U.S. patent application number 13/182779 was filed with the patent office on 2012-03-15 for biomarkers for diagnosis of stroke and its causes.
This patent application is currently assigned to Regents of the University of California. Invention is credited to Glen C. Jickling, Frank Sharp, Boryana Stamova.
Application Number | 20120065087 13/182779 |
Document ID | / |
Family ID | 45470084 |
Filed Date | 2012-03-15 |
United States Patent
Application |
20120065087 |
Kind Code |
A1 |
Sharp; Frank ; et
al. |
March 15, 2012 |
BIOMARKERS FOR DIAGNOSIS OF STROKE AND ITS CAUSES
Abstract
The present invention provides compositions and methods for the
diagnosis of the occurrence and cause of stroke.
Inventors: |
Sharp; Frank; (Davis,
CA) ; Stamova; Boryana; (Davis, CA) ;
Jickling; Glen C.; (Sacramento, CA) |
Assignee: |
Regents of the University of
California
Oakland
CA
|
Family ID: |
45470084 |
Appl. No.: |
13/182779 |
Filed: |
July 14, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61364449 |
Jul 15, 2010 |
|
|
|
Current U.S.
Class: |
506/9 ; 435/6.11;
506/16 |
Current CPC
Class: |
G01N 2800/2871 20130101;
C12Q 1/6883 20130101; C12Q 2600/16 20130101; G01N 2800/50 20130101;
G01N 33/6896 20130101; C12Q 2600/158 20130101 |
Class at
Publication: |
506/9 ; 506/16;
435/6.11 |
International
Class: |
C40B 30/04 20060101
C40B030/04; C12Q 1/68 20060101 C12Q001/68; C40B 40/06 20060101
C40B040/06 |
Goverment Interests
STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED
RESEARCH AND DEVELOPMENT
[0002] This invention was made with Government support under Grant
No. NS056302, awarded by the National Institutes of Health and
National Institute of Neurological Disorders and Stroke (NINDS) and
Grant No. 077501 4N, awarded by the American Heart Association. The
government has certain rights in this invention.
Claims
1. A method for diagnosing the occurrence and cause of ischemic
stroke or a predisposition for developing ischemic stroke, the
method comprising: a) determining a level of expression of at least
15 ischemic stroke-associated biomarkers in a biological sample
from a patient, wherein the biomarkers comprise a plurality of
biomarkers selected from Table 7A, a plurality of biomarkers
selected from Table 13A, a plurality of biomarkers selected from
Table 14 and a plurality of biomarkers selected from Table 15; and
b) comparing the level of expression of the ischemic
stroke-associated biomarkers to the expression level of a plurality
of stably expressed endogenous reference biomarkers, wherein an
increase or decrease of the expression level of the plurality of
biomarkers selected from Table 7A compared to the expression level
of the plurality of endogenous reference biomarkers indicates that
the patient suffers from or is at risk of developing ischemic
stroke; wherein an increase or decrease of the expression level of
the plurality of biomarkers selected from Table 13A compared to the
expression level of the plurality of endogenous reference
biomarkers indicates that the patient suffers from or is at risk of
developing cardioembolic stroke; wherein an increase or decrease of
the expression level of the plurality of biomarkers selected from
Table 14 compared to the expression level of the plurality of
endogenous reference biomarkers indicates that the patient suffers
from or is at risk of developing carotid stenosis; and wherein an
increase or decrease of the expression level of the plurality of
biomarkers selected from Table 15 compared to the expression level
of the plurality of endogenous reference biomarkers indicates that
the patient suffers from or is at risk of developing atrial
fibrillation, thereby diagnosing the occurrence and cause of
ischemic stroke or the predisposition for developing ischemic
stroke.
2-6. (canceled)
7. The method claim 1, wherein the patient has at least one
vascular risk factor and an increased expression level of one or
more ischemic stroke-associated biomarkers of Table 7A selected
from the group consisting of RNF141, CLEC4E, TIMP2, PHTF1, CKLF,
CKLF, RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B,
ENTPD1, BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3,
AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6,
HNRNPH2, GAB1, UBR5 and VAPA, and/or wherein a decreased expression
level of one or more ischemic stroke-associated biomarkers of Table
7A selected from the group consisting of PGM5,
CCDCl44C///LOC100134159, LECT2, SHOX, TBX5, SPTLC3, SNIP, RBMS3,
P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1, CXADR, OVOL2, SPIB,
BXDC5, UNC5B, ASTN2, F1135934, ANKRD28, CCDCl44A, TIMM8A, ALDOAP2,
LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1, HNRNPUL2, FCRL4, ELAVL2,
PRTG, DLX6, FOXA2, SCD5, GABRB2, GYPA, LOC283027, LOC344595, RPL22,
LOC100129488, RPL22 and SH3GL3 indicates that the patient suffers
from or is at risk of developing ischemic stroke.
8-11. (canceled)
12. The method of claim 1, wherein an increased expression level of
one or more ischemic stroke-associated biomarkers of Table 13A
selected from the group consisting of IRF6, ZNF254, GRM5, EXT2,
AP3S2, PIK3C2B, ARHGEF5, COL13A1, PTPN20A///PTPN20B, LHFP, BANK1,
HLA-DOA, EBF1, TMEM19, LHFP, FCRL1, OOEP and LRRC37A3, and/or
wherein a decreased expression level of one or more ischemic
stroke-associated biomarkers of Table 13A selected from the group
consisting of LOC284751, CD46, ENPP2, C19orf28, TSKS, CHURC1,
ADAMTSL4, FLJ40125, CLEC18A, ARHGEF12, C16orf68, TFDP1 and GSTK1
indicates that the patient has experienced or is at risk for
cardioembolic stroke.
13. (canceled)
14. The method of claim 1, wherein an increased expression level of
one or more ischemic stroke-associated biomarkers of Table 14
selected from the group consisting of NT5E, CLASP2, GRM5, PROCR,
ARHGEF5, AKR1C3, COL13A1, LHFP, RNF7, CYTH3, EBF1, RANBP10, PRSS35,
C12orf42 and LOC100127980, and/or wherein a decreased expression
level of one or more ischemic stroke-associated biomarkers of Table
14 selected from the group consisting of FLJ31945, LOC284751,
LOC100271832, MTBP, ICAM4, SHOX2, DOPEY2, CMBL, LOC146880, SLC20A1,
SLC6A19, ARHGEF12, C16orf68, GIPC2 and LOC100144603 indicates that
the patient has experienced or is at risk for carotid stenosis.
15. (canceled)
16. The method of claim 1, wherein an increased expression level of
one or more ischemic stroke-associated biomarkers of Table 15
selected from the group consisting of SMC1A, SNORA68, GRLF1, SDC4,
HIPK2, LOC100129034, CMTM1 and TTC7A, and/or wherein a decreased
expression level of one or more ischemic stroke-associated
biomarkers of Table 15 selected from the group consisting of
LRRC43, MIF///SLC2A11, PER3, PPIE, COL13A1, DUSP16, LOC100129034,
BRUNOL6, GPR176, C6orf164 and MAP3K71P1 indicates that the patient
has experienced or is at risk for atrial fibrillation.
