-146b-5p, hsa-miR-222-3p, hsa-miR-
221-3p,
hsa-miR-21-5p and hsa-miR-31-5p) are up-regulated in the papillary carcinoma
smear.
Figures 2A-2B: Novel microRNAs detected by next generation sequencing. Fig.2A
shows the predicted secondary structure of two novel microRNAs, MD2-495 (top)
and MD2-437
(bottom) detected in thyroid tissue. Fig.2B shows the expression of the two
novel microRNAs in
each one of 11 resected thyroid samples.
Figures 3A-3B: MicroRNA expression in malignant versus benign samples. The
scatter
plot shows the median microRNA expression levels of microRNA, including miR-
125b-5p, miR-
222-3p and miR-146b-5p (highlighted) in malignant nodules (y-axis) versus
benign nodules (x-
axis). Each cross represents a microRNA, and includes control sequences,
microRNAs with low
expression and non-reliable probes (NT). The dashed line represents 1.5-fold.
Fig.3A shows the
analysis in cohort I. Fig.3B shows the analysis in cohort II.
Figure 4: MiR-375 expression in medullary lesions. The plot shows the
expression of
miR-375 in medullary lesions (diamonds, Med) in comparison to malignant non-
medullary
(squares, Mal-n-med) combined with benign lesions (circles, B). Lines
represent the median
expression for each group. p-value=1.2e-42. Fold change=201.4.
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Figures 5A-5B: Samples stained with different dyes can be processed and
microRNA
can be detected. The plots shows the median expression levels of miR-146b-5p
in malignant (M)
or benign (B) samples stained with different dyes. Fig.5A shows miR expression
in samples
stained with May-Grunwald Giemsa compared with DiffQuik. p-value=0.18
(Wilcoxon). Median
fold-change (med.f-ch) =1Ø Fig.5B shows miR expression in samples stained
with DiffQuik 5
compared with Papanicolaou. p-value=0.56 (Wilcoxon). Median fold-change (med.f-
ch) =1.1.
Figures 6A-6B: Hurthle cell marker. The plots shows higher expression of MID-
16582
in follicular adenoma presenting Hurthle cells versus follicular adenomas with
no indication of
Hurthle cells. Sign.=significant; Diff.=differential; f-ch=fold change;
B1.=blood; NT, not tested.
Fig.6A: The y and x axes show the median array expression levels of the miRs
in FA (follicular 10
adenoma) samples not documented as having Hurthle cells (n=22) versus FA
samples with
Hurthle cells (n=9). The dashed factor line = x1.5. B1.=blood. NT, not tested.
Fig.6B: The y and
x axes show the median PCR expression levels of the miRs in FA samples with no
indication of
Hurthle cells (n=21) versus FA samples with Hurthle cells (n=9). The dashed
factor line = 0.6.
Figure 7: Profiling of malignant and benign samples with Thyroid assay set of
15
microRNAs. The x and y axis show the expression levels of the miRs in benign
(B) (n=166)
versus malignant (M) (n=187) samples, respectively. The microRNA median
expression levels
for hsa-miR-222-3p (SEQ ID NO.1-2), hsa-miR-551b-3p (SEQ ID NO.3-4), hsa-miR-
31-5p
(SEQ ID NO.5-7), hsa-miR-125b-5p (SEQ ID NO.9), hsa-miR-146b-5p (SEQ ID NO.10-
11),
hsa-miR-152-3p (SEQ ID NO.12-13), hsa-miR-346 (SEQ ID NO.14), hsa-miR-181c-5p
(SEQ 20
ID NO.15), hsa-miR-424-3p (SEQ ID NO.16), and hsa-miR-375 (SEQ ID NO.8) is
highlighted.
The numbers refer to (50 ¨ normalized Ct value). Diamonds (.) represent the
normalizers.
Sign.=significant; Diff.=differential; f-ch=fold change. The dashed factor
line = 0.6.
Figures 8A-8C: A Discriminant Analysis classifier was used to classify
malignant
(diamonds, M) from benign (squares, B) samples, using microRNA expression
values. Fig.8A: 25
The normalized values of two microRNAs (hsa-miR-551b-3p and hsa-miR-146b-5p)
were used
as the features for the classification. The sensitivity of this classifier is
84.8% and the specificity
is 68.9%. The grey shaded area marks the space in which a sample is classified
as malignant, as
determined by the classifier. Fig.8B: The normalized values of three microRNAs
(hsa-miR-
551b-3p, hsa-miR-146b-5p, and hsa-miR-31-5p) were used as the features for the
classification. 30
The sensitivity of this classifier is 82.9% and the specificity is 72.2%.
Misclassified samples
(miscl.) are represented by a dot. Fig.8C: The normalized values of 8 microRNA
(hsa-miR-
551b-3p; hsa-miR-146b-5p; hsa-miR-31-5p; hsa-miR-222-3p; hsa-miR-375; hsa-miR-
125b-5p;
hsa-miR-152-3p; hsa-miR-181c-5p) were used as the features for the
classification. The figure
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WO 2015/175660 PCT/US2015/030564
shows a confusion matrix where the x-axis shows the classifier answer (Clas.
Ans.) while the y-
axis shows the true diagnosis (Real class=re.c1.). The sensitivity of this
classifier is 83.5% and
the specificity is 81.5%.
Figures 9A-9C: A Discriminant Analysis classifier was used to classify
malignant
(diamonds, M) from benign (squares, B) samples, using normalized values of
microRNA 5
expression ratios. Fig.9A: The normalized values of two microRNA ratios (hsa-
miR-146b-
5p:hsa-miR-342-3p and hsa-miR-31-5p:hsa-miR-342-3p) were used as the features
for the
classification. The sensitivity of this classifier is 78% and the specificity
is 79.5%. The grey
shaded area marks the space in which a sample is classified as malignant , as
determined by the
classifier. Fig. 9B: The normalized values of three microRNA ratios (hsa-miR-
146b-5p:hsa- 10
miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-138-5p) were
used as
the features for the classification. The sensitivity of this classifier is
81.1% and the specificity is
82.1%. Misclassified samples (miscl.) are represented by a dot. Fig.9C: The
normalized values
of 8 microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-
342-3p; hsa-
miR-125b-5p:hsa-miR-138-5p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-222-
3p:hsa-miR- 15
486-5p; hsa-miR-200c-3p:hsa-miR-486-5p; MID-16582:hsa-miR-200c-3p; MID-
16582:hsa-
miR-138-5p) were used as the features for the classification. The figure shows
a confusion
matrix where the x-axis shows the classifier answer (Clas. Ans.) while the y-
axis shows the true
diagnosis (Real class=re.c1.). The sensitivity of this classifier is 74.4% and
the specificity is
84.1%. 20
Figure 10A-10C: A Discriminant Analysis classifier was used to classify
malignant
(diamonds, M) from benign (squares, B) samples, using normalized values of a
combination of
microRNAs and microRNA ratios. Fig.10A: Normalized values of one microRNA
ratio and one
microRNA (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-551h-3p) were used as the
features for
the classification. The sensitivity of this classifier is 82.9% and the
specificity is 82.8%. The 25
grey shaded area marks the space in which a sample is classified as malignant,
as determined by
the classifier. Fig.10B: The normalized values of one microRNA ratio and two
microRNAs (hsa-
miR-146b-5p:hsa-miR-342-3p; hsa-miR-551b-3p; hsa-miR-146b-5p) were used as the
features
for the classification. The sensitivity of this classifier is 82.9% and the
specificity is 82.8%.
Fig.10C: The normalized values of 5 microRNAs and 3 microRNA ratios (hsa-miR-
146b- 30
5p:hsa-miR-342-3p; hsa-miR-55 lb-3p; hsa-miR-146b-5p; hsa-miR-31-5p:hsa-miR-
342-3p; hsa-
miR-31-5p; hsa-miR-222-3p; hsa-miR-125b-5p:hsa-miR-138-5p; hsa-miR-375) were
used as the
features for the classification. The figure shows a confusion matrix where the
x-axis shows the
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classifier answer (Clas. Ans.) while the y-axis shows the true diagnosis (Real
class=re.c1.) (Real
class=re.c1.). The sensitivity of this classifier is 93.3% and the specificity
is 42.4%.
Figure 11A-11C: A K-nearest neighbor (KNN) classifier was used to classify
malignant
(M) from benign (B) samples using normalized values of microRNAs. Fig.11A: The
normalized
values of 6 microRNAs (hsa-miR-551b-3p; hsa-miR-146b-5p; hsa-miR-31-5p; hsa-
miR-222-3p; 5
hsa-miR-375; hsa-miR-125b-5p) were used as the features for the
classification. The figure
shows a confusion matrix where the x-axis shows the classifier answer (Clas.
Ans.) while the y-
axis shows the true diagnosis (Real class=re.c1.). The sensitivity of this
classifier is 82.3% and
the specificity is 68.2%. Fig.11B: The normalized values of 8 microRNAs (hsa-
miR-55 lb-3p;
hsa-miR-146b-5p; hsa-miR-31-5p; hsa-miR-222-3p; hsa-miR-375; hsa-miR-125b-5p;
hsa-miR- 10
152-3p; hsa-miR-181c-5p) were used as the features for the classification. The
figure shows a
confusion matrix where the x-axis shows the classifier answer (Clas. Ans.)
while the y-axis
shows the true diagnosis (Real class=re.c1.). The sensitivity of this
classifier is 82.9% and the
specificity is 74.2%. Fig.11C: The normalized values of 12 microRNAs (hsa-miR-
551b-3p; hsa-
miR-146b-5p; hsa-miR-31-5p; hsa-miR-222-3p; hsa-miR-375; hsa-miR-125b-5p; hsa-
miR-152- 15
3p; hsa-miR-181c-5p; hsa-miR-486-5p; hsa-miR-424-3p; hsa-miR-200c-3p; hsa-miR-
346) were
used as the features for the classification. The figure shows a confusion
matrix where the x-axis
shows the classifier answer (Clas. Ans.) while the y-axis shows the true
diagnosis (Real
class=re.c1.). The sensitivity of this classifier is 81.1% and the specificity
is 68.9%.
Figure 12A-12B: A KNN classifier was used to classify malignant (M) from
benign (B) 20
samples using normalized values of microRNA ratios Fig.12A: The normalized
values of 6
microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p;
hs a-
miR-125b-5p:hsa-miR-138-5p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-222-
3p:hsa-miR-
486-5p; hsa-miR-200c-3p:hsa-miR-486-5p) were used as the features for the
classification. The
figure shows a confusion matrix where the x-axis represents the classifier
answer (Clas. Ans.) 25
and the y-axis represents the true diagnosis (Real class=re.c1.). The
sensitivity of this classifier is
78% and the specificity is 58.9%. Fig. 12B: The normalized values of 8 miR
ratios (hsa-miR-
146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-
138-5p;
hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-222-3p:hsa-miR-486-5p; hsa-miR-200c-
3p:hsa-
miR-486-5p; MID-16582:hsa-miR-200c-3p; MID-16582:hsa-miR-138-5p) were used as
the 30
features for the classification. The figure shows a confusion matrix where the
x-axis represents
the classifier answer (Clas. Ans.) and the y-axis represents the true
diagnosis (Real class=re.c1.).
The sensitivity of this classifier is 80.5% and the specificity is 65.6%.
CA 02945531 2016-10-11
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Figure 13A-13C: A KNN classifier was used to classify malignant (M) from
benign (B)
samples using normalized values of a combination of microRNAs and microRNA
ratios.
Fig.13A: The normalized values of 4 microRNAs and 2 microRNA ratios (hsa-miR-
146b-
5p:hsa-miR-342-3p; hsa-miR-55 lb-3p; hsa-miR-146b-5p; hsa-miR-31-5p:hsa-miR-
342-3p; hsa-
miR-31-5p; hsa-miR-222-3p) were used as the features for the classification.
The figure shows a 5
confusion matrix where the x-axis represents the classifier answer (Clas.
Ans.) while the y-axis
represents the true diagnosis (Real class=re.c1.). The sensitivity of this
classifier is 85.4% and the
specificity is 66.9%. Fig.13B: The noimalized values of 5 microRNAs and 3
microRNA ratios
(hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-55 lb-3p; hsa-miR-146b-5p; hsa-miR-31-
5p:hsa-
miR-342-3p; hsa-miR-31-5p; hsa-miR-222-3p; hsa-miR-125b-5p:hsa-miR-138-5p; hsa-
miR- 10
375) were used as the features for the classification. The figure shows a
confusion matrix where
the x-axis represents the classifier answer (Clas. Ans.) while the y-axis
represents the true
diagnosis (Real class=re.c1.). The sensitivity of this classifier is 83.5% and
the specificity is
70.9%. Fig.13C: The normalized values of 7 microRNAs and 5 microRNA ratios
(hsa-miR-
146b-5p:hsa-miR-342-3p; hsa-miR-551b-3p; hsa-miR-146b-5p; hsa-miR-31-5p:hsa-
miR-342- 15
3p; hsa-miR-31-5p; hsa-miR-222-3p; hsa-miR-125b-5p:hsa-miR-138-5p; hsa-miR-
375; hsa-
miR-125b-5p:hsa-miR-200c-3p; hsa-miR-125b-5p; hsa-miR-222-3p:hsa-miR-486-5p;
hsa-miR-
152-3p) were used as the features for the classification. The figure shows a
confusion matrix
where the x-axis represents the classifier answer (Clas. Ans.) while the y-
axis represents the true
diagnosis (Real class=re.c1.). The sensitivity of this classifier is 83.5% and
the specificity is 20
66.9%.
Figure 14A-14C: A Support Vector Machine (SVM) classifier was used to classify
malignant (diamonds, M) from benign (squares, B) samples using normalized
microRNA values.
Fig.14A: The normalized values of three microRNAs (hsa-miR-551b-3p; hsa-miR-
146b-5p; hs a-
miR-31-5p) were used as the features for the classification. The sensitivity
of this classifier is 25
82.3% and the specificity is 68.2%. Misclassified samples (miscl.) are
represented by a dot.
Fig.14B: The normalized values of 6 microRNAs (hsa-miR-551b-3p; hsa-miR-146b-
5p; hsa-
miR-31-5p; hsa-miR-222-3p; hsa-miR-375; hsa-miR-125b-5p) were used as the
features for the
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer
(Clas. Ans.) while the y-axis shows the true diagnosis (Real class=re.c1.).
The sensitivity of this 30
classifier is 83.5% and the specificity is 75.5%. Fig.14C: The normalized
values of 8
microRNAs (hsa-miR-551b-3p; hsa-miR-146b-5p; hsa-miR-31-5p; hsa-miR-222-3p; hs
a-miR-
375; hsa-miR-125b-5p; hsa-miR-152-3p; hsa-miR-181c-3p) were used as the
features for the
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer
11
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(Clas. Ans.) and the y-axis shows the true diagnosis (Real class=re.c1.). The
sensitivity of this
classifier is 86% and the specificity is 75.5%.
Figure 15A-15C: A SVM classifier was used to classify malignant (diamonds, M)
from
benign (squares, B) samples, using normalized values of microRNA ratios.
Fig.15A: The
normalized values of three microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p;
hsa-miR-31- 5
5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-138-5p) were used as the features
for the
classification. The sensitivity of this classifier is 83.5% and the
specificity is 80.8%.
Misclassified samples (miscl.) are represented by a dot. Fig.15B: The
normalized values of 6
microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p
hs a-
miR-125b-5p:hsa-miR-138-5p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-222-
3p:hsa-miR- 10
486-5p; hsa-miR-200c-3p:hsa-miR-486-5p) were used as the features for the
classification. The
figure shows a confusion matrix where the x-axis shows the classifier answer
(Clas. Ans.) and
the y-axis shows the true diagnosis (Real class=re.c1.). The sensitivity of
this classifier is 83.5%
and the specificity is 80.1%. Fig.15C: The normalized values of 8 microRNA
ratios (hsa-miR-
146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-
138-5p; 15
hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-222-3p:hsa-miR-486-5p; hsa-miR-200c-
3p:hsa-
miR-486-5p; MID-16582:hsa-miR-200c-3p; M1D-16582:hsa-miR-138-5p) were used as
the
features for the classification. The figure shows a confusion matrix where the
x-axis shows the
classifier answer (Clas. Ans.) and the y-axis shows the true diagnosis (Real
class=re.c1.). The
sensitivity of this classifier is 82.9% and the specificity is 80.8%. 20
Figure 16A-16C: A SVM classifier was used to classify malignant (diamonds, M)
from
benign (squares, B) samples, using normalized values of a combination of
microRNA values and
microRNA ratios. Fig.16A: The normalized values of 2 microRNAs and one
microRNA ratio
(hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-551b-3p; hsa-miR-146b-5p) were used
as the
features for the classification. The sensitivity of this classifier is 82.9%
and the specificity is 25
83.4%. Misclassified samples (miscl.) are represented by a dot. Fig.16B: The
normalized values
of 4 microRNA and 2 microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-
551b-3p;
hsa-miR-146b-5p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-31-5p; hsa-miR-222-3p)
were used
as the features for the classification. The figure shows a confusion matrix
where the x-axis
shows the classifier answer (Clas. Ans.) and the y-axis shows the true
diagnosis (Real 30
class=re.c1.). The sensitivity of this classifier is 86% and the specificity
is 80.1%. Fig.16C: The
normalized values of 5 microRNAs and 3 microRNA ratios (hsa-miR-146b-5p:hsa-
miR-342-3p;
hsa-miR-551b-3p; hsa-miR-146b-5p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-31-5p;
hsa-miR-
222-3p; hsa-miR-125b-5p:hsa-miR-138-5p; hsa-miR-375) were used as the features
for the
12
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classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer
(Clas. Ans.) and the y-axis shows the true diagnosis (Real class=re.c1.). The
sensitivity of this
classifier is 86.6% and the specificity is 79.5%.
Figure 17A-17C: A Discriminant Analysis Ensemble classifier was used to
classify
malignant (diamonds, M) from benign (squares, B) samples, using normalized
values of 5
microRNAs. Fig.17A: The normalized values of two microRNAs (hsa-miR-55 lb-3p;
hsa-miR-
146b-5p) were used as the features for the classification. The sensitivity of
this classifier is
84.8% and the specificity is 64.2%. The grey shaded area marks the space in
which a sample is
classified as malignant, as determined by the classifier. Fig.17B: The
normalized values of three
microRNAs (hsa-miR-551b-3p; hsa-miR-146b-5p; hsa-miR-31-5p) were used as the
features for 10
the classification. The sensitivity of this classifier is 84.1% and the
specificity is 65.6%.
Misclassified samples (miscl.) are represented by a dot. Fig.17C: The
normalized values of 8
microRNAs (hsa-miR-551b-3p; hsa-miR-146b-5p; hsa-miR-31-5p; hsa-miR-222-3p; hs
a-miR-
375; hsa-miR-125b-5p; hsa-miR-152-3p; hsa-miR-181c-3p) were used as the
features for the
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer 15
(Clas. Ans.) and the y-axis shows the true diagnosis (Real class=re.c1.). The
sensitivity of this
classifier is 84.8% and the specificity is 74.8%.
Figure 18A-18C: A Discriminant Analysis Ensemble classifier was used to
classify
malignant (diamonds, M) from benign (squares, B) samples, using normalized
values of
microRNA ratios. Fig.18A: The normalized values of two microRNA ratios (hsa-
miR-146b- 20
5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p) were used as the features for
the
classification. The sensitivity of this classifier is 83.5% and the
specificity is 73.5%. The grey
shaded area marks the space in which a sample is classified as malignant, as
determined by the
classifier. Fig.18B: The normalized values of three microRNA ratios (hsa-miR-
146b-5p:hsa-
miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-138-5p) were
used as 25
the features for the classification. The sensitivity of this classifier is 86%
and the specificity is
79.5%. Misclassified samples (miscl.) are represented by a dot. Fig.18C: The
normalized values
of 8 microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-
342-3p; hsa-
miR-125b-5p: hsa-miR-138-5p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-222-
3p:hsa-miR-
486-5p; hsa-miR-200c-3p:hsa-miR-486-5p; MID-16582:hsa-miR-200c-3p; MID-
16582:hsa- 30
miR-138-5p) were used as the features for the classification. The figure shows
a confusion
matrix where the x-axis shows the classifier answer (Clas. Ans.) and the y-
axis shows the true
diagnosis (Real class=re.c1.). The sensitivity of this classifier is 84.1% and
the specificity is
78.1%.
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Figure 19A-19C: A Discriminant Analysis Ensemble classifier was used to
classify
malignant (diamonds, M) from benign (squares, B) samples, using a combination
of normalized
values of microRNAs and microRNA ratios. Fig.19A: The normalized values of one
microRNA
and one microRNA ratio (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-551b-3p) were
used as
the features for the classification. The sensitivity of this classifier is
85.4% and the specificity is 5
78.8%. The grey shaded area marks the space in which a sample is classified as
malignant, as
determined by the classifier. Fig.19B: The normalized values of two microRNAs
and one
microRNA ratio (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-551b-3p; hsa-miR-146b-
5p) were
used as the features for the classification. The sensitivity of this
classifier is 85.4% and the
specificity is 78.1%. Misclassified samples (miscl.) are represented by a dot.
Fig. 19C: The 10
normalized values of 5 microRNAs and 3 microRNA ratios (hsa-miR-146b-5p:hsa-
miR-342-3p;
hsa-miR-551b-3p; hsa-miR-146b-5p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-31-5p;
hsa-miR-
222-3p; hsa-miR-125b-5p:hsa-miR-138-5p; hsa-miR-375) were used as the features
for the
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer
(Clas. Ans.) while the y-axis shows the true diagnosis (Real class=re.c1.).
The sensitivity of this 15
classifier is 86% and the specificity is 82.8%.
Figure 20: The normalized levels of hsa-miR-375 expression (Exp.) is shown as
a dot
plot for Medullary ("Med."), non-medullary Malignant ("Mal.") and for benign
("Ben.")
samples. Lines represent the median values for each group. Within each group,
dots are
randomly distributed along the x-axis, in order to improve visibility of the
dots. 20
Figure 21: The normalized levels of hsa-miR-146b-5p expression (Exp.) is shown
as a
dot plot for non-medullary Malignant ("Mal.") and for benign ("Ben.") samples.
Lines represent
the median values for each group. Within each group, dots are randomly
distributed along the x-
axis, in order to improve visibility of the dots.
Figure 22: The normalized expression (Exp.) levels of the miR ratio 25
hsa-miR-146b-5p:hsa-miR-342-3p is shown as a dot plot for non-medullary
Malignant ("Mal.")
and for benign ("Ben.") samples. Lines represent the median values for each
group. Within
each group, dots are randomly distributed along the x-axis, in order to
improve visibility of the
dots.
Figure 23A-23C: A Discriminant Analysis classifier was used to classify
Indeterminate 30
malignant (diamonds, M) from benign (squares, B) samples, using normalized
values of
microRNAs. Fig.23A: The normalized values of two microRNAs (hsa-miR-146b-5p;
hsa-miR-
551b-3p) were used as the features for the classification. The sensitivity of
this classifier is 80%
and the specificity is 56.3%. The grey shaded area marks the space in which a
sample is
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classified as malignant, as determined by the classifier. Fig.23B: The
normalized values of three
microRNAs (hsa-miR-146b-5; hsa-miR-551b-3p; hsa-miR-222-3p) were used as the
features for
the classification. The sensitivity of this classifier is 82.6% and the
specificity is 59.5%.
Misclassified samples (miscl.) are represented by a dot. Fig.23C: The
normalized values of 8
microRNAs (hsa-miR-146b-5p,hsa-miR-551b-3p,hsa-miR-222-3p,hsa-miR-125b-5p,hsa-
miR- 5
31-5p,hsa-miR-375,hsa-miR-152-3p,hsa-miR-181c-5p) were used as the features
for the
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer
and the y-axis shows the true diagnosis. The sensitivity of this classifier is
81.7% and the
specificity is 71.4%. The figure shows a confusion matrix where the x-axis
shows the classifier
answer (Clas. Ans.) while the y-axis shows the true diagnosis (Real
class=re.c1.). 10
Figure 24A-24C: A Discriminant Analysis classifier was used to classify
Indeterminate
malignant (diamonds, M) from benign (squares, B) samples, using normalized
values of
microRNA ratios. Fig.24A: The normalized values of two microRNA ratios (hsa-
miR-146b-5p -
hsa-miR-342-3p,hsa-miR-31-5p - hsa-miR-342-3p) were used as the features for
the
classification. The sensitivity of this classifier is 80% and the specificity
is 72.2%. The grey 15
shaded area marks the space in which a sample is classified as malignant, as
determined by the
classifier. Fig.24B: 'the normalized values of three microRNA ratios (hsa-miR-
146b-5p:hsa-
miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-200c-3p)
were used as
the features for the classification. The sensitivity of this classifier is 80%
and the specificity is
69%. Misclassified samples (miscl.) are represented by a dot. Fig.24C: The
normalized values of 20
8 microRNA ratios indeterminate malignant from benign samples. The notmalized
values of 8
microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p
; hs a-
miR-125b-5p:hsa-miR-200c-3p; hsa-miR-125b-5p:hsa-miR-138-5p; hsa-miR-222-
3p:hsa-miR-
486-5p; MID-16582:hsa-miR-200c-3p; MID-16582:hsa-miR-138-5p; hsa-miR-200c-
3p:hsa-
miR-486-5p) were used as the features for the classification. The figure shows
a confusion 25
matrix where the x-axis shows the classifier answer and the y-axis shows the
true diagnosis. The
sensitivity of this classifier is 80% and the specificity is 66.7%. The figure
shows a confusion
matrix where the x-axis shows the classifier answer (Clas. Ans.) while the y-
axis shows the true
diagnosis (Real class=re.c1.).
Figure 25A-25C: A Discriminant Analysis classifier was used to classify
Indeterminate 30
malignant (diamonds, M) from benign (squares, B) samples, using a combination
of normalized
values of microRNAs and microRNA ratios. Fig.25A: The normalized values of one
microRNA
and one microRNA ratio (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-146b-5p) were
used as
the features for the classification. 'the sensitivity of this classifier is
80% and the specificity is
CA 02945531 2016-10-11
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73.8%.The grey shaded area marks the space in which a sample is classified as
malignant, as
determined by the classifier Fig.25B: The normalized values of two microRNAs
and one
microRNA ratio (hsa-rniR-146b-5p:hsa-miR-342-3p; hsa-miR-146b-5p; hsa-miR-551b-
3p) were
used as the features for the classification. The sensitivity of this
classifier is 79.1% and the
specificity is 73%. Fig.25C: The normalized values of 5 microRNAs and 3
microRNA ratios 5
(hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-146b-5p; hsa-miR-551b-3p; hsa-miR-222-
3p; hsa-
miR-31 -5p :hsa-miR-342-3p ; hsa-miR-125b-5p :hsa-miR-200c-3p ; hsa-miR-125b-
5p; hsa-miR-
31-5p) were used as the features for the classification. The sensitivity of
this classifier is 87.8%
and the specificity is 67.5%. The figure shows a confusion matrix where the x-
axis shows the
classifier answer (Clas. Ans.) while the y-axis shows the true diagnosis (Real
class=re.c1.). 10
Figure 26A-26C: A KNN classifier was used to classify Indeterminate malignant
(M)
from benign (B) samples, using normalized values of microRNAs. Fig.26A: The
normalized
values of 6 microRNAs (hsa-miR-146b-5p; hsa-miR-551b-3p ; hsa-miR-222-3p ; hs
a-miR-125b-
Sp; hsa-miR-31-5p; hsa-miR-375) were used as the features for the
classification. The figure
shows a confusion matrix where the x-axis shows the classifier answer (Clas.
Ans.) while the y- 15
axis shows the true diagnosis (Real class=re.c1.). The sensitivity of this
classifier is 78.3% and
the specificity is 65.9%. Fig.26B: The normalized values of 8 microRNAs (hsa-
miR-146b-5p;
hsa-miR-551b-3p; hsa-miR-222-3p; hsa-miR-125b-5p; hsa-miR-31-5p; hsa-miR-375;
hsa-miR-
152-3p; hsa-miR-181c-5p) were used as the features for the classification. The
figure shows a
confusion matrix where the x-axis shows the classifier answer (Clas. Ans.)
while the y-axis 20
shows the true diagnosis (Real class=re.c1.). The sensitivity of this
classifier is 82.6% and the
specificity is 73%. Fig.26C: The normalized values of 12 microRNAs (hsa-miR-55
lb-3p; hsa-
miR-146b-5p; hsa-miR-222-3p; hsa-miR-125b-5p; hsa-miR-31-5p; hsa-miR-375; hsa-
miR-152-
3p; hsa-miR-181c-5p; hsa-miR-424-3p; hsa-miR-486-5p; hsa-miR-200c-3p; hsa-miR-
346) were
used as the features for the classification. The figure shows a confusion
matrix where the x-axis 25
shows the classifier answer (Clas. Ans.) while the y-axis shows the true
diagnosis (Real
class=re.c1.). The sensitivity of this classifier is 73.9% and the specificity
is 68.3%.
Figure 27A-27B: A KNN classifier was used to classify Indeterminate malignant
(M)
from benign (B) samples, using normalized values of microRNA ratios. Fig.27A:
The
normalized values of 6 microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-
miR-31- 30
5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-125b-5p:hsa-miR-
138-5p;
hsa-miR-222-3p:hsa-miR-486-5p; MID-16582:hsa-miR-200c-3p) were used as the
features for
the classification. The figure shows a confusion matrix where the x-axis shows
the classifier
answer (Clas. Ans.) while the y-axis shows the true diagnosis (Real
class=re.c1.). 'the sensitivity
16
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of this classifier is 80.9% and the specificity is 65.9%. Fig.27B: The
normalized values of 8
microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p
; hs a-
miR-125b-5p:hs a-miR-200c-3p ; hsa-miR-125b-5p :hs a-miR- l38-Sp; hs a-miR-222-
3p :hsa-miR-
486-5p; MID-16582:hsa-miR-200c-3p; MID-16582:hsa-miR-138-5p; hsa-miR-200c-
3p:hsa-
miR-486-5p) were used as the features for the classification. The figure shows
a confusion 5
matrix where the x-axis shows the classifier answer (Clas. Ans.) while the y-
axis shows the true
diagnosis (Real class=re.c1.). The sensitivity of this classifier is 76.5% and
the specificity is
62.7%.
Figure 28A-28C: A KNN classifier was used to classify Indeterminate malignant
(M)
from benign (B) samples, using normalized values of microRNAs and microRNA
ratios. 10
Fig.27C: The normalized values of 3 microRNAs and 3 microRNA ratios (hsa-miR-
146b-
5p:hsa-miR-342-3p; hsa-miR-146b-5p; hsa-miR-55 lb-3p; hsa-miR-222-3p; hsa-miR-
31-5p:hsa-
miR-342-3p; hsa-miR-125b-5p:hsa-miR-200c-3p) were used as the features for the
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer
(Clas. Ans.) while the y-axis shows the true diagnosis (Real class=re.c1.).
The sensitivity of this 15
classifier is 76.5% and the specificity is 57.9%. Fig.28B: The normalized
values of 5
microRNAs and 3 microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-551b-
3p; hsa-
miR-146b-5p; hsa-miR-222-3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-
miR-
200c-3p; hsa-miR-125b-5p; hsa-miR-31-5p) were used as the features for the
classification. The
figure shows a confusion matrix where the x-axis shows the classifier answer
(Clas. Ans.) while 20
the y-axis shows the true diagnosis (Real class=re.c1.). The sensitivity of
this classifier is 78.3%
and the specificity is 64.3% Fig.28C: The normalized values of 12 microRNA and
microRNA
ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-146b-5p; hsa-miR-551b-3p; hsa-
miR-222-
3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-
125b-5p;
hsa-miR-31-5p; hsa-miR-375; hsa-miR-125b-5p:hsa-miR-138-5p; hsa-miR-222-3p:hsa-
miR- 25
486-5p; hsa-miR-152-3p) were used as the features for the classification. The
figure shows a
confusion matrix where the x-axis shows the classifier answer (Clas. Ans.)
while the y-axis
shows the true diagnosis (Real class=re.c1.). The sensitivity of this
classifier is 80.9% and the
specificity is 67.5%.
Figure 29A-29C: A SVM classifier was used to classify Indeterminate malignant
30
(diamonds, M) from benign (squares, B) samples, using the normalized values of
microRNAs.
Fig.29A: The normalized values of three microRNAs (hsa-miR-146b-5p; hsa-miR-
551b-3p; hsa-
miR-222-3p) were used as the features for the classification. The sensitivity
of this classifier is
82.6% and the specificity is 54.8% Misclassified samples (miscl.) are
represented by a dot.