17-24. (canceled)
25. A method for diagnosing ischemic stroke or a predisposition for
developing ischemic stroke, the method comprising: determining a
level of expression of a plurality of ischemic stroke-associated
biomarkers in a biological sample from a patient, wherein an
increase or decrease of the level compared to a control indicates
that the patient suffers from or is at risk of developing ischemic
stroke, wherein the plurality of ischemic stroke-associated
biomarkers is selected from the biomarkers set forth in Table
7A.
26. (canceled)
28. The method of claim 25, wherein the patient has at least one
vascular risk factor and an increased expression level of one or
more ischemic stroke-associated biomarkers of Table 7A selected
from the group consisting of RNF141, CLEC4E, TIMP2, PHTF1, CKLF,
CKLF, RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B,
ENTPD1, BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3,
AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6,
HNRNPH2, GAB1, UBR5 and VAPA, and/or wherein a decreased expression
level of one or more ischemic stroke-associated biomarkers of Table
7A selected from the group consisting of PGM5,
CCDCl44C///LOC100134159, LECT2, SHOX, TBX5, SPTLC3, SNIP, RBMS3,
P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1, CXADR, OVOL2, SPIB,
BXDC5, UNC5B, ASTN2, F1135934, ANKRD28, CCDCl44A, TIMM8A, ALDOAP2,
LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1, HNRNPUL2, FCRL4, ELAVL2,
PRTG, DLX6, FOXA2, SCD5, GABRB2, GYPA, LOC283027, LOC344595, RPL22,
LOC100129488, RPL22 and SH3GL3 indicates that the patient suffers
from or is at risk of developing ischemic stroke.
29-58. (canceled)
59. A method for determining the cause of ischemic stroke in a
patient in need thereof, the method comprising: determining a level
of expression of a plurality of ischemic stroke-associated
biomarkers in a biological sample from a patient who has suffered
ischemic stroke, wherein an increase or decrease of the level
compared to a control indicates that the patient has experienced
cardioembolic stroke, wherein the plurality of ischemic
stroke-associated biomarkers is selected from the biomarkers set
forth in Table 13A.
60. The method of claim 59, wherein an increased expression level
of one or more ischemic stroke-associated biomarkers of Table 13A
selected from the group consisting of IRF6, ZNF254, GRM5, EXT2,
AP3S2, PIK3C2B, ARHGEF5, COL13A1, PTPN20A///PTPN20B, LHFP, BANK1,
HLA-DOA, EBF1, TMEM19, LHFP, FCRL1, OOEP and LRRC37A3, and/or
wherein a decreased expression level of one or more ischemic
stroke-associated biomarkers of Table 13A selected from the group
consisting of LOC284751, CD46, ENPP2, C19orf28, TSKS, CHURC1,
ADAMTSL4, F1140125, CLEC18A, ARHGEF12, C16orf68, TFDP1 and GSTK1
indicates that the patient has experienced cardioembolic
stroke.
61-75. (canceled)
76. A method for determining the cause of ischemic stroke in a
patient in need thereof, the method comprising: determining a level
of expression of a plurality of ischemic stroke-associated
biomarkers in a biological sample from a patient who has suffered
ischemic stroke, wherein an increase or decrease of the level
compared to a control indicates that the patient has experienced
carotid stenosis, wherein the plurality of ischemic
stroke-associated biomarkers is selected from the biomarkers set
forth in Table 14.
77. The method of claim 76, wherein an increased expression level
of one or more ischemic stroke-associated biomarkers of Table 14
selected from the group consisting of NT5E, CLASP2, GRM5, PROCR,
ARHGEF5, AKR1C3, COL13A1, LHFP, RNF7, CYTH3, EBF1, RANBP10, PRSS35,
C12orf42 and LOC100127980, and/or wherein a decreased expression
level of one or more ischemic stroke-associated biomarkers of Table
14 selected from the group consisting of F1131945, LOC284751,
LOC100271832, MTBP, ICAM4, SHOX2, DOPEY2, CMBL, LOC146880, SLC20A1,
SLC6A19, ARHGEF12, C16orf68, GIPC2 and LOC100144603 indicates that
the patient has experienced carotid stenosis.
78-91. (canceled)
92. A method for determining the risk or susceptibility of
experiencing atrial fibrillation in a patient, the method
comprising: determining a level of expression of a plurality of
ischemic stroke-associated biomarkers in a biological sample from
the patient, wherein an increase or decrease of the level compared
to a control indicates that the patient has experienced or is at
risk for experiencing atrial fibrillation, wherein the plurality of
ischemic stroke-associated biomarkers is selected from the
biomarkers set forth in Table 15.
93. The method of claim 92, wherein an increased expression level
of one or more ischemic stroke-associated biomarkers of Table 15
selected from the group consisting of SMC1A, SNORA68, GRLF1, SDC4,
HIPK2, LOC100129034, CMTM1 and TTC7A, and/or wherein a decreased
expression level of one or more ischemic stroke-associated
biomarkers of Table 15 selected from the group consisting of
LRRC43, MIF///SLC2A11, PER3, PPIE, COL13A1, DUSP16, LOC100129034,
BRUNOL6, GPR176, C6orf164 and MAP3K71P1 indicates that the patient
has experienced or is at risk for atrial fibrillation.
94-108. (canceled)
109. A solid support comprising a plurality of nucleic acids that
hybridize to a plurality of the genes set forth in Table 7A, a
plurality of nucleic acids that hybridize to a plurality of the
genes set forth in Table 13A, a plurality of nucleic acids that
hybridize to a plurality of the genes set forth in Table 14 and a
plurality of nucleic acids that hybridize to a plurality of the
genes set forth in Table 15.
110-121. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 61/364,449, filed on Jul. 15, 2010, the entire
disclosure of which is hereby incorporated herein by reference for
all purposes.
FIELD OF THE INVENTION
[0003] The present invention provides compositions and methods for
diagnosing stroke and the risk of stroke, as well as the cause of
stroke.
BACKGROUND OF THE INVENTION
[0004] Stroke is a leading cause of adult death and disability
[Thom T et al., Circulation, 113:e85-151 (2006); WHO, The atlas of
heart disease and stroke (2005)]. The diagnosis of ischemic stroke
(IS) is made with clinical assessment in combination with brain
imaging. However, the diagnosis is not always straightforward,
particularly in the acute setting where an accurate, inexpensive
and rapid diagnosis is critical to optimally treat patients.
[0005] Extensive efforts have been directed toward identifying
blood based biomarkers for IS. More than 58 proteins and 7 panels
of proteins have been described as biomarkers of IS [Whiteley W et
al., Stroke, 39:2902-2909 (2008); Foerch C et al., Neurology,
73:393-399 (2009); Jensen M B et al., Expert Rev Cardiovasc Ther.,
7:389-393 (2009)]. RNA expression profiles in the blood have also
been described in IS [Tang Y et al., J Cereb Blood Flow Metab.,
26:1089-1102 (2006); Moore D F et al., Circulation, 111:212-221
2005]. We previously reported a 29-probe set expression profile
predictive of IS [Tang Y et al., J Cereb Blood Flow Metab.,
26:1089-1102 (2006)]. This profile required validation in a second
cohort, which has been done in the current study. Herein is
described a 97-probe set expression profile that differentiates IS
from controls, e.g., individuals who are healthy, have vascular
risk factors, or who have experienced myocardial infarction. These
profiles represent further refinement of gene expression as a
diagnostic tool in patients with acute IS.