17
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Fig.29B: The normalized values of 6 microRNAs (hsa-miR-146b-5p; hsa-miR-551b-
3p; hsa-
miR-222-3p; hsa-miR-125b-5p; hsa-miR-31-5p; hsa-miR-375) were used as the
features for the
classification. The sensitivity of this classifier is 82.6% and the
specificity is 59.5%. The figure
shows a confusion matrix where the x-axis shows the classifier answer (Clas.
Ans.) while the y-
axis shows the true diagnosis (Real class=re.c1.). Fig.29C: Figure 20: The
normalized values of 8 5
microRNAs (hsa-miR-146b-5p; hsa-miR-551b-3p ; hsa-miR-222-3p; hsa-miR-125b-5p;
hsa-miR-
31-5p; hsa-miR-375; hsa-miR-152-3p; hsa-miR-181c-5p) were used as the features
for the
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer
(Clas. Ans.) while the y-axis shows the true diagnosis (Real class=re.c1.).
The sensitivity of this
classifier is 90.4% and the specificity is 60.3%. 10
Figure 30A-30C: A SVM classifier was used to classify Indeterminate malignant
(diamonds, M) from benign (squares, B) samples, using the normalized values of
microRNA
ratios. Fig.30A: The normalized values of three microRNA ratios (hsa-miR-146b-
5p:hsa-miR-
342-3p; hs a-miR-31 -5p :hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-200c-3p) were
used as the
features for the classification. The sensitivity of this classifier is 81.7%
and the specificity is 15
67.5%. Misclassified samples (miscl.) are represented by a dot. Fig.30B: The
normalized values
of 6 microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-
342-3p; hsa-
miR-125b-5p:hsa-miR-200c-3p; hsa-miR-125b-5p:hsa-miR-138-5p; hsa-miR-222-
3p:hsa-miR-
486-5p; MID-16582:hsa-miR-200c-3p) were used as the features for the
classification. The
figure shows a confusion matrix where the x-axis shows the classifier answer
(Clas. Ans.) while 20
the y-axis shows the true diagnosis (Real class=re.c1.). The sensitivity of
this classifier is 88.7%
and the specificity is 63.5%. Fig. 30C: The normalized values of 8 microRNA
ratios (hsa-miR-
146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-
200c-3p;
hsa-miR-125b-5p:hsa-miR-138-5p; hsa-miR-222-3p:hsa-miR-486-5p; MID-16582:hsa-
miR-
200c-3p; MID-16582:hsa-miR-138-5p; hsa-miR-200c-3p:hsa-miR-486-5p) were used
as the 25
features for the classification. The figure shows a confusion matrix where the
x-axis shows the
classifier answer (Clas. Ans.) while the y-axis shows the true diagnosis (Real
class=re.c1.). The
sensitivity of this classifier is 87.8% and the specificity is 58.7%.
Figure 31A-31C: A SVM classifier was used to classify Indeterminate malignant
(diamonds, M) from benign (squares, B) samples, using the combination of
normalized values of 30
microRNAs and microRNA ratios. Fig. 31A: The normalized values of two
microRNAs and one
microRNA ratio (hsa-miR-146b-5p:hsa-miR-342-3p ; hsa-miR-146b-5p; hsa-miR-551b-
3p)
were used as the features for the classification. The sensitivity of this
classifier is 80% and the
specificity is 71.4%. Fig. 31B: The normalized values of 4 microRNAs and two
microRNA
18
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ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-146b-5p; hsa-miR-551b-3p; hsa-
miR-222-
3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-125b-5p:hsa-miR-200c-3p) were used
as the
features for the classification. The figure shows a confusion matrix where the
x-axis shows the
classifier answer (Clas. Ans.) while the y-axis shows the true diagnosis (Real
class=re.c1.). The
sensitivity of this classifier is 89.9% and the specificity is 51.6%. Fig.
31C: The normalized 5
values of 5 microRNAs and 3 microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p;
hsa-miR-
551b-3p; hsa-miR-146b-5p; hsa-miR-222-3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-
miR-125b-
5p:hsa-miR-200c-3p; hsa-miR-125b-5p; hsa-miR-31-5p) were used as the features
for the
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer
(Clas. Ans.) while the y-axis shows the true diagnosis (Real class=re.c1.).
The sensitivity of this 10
classifier is 84.3% and the specificity is 68.3%.
Figure 32A-32C: A Discriminant analysis ensemble classifier was used to
classify
Indeterminate malignant (diamonds, M) from benign (squares, B) samples using
the normalized
values of microRNAs. Fig.32A: The normalized values of two microRNA (hsa-miR-
146b-5p;
hsa-miR-551b-3p) were used as the features for the classification. The
sensitivity of this 15
classifier is 85.2% and the specificity is 45.2%. The grey shaded area marks
the space in which a
sample is classified as malignant, as determined by the classifier. Fig.32B:
The normalized
values of three microRNAs (hsa-miR-551b-3p; hsa-miR-146b-5p; hsa-miR-222-3p)
were used as
the features for the classification. The sensitivity of this classifier is
84.3% and the specificity is
45.2%. Misclassified samples (miscl.) are represented by a dot. Fig.32C: The
normalized values 20
of 8 microRNAs (hsa-miR-146b-5p; hsa-miR-551b-3p; hsa-miR-222-3p; hsa-miR-125b-
5p; hsa-
miR-31-5p; hsa-miR-375; hsa-miR-152-3p; hsa-miR-181c-5p) were used as the
features for the
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer
(Clas. Ans.) while the y-axis shows the true diagnosis (Real class=re.c1.).
The sensitivity of this
classifier is 88.7% and the specificity is 64.3%. 25
Figure 33A-33C: A Discriminant analysis ensemble classifier was used to
classify
Indeterminate malignant (diamonds, M) from benign (squares, B) samples using
the normalized
values of microRNA ratios. Fig.33A: The normalized values of two microRNA
ratios (hsa-miR-
146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p) were used as the
features for the
classification. The sensitivity of this classifier is 86.1% and the
specificity is 61.1%. The grey 30
shaded area marks the space in which a sample is classified as malignant, as
determined by the
classifier. Fig.33B: The normalized values of three microRNA ratios (hsa-miR-
146b-5p:hsa-
miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p; hsa-miR-125h-5p:hsa-miR-200c-3p)
were used as
the features for the classification. The sensitivity of this classifier is 87%
and the specificity is
19
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
57.1%. Misclassified samples (miscl.) are represented by a dot. Fig.33C: The
normalized values
of 8 microRNA ratios (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-
342-3p; hsa-
miR-125b-5p:hsa-miR-200c-3p; hsa-miR-125b-5p :hs a-miR- l38-5p; hs a-miR-222-
3p :hsa-miR-
486-5p; MID-16582:hsa-miR-200c-3p; MID-16582:hsa-miR-138-5p; hsa-miR-200c-
3p:hsa-
miR-486-5p) were used as the features for the classification. The figure shows
a confusion 5
matrix where the x-axis shows the classifier answer (Clas. Ans.) while the y-
axis shows the true
diagnosis (Real class=re.c1.). The sensitivity of this classifier is 89.6% and
the specificity is
65.1%.
Figure 34A-34C: A Discriminant analysis ensemble classifier was used to
classify
Indeterminate malignant (diamonds, M) from benign (squares, B) samples using a
combination 10
of normalized values of microRNAs and microRNA ratios. Fig.34A: The normalized
values of
one microRNA and one microRNA ratio (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-
146b-5p)
were used as the features for the classification. The sensitivity of this
classifier is 83.5% and the
specificity is 58.7%. The grey shaded area marks the space in which a sample
is classified as
malignant, as determined by the classifier. Fig.34B: The normalized values of
two microRNAs 15
and one microRNA ratio (hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-146b-5p; hsa-
miR-551b-
3p) were used as the features for the classification. The sensitivity of this
classifier is 85.2% and
the specificity is 65.9%. Misclassified samples (miscl.) are represented by a
dot. Fig.34C: The
normalized values of 5 microRNAs and 3 microRNA ratios (hsa-miR-146b-5p:hsa-
miR-342-3p;
hsa-miR-146b-5p; hsa-miR-551b-3p; hsa-miR-222-3p; hsa-miR-31-5p:hsa-miR-342-
3p; hsa- 20
miR-125b-5p:hsa-miR-200c-3p; hsa-miR-125b-5p; hsa-miR-31-5p) were used as the
features for
the classification. The figure shows a confusion matrix where the x-axis shows
the classifier
answer (Clas. Ans.) while the y-axis shows the true diagnosis (Real
class=re.c1.). The sensitivity
of this classifier is 87.8% and the specificity is 62.7%.
Figure 35: Normalized expression (Exp.) levels of hsa-miR-146b-5p is shown as
a dot 25
plot for Indeterminate non-medullary malignant ("Mal.") and benign ("Ben.")
samples. Lines
represent the median values for each group. Within each group, dots are
randomly distributed
along the x-axis, in order to improve visibility of the dots.
Figure 36: The noimalized expression levels (Exp.) of the miR ratio hsa-miR-
146b-
5p:hsa-miR-342-3p is shown as a dot plot for Indeterminate non-medullary
malignant ("Mal.") 30
and benign ("Ben.") samples. Lines represent the median values for each group.
Within each
group, dots are randomly distributed along the x-axis, in order to improve
visibility of the dots.
Figure 37A-37C: A Discriminant analysis classifier was used to classify
Bethesda IV
malignant (diamonds, M) from benign (squares, B) samples, using the normalized
values of
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
microRNAs. Fig.37A: The normalized values of two microRNAs (hsa-miR-125b-5p;
hsa-miR-
551b-3p) were used as the features for the classification. The sensitivity of
this classifier is
91.5% and the specificity is 42.9%. The grey shaded area marks the space in
which a sample is
classified as malignant, as determined by the classifier. Fig.37B: The
normalized values of three
microRNAs (hsa-miR-125b-5p; hsa-miR-551b-3p; hsa-miR-222-3p) were used as the
features 5
for the classification. The sensitivity of this classifier is 91.5% and the
specificity is 39.7%.
Misclassified samples (miscl.) are represented by a dot. Fig.37C: The
normalized values of 8
microRNAs (hsa-miR-125b-5p; hsa-miR-551b-3p ; hsa-miR-222-3p; hsa-miR-146b-5p;
hsa-miR-
375; hsa-miR-181c-5p; hsa-miR-31-5p; hsa-miR-138-5p) were used as the features
for the
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer 10
(Clas. Ans.) while the y-axis shows the true diagnosis (Real class=re.c1). The
sensitivity of this
classifier is 89.4% and the specificity is 47.6%.
Figure 38A-38C: A Discriminant analysis classifier was used to classify
Bethesda IV
malignant (diamonds, M) from benign (squares, B) samples, using the normalized
values of
microRNA ratios. Fig.38A: The normalized values of two microRNA ratios (hsa-
miR-125b- 15
5p:hsa-miR-200c-3p; hsa-miR-146b-5p:hsa-miR-342-3p) were used as the features
for the
classification. 'the sensitivity of this classifier is 89.4% and the
specificity is 28.6%. 'The grey
shaded area marks the space in which a sample is classified as malignant, as
determined by the
classifier. Fig.38B: The normalized values of three microRNA ratios (hsa-miR-
125b-5p:hsa-
miR-200c-3p; hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p)
were used as 20
the features for the classification. The sensitivity of this classifier is
91.5% and the specificity is
30.2%. Misclassified samples (miscl.) are represented by a dot. Fig.38C: The
normalized values
of 8 microRNA ratios (hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-146b-5p:hsa-miR-
342-3p;
hsa-miR-31-5p:hsa-miR-342-3p; MID-16582:hsa-miR-138-5p; hsa-miR-222-3p:hsa-miR-
486-
5p; MID-16582:hsa-miR-200c-3p; hsa-miR-125b-5p: hsa-miR-138-5p; hsa-miR-200c-
3p:hsa- 25
miR-486-5p) were used as the features for the classification. The figure shows
a confusion
matrix where the x-axis shows the classifier answer (Clas. Ans.) while the y-
axis shows the true
diagnosis (Real class=re.c1.). The sensitivity of this classifier is 80.9% and
the specificity is
57.1%.
Figure 39A-39C: A Discriminant analysis classifier was used to classify
Bethesda IV 30
malignant (diamonds, M) from benign (squares, B) samples, using the normalized
values of
microRNAs and microRNA ratios. Fig. 39A: The normalized values of one microRNA
and one
microRNA ratio (hsa-miR-125b-5p; hsa-miR-125b-5p:hsa-miR-200c-3p) were used as
the
features for the classification. The sensitivity of this classifier is 93.6%
and the specificity is
21
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WO 2015/175660 PCT/US2015/030564
33.3%. The grey shaded area marks the space in which a sample is classified as
malignant, as
determined by the classifier. Fig. 39B: The normalized values of one microRNA
and two
microRNA ratios (hsa-miR-125b-5p ; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-
146b-
5p:hsa-miR-342-3p) were used as the features for the classification. The
sensitivity of this
classifier is 89.4% and the specificity is 41.3%. Misclassified samples
(miscl.) are represented by 5
a dot.
Fig. 39C: The normalized values of 4 microRNAs and 4 microRNA ratios (hsa-miR-
125b-5p;
hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-146b-5p: hsa-miR-342-3p; hsa-miR-55
lb-3p; hsa-
miR-222-3p; hsa-miR-146b-5p; hsa-miR-31-5p:hsa-miR-342-3p; MID-16582:hsa-miR-
138-5p)
were used as the features for the classification. The figure shows a confusion
matrix where the 10
x-axis shows the classifier answer (Clas. Ans.) while the y-axis shows the
true diagnosis (Real
class=re.c1). The sensitivity of this classifier is 87.2% and the specificity
is 46%.
Figure 40A-40C: A KNN classifier was used to classify Bethesda IV malignant
from
benign samples, using the normalized values of microRNAs. The figures show a
confusion
matrix where the x-axis shows the classifier answer (Clas. Ans.) while the y-
axis shows the true 15
diagnosis (Real class=re.c1.). Fig.40A: The normalized values of 6 microRNAs
(hsa-miR-125b-
5p; hsa-miR-551b-3p; hsa-miR-222-3p; hsa-miR-146b-5p; hsa-miR-375; hsa-miR-18
lc-5p)
were used as the features for the classification. The sensitivity of this
classifier is 72.3% and the
specificity is 39.7%. Fig.40B: The normalized values of 8 microRNA (hsa-miR-
125b-5p; hsa-
miR-551b-3p; hsa-miR-222-3p; hsa-miR-146b-Sp; hsa-miR-375; hsa-miR-181c-5p;
hsa-miR-31- 20
5p; hsa-miR-138-5p) were used as the features for the classification. The
sensitivity of this
classifier is 66% and the specificity is 61.9%. Fig.40C: The nottnalized
values of 12 microRNA
(hsa-miR-125b-5p; hsa-miR-551b-3p; hsa-miR-222-3p; hsa-miR-146b-5p; hsa-miR-
375; hsa-
miR-18 lc-5p; hsa-miR-31-5p; hsa-miR-138-5p; hsa-miR-200c-3p; MID-16582; hsa-
miR-346;
hsa-miR-152-3p) were used as the features for the classification. The
sensitivity of this classifier 25
is 66% and the specificity is 61.9%.
Figure 41A-41B: A KNN classifier was used to classify Bethesda IV malignant
from
benign samples, using the normalized values of microRNA ratios. The figures
show a confusion
matrix where the x-axis shows the classifier answer (Clas. Ans.) while the y-
axis shows the true
diagnosis (Real class=re.c1.). Fig.41A: The normalized values of 6 microRNA
ratios (hsa-miR- 30
125b-5p:hsa-miR-200c-3p; hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-
342-3p;
MID-16582:hsa-miR-138-5p; hsa-miR-222-3p:hsa-miR-486-5p; MID-16582:hsa-miR-
200c-3p)
were used as the features for the classification. The sensitivity of this
classifier is 78.7% and the
specificity is 61.9%. Fig.41B: The normalized values of 8 microRNA ratios (hsa-
miR-125b-
27
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
5p:hsa-miR-200c -3p; hsa-miR-146b-5p:hsa-miR-342-3p;
hsa-miR-31-5p:hsa-miR-342-3p;
MID-16582:hsa-miR-138-5p; hsa-miR-222-3p:hsa-miR-486-5p; MID-16582:hsa-miR-
200c-3p;
hsa-miR-125b-5p:hsa-miR-138-5p; hsa-miR-200c-3p:hsa-miR-486-5p) were used as
the features
for the classification. The sensitivity of this classifier is 80.9% and the
specificity is 50.8%.
Figure 42A-42C: A KNN classifier was used to classify Bethesda IV malignant
from 5
benign samples, using the normalized values of microRNAs and microRNA ratios.
The figures
show a confusion matrix where the x-axis shows the classifier answer (Clas.
Ans.) while the y-
axis shows the true diagnosis (Real class=re.c1.). Fig.42A: The normalized
values of 4
microRNAs and 2 microRNA ratios (hsa-miR-125b-5p; hsa-miR-125b-5p:hsa-miR-200c-
3p;
hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-551b-3p; hsa-miR-222-3p; hsa-miR-146b-
5p) were 10
used as the features for the classification. The sensitivity of this
classifier is 63.8% and the
specificity is 46%. Fig.42B: The noimalized values of 4 microRNAs and 4
microRNA ratios
(hsa-miR-125b-5p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-146b-5p:hsa-miR-342-
3p; hsa-
miR-55 lb-3p; hsa-miR-222-3p; hsa-miR-146b-5p; hsa-miR-31-5p:hsa-miR-342-3p;
MID-
16582:hsa-miR-138-5p) were used as the features for the classification. The
sensitivity of this 15
classifier is 68.1% and the specificity is 49.2%. Fig.42C: The normalized
values of 6 microRNA
and 6 microRNA ratios (hsa-miR-125b-5p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-
miR-146b-
5p:hsa-miR-342-3p; hsa-miR-55 lb-3p; hsa-miR-222-3p; hsa-miR-146b-5p; hsa-miR-
31-5p:hsa-
miR-342-3p; MID-16582:hsa-miR-138-5p; hsa-miR-375; hsa-miR-222-3p:hsa-miR-486-
5p;
hsa-miR-181c-5p; MID-16582:hsa-miR-200c-3p) were used as the features for the
classification. 20
The sensitivity of this classifier is 74.5% and the specificity is 58.7%.
Figure 43A-43C: A SVM classifier was used to classify Bethesda IV malignant
from
benign samples, using the normalized values of microRNAs. Fig.43A: The
normalized values of
three microRNA (hsa-miR-125b-5p; hsa-miR-551b-3p; hsa-miR-222-3p) were used as
the
features for the classification. The sensitivity of this classifier is 97.9%
and the specificity is 25
22.2%. Malignant=M (diamonds); Benign=B (squares). Fig.43B: The normalized
values of 6
microRNAs (hsa-miR-125b-5p; hsa-miR-551b-3p; hsa-miR-222-3p; hsa-miR-146b-5p;
hsa-miR-
375; hsa-miR-181c-5p) were used as the features for the classification. The
figure shows a
confusion matrix where the x-axis shows the classifier answer (Clas. Ans.)
while the y-axis
shows the true diagnosis (Real class=re.cl. The sensitivity of this classifier
is 89.4% and the 30
specificity is 38.1%. Fig.43C: The normalized values of 8 microRNA (hsa-miR-
125b-5p; hsa-
miR-55 lb-3p; hsa-miR-222-3p; hsa-miR-146b-5p; hsa-miR-375; hsa-miR-181c-5p;
hsa-miR-31-
5p; hsa-miR-138-5p) were used as the features for the classification. The
figure shows a
confusion matrix where the x-axis shows the classifier answer (Clas. Ans.)
while the y-axis
23
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
shows the true diagnosis (Real class=re.c1.). The sensitivity of this
classifier is 91.5% and the
specificity is 55.6%.
Figure 44A-44C: A SVM classifier was used to classify Bethesda IV malignant
(diamonds, M) from benign (squares, B) samples, using the normalized values of
microRNA
ratios. Fig.44A: The normalized values of three microRNA ratios (hsa-miR-125b-
5p:hsa-miR- 5
200c-3p; hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p) were
used as the
features for the classification. The sensitivity of this classifier is 100%.
Fig.44B: The
normalized values of 6 microRNA ratios (hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-
miR-146b-
5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p; MID-16582:hsa-miR- 138-5p;
hsa-miR-
222-3p:hsa-miR-486-5p; MID-16582:hsa-miR-200c-3p) were used as the features
for the 10
classification. The figure shows a confusion matrix where the x-axis shows the
classifier answer
(Clas. Ans.) while the y-axis shows the true diagnosis (Real class=re.c1.).
The sensitivity of this
classifier is 93.6% and the specificity is 33.3%. Fig.44C: The normalized
values of 8 microRNA
ratios (hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-146b-5p:hsa-miR-342-3p; hsa-
miR-31-
5p:hsa-miR-342-3p; MID-16582:hsa-miR-138-5p; hsa-miR-222-3p:hsa-miR-486-5p;
MID- 15
16582:hsa-miR -200c-3p ; hsa-miR-125h-5p:hs a-miR -138-5p; h sa-miR -200c-3p
:h sa-miR-486-
5p) were used as the features for the classification. 'Ibe figure shows a
confusion matrix where
the x-axis shows the classifier answer (Clas. Ans.) while the y-axis shows the
true diagnosis
(Real class=re.c1.). The sensitivity of this classifier is 93.6% and the
specificity is 31.7%.
Figure 45A-45C: A SVM classifier was used to classify Bethesda IV malignant 20
(diamonds, M) from benign (squares, B) samples, using a combination normalized
values of
microRNAs and microRNA ratios. Fig.45A: The normalized values of one microRNA
and two
microRNA ratios (hsa-miR-125b-5p ; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-
146b-
5p:hsa-miR-342-3p) were used as the features for the classification. The
sensitivity of this
classifier is 93.6% and the specificity is 22.2%. Misclassified samples
(miscl.) are represented by 25
a dot. Fig.45B: The normalized values of 4 microRNAs and 2 microRNA ratios
(hsa-miR-125b-
5p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-
55 lb-3p;
hsa-miR-222-3p; hsa-miR-146b-5p) were used as the features for the
classification. The figure
shows a confusion matrix where the x-axis shows the classifier answer (Clas.
Ans.) while the y-
axis shows the true diagnosis (Real class=re.c1.). The sensitivity of this
classifier is 95.7% and 30
the specificity is 31.7%. Fig.45C: The normalized values of 4 microRNAs and 4
microRNA
ratios (hsa-miR-125b-5p; hsa-miR-125b-5p:hsa-miR-200c-3p; hs a-miR-146b-5p :hs
a-miR-342-
3p; hsa-miR-551b-3p; hsa-miR-222-3p; hsa-miR-146b-5p; hsa-miR-31-5p:hsa-miR-
342-3p;
MID-16582:hsa-miR-138-5p) were used as the features for the classification.
The figure shows
24
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
a confusion matrix where the x-axis shows the classifier answer (Clas. Ans.)
while the y-axis
shows the true diagnosis (Real class=re.c1.). The sensitivity of this
classifier is 91.5% and the
specificity is 36.5%.
Figure 46A-46C: A Discriminant Analysis Ensemble classifier was used to
classify
Bethesda IV malignant (diamonds, M) from benign (squares, B) samples, using
normalized 5
values of microRNAs. Fig.46A: The normalized values of two microRNAs (hsa-miR-
125b-5p;
hsa-miR-551b-3p) were used as the features for the classification. The
sensitivity of this
classifier is 91.5% and the specificity is 39.7%. The grey shaded area marks
the space in which
a sample is classified as malignant, as determined by the classifier. Fig.46B:
The normalized
values of three microRNAs (hsa-miR-125b-5p; hsa-miR-551b-3p; hsa-miR-222-3p)
were used as 10
the features for the classification. The sensitivity of this classifier is
89.4% and the specificity is
39.7%. Fig.46C: The normalized values of 8 microRNAs (hsa-miR-125b-5p; hsa-miR-
551b-3p;
hsa-miR-222-3p; hsa-miR-146b-5p; hsa-miR-375; hsa-miR-181c-5p; hsa-miR-31-5p;
hsa-miR-
138-5p) were used as the features for the classification. The figure shows a
confusion matrix
where the x-axis shows the classifier answer (Clas. Ans.) while the y-axis
shows the true 15
diagnosis (Real class=re.c1.). The sensitivity of this classifier is 93.6% and
the specificity is
46%.
Figure 47A-47C: A Discriminant Analysis Ensemble classifier was used to
classify
Bethesda IV malignant (diamonds, M) from benign (squares, B) samples, using
normalized
values of microRNA ratios. Fig.47A: The normalized values of two microRNA
ratios (hsa-miR- 20
125b-5p:hsa-miR-200c-3p; hsa-miR-146b-5p:hsa-miR-342-3p) were used as the
features for the
classification. The sensitivity of this classifier is 93.6% and the
specificity is 19%. The grey
shaded area marks the space in which a sample is classified as malignant, as
determined by the
classifier. Fig.47B: The normalized values of three microRNA ratios (hsa-miR-
125b-5p:hsa-
miR-200c-3p; hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-31-5p:hsa-miR-342-3p)
were used as 25
the features for the classification. The sensitivity of this classifier is
93.6% and the specificity is
17.5%. Misclassified samples (miscl.) are represented by a dot. Fig.47C: The
normalized values
of 8 microRNA ratios (hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-146b-5p:hsa-miR-
342-3p;
hsa-miR-31-5p:hsa-miR-342-3p; MID-16582:hsa-miR-138-5p; hsa-miR-222-3p:hsa-miR-
486-
5p; MID-16582:hsa-miR-200c-3p; hsa-miR-125b-5p:hsa-miR-138-5p; hsa-miR-200c-
3p:hsa- 30
miR-486-5p) were used as the features for the classification. The figure shows
a confusion
matrix where the x-axis shows the classifier answer (Clas. Ans.) while the y-
axis shows the true
diagnosis (Real class=re.c1.). The sensitivity of this classifier is 89.4% and
the specificity is
44.4%.
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
Figure 48A-48C: A Discriminant Analysis Ensemble classifier was used to
classify
Bethesda IV malignant (diamonds, M) from benign (squares, B) samples, using a
combination of
normalized values of microRNAs and microRNA ratios. Fig.48A: The normalized
values of one
microRNA and one microRNA ratio (hsa-miR-125b-5p; hsa-miR-125b-5p:hsa-miR-200c-
3p)
were used as the features for the classification. The sensitivity of this
classifier is 91.5% and the 5
specificity is 33.3%. The grey shaded area marks the space in which a sample
is classified as
malignant, as determined by the classifier. Fig.48B: The normalized values of
one microRNA
and two microRNA ratios (hsa-miR-125b-5p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-
miR-
146b-5p:hsa-miR-342-3p) were used as the features for the classification. The
sensitivity of this
classifier is 89.4% and the specificity is 36.5%. Misclassified samples
(miscl.) are represented by 10
a dot. Fig.48C: The normalized values of 4 microRNA and 4 microRNA ratios (hsa-
miR-125b-
5p; hsa-miR-125b-5p:hsa-miR-200c-3p; hsa-miR-146b-5p:hsa-miR-342-3p; hsa-miR-
55 lb-3p;
hsa-miR-222-3p; hsa-miR-146b-5p; hsa-miR-31-5p:hsa-miR-342-3p; MID-16582:hsa-
miR-138-
5p) were used as the features for the classification. The figure shows a
confusion matrix where
the x-axis shows the classifier answer (Clas. Ans.) while the y-axis shows the
true diagnosis 15
(Real class=re.c1.). The sensitivity of this classifier is 91.5% and the
specificity is 34.9%.
Figure 49: The normalized expression (Exp.) levels of hsa-miR-146b-5p is shown
as a
dot plot for Bethesda IV non-medullary malignant ("Mal.") and for benign
("Ben.") samples.
Lines represent the median values for each group. Within each group, dots are
randomly
distributed along the x-axis. 20
Figure 50: The normalized expression (Exp.) levels of the microRNA ratio hsa-
miR-
146b-5p:hsa-miR-342-3p is shown as a dot plot for Bethesda IV non-medullary
malignant
("Mal.") and for benign ("Ben.") samples. Lines represent the median values
for each group.
Within each group, dots are randomly distributed along the x-axis.
Figure 51: A Discriminant Analysis classifier was used to classify malignant
(diamonds, 25
M) from benign (squares, B) samples, wherein the malignant group included
samples of
medullary tumor. The normalized values of two microRNA (hsa-miR-222-3p; hsa-
miR-55 lb-3p)
were used as features for the classification. The sensitivity of this
classifier is 85.2% and the
specificity is 53.6%. The grey shaded area marks the space in which a sample
is classified as
malignant, as determined by the classifier. 30
Figure 52: A Discriminant Analysis classifier was used to classify malignant
(diamonds,
M) from benign (squares, B) samples, wherein the malignant group included
samples of
medullary tumor. The normalized values of two microRNA ratios (hsa-miR-12511-
5p:hsa-miR-
138-5p; hsa-miR-146b-5p:hsa-miR-342-3p) were used as the features for the
classification. The
26
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
sensitivity of this classifier is 84.7% and the specificity is 80.8%. The grey
shaded area marks
the space in which a sample is classified as malignant, as determined by the
classifier.
Figure 53: Expression pattern of hsa-miR-486-5p and hsa-miR-200c-3p is
determinant
for the quality of the sample. Four samples of blood smears (BS) were analyzed
for the
expression of hsa-miR-486-5p (SEQ ID NO.22) and hsa-miR-200c-3p (SEQ ID NO.23-
24) in 5
comparison with their expression in malignant (M) and benign (B) thyroid
samples. Normalized
values for the two miRs are shown (normalized using all normalizers).
Figure 54: Sub-typing of Benign Thyroid Tumors. microRNA expression profile
(median) was established for two sub-types of benign tumors, Follicular
Adenoma (FA, y axis,
n=81) and Hashimoto (Hash., x axis, n=6). Each cross represents a microRNA or
a microRNA 10
ratio. The ratio hsa-miR-125b-5p:hsa-miR-200c-3p correlated to FA, while
expression of hsa-
miR-342-3p and hsa-miR-31-5p correlated with Hashimoto. Diamonds represent
normalizers.
Significant microRNAs (p-value for t-test < 0.05) are represented by circles.
Figure 55: Sub-typing of Malignant Thyroid Tumors. microRNA expression profile
was
established for two sub-types of malignant thyroid tumors, papillary carcinoma
(Pap.; y-axis, 15
n=161) and follicular carcinoma (FC; x-axis, n=16). Each cross represents a
microRNA or a
microRNA ratio. Diamonds are normalizers. Significant microRNAs (p-value for t-
test < 0.05)
are encircled. Only normalized microRNA values are labeled. Unlabeled circles
represent
significant ratios.
Figure 56: Flowchart representing the protocol for diagnosis of indeterminate
thyroid 20
nodule samples obtained through FNA.
DETAILED DESCRIPTION OF THE INVENTION
Despite accumulated efforts in the search for accurate diagnosis of thyroid
lesions, a great
number of technical problems remain with no solution in sight. As a result of
the quality of the 25
material obtained, the diagnosis of thyroid lesions in fine needle aspiration
(FNA) samples is still
challenging. The low number of cells, the amount of blood, the ratio between
thyroid tumor cells
and non-thyroid tumor cells in the sample, make it challenging to extract
enough material that
will provide conclusive results.
The present invention provides a sensitive, specific and accurate methodology
for 30
distinguishing between malignant and benign thyroid tumors, as well as
particular subtypes of
thyroid tumors. Distinguishing between different subtypes of thyroid tumors is
essential for
providing the patient with the best and most suitable treatment. The present
invention provides a
27
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
significant improvement of the technologies currently available in the field
of thyroid tumor
classification and diagnosis.
The present inventors have developed an integrative platform for the
classification of
thyroid lesions, by profiling and characterizing microRNA expression in
thyroid clinical samples
obtained by FNA biopsies, while also overcoming hindrances such as low number
of cells in the 5
sample and the amount of blood in the sample by microRNA profiling. This
technological
platform was applied to stratify thyroid lesions into benign or malignant
neoplasms, as well as
subtypes of thyroid tumors, as an adjunctive tool in the pre-operative
management of thyroid
nodules. The inventors have exceptionally developed a method for
classification of benign and
malignant thyroid lesions, and specific subtypes of thyroid cancer and
follicular lesions, while 10
integrating steps for filtering out sub-optimal samples, by implementing
specific algorithms
based on microRNA profiling. The method is part of an overall protocol, in
which existing or
available clinical cytological slides having smears from FNA samples may be
used, without the
need to generate or collect additional material from the patients.
The present method further incorporates the analysis of microRNAs in minute
amounts 15
of RNA material from cytological samples. Once an FNA sample is collected,
between one and
several passes of material are smeared onto slides. Currently available
methods usually require
the use of several passes for having enough material for analysis. The present
inventors
developed a method in which even only one FNA slide provides sufficient
material for
microRNA detection. Furthermore, the present inventors were able to measure
microRNA 20
abundance in FNA samples obtained from thyroid nodules as small as 0.1 cm.
This is
particularly relevant considering that approximately 50% of thyroid lesions
are smaller than lcm
[Jung et al. (2014)J Clin Endocrinol Metab 99: E276¨E2851. In addition, the
method developed
by the inventors allows for the aanalysis of samples having very small amounts
of cells, such as
samples having 50 cells, up to 120 cells and over. 25
The present method includes steps for eliminating or disqualifying samples
that lack
thyroid cells and/or in which non-thyroid cells, such as blood cells, are over-
represented.
The present inventors have identified a unique microRNA expression signature
for
thyroid lesions through profiling the expression of the microRNAs denoted by
SEQ ID NOs.1-
308. 30
More specifically, the present inventors have develop a platform for
classification of
thyroid clinical samples based on the levels of expression of a set of
microRNAs, comprising at
least two microRNAs, selected from the group consisting of hsa-miR-31-5p (SEQ
ID NO.5-7),
hsa-miR-424-3p (SEQ 11) NO.16). hsa-miR-222-3p (SEQ ID NO.1-2), hsa-miR-146b-
5p (SEQ
28
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
ID NO.10-11), hsa-miR-346 (SEQ ID NO.14), MID-16582 (SEQ ID NO.25), hsa-miR-
342-3p
(SEQ ID NO.17-18), hsa-miR-181c-5p (SEQ ID NO.15), hsa-miR-125b-5p (SEQ ID
NO.9), hsa-
miR-375 (SEQ ID NO.8). hsa-miR-486-5p (SEQ ID NO.22), hsa-miR-551b-3p (SEQ ID
NO.3-
4), hsa-miR-152-3p (SEQ ID NO.12-13), hsa-miR-200c-3p (SEQ ID NO.23-24) and
hsa-miR-
138-5p (SEQ ID NO.19-21); or a sequence at least 80%, at least 85%, or at
least 90% identical 5
thereto. The platform was established based on a training study with a robust
cohort, and which
also included the measurement of additional microRNAs that served as
normalizers.
The present invention is particularly useful for the 25% of the cases in which
FNA
specimens present inconclusive results in cytopathology, usually referred to
as "indeterminate",
and which include thyroid lesion samples classified in Bethesda categories
III, IV and V. In 10
current medical practice, patients with specimens falling within this category
undergo repeat
FNA procedure, and surgery, including lobectomy and thyroidectomy.
Thus, in one embodiment, the present invention provides a method of
classification for
thyroid lesion samples that fall into the "indeterminate" cases, classified in
categories III, IV and
V of the Bethesda System (described further herein). In one particular
embodiment, the present 15
invention provides a method of classification for thyroid lesion samples
classified in category
IV of the Bethesda System, which relates to "Follicular Neoplasm" or
"Suspicious of a
Follicular Neoplasm", which is known to be the most difficult category to be
classified.
Thus, the present invention presents primarily a protocol for management of
thyroid
lesion samples which failed to be classified by cytopathological analysis.
Particular samples that 20
are of interest are those obtained by FNA. In one embodiment, routine smears
from FNA
samples are used. In another embodiment, FNA samples in preservative solutions
may be used.
Total RNA is extracted from the FNA samples, and the expression of microRNAs
is measured.
In one embodiment, the expression of about 2200 microRNAs is measured. In
another
embodiment, the expression of 182 microRNAs, comprising the sequences of SEQ
ID NO. 1- 25
182 is measured. In a further embodiment, the expression of the microRNAs
comprising the
sequences of SEQ ID NO.1-37 is measured. In another further embodiment, at
least three, at
least four, at least five, at least six, at least seven, at least eight, at
least nine, at least ten, at least
eleven, at least twelve, at least thirteen, at least fourteen, or all
microRNAs from the group
selected from hsa-miR-31-5p (SEQ ID NO.5-7), hsa-miR-424-3p (SEQ ID NO.16),
hsa-miR- 30
222-3p (SEQ ID NO.1-2), hsa-miR-146b-5p (SEQ ID NO.10-11), hsa-miR-346 (SEQ ID
NO.14). MID-16582 (SEQ ID NO.25), hsa-miR-342-3p (SEQ ID NO.17-18), hsa-miR-
181c-5p
(SEQ ID NO.15), hsa-miR-125b-5p (SEQ ID NO.9), hsa-miR-375 (SEQ ID NO.8), hsa-
miR-
486-5p (SEQ Ill NO.22), hsa-miR-551b-3p (SEQ Ill NO.3-4), hsa-miR-152-3p (SEQ
ID NO.12-
29
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
13), hsa-miR-200c-3p (SEQ ID NO.23-24) and hsa-miR-138-5p (SEQ ID NO.19-21),
or a
sequence at least 80%, at least 85%, or at least 90% identical thereto, are
measured and used in
the classification.
In a further embodiment, classification of the thyroid sample as malignant or
benign
comprises measuring the expression levels of hsa-miR-222-3p (SEQ ID NO.1-2),
hsa-miR-551b- 5
3p (SEQ ID NO.3-4), hsa-miR-31-5p (SEQ ID NO.5-7), hsa-miR-375 (SEQ ID NO.8),
hsa-miR-
125b-5p (SEQ ID NO.9), hsa-miR-146b-5p (SEQ ID NO.10-11), hsa-miR-152-3p (SEQ
ID
NO.12-13), hsa-miR-346 (SEQ ID NO.14), hsa-miR-181c-5p (SEQ ID NO.15), hsa-miR-
424-3p
(SEQ ID NO.16). hsa-miR-342-3p (SEQ ID NO.17-18), hsa-miR-138-5p (SEQ ID NO.19-
21),
hsa-miR-486-5p (SEQ ID NO.22), hsa-miR-200c-3p (SEQ ID NO.23-24), MID-16582
(SEQ ID 10
NO.25). or any combination thereof, or a sequence at least 80%, at least 85%,
or at least 90%
identical thereto, providing the levels of expression to a classifier which
analyzes and classifies
the sample as malignant or benign.
Thus, the present invention provides a method for distinguishing between
malignant and
benign thyroid tumor lesions in a subject in need, said method comprising
obtaining a thyroid 15
tumor lesion sample from said subject, or provided a biological sample
obtained from said
subject, determining an expression profile in said sample of one or more, or
at least four
microRNAs comprising SEQ ID NOS: 1-25, or a sequence at least 80%, at least
85%, or at least
90% identical thereto, or any combination of said microRNAs, by hybridization
or by
amplification, comparing said expression profile to a reference threshold
value by using a 20
classifier algorithm; and determining whether the thyroid lesion is malignant
or benign. In one
particular embodiment, the method of the invention is for distinguishing sub-
types of malignant
or benign thyroid tumor lesions.
In one embodiment, the method of the invention comprises measuring the
expression of
at least four of the microRNAs comprising SEQ ID NOS: 1-25, obtaining the
microRNA 25
expression profile value of said sample, and using a classifier to establish,
based on said value,
whether the thyroid lesion is malignant or benign, and optionally further
classifying the sample
into one of the malignant or benign subtypes.
In one particular embodiment, said determining an expression profile by
hybridization
comprises contacting the sample with probes that hybridize to each of SEQ ID
NOS: 1-25, or to 30
a sequence at least 80%, at least 85%, or at least 90% identical thereto. In
another embodiment,
said determining an expression profile by hybridization comprises contacting
the sample with
probes that hybridize with at least eight, at least ten, at least twelve, at
least fourteen, or at least
sixteen contiguous nucleotides of said microRNA comprising SEQ Ill NOS: 1-25.
CA 02945531 2016-10-11
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The present invention further provides a method of classifying a sample as
malignant or
benign, and/or sub-typing said sample, whereby, further to measuring the
expression levels of
microRNAs in the sample, obtaining an expression profile and optionally
calculating microRNA
ratios, applying a multi-step analysis of the expression data. Said multi-step
analysis comprising
applying one or more algorithms, in parallel or sequentially, to at least one
of the microRNA 5
expression profiles, microRNA ratios, or a combination thereof. Said multi-
step analysis may
also further include analyzing the expression of one or more single microRNA
levels which may
be indicative of the overall quality of the sample.
Examples of criteria that may be included in the multi-step analysis, in any
order and in
any combination, are: the expression of non-malignant cell markers, the
expression of 10
microRNAs that correlate with a specific sub-type of thyroid tumor, and the
like. Thus for
example, one step may be examining whether the expression of non-thyroid cell
markers is
higher or lower than the threshold established in the data set, e.g. the
training data set, in which
case the sample may be disqualified. Another further step may be examining the
expression of a
microRNA or microRNA ratio that correlates with a thyroid tumor sub-type, e.g.
if the 15
expression of hsa-miR-342-3p (SEQ ID NO.17-18) is very high compared to the
threshold
established in the data set, e.g. the training data set, the sample may be
classified as benign, and
further sub-typed as being Hashimoto. Alternatively, if the expression of hsa-
miR-342-3p (SEQ
ID NO.17-18) is very high compared to the threshold established in the data
set, e.g. the training
data set, the sample may be disqualified for lack of sufficient thyroid cells.
Another further 20
optional step may relate to the level of expression of MID-16582 (SEQ ID
NO.25), may be used
to determine whether the sample may be discarded, or analyzed using a
classifier specific for
these samples in which MID-16582 (SEQ ID NO.25) is high (compared to the
threshold
established in the training set).
In one particular embodiment of the invention, said non-thyroid cell marker is
a blood 25
cell marker.
In another particular embodiment of the invention, said cell marker is an
epithelial cell
marker.
In a further particular embodiment of the invention, said cell marker is a
blood cell
marker, a white blood cell marker or an epithelial cell marker. Examples of
blood cell markers 30
are hsa-miR-486-5p (SEQ ID NO.22), hsa-miR-320a (SEQ ID NO.173), hsa-miR-106a-
5p
(SEQ ID NO.150), hsa-miR-93-5p (SEQ ID NO.182), hsa-miR-17-3p (SEQ ID NO.160),
hsa-let-7d-5p (SEQ ID NO.144), hsa-miR-107 (SEQ ID NO.152), hsa-miR-103a-3p
(SEQ ID NO.149), hsa-miR-17-5p (SEQ ID NO.161), hsa-miR-191-5p (SEQ Ill
NO.163),
31
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
hsa-miR-25-3p (SEQ ID NO.167), hsa-miR-106b-5p (SEQ ID NO.151), hsa-miR-20a-5p
(SEQ ID NO.166), hsa-miR-18a-5p (SEQ ID NO.40), hsa-miR-144-3p (SEQ ID
NO.154),
hsa-miR-140-3p (SEQ ID NO.51), hsa-miR-15b-5p (SEQ ID NO.157), hsa-miR-16-5p
(SEQ ID NO.159), hsa-miR-92a-3p (SEQ ID NO.181), hsa-miR-484 (SEQ ID NO.179),
hsa-miR-151a-5p (SEQ ID NO.156), hsa-let-7f-5p (SEQ ID NO., hsa-let-7a-5p (SEQ
ID 5
NO.141), hsa-let-7c-5p (SEQ ID NO.143), hsa-let-7b-5p (SEQ ID NO.142), hsa-let-
7g-5p
(SEQ ID NO.146), hsa-let-7i-5p (SEQ ID NO.147), hsa-miR-185-5p (SEQ ID
NO.162),
hsa-miR-30d-5p (SEQ ID NO.172), hsa-miR-30b-5p (SEQ ID NO.170), hsa-miR-30c-5p
(SEQ ID NO.171), hsa-miR-19b-3p, hsa-miR-26a-5p (SEQ ID NO.168), hsa-miR-26b-
5p
(SEQ ID NO.169), hsa-miR-425-5p (SEQ ID NO.176), MID-19433 (SEQ ID NO.133),
and 10
hsa-miR-4306 (SEQ ID NO.177). Examples of white blood cell markers are hsa-miR-
342-3p
(SEQ ID NO.17-18), hsa-miR-146a-5p and hsa-miR-150-5p (SEQ ID NO.59). Examples
of
epithelial markers are hsa-miR-200c-3p (SEQ ID NO.23-24), hsa-miR-138-5p (SEQ
ID NO.19-
21), hsa-miR-3648 (SEQ ID NO.174), hsa-miR-125b-5p (SEQ ID NO.9), hsa-miR-125a-
5p
(SEQ ID NO.153), hsa-miR-192-3p (SEQ ID NO.164), hsa-miR-4324 (SEQ ID NO.178).
hsa- 15
miR-376a-3p (SEQ ID NO.175).
As referred to herein, said microRNA ratio is the ratio between the normalized
expression level of a pair of microRNAs, wherein the normalized expression
level of one
microRNA is used as the numerator and the normalized expression level of a
second microRNA
is the denominator. 20
In another particular embodiment, said determining an expression profile
comprises
contacting the sample with RT-PCR reagents, including forward and reverse
primers as
exemplified herein in the Examples, and generating RT-PCR products.
In a further particular embodiment, said method comprises contacting RT-PCR
products
with specific or general probes, or a combination thereof, as exemplified
herein in the Examples, 25
detecting and measuring the PCR products.
In a further embodiment, said determining an expression profile comprises
measuring
microRNA expression by hybridization, using microarrays and the like. In
another further
embodiment, said determining an expression profile comprises measuring
microRNA expression
by next-generation sequencing. 30
In another embodiment, said method comprises optionally further determining
the
expression profile of at least one microRNA to be used as normalizer. In one
embodiment, any
microRNA as described in Table 1 may be used as a normalizer. In one
particular embodiment,
32
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
any of the microRNAs comprising SEQ ID NO. 26-37, or a sequence at least 80%,
85%, 90%, or
95% identical thereto, are used as normalizers.
The present inventors have surprisingly found that the classification of a
thyroid tumor
sample is improved when a number of markers, from different categories as
defined and
exemplified herein are used. Said markers may be any one of malignant markers,
secondary 5
markers and cell-type markers, or any combination thereof, comprising SEQ ID
NOS:1-25, or a
sequence at least 80%, 85%, 90%, or 95% identical thereto. In order to perform
the method of
the invention, the full set of markers may be used. Alternatively, any
combination of malignant,
secondary and cell-type markers may be used. Thus, the method may comprise at
least one
malignant marker, in association with at least one secondary marker and/or at
least one cell-type 10
marker.
Depending on the analysis of the data, each of the cell type markers may be
used as in the
form of raw or normalized signals. Alternatively, the cell type markers may be
used as a
preliminary test prior to performing the classification, in order to determine
whether the sample
has sufficient relevant material to perform classification, or whether the
sample should be 15
discarded. Yet another option is to use the cell-type markers as part of the
final classifier, where
the signal of the cell type marker is used by the classifier. A further option
is to use the cell-type
markers as the denominator of a miR ratio optionally used by the classifier.
For example, the
expression level of a malignant or a secondary marker may be divided by the
expression level of
a cell-type marker, and the resulting miR ratio used in the classifier. 20
Thus, in a further embodiment of the method for distinguishing between
malignant and
benign thyroid tumor lesions in a subject in need, said classifier may be any
one of a single
classifier, a multi-step classifier, a classifier which uses all the malignant
markers, a classifier
which uses a subset of the malignant markers, a classifier which uses all the
malignant markers
and the secondary markers, a classifier which uses a subset of the malignant
markers and a 25
subset of the secondary markers, a classifier which uses all the malignant
markers and the
secondary markers and the cell type markers, a classifier which employs a
subset of all the
malignant markers and the secondary markers and the cell type markers, a
classifier which uses
all or a subset of the malignant markers and all or a subset of the cell type
markers.
In another further embodiment of the method or the protocol of the invention,
the 30
performance of the classification may be improved by further combining the
result from the
algorithm classifier with additional clinical or molecular data available for
the thyroid sample
being analyzed. Additional data available may be related to the thyroid
lesion, such as the size of
the nodule, the number of nodules; it may relate to other clinical information
available for the
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WO 2015/175660 PCT/US2015/030564
subject from whom the sample was obtained, such as molecular test results,
like the expression
of other molecular markers, genetic markers, biochemical test results, blood
test results, urine
test results, recurrence, prognosis data, family history, patient medical
history, and the like.
Other data that may also be combined is thyroid genetic data, such as mutation
analysis, gene
fusions, chromosomal rearrangements, gene expression, protein expression, and
the like. 5
Therapeutic indications may vary according to the diagnostic obtained with the
method
or protocol of the invention. Typically there are five types of therapy that
may be administered to
a thyroid cancer patient: surgery, radiation therapy, chemotherapy, thyroid
hormone therapy and
targeted therapy.
Surgery is the most common treatment of thyroid cancer. One of the following
10
procedures may be used:
- Lobectomy: Removal of the lobe in which thyroid cancer is found. Biopsies
of lymph
nodes in the area may be done to see if they contain cancer.
- Near-total thyroidectomy: Removal of all but a very small part of the
thyroid.
- Total
thyroidectomy: Removal of the whole thyroid. 15
- Iymphadenectomy: Removal of lymph nodes in the neck that contain cancer.
Thyroidectomy is a surgical procedure that has several potential complications
or sequela
including: temporary or permanent change in voice, temporary or permanently
low calcium, need
for lifelong thyroid hormone replacement, bleeding, infection, and the remote
possibility of
airway obstruction due to bilateral vocal cord paralysis. Therefore, accurate
diagnosis which 20
would prevent the unnecessary removal of the thyroid gland is very desirable.
Radiation therapy uses high-energy x-rays or other types of radiation to
eliminate
cancer cells or inhibit their proliferation. There are two types of radiation
therapy. External
radiation therapy uses a machine outside the body to send radiation toward the
cancer. Internal
radiation therapy uses a radioactive substance sealed in needles, seeds,
wires, or catheters that 25
are placed directly into or near the cancer. The radiation therapy of choice
will be dependent on
the type and stage of the thyroid cancer. Radiation therapy may be
supplementary to surgery in
order to eliminate cancer cells that were not successfully removed. Follicular
and papillary
thyroid cancers may be treated with radioactive iodine (RAI) therapy. RAI is
administered orally
and collects in any remaining thyroid tissue, including thyroid cancer cells
that have spread to 30
other places in the body. Since only thyroid tissue takes up iodine, the RAI
destroys thyroid
tissue and thyroid cancer cells without harming other tissues. Before a full
treatment dose of RAI
is given, a small test-dose is given to see if the tumor takes up the iodine.
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Chemotherapy is another option for thyroid cancer treatment. Chemotherapy may
be
administered orally or by injection, intravenous or intramuscular.
Chemotherapy may also be
administered directly into the cancer affected area instead of systemically.
The choice of
administration will depend on the type and stage of the cancer. A few examples
of drugs that
have been approved for thyroid cancer treatment are: Adriamycin PFS
(Doxorubicin 5
Hydrochloride), Adriamycin RDF (Doxorubicin Hydrochloride), Cabozantinib-S-
Malate,
Caprelsa (Vandetanib), Cometriq (Cabozantinib-S-Malate), Doxorubicin
Hydrochloride,
Nexavar (Sorafenib Tosylate), Sorafenib Tosylate and Vandetanib.
Thyroid hormone therapy is a cancer treatment that removes hormones or blocks
their
action and inhibits cancer cell proliferation. In the treatment of thyroid
cancer, drugs may be 10
given to prevent thyroid-stimulating hormone (TSH) production, in order to
avoid that the
hormone would induce the growth or recurrence of the thyroid cancer.
Also, because thyroid cancer treatment specifically targets thyroid cells, the
thyroid is not
able to make enough thyroid hormone. Patients are given thyroid hormone
replacement pills.
Targeted therapy uses drugs or other substances to identify and attack
specific cancer 15
cells without harming normal cells. Tyrosine kinase inhibitor (TKI) therapy
blocks signal
transduction in thyroid cancer cells, inhibiting their growth. Vandetanib is a
TK1 used to treat
thyroid cancer.
Dosage and duration of any therapy will depend on individual evaluation of the
patient
and on standard practice known by the health care provider. The duration of
treatment is the 20
period of time during which doses of a pharmaceutical agent or pharmaceutical
composition are
administered.
The identification and differentiation of the thyroid tumor, firstly as benign
or malignant,
and subsequently its classification into the various subtypes through the
analysis of differentially
expressed microRNAs can provide further clues to the biological differences
between the 25
subtypes, their diverging oncogenetic processes and possible new targets for
type-specific target
therapy.
The present invention provides diagnostic assays and methods, both
quantitative and
qualitative, for detecting, diagnosing, monitoring, staging and
prognosticating thyroid cancers by
comparing levels of the specific microRNA molecules as described herein. Such
levels are 30
measured in a patient sample, which may be from a biopsy, tumor samples,
cells, tissues and/or
bodily fluids.
Thus, the method of the invention is particularly useful for discriminating
between
different subtypes of malignant thyroid tumors, such types being follicular
carcinoma, papillary
CA 02945531 2016-10-11
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carcinoma, follicular variant of papillary carcinoma (FVPC or FVPTC),
encapsulated FVPC (or
encapsulated FVPTC), medullary carcinoma, anaplastic thyroid cancer, poorly
differentiated
thyroid cancer, and for determining the therapeutic course to be followed
after diagnosis. In a
further embodiment, the present invention provides a method for classifying
sub-types of benign
thyroid tumor, e.g. follicular adenoma, Hashimoto thyroiditis, hyperplasia
(Goiter). 5
The present invention also provides a method of treatment of thyroid cancer,
said method
comprising the method of distinguishing between benign or malignant thyroid
tumor as
described herein, optionally subtyping the thyroid tumor type, and
administering the treatment
according to the diagnosis provided by the present method.
All the methods of the present invention may optionally further include
measuring levels 10
of other cancer markers. Other cancer markers, in addition to said microRNA
molecules useful in
the present invention, will depend on the cancer being tested and are known to
those of skill in
the art.
Assay techniques that can be used to determine levels of gene expression, such
as the
nucleic acid sequence of the present invention, in a sample derived from a
patient are well 15
known to those of skill in the art. Such assay methods include, but are not
limited to,
radioimmunoassays, reverse transcriptase PCR (RT-PCR) assays,
immunohistochemistry assays,
in situ hybridization assays, competitive-binding assays, northern blot
analyses, ELISA assays,
nucleic acid microarrays and biochip analysis.
An arbitrary threshold on the expression level of one or more nucleic acid
sequences can 20
be set for assigning a sample or tumor sample to one of two groups.
Alternatively, in a preferred
embodiment, expression levels of one or more nucleic acid sequences of the
invention are
combined by taking ratios of expression levels of two nucleic acid sequences
and/or by a method
such as logistic regression to define a metric which is then compared to
previously measured
samples or to a threshold. The threshold for assignment is treated as a
parameter, which can be 25
used to quantify the confidence with which samples are assigned to each class.
The threshold for
assignment can be scaled to favor sensitivity or specificity, depending on the
clinical scenario.
The correlation value to the reference data generates a continuous score that
can be scaled and
provides diagnostic information on the likelihood that a samples belongs to a
certain class of
thyroid subtype. In multivariate analysis, the microRNA signature provides a
high level of 30
prognostic information.
The present invention also provides novel microRNA molecules, comprising
nucleic
acids denoted by SEQ ID NOS.27-29, 33, 34, 139, 140, 307 and 308. It is to be
understood, that
36
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
the cDNA, complement sequence, and anti-miR corresponding to any one of SEQ ID
NOS.27-
29, 33, 34, 139, 140, 307 and 308 are also encompassed by the present
invention.
Further, the present application provides compositions, formulations and
medicaments
comprising the microRNAs described herein. In one particular embodiment, the
present
invention provides compositions, formulations and medicaments comprising as an
active agent 5
the microRNA comprising any one of SEQ ID NOS.27-29, 33, 34, 139, 140, 307 and
308,
variants thereof, or a sequence at least 80%, at least 85%, or at least 90%
identical thereto. Said
compositions, formulations and medicaments may further optionally comprise any
one of
adjuvants, carriers. diluents and excipients. The microRNAs described herein
can be formulated
into compositions, formulations and medicaments by combination with
appropriate, 10
pharmaceutically acceptable carriers or diluents, and can be formulated into
preparations in solid,
semi-solid, liquid or gaseous forms, such as tablets, capsules, powders,
granules, ointments,
solutions, suppositories, injections, inhalants and aerosols. As such,
administration of the
microRNA or a pharmaceutical composition comprising thereof can be achieved in
various
ways, including oral, buccal, rectal, parenteral, intraperitoneal,
intradermal, transdermal, 15
intratracheal, etc.
In certain embodiments, pharmaceutical compositions of the present invention
comprise
one or more nucleic acids of the invention and one or more excipients. In
certain such
embodiments, excipients are selected from water, salt solutions, alcohol,
polyethylene glycols,
gelatin, lactose, amylase, magnesium stearate, talc, silicic acid, viscous
paraffin, 20
hydroxymethylcellulose and polyvinylpyffolidone.
In certain embodiments, a pharmaceutical composition of the present invention
is
prepared using known techniques, including, but not limited to mixing,
dissolving, granulating,
dragee-making, levigating, emulsifying, encapsulating, entrapping or
tabletting processes.
Methods for the preparation of pharmaceutical compositions may be found in the
literature, e.g. 25
in Gennaro, A. R. (2000) Remington: The Science and Practice of Pharmacy, 20th
ed.
In certain embodiments, a pharmaceutical composition of the present invention
is a liquid
(e.g., a suspension, elixir and/or solution). In certain of such embodiments,
a liquid
pharmaceutical composition is prepared using ingredients known in the art,
including, but not
limited to, water, glycols, oils, alcohols, flavoring agents, preservatives,
and coloring agents. 30
In certain embodiments, a pharmaceutical composition of the present invention
is a solid
(e.g., a powder, tablet, and/or capsule). In certain of such embodiments, a
solid pharmaceutical
composition comprising one or more nucleic acids of the invention is prepared
using ingredients
37
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
known in the art, including, but not limited to, starches, sugars, diluents,
granulating agents,
lubricants, binders, and disintegrating agents.
Further, the present application provides vectors and probes comprising the
compounds
(the nucleic acids) disclosed herein. In one particular embodiment, the
present application
provides vectors and probes comprising nucleic acids denoted by SEQ ID NOS.27-
29, 33, 34, 5
139, 140, 307 and 308, variants thereof or a sequence at least 80%, at least
85%, or at least 90%
identical thereto.
It is to be understood that the terminology used herein is for the purpose of
describing
particular embodiments only and it is not intended to be limiting. It must be
noted that, as used in
the specification and the appended claims, the singular forms "a," "an" and
"the" include plural 10
referents unless the context clearly dictates otherwise.
For the recitation of numeric ranges herein, each intervening number there
between with
the same degree of precision is explicitly contemplated. For example, for the
range of 6-9, the
numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-
7.0 for example,
the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9 and 7.0 are
explicitly contemplated. 15
As used herein, the term "aberrant proliferation" means cell proliferation
that deviates
from the normal, proper, or expected course. Aberrant cell proliferation may
include cell
proliferation whose characteristics are associated with an indication caused
by, mediated by, or
resulting in inappropriately high levels of cell division, inappropriately low
levels of apoptosis,
or both. Such indications may be characterized, for example, by single or
multiple local 20
abnormal proliferations of cells, groups of cells, or tissue(s), whether
cancerous or non-
cancerous, benign or malignant. Aberrant proliferation is one of the main
features of cancer.
As used herein, the term "about" refers to +/-10%.
"Attached" or "immobilized", as used herein to refer to a probe and a solid
support,
means that the binding between the probe and the solid support is sufficient
to be stable under 25
conditions of binding, washing, analysis, and removal. The binding may be
covalent or non-
covalent. Covalent bonds may be formed directly between the probe and the
solid support or
may be formed by a cross linker or by inclusion of a specific reactive group
on either the solid
support or the probe or both molecules. Non-covalent binding may be one or
more of
electrostatic, hydrophilic, and hydrophobic interactions. Included in non-
covalent binding is the 30
covalent attachment of a molecule, such as streptavidin, to the support and
the non-covalent
binding of a biotinylated probe to the streptavidin. Immobilization may also
involve a
combination of covalent and non-covalent interactions.
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"Biological sample" or "sample", as used herein, means a sample of biological
tissue or
fluid that comprises nucleic acids, microRNA in particular. Such samples
include, but are not
limited to, tissue or fluid isolated from subjects. Biological samples also
include sections of
tissues such as biopsy and autopsy samples, fine-needle aspiration (FNA)
samples, frozen
sections taken for histological purposes, blood, blood fraction, plasma,
serum, and the like. A 5
biological sample may be provided by removing a sample of cells from a
subject, but can also be
accomplished by using previously isolated cells (e.g., isolated by another
person, at another time,
and/or for another purpose), which may then be cultured or not. Archival
tissues, such as those
having treatment or outcome history, may also be used.
In another embodiment of the invention, the FNA biopsy is prepared as a smear.
10
The term "classification" refers to a procedure and/or algorithm in which
individual items
are placed into groups or classes based on quantitative information on one or
more characteristics
inherent in the items (referred to as traits, variables, characters, features,
etc.) and based on a
statistical model and/or a training set of previously labeled items.
As used herein, the term "classifying thyroid tumors" refers to the
identification of one or 15
more properties of a thyroid tissue sample (e.g., including but not limited
to, the presence of
microRNAs expressed in cancerous tissue, the presence of microRNAs expressed
in pre-
cancerous tissue that is likely to become cancerous, and the presence of
microRNAs expressed in
cancerous tissue that is likely to metastasize).
The term "classifier" as used herein refers to an algorithm used to classify,
distinguish or 20
identify thyroid tumors (or lesions) as benign or malignant, or to classify,
distinguish or identify
sub-types of thyroid tumor. Once the microRNA expression profile of the
samples of any study
cohort is acquired, for example from the training cohort, a database is
generated in which the
expression levels of all the microRNAs in the samples of the cohorts are
stored. This database is
also referred to as "the training data" and it is used to choose an optimal
algorithm for 25
classification. Nucleic acid (or microRNA) ratios, alone or in combination
with nucleic acid (or
microRNA) levels may also be used by the algorithm for the classification of
thyroid samples.
In one embodiment, the algorithm to be used in the method or protocol of the
invention is
a machine-learning algorithm. Examples of machine-learning algorithms are
discriminant
analysis, K-nearest neighbor classifier (KNN), Support Vector Machine (SVM)
classifierõ 30
logistic regression classifier, neural network classifier, Gaussian mixture
model (GMM), nearest
centroid classifier, linear regression classifier, decision tree classifier,
and random forest
classifier, ensemble of classifiers, or any combination thereof.
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When a discriminant analysis classifier is used, the discriminant may be any
one of a
linear, quadratic, a diagonal of the linear covariance matrix, diagonals of
the quadratic
covariance matrices, pseudoinverse of the linear covariance matrix, and
pseudoinverse of the
quadratic covariance matrices. When a KNN classifier is used, the k may be
altered and the
distance metric can be either Pearson correlation, spearman correlation,
Euclidean or cityblock 5
(Manhattan) distance. When a SVM classifier is used, the kernel may be linear,
Gaussian or
polynomial. When an ensemble method classifier is used, it usually applies
algorithms such as
classification trees, KNN or discriminate analysis classifiers. The ensembles
can be either
created using boosting or bagging algorithms and the number of ensemble
learning cycles can
range from two up to a few thousand. 10
As used herein, "confusion matrix" refers to a specific table layout that
allows
visualization of the performance of an algorithm, typically a supervised
learning one. A
"confusion matrix" may also be referred to as a contingency table or an error
matrix.
"Complement" or "complementary", as used herein to refer to a nucleic acid,
may mean
Watson-Crick (e.g., A-T/U and C-G) or Hoogsteen base pairing between
nucleotides or 15
nucleotide analogs of nucleic acid molecules. A full complement or fully
complementary means
100% complementary base pairing between nucleotides or nucleotide analogs of
nucleic acid
molecules. In some embodiments, the complementary sequence has a reverse
orientation (5'-3').
The present invention also provides the complement of the nucleic acids
denoted by SEQ ID
NOS. 7-29, 33, 34, 139, and 140. 20
As used herein, "CT signals" or "Cr" represent the first cycle of PCR where
amplification
crosses a threshold (cycle threshold) of fluorescence. Accordingly, low values
of CT represent
high abundance or expression levels of the microRNA. In some embodiments the
PCR CT signal
is normalized such that the normalized CT is inversed from the expression
level. In other
embodiments the PCR CT signal may be normalized and then inverted such that
low normalized- 25
inverted CT represents low abundance or expression levels of the microRNA.