[0006] Ischemic stroke is most commonly classified using the Trial
of ORG 10172 in Acute Stroke Treatment (TOAST) criteria, dividing
patients into cardioembolic, large vessel, small vessel lacunar,
other, and cryptogenic causes [Adams H P, Jr., et al., Stroke,
24:35-41 (1993)]. TOAST criteria improves rater reliability and
guides treatment when a known cause can be clearly identified
[Goldstein L B et al., Stroke, 32:1091-1098 (2001); Ay H et al.,
Stroke, 38:2979-2984 (2007)]. However, in many patients the cause
of stroke remains unknown or cryptogenic in spite of extensive
investigation. Given cryptogenic stroke accounts for approximately
30% of all ischemic strokes, better tools identify the cause of
stroke are required [Ionita C C et al., Prey Cardiol., 8:41-46
(2005)].
[0007] Blood based biomarkers present a valuable tool to determine
the cause of stroke. A number of protein biomarkers have been
associated with stroke subtypes. For example, cardioembolic stroke
is associated with brain natriuretic peptide and D-dimer; large
vessel stroke is associated with C-reactive protein; and small
vessel lacunar stroke is associated with homocysteine, ICAM-1, and
thrombomodulin [Laskowitz D T et al., Stroke, 40:77-85 (2009);
Shibazaki K et al., Intern Med., 48:259-264 (2009); Montaner J et
al., Stroke, 39:2280-2287 (2008); Hassan A et al., Brain,
126:424-432 (2003)]. However, biomarkers of ischemic stroke subtype
currently lack sufficient sensitivity and specificity to be used in
clinical practice. Thus, a combination of biomarkers into a
biomarker profile might be one method by which diagnostic
specificity and sensitivity can be improved.
[0008] The present study determined that gene expression signatures
in blood can be used to distinguish cardioembolic from large vessel
ischemic stroke, and can be used to predict the cardioembolic and
large vessel causes in patients with cryptogenic stroke. The
rationale for why changes in blood cell RNA expression occur in
ischemic stroke include inflammatory changes associated with acute
cerebral ischemia, symptomatic atherosclerosis and thromboembolism
[Xu H et al., J Cereb Blood Flow Metab., 28:1320-1328 (2008)9; Tang
Y et al., J Cereb Blood Flow Metab., 26:1089-1102 (2006); Du X et
al., Genomics, 87:693-703 (2006)]. Using whole genome microarrays,
a 40 gene profile was identified to distinguish cardioembolic
stroke from large vessel stroke, and a 37 gene profile was
identified to distinguish cardioembolic stroke due to atrial
fibrillation from non-atrial fibrillation causes. These genes play
roles in inflammation and represent a step toward better
determining the cause of cryptogenic stroke.
BRIEF SUMMARY OF THE INVENTION
[0009] The present invention provides methods for diagnosing or
predicting the occurrence of stroke and the cause of stroke by
determining the overexpression and underexpression of biomarkers in
blood.
[0010] Accordingly, in one aspect, the invention provides methods
for diagnosing the occurrence and cause of ischemic stroke or a
predisposition for developing ischemic stroke, the method
comprising:
[0011] a) determining a level of expression of at least 15 ischemic
stroke-associated biomarkers in a biological sample from a patient,
wherein the biomarkers are selected from the group consisting of a
plurality of biomarkers selected from Table 7A, a plurality of
biomarkers selected from Table 13A, a plurality of biomarkers
selected from Table 14 and a plurality of biomarkers selected from
Table 15;
[0012] b) comparing the level of expression of the ischemic
stroke-associated biomarkers to the expression level of a plurality
of stably expressed endogenous reference biomarkers, wherein an
increase or decrease of the expression level of the plurality of
biomarkers selected from Table 7A compared to the expression level
of the plurality of endogenous reference biomarkers indicates that
the patient suffers from or is at risk of developing ischemic
stroke;
[0013] wherein an increase or decrease of the expression level of
the plurality of biomarkers selected from Table 13A compared to the
expression level of the plurality of endogenous reference
biomarkers indicates that the patient suffers from or is at risk of
developing cardioembolic stroke;
[0014] wherein an increase or decrease of the expression level of
the plurality of biomarkers selected from Table 14 compared to the
expression level of the plurality of endogenous reference
biomarkers indicates that the patient suffers from or is at risk of
developing carotid stenosis;
[0015] wherein an increase or decrease of the expression level of
the plurality of biomarkers selected from Table 15 compared to the
expression level of the plurality of endogenous reference
biomarkers indicates that the patient suffers from or is at risk of
developing atrial fibrillation, thereby diagnosing the occurrence
and cause of ischemic stroke or the predisposition for developing
ischemic stroke. The levels of expression of the plurality of
biomarkers can be concurrently or sequentially determined.
[0016] In a related aspect, the invention provides methods for
diagnosing the occurrence and cause of ischemic stroke or a
predisposition for developing ischemic stroke, the method
comprising:
[0017] a) determining a level of expression of a plurality, e.g.,
at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or more or
all, ischemic stroke-associated biomarkers in a biological sample
from a patient, wherein the biomarkers are selected from the group
consisting of a plurality of biomarkers selected from Table 7A, a
plurality of biomarkers selected from Table 13A, a plurality of
biomarkers selected from Table 14 and a plurality of biomarkers
selected from Table 15;
[0018] b) comparing the level of expression of the ischemic
stroke-associated biomarkers to a control expression level,
[0019] wherein an increase or decrease of the expression level of
the plurality of biomarkers selected from Table 7A compared to the
control expression level indicates that the patient suffers from or
is at risk of developing ischemic stroke;
[0020] wherein an increase or decrease of the expression level of
the plurality of biomarkers selected from Table 13A compared to the
control expression level indicates that the patient suffers from or
is at risk of developing cardioembolic stroke;
[0021] wherein an increase or decrease of the expression level of
the plurality of biomarkers selected from Table 14 compared to the
control expression level indicates that the patient suffers from or
is at risk of developing carotid stenosis;
[0022] wherein an increase or decrease of the expression level of
the plurality of biomarkers selected from Table 15 compared to the
control expression level indicates that the patient suffers from or
is at risk of developing atrial fibrillation, thereby diagnosing
the occurrence and cause of ischemic stroke or the predisposition
for developing ischemic stroke. The levels of expression of the
plurality of biomarkers can be concurrently or sequentially
determined. The control expression level can be, e.g., with respect
to a plurality of stably expressed endogenous reference biomarkers,
with respect to the expression level of the same
ischemia-associated biomarker in an otherwise healthy individual
(optionally normalized to the expression levels of a plurality of
stably expressed endogenous reference biomarkers), or with respect
to a threshold level representative of the expression level of the
same ischemia-associated biomarker in an otherwise healthy
individual (optionally normalized to the expression levels of a
plurality of stably expressed endogenous reference biomarkers).
[0023] In various embodiments, the plurality of biomarkers
determined are from Table 7A. In various embodiments, the plurality
of biomarkers determined are from Table 13A. In various
embodiments, the plurality of biomarkers determined are from Table
14. In various embodiments, the plurality of biomarkers determined
are from Table 15. In various embodiments, the plurality of
biomarkers determined are from two or more of Table 7A, Table 13A,
Table 14 and Table 15.