As used herein, a "data processing routine" refers to a process that can be
embodied in
software that determines the biological significance of acquired data (i.e.,
the ultimate results of
an assay or analysis) with respect to one or more samples. For example, the
data processing
routine can make determination of whether a thyroid lesion from which a sample
was collected 30
or obtained is benign or malignant, or of a specific sub-type, based upon the
data collected. In
the systems and methods herein, the data processing routine can also control
the data collection
routine based upon the results determined. The data processing routine and the
data collection
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
routines can be integrated and provide feedback to operate the data
acquisition, and hence
provide assay-based judging methods.
"Detection" means detecting the presence of a component in a sample. Detection
also
means detecting the absence of a component. Detection also means determining
the level of a
component, either quantitatively or qualitatively. 5
"Differential expression" or a "difference in expression levels" means
qualitative or
quantitative differences in the microRNA expression patterns in thyroid
samples. Thus, a
differentially expressed microRNA may qualitatively have its expression
altered, including an
activation or inactivation, in, e.g., nonnal versus diseased thyroid tissue. A
qualitatively
regulated microRNA may exhibit an expression pattern within a thyroid sample
or cell type 10
which may be detectable by standard techniques. Some microRNAs may be
expressed in one
thyroid sample or cell type, and not in other, or expressed at different
levels between different
cell types or different samples. Thus, the difference in expression may be
quantitative, e.g., in
that expression is modulated, up-regulated, resulting in an increased amount
of microRNA, or
down-regulated, resulting in a decreased amount of microRNA. The degree to
which expression 15
differs needs only be large enough to quantify via standard characterization
techniques such as
expression arrays, next generation sequencing (NGS), quantitative reverse
transcriptase PCR,
northern blot analysis, real-time PCR, in situ hybridization and RNase
protection.
The term "expression profile" is used broadly to include a genomic expression
profile, as
well as an expression profile of microRNAs, for example. As used herein,
expression profile 20
means the set of data obtained for the nucleic acid (or microRNA) expression.
It may refer to the
raw data or to the normalized expression values. Expression profiles may be
generated by any
convenient means for determining a level of a nucleic acid sequence e.g.
quantitative
hybridization of microRNA, labeled microRNA, amplified microRNA, cDNA, etc.,
quantitative
PCR, and the like. Further to measuring nucleic acid sequence levels, the data
obtained may be 25
normalized ¨ normalization of data has been discussed somewhere else in this
application.
Expression profiles allow the analysis of differential gene expression between
two or more
samples, as well as between samples and thresholds. Further, classifiers may
be applied to
expression profiles in order to obtain information about the sample, such as
classification,
diagnosis, sub-typing of the sample, and the like. Nucleic acid sequences of
interest are nucleic 30
acid sequences that are found to be predictive, including the nucleic acid
sequences provided
herein in Table 1, where the expression profile may include expression data
for 5, 10, 20, 25, 50,
100 or more of, including all of the listed nucleic acid sequences. According
to some
embodiments, the term "expression profile" means measuring the abundance of
the nucleic acid
41
I I
CA 2945531 2017-04-18
sequences in the measured samples. In a specific embodiment, niicroRNA
expression profiles
are characterized in each thyroid sample.
"Expression ratio", as used herein, refers to relative expression levels of
two or more
nucleic acids, i.e. microRNAs, as determined by detecting the relative
expression levels of the
corresponding nucleic acids in a biological sample, such as a thyroid sample.
Since microRNA 5
expression levels are expressed as Grs, which are obtained in log scale, in
practice expression
ratios are obtained by subtraction of the CTS, rather than by division.
As used herein, "FDR" or "False Discovery Rate", is a statistical method used
in multiple
hypothesis testing to correct for multiple comparisons. When performing
multiple statistical
tests, for example in comparing the signal between two groups in multiple data
features, there is 10
an increasingly high probability of obtaining false positive results, by
random differences
between the groups that can reach levels that would otherwise be considered as
statistically
significant. In order to limit the proportion of such false discoveries,
statistical significance is
defined only for data features in which the differences reached a p-value (by
two-sided t-test)
below a threshold, which is dependent on the number of tests performed and the
distribution of 15
p-values obtained in these tests.
As used herein, "FNA" relates to "fine needle aspiration". Fine-needle
aspiration biopsy
(FNAB, FNA or NAB), or fine-needle aspiration cytology (FNAC), is a diagnostic
procedure
used to investigate superficial (just under the skin) lumps or masses, and it
is particularly useful
for thyroid lesion biopsies. A biopsy is collected by inserting a thin, hollow
needle into the mass 20
for sampling of cells that, after being stained, will be examined under a
microscope. There could
be cytology exam of aspirate (cell specimen evaluation, FNAC) or histological
(biopsy - tissue
specimen evaluation, FNAB). FNA is a popular biopsy method used for thyroid
nodules since a
major surgical (excisional or open) biopsy can be avoided by performing a
needle aspiration
biopsy instead. A detailed description of specimen collection and preparation
may be found in 25
"Atlas of Fine Needle Aspiration Cytology" by Henryk A. Domanski (2014). The
preparation of aspiration specimens has been well described in the art.
Usually, a suitable
amount of aspirate (usually about one drop) is spread thinly and evenly over a
microscopic slide which is then stained and mounted. FNA specimen prepared in
this
manner are also referred to as ''smear". The result should be compatible to a
sectioned 30
histological slide with regard to specimen thickness and evenness. Fixation of
FNA smears
is usually by air drying (generally referred to as "routine air dried FNAB")
or wet fixing
using either 95% ethanol or cyto-spray as fixative. Other suitable liquid
CA 2945531 2017-04-18
fixatives are methanol, acetone, isopropyl alcohol, acetone/methanol and the
like. Alternatively,
FNA samples may be added to or mixed with preservatives in a tube.
As referred to herein, a "follicular" lesion may be any one of follicular
adenoma (FA),
follicular carcinoma (FC) and follicular variant of papillary carcinoma
(FVPCA).
"Fragment" is used herein to indicate a non-full-length part of a nucleic
acid. Thus, a 5
fragment is itself also a nucleic acid.
"Groove binder" and/or "minor groove binder" (MOB), as used herein, may be
used
interchangeably and refer to small molecules that fit into the minor groove of
double-stranded
DNA, typically in a sequence-specific manner. Minor groove binders may be
long, flat
molecules that can adopt a crescent-like shape and thus, fit snugly into the
minor groove of a 10
double helix, often displacing water. Minor groove binding molecules may
typically comprise
several aromatic rings connected by bonds with torsional freedom such as
furan, benzene, or
pyrrole rings. Minor groove binders may be antibiotics such as netropsin,
distamycin, berenil,
pentamidine and other aromatic diamidines, Hoechst 33258, SN 6999, aureolic
anti-tumor drugs
such as chsomomycin and mithramycin, CC-1065, dihydrocyclopyrroloindole
tripeptide (DPI3), 15
1,2-dihydro-(31-1)-pyrrolo[3,2-e]indole-7-carboxylate (CDPI3), and related
compounds and
analogues, including those described in Nucleic Acids in Chemistry and
Biology, 2d ed.,
Blackburn and Gait, eds., Oxford University Press, 1996, and PCT Published
Application No.
WO 03/078450. A minor groove binder may be a component of a primer, a probe, a
hybridization tag complement, or combinations thereof. Minor groove binders
may 20
increase the Trn of the primer or a probe to which they are attached, allowing
such primers
or probes to effectively hybridize at higher temperatures.
"Identical" or "identity", as used herein in the context of two or more
nucleic acid
sequences, mean that the sequences have a specified percentage of residues
that are the same 25
over a specified region. The percentage may be calculated by optimally
aligning the two
sequences, comparing the two sequences over the specified region, determining
the number of
positions at which the identical residue occurs in both sequences to yield the
number of matched
positions, dividing the number of matched positions by the total number of
positions in the
specified region, and multiplying the result by 100 to yield the percentage of
sequence identity. 30
In cases where the two sequences are of different lengths or the alignment
produces one or more
staggered ends and the specified region of comparison includes only a single
sequence, the
residues of single sequence are included in the denominator but not the
numerator of the
calculation. When comparing DNA and RNA sequences, thymine (T) and uracil (U)
may be
43
CA 02945531 2016-10-11
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considered equivalent. Identity may be performed manually or by using a
computer sequence
algorithm such as BLAST, BLAST 2.0, and the like.
"In situ detection", as used herein, means the detection of expression or
expression levels
in the original site hereby meaning in a tissue sample such as biopsy.
"Label", as used herein, means a composition detectable by spectroscopic, 5
photochemical, biochemical, immunochemical, chemical, or other physical means.
The label
may be any entity that does not naturally occur in a protein or nucleic acid
and allows the nucleic
acid or protein to be detectable. For example, useful labels include 32P,
fluorescent dyes,
electron-dense reagents, enzymes, biotin, digoxigenin, or haptens and other
entities which can be
made detectable, and the like. A label may be incorporated into nucleic acids
and proteins at any 10
position.
"Logistic regression" is part of a category of statistical models called
generalized linear
models. Logistic regression allows one to predict a discrete outcome, such as
group membership,
from a set of variables that may be continuous, discrete, dichotomous, or a
mix of any of these.
The dependent or response variable can be dichotomous, for example, one of two
possible types 15
of cancer. Logistic regression models the natural log of the odds ratio, i.e.
the ratio of the
probability of belonging to the first group (P) over the probability of
belonging to the second
group (1¨P), as a linear combination of the different expression levels (in
log-space). The
logistic regression output can be used as a classifier by prescribing that a
case or sample will be
classified into the first type is P is greater than 0.5 or 50%. Alternatively,
the calculated 20
probability P can be used as a variable in other contexts such as a 1D or 2D
threshold classifier.
As used herein, the term "prior" refers to a probability for each class, e.g.,
given to the
different classes, and used by the likelihood that a sample is malignant or
benign, without any
additional knowledge regarding the expression profile of the sample in a
classification. Priors
may be set at different ratios, such as for example 80%-20% malignant-benign,
75%-25% 25
malignant-benign, 70%-30% malignant-benign, 65%-35% malignant-benign, 60%-40%
malignant-benign, 50%-50% malignant-benign (i.e., uniform). In addition,
priors may be
empirical, i.e., based on the distribution of the samples in training cohort.
Priors may be adjusted
in order to achieve a predetermined sensitivity or specificity.
As used herein, a "marker" is a microRNA, or a nucleic acid sequence, whose
presence 30
and abundance is measured in a sample. A "marker" further provides an
indication of the status
of the sample.
44
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WO 2015/175660 PCT/US2015/030564
As used herein, "malignant marker" is a microRNA, or a nucleic acid sequence
which is
present at higher levels in malignant samples versus benign samples. A
malignant marker may or
may not be present in test samples.
As used herein, "secondary marker" is a microRNA, or a nucleic acid sequence,
which is
used to differentiate between malignant and benign samples, and for which the
difference, or the 5
ratio, in the expression levels of said secondary marker in malignant and
benign samples is less
than the difference, or the ratio, in the expression levels of malignant
markers. A secondary
marker may or may not be present in test samples.
As used herein, "cell type marker" refers to a microRNA, or nucleic acid
sequence,
whose expression correlates with certain cell types. Said cell types may
generally be found in a 10
sample, e.g. blood cells, white blood cells, red blood cells, epithelial
cells, Hurthle cells,
mitochondrial-rich cells, lymphocytes, follicular cells, parafollicular cells
(C cells), metastatic
cells, immune cells, macrophages and the like. Other markers included as "cell
type markers"
may be species-specific markers, such as markers from bacteria, fungi, and the
like.
"Normalize'", as used herein, means a microRNA or a nucleic acid sequence
whose 15
signal (i.e., level of expression) is used in order to normalize each sample.
A normalizer may be
used alone (one microRNA as normalizer), or as part of a set of normalizers
(more than one
microRNA as normalizer, for example two, three, four, five, six, seven eight,
nine, ten eleven,
twelve, thirteen fourteen, sixteen or seventeen microRNAs may be used as
normalizers in a set).
As referred to herein, any microRNA detected in the sample may be used as a
normalizer. To 20
that effect, the microRNAs defined herein as "markers" may also be used as
"nollnalizers".
Essentially, any microRNA may be used as a normalizer. To that effect,
microRNAs denoted by
any one of SEQ ID NOs 1-182 may be used as normalizers. MicroRNAs denoted by
any one of
SEQ ID NOs. 1-37 may be used as normalizers. Particular examples of microRNAs
that may be
used as normalizers are hsa-miR-23a-3p, MID-20094, MID-50969, hsa-miR-345-5p,
hsa-miR- 25
3074-5p, MID-50976, MID-50971, hsa-miR-5701 and hsa-miR-574-3p.
"Normalization" of data values refers to mapping the original data range into
another
scale. Normalization may be done by subtracting the mean expression of the set
of normalizers,
subtracting the median expression of the set of normalizers, fitting the
expression values of the
normalizers to a reference set of values (using a polynomial fit) and applying
this fit to all 30
signals. All the normalizers, or a subset of the normalizers may be used.
"Nucleic acid" or "oligonucleotide" or "polynucleotide", as used herein, means
at least
two nucleotides covalently linked together. The depiction of a single strand
also defines the
sequence of the complementary strand. Thus, a nucleic acid also encompasses
the
CA 2945531 2017-04-18
complementary strand of a depicted single strand. Many variants of a nucleic
acid may be used
for the same purpose as a given nucleic acid. Thus, a nucleic acid also
encompasses substantially
identical nucleic acids and complements thereof. A single strand may provide a
probe that
hybridizes to a target sequence under stringent hybridization conditions.
Thus, a nucleic acid also
encompasses a probe that hybridizes under stringent hybridization conditions.
5
Nucleic acids may be single-stranded or double-stranded, or may contain
portions of both
double-stranded and single-stranded sequences. The nucleic acid may be DNA,
both genomic
and cDNA, RNA, or a hybrid, where the nucleic acid may contain combinations of
deoxyribo-
and ribo-nucleotides, and combinations of bases including uracil, adenine,
thymine, cytosine,
guanine, inosine, xanthine, hypoxanthine, isocytosine and isoguanine. Nucleic
acids may be 10
obtained by chemical synthesis methods or by recombinant methods.
A nucleic acid will generally contain phosphodiester bonds, although nucleic
acid
analogs may be included. The analog may include a non-naturally occurring
linkage, backbone,
or nucleotide. The analog may have at least one different linkage, e.g.,
phosphoramidate,
phosphorothioate, phosphorodithioate, or 0-methylphosphoroamidite linkages and
peptide 15
nucleic acid backbones and linkages. Other analog nucleic acids include those
with positive
backbones; non-ionic backbones, and non-ribose backbones, including those
described in US
5,235,033 and US 5,034,506. Nucleic acids containing one or more non-naturally
occurring
or modified nucleotides are also included within one definition of nucleic
acids. The
modified nucleotide analog may be located for example at the 5'-end and/or the
3'-end of 20
the nucleic acid molecule. Representative examples of nucleotide analogs may
be selected
from sugar- or backbone-modified ribonucleotides. It should be noted, however,
that also
nucleobase-modified ribonucleotides, i.e., ribonucleotides containing a non-
naturally
occurring nucleobase instead of a naturally occurring nucleobase such as
uridines or
cytidines modified at the 5-position, e.g., 5-(2-amino) propyl uridine, 5-
bromo uridine; 25
adenosines and guanosines modified at the 8-position, e.g., 8-bromo guanosine;
deaza
nucleotides, e.g., 7-deaza-adenosine; 0- and N-alkylated nucleotides, e.g., N6-
methyl
adenosine are suitable. The 2'-0H-group may be replaced by a group selected
from H, OR,
R, halo, SH, SR, NH2, NHR, NR2 or CN, wherein R is CI-C6 alkyl, alkenyl or
alkynyl and
halo is F, Cl, Br or I. Modified nucleotides also include nucleotides
conjugated with 30
cholesterol through, e.g., a hydroxyprolinol linkage as described in
Krutzfeldt et al. (Nature
2005; 438:685-689), Soutschek et al. (Nature 2004; 432:173-178), and WO
2005/079397.
Modifications of the ribose-phosphate backbone may be done for a variety of
reasons, e.g.,
to increase the stability and half-life of such molecules in physiological
environments, to
46
I I
CA 2945531 2017-04-18
enhance diffusion across cell membranes, or as probes on a biochip. The
backbone modification
may also enhance resistance to degradation, such as in the harsh endocytic
environment of cells.
The backbone modification may also reduce nucleic acid clearance by
hepatocytes, such as in the
liver and thyroid. Mixtures of naturally occurring nucleic acids and analogs
may be made.
Alternatively, mixtures of different nucleic acid analogs, and mixtures of
naturally occurring 5
nucleic acids and analogs may be made.
Thus, novel isolated nucleic acids are provided herein. The nucleic acids
provided herein
may be non-naturally occurring, synthesized nucleic acids. Thus, the nucleic
acid provided
herein may be a synthetic nucleic acid. Methods of synthesizing nucleic acids
are known to the
man skilled in the art, and are described, e.g., in US 7,579,451. The nucleic
acids may io
comprise at least one o f the sequences of SEQ ID NOS: 1-308 or a variant
thereof. In one
embodiment, the nucleic acids comprise at least one of the sequences of SEQ ID
NOS: 1-
= 182. The variant may be a complement of the referenced nucleotide
sequence. The variant
may be a nucleotide sequence that is 70%, 75%, 80%, 85%, 90% or 95% identical
to the
referenced nucleotide sequence or the complement thereof. The variant may be a
15
nucleotide sequence which hybridizes under stringent conditions to the
referenced
nucleotide sequence, complements thereof, or nucleotide sequences
substantially identical
thereto.
A nucleic acid as described herein may have a length of from about 10 to about
250
nucleotides. The nucleic acid may have a length of at least 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 60, 70, 80, 90,
100, 125, 150, 175, 200
or 250 nucleotides. The nucleic acid may be synthesized or expressed in a cell
(in vitro or in
vivo) using a synthetic gene. The nucleic acid may be synthesized as a single
strand molecule
and hybridized to a substantially complementary nucleic acid to form a duplex.
The nucleic acid
may be introduced to a cell, tissue or organ in a single- or double-stranded
form or capable of 25
being expressed by a synthetic gene using methods well known to those skilled
in the art,
including as described in US 6,506,559.
The nucleic acid may comprise a microRNA sequence shown in Table 1, or a
variant
thereof. In some instances, variants of the same microRNA are also provided in
Table 1. It is to 30
be noted that SEQ ID NOs.1-180 in Table I present the cDNA corresponding to
the sequence.of
the naturally occurring microRNA, i.e., the sequences present thymine (T)
instead of uracil (U).
It is to be understood that nucleic acid refers to deoxyribonucleotides,
ribonucleotides, or
modified nucleotides, and polymers thereof in single- or double-stranded form.
The term
47
CA 2945531 2017-04-18
encompasses nucleic acids containing known nucleotide analogs or modified
backbone residues
or linkages, which are synthetic, naturally occurring, and non-naturally
occurring, which have
similar binding properties as the reference nucleic acid, and which are
metabolized in a manner
similar to the reference nucleotides. Examples of such analogs include,
without limitation,
phosphorothioates, phosphoramidates, methyl phosphonates, chiral-methyl
phosphonates, 2-0- 5
methyl ribonucleotides, peptide-nucleic acids (PNAs) and unlocked nucleic
acids (UNAs; see,
e.g., Jensen et al. Nucleic Acids Symposium Series 52: 133-4), and derivatives
thereof.
Nucleotide is used as recognized in the art, to include those with natural
bases
(standard), and modified bases well known in the art. Such bases are generally
located at the
Pposition of a nucleotide sugar moiety. Nucleotides generally comprise a base,
sugar and 10
a phosphate group. The nucleotides can be unmodified or modified at the sugar,
phosphate
and/or base moiety, also referred to interchangeably as nucleotide analogs,
modified
nucleotides, non-natural nucleotides, non-standard nucleotides and other (see,
e.g.,
WO 92/07065; WO 93/15187. There are several examples of modified nucleic acid
bases
known in the art as summarized by Limbach, et al, Nucleic Acids Res. 22:2183,
1994. Some of 15
the non-limiting examples of base modifications that can be introduced into
nucleic acid
molecules include, hypoxanthine, purine, pyridin-4-one, pyridin-2-one, phenyl,
pseudouracil,
2,4,6-trimethoxy benzene, 3-methyl uracil, dihydrouridine, .naphthyl,
aminophenyl, 5-
alkylcytidines (e.g., 5-methylcytidine), 5-alkyluridines (e.g.,
ribothymidine), 5-halouridine (e.g.,
5-bromouridine) or 6-azapyrimidines or 6-alkylpyrimidines (e.g. 6-
methyluridine), propyne, and 20
others (Burgin, et al., Biochemistry 35:14090, 1996). By "modified bases" in
this aspect is meant
nucleotide bases other than adenine, guanine, cytosine and uracil at 1'
position or their
equivalents.
Modified nucleotide refers to a nucleotide that has one or more modifications
to the
nucleoside, the nucleobase, pentose ring, or phosphate group. Modifications
include those 25
naturally occurring that result from modification by enzymes that modify
nucleotides, such as
methyltransferases., Modified nucleotides also include synthetic or non-
naturally occurring
nucleotides. Synthetic or non-naturally occurring modifications in nucleotides
include those with
2 modifications, e.g., 2'-methoxyethoxy, 2'-fluoro, 2-ally], 2'-0[2-
(methylamino)-2-oxoethyl],
4'-thio, 4'-
(CH2) 2-0-2'-bridge, 2'-LNA or other bicyclic or "bridged" 30
nucleoside analog, and 2'-0-(N-methylcarbaniate) or those comprising base
analogs. In
connection with 2'-modified nucleotides as described for the present
disclosure, by "amino" is
meant 2'-NI-I2 or 2'-0-NH2, which can be modified or unmodified. Such modified
groups are
48
CA 2945531 2017-04-18
described, e.g., in US 5,672,695 and US 6,248,878. "Modified nucleotides" of
the instant
invention can also include nucleotide analogs as described above.
As used herein, "base analog" refers to a heterocyclic moiety which is located
at
the l' position of a nucleotide sugar moiety in a modified nucleotide that can
be incorporated
into a nucleic acid duplex (or the equivalent position in a nucleotide sugar
moiety 5
substitution that can be incorporated into a nucleic acid duplex). A base
analog may be
generally a purine or a pyrimidine base, excluding the common bases guanine
(G), cytosine
(C), adenine (A), thymine (T), and uracil (U). Base analogs can duplex with
other bases or
base analogs in dsRNAs. Base analogs include those useful in the compounds and
methods
of the invention, e.g., those disclosed in US 5,432,272, US 6,001,983 and US
7,579,451. Non- 10
limiting examples of bases include hypoxanthine (I), xanthine (X), 313-D-
ribofuranosyl-(2,6-diamiaopyrimidine) (K), 3-gamma-D-ribofuranosyl-(1-methyl-
pyrazolo[4,3-
d]pyrimidine-5,7(4H,6H)-dione) (P), iso-cytosine (iso-C), iso-guanine (iso-G),
1-gamma-D-
ribofuranosyl-(5-nitroindole), 1-gamma-D-ribofuranosyl-(3-nitropyrrole), 5-
bromouracil, 2-
aminopurine, 4-thio-dT, 7-(2-thieny1)-imidazo[4,5-1Apyridine (Ds) and pyrrole-
2-carbaldehyde 15
(Pa), 2-amino-6-(2-thienyl)purine (S), 2-oxopyridine (Y), difluorotolyl, 4-
fluoro-6-
methylbenzimidazole, 4-methylbenzimidazole, 3-methyl isocarbostyrilyl, 5-
methyl
isocarbostyrilyl, and 3-methyl-7-propynyl isocarbostyrilyl, 7-azaindolyl, 6-
methy1-7-azaindolyl,
imidizopyridinyl, 9-methyl-imidizopyridinyl, pyrrolopyrizinyl,
isocarbostyrilyl, 7-propynyl
isocarbostyrilyl, propyny1-7-azaindolyl, 2,4,5-trimethylphenyl, 4-
methylindolyl, 4,6- 20
dimethylindolyl, phenyl, napthalenyl, anthracenyl, phenanthracenyl, pyrenyl,
stilbenzyl,
tetracenyl, pentacenyl, and structural derivates thereof (Schweitzer et al.,
J. Org. Chem.,
59:7238-7242 (1994); Berger et al., Nucleic Acids Research, 28(15):2911-2914
(2000); Moran et
al., J. Am. Chem. Soc., 119:2056-2057 (1997); Morales et al., J. Am. Chem.
Soc., 121:2323-
2324 (1999); Guckian et al., J. Am. Chem. Soc., 118:8182-8183 (1996); Morales
et al., J. Am. 25
Chem. Soc., 122(6):1001-1007 (2000); McMinn et al., J. Am. Chem. Soc.,
121:11585-11586
(1999); Guckian et al., J. Org. Chem., 63:9652-9656 (1998); Moran et al.,
Proc. Natl. Acad. Sci.,
94:10506-10511 (1997); Das et al., J. Chem. Soc., Perkin Trans., 1:197-206
(2002); Shibata et
al., J. Chem. Soc., Perkin Trans., 1: 1605-1611 (2001); Wu et al., J. Am.
Chem. Soc.,
122(32):7621-7632 (2000); O'Neill et al., J. Org. Chem., 67:5869-5875 (2002);
Chaudhuri et al., 30
J. Am. Chem. Soc., 117:10434-10442 (1995); and U.S. Pat. No. 6,218,108.). Base
analogs may
also be a universal base.
"Universal base" refers to a heterocyclic moiety located at the l' position of
a nucleotide
sugar moiety in a modified nucleotide, or the equivalent position in a
nucleotide sugar moiety
49
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
substitution, that, when present in a nucleic acid duplex, can be positioned
opposite more than
one type of base without altering the double helical structure (e.g., the
structure of the phosphate
backbone). Additionally, the universal base does not destroy the ability of
the single stranded
nucleic acid in which it resides to duplex to a target nucleic acid.