[0024] In some embodiments, the plurality of stably expressed
endogenous reference biomarkers are selected from the biomarkers
listed in Table 16. In some embodiments, the ischemic
stroke-associated biomarkers are overexpressed or underexpressed at
least about 1.2-fold, 1.3-fold, 1.4-fold, 1.5-fold, 1.6-fold,
1.7-fold, 1.8-fold, 1.9-fold, 2.0-fold, 2.1 fold, 2.2-fold,
2.3-fold, 2.4-fold, 2.5-fold, 2.6-fold, 2.7-fold, 2.8-fold,
2.9-fold, 3.0-fold, 3.1-fold, 3.2-fold, 3.3-fold, 3.4-fold or
3.5-fold, or more, in comparison to the expression levels of a
plurality of stably expressed endogenous reference biomarkers,
e.g., those listed in Table 16. In some embodiments, the expression
levels of 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, or all,
the endogenous reference biomarkers selected from the group
consisting of USP7, MAPRE2, CSNK1G2, SAFB2, PRKAR2A, PI4 KB, CRTC1,
HADHA, MAP1LC3B, KAT5, CDC2L1///CDC2L2, GTSE1, CDC2L1///CDC2L2,
TCF25, CHP, LRRC40, hCG 2003956///LYPLA2///LYPLA2P1, DAXX, UBE2NL,
EIF1, KCMF1, PRKRIP1, CHMP4A, TMEM184C, TINF2, PODNL1, FBXO42,
LOC441258, RRP1, C10orf104, ZDHHC5, C9orf23, LRRC45, NACC1,
LOC100133445///LOC115110, PEX16 are determined as a control.
[0025] In some embodiments, the level of expression of about 15-85,
20-70, 30-60 or 40-50 biomarkers are determined. In some
embodiments, about 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70,
75, 80, 85, 90, 95 or 100 biomarkers are determined. In some
embodiments, the expression levels of at least about 3, 5, 10, 15,
20, 25, 30 or more biomarkers from Table 7A are determined. In some
embodiments, the expression levels of at least about 3, 5, 10, 15,
20, 25, 30 or more biomarkers from Table 13A are determined. In
some embodiments, the expression levels of at least about 3, 5, 10,
15, 20, 25, 30 or more biomarkers from Table 14 are determined. In
some embodiments, the expression levels of at least about 3, 5, 10,
15, 20, 25, 30 or more biomarkers from Table 15 are determined. In
some embodiments, the level of expression of all biomarkers listed
in Table 7A are determined. In some embodiments, the level of
expression of all biomarkers listed in Table 13A are determined. In
some embodiments, the level of expression of all biomarkers listed
in Table 14 are determined. In some embodiments, the level of
expression of all biomarkers listed in Table 15 are determined.
Stroke-associated biomarkers with increased and/or decreased
expression levels, e.g., in comparison to a control expression
level, can be determined.
[0026] In some embodiments, the level of expression of biomarkers
indicative of the occurrence of stroke is determined within 3 hours
of a suspected ischemic event. In an otherwise healthy individual
(i.e., no myocardial infarction, no vascular risk factors), an
increased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of FAT3, GADL1, CXADR, RNF141, CLEC4E, TIMP2, ANKRD28,
TIMM8A, PTPRD, CCRL1, FCRL4, DLX6, GABRB2, GYPA, PHTF1, CKLF, CKLF,
RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B, ENTPD1,
BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3, AMN1, LAMP2,
FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6, HNRNPH2, GAB1,
UBR5, VAPA, MCTP1 and SH3GL3 indicates that the patient suffers
from or is at risk of developing ischemic stroke. In an otherwise
healthy individual (i.e., no myocardial infarction, no vascular
risk factors), a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX,
TBX5, SPTLC3, SNIP, RBMS3, P704P, THSD4, SNRPN, GLYATL1,
DKRzP434L187, OVOL2, SPIB, BXDC5, UNC5B, ASTN2, FLJ35934, CCDCl44A,
ALDOAP2, LDB3, LOC729222///PPFIBP1, HNRNPUL2, ELAVL2, PRTG, FOXA2,
SCD5, LOC283027, LOC344595, RPL22, LOC100129488 and RPL22 indicates
that the patient suffers from or is at risk of developing ischemic
stroke.
[0027] In some embodiments, the level of expression of biomarkers
indicative of the occurrence of stroke is determined within 3 hours
of a suspected ischemic event. In an otherwise healthy individual
(i.e., no myocardial infarction, no vascular risk factors), an
increased expression level of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, or more or all, ischemic stroke-associated
biomarkers of Table 7A selected from the group consisting of FGD4,
F5, ABCA1, LOC100290882, LTB4R, UBXN2B, CKLF, CLEC4E, PHTF1,
ENTPD1, OSBPL1A, RRAGD, CPEB2, CKLF, BST1 and CKLF indicates that
the patient suffers from or is at risk of developing ischemic
stroke.
[0028] In various embodiments, in an individual presenting with
myocardial infarction within 3 hours of a suspected ischemic event,
an increased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of CLEC4E, TIMP2, FGD4, CPEB2, LTB4R and VNN3 indicates
that the individual suffers from or is at risk of developing
ischemic stroke. In various embodiments, in an individual
presenting with myocardial infarction within 3 hours of a suspected
ischemic event, a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX,
TBX5, SPTLC3, SNIP, RBMS3, P704P, THSD4, FAT3, SNRPN, GLYATL1,
GADL1, CXADR, OVOL2, RNF141, SPIB, BXDC5, UNC5B, ASTN2, FLJ35934,
ANKRD28, CCDCl44A, TIMM8A, ALDOAP2, LDB3, PTPRD,
LOC729222///PPFIBP1, CCRL1, HNRNPUL2, FCRL4, ELAVL2, PRTG, DLX6,
FOXA2, SCD5, GABRB2, GYPA, PHTF1, CKLF, CKLF, RRAGD, CKLF,
LOC100290882, UBXN2B, ENTPD1, BST1, F5, IFRD1, KIAA0319, CHMP1B,
MCTP1, AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6,
HNRNPH2, GAB1, UBR5, VAPA, LOC283027, LOC344595, RPL22,
LOC100129488, RPL22, MCTP1 and SH3GL3 indicates that the individual
suffers from or is at risk of developing ischemic stroke.
[0029] In various embodiments, in an individual presenting with
myocardial infarction within 3 hours of a suspected ischemic event,
a decreased expression level of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, or more or all, ischemic stroke-associated biomarkers of
Table 7A selected from the group consisting of THSD4, SNRPN, ASTN2,
SNIP, FAT3, TIMM8A, CCDCl44C///LOC100134159, ANKRD28, TBX5, PGM5,
SCD5, FCRL4, SHOX, CCRL1, LECT2, PTPRD, CCDCl44A, LDB3,
LOC729222///PPFIBP1, RBMS3, P704P, GYPA, PRTG, GABRB2, HNRNPUL2,
ELAVL2, SPTLC3, FOXA2, DLX6, ALDOAP2, and FLJ35934 indicates that
the individual suffers from or is at risk of developing ischemic
stroke. In various embodiments, in an individual presenting with
myocardial infarction within 3 hours of a suspected ischemic event,
a decreased expression level of 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10, or
more or all, ischemic stroke-associated biomarkers of Table 7A
selected from the group consisting of THSD4, SNRPN, ASTN2, SNIP,
FAT3, TIMM8A, CCDCl44C///LOC100134159, ANKRD28, TBX5, PGM5
indicates that the individual suffers from or is at risk of
developing ischemic stroke.
[0030] In various embodiments, in an individual presenting with one
or more vascular risk factors (e.g., hypertension, diabetes
mellitus, hyperlipidemia, or tobacco smoking) within 3 hours of a
suspected ischemic event, an increased expression level of one or
more or all ischemic stroke-associated biomarkers of Table 7A
selected from the group consisting of RNF141, CLEC4E, TIMP2, PHTF1,
CKLF, CKLF, RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B,
ENTPD1, BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3,
AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6,
HNRNPH2, GAB1, UBR5, VAPA and MCTP1 indicates that the individual
suffers from or is at risk of developing ischemic stroke. In
various embodiments, in an individual presenting with one or more
vascular risk factors within 3 hours of a suspected ischemic event,
a decreased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX, TBX5,
SPTLC3, SNIP, RBMS3, P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1,
CXADR, OVOL2, SPIB, BXDC5, UNC5B, ASTN2, FLJ35934, ANKRD28,
CCDCl44A, TIMM8A, ALDOAP2, LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1,
HNRNPUL2, FCRL4, ELAVL2, PRTG, DLX6, FOXA2, SCD5, GABRB2, GYPA,
LOC283027, LOC344595, RPL22, LOC100129488, RPL22 and SH3GL3
indicates that the individual suffers from or is at risk of
developing ischemic stroke.
[0031] In various embodiments, in an individual presenting with one
or more vascular risk factors (e.g., hypertension, diabetes
mellitus, hyperlipidemia, or tobacco smoking) within 3 hours of a
suspected ischemic event, an increased expression level of 1, 2, 3
or 4 ischemic stroke-associated biomarkers of Table 7A selected
from the group consisting of RNF141, ELL2, TIMP2 and CLEC4E
indicates that the individual suffers from or is at risk of
developing ischemic stroke. In various embodiments, in an
individual presenting with one or more vascular risk factors within
3 hours of a suspected ischemic event, a decreased expression level
of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or all,
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of SNIP, BXDC5, FAT3, LECT2, THSD4,
CCDCl44C///LOC100134159, OVOL2, SPTLC3, GLYATL1, RBMS3, SPIB,
DKFZP434L187, GADL1, SHOX, TBX5, UNC5B, PGM5 and CXADR indicates
that the individual suffers from or is at risk of developing
ischemic stroke.
[0032] In some embodiments, the level of expression of biomarkers
indicative of the occurrence of stroke is determined 3 or more
hours after a suspected ischemic event. In an otherwise healthy
individual (i.e., no myocardial infarction, no vascular risk
factors), an increased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX,
TBX5, SNIP, RBMS3, P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1,
CXADR, OVOL2, RNF141, CLEC4E, BXDC5, UNC5B, TIMP2, ASTN2, FLJ35934,
ANKRD28, CCDCl44A, TIMM8A, ALDOAP2, LDB3, PTPRD,
LOC729222///PPFIBP1, CCRL1, FCRL4, ELAVL2, PRTG, DLX6, SCD5,
GABRB2, GYPA, PHTF1, CKLF, CKLF, RRAGD, CLEC4E, CKLF, FGD4, CPEB2,
LOC100290882, UBXN2B, ENTPD1, BST1, LTB4R, F5, IFRD1, KIAA0319,
CHMP1B, MCTP1, VNN3, AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25,
FAR2, ST3GAL6, HNRNPH2, GAB1, UBR5, VAPA, LOC283027, LOC344595,
RPL22, LOC100129488 and MCTP1 indicates that the patient suffers
from or is at risk of developing ischemic stroke. In an otherwise
healthy individual (i.e., no myocardial infarction, no vascular
risk factors), a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of SPTLC3, DKRZP434L187, SPIB, HNRNPUL2, FOXA2,
RPL22 and SH3GL3 indicates that the patient suffers from or is at
risk of developing ischemic stroke.
[0033] In some embodiments, the level of expression of biomarkers
indicative of the occurrence of stroke is determined at least 24
hours after a suspected ischemic event. In an otherwise healthy
individual (i.e., no myocardial infarction, no vascular risk
factors), an increased expression level of 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of ZNF608, FCHO2, ST3GAL6, ABCA1, THBD, AMN1, QKI,
KIAA0319, MCTP1, VNN3, UBR5, FAR2, RBM25, CHMP1B, LAMP2, VAPA,
IFRD1, HNRNPH2, REM2 and GAB1 indicates that the patient suffers
from or is at risk of developing ischemic stroke. In an otherwise
healthy individual (i.e., no myocardial infarction, no vascular
risk factors), an increased expression level of 1, 2, 3, 4, 5, 6,
7, 8, 9 or 10, or more or all, ischemic stroke-associated
biomarkers of Table 7A selected from the group consisting of
ZNF608, FCHO2, ST3GAL6, ABCA1, THBD, AMN1, QKI, KIAA0319, MCTP1 and
VNN3 indicates that the patient suffers from or is at risk of
developing ischemic stroke.
[0034] In various embodiments, in an individual presenting with
myocardial infarction 3 or more hours after a suspected ischemic
event, an increased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of RNF141, CLEC4E, TIMP2, HNRNPUL2, PHTF1, CKLF, CKLF,
RRAGD, CLEC4E, CKLF, FGD4, CPEB2, UBXN2B, BST1, LTB4R, F5, IFRD1,
KIAA0319, MCTP1, VNN3, AMN1, LAMP2, ZNF608, FAR2, GAB1, VAPA and
MCTP1 indicates that the individual suffers from or is at risk of
developing ischemic stroke. In various embodiments, in an
individual presenting with myocardial infarction 3 or more hours
after a suspected ischemic event, a decreased expression level of
one or more or all ischemic stroke-associated biomarkers of Table
7A selected from the group consisting of PGM5,
CCDCl44C///LOC100134159, LECT2, SHOX, TBX5, SPTLC3, SNIP, RBMS3,
P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1, CXADR, OVOL2, SPIB,
BXDC5, UNC5B, ASTN2, F1135934, ANKRD28, CCDCl44A, TIMM8A, ALDOAP2,
LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1, FCRL4, ELAVL2, PRTG, DLX6,
FOXA2, SCD5, GABRB2, GYPA, LOC100290882, ENTPD1, CHMP1B, FCHO2,
LOC283027, REM2, QKI, RBM25, ST3GAL6, HNRNPH2, UBR5, LOC344595,
RPL22, LOC100129488, RPL22 and SH3GL3 indicates that the individual
suffers from or is at risk of developing ischemic stroke.
[0035] In various embodiments, in an individual presenting with
myocardial infarction at least 24 hours after a suspected ischemic
event, a decreased expression level of 1, 2, 3, 4, 5, 6, 7, or more
or all, ischemic stroke-associated biomarkers of Table 7A selected
from the group consisting of RPL22, LOC100129488, LOC283027,
LOC344595, THSD4, FAT3, P704P indicates that the individual suffers
from or is at risk of developing ischemic stroke.