Table 1: The microRNAs of the invention
miR name SEQ ID NO. Sequence
hsa-miR-222-3p 1 AGCTACATCTGGCTACTGGGT
AGCTACATCTGGCTACTGGGTCTC
hsa-miR-55 lb-3p 3 GACCCATACTTGGTTTCAGAGG
4 GCGACCCATACTTGGTTTCAG
hsa-miR-31-5p 5 AGGCAAGATGCTGGCATAGCT
6 AGGCAAGATGCTGGCATAGCTGT
7 GGCAAGATGCTGGCATAGCTG
hsa-miR-375 8 TTTGTTCGTTCGGCTCGCGTGA
hsa-miR-125b-5p 9 TCCCTGAGACCCTAACTTGTGA
hsa-miR-146b-5p 10 TGAGAACTGAATTCCATAGGCT
11 TGAGAACTGAATTCCATAGGCTGT
hsa-miR-152-3p 12 TCAGTGCATGACAGAACTTGG
13 TCAGTGCATGACAGAACTTGGG
hsa-miR-346 14 TGTCTGCCCGCATGCCTGCCTCT
hsa-miR-181c-5p 15 AACATTCAACCTGTCGGTGAGT
hsa-miR-424-3p 16 CAAAACGTGAGGCGCTGCTAT
hsa-miR-342-3p 17 TCTCACACAGAAATCGCACCCGT
18 TCTCACACAGAAATCGCACCCGTC
hsa-miR-138-5p 19 AGCTGGTGTTGTGAATC
20 AGCTGGTGTTGTGAATCAGGCCG
21 AGCTGGTGTTGTGAATCAGGCCGT
hsa-miR-486-5p 29 TCCTGTACTGAGCTGCCCCGAG
hsa-miR-200c-3p 23 TAATACTGCCGGGTAATGATGG
24 TAATACTGCCGGGTAATGATGGA
MID-16582 25 AGTGAAGCATTGGACTGTA
hsa-miR-23a-3p 26 ATCACATTGCCAGGGATTTCC
MID-20094 27 TAAGCCAGTTTCTGTCTGATA
28 TTTCTAAGCCAGTTTCTGTCTGATA
MID-50969 29 ATGACAGATTGACATGGACAATT
hsa-miR-345-5p 30 GCTGACTCCTAGTCCAGGGCTC
31 TGCTGACTCCTAGTCCAGGGC
hsa-miR-3074-5p 32 GTTCCTGCTGAACTGAGCCAG
MID-50976 33 CTGTCTGAGCGCCGCTC
MID-50971 34 ATACTCTGGTTTCTTTTC
hsa-miR-5701 35 TTATTGTCACGTTCTGATT
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hsa-miR-574-3p 36 CACGCTCATGCACACACCCAC
37 CACGCTCATGCACACACCCACA
hsa-miR-7-5p 38 TGGAAGACTAGTGATTTTGTTGT
hsa-miR-10a-5p 39 TACCCTGTAGATCCGAATTTGTG
hsa-miR-18a-5p 40 TAAGGTGCATCTAGTGCAGATAG
hsa-miR-21-3p 41 CAACACCAGTCGATGGGCTGT
hsa-miR-21-5p 47 TAGCTTATCAGACTGATGTTGA
hsa-miR-30e-5p 43 TGTAAACATCCTTGACTGGAAG
hsa-miR-31-3p 44 TGCTATGCCAACATATTGC CAT
hsa-miR-34a-5p 45 TGGCAGTGTCTTAGCTGGTTGTT
hsa-miR-92b-5p 46 AGGGACGGGACGCGGTGCAGTG
hsa-miR-96-5p 47 TTTGGCACTAGCACATTTTTGCT
hsa-miR-100-5p 48 AACCCGTAGATCCGAACTTGTG
hsa-miR-126-3p 49 TCGTACCGTGAGTAATAATGCG
hsa-miR-138-1-3p 50 GCTACTTCACAACACCAGGGCC
hsa-miR-140-3p 51 TACCACAGGGTAGAACCACGG
hsa-miR-141-3p 52 TAACACTGTCTGGTAAAGATGG
hsa-miR-142-3p 53 TGTAGTGTTTCCTACTTTATGGA
hsa-miR-142-5p 54 CATAAAGTAGAAAGCACTACT
hsa-miR-146b-3p 55 TGCCCTGTGGACTCAGTTCTGG
hsa-miR-146a-5p 56 TGAGAACTGAATTCCATGGGTT
hsa-miR-148a-3p 57 TCAGTGCACTACAGAACTTTGT
hsa-miR-150-3p 58 CTGGTACAGGCCTGGGGGACAG
hsa-miR-150-5p 59 TCTCCCAACCCTTGTACCAGTG
hsa-miR-155-5p 60 TTAATGCTAATCGTGATAGGGGT
hsa-miR-181 a-5p 61 AACATTCAACGCTGTCGGTGAGT
hsa-miR-181b-5p 67 AACATTCATTGCTGTCGGTGGGT
hsa-miR-182-5p 63 TTTGGCAATGGTAGAACTCACACT
hsa-miR-187-3p 64 TCGTGTCTTGTGTTGCAGCCGG
hsa-miR-193a-3p 65 AACTGGCCTACAAAGTCCCAGT
hsa-miR-195-5p 66 TAGCAGCACAGAAATATTGGC
hsa-miR-197-5p 67 CGGGTAGAGAGGGCAGTGGGAGG
hsa-miR-199 a-3p 68 ACAGTAGTCTGCACATTGGTTA
hsa-miR-200 a-3p 69 TAACACTGTCTGGTAACGATGTT
hsa-miR-200b-3p 70 TAATACTGCCTGGTAATGATGA
hsa-miR-199a-5p 71 CCCAGTGTTCAGACTACCTGTTC
hsa-miR-199b-5p 77 CCCAGTGTTTAGACTATCTGTTC
hsa-miR-205-5p 73 TCCTTCATTCCACCGGAGTCTG
hsa-miR-210-3p 74 CTGTGCGTGTGACAGCGGCTGA
hsa-miR-214-3p 75 ACAGCAGGCACAGACAGGCAGT
hsa-miR-221-3p 76 AGCTACATTGTCTGCTGGGTTTC
hsa-miR-221 -5p 77 ACCTGGCATACAATGTAGATTT
hsa-miR-223-3p 78 TGTCAGTTTGTCAAATACCCCA
hsa-miR-222-5p 79 CTCAGTAGCCAGTGTAGATCCT
hsa-miR-224-5p 80 CAAGTCACTAGTGGTTCCGTTTAG
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hsa-miR-342-5p 81 AGGGGTGCTATCTGTGATTGA
hsa-miR-429 82 TAATACTGTCTGGTAAAACCGT
hsa-miR-455 -3p 83 GCAGTCCATGGGCATATACAC
hsa-miR-483-5p 84 AAGACGGGAGGAAAGAAGGGAG
hsa-miR-487b-3p 85 AATCGTACAGGGTCATCCACTT
hsa-miR-497-5p 86 CAGCAGCACACTGTGGTTTGT
hsa-miR-513a-5p 87 TTCACAGGGAGGTGTCATTTAT
hsa-miR-542-5p 88 TCGGGGATCATCATGTCACGAGA
hsa-miR-625-5p 89 AGGGGGAAAGTTCTATAGTCC
hsa-miR-650 90 AGGAGGCAGCGCTCTCAGGAC
hsa-miR-658 91 GGCGGAGGGAAGTAGGTCCGTTGGT
hsa-miR-664b-5p 92 TGGGCTAAGGGAGATGATTGGGTA
hsa-miR-708-5p 93 AAGGAGCTTACAATCTAGCTGGG
hsa-miR-765 94 TGGAGGAGAAGGAAGGTGATG
hsa-miR-1229-5p 95 GTGGGTAGGGTTTGGGGGAGAGCG
hsa-miR-2392 96 TAGGATGGGGGTGAGAGGTG
hsa-miR-3141 97 GAGGGCGGGTGGAGGAGGA
hsa-miR-3162-5p 98 TTAGGGAGTAGAAGGGTGGGGAG
hsa-miR-3679-5p 99 TGAGGATATGGCAGGGAAGGGGA
hsa-miR-3687 100 CCCGGACAGGCGTTCGTGCGACGT
hsa-miR-3940-5p 101 GTGGGTTGGGGCGGGCTCTG
hsa-miR-4270 102 TCAGGGAGTCAGGGGAGGGC
hsa-miR-4284 103 GGGCTCACATCACCCCAT
hsa-miR-4443 104 TTGGAGGCGTGGGTTTT
hsa-miR-4447 105 GGTGGGGGCTGTTGTTT
hsa-miR-4448 106 GGCTCCTTGGTCTAGGGGTA
hsa-miR-4454 107 GGATCCGAGTCACGGCACCA
hsa-miR-4534 108 GGATGGAGGAGGGGTCT
hsa-miR-4538 109 GAGCTTGGATGAGCTGGGCTGA
hsa-miR-4539 110 GCTGAACTGGGCTGAGCTGGGC
hsa-miR-4689 111 TTGAGGAGACATGGTGGGGGCC
hsa-miR-4690-5p 112 GAGCAGGCGAGGCTGGGCTGAA
hsa-miR-4739 113 AAGGGAGGAGGAGCGGAGGGGCCCT
hsa-miR-5001-5p 114 AGGGCTGGACTCAGCGGCGGAGCT
hsa-miR-5100 115 TTCAGATCCCAGCGGTGCCTCT
hsa-miR-5684 116 AACTCTAGCCTGAGCAACAG
hsa-miR-5698 117 TGGGGGAGTGCAGTGATTGTGG
hsa-miR-5739 118 GCGGAGAGAGAATGGGGAGC
hsa-miR-6076 119 AGCATGACAGAGGAGAGGTGG
hsa-miR-6086 120 GGAGGTTGGGAAGGGCAGAG
hsa-miR-6127 121 TGAGGGAGTGGGTGGGAGG
MID-00078 122 AAGTGATTGGAGGTGGGTGGGG
M1D-00321 123 CCTGTCTGAGCGACGCT
MID-00387 124 GAGACTCTCCTGTGCAG
MID-00671 125 TGCAGATTGTGGGTGGGAGGAC
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MID-00672 126 TGCAGCTGGTGGAGTCTGGGGG
MID-00690 127 TGGAGAAGACTGGAGAGGGTAT
MID-15965 128 ACTACCCCAGGATGCCAGCATAGTT
MID-16318 129 AGCTGGTTTGATGGGGAGCCAT
MID-17144 130 CACTGATTATCGAGGCGATTCT
M1D-17866 131 CGCCTGTGAATAGTCACTGCAC
MID-18468 132 GACGTGAGGGGGTGCTACATAC
MID-19433 133 GGCTGGTCCGAAGGTAGTGAGTT
MID-19434 134 GGCTGGTCCGAGTGCAGTGGTGTTT
MID-23168 135 TGTCCAAAGTAAACGCCCTGACGCA
MID-23794 136 TTCCCGGCCAATGCATTA
MID-24496 137 TTTGGAGGGGCCGTGACAGATG
MID-24705 138 CTCCCACTGCTTCACTTGACTA
MD2-495 139 NGGGCCGAGGGAGCGAGAG1
MD2-437 140 AGUGCUUGGCUGAGGAGCU
hsa-let-7a-5p 141 TGAGGTAGTAGGTTGTATAGTT
hsa-let-7b-5p 142 TGAGGTAGTAGGTTGTGTGGTT
hsa-let-7c-5p 143 TGAGGTAGTAGGTTGTATGGTT
hsa-let-7d-5p 144 AGAGGTAGTAGGTTGCATAGTT
hsa-let-7f-5p 145 TGAGGTAGTAGATTGTATAGTT
hsa-let-7g-5p 146 TGAGGTAGTAGTTTGTACAGTT
hsa-let-7i-5p 147 TGAGGTAGTAGTTTGTGCTGTT
hsa-miR-103 a-2-5p 148 AGCTTCTTTACAGTGCTGCCTTG
hsa-miR-103a-3p 149 AGCAGCATTGTACAGGGCTATGA
hsa-miR-106a-5p 150 AAAAGTGCTTACAGTGCAGGTAGC
hsa-miR-106b-5p 151 TAAAGTGCTGACAGTGCAGAT
hsa-miR-107 152 AGCAGCATTGTACAGGGCTATCA
hsa-miR-125 a-5p 153 TCCCTGAGACCCTTTAACCTGTGA
hsa-miR-144-3p 154 TACAGTATAGATGATGTACT
hsa-miR-149-5p 155 TCTGGCTCCGTGTCTTCACTCCC
hsa-miR-151a-5p 156 TCGAGGAGCTCACAGTCTAGTA
hsa-miR-15b-5p 157 TAGCAGCACATCATGGTTTACA
hsa-miR-16-1-3p 158 CCAGTATTAACTGTGCTGCTGA
hsa-miR-16-5p 159 TAGCAGCACGTAAATATTGGCG
hsa-miR-17-3p 160 ACTGCAGTGAAGGCACTTGTAG
hsa-miR-17-5p 161 CAAAGTGCTTACAGTGCAGGTAGT
hsa-miR-185-5p 162 TGGAGAGAAAGGCAGTTCCTGA
hsa-miR-191-5p 163 CAACGGAATCCCAAAAGCAGCTG
hsa-miR-192-3p 164 CTGCCAATTCCATAGGTCACAG
hsa-miR-19b-3p 165 TGTGCAAATCCATGCAAAACTGA
hsa-miR-20a-5p 166 TAAAGTGCTTATAGTGCAGGTAG
hsa-miR-25-3p 167 cATTGCACTTGTCTCGGTCTGA
hsa-miR-26a-5p 168 TTCAAGTAATCCAGGATAGGCT
hsa-miR-26b-5p 169 TTCAAGTAATTCAGGATAGGT
hsa-miR-30b-5p 170 TGTAAACATCCTACACTCAGCT
53
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hsa-miR-30c-5p 171 TGTAAACATCCTACACTCTCAGC
hsa-miR-30d-5p 172 TGTAAACATCCCCGACTGGAAG
hsa-miR-320a 173 AAAAGCTGGGTTGAGAGGGCGAA
hsa-miR-3648 174 AGCCGCGGGGATCGCCGAGGG
hsa-miR-376a-3p 175 ATCATAGAGGAAAATCCACGT
hsa-miR-425-5p 176 AATGACACGATCACTCCCGTTGA
hsa-miR-4306 177 TGGAGAGAAAGGCAGTA
hsa-miR-4324 178 CCCTGAGACCCTAACCTTAA
hsa-miR-484 179 TCAGGCTCAGTCCCCTCCCGAT
hsa-miR-624-5p 180 TAGTACCAGTACCTTGTGTTCA
hsa-miR-92a-3p 181 TATTGCACTTGTCCCGGCCTGT
hsa-miR-93-5p 182 CAAAGTGCTGTTCGTGCAGGTAG
1 "N" may be any one of G, C, A. T/U.
miR name is the miRBase registry name (release 20), except for the miR names
represented by
MID- [numeral] or MD2-[numeral[.
MID-00078, MID-00321, MID-00387, MID-00671, MID-00672, MID-00690, MID-15965,
MID-16318, MID-17144, MID-17866, MID-18468, MID-19433, MID-19434, MID-23168, 5
MID-23794, MID-24496, MID-24705, MD2-495 and MD2-437 are putative microRNAs,
which
were predicted and/or cloned at Rosetta Genomics.
The nucleic acid may also comprise a miR hairpin sequence shown in Table 2, or
a variant
thereof. 10
Table 2: Hairpins of the microRNAs of the invention
Hairpin
miR name SEQ ID Hairpin Sequence
NO.
GTGGACCGGCTGGCCCCATCTGGAAGACTAGTGATTTTGTTGTTGTCT
hsa-mir-7 183 TACTGCGCTCAACAACAAATCCCAGTCTACCTAATGGTGCCAGCCATC
GC
GTCTTCTGTATATACCCTGTAGATCCGAATTTGTGTAAGGAATTTTGT
hsa-mir-10a 184
GGTCACAAATTCGTATCTAGGGGAATATGTAGTTGAC
GTTCTAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATCTACT
hsa-mir-18a 185
GCCCTAAGTGCTCCTTCTGGC
GTACCACCTTGTCGGGTAGCTTATCAGACTGATGTTGACTGTTGAATC
hsa-mir-21 186
TCATGGCAACACCAGTCGATGGGCTGTCTGACATTTTGGTAT
GGCCGGCTGGGGITCCTGGGGATGGGATTTGCTTCCTGTCACAAATCA
hsa-mir-23a 187
CATTGCCAGGGATTTCCAACCGACC
GGCAGTCTTTGCTACTGTAAACATCCTTGACTGGAAGCTGTAAGGTGT
hsa-mir-30e 188
TCAGAGGAGCTTTCAGTCGGATGTTTACAGCGGCAGGCTGCC
GGAGAGGAGGCAAGATGCTGGCATAGCTGTTGAACTGGGAACCTGCTA
hsa-mir-31 189
TGCCAACATATTGCCATCTTTCC
GTGAGTGTTTCTTTGGCAGTGTCTTAGCTGGTTGTTGTGAGCAATAGT
hsa-mir-34a 190
AAGGAAGCAATCAGCAAGTATACTGCCCTAGAAGTGCTGCAC
GGGGAGCGGGATCCCGGGCCCCGGGCGGGCGGGAGGGACGGGACGCGG
hsa-mir-92b 191 TGCAGTGTTGTTITTTCCCCCGCCAATATTGCACTCGTCCCGGCCTCC
GGCCCCCCCGGCCCCCCGGCCTCCCCGCTACCCC
hsa-mir-96 192 TCTGCTTGGCCGATITTGGCACTAGCACATTTTTGCTTGIGICTCTCC
54
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Hairpin
miR name SEQ ID Hairpin Sequence
NO.
GCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAAAGCAGG
GCCTGTTGCCACAAACCCGTAGATCCGAACTTGTGGTATTAGTCCGCA
hsa-mir-100 193
CAAGCTTGTATCTATAGGTATGTGTCTGTTAGGC
GCTGGCGACGGGACATTATTACTTTTGGTACGCGCTGTGACACTTCAA
hsa-mir-126 194
ACTCGTACCGTGAGTAATAATGCGCCGTCCACGGC
TGCGCTCCTCTCAGTCCCTGAGACCCTAACTTGTGATGTTTACCGTTT
hsa-mir-125b-1 195
AAATCCACGGGTTAGGCTCTTGGGAGCTGCGAGTCGTGCT
ACCAGACTTTTCCTAGTCCCTGAGACCCTAACTTGTGAGGTATTTTAG
hsa-mir-125b-2 196
TAACATCACAAGICAGGCTCTTGGGACCTAGGCGGAGGGGA
TGGTGTGGTGGGGCAGCTGGTGTTGTGAATCAGGCCGTTGCCAATCAG
197
AGAACGGCTACTICACAACACCAGGGCCACACCACACTA
hsa-mir-138-1 CCCTGGCATGGTGTGGTGGGGCAGCTGGTGTTGTGAATCAGGCCGTTG
198 CCAATCAGAGAACGGCTACTTCACAACACCAGGGCCACACCACACTAC
AGG
CGTTGCTGCAGCTGGTGTTGTGAATCAGGCCGACGAGCAGCGCATCCT
199
CTTACCCGGCTATTTCACGACACCAGGGTTGCATCA
hsa-mir-138-2 GAGGAAGCCGGCGGAGTTCTGGTATCGTTGCTGCAGCTGGTGTTGTGA
200 ATCAGGCCGACGAGCAGCGCATCCTCTTACCCGGCTATTICACGACAC
CAGGGTTGCATCATACCCATCCTCTCCAGGCGAGCCTC
GCGCCCTGTGTGTGICTCTCTCTGTGTCCTGCCAGTGGTTTTACCCTA
hsa-mir-140 201 TGGTAGGTTACGTCATGCTGTTCTACCACAGGGTAGAACCACGGACAG
GATACCGGGGCACCCTCTGCGT
GTCGGCCGGCCCIGGGTCCATCTTCCAGTACAGTGTTGGATGGTCTAA
hsa-mir-141 202 TTGTGAAGCTCCTAACACTGTCTGGTAAAGATGGCTCCCGGGTGGGTT
CTCTCGGC
ACAGTGCAGTCACCCATAAAGTAGAAAGCACTACTAACAGCACTGGAG
hsa-mir-142 203
GGTGTAGTGTTTCCTACTTTATGGATGAGTGTACTGT
CCTGGCACTGAGAAC T GAATTCCATAGGCTGTGAGCTCTAGCAATGCC
hsa-mir-146b 204
CTGTGGACTCAGTTCTGGTGCCCGG
GTATCCTCAGCTITGAGAACTGAATTCCATGGGTTGTGTCAGTGTCAG
hsa-mir-146a 205
ACCTCTGAAATTCAGTTCTTCAGCTGGGATAT
GGTCTTTTGAGGCAAAGTTCTGAGACACTCCGACTCTGAG TAT GATAG
hsa-mir-148a 206
AAGTCAGTGCACTACAGAACTTTGTCTCTAGAGGCT
TCCCCATGGCCCIGICTCCCAACCCTTGTACCAGTGCTGGGCTCAGAC
hsa-mir-150 207
CCTGGTACAGGCCTGGGGGACAGGGACCTGGGGA
GTCCCCCCCGGCCCAGGTTCTGTGATACACTCCGACTCGGGCTCTGGA
208
GCAGTCAGTGCATGACAGAACTTGGGCCCGGAAGGAC
hsa-mir-152
TGTCCCCCCCGGCCCAGGTTCTGTGATACACTCCGACTCGGGCTCTGG
209
AGCAGTCAGTGCATGACAGAACTTGGGCCCGGAAGGACC
TAGGCTGTATGCTGITAATGCTAATCGTGATAGGGGTTTITGCCTCCA
hsa-mir-155 210
ACTGACTCCTACATATTAGCATTAACAGTGTATGATGCCTG
GGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTTTGGAATTAAAA
hsa-mir-181a 211
TCAAAACCATCGAC C GTTGATTGTACCCTATGGCTAACC
GGTCACAATCAACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACA
hsa-mir-181b 212
AGCTCACTGAACAATGAATGCAACTGTGGCC
CGGAAAATTTGCCAAGGGTTTGGGGGAACATTCAACCTGTCGGTGAGT
hsa-mir-181c 213 TTGGGCAGCTCAGGCAAACCATCGACCGTTGAGTGGACCCTGAGGCCT
GGAATTGCCATCCT
CCTCCCCCCGTTITTGGCAATGGTAGAACTCACACTGGTGAGGTAACA
hsa-mir-182 214
GGATCCGGTGGTTCTAGACTTGCCAACTATGGGGCGAGG
CCTCGGGCTACAACACAGGACCCGGGCGCTGCTCTGACCCCTCGTGTC
hsa-mir-187 215
TTGTGTTGCAGCCGGAGG
CA 02945531 2016-10-11
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Hairpin
miR name SEQ ID Hairpin Sequence
NO.
GGGAGCTGAGGGCTGGGTCTTTGCGGGCGAGATGAGGGTGTCGGATCA
hsa-mir-193a 216
ACTGGCCTACAAAGTCCCAGTTCTCGGCCCC
CCTGGCTCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTGCC
hsa-mir-195 217
AATATTGGCTGTGCTGCTCCAGG
TGTGCTCTGGGGGCTGTGCCGGGTAGAGAGGGCAGTGGGAGGTAAGAG
hsa-mir-197 218
CTCTTCACCCTTCACCACCTTCTCCACCCAGCATGGCCGGCACA
GGCCCCGCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTCTCAAT
hsa-mir-199a 219
CTGTACAGTACTCTCCACATTGGTTAGGCTGGGCT
GAGCATCTTACCGGACAGTGCTGGATTTCCCAGCTTGACICTAACACT
hsa-mir-200a 220
GTCTGGTAACGATGTTC
GCTCGGGCAGCCGTGGCCATCTTACTGGGCAGCATTGGATGGAGTCAG
hsa-mir-200b 221
GTCTCTAATACTGCCTGGTAATGATGACGGCGGAGCCCTGC
GGGCGGGGGCCCTCGTCTTACCCAGCAGTGTTTGGGTGCGGTTGGGAG
222
TCTCTAATACTGCCGGGTAATGATGGAGGCCCCTGTCC
hsa-mir-200c
CCCTCGTCTTACCCAGCAGTGTTTGGGTGCGGTTGGGAGICTCTAATA
723
CTGCCGGGTAATGATGGAGG
GGCCCCGCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTCTCAAT
hsa-mir-199a 774
GTGTACAGTAGTCTGCACATTGGTTAGGCTGGGCT
GTCTACCCAGTGITTAGACTATCTGTTCAGGACTCCCAAATIGTACAG
hsa-mir-199b 225
TAGTCTGCACATTGGTTAGGC
TCCATGTGCTTCTCTTGTCCTTCATTCCACCGGAGTCTGICTCATACC
hsa-mir-205 226
CAACCAGATTTCACTCGAGTGAAGTTCAGGAGGCATGGA
CCAGGCGCAGGGCAGCCCCTGCCCACCGCACACTGCGCTGCCCCAGAC
hsa-mir-210 227
CCACTGTGCGTGTGACAGCGGCTGATCTGTGCCTGG
GGCTGGACAGAGTTGTCATGTGTCTGCCTGTCTACACTTGCTGTGCAG
hsa-mir-214 928 AACATCCGCTCACCIGTACAGCAGGCACAGACAGGCAGTCACATGACA
ACCCAGCC
GAACATCCAGGTCTGGGGCATGAACCTGGCATACAATGTAGATTTCTG
hsa-mir-221 229 TGTTCGTTAGGCAACAGCTACATTGTCTGCTGGGTTTCAGGCTACCTG
GAAACATGTTC
CAGCTGCTGGAAGGIGTAGGTACCCTCAATGGCTCAGTAGCCAGTGTA
230 GATCCTGTCTTTCGTAATCAGCAGCTACATCTGGCTACTGGGTCTCTG
ATGGCATCTTCTAGCTTCTG
hsa-mir-222
GCTGCTGGAAGGIGTAGGTACCCTCAATGGCTCAGTAGCCAGTGTAGA
231 TCCTGTCTTTCGTAATCAGCAGCTACATCTGGCTACTGGGTCTCTGAT
GGCATCTTCTAGCT
GCTCTTGGCCTGGCCTCCTGCAGTGCCACGCTCCGTGTATTTGACAAG
hsa-mir-223 212 CTGAGTTGGACACTCCATGTGGTAGAGTGTCAGTTTGTCAAATACCCC
AAGTGCGGCACATGCTTACCAGCTCTAGGCCAGGGC
GGGGCTTTCAAGTCACTAGTGGTTCCGTTTAGTAGATGATTGTGCATT
hsa-mir-224 233
GTTTCAAAATGGTGCCCTAGTGACTACAAAGCCCC
GTGAAACTGGGCTCAAGGTGAGGGGTGCTATCTGTGATTGAGGGACAT
234 GGTTAATGGAATTGICTCACACAGAAATCGCACCCGTCACCITGGCCT
ACTTATCAC
hsa-mir-342
GAAACTGGGCTCAAGGTGAGGGGTGCTATCTGTGATTGAGGGACATGG
235 TTAATGGAATTGICTCACACAGAAATCGCACCCGTCACCITGGCCTAC
TTA
ACCCAAACCCTAGGICTGCTGACTCCTAGTCCAGGGCTCGTGATGGCT
hsa-mir-345 236 GGTGGGCCCTGAACGAGGGGTCTGGAGGCCTGGGTTTGAATATCGACA
GC
GGTCTCTGTGTTGGGCGTCTGTCTGCCCGCATGCCTGCCTCTCTGTTG
hsa-mir-346 237
CTCTGAAGGAGGCAGGGGCTGGGCCTGCAGCTGCCTGGGCAGAGCGG
56
CA 02945531 2016-10-11
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Hairpin
miR name SEQ ID Hairpin Sequence
NO.
238 CGCTCCCGCCCCGCGACGAGCCCCTCGCACAAACCGGACCTGAGCGTT
TTGTTCGTTCGGCTCGCGTGAGGCAGGGGCG
hsa-mir-375
CCCCGCGACGAGCCCCTCGCACAAACCGGACCTGAGCGTITTGTTCGT
239
TCGGCTCGCGTGAGGC
CGAGGGGATACAGCAGCAATTCATGTTTTGAAGTGTTCTAAATGGTTC
hsa-mir-424 240 AAAACGTGAGGCGCTGCTATACCCCCTCGTGGGGAAGGTAGAAGGTGG
GG
GATGGGCGTCTTACCAGACATGGTTAGACCTGGCCCTCTGTCTAATAC
hsa-mir-429 241
TGTCTGGTAAAACCGTCCATC
GGCGTGAGGGTATGTGCCTTTGGACTACATCGTGGAAGCCAGCACCAT
hsa-mir-455 '242
GCAGTCCATGGGCATATACACTTGCCTCAAGGCC
ACCCCAAGGTGGAGCCCCCAGCGACCTTCCCCTTCCAGCTGAGCATTG
CTGTGGGGGAGAGGGGGAAGACGGGAGGAAAGAAGGGAGIGGTTCCAT
hsa-mir-483 243 CACGCCTCCTCACTCCTCTCCTCCCGTCTTCTCCTCTCCTGCCCTTGT
CTCCCTGTCTCAGCAGCTCCAGGGGTGGTGTGGGCCCCTCCAGCCTCC
TAGGTGGT
GTGCTAACCTTTGGTACTTGGAGAGTGGTTATCCCTGTCCTGTTCGTT
hsa-mir-487b 244 TTGCTCATGTCGAATCGTACAGGGTCATCCACTTTTTCAGTATCAAGA
GC CC
CTGATCTCCATCCTCCCTGGGGCATCCTGTACTGAGCTGCCCCGAGGC
245 CCTTCATGCTGCCCAGCTCGGGGCAGCTCAGTACAGGATACTCGGGGT
hsa-mir-486 GGGAGTCAGCAGGAGGTGAG
246 GCATCCTGTACTGAGCTGCCCCGAGGCCCTTCATGCTGCCCAGCTCGG
GGCAGCTCAGTACAGGATAC
TCCTGTACTGAGCTGCCCCGAGCTGGGCAGCATGAAGGGCCTCGGGGC
hsa-mir-486-2 247
AGCTCAGTACAGGATG
CGGTCCTGCTCCCGCCCCAGCAGCACACTGTGGTTTGTACGGCACTGT
hsa-mir-497 248 GGCCACGTCCAAACCACACTGTGGTGTTAGAGCGAGGGTGGGGGAGGC
AC CG
GGGATGCCACATTCAGCCATTCAGCGTACAGTGCCTTTCACAGGGAGG
hsa-mir-513a 249 TGTCATTTATGTGAACTAAAATATAAATTTCACCTTTCTGAGAAGGGT
AATGTACAGCATGCACTGCATATGTGGTGTCCC
GGATGCACAGATCTCAGACATCTCGGGGATCATCATGTCACGAGATAC
hsa-mir-542 250 CAGTGTGCACTTGTGACAGATTGATAACTGAAAGGTCTGGGAGCCACT
CATCT
TGCCAGATGTGCTCTCCTGGCCCATGAAATCAAGCGTGGGTGAGACCT
251 GGTGCAGAACGGGAAGGCGACCCATACTTGGTTTCAGAGGCTGTGAGA
hsa-mir-551b ATAACTGCA
252 AGATGTGCTCTCCTGGCCCATGAAATCAAGCGTGGGTGAGACCTGGTG
CAGAACGGGAAGGCGACCCATACTTGGTTTCAGAGGCTGTGAGAATAA
GGGACCTGCGTGGGTGCGGGCGTGTGAGTGTGTGTGTGTGAGTGTGTG
hsa-mir-574 253
TCGCTCCGGGTCCACGCTCATGCACACACCCACACGCCCACACTCAGG
TGGTAAGGGTAGAGGGATGAGGGGGAAAGTTCTATAGTCCTGTAATTA
hsa-mir-625 '254
GATCTCAGGACTATAGAACTTTCCCCCTCATCCCTCTGCCCICTACCA
TCTCAGGAGGCAGCGCTCTCAGGACGTCACCACCATGGCCTGGGCTCT
hsa-mir-650 255
GCTCCTCCTCA
57
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Hairpin
miR name SEQ ID Hairpin Sequence
NO.
CTCGGTTGCCGTGGITGCGGGCCCTGCCCGCCCGCCAGCTCGCTGACA
hsa-mir-658 256 GCACGACTCAGGGCGGAGGGAAGTAGGTCCGTTGGTCGGICGGGAACG
AG
GTTCAGTCCAGGGCAGCTTCCCTGTTCTGTTAATTAAACTTIGGGACA
TTAAAATGGGCTAAGGGAGATGATTGGGTAGAAAGTATTATTCTATTC
hsa-mir-664b 257
ATTTGCCTCCCAGCCTACAAAAATGCCTGCTTGGGGTCTAATACTTCA
ACGGTTAAAGATGCCTGGAAGAGGGC
GGTAACTGCCCTCAAGGAGCTTACAATCTAGCTGGGGGTAAATGACTT
hsa-mir-708 258
GCACATGAACACAACTAGACTGTGAGCTTCTAGAGGGCAGGGACC
TTAGGCGCTGATGAAAGTGGAGTTCAGTAGACAGCCCTTITCAAGCCC
hsa-mir-765 259 TACGAGAAACTGGGGTTTCTGGAGGAGAAGGAAGGTGATGAAGGATCT
GTTCTCGTGAGCCTGA
GTGGGTAGGGTTIGGGGGAGAGCGTGGGCTGGGGTTCAGGGACACCCT
hsa-mir-1229 260
CTCACCACTGCCCTCCCACAG
TGGTCCCTCCCAATCCAGCCATTCCTCAGACCAGGTGGCTCCCGAGCC
hsa-mir-2392 261
ACCCCAGGCTGTAGGATGGGGGTGAGAGGTGCTA
GCTCGACTCCTGTTCCTGCTGAACTGAGCCAGTGTGTAAAATGAGAAC
hsa-mir-3074 262
TGATATCAGCTCAGTAGGCACCGGAGGGCGGGT
CCCGGTGAGGGCGGGTGGAGGAGGAGGGTCCCCACCATCAGCCTTCAC
hsa-mir-3141 263
TGGGACGGG
AAGTTAATTTTGAAGCTGACTTTTTTAGGGAGTAGAAGGGTGGGGAGC
hsa-mir-3162 264 ATGAACAATGTTTCTCACTCCCTACCCCTCCACTCCCCAAAAAAGTCA
GCTTCTCTTGTTAACTT
GGCCCCACGTGGTGAGGATATGGCAGGGAAGGGGAGTTTCCCTCTATT
hsa-mir-3679 265
CCCTTCCCCCCAGTAATCTTCATCATGCGGTGTC
GCGCGTGCGCCCGAGCGCGGCCCGGTGGTCCCTCCCGGACAGGCGTTC
hsa-mir-3687 266
GTGCGACGTGT
GAGGAAAAGATCGAGGTGGGTTGGGGCGGGCTCTGGGGATTTGGTCTC
hsa-mir-3940 267
ACAGCCCGGATCCCAGCCCACTTACCTTGGTTACTCTCCIT
CAAATAGCTTCAGGGAGTCAGGGGAGGGCAGAAATAGATGGCCTTCCC
hsa-mir-4270 268
CTGCTGGGAAGAAAGTG
TTCTGTGAGGGGCTCACATCACCCCATCAAAGTGGGGACICATGGGGA
hsa-mir-4284 269
GAGGGGGTAGTTAGGAGCTTTGATAGAG
GGTGGGGGTTGGAGGCGTGGGTTTTAGAACCTATCCCTTTCTAGCCCT
hsa-mir-4443 270
GAGCA
GTTCTAGAGCATGGITTCTCATCATTTGCACTACTGATACTIGGGGTC
hsa-mir-4447 271
AGATAATTGTTTGTGGTGGGGGCTGTTGTTTGCATTGTAGGAT
GGAGTGACCAAAAGACAAGAGTGCGAGCCTTCTATTATGCCCAGACAG
hsa-mir-4448 272
GGCCACCAGAGGGCTCCTTGGTCTAGGGGTAATGCC
CCGGATCCGAGTCACGGCACCAAATTTCATGCGTGTCCGTGIGAAGAG
hsa-mir-4454 273
ACCACCA
GTGAATGACCCCCTICCAGAGCCAAAATCACCAGGGATGGAGGAGGGG
hsa-mir-4534 274
TCTTGGGTAC
AACTGGGCTGGGCTGAACTGGGCTGGGCTGAGCTGAGCTTGGATGAGC
hsa-mir-4538 275 TGGGCTGAACTGGGCTGGGTTGAGCTGGGCTGGGCTGAGTTGAGCCAG
GCTGATCTGGGCTGAGCCGAGCTGGGTTAAGCCGAGCTGGGIT
GGCTGGGCTGGGCTGGGCTCTGCTGTGCTGTGCTGAACAGGGCTGAGC
TGAACTGAGCTGAGCTGGGCTGAGCTGGGCTCTGCTGTGCTGTGCTGA
hsa-mir-4539 276 GCAGGGCTGAGCTGAACTGGGCTGAGCTGGGCTGAGCTGGGCTGAGTT
GAGCAGAGCTGGGTTGAGCAGAGCTGGGCTGGGCTGGGCTGAGTTGAG
CC
hsa-mir-4689 277 CGGTTTCTCCTTGAGGAGACATGGTGGGGGCCGGTCAGGCAGCCCATG
58
CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
Hairpin
miR name SEQ ID Hairpin Sequence
NO.
CCATGTGTCCTCATGGAGAGGCCG
GGCAGGTGAGCAGGCGAGGCTGGGCTGAACCCGTGGGTGAGGAGTGCA
hsa-mir-4690 278
GCCCAGCTGAGGCCICTGCTGTCTTATCTGTC
GTGGGCAGGGGAGGAAGAAGGGAGGAGGAGCGGAGGGGCCCITGTCTT
hsa-mir-4739 279
CCCAGAGCCTCTCCCTTCCTCCCCTCCCCCTCCCTCTGCTCAT
GGGCGGCTGCGCAGAGGGCTGGACTCAGCGGCGGAGCTGGCTGCTGGC
hsa-mir-5001 280
CTCAGTTCTGCCTCTGTCCAGGTCCTTGTGACCCGCCC
CTGGGGGTAGGAGCGTGGCTTCTGGAGCTAGACCACATGGGITCAGAT
hsa-mir-5100 281
CCCAGCGGTGCCICTAACTG
GAGCTATGATTGIGTAGCTGAACTCTAGCCTGAGCAACAGAGTGAGAT
hsa-mir-5684 282 GGTCTTGTTTTGTTGCCCAGGCTGGAGTCCAGTGTCAAGATCATGGCT
GAGCTCCAAATCTGTGCACCTGGGGGAGTGCAGTGATTGIGGAATGCA
hsa-mir-5698 283
AAGTCCCACAATCACTGTACTCCCCAGGTGCACAGATTCICICTC
GATTGGACTTTATTGTCACGTTCTGATTGGTTAGCCTAAGACTTGTTC
hsa-mir-5701-1 284
TGATCCAATCAGAACATGAAAATAACGTCCAATC
GATTGGACTTTATTGTCACGTTCTGATTGGTTAGCCTAAGACTTGTTC
hsa-mir-5701-2 285
TGATCCAATCAGAACATGAAAATAACGTCCAATC
TTGGCTATAACTATCATTTCCAAGGTTGTGCTTTTAGGAAATGTTGGC
hsa-mir-5739 286
TGTCCTGCGGAGAGAGAATGGGGAGCCAG
AGCATGACAGAGGAGAGGTGGAGGTAGGCGAGAGTAATATAATTTCTC
hsa-mir-6076 287 CAGGAGAACATCTGAGAGGGGAAGTTGCTTTCCTGCCCTGGCCCTTTC
ACCCTCCTGAGTTTGGG
AGGAGGTTGGGAAGGGCAGAGATGAGCATAAAGTTTTTGCCITGTTTT
hsa-mir-6086 288
TCTTTTT
AAGATGAGGGAGIGGGTGGGAGGTGGGAAGGCTGCCCCAAATGGCCTC
hsa-mir-6127 289
TAACATCCCTTCCAGTCTCCTCCTCCTCCTCCTCCTTCTICTT
TATGTACCCGGAGCCAAAAGTGATTGGAGGTGGGTGGGGITAATGAAT
MID-00078 290 AGACAAGTGTTAAAACTAAAAGTCACGTCTCTCTCTCCTICCTCCTCA
GTTTTGGCTTGATTTTTCATG
CTTACCTAGAAATTGTTGCCTGTCTGAGCGACGCTTCAAACTCAGCTT
MID-00321 291
CAGCAGGTCTGCAGGGACATCAGGTAGG
GTGTCTCTGTGTTTGCAGGTGTCCAGTGTGAGGTGCAGCTGGTGGAGT
MID-00387 292 CTGGGGGAGGCTTGGTACAGCCTGGGGGATCCCTGAGACTCTCCTGTG
CAGCCTCTGGATTCACCTTCAGTAACAGTGACAT
GTCAGCCTGCAATTAGTGAAATGGAGGCACACATGCTGGITTGCAGAT
MID-00671 293
TGTGGGTGGGAGGAC
GTGTCTCTGTGTITGCAGGTGTCCAGTGTGAGGTGCAGCTGGTGGAGT
MID-00672 294 CTGGGGGAGGCTTGGTACAGCCTGGGGGATCCCTGAGACTCTCCTGTG
CAGCCTCTGGATTCACCTTCAGTAACAGTGACAT
GGCCTTGGATGGAGAAGACTGGAGAGGGTATGGAAGTGCTTGGACGTA
MID-00690 295
GGACATCTGCCTCTCTGGTCTTTGTCCATCCCACAGGGCC
AGCTGGTTGGCATTCTGGCCCTGGTTCATGCCAACTCTTGTGTTGACT
MID-15965 296
ACCCCAGGATGCCAGCATAGTTG
CTGCCAAAGAGCAGCAAGATGAGCTGGTTTGATGGGGAGCCATCCCTT
MID-16318 297 GATGAGGAGAACCCTTCCCACTCTCACTCAGCCTCACCCAGCTGCCCT
GAGGCAG
GCTCAGAAGTGATGAATTGATCAGATAGACGAGGCCGGGCTTGTCCCC
MID-17144 298
GGCCACTGATTATCGAGGCGATTCTGATCTGGGC
GCTGGGTGCAGTAGCTTATGTCTGTAGTCCCAGCTACTTGGGAGGCTG
M1D-17866 299 AGGTGGGAGGATCACCTGAGGTCAGGAGTTTGGGTCTGCCGTGAGCTG
TGATTGCGCCTGTGAATAGTCACTGCACTCCAGC
59
I I
CA 2945531 2017-04-18
Hairpin
miR name SEQ ID Hairpin Sequence
NO.
MID 1 GACGTGAGGGGGTGCTACATACAGCAGCTGTGTGTAGTATGTGCCTTT
- 8468 300
CTCTGTT
TAGGAATTCTGGACCAGGCTTAAAAGACTGGGATGAGGCTGGTCCGAA
MID-19433 301 GGTAGTGAGTTATCTCCATTGATAGTTCAGTCTGTAACAGATCAAACT
CCTTGTTCTACTCTTTTTTTTTTTTTTAGACAGA
TGGGCTGGTCCGAGTGCAGTGGTGTTTACAAGTATTTGATTATAACTA
MID-19434 302 GTTACAGATTTCTTTGTTTCCTTCTCCACTCCCACTGCCTCACTTGAC
TGGCCTA
MID 23168 GCTCTGTCCAAAGTAAACGCCCTGACGCACTGTGGGAAGGGTGAGATG
- 303
GGCACCGC
MID 237N GTGAGTGGGAGGGGGGCTGCAGCCCAAAGAGGCAACAAAGGCCCTTCC
- 304
CGGCCAATGCATTAC
TGTCCTCAGGCCTGCTACTGATCCTGCAGCCAGAAGTTCCAGAAAGTG
MID 24496 3 AAGGGATTTGGAGGGGCCGTGACAGATGCAGGTGCCCTCAACATCCTT
- 05
GCCCTGTCACCCCCTGCCCAGAATTTGCTACTTAAATGGTACTTCTCT
GAAGAAGATGAGGAGGAAGGGGACA
ACAGAATTCCTCTTCTCCCTICTCCTATAACCTGTITTATTTAATTAA
MID-24705 306 TTAATTTITTAGGCTAGTCAAGTGAAGCAGTGGGAGTGGAAGGAACAA
AGAAATCTGT
MD2-495 307 UGAGCUCUGCGGCGCCAAGGGACCGAGGGGCCGAGGGAGCGAGAG
MD2 437 308 AGUGCUUGGCUGAGGAGCUGGGGCCAAGGGGGAACACAAAUAUGGUCC
-
UGACCCUACAUUCCCAGCCCUGCCUCU
It is to be noted that SEQ ID NOs.183-306 in Table 2 present the cllNA
corresponding to
the sequence of the naturally occurring pre-miR, i.e., the sequences present
thymine (T) instead
of uracil (U).
The nucleic acid may be in the form of a nucleic acid complex, and may further
comprise 5
one or more of the following: a peptide, a protein, a RNA-DNA hybrid, an
antibody, an antibody
fragment, a Fab fragment, or an aptarner.
The nucleic acid may comprise a sequence of a pri-miRNA or a variant thereof.
The pri-
microRNA sequence may comprise from 45-30,000, 50-25,000, 100-20,000, 1,000-
1,500 or 80-
100 nucleotides. The sequence of the pri-miRNA may comprise a pre-miRNA, miRNA
and 10
iiiiRNA*, as set forth herein, and variants thereof. The sequence of the pri-
miRNA may
comprise any of the sequences of SEQ ID NOS: 183-308 or variants thereof.
The pri-miRNA may comprise a hairpin structure. The hairpin may comprise a
first and a
second nucleic acid sequence that are substantially complimentary. The first
and second nucleic
acid sequence may be from 37-50 nucleotides. The first and second nucleic acid
sequence may 15
be separated by a third sequence of from 8-12 nucleotides. The hairpin
structure may have a free
energy of less than -25 Kcal/mole as calculated by the Vienna algorithm with
default parameters,
I I
CA 2945531 2017-04-18
as described in Hofacker et al. (Monatshefte f. Chemie 1994: 125:167-188). The
hairpin
may comprise a terminal loop of 4-20, 8-12 or 10 nucleotides. The pri-miRNA
may
comprise at least 19% adenosine nucleotides, at least 16% cytosine
nucleotides, at least
23% thymine nucleotides and at least 19% guanine nucleotides.
The nucleic acid may also comprise a sequence of a pre-miRNA or a variant
thereof. The 5
pre-miRNA sequence may comprise from 45-90, 60-80 or 60-70 nucleotides. The
sequence of
the pre-miRNA may comprise a miRNA and a iniRNA* as set forth herein. The
sequence of the
pre-miRNA may also be that of a pri-miRNA excluding from 0-160 nucleotides
from the 5' and
3' ends of the pri-miRNA. The sequence of the pre-miRNA may comprise the
sequence of SEQ
ID NOS: 183-308 or variants thereof. 10
As described herein, the nucleic acid may be at least 70%, 75%, 80%, 85%, 90%,
95%,
97%, 98% or 99% identical to the nucleic acid sequences in Tables 1 or 2 (with
increments of
1% from 80 to 99%), over a region of 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23,
24, 25, 30, 35, 40, 45, 50 or more nucleotides.
The nucleic acid may also comprise a sequence of a microRNA (including a
miRNA*) or 15
a variant thereof, including those putative microRNAs represented by MID-
[numeral]. As
referred to herein, microRNAs include those miRs which have been listed in the
miRBase
registry name (release 20), as well as putative microRNAs which have been
predicted and/or
cloned by Rosetta Genomics and which are represented by MID-[numera11. The
microRNA
sequence may comprise from 13-33, 18-24 or 21-23 nucleotides. The microRNA may
also 20
comprise a total of at least 5, 67, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or 40 nucleotides.
The sequence of the
microRNA may be the first 13-33 nucleotides of the pre-miRNA. The sequence of
the
microRNA may also be the last 13-33 nucleotides of the pre-miRNA. The sequence
of the
microRNA may comprise the sequence of any one of SEQ ID NOS: 1-182 or a
variant thereof. 25
The present invention employs microRNAs for the identification, classification
and diagnosis of
thyroid nodules.
"Variant", as used herein referring to a nucleic acid, means (i) a portion of
a referenced
nucleotide sequence; (ii) the complement of a referenced nucleotide sequence
or portion thereof;
(iii) a nucleic acid that differs from the referenced nucleotide sequence by a
point-mutation or 30
the complement thereof; (iv) a naturally-occurring variant of the referenced
nucleotide sequence
present in the general population or the complement thereof; or (iv) a nucleic
acid that hybridizes
under stringent conditions to the referenced nucleic acid, of the complement
thereof.
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"Probe", as used herein, means an oligonucleotide capable of binding to a
target nucleic
acid of complementary sequence through one or more types of chemical bonds,
usually through
complementary base pairing, usually through hydrogen bond formation. Probes
may bind target
sequences lacking complete complementarity with the probe sequence depending
upon the
stringency of the hybridization conditions. For example, for hybridization
assays, the probe may 5
be complementary to at least 8, at least 9, at least 10, at least 11, at least
12, at least 13, at least
14, at least 15, at least 16, at least 17, at least 18, at least 19, at least
20 contiguous nucleotides of
the sequence of the microRNA being detected. Alternatively, for PCR assays,
the probe may be
complementary to at least 8, at least 9, at least 10, at least 11, at least
12, at least 13, at least 14,
at least 15, at least 16, at least 17, at least 18, at least 19, at least 20
contiguous nucleotides of the 10
sequence of the PCR product being detected.
Thus, a probe may be complementary to, or may hybridize to at least 60%, 65%,
70%,
75%, 80%, 85%, 90%, 95%, 97%, 98% or 99% of its target nucleic acid.
A probe may be single-stranded or partially single- and partially double-
stranded. The
strandedness of the probe is dictated by the structure, composition and
properties of the target 15
sequence. Probes may include a label, an attachment, or a nucleotide sequence
that does not
naturally occur in a nucleic acid described herein. Probes may be directly
labeled or indirectly
labeled such as with biotin to which a streptavidin complex may bind.
"Probe- may be an agent for detecting the nucleic acid sequences described
herein. Probe
may be a labeled nucleic acid probe capable of hybridizing to a portion of the
nucleic acid 20
sequence of the invention, or amplification products derived therefrom. In
some embodiments,
the nucleic acid probe is reverse complementary nucleic acid molecule of the
nucleic acid
sequence disclosed herein. A probe may be a nucleic acid sequence which
sufficiently
specifically hybridizes under stringent conditions to the nucleic acid
disclosed herein. A probe is
optionally labeled with a fluorescent molecule such as a fluorescein, e.g. 6-
carboxyfluorescein 25
(FAM), an indocarbocyanine, e.g. QUASAR-670 (QUA), a hexafluorocine, such as 6-
carboxyhexafluorescein (HEX), or other fluorophore molecules and optionally a
quencher. A
quencher is appreciated to be matched to a fluorophore. Illustrative examples
of a quencher
include the black hole quenchers BHQ1, and BHQ2, or minor groove binders
(MGB), e.g.
dihydrocyclopyffoloindole tripeptide. Other fluorophores and quenchers are
known in the art and 30
are similarly operable herein.
Thus, the present invention also provides a probe, said probe comprising the
novel
nucleic acid sequences described herein, defined by any one of SEQ ID NOs. 27-
29, 33, 34, 139,
140, 307 and 308, or variants thereof. Probes may be used for screening and
diagnostic methods.
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The probe may be attached or immobilized to a solid substrate, such as a
biochip. The probe may
have a length of from 8 to 500, 10 to 100 or 20 to 60 nucleotides. The probe
may have a length
of at least 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 35,
40, 45, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280
or 300 nucleotides.
The probe may further comprise a linker sequence of from 10-60 nucleotides.The
probe may 5
further comprise a linker. The linker may comprise a sequence that does not
occur naturally in a
nucleic acid described herein. The linker may be 10-60 nucleotides in length.
The linker may be
20-27 nucleotides in length. The linker may be of sufficient length to allow
the probe to be a
total length of 45-60 nucleotides. The linker may not be capable of forming a
stable secondary
structure, or may not be capable of folding on itself, or may not be capable
of folding on a non- 10
linker portion of a nucleic acid contained in the probe. The sequence of the
linker is
heterogenous, and it may not appear in the genome of the animal from which the
probe non-
linker nucleic acid is derived.
As used herein, the term "reference value" means a value that statistically
correlates to a
particular outcome when compared to an assay result. In one embodiment, the
reference value is 15
determined from statistical analysis of studies that compare microRNA
expression with known
clinical outcomes. In another embodiment, the reference value may vary
according to the
classifier (i.e. the algorithm) used. Hence, the reference value may be the
expression levels (or
values) of all the microRNAs in the training data. The reference value may be
one or more
thresholds established by the classifier. The reference value may further be a
coefficient or set of 20
coefficients. Essentially the reference value refers to any parameter needed
or used by the
algorithm.
"Sensitivity", as used herein, may mean a statistical measure of how well a
binary
classification test correctly identifies a condition, for example, how
frequently it correctly
classifies a cancer into the correct type out of two possible types. The
sensitivity for class A is 25
the proportion of cases that are determined to belong to class "A" by the test
out of the cases that
are in class "A", as determined by some absolute or gold standard.
"Sensitivity", as used herein, may mean a statistical measure of how well a
classification
test correctly identifies a condition or conditions, for example, how
frequently it correctly
classifies a cancer into the correct type out of two or more possible types.
The sensitivity for 30
class A is the proportion of cases that are determined to belong to class "A"
by the test out of the
cases that are in class "A", as determined by some absolute or gold standard.
"Smear", as used herein, refers to a sample of thyroid tissue spread thinly on
a
microscope slide for examination, typically for medical diagnosis. Smears from
FNAs usually
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WO 2015/175660 PCT/US2015/030564
have very small amounts of cells, which results in small amounts of RNA, which
may range
from 1-1000 ng. 1-100 ng, 1-50 ng, 1-40 ng, accordingly. Smears may be stained
with any stain
known to the man skilled in the art of cytology, histology or pathology, such
as any stain used to
differentiate cells in pathologic specimens. Examples of stains are
multichromatic stains, like
Papanicolaou, which are a combination of nuclear stain and cytoplasm stain;
cellular structure 5
stains such as Wright, Giemsa, Romanowsky and the like; nuclear stains, such
as Hoescht stains
and the like; cell viability stains, such as Trypan blue, and the like, enzyme
activity, such as
benzidine for HRP to folln visible precipitate and the like.
"Specificity", as used herein, may mean a statistical measure of how well a
binary
classification test correctly identifies cases that do not have a specific
condition, for example, 10
how frequently it correctly classifies a sample as non-cancer when indeed it
is a non-cancerous
sample. The specificity for class A is the proportion of cases that are
determined to belong to
class "not A" by the test out of the cases that are in class "not A", as
determined by some
absolute or gold standard.
"Specificity", as used herein, may mean a statistical measure of how well a
classification 15
test correctly identifies cases that do not have a specific condition. The
specificity for class A is
the proportion of cases that are determined by the test not to belong to class
A out of the cases
that are not in class A, as determined by some absolute or gold standard.
As used herein, the term "stage of cancer" refers to a numerical measurement
of the level
of advancement of a cancer. Criteria used to determine the stage of a cancer
include, but are not 20
limited to, the size of the tumor, whether the tumor has spread to other parts
of the body and
where the cancer has spread (e.g., within the same organ or region of the body
or to another
organ).
"Stringent hybridization conditions", as used herein, mean conditions under
which a first
nucleic acid sequence (e.g., probe) will hybridize to a second nucleic acid
sequence (e.g., target), 25
such as in a complex mixture of nucleic acids. Stringent conditions are
sequence-dependent and
will be different in different circumstances. Stringent conditions may be
selected to be about 5-
C lower than the thermal melting point (T.) for the specific sequence at a
defined ionic
strength pH. The T. may be the temperature (under defined ionic strength, pH
and nucleic
concentration) at which 50% of the probes complementary to the target
hybridize to the target 30
sequence at equilibrium (as the target sequences are present in excess, at T.,
50% of the probes
are occupied at equilibrium). Stringent conditions may be those in which the
salt concentration is
less than about 1.0 M sodium ion, such as about 0.01-1.0 M sodium ion
concentration (or other
salts) at pH 7.0 to 8.3 and the temperature is at least about 30 C for short
probes (e.g., about 10-
64
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CA 2945531 2017-04-18
50 nucleotides) and at least about 60 C for long probes (e.g., greater than
about 50 nucleotides).
Stringent conditions may also be achieved with the addition of destabilizing
agents such as
forniamide. For selective or specific hybridization, a positive signal may be
at least 2 to 10 times
background hybridization. Exemplary stringent hybridization conditions include
the following:
50% fomiamide, 5x SSC, and 1% SDS, incubating at 42 C, or, 5x SSC, 1% SDS,
incubating at 5
65 C, with wash in 0.2x SSC, and 0.1% SDS at 65 C, DMSO, 6X SSPE + 0.005% N-
Lauroylsarcosine +0.005% Triton X-102, 0.06X SSPE + 0.005% N-Lauroylsarcosine
+0.005%
TritonTm X-102.
As used herein, the term "subject" refers to a mammal, including both human
and other
mammals. The methods of the present invention are preferably applied to human
subjects. 10
As used herein, the term "subtype of cancer" refers to different types of
cancer that affect
the same organ (e.g., papillary, follicular carcinoma and follicular variant
papillary carcinoma of
the thyroid).
"Thyroid lesion" as used herein, may mean a thyroid tumor, including sub-types
of
thyroid tumors, such as Hashimoto disease, follicular carcinoma, papillary
carcinoma, follicular 15
variant of papillary carcinoma (FVPC or FVPTC), encapsulated FVPC (or
encapsulated
FVPTC), non-encapsulated (infiltrative/diffuse) FVPC or FVPTC, medullary
carcinoma,
anaplastic thyroid cancer, or poorly differentiated thyroid cancer.
As used herein, the phrase "threshold expression profile" refers to a
criterion expression
profile to which measured values are compared in order to classify a tumor.
20
As used herein, a tissue sample is tissue obtained from a tissue biopsy using
methods
well known to those of ordinary skill in the related medical arts. The phrase
"suspected of being
cancerous", as used herein, means a cancer tissue sample believed by one of
ordinary skill in the
medical arts to contain cancerous cells. Methods for obtaining the sample from
the biopsy
include gross apportioning of a mass, microdissection, laser-based
microdissection, or other art- 25
known cell-separation methods.
"Tumor", as used herein, refers to all neoplastic cell growth and
proliferation, whether
malignant or benign, and all pre-cancerous and cancerous cells and tissues.
The cytological
classification of the thyroid lesions or tumor samples used herein is based on
"The Bethesda
System for Reporting Thyroid Cytopathology", the "BSRTC" (Syed, Z. Ali and
Edmund S. 30
Cibas, eds.; DO! 10.1007/978-0-387-87666-5_1; Springer Science+Business Media,
LLC 2010).
The BSRTC recommends that each thyroid FNA report be accompanied by a general
diagnostic
category, in which each category has an implied cancer risk.
CA 02945531 2016-10-11
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Recommended nomenclature for the Bethesda categories are as follows:
Non-diagnostic or Unsatisfactory
Cyst fluid only
Virtually acellular specimen
Other (obscuring blood, clotting artifact. etc.) 5
Benign
Consistent with a benign follicular nodule (includes adenomatoid nodule,
colloid nodule,
etc.)
Consistent with lymphocytic (Hashimoto) thyroiditis in the proper clinical
context
Consistent with granulomatous (subacute) thyroiditis 10
Other
Atypia of Undetermined Significance or Follicular Lesion of IJndetermined
Significance
IV. Follicular Neoplasm or suspicious of a Follicular Neoplasm
Specific in Hurthle cell (oncocytic) type
V. Suspicious for Malignancy 15
Suspicious for papillary carcinoma
Suspicious for medullary carcinoma
Suspicious for metastatic carcinoma
Suspicious for lymphoma
Other 20
VI. Malignant
Papillary thyroid carcinoma
Poorly differentiated carcinoma
Medullary thyroid carcinoma
Undifferentiated (anaplastic) carcinoma 25
Squamous cell carcinoma
Carcinoma with mixed features
Metastatic carcinoma
Non-Hodgkin lymphoma
Other 30
As used herein, "Indeterminate" refers to thyroid lesions or tumor samples
examined for
cytology and classified according to the Bethesda classification in categories
III, IV and V.
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The present invention further provides a method for identifying subtypes of
thyroid
lesions in a subject, said subtypes of thyroid lesions being said subtypes of
malignant or benign
thyroid tumor. subtype is any one of follicular carcinoma, papillary
carcinoma, follicular variant
of papillary carcinoma (FVPC or FVPTC), encapsulated FVPC (or encapsulated
FVPTC), non-
encapsulated FVPC (or non-encapsulated FVPTC), medullary carcinoma, anaplastic
thyroid 5
cancer or poorly differentiated thyroid cancer.
In another further embodiment, said subtype is any one of Hashimoto
thyroiditis,
follicular adenoma or hyperplasia.
In another further embodiment, said subtype is Hurthle cell carcinoma.
In another aspect, the present invention provides a method for distinguishing
between 10
follicular adenoma and follicular carcinoma.
In another further aspect, the present invention provides a method for
distinguishing
follicular adenoma from papillary carcinoma.
In another further aspect, the present invention provides a method for
distinguishing
follicular adenoma from follicular variant of papillary carcinoma. 15
In another further aspect, the present invention provides a method for
distinguishing non-
encapsulated follicular variant of papillary carcinoma from benign lesions.
In another further aspect the present invention provides a method for
distinguishing
papillary carcinoma and Hashimoto thyroiditis.
"Vector" refers to any known vector such as a plasinid vector, a phage vector,
a 20
phagemid vector, a cosmid vector, or a virus vector. The nucleic acid
described herein may be
comprised in a vector. The vector may be used for delivery of the nucleic
acid. The vector
preferably contains at least a promoter that enhances expression of the
nucleic acid carried, and
in this case the nucleic acid is preferably operably linked to such a
promoter. The vector may or
may not be replicable in a host cell, and the transcription of a gene may be
carried out either 25
outside the nucleus or within the nucleus of a host cell. In the latter case,
the nucleic acid may be
incorporated into the genome of a host cell. A vector may be a DNA or RNA
vector. A vector
may be either a self-replicating extrachromosomal vector or a vector that
integrates into a host
genome.
In one embodiment of the method or protocol of the invention, the levels of
microRNAs 30
are measured by reverse transcription polymerase chain reaction (RT-PCR).
Target sequences of
a cDNA are generated by reverse transcription of a target RNA, which may be a
nucleic acid
described herein (comprising a sequence provided in Tables 1 and 2). Known
methods for
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CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
generating cDNA involve reverse transcribing either polyadenylated RNA or
alternatively, RNA
with a ligated adaptor sequence.
RNA may be ligated to an adaptor sequence prior to reverse transcription. A
ligation
reaction may be performed by T4 RNA ligase to ligate an adaptor sequence at
the 3' end of the
RNA. Reverse transcription (RT) reaction may then be performed using a primer
comprising a 5
sequence that is complementary to the 3' end of the adaptor sequence.
Alternatively, polyadenylated RNA may be used in a reverse transcription (RT)
reaction
using a poly(T) primer comprising a 5' adaptor sequence. The poly(T) sequence
may comprise 8,
9, 10, 11, 12, 13, or 14 consecutive thymines.
The reverse transcript of the RNA may then be amplified by real-time PCR,
using a 10
specific forward primer comprising at least 15 nucleic acids complementary to
the target nucleic
acid and a 5' tail sequence; a reverse primer that is complementary to the 3'
end of the adaptor
sequence; and a probe comprising at least 8 nucleic acids complementary to the
target nucleic
acid. The probe may be partially complementary to the 5' end of the adaptor
sequence.
The amplification of the reverse transcripts of the target nucleic acids
(microRNAs, 15
including herein described putative microRNAs) may be by PCR or the like. The
first cycles of
the PCR reaction may have an annealing temperature of 56 C, 57 C, 58 C, 59 C,
or 60 C. The
first cycles may comprise 1-10 cycles. The remaining cycles of the PCR
reaction may be 60 C.
The remaining cycles may comprise 2-40 cycles.
The PCR reaction comprises a forward primer. In one embodiment, the forward
primer 20
may comprise 15, 16, 17, 18, 19, 20, or 21 nucleotides identical to the target
nucleic acid. The 3'
end of the forward primer may be sensitive to differences in sequence between
a target nucleic
acid and highly similar sequences.
The forward primer may also comprise a 5' overhanging tail. The 5' tail may
increase the
melting temperature of the forward primer. The sequence of the 5' tail may
comprise a sequence 25
that is non-identical to the target nucleic acid. The sequence of the 5' tail
may also be synthetic.
The 5' tail may comprise 8, 9, 10, 11, 12, 13, 14, 15, or 16 nucleotides.
Examples of forward
primers used in the invention are provided in Table 8.
The PCR reaction comprises a reverse primer. The reverse primer may be
complementary
to a target nucleic acid. The reverse primer may also comprise a sequence
complementary to an 30
adaptor sequence. Examples of reverse primers used in the invention are
provided in Example 8.
The probes used to detect products of RT-PCR amplification may be general
probes or
sequence-specific probes. General probes are designed to detect (or hybridize
with) RT-PCR
amplification products in a non-sequence specific manner. Said probes arc
between 16 and 20
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CA 02945531 2016-10-11
WO 2015/175660 PCT/US2015/030564
nucleotides long. preferably 18 nucleotides long, and comprise a sequence
which is the reverse
complement of the RT primer, including 4 adenines (As) at the 5' end. Sequence-
specific probes
are designed to detect (or hybridize with) RT-PCR amplification products based
on total or
partial complementarity between the sequence of the probe and the sequence of
the RT-PCR
product. Said probes are between 20 and 28 nucleotides longs, preferably 24
nucleotides long, 5
and comprising at the 5'end three nucleotides from each at least two are
complementary to the
RT primer, followed by between 10 to 14, preferably 12 thymines (Ts), followed
by between 6 to
10, preferably 8 contiguous nucleotides which correspond to the reverse
complementary
sequence of the specific corresponding microRNA.
A biochip comprising novel nucleic acids described herein is provided. In one
10
embodiment, the biochip may comprise probes that recognize the novel nucleic
acids described
herein. Said nucleic acids are isolated nucleic acids comprising at least 12
contiguous
nucleotides at least 80% identical to the sequence of any one of SEQ ID NOs.
27-29, 33, 34, 139,
140, 307 and 308. In one embodiment, said isolated nucleic acid comprises at
least 13, at least
14, at least 15, at least 16, at least 17, at least 18, at least 19, or at
least 20 contiguous nucleotides 15
identical to the sequence of any one of SEQ Ti) NOs. 27-29, 33, 34, 139, 140,
307 and 308. The
biochip may comprise a solid substrate comprising an attached nucleic acid,
probe or plurality of
probes described herein. The probes may be capable of hybridizing to a target
sequence under
stringent hybridization conditions. The probes may be attached at spatially
defined addresses on
the substrate. More than one probe per target sequence may be used, with
either overlapping 20
probes or probes to different sections of a particular target sequence. The
probes may be capable
of hybridizing to target sequences associated with a single disorder
appreciated by those in the
art. The probes may either be synthesized first, with subsequent attachment to
the biochip, or
may be directly synthesized on the biochip.
The solid substrate may be a material that may be modified to contain discrete
individual 25
sites appropriate for the attachment or association of the probes and is
amenable to at least one
detection method. Representative examples of substrates include glass and
modified or
functionalized glass, plastics (including acrylics, polystyrene and copolymers
of styrene and
other materials, polypropylene, polyethylene, polybutylene, polyurethanes,
TeflonJ, etc.),
polysaccharides, nylon or nitrocellulose, resins, silica or silica-based
materials including silicon 30
and modified silicon, carbon, metals, inorganic glasses and plastics. The
substrates may allow
optical detection without appreciably fluorescing.
The substrate may be planar, although other configurations of substrates may
be used as
well. For example, probes may be placed on the inside surface of a tube, for
flow-through sample
69
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CA 2945531 2017-04-18
analysis to minimize sample volume. Similarly, the substrate may be flexible,
such as flexible
foam, including closed cell foams made of particular plastics.
The biochip and the probe may be derivatized with chemical functional groups
for
subsequent attachment of the two. For example, the biochip may be derivatized
with a chemical
functional group including, but not limited to, amino groups, carboxyl groups,
oxo groups or 5
thiol groups. Using these functional groups, the probes may he attached using
functional groups
on the probes either directly or indirectly using a linker. The probes may be
attached to the solid
support by either the 5' terminus, 3' terminus, or via an internal nucleotide.
The probe may also be attached to the solid support non-covalently. For
example,
biotinylated oligonucleotides can be made, which may bind to surfaces
covalently coated with 10
streptavidin, resulting in attachment. Alternatively, probes may be
synthesized on the surface
using techniques such as photopolymerization and photolithography.
In a further embodiment of the invention, measuring the microRNAs for
classification of
thyroid lesions may be effected by high throughput sequencing. High throughput
sequencing can
involve sequencing-by-synthesis, sequencing-by-ligation, and ultra-deep
sequencing. Sequence- 15
by-synthesis can be initiated using sequencing primers complementary to the
sequencing element
on the nucleic acid tags. The method involves detecting the identity of each
nucleotide
immediately after (substantially real-time) or upon (real-time) the
incorporation of a labeled
nucleotide or nucleotide analog into a growing strand of a complementary
nucleic acid sequence
in a polymerase reaction. After the successful incorporation of a label
nucleotide, a signal is 20
measured and then nulled by methods known in the art. Examples of sequence-by-
synthesis
methods are known in the art, and are described for example in US 7,056,676,
US 8,802,368 and
US 7,169,560. Examples of labels that can be used to label nucleotide or
nucleotide analogs
for sequencing-by-synthesis include, but are not limited to, chromophores,
fluorescent
moieties, enzymes, antigens, heavy metal, magnetic probes, dyes,
phosphorescent groups, 95
radioactive materials, chemiluminescent moieties, scattering or fluorescent
nanoparticles,
Raman signal generating moieties, and electrochemical detection moieties.
Sequencing-by-
synthesis can generate at least 1,000, at least 5,000, at least 10,000, at
least 20,000, 30,000,
at least 40,000, at least 50,000, at least 100,000 or at least 500,000 reads
per hour. Such
reads can have at least 40, at least 45, at least 50, at least 60, at least
70, at least 80, at least 30
90, at least 100, at least 120 or at least 150 bases per read.
Sequencing-by-synthesis may be performed on a solid surface (or a chip) using
fold-back
PCR and anchored primers. Since inicroRNAs occur as small nucleic acid
fragments ¨ adaptors
are added to the 5' and 3' ends of the fragments. Nucleic acid fragments that
are attached to the
CA 02945531 2016-10-11
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surface of flow cell channels are extended and bridge amplified. The fragments
become double
stranded, and the double stranded molecules are denatured. Multiple cycles of
the solid-phase
amplification followed by denaturation can create several million clusters of
approximately
1,000 copies of single-stranded nucleic acid molecules of the same template in
each channel of
the flow cell. Primers, polymerase and four fluorophore-labeled, reversibly
terminating 5
nucleotides are used to perform sequential sequencing. After nucleotide
incorporation, a laser is
used to excite the fluorophores, an image is captured and the identity of the
first base is recorded.