[0036] In various embodiments, in an individual presenting with one
or more vascular risk factors 3 or more hours after a suspected
ischemic event, an increased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of RNF141, CLEC4E, TIMP2, PHTF1, CKLF, CKLF,
RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B, ENTPD1,
BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3, AMN1, LAMP2,
FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6, HNRNPH2, GAB1,
UBR5, VAPA and MCTP1 indicates that the individual suffers from or
is at risk of developing ischemic stroke. In various embodiments,
in an individual presenting with one or more vascular risk factors
3 or more hours after a suspected ischemic event, a decreased
expression level of one or more or all ischemic stroke-associated
biomarkers of Table 7A selected from the group consisting of PGM5,
CCDCl44C///LOC100134159, LECT2, SHOX, TBX5, SPTLC3, SNIP, RBMS3,
P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1, CXADR, OVOL2, SPIB,
BXDC5, UNC5B, ASTN2, F1135934, ANKRD28, CCDCl44A, TIMM8A, ALDOAP2,
LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1, HNRNPUL2, FCRL4, ELAVL2,
PRTG, DLX6, FOXA2, SCD5, GABRB2, GYPA, LOC283027, LOC344595, RPL22,
LOC100129488, RPL22 and SH3GL3 indicates that the individual
suffers from or is at risk of developing ischemic stroke.
[0037] In various embodiments, in an individual presenting with one
or more vascular risk factors at least 24 hours after a suspected
ischemic event, an increased expression level of one or both
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of TIMP2 and MCTP1 indicates that the individual
suffers from or is at risk of developing ischemic stroke. In
various embodiments, in an individual presenting with one or more
vascular risk factors at least 24 hours after a suspected ischemic
event, a decreased expression level of 1, 2, 3, 4, 5, 6, or 7
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of RPL22, SNIP, SH3GL3, FAT3, SPTLC3, RBMS3 and
SNRPN indicates that the individual suffers from or is at risk of
developing ischemic stroke.
[0038] With respect to the determination of the cause of stroke, in
some embodiments an increased expression level of one or more or
all ischemic stroke-associated biomarkers of Table 13A selected
from the group consisting of IRF6, ZNF254, GRM5, EXT2, AP3S2,
PIK3C2B, ARHGEF5, COL13A1, PTPN20A///PTPN20B, LHFP, BANK1, HLA-DOA,
EBF1, TMEM19, LHFP, FCRL1, OOEP and LRRC37A3 indicates that the
patient has experienced or is at risk for cardioembolic stroke. In
some embodiments, a decreased expression level of one or more or
all ischemic stroke-associated biomarkers of Table 13A selected
from the group consisting of LOC284751, CD46, ENPP2, C19orf28,
TSKS, CHURC1, ADAMTSL4, FLJ40125, CLEC18A, ARHGEF12, C16orf68,
TFDP1 and GSTK1 indicates that the patient has experienced or is at
risk for cardioembolic stroke.
[0039] In some embodiments an increased expression level of 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20
ischemic stroke-associated biomarkers of Table 13A selected from
the group consisting of EBF1, GRM5, AP3S2, LRRC37A3, IRF6, LHFP,
BANK1, ARHGEF5, ZNF254, COL13A1, P2RX5, LHFP, PIK3C2B, EXT2,
HLA-DOA, OOEP, ZNF185, TMEM19, FCRL1 and PTPN20A///PTPN20B
indicates that the patient has experienced or is at risk for
cardioembolic stroke. In some embodiments, a decreased expression
level of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or 13 ischemic
stroke-associated biomarkers of Table 13A selected from the group
consisting of TSKS, ENPP2, C16orf68, LOC284751, TFDP1, GSTK1,
ADAMTSL4, CHURC1, FLJ40125, ARHGEF12, CLEC18A, CD46 and C19orf28
indicates that the patient has experienced or is at risk for
cardioembolic stroke.
[0040] In some embodiments an increased expression level of 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, or more or all, ischemic stroke-associated
biomarkers of Table 13A selected from the group consisting of EBF1,
GRM5, AP3S2, LRRC37A3, IRF6, LHFP, BANK1, ARHGEF5, ZNF254 and
COL13A1 indicates that the patient has experienced or is at risk
for cardioembolic stroke. In some embodiments, a decreased
expression level of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more or all,
ischemic stroke-associated biomarkers of Table 13A selected from
the group consisting of TSKS, ENPP2, C16orf68, LOC284751, TFDP1,
GSTK1, ADAMTSL4, CHURC1, FLJ40125 and ARHGEF12 indicates that the
patient has experienced or is at risk for cardioembolic stroke.
[0041] With respect to the determination of the cause of stroke, in
some embodiments, an increased expression level of one or more or
all ischemic stroke-associated biomarkers of Table 14 selected from
the group consisting of NT5E, CLASP2, GRM5, PROCR, ARHGEF5, AKR1C3,
COL13A1, LHFP, RNF7, CYTH3, EBF1, RANBP10, PRSS35, C12orf42 and
LOC100127980 indicates that the patient has experienced or is at
risk for carotid stenosis. In some embodiments, a decreased
expression level of one or more or all ischemic stroke-associated
biomarkers of Table 14 selected from the group consisting of
FLJ31945, LOC284751, LOC100271832, MTBP, ICAM4, SHOX2, DOPEY2,
CMBL, LOC146880, SLC20A1, SLC6A19, ARHGEF12, C16orf68, GIPC2 and
LOC100144603 indicates that the patient has experienced or is at
risk for carotid stenosis.
[0042] With respect to the determination of the cause of stroke, in
some embodiments, an increased expression level of 2, 5, 10, 15, or
more or all, ischemic stroke-associated biomarkers of Table 14
selected from the group consisting of EBF1, COL13A1, LHFP, GRM5,
ARHGEF5, RNF7, CLASP2, RANBP10, LOC100127980, CYTH3, PROCR,
C12orf42, PRSS35, NT5E, and AKR1C3 indicates that the patient has
experienced or is at risk for carotid stenosis. In some
embodiments, a decreased expression level of 2, 5, 10, 15, or more
or all ischemic stroke-associated biomarkers of Table 14 selected
from the group consisting of FLJ31945, C16orf68, SLC20A1, DOPEY2,
LOC284751, LOC100144603, MTBP, SHOX2, GIPC2, CMBL, LOC146880,
SLC6A19, ICAM4, ARHGEF12, and LOC10027183 indicates that the
patient has experienced or is at risk for carotid stenosis.
[0043] With respect to the determination of the cause of stroke, in
some embodiments, an increased expression level of one or more or
all ischemic stroke-associated biomarkers of Table 15 selected from
the group consisting of SMC1A, SNORA68, GRLF1, SDC4, HIPK2,
LOC100129034, CMTM1 and TTC7A indicates that the patient has
experienced or is at risk for atrial fibrillation. In some
embodiments, a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 15 selected from the
group consisting of LRRC43, MIF///SLC2A11, PER3, PPIE, COL13A1,
DUSP16, LOC100129034, BRUNOL6, GPR176, C6orf164 and MAP3K71P1
indicates that the patient has experienced or is at risk for atrial
fibrillation.