The 3' terminators and fluorophores from each incorporated base are removed
and the
incorporation, detection and identification steps are repeated. This
technology is used, for
example, in the Illumina() sequencing platform. 10
Another sequencing method involves hybridizing the amplified regions to a
primer
complementary to the sequence element in an LST (a file listing the names of
fasta files). This
hybridization complex is incubated with a polymerase, ATP sulfurylase,
luciferase, apyrase, and
the substrates luciferin and adenosine 5' phosphosulfate. Next,
deoxynucleotide triphosphates
corresponding to the bases A, C, G, and T (U) are added sequentially. Each
base incorporation is 15
accompanied by release of pyrophosphate, converted to ATP by sulfurylase,
which drives
synthesis of oxyluciferin and the release of visible light. Since
pyrophosphate release is
equimolar with the number of incorporated bases, the light given off is
proportional to the
number of nucleotides adding in any one step. The process is repeated until
the entire sequence is
determined. Yet another sequencing method involves a four-color sequencing by
ligation scheme 20
(degenerate ligation), which involves hybridizing an anchor primer to one of
four positions. Then
an enzymatic ligation reaction of the anchor primer to a population of
degenerate nonamers that
are labeled with fluorescent dyes is performed. At any given cycle, the
population of nonamers
that is used is structure such that the identity of one of its positions is
correlated with the identity
of the fluorophore attached to that nonamer. To the extent that the ligase
discriminates for 25
complementarily at that queried position, the fluorescent signal allows the
inference of the
identity of the base. After performing the ligation and four-color imaging,
the anchor
primer:nonamer complexes are stripped and a new cycle begins. Methods to image
sequence
information after performing ligation are known in the art. In some cases,
high throughput
sequencing involves the use of ultra-deep sequencing, such as described in
Marguiles et al., 30
Nature 437 (7057): 376-80 (2005).
MicroRNA sequencing (miRNA-seq) is a type of RNA Sequencing (RNA-Seq) which
uses next-generation sequencing or massively parallel high-throughput DNA
sequencing to
sequence microRNAs. miRNA-seq differs from other forms of RNA-seq in that
input material is
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often enriched for small RNAs. miRNA-seq provides tissue specific expression
patterns, which
may lead to disease associations and microRNAs isoforms. miRNA-seq is also
used for the
discovery of previously uncharacterized microRNAs, such as the nucleic acid
sequences denoted
by SEQ ID NOs 139-140 and 307-308.
As used herein, the term "diagnosing" refers to classifying pathology, or a
symptom, 5
determining a severity of the pathology (grade or stage), monitoring pathology
progression,
forecasting an outcome of pathology and/or prospects of recovery.
As used herein, the phrase "subject in need thereof' refers to an human
subject who is
known to have cancer, at risk of having cancer (e.g., a genetically
predisposed subject, a subject
with medical and/or family history of cancer, a subject who has been exposed
to carcinogens, 10
occupational hazard, environmental hazard) and/or a subject who exhibits
suspicious clinical
signs of cancer (e.g., nodules in the thyroid). Additionally or alternatively,
the subject in need
thereof can be a healthy human subject undergoing a routine well-being check-
up.
Analyzing presence of malignant or pre-malignant cells can be effected in vivo
or ex vivo,
whereby a biological sample (e.g., biopsy) is retrieved. Such biopsy samples
comprise cells and 15
may be an incisional or excisional biopsy. The sample may be retrieved from
the thyroid of the
subject, and may be retrieved using FNA. Alternatively the cells may be
retrieved from a
complete resection.
While employing the present teachings, additional information may be gleaned
pertaining
to the determination of treatment regimen, treatment course and/or to the
measurement of the 20
severity of the disease.
As used herein, the phrase "treatment regimen" refers to a treatment plan that
specifies
the type of treatment, dosage, schedule and/or duration of a treatment
provided to a subject in
need thereof (e.g., a subject diagnosed with a pathology). The selected
treatment regimen can be
an aggressive one which is expected to result in the best clinical outcome
(e.g., complete cure of 25
the pathology) or a more moderate one which may relieve symptoms of the
pathology yet results
in incomplete cure of the pathology. It will be appreciated that in certain
cases the treatment
regimen may be associated with some discomfort to the subject or adverse side
effects (e.g.,
damage to healthy cells or tissue). The type of treatment can include a
surgical intervention (e.g.,
removal of lesion, diseased cells, tissue, or organ), a cell replacement
therapy, an administration 30
of a therapeutic drug (e.g., receptor agonists, antagonists, hormones,
chemotherapy agents) in a
local or a systemic mode, an exposure to radiation therapy using an external
source (e.g.,
external beam) and/or an internal source (e.g., brachytherapy) and/or any
combination thereof.
The dosage, schedule and duration of treatment can vary, depending on the
severity of pathology
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and the selected type of treatment, and those of skills in the art are capable
of adjusting the type
of treatment with the dosage, schedule and duration of treatment.
A method of diagnosis is also provided. The method comprises detecting an
expression
level of a specific cancer-associated nucleic acid in a biological sample.
Diagnosis of a specific
cancer state in a patient may allow for prognosis and selection of therapeutic
strategy. Further, 5
the developmental stage of cells may be classified by determining temporarily
expressed specific
cancer-associated nucleic acids.
In situ hybridization of labeled probes to tissue sections or FNA smears may
be
performed. When comparing the fingerprints between individual samples the
skilled artisan can
make a diagnosis, a prognosis, or a prediction based on the findings. It is
further understood that 10
the nucleic acid sequence which indicate the diagnosis may differ from those
which indicate the
prognosis and molecular profiling of the condition of the cells may lead to
distinctions between
responsive or refractory conditions or may be predictive of outcomes.
A kit is also provided and may comprise a nucleic acid described herein
together with
any or all of the following: assay reagents, buffers, probes and/or primers,
and sterile saline or 15
another pharmaceutically acceptable emulsion and suspension base. In addition,
the kits may
include instructional materials containing directions (e.g., protocols) for
the practice of the
methods described herein. The kit may further comprise a software package for
data analysis of
expression profiles.
For example, the kit may be a kit for the amplification, detection,
identification or 20
quantification of a target nucleic acid sequence. The kit may comprise a poly
(T) primer, a
forward primer, a reverse primer, and a probe.
Any of the compositions described herein may be comprised in a kit. In a non-
limiting
example, reagents for isolating microRNA, labeling microRNA. and/or evaluating
a microRNA
population using an array are included in a kit. The kit may further include
reagents for creating 25
or synthesizing microRNA probes. The kits will thus comprise, in suitable
container means, an
enzyme for labeling the microRNA by incorporating labeled nucleotide or
unlabeled nucleotides
that are subsequently labeled. It may also include one or more buffers, such
as reaction buffer,
labeling buffer, washing buffer, or a hybridization buffer, compounds for
preparing the
microRNA probes, components for in situ hybridization and components for
isolating 30
microRNA. Other kits of the invention may include components for making a
nucleic acid array
comprising microRNA, and thus, may include, for example, a solid support.
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The following examples are presented in order to more fully illustrate some
embodiments
of the invention. They should in no way be construed, however, as limiting the
broad scope of
the invention.
EXAMPLES 5
Materials and Methods
1. microRNA analysis
The presence and/or level of microRNAs in thyroid tumor samples may be
evaluated
using methods known in the art, e.g.. Northern blot, RNA expression assays,
e.g., microarray
analysis, RT-PCR, high throughput sequencing (next generation sequencing),
cloning, and 10
quantitative real time polymerase chain reaction (q1U-PCR). Analytical
techniques to determine
RNA expression are known in the art, see e.g. Sambrook et al., Molecular
Cloning: A Laboratory
Manual, 3rd Ed., Cold Spring Harbor Press, Cold Spring Harbor, N.Y. (2001).
Examples of
specific methods used herein are described in more detail below.
2. RNA extraction 15
FNA Cell Block samples
Total RNA was isolated from seven to ten 10 i.tm-thick tissue sections.
Sections were
incubated a few times (1-3 times) in xylene at 57 C for 5 minutes in order to
remove excess
paraffin, followed by centrifugation at ambient temperature for 2 minutes at
10,000g. The
specimen was then washed several times (about 3 times) with 1 ml 100% ethanol
in order to 20
wash the xylene out of the tissue, followed by centrifugation at ambient
temperature for 10
minutes at 10,000g. The supernatant was discarded and the tissue dried at 65 C
for 5 minutes.
Proteins were degraded by proteinase K solution (5-12 j.il Proteinase K (e.g.,
Sigma or ABI) in
500 pl of Buffer B (10 mM NaC1, 500 mM Tris pH 7.5, 20 mM EDTA pH 8, 1% SDS),
at 45 C
for a few hours (about 16 hours). Proteinase K was inactivated by incubation
at 95 C for 7 25
minutes. After the tubes were chilled 10 pi of RNA synthetic spikes was added
(e.g., 2 spikes of
0.15 fmol/p1). RNA was extracted using acid phenol/chloroform equal volume,
vortexing,
followed by centrifugation at 4 C for 15 minutes at 12000g. RNA was then
precipitated using 8
p.1 linear acrylamide. 0.1 volumes of 3M Na0Ac pH 5.2, and 3 volumes of
absolute 100%
ethanol, for 30 minutes to 16 hours followed by centrifugation at 4 C for at
least 40 minutes at 30
20000g (14,000 rpm). The pellet was washed by adding 1 ml 85% cold Ethanol.
DNAses were
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introduced at 37 C for 60 minutes to digest DNA (e.g. 10 1.11 TurboTm DNase),
followed by
extraction using acid phenol/chloroform and ethanol precipitated as described
above.
FNA smears samples (e.2.)
Total RNA was isolated from FNA smear samples in slides, either non-stained or
stained
(e.g. by Papanicolaou, Giemsa or Diff-Quick) after removal of the coverslip
(when present) by 5
dipping the slides for several hours (about 2-20 hours, usually about 16
hours) in xylene at
ambient temperature, in order to remove excess paraffin or glue. Further the
slides were washed
several times (about 3 times) with 100% ethanol in order to wash the xylene
out. Slides were
dipped for 1 minute in double-distilled water (DDW). The cells were scraped
from the slide
using a scalpel. The slide was then washed with 500 .1 buffer B (10mM NaC1,
500mM Tris pH 10
7.5, 20mM EDTA pH 8, 1% SDS), and transferred to a 1.7 ml tube. Proteins were
degraded by
proteinase K (e.g., 5-12 p.1 Sigma or ABI) at 45 C for a few hours (about 16
hours). Proteinase K
was inactivated by incubating the tubes at 95 C for 7 minutes. After chilling
the tubes, 10 1 of
RNA synthetic spikes (e.g., 2 spikes of 0.15fmol/p.1) was added. RNA was
extracted using acid
phenol/chloroform equal volume, vortexing, spinning down at 4 C for 15 minutes
at 12000g. 15
RNA was then precipitated using 8 1 linear acrylamide, 0.1 volumes of 3M
Na0Ac pH 5.2, and
3 volumes of absolute ethanol from 30 minutes to 16 hours. The tubes were then
spun down at
4 C for at least 40 minutes at 20000g (14,000 rpm). The pellet was washed with
about 1 ml 85%
cold ethanol. DNAses were introduced at 37 C for 60 minutes to digest DNA
(e.g. 10 p 1
Turbo TM DNase, Ambion, Life Technologies), followed by extraction using acid
20
phenol/chloroform and ethanol precipitation as described above.
3. Total RNA quantification
Total RNA quantification was performed by fluorospectrometry in a Nanollrop
3300
(ND3300) fluorospectrometer using the RiboGreen0 dye (Thenno Fisher Scientific
,
Wilmington, DE). The ND3300 RNA detection range is of 25 ng/ml - 1000 ng/ml
when using a 25
high concentration of RiboOreen dye (1:200 dilution), and 5 ng/ml - 50 ng/ml
when using a
1:2000 dilution of RiboGreen dye. The RNA amounts which were determined by
ND3300
were highly correlated to the detected expressed microRNA.
4. MicroRNA profiling in microarray
Custom microai-rays (Agilent Technologies, Santa Clara, CA) were generated by
printing 30
DNA oligonucleotide probes to: 2172 miRs sequences, 17 negative controls, 23
spikes, and 10
positive controls (total of 2222 probes). Each microRNA probe, printed in
triplicate, carried up
to 28-nucleotide (nt) linker at the 3' end of the microRNAs' complement
sequence. Negative
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spikes and positive probes were printed from 3 to 200 times. Seventeen (17)
negative control
probes were designed using sequences that do not match the genome. Two groups
of positive
control probes were designed to hybridize to the microRNA array: (i) synthetic
small RNAs
were spiked to the RNA before labeling to verify the labeling efficiency; and
(ii) probes for
abundant small RNA, e.g., small nuclear RNAs (U43, U24, Z30, U6, U48, U44),
5.8s and 5s 5
ribosomal RNA were spotted on the array to verify RNA quality.
5. Cy-dye labeling of microRNA for microarray
Total RNA (20-1000ng) was labeled by ligation (Thomson et al. Nature Methods
2004;
1:47-53) with an RNA linker, p-rCrU-Cy/dye or several sequential Cys
(BioSpring GmbH, IBA
GmbH or equivalent), to the 3' end with Cy3 or Cy5. The labeling reaction
contained total RNA, 10
spikes (0.1-100 fmoles), 250-400 ng RNA-linker-dye, 15% DMSO, lx ligase buffer
and 20 units
of T4 RNA ligase (NEB or equivalent), and proceeded at 4 C for 1 hour,
followed by 1 hour at
37 C, followed by 4 C up to 40 minutes.
The labeled RNA was mixed with 30 pl hybridization mixture (mixture of 45 L
of the
10X GE Agilent Blocking Agent and 246 I- of 2X Hi-RPM Hybridization). The
labeling 15
mixture was incubated at 100 C for 5 minutes followed by ice incubation in
water bath for 5
minutes. Slides were hybridized at 54-55 C for 16-20 hours, followed by two
washes. The first
wash was conducted at room temperature with Agilent GE Wash Buffer 1 (e.g. 6X
SSPE +
0.005% N-Lauroylsarcosine +0.005% Triton X-102,) for 5 minutes followed by a
second wash
with Agilent GE Wash Buffer 2 at 37 C for 5 minutes (e.g. 0.06X SSPE + 0.005%
N- 20
Lauroylsarcosine +0.005% Triton X-102).
Arrays were scanned using a microarray scanner (Agilent Microarray Scanner
Bundle
G2565BA, resolution of 5 pm at XDR Hi 100%, XDR Lo 10%). Array images were
analyzed
using appropriate software (Feature Extraction 10.7 software, Agilent).
6. RT-PCR 25
Poly-adenylation and reverse transcription was performed on 1-50Ong of total
RNA.
RNA was incubated in the presence of poly (A) polymerase (Poly (A) Polymerase
NEB-
M0276L), ATP, an oligodT primer harboring a consensus sequence and reverse
transcriptase
(SuperScript() II RT, Invitrogen, Carlsbad, CA) for 1 hour at 37 C. Next, the
cDNA was
amplified by RT-PCR. The amplification reaction included a microRNA-specific
forward 30
primer, being a TaqMan (MGB) probe complementary to the 3' of the specific
microRNA
sequence and or to part of the polyA adaptor sequence, and a universal reverse
primer
complementary to the consensus 3' sequence of the oligodT tail. Detailed
description of the RT-
76
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PCR methodology may be found in publication WO 2008/029295.
The cycle threshold (CT, the PCR cycle at which probe signal reaches the
threshold) was
determined for each microRNA.
In order to allow comparison between microRNA expression results from RT-PCR
with 5
microRNA expression results from microarray, each value obtained by RT-PCR was
subtracted
from 50 (50-CT). The 50-CT expression for each microRNA for each patient was
compared with
the signal obtained by the microarray method.
7. Array data normalization
The initial data set consisted of signals measured for multiple probes for
every sample. 10
For the analysis, signals were used only for probes that were designed to
measure the expression
levels of known or validated human microRNAs.
Triplicate spots were combined into one signal by taking the logarithmic mean
of the
reliable spots. All data was log-transformed and the analysis was performed in
log-space. A
reference data vector for normalization, R, was calculated by taking the mean
expression level 15
for each probe in two representative samples, one from each tumor type.
For each sample k with data vector Sk, a 2nd degree polynomial Fk was found so
as to
provide the best fit between the sample data and the reference data, such that
RzP4(Sk). Remote
data points ("outliers") were not used for fitting the polynomials F. For each
probe in the sample
(element SI' in the vector Sk), the normalized value (in log-space) A4'," is
calculated from the 20
initial value Si" by transfomiing it with the polynomial function Fk, so that
=F"( S").
Statistical analysis is performed in log-space. For presentation and
calculation of fold-change,
data is translated back to linear-space by taking the exponent.
8. miRNA-seq Sequence Library Construction
Sequence library construction may be performed using a variety of different
kits 25
depending on the high-throughput sequencing platform being employed. However,
there are
several common steps for small RNA sequencing preparation. The ligation step
adds DNA
adaptors to both ends of the small RNAs, which act as primer binding sites
during reverse
transcription and PCR amplification. An adenylated single strand DNA 3'
adaptor followed by a
5'adaptor is ligated to the small RNAs using a ligating enzyme such as T4 RNA
ligase or adding 30
5' adaptor using 5' RACE reaction 2. The adaptors are also designed to capture
small RNAs with
a 5' phosphate group, characteristic microRNAs, rather than RNA degradation
products with a 5'
hydroxyl group. Reverse transcription and PCR amplification steps convert the
small adaptor
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ligated RNAs into cDNA clones used in the sequencing reaction. PCR is then
carried out to
amplify the pool of cDNA sequences. Primers designed with unique nucleotide
tags may also be
used in this step to create ID tags in pooled library multiplex sequencing.
9. Next generation sequencing (NGS)
50Ong of RNA from each FFPE sample were used for small RNA deep sequencing 5
(miRSeq). Libraries were loaded on two lanes of the sequence analyzer
(Illumina HiSee
2000 DNA). An average of about 6.3 million reads per library were obtained. To
find novel
microRNAs, sequence analysis software (miRDeep2, Friedlander MR et al. Nucleic
Acids Res.
2012 Jan;40(1):37-52) was applied on the raw sequencing data (primer-adapter
sequences were
trimmed). 10
10. Statistical analysis
P-values were calculated using a two-sided (unpaired) Student's t-test on the
log-
transformed normalized fluorescence signal. The threshold for significant
differences was
determined by setting a false discovery rate (FDR) of 0.05 to 0.1, to correct
for effects of
multiple hypothesis testing, resulting in p-value cutoffs in the range of 0.01-
0.06. For each 15
differentially expressed microRNA, the fold-difference (ratio of the median
normalized
fluorescence) and the area under curve (AUC) of the response operating
characteristic (ROC)
curve were calculated. Three sets of miRs were excluded from the statistical
analysis: (a) miRs
that were previously found as highly expressed in blood samples (due to high
percentages of
blood in FNA samples), (b) miRs whose level of expression did not correlate
with decreasing 20
amounts of RNA, i.e: these miRs did not show linear decrease in signal in
association with
decreasing measured RNA amounts, and (c) miRs whose level of expression
correlated with
miRs in set (b).
Example 1: Detection of microRNA in pre-operative samples 25
A pilot study of microRNA profiling was conducted in a few Papanicolaou,
Giemsa and
Diff-Quick stained smears from ex-vivo FNA biopsy samples in order to ensure
feasibility of the
methodology. Since FNA smears often have very few cells, providing a minuscule
amount of
RNA for analysis, e.g. 1-1000 ng, it was first necessary to evaluate whether
microRNA would
be detectable under such low RNA amounts. Thus, microRNA expression levels of
about 2200 30
individual microRNAs was measured in Giemsa-stained papillary carcinoma and
non-papillary
carcinoma smears. Five microRNAs (hsa-miR-146b-5p, hsa-miR-31-5p, hsa-miR-222-
3p, hsa-
miR-221-3p, and hsa-miR-21-5p), previously shown to correlate with papillary
carcinoma were
found over-represented in the papillary-carcinoma smears. Figure 1 shows a
comparison of
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microRNA expression between Giemsa-stained papillary carcinoma and non-
papillary carcinoma
samples, and reveals the highly up-regulated microRNA markers in the papillary
carcinoma.
These results strongly suggested that microRNA profiles can be successfully
determined in FNA
smears.
Example 2: Differential microRNA expression between malignant and benign
thyroid
lesions
The cohort of samples used in the experimental analysis consisted of 73 pre-
operative
thyroid FNA cell blocks selected from archived materials of the Department of
Pathology
Temple University Hospital (Philadelphia, USA). The 73 specimens included
samples of 35 10
benign and 38 malignant thyroid lesions. The 35 benign tumors consisted of 18
follicular
adenoma, eight (8) Hashimoto thyroiditis, and nine (9) hyperplasia (Goiter)
samples. The 38
malignant tumors consisted of: 10 follicular carcinoma and 28 papillary
carcinoma. Of the 28
papillary carcinoma samples, nine (9) were papillary carcinoma, 13 were
papillary carcinoma
follicular variant encapsulated, and six (6) were papillary carcinoma
follicular variant non- 15
encapsulated. The histological diagnosis assessed ultimately the malignancy or
benignity of the
thyroid lesions. The cytological classification was based on ¨The Bethesda
System for Reporting
Thyroid Cytopathology" (Syed. Z. Ali and Edmund S. Cibas, eds.; DOI
10.1007/978-0-387-
87666-5_1; Springer Science+Business Media, LLC 2010). The study protocol was
approved by
the Institutional Review Board (IRB, equivalent to Ethical Review Board) of
the contributing 20
institution. Tumor classification was based on the World Health Organization
(WHO) guidelines.
An additional cohort consisted of 13 thyroid ex-vivo FNA smears, prepared
after thyroidectomy,
and obtained from the University Milano-Bicocca (Milan, Italy).
Total RNA (at least 10 ng) was extracted from these samples, and microRNA
expression
was profiled using custom microarrays containing about 2200 miRs. The results
exhibited a 25
significant difference in the expression pattern between benign and malignant
lesions of several
miRs listed in Table 3 (upregulated or downregulated in malignant versus
benign).
Table 3: miRNAs up- or downregulated in malignant versus benign thyroid tumor
miR name p-value fold-change AUC median
malignant benign
hsa-miR-146b-5p 3.80E-05 2.57 (+) 0.77 5.70E+02
2.20E+02
hsa-miR-222-3p 1.80E-03 2.20 (+) 0.71 4.70E+03 2.10E+03
hsa-miR-221 -3p 1.80E-03 2.09 (+) 0.71 4.10E+03
2.00E+03
hsa-miR-181b-5p 2.50E-02 1.38 (+) 0.65 5.00E+02
3.60E+02
hsa-miR-29b-3p 9.50E-03 1.32 (+) 0.64 2.10E+03 1.60E+03
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miR name p-value fold-change A UC median
malignant benign
hsa-miR-200b-3p 2.60E-02 1.27 (+) 0.65 3.10E+02
2.40E+02
hsa-miR-200a-3p 3.90E-02 1.27 (+) 0.64 3.00E+02
2.40E+02
hsa-miR-29c-3p 8.80E-03 1.22 (+) 0.64 1.40E+03 1.10E+03
hsa-miR-130a-3p 3.30E-02 1.20 (+) 0.64 1.00E+03
8.70E+02
hsa-miR-148b-3p 3.60E-02 1.13 (+) 0.64 5.00E+02
4.50E+02
MID-23794 2.60E-05 2.34 (-) 0.78 6.00E+02 1.40E+03
hsa-miR-197-5p 2.20E-03 1.90 (-) 0.74 3.40E+02 6.60E+02
hsa-miR-486-3p 3.60E-05 1.73 (-) 0.79 2.00E+02 3.50E+02
hsa-miR-574-3p 1.40E-02 1.44 (-) 0.68 2.30E+02 3.30E+02
hsa-miR-532-3p 4.80E-03 1.30 (-) 0.71 4.50E+02 5.80E+02
hsa-miR-199a-5p 2.50E-03 1.25 (-) 0.73 3.90E+02
4.80E+02
hsa-miR-22-3p 3.90E-02 1.11 (-) 0.62 3.40E+03 3.70E+03
p-values were calculated using a two-sided (unpaired) Student's t-test.
The fold-change represents the ratio between the median values of each group.
AUC: Area under the curve when using the miRNAs to classify the two groups.
Median: median of expression values (rounded).
A classification algorithm for differentiating between malignant and benign
thyroid
tumor was developed based on miRNA expression in 35 benign and 38 malignant
FNA samples.
A logistic regression classifier was trained to distinguish between malignant
and benign thyroid
lesions, based on eight miRs (hsa-miR-125b-5p, hsa-miR-2 -5p, hsa-miR-222-3p,
hsa-miR-221-
3p, hsa-miR-146b-5p, hsa-miR-181a-5p, hsa-miR-138-5p, and MID-23794) that were
found to 10
be differentially expressed in these conditions, either between benign or
malignant or between
specific thyroid tumor subtypes (data not shown). The classifier reached 89%
accuracy with
sensitivity of 87% and specificity of 91% for identifying malignant samples.
hsa-miR-125b-5p,
hsa-miR-21-5p, hsa-miR-222-3p, hsa-miR-221-3p, hsa-miR-146b-5p and hsa-miR-
181a-5p
exhibited higher expression in malignant lesions, while hsa-miR-138-5p and MID-
23794 15
exhibited higher expression in benign lesions (data not shown).
Example 3: Distinguishing different sub-types of malignant and benign thyroid
lesions
Expression levels of miRs were compared in 18 follicular adenoma samples and
10
follicular carcinoma samples. microRNAs that were upregulated or downregulated
in follicular 20
adenoma relative to follicular carcinoma are presented in Table 4
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Table 4: miRNAs up- or downregulated in follicular adenoma versus follicular
carcinoma
Median
fold-
miR name p-value change AUC Follicular Follicular
adenoma carcinoma
hsa-miR-486-3p 2.80E-02 2.04 (+) 0.77 4.80E+02 2.40E+02
MID-01141 5.50E-02 1.91 (+) 0.73 3.50E+02 1.80E+02
2.20E+02
hsa-miR-193a-3p 2.70E-02 1.45 (+) 0.76 3.10E+02
hsa-miR-148b-3p 3.90E-02 1.25 (-) 0.71 4.50E+02 5.60E+02
p-values were calculated using a two-sided (unpaired) Student's t-test.
The fold-change represents the ratio between the median values of each group.
AUC: Area under the curve when using the miRNAs to classify the two groups.
Median: median of expression values (rounded). 5
Expression levels of miRs were compared in 18 follicular adenoma samples
versus 9
papillary carcinoma (non-follicular variant) samples, and a classifier was
generated for
distinguishing between follicular adenoma and papillary carcinoma samples
using the expression
levels of hsa-miR-146b-5p and hsa-miR-21-5p, with 100% accuracy (data not
shown). 10
Expression levels of miRs were compared in 18 follicular adenoma samples
versus 19
follicular variant of papillary carcinoma samples. microRNAs that were
upregulated or
downregulated in follicular variant of papillary carcinoma relative to
follicular adenoma are
presented in Table 5.
Table 5: miRNAs up- or downregulated in follicular variant papillary carcinoma
(FVPC)
versus follicular adenoma (FA)
median
miR name p-value fold-change AUC
FVPC FA
hsa-miR-146b-5p 4.00E-02 2.36 (+) 0.71 5.40E+02
2.30E+02
hsa-miR-29c-3p 2.00E-03 1.66 (+) 0.76 1.40E+03
8.30E+02
hsa-miR-200a-3p 2.50E-02 1.65 (+) 0.73 3.00E+02
1.80E+02
hsa-miR-200b-3p 1.70E-02 1.56 (+) 0.73 3.10E+02
2.00E+02
hsa-miR-125a-5p 3.30E-02 1.42 (+) 0.69 1.70E+03
1.20E+03
hsa-miR-148b-3p 2.10E-02 1.20 (+) 0.70 5.40E+02
4.50E+02
hsa-miR-199a-3p 4.10E-02 1.09 (+) 0.70 3.30E+02
3.10E+02
hsa-miR-197-5p 5.60E-05 3.73 (-) 0.89 2.70E+02
1.00E+03
MID-23794 6.50E-05 2.39 (-) 0.84 7.70E+02 1.80E+03
hsa-miR-486-3p 2.00E-05 2.34 (-) 0.89 2.10E+02
4.80E+02
hsa-miR-532-3p 8.50E-04 1.70 (-) 0.82 4.40E+02
7.60E+02
hsa-miR-22-3p 8.10E-03 1.33 (-) 0.75 3.40E+03 4.50E+03
hsa-miR-199a-5p 5.80E-03 1.30 (-) 0.76 3.70E+02
4.80E+02
hsa-miR-23a-3p 4.50E-02 1.26 (-) 0.68 2.60E+03
3.30E+03
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miR name p-value fold-change AUC median
FVPC FA
hsa-miR-34a-5p 4.10E-02 1.09 (-) 0.63 6.00E+02
6.60E+02
p-values were calculated using a two-sided (unpaired) Student's t-test.
The fold-change represents the ratio between the median values of each group.
AUC: Area under the curve when using the miRNAs to classify the two groups.
Median: median of expression values (rounded).
Expression levels of miRs were compared in 6 non-encapsulated follicular
variant of
papillary carcinoma samples versus 35 benign samples, and a classifier was
generated using the
expression levels of hsa-miR-221-3p and hsa-miR-200b-3p, with 98% accuracy,
83% sensitivity
and 100% specificity (data not shown).
Expression levels of miRs were compared in 8 Hashimoto thyroiditis samples and
9 (non- 10
follicular) papillary carcinoma samples. microRNAs that were upregulated or
downregulated in
papillary carcinoma relative to Hashimoto thyroiditis are presented in Table
6. The miRs that are
the best candidates for the profile signature for comparing these two thyroid
lesions are hsa-miR-
146b-5p, hsa-miR-200a-3p and MID-23794.
Table 6: miRNAs upregulated or downregulated in papillary carcinoma (PC)
versus
Hashimoto thyroiditis (Ht)
median
miR name p-value fold-change AUC
PC Ht
hsa-miR-146b-5p 2.20E-02 2.46 (+) 0.75 7.90E+02
3.20E+02
hsa-miR-200a-3p 2.30E-02 2.46 (+) 0.75 4.50E+02
1.80E+02
hsa-miR-200b-3p 3.40E-02 2.13 (+) 0.76 4.30E+02
2.00E+02
MID-23794 4.10E-05 4.85 (-) 0.88 4.80E+02
2.30E+03
MID-00387 8.70E-07 4.18 (-) 0.92 7.70E+01
3.20E+02
hsa-miR-486-3p 5.30E-04 2.03 (-) 0.80 1.80E+02
3.70E+02
p-values were calculated using a two-sided (unpaired) Student's t-test.
The fold-change represents the ratio between the median values of each group.
AUC: Area under the curve when using the miRNAs to classify the two groups.
20
Median: median of expression values (rounded).
Example 4: Identification of Novel microRNAs Biomarkers by Deep-sequencing
Eleven (11) FFPE (Formalin Fixed Paraffin Embedded) thyroid resection samples
(obtained from surgical biopsies and fixed in founalin and preserved in
paraffin) from follicular 25
lesions were obtained from the Department of Pathology at Rabin Medical
Center. The
specimens included 6 follicular adenomas and 5 follicular carcinomas. Tumor
cellular content
was higher than 50% in all the samples.
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A total of 386 novel candidate microRNAs were found with sequence analysis
software,
and 27 of those were selected for validation, performed by qPCR. Two novel
microRNAs are
disclosed herein, MD2-495 and MD2-437, and their sequences are presented in
Table 1, and
their respective hairpins are shown in Table 2. Figure 2A shows the secondary
structures of the
two novel microRNAs, predicted by sequence analysis software. Figure 2B shows
the expression 5
of the two novel microRNAs (normalized number of reads) in each of the 11
samples. The color-
coded bar on the right represents a scale for expression.
Example 5: Specific microRNAs are differentially expressed between benign and
malignant
thyroid lesions 10
Stained thyroid FNA smears were obtained from a medical center in Israel
(Cohort I);
and thyroid FNA cell blocks were obtained from a medical center in the USA
(Cohort II). For
both cohorts, thyroid lesions were ultimately classified as malignant or
benign based on
histological diagnosis of the resected tumor. A summary of the breakdown of
the samples from
the two cohorts is shown in Table 7. 15
Table 7: FNA Samples ¨ Cohorts I and II
FNA Sample Description Cohort I Cohort II
Number of lesions (#patients)1 81(65) 73 (73)
Nodular hyperplasia (nodular Goiter) 13 9
Follicular adenoma 27 18
Graves' disease 3 0
Hashimoto thyroiditis 3 8
Total Benign Nodules 46 35
Papillary carcinoma 10 9
Follicular variant of papillary carcinoma 13 19
Follicular carcinoma 4 10
Medullary carcinoma 6 0
Thyroid carcinoma (Mix histology) 2 0
Total Malignant Nodules 35 38
Bethesda2 class II, VI 33 0
Bethesda2 class III, IV, V 48 73
1
Some patients had more than one lesion.