[0044] With respect to the determination of the cause of stroke, in
some embodiments, an increased expression level of 1, 2, 3, 4, 5,
6, 7 or 8 ischemic stroke-associated biomarkers of Table 15
selected from the group consisting of CMTM1, SDC4, SNORA68, HIPK2,
TTC7A, GRLF1, LOC100129034, SMC1A indicates that the patient has
experienced or is at risk for atrial fibrillation. In some
embodiments, a decreased expression level of 1, 2, 3, 4, 5, 6, 7,
8, 9 or 10 ischemic stroke-associated biomarkers of Table 15
selected from the group consisting of COL13A1, C6orf164, GPR176,
BRUNOL6, MIF///SLC2A11, DUSP16, PPIE, MAP3K71P1, PER3, LRRC43
indicates that the patient has experienced or is at risk for atrial
fibrillation.
[0045] In a related aspect, the invention provides methods for
determining whether a stroke has occurred or predicting whether a
stroke will occur. Accordingly, the invention provides methods for
diagnosing ischemic stroke or a predisposition for developing
ischemic stroke, the method comprising: determining a level of
expression of a plurality of ischemic stroke-associated biomarkers
in a biological sample from a patient, wherein an increase or
decrease of the level compared to a control indicates that the
patient suffers from or is at risk of developing ischemic stroke,
wherein the plurality of ischemic stroke-associated biomarkers is
selected from the biomarkers set forth in Table 7A. In some
embodiments, the methods for determining the occurrence of stroke
comprise further determining the level of expression of one or
biomarkers listed in Table 7B. In some embodiments, the ischemic
stroke is a member selected from the group consisting of: embolic
stroke, thrombotic stroke, transient ischemic attack, cardioembolic
stroke and atherothrombotic stroke.
[0046] In some embodiments, the level of expression of biomarkers
indicative of the occurrence of stroke is determined within 3 hours
of a suspected ischemic event. In an otherwise healthy individual
(i.e., no myocardial infarction, no vascular risk factors), an
increased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of FAT3, GADL1, CXADR, RNF141, CLEC4E, TIMP2, ANKRD28,
TIMM8A, PTPRD, CCRL1, FCRL4, DLX6, GABRB2, GYPA, PHTF1, CKLF, CKLF,
RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B, ENTPD1,
BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3, AMN1, LAMP2,
FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6, HNRNPH2, GAB1,
UBR5, VAPA, MCTP1 and SH3GL3 indicates that the patient suffers
from or is at risk of developing ischemic stroke.
[0047] In an otherwise healthy individual (i.e., no myocardial
infarction, no vascular risk factors), a decreased expression level
of one or more or all ischemic stroke-associated biomarkers of
Table 7A selected from the group consisting of PGM5,
CCDCl44C///LOC100134159, LECT2, SHOX, TBX5, SPTLC3, SNIP, RBMS3,
P704P, THSD4, SNRPN, GLYATL1, DKRZP434L187, OVOL2, SPIB, BXDC5,
UNC5B, ASTN2, FLJ35934, CCDCl44A, ALDOAP2, LDB3,
LOC729222///PPFIBP1, HNRNPUL2, ELAVL2, PRTG, FOXA2, SCD5,
LOC283027, LOC344595, RPL22, LOC100129488 and RPL22 indicates that
the patient suffers from or is at risk of developing ischemic
stroke.
[0048] In various embodiments, in an individual presenting with
myocardial infarction within 3 hours of a suspected ischemic event,
an increased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of CLEC4E, TIMP2, FGD4, CPEB2, LTB4R and VNN3 indicates
that the individual suffers from or is at risk of developing
ischemic stroke. In various embodiments, in an individual
presenting with myocardial infarction within 3 hours of a suspected
ischemic event, a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX,
TBX5, SPTLC3, SNIP, RBMS3, P704P, THSD4, FAT3, SNRPN, GLYATL1,
GADL1, CXADR, OVOL2, RNF141, SPIB, BXDC5, UNC5B, ASTN2, FLJ35934,
ANKRD28, CCDCl44A, TIMM8A, ALDOAP2, LDB3, PTPRD,
LOC729222///PPFIBP1, CCRL1, HNRNPUL2, FCRL4, ELAVL2, PRTG, DLX6,
FOXA2, SCD5, GABRB2, GYPA, PHTF1, CKLF, CKLF, RRAGD, CKLF,
LOC100290882, UBXN2B, ENTPD1, BST1, F5, IFRD1, KIAA0319, CHMP1B,
MCTP1, AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6,
HNRNPH2, GAB1, UBR5, VAPA, LOC283027, LOC344595, RPL22,
LOC100129488, RPL22, MCTP1 and SH3GL3 indicates that the individual
suffers from or is at risk of developing ischemic stroke.
[0049] In various embodiments, in an individual presenting with one
or more vascular risk factors (e.g., hypertension, diabetes
mellitus, hyperlipidemia, or tobacco smoking) within 3 hours of a
suspected ischemic event, an increased expression level of one or
more or all ischemic stroke-associated biomarkers of Table 7A
selected from the group consisting of RNF141, CLEC4E, TIMP2, PHTF1,
CKLF, CKLF, RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B,
ENTPD1, BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3,
AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6,
HNRNPH2, GAB1, UBR5, VAPA and MCTP1 indicates that the individual
suffers from or is at risk of developing ischemic stroke. In
various embodiments, in an individual presenting with one or more
vascular risk factors within 3 hours of a suspected ischemic event,
a decreased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX, TBX5,
SPTLC3, SNIP, RBMS3, P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1,
CXADR, OVOL2, SPIB, BXDC5, UNC5B, ASTN2, FLJ35934, ANKRD28,
CCDCl44A, TIMM8A, ALDOAP2, LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1,
HNRNPUL2, FCRL4, ELAVL2, PRTG, DLX6, FOXA2, SCD5, GABRB2, GYPA,
LOC283027, LOC344595, RPL22, LOC100129488, RPL22 and SH3GL3
indicates that the individual suffers from or is at risk of
developing ischemic stroke.
[0050] In some embodiments, the level of expression of biomarkers
indicative of the occurrence of stroke is determined 3 or more
hours after a suspected ischemic event. In an otherwise healthy
individual (i.e., no myocardial infarction, no vascular risk
factors), an increased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX,
TBX5, SNIP, RBMS3, P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1,
CXADR, OVOL2, RNF141, CLEC4E, BXDC5, UNC5B, TIMP2, ASTN2, FLJ35934,
ANKRD28, CCDCl44A, TIMM8A, ALDOAP2, LDB3, PTPRD,
LOC729222///PPFIBP1, CCRL1, FCRL4, ELAVL2, PRTG, DLX6, SCD5,
GABRB2, GYPA, PHTF1, CKLF, CKLF, RRAGD, CLEC4E, CKLF, FGD4, CPEB2,
LOC100290882, UBXN2B, ENTPD1, BST1, LTB4R, F5, IFRD1, KIAA0319,
CHMP1B, MCTP1, VNN3, AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25,
FAR2, ST3GAL6, HNRNPH2, GAB1, UBR5, VAPA, LOC283027, LOC344595,
RPL22, LOC100129488 and MCTP1 indicates that the patient suffers
from or is at risk of developing ischemic stroke. In an otherwise
healthy individual (i.e., no myocardial infarction, no vascular
risk factors), a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of SPTLC3, DKRZP434L187, SPIB, HNRNPUL2, FOXA2,
RPL22 and SH3GL3 indicates that the patient suffers from or is at
risk of developing ischemic stroke.