2
The Bethesda System for Reporting Thyroid Cytopathology (BSRTC) resulted from
a
conference held at the National Institutes of Health in 2007 (Cibas ES, Ali
SZ. The Bethesda 20
System for Reporting Thyroid Cytopathology. Am J Clin Pathol 2009;132:658-65).
The system
led to standardization of FNA reports based on six diagnostic categories: DC I
= non-diagnostic,
DC II = benign, DC III = atypia/follicular lesion of undetermined significance
(AUS/FLUS). DC
IV = follicular neoplasm/suspicion for a follicular neoplasm (FN/SFN), DC V =
suspicious for
malignancy, and DC VI = malignant. 25
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Highly purified RNA, including the microRNA fraction, was extracted from
samples
using in-house developed protocols as described above. FFPE and cytological
(FNA) samples
were profiled by custom printed microarrays measuring over 2000 microRNAs to
identify
differentially expressed microRNAs and to develop a classifier.
Over 150 thyroid FNA samples (Table 7) were profiled by custom-printed
microarrays 5
measuring over 2000 microRNAs and on 96 microRNAs by qPCR. Figures 3A (cohort
I) and 3B
(cohort II) show the median microRNA expression levels on microarrays in
patients with
malignant nodules (y-axis) and in patients with benign nodules (x-axis). For
each microRNA, the
values in the two groups were compared by Mann-Whitney test with FDR=0.1.
Differential expression of microRNAs was found between benign and malignant 10
neoplasms. Classification of malignant vs. benign smears based on two
microRNAs: hsa-miR-
146b-5p and hsa-miR-375 results in over 85% accuracy (based on the median of
ten 10-fold
cross-validation runs, data not shown).
Example 6: hsa-miR-375 is a significant marker for medullary thyroid carcinoma
in FNA 15
samples
Expression level of hsa-miR-375 (SEQ ID NO.8) in FNA cohort I was compared
between medullary thyroid cancer samples (n=6) and samples from other thyroid
nodules (n=75).
Results are shown in Figure 4. Thus, hsa-miR-375 is a significant marker for
medullary thyroid
carcinoma. 20
Example 7: Stained thyroid smears can be used for microRNA profiling
MicroRNA expression level in samples stained with different dyes was compared
in
order to evaluate microRNA stability and reproducibility of the microRNA level
detection upon
staining. A total of 143 smears from FNA cohort I were stained as follows: 60
with May- 25
GrUnwald Giemsa, 64 with DiffQuik and 19 with Papanicolaou. MicroRNA
expression levels in
duplicates of the same sample stained with different dyes showed significant
correlation (more
than expected). The similarity of miR-146b-5p expression levels between the
different stains is
further demonstrated in Figures 5A-5B, which shows that the normalized
expression level of
hsa-miR-146b-5p (SEQ ID NO.10-11) is similar when the same sample is stained
with different 30
dyes, as can be seen for the 52 May-Gri.inwald Giemsa -DiffQuik pairs (Fig.5A)
and for the 15
DiffQuik-Papanicolaou pairs (Fig.5B).
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Therefore, different cytological dyes used in the clinical setting
(Papanicolaou; May-
Grtinwald Giemsa; and DiffQuik) do not affect the detection and quantification
of microRNA
expression.
Example 8: Thyroid Classification ¨ Assay Development 5
A total of twenty-four (24) microRNAs overall were chosen for establishing the
status of
thyroid samples as malignant versus benign (Table 12). MicroRNA expression was
measured by
RT-PCR as described above. The list of miRs and their respective forward
primers are provided
in Table 8. First-strand generation was done using polyT adaptor presented
below. Forward
primers were sequence-specific while the reverse primer was universal.
Detection of the RT- 10
PCR products was done with the universal MGB probe for miRs hsa-miR-31-5p (SEQ
ID NO.5-
7) , hsa-miR-5701 (SEQ ID NO.35), hsa-miR-424-3p (SEQ ID NO.16), MID-50971
(SEQ ID
NO.34). MID-20094 (SEQ ID NO.27-28), MID-50976 (SEQ ID NO.33), hsa-miR-3074-5p
(SEQ ID NO.32). hsa-miR-222-3p (SEQ ID NO.1-2), MID-50969 (SEQ ID NO.29), hsa-
miR-
146b-5p (SEQ ID NO.10-11), hsa-miR-346 (SEQ ID NO.14), MID-16582 (SEQ ID
NO.25), or 15
with probes specific for the miRs as provided in Table 9.
The sequences of the reverse primer, the poly'I' adaptor and the MGB probe are
provided
below:
- Reverse primer
GCGAGCACAGAATTAATACGAC (SEQ ID NO.309); 20
- PolyT adaptor
GCGAGCACAGAATTAATACGACTCACTATCGGTTITTITTITTIVN (SEQ ID NO. 310),
where "V" may be any one of A, G or C; and "NT" may be any one of G, C. A or
U/T;
Universal MGB probe
AAAACCGATAGTGAGTCG (SEQ ID NO.311).
Table 8: Assay Development - MicroRNAs and forward primers
SEQ IDSEQ ID
microRNA Forward primer
NO. NO.
hsa-miR-222-3p 1,2 GCAGCTACATCTGGCTACTGGGT 312
hsa-miR-55 lb-3p 3,4 CAGTCATTTGGCGCGACCCATACTTGGT 313
hsa-miR-31-5p 5,6,7 AGGCAAGATGCTGGCATAGCT 314
hsa-miR-375 8 CAGTCATTTGGGTTTGTTCGTTCGGCTC 315
hsa-miR-125b-5p 9 CAGTCATTTGGGTCCCTGAGACCCTAAC 316
hsa-miR-146b-5p 10,11 TGGCTGAGAACTGAATTCCATAGGCT 317
hsa-miR-152-3p 12,13 CAGTCATTTGGCTCAGTGCATGACAGAA 318
hsa-miR-346 14 TGTCTGCCCGCATGCCTGCCTCT 319
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SEQ IDSEQ ID
microRNA Forward primer
NO. NO.
hsa-miR-181c-5p 15 CAGTCATTTGGCAACATTCAACCTGTCG 320
hsa-miR-424-3p 16 CAAAACGTGAGGCGCTGCTAT 321
hsa-miR-342-3p 17,18 CAGTCATTTGGGTCTCACACAGAAATCG 377
hsa-miR-138-5p 19,20,21 CAGTCATTTGGCAGCTGGTGTTGTGAAT 323
hsa-miR-486-5p 22 CAGTCATTTGGCTCCTGTACTGAGCTGC 324
hsa-miR-200c-3p 23,24 CAGTCATTTGGGTAATACTGCCGGGTAA 325
MID-16582 25 TTGGCAGTGAAGCATTGGACTGTA 326
hsa-miR-23a-3p 26 CAGTCATTTGGCATCACATTGCCAGGGA 327
MID-20094 27,28 CATTTGGCTAAGCCAGTTTCTGTCTGATA 328
MID-50969 29 TGGCATGACAGATTGACATGGACAATT 329
hsa-miR-345-5p 30,31 CAGTCATTTGGCGCTGACTCCTAGTCCA 330
hsa-miR-3074-5p 32 CGTTCCTGCTGAACTGAGCCAG 331
MID-50976 33 CCTGTCTGAGCGCCGCTC 332
MID-50971 34 CAGTCATTTGGCATACTCTGGTTTCTTTTC 333
hsa-miR-5701 35 AGTCATTTGGCTTATTGTCACGTTCTGATT 334
hsa-miR-574-3p 36,37 CAGTCATTTGGCCACGCTCATGCACACA 335
Table 9: Assay Development - MicroRNA Specific probes
microRNA Specific probe sequence SEQ ID NO.
hsa-miR-342-3p CCGTTTTTTTTTTTTACGGGTGC 336
hsa-miR-181c-5p CCGTTTTTTTTTTTTACTCACCG 337
hsa-miR-125b-5p CCGTTTTTTTTTTTTCACAAGTT 338
hsa-miR-375 CCGTTTTTTTTTTTTCACGCGAG 339
hsa-miR-486-5p CCGTTTTTTTTTTTTCTCGGGGC 340
hsa-miR-551b-3p CCGTTTTTTTTTTTTCTGAAACC 341
hsa-miR-23a-3p CCGTTTTTTTTTTTTGGAAATCC 342
hsa-miR-574-3p CCGTTTTTTTTTTTTGTGGGTGT 343
hsa-miR-152-3p CGTTTTTTTTTTTTCCAAGTTC 344
hsa-miR-200c-3p CGTTTTTTTTTTTTCCATCATT 345
hsa-miR-138-5p CGTTTTTTTTTTTTCGGCCTGA 346
hsa-miR-345-5p CGTTTTTTTTTTTTGAGCCCTG 347
Table 11: microRNA Markers in Thyroid Assay
microRNA SEQ ID NO. Marker
type
hsa-miR-222-3p 1,2
hsa-miR-551b-3p 3,4
hsa-miR-31-5p 5.6,7
Malignant
hsa-miR-375 8
hsa-miR-125b-5p 9
hsa-miR-146b-5p 10,11
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microRNA SEQ ID NO. Marker
type
hsa-miR-152-3p 12,13
hsa-miR-346 14
hsa-miR-181c-5p 15
hsa-miR-424-3p 16
hsa-miR-342-3p 17,18
hsa-miR-138-5p 19,20,21
hsa-miR-486-5p 22 Cell-type
specific
hsa-miR-200c-3p 23,24
MID-16582 25
hsa-miR-23a-3p 26
MID-20094 27,28
MID-50969 29
hs a-miR-345 -5p 30,31
hsa-miR-3074-5p 32 Normalizer
MID-50976 33
MID-50971 34
hsa-miR-5701 35
hsa-miR-574-3p 36,37
Marker microRNAs were selected based on their patterns of expression in
several
preliminary studies performed by the inventors (data not shown), and provided
the reasoning for
classifying the same as "malignant", "cell type" or alternatively, to be used
as normalizers.
"Malignant markers" hsa-miR-222-3p, hsa-miR-551b-3p. hsa-miR-31-5p, hsa-miR-
375, 5
hsa-miR-125b-5p, hsa-miR-152-3p, hsa-miR-346, hsa-miR-181c-5p, hsa-miR-424-3p
and hsa-
miR-146b-5p were established according to the level of expression of these
microRNAs in
malignant samples when compared with their expression in benign samples.
"Cell type" markers hsa-miR-486-5p, hsa-miR-342-3p, hsa-miR- l38-Sp' hsa-miR-
200c-
3p, and MID-16582 were chosen by the inventors according to their pattern or
expression as 10
exemplified below.
hsa-miR-486-5p (SEQ 11) NO.22) was found enriched in whole blood relative to
thyroid
epithelial cells. Along with other microRNAs (data not shown), it was found to
be associated
with the amount of blood in thyroid FNA samples. Thus, hsa-miR-486-5p (SEQ ID
NO.22) is
one example of whole blood marker. Several microRNAs were detected in high
correlation 15
(>0.85) with miR-486-5p, and may also be considered blood markers, including
hsa-miR-320a,
hsa-miR-106a-5p, hsa-miR-93-5p, hsa-miR- l7-3p, hsa-let-7d-5p,
hsa-miR- 107,
hsa-miR-103a-3p, hsa-miR-17-5p, hsa-miR-191-5p, hsa-miR-25-3p, hsa-miR-106b-
5p,
hsa-miR-20a-5p, hsa-miR- 18a-5p, hsa-miR- l44-3p, hsa-miR-140-3p, hsa-miR- 15b-
5p,
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hsa-miR-16-5p, hsa-miR-92a-3p, hsa-miR-484, hsa-miR-151a-5p, hsa-let-7f-5p,
hsa-let-7a-5p,
hsa-let-7c-5p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-185-5p,
hsa-miR-30d-5p,
hsa-miR-30b-5p, hsa-miR-30c-5p, hsa-miR-19b-3p, hsa-miR-26a-5p, hsa-miR-26b-
5p,
hsa-miR-425-5p, MID-19433, and hsa-miR-4306.
The inventors have observed upon measuring the microRNA profile of the blood 5
compartments, that a number of microRNAs were found elevated in different
blood cell types
(data not shown). Thus, hsa-miR-342-3p (SEQ ID NO.17-18) was one of the
microRNAs,
amongst others, which was enriched in white blood cells, and may therefore be
considered an
example of white blood cell marker. Interestingly, hsa-miR-342-3p showed to be
expressed in
correlation with hsa-miR-150-5p, suggesting that also hsa-miR-150-5p is a
white blood cell 10
marker. In addition, hsa-miR-146a-5p was also shown to be expressed in white
blood cells (data
not shown).
hsa-miR-200c-3p (SEQ ID NO.23-24) and hsa-miR-138-5p (SEQ ID NO.19-21) were
found enriched in epithelial cells. In a preliminary experiment, smears were
generated with
blood in the absence of thyroid tissue material, and compared with smears from
thyroid tissue. 15
Both hsa-miR-200c-3p (SEQ ID NO.23-24) and hsa-miR-138-5p (SEQ ID NO.19-21)
were
found to be expressed at much higher levels in the thyroid smears (both benign
and malignant)
compared to blood smears (data not shown). Other microRNAs were also found
enriched in
epithelial cells (data not shown). Thus, hsa-miR-200c-3p (SEQ ID NO.23-24) and
hsa-miR-138-
5p (SEQ ID NO.19-21) are examples of epithelial cell markers. Interestingly,
the inventors found 20
that the expression of hsa-miR-138-5p correlated with the presence of
epithelial cells, and in
certain subsets of the data hsa-miR-138-5p was found to be upregulated in
benign samples (data
not shown).
MID-16582 (SEQ ID NO.25) was found at higher expression levels in Hurthle
cells. In
preliminary studies, the inventors have surprisingly found that this microRNA
is upregulated in 25
follicular adenoma presenting Hurthle cells versus follicular adenomas not
indicated to have
Hurthle cells (Figures 6A-6B). This result may be attributed to the
mitochondrial enrichment
found in Hurthle cells. The present inventors have found that the sequence of
MID-16582 (SEQ
ID NO.25), as well as other nucleic acid sequences found in Hurthle cells, can
be mapped to
mitochondrial DNA (data not shown). Thus, MID-16582 is an example of Hurthle
cell marker. 30
Assay development training set included about 360 distinct samples. Most of
the samples
were stained FNA smears (Papanicolaou, May-GrUnwald Giemsa or Diff-Quik).
Forty-five (45)
FNA samples were in cell blocks. The samples were collected from medical
centers in Israel,
Europe and USA. Most of the samples were "indeterminate" FNAs (according to
Bethesda
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classification, 71 of class III. 113 of class IV and 74 of class V) while
others were "determinate"
(38 of class II, 60 of class VI). The training set was composed of malignant
(n=197) and benign
(n=155) thyroid nodules, and contained representatives of the eight main
histological subtypes of
thyroid nodules. Thirty-three of the samples came from thyroid nodules that
were less than 1 cm
in size. The smallest nodule size was 0.1 cm. Samples of medullary carcinoma
were excluded 5
from most of the analyses, unless where indicated. Table 10 provides the
distribution of the
samples per category.
Table 10: Training Study Cohort composition and Bethesda distribution
Histological type No.
Papillary carcinoma 84
Papillary carcinoma, follicular variant 77
Follicular carcinoma 16
Unspecified carcinoma 6
Medullary 14
Nodular hypeiplasia 65
Follicular adenoma 81
Hashimoto 6
Graves 3
Total Malignant 197
Total Benign 155
inconclusive 4
Bethesda No.
0
II 38
III 71
IV 113
V 74
VI 60
unknown 98
Determinate total 258
Indeterminate total 84
Samples from FNA smears routinely prepared as well as cell blocks were used
for total
RNA extraction and RT-PCR amplification. All the samples were tested with a
panel of 15
marker microRNAs and 9 microRNAs used as normalizers (Table 11).
Results of the training in a sub-set of samples (n=353) are shown in Figure 7.
Expression
of microRNAs hsa-miR-222-3p (SEQ ID NO.1-2), hsa-miR-551b-3p (SEQ ID NO.3-4),
hsa- 15
miR-31-5p (SEQ ID NO.5-7), hsa-miR-125b-5p (SEQ ID NO.9), hsa-miR-146b-5p (SEQ
ID
NO.10-11), hsa-miR-346 (SEQ ID NO.14), hsa-miR-181c-5p (SEQ NO.15), and hsa-
miR-
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375 (SEQ ID NO.8) above the threshold are found in correlation with malignant
samples. The
expression levels shown in Figure 7 were obtained by the following formula:
[50 - normalized
Ct of each marker]. The normalization was done by subtracting the mean signal
of the
normalizers. The value of the mean signal of the normalizers over all the
samples used, was
added to all the expression values detected, in order to bring the values to a
range more 5
manageable for calculation. Interestingly, expression levels of hsa-miR-125a-
5p correlate with
that of hs a -miR-125b-5p .
Example 9: Establishment of Classifiers for the Thyroid Assay
Four algorithms were used in order to establish the best classifier to be
implemented in 10
the thyroid assay, Discriminant Analysis, K-nearest neighbor (KNN), support
vector machine
(SIJV) and Ensemble of discriminant analysis classifiers (Discriminant
Analysis Ensemble).
The following parameters were established a priori:
- Priors: For all the algorithms used, priors were set to 70% for the
malignant and 30% for
the benign samples. 15
- Sample Set: In this example, three sample sets were analyzed. One sample
set included
malignant (n=183) plus benign (n=155) samples, which excludes the malignant
medullary
samples; referred to below and in the Figures as "malignant+benige. Another
sample set
included all "indeterminate- samples, which includes all samples classified as
Bethesda III, IV
and V, referred to below and in the Figures as "indeterminate". A third sample
set included 20
samples classified as Bethesda IV only, referred to below and in the Figures
as "Bethesda".
Samples from thyroid lesions classified as Bethesda IV are usually difficult
to classify by
cytological parameters. Therefore, it is important to establish a classifier
that is based on this
sub-group of samples. In addition, specific samples that presented technical
problems due to a
variety of reasons (e.g. malignant samples with Bethesda II; sample taken from
lymph nodes) 25
were excluded.
- Medullary samples were excluded from the classification. Therefore, in
this Example,
when referring to malignant samples it means non-medullary malignant.
- Normalization of microRNA expression levels: MicroRNA expression levels
were
normalized with the so-called normalizer microRNAs [hsa-miR-23a-3p, MID-20094,
MID- 30
50969, hsa-miR-345-5p, hsa-miR-3074-5p, MID-50976, MID-50971, hsa-miR-5701 and
hsa-
miR-574-3p1 and were subtracted from 50, in order for lower CTs to be
associated with higher
expression values.
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MicroRNA Ratios: Ratios were obtained from pairs of microRNAs in an attempt to
subtract certain factors from the classifier. Thus e.g. a ratio of hsa-miR-31-
5p:hsa-miR-342-3p
enables to reduce the contribution of white blood cells (through the
expression of hsa-miR-342-
3p, the denominator) in the expression of hsa-miR-31-5p (the numerator). Since
C is are in log-
scale, ratios were created by subtracting one miR expression from the other.
Each ratio was 5
further normalized by adding a constant, in order for the ratios to be within
the same range as the
microRNA normalized values.
Example 9.1: Discriminant Analysis Classifier
When discriminant analysis was used as the algorithm, a linear discriminant
type of 10
discriminant analysis was applied, in three sets of samples as mentioned above
(using as features
either different combinations of microRNA expression levels (Fig.8A-8C,
Fig.23A-23C and
Fig.37A-37C), microRNA ratios (Fig.9A-9C, Fig.24A-24C and Fig.38A-38C), or a
combination
of microRNA expression levels and microRNA ratios (Fig.10A-10C, Fig.25A-25C
and Fig.39A-
39C). 15
As mentioned above, three sets of samples were run with this algorithm.
Figures 8A-8C,
Fig.9A-9C and Fig. 10A-10C provide the results of this algorithm on
malignant+benign samples.
Figures 23A-23C, Fig.24A-24C and Fig. 25A-25C provide the results of this
algorithm on
indeterminate samples. Figures 37A-37C, Fig.38A-38C and Fig. 39A-39C provide
the results of
this algorithm on Bethesda IV samples. 20
Example 9.2: KNN Classifier
One analysis was performed using KNN (k-nearest neighbors) as the algorithm,
in which
k=5 was used with a distance metric of Pearson correlation. The analysis with
the KNN
algorithm was applied to three sets of samples as mentioned above
(malignant+benign, 25
indeterminate and Bethesda IV) using as features either different combinations
of microRNA
expression levels (Fig.11A-11C, Fig.26A-26C and Fig.40A-40C), microRNA ratios
(Fig.12A-
12B, Fig.27A-27B and Fig.41A-41B), or a combination of microRNA expression
levels and
microRNA ratios (Fig.13A-13C, Fig.28A-28C and Fig. 42A-42C).
As mentioned above, three sets of samples were run with this algorithm.
Figures 11A- 30
11C, Fig.12A-12B and Fig. 13A-13C provide the results of this algorithm on
malignant+benign
samples. Figures 26A-26C, Fig.27A-27B and Fig. 28A-28C provide the results of
this algorithm
on indeterminate samples. Figures 40A-40C, Fig.41A-41C and Fig. 42A-42C
provide the results
of this algorithm on Bethesda IV samples.
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Example 9.3: SVM Classifier
A third analysis was performed applying SVM (Support vector machine) as the
algorithm, in which linear kernel was used. The analysis with the SVM
algorithm was applied to
three sets of samples as mentioned above (malignant+benign, indeterminate and
Bethesda IV), 5
using as features either different combinations of microRNA expression levels
(Fig.14A-14C,
Fig.29A-29C and Fig.43A-43C), microRNA ratios (Fig.15A-15C, Fig.30A-30C and
Fig.44A-
44C), or a combination of microRNA expression levels and microRNA ratios
(Fig.16A-16C,
Fig. 31A-31C and Fig. 45A-45C), respectively.
As mentioned above, three sets of samples were run with this algorithm.
Figures 14A- 10
14C, Fig.15A-15C and Fig. 16A-16C provide the results of this algorithm on
malignant+benign
samples. Figures 29A-29C, Fig.30A-30C and Fig. 31A-31C provide the results of
this algorithm
on indeterminate samples. Figures 43A-43C, Fig.44A-44C and Fig. 45A-45C
provide the results
of this algorithm on Bethesda IV samples.
Example 9.4: Ensemble Methods Classifier
A fourth analysis was performed applying Ensemble methods as the algorithm. An
ensemble of up to 100 discriminant analysis classifiers was created using
AdaBoost and applied
to the data. The analysis with the Ensemble algorithm was applied to three
sets of samples as
mentioned above (malignant+benign, indeterminate and Bethesda IV). using as
features either 20
different combinations of microRNA expression levels (Fig.17A-17C, Fig.32A-32C
and
Fig.46A-46C), microRNA ratios (Fig.18A-18C, Fig.33A-33C and Fig.47A-47C), or a
combination of microRNA expression levels and microRNA ratios (Fig.19A-19C,
Fig.34A-34C
and Fig.48A-48C).
As mentioned above, three sets of samples were run with this algorithm.
Figures 17A- 25
17C, Fig.18A-18C and Fig. 19A-19C provide the results of this algorithm on
malignant+benign
samples. Figures 32A-32C, Fig.33A-33C and Fig. 34A-34C provide the results of
this algorithm
on indeterminate samples. Figures 46A-46C, Fig.47A-47C and Fig. 48A-48C
provide the results
of this algorithm on Bethesda IV samples.
Example 10. A classifier for malignant samples including medullary
The same sample set used in Example 9, but including medullary malignant
samples was
used for establishing a classifier. All classifiers were applied in this set
of samples, and a
representative set of results from the discriminant analysis algorithm applied
in the set of
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samples is presented in Figures 51 and 52. When normalized values of two
microRNA ratios
(hsa-miR-125b-5p:hsa-miR-138-5p; and hs a-miR-146b-5p :hsa-miR-342-3p) were
used as the
features for the classification, the sensitivity of the classifier was 84.7%
and the specificity,
80.8%. When the normalized values of two microRNAs (hsa-miR-222-3p and hsa-miR-
55 lb-3p)
were used as the features for the classification, the sensitivity was 85.2%
and the specificity, 5
53.6%.
Example 11: Elimination of Samples through the Expression of Cell Specific
Markers
One important consideration throughout this study was the accuracy of the
result that is
to be provided to a patient who has had an FNA sample collected. Laboratories
tend to err in 10
order not to provide false-negative results. On the other hand, in the
analysis of FNA specimens,
a suspicious diagnostic will send the patient to surgery, which in more than
25% of the cases
turns out to be unnecessary. For example, at least one report in the
literature described that
thyroid tumor samples with large amounts of blood, or even pure blood, are
misdiagnosed as
suspicious in 7 out of 9 cases (Walsh et al. (2012) J Clin Endocrin Metab.
doi:10.1210/jc.2012- 15
1923).
With this goal in mind, the present inventors searched for microRNAs that
could be used
as cell type markers and aid in the screening of the quality of the specimen
examined. Thus, the
expression of hsa-miR-486-5p (SEQ ID NO.22) and hsa-miR-200c-3p (SEQ ID NO.23-
24) was
evaluated in the training cohort, including cell blocks, having samples from
benign and 20
malignant (non-medullary) thyroid lesions, as well as four samples of blood
only (slides of blood
smears were generated for this purpose, and RNA extracted as described
herein). Figure 53
shows the result of this experiment. The blood microRNA marker, hsa-miR-486-5p
is very high
and the epithelial marker, hsa-miR-200c-3p, is very low, compared to the
threshold established
in the training set. The blood smear samples were therefore filtered out using
these markers. This 25
expression pattern indicates that these samples do not have enough epithelial
cells (for lack of
the epithelial cell marker) to continue the test. In a test situation, these
four samples of blood
smears would be disqualified and discarded. Expression of hsa-miR-138-5p (SEQ
ID NO.19-21)
has also been shown to be low, compared to the threshold, in blood smears
(data not shown).
Samples with this profile are eligible to be disqualified and/or discarded
from the protocol for 30
classification of thyroid lesion samples.
The inventors had previously established that expression of hsa-miR-342-3p
(SEQ ID
NO.17-18) correlates with white blood cells (data not shown). Hence, high
expression of hsa-
miR-342-3p compared to the threshold indicated lack of sufficient thyroid
cells, and samples
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with this profile are eligible to be disqualified and/or discarded from the
protocol for
classification of thyroid lesion samples.
In parallel, high expression of hsa-miR-200c-3p is an indicator of the
presence of
epithelial cells in general, and specifically thyroid cells (data not shown
and Figure 53). Hence,
the expression of hsa-miR-200c-3p above a threshold may be used as an
indicator of sufficiency 5
of thyroid cells in the sample.
Example 12: Classification of Thyroid Tumor Sub-types
Classification of benign thyroid tumor sub-types was done using samples from
Hashimoto (n=6) and follicular adenoma (FA; n=81), from the Training cohort.
The results are 10
presented in Figure 54. Expression of hsa-miR-342-3p (SEQ ID NO.17-18) and hsa-
miR-31-5p
in Hashimoto samples was high compared to the threshold established in the
training set. Thus,
high expression of hsa-miR-342-3p alone or in combination with hsa-miR-31-5p
may be used for
the classification of samples as benign, and further sub-typing as Hashimoto.
Further, the inventors also tested microRNA ratios for sub-typing benign
thyroid tumors. 15
In this context, the miR ratio of hsa-miR-125b-5p:hsa-miR-200c-3p was
significant for
classifying follicular adenoma (FA) versus Hashimoto samples (data not shown).
Classification of malignant thyroid tumor sub-types was done using a subset of
samples
(n=177) of the Training cohort. Figure 55 provides one example of an analysis,
in which 146b-
5p, 222-3p, 31-5p, 125b-5p, 551-3p and 375 were found to be highly expressed
in papillary 20
carcinoma, while MID-16582 was found to be highly expressed in follicular
carcinoma.
The ratios of the following miR pairs were significant for classifying
Papillary
Carcinoma (PC) versus Follicular Carcinoma samples: hsa-miR-146b-5p:hsa-miR-
342-3p, hsa-
miR-125b-5p:hsa-miR-200c-3p, hsa-miR-222-3p :hsa-miR-486-5p, hsa-miR-31-5p :
hsa-miR-
342-3p, MID-16582 :hs a-miR-200c-3p, MID-16582 :hs a-miR-138-5p (data not
shown). 25
Therefore, the inventors have demonstrated that malignant thyroid tumor sub-
typing may
be performed using miR ratios, particularly miR ratios where the denominator
is a cell marker
microRNA, such as hsa-miR-486-5p, hsa-miR-200c-3p, hsa-miR-138-5p, and hsa-miR-
342-3p.
Example 13: Protocol for the classification of thyroid nodules as malignant or
benign 30
Figure 56 presents a flowchart with the protocol for thyroid nodule sample
analysis, from
collection of FNA samples to laboratory analysis and diagnostic. FNA samples
are collected
from patients having thyroid nodules, and are routinely processed. Smears are
prepared from the
FNA samples. As a first step, a specialist in cytopathology examines the FNA
sample and
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provides an analysis. In cases where the analysis is inconclusive,
particularly in samples
classified as Bethesda III, IV, or V, i.e. so-called "indeterminate", the
sample is sent to Rosetta
Genomics' laboratories to undergo microRNA profiling and conclusive
diagnostic. Total RNA is
extracted from the sample, which undergoes microRNA profiling. MicroRNA
profiling may be
performed by amplification (RT-PCR or NGS) or hybridization (microarray), as
shown in the 5
Examples above.
The protocol may include any one of the following:
- One or more algorithms may be used during classification, and will be
applied on data
comprising single microRNAs expression, microRNA ratios, or a combination
thereof.
- Samples wherein the hsa-miR-375 expression level is above a specific
threshold may be 10
determined as malignant (e.g. a threshold of at least 10, or a threshold of at
least 18), as
demonstrated for example in Figures 4 (expression analyzed by array) and 20
(expression
analyzed by PCR). The threshold is dependent on the normalization of the
samples, as well as on
the methodology used for measuring the microRNAs. The threshold may also be a
function of
the target sensitivity and specificity. 15
- Samples wherein the hsa-miR-146b-5p expression level is above a specific
threshold will
be determined as malignant (e.g. a threshold of at least 16), as demonstrated
for example in
Figures 21, 35 and 49. The threshold is dependent on the normalization of the
samples, as well as
on the methodology used for measuring the microRNAs. The threshold may also be
a function of
the target sensitivity and specificity. 20
- Samples wherein the ratio hsa-miR-146b-5p:hsa-miR-342-3p, further to
normalization, is
above a specific threshold will be determined as malignant (e.g. a threshold
of at least 16), as
demonstrated for example in Figures 22, 36 and 50. The threshold is dependent
on the
normalization of the samples, as well as on the methodology used for measuring
the microRNAs.
- The level of expression of the normalizers may be used as an indicator
for discarding 25
samples, due to insufficient tumor-derived material. Thus, samples presenting
low levels of any
of the normalizers, or the minimal, median or maximal value of expression for
the normalizers
may be discarded. For example, low levels of hsa-miR-23a-3p (compared to the
overall levels of
hsa-miR-23a-3p expression in the cohort) are likely to be misclassified. In
counterpart, high
levels of hsa-miR-23a-3p improve the classification by improving sensitivity
and specificity 30
(data not shown).
Analysis of the microRNA profiling data leads to diagnostic of the thyroid
nodule as
benign or malignant. Results permitting, which include the expression of
microRNAs that may
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be associated with thyroid tumor sub-types, as shown in Figures 54 and 55, for
example, the
sample is further classified according to its thyroid tumor subtype.
The foregoing description of the specific embodiments so fully reveals the
general nature
of the invention that others can, by applying current knowledge, readily
modify and/or adapt for 5
various applications such specific embodiments without undue experimentation
and without
departing from the generic concept, and, therefore, such adaptations and
modifications should
and are intended to be comprehended within the meaning and range of
equivalents of the
disclosed embodiments. Although the invention has been described in
conjunction with specific
embodiments thereof, it is evident that many alternatives, modifications and
variations will be 10
apparent to those skilled in the art. Accordingly, it is intended to embrace
all such alternatives,
modifications and variations that fall within the spirit and broad scope of
the appended claims.
It should be understood that the detailed description and specific examples,
while
indicating preferred embodiments of the invention, are given by way of
illustration only, since
various changes and modifications within the spirit and scope of the invention
will become 15
apparent to those skilled in the art from this detailed description.
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