[0051] In various embodiments, in an individual presenting with
myocardial infarction 3 or more hours after a suspected ischemic
event, an increased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of RNF141, CLEC4E, TIMP2, HNRNPUL2, PHTF1, CKLF, CKLF,
RRAGD, CLEC4E, CKLF, FGD4, CPEB2, UBXN2B, BST1, LTB4R, F5, IFRD1,
KIAA0319, MCTP1, VNN3, AMN1, LAMP2, ZNF608, FAR2, GAB1, VAPA and
MCTP1 indicates that the individual suffers from or is at risk of
developing ischemic stroke. In various embodiments, in an
individual presenting with myocardial infarction 3 or more hours
after a suspected ischemic event, a decreased expression level of
one or more or all ischemic stroke-associated biomarkers of Table
7A selected from the group consisting of PGM5,
CCDCl44C///LOC100134159, LECT2, SHOX, TBX5, SPTLC3, SNIP, RBMS3,
P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1, CXADR, OVOL2, SPIB,
BXDC5, UNC5B, ASTN2, FLJ35934, ANKRD28, CCDCl44A, TIMM8A, ALDOAP2,
LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1, FCRL4, ELAVL2, PRTG, DLX6,
FOXA2, SCD5, GABRB2, GYPA, LOC100290882, ENTPD1, CHMP1B, FCHO2,
LOC283027, REM2, QKI, RBM25, ST3GAL6, HNRNPH2, UBR5, LOC344595,
RPL22, LOC100129488, RPL22 and SH3GL3 indicates that the individual
suffers from or is at risk of developing ischemic stroke.
[0052] In various embodiments, in an individual presenting with one
or more vascular risk factors 3 or more hours after a suspected
ischemic event, an increased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of RNF141, CLEC4E, TIMP2, PHTF1, CKLF, CKLF,
RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B, ENTPD1,
BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3, AMN1, LAMP2,
FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6, HNRNPH2, GAB1,
UBR5, VAPA and MCTP1 indicates that the individual suffers from or
is at risk of developing ischemic stroke. In various embodiments,
in an individual presenting with one or more vascular risk factors
3 or more hours after a suspected ischemic event, a decreased
expression level of one or more or all ischemic stroke-associated
biomarkers of Table 7A selected from the group consisting of PGM5,
CCDCl44C///LOC100134159, LECT2, SHOX, TBX5, SPTLC3, SNIP, RBMS3,
P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1, CXADR, OVOL2, SPIB,
BXDC5, UNC5B, ASTN2, F1135934, ANKRD28, CCDCl44A, TIMM8A, ALDOAP2,
LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1, HNRNPUL2, FCRL4, ELAVL2,
PRTG, DLX6, FOXA2, SCD5, GABRB2, GYPA, LOC283027, LOC344595, RPL22,
LOC100129488, RPL22 and SH3GL3 indicates that the individual
suffers from or is at risk of developing ischemic stroke.
[0053] In a further aspect, the invention provides methods for
determining the occurrence of or the predisposition of a subject to
experience cardioembolic stroke, the method comprising: determining
a level of expression of a plurality of ischemic stroke-associated
biomarkers in a biological sample from a patient, wherein an
increase or decrease of the level compared to a control indicates
that the patient has experienced cardioembolic stroke, wherein the
plurality of ischemic stroke-associated biomarkers is selected from
the biomarkers set forth in Table 13A. In some embodiments, an
increased expression level of one or more or all ischemic
stroke-associated biomarkers selected from the group consisting of
IRF6, ZNF254, GRM5, EXT2, AP3S2, PIK3C2B, ARHGEF5, COL13A1,
PTPN20A///PTPN20B, LHFP, BANK1, HLA-DOA, EBF1, TMEM19, LHFP, FCRL1,
OOEP and LRRC37A3 indicates that the patient has experienced or is
at risk for cardioembolic stroke. In some embodiments, a decreased
expression level of one or more or all ischemic stroke-associated
biomarkers selected from the group consisting of LOC284751, CD46,
ENPP2, C19orf28, TSKS, CHURC1, ADAMTSL4, FLJ40125, CLEC18A,
ARHGEF12, C16orf68, TFDP1 and GSTK1 indicates that the patient has
experienced or is at risk for cardioembolic stroke. In some
embodiments, a level of expression of a plurality of ischemic
stroke-associated biomarkers listed in Table 13B is further
determined, wherein an increase or decrease of the level compared
to a control indicates that the patient has experienced or is at
risk for cardioembolic stroke. In some embodiments an increased
expression level of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more or all,
ischemic stroke-associated biomarkers of Table 13A selected from
the group consisting of EBF1, GRM5, AP3S2, LRRC37A3, IRF6, LHFP,
BANK1, ARHGEF5, ZNF254 and COL13A1 indicates that the patient has
experienced or is at risk for cardioembolic stroke. In some
embodiments, a decreased expression level of 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, or more or all, ischemic stroke-associated biomarkers of
Table 13A selected from the group consisting of TSKS, ENPP2,
C16orf68, LOC284751, TFDP1, GSTK1, ADAMTSL4, CHURC1, F1140125 and
ARHGEF12 indicates that the patient has experienced or is at risk
for cardioembolic stroke.
[0054] In a further aspect, the invention provides methods for
determining the occurrence of or the predisposition of a subject to
experience carotid stenosis, the method comprising: determining a
level of expression of a plurality of ischemic stroke-associated
biomarkers in a biological sample from a patient who has suffered
ischemic stroke, wherein an increase or decrease of the level
compared to a control indicates that the patient has experienced
carotid stenosis, wherein the plurality of ischemic
stroke-associated biomarkers is selected from the biomarkers set
forth in Table 14. In some embodiments, an increased expression
level of one or more or all ischemic stroke-associated biomarkers
selected from the group consisting of NT5E, CLASP2, GRM5, PROCR,
ARHGEF5, AKR1C3, COL13A1, LHFP, RNF7, CYTH3, EBF1, RANBP10, PRSS35,
C12orf42 and LOC100127980 indicates that the patient has
experienced or is at risk for carotid stenosis. In some
embodiments, a decreased expression level of one or more or all
ischemic stroke-associated biomarkers selected from the group
consisting of FLJ31945, LOC284751, LOC100271832, MTBP, ICAM4,
SHOX2, DOPEY2, CMBL, LOC146880, SLC20A1, SLC6A19, ARHGEF12,
C16orf68, GIPC2 and LOC100144603 indicates that the patient has
experienced or is at risk for carotid stenosis. In some
embodiments, an increased expression level of 2, 5, 10, 15, or more
or all, ischemic stroke-associated biomarkers of Table 14 selected
from the group consisting of EBF1, COL13A1, LHFP, GRM5, ARHGEF5,
RNF7, CLASP2, RANBP10, LOC100127980, CYTH3, PROCR, C12orf42,
PRSS35, NT5E, and AKR1C3 indicates that the patient has experienced
or is at risk for carotid stenosis. In some embodiments, a
decreased expression level of 2, 5, 10, 15, or more or all ischemic
stroke-associated biomarkers of Table 14 selected from the group
consisting of FLJ31945, C16orf68, SLC20A1, DOPEY2, LOC284751,
LOC100144603, MTBP, SHOX2, GIPC2, CMBL, LOC146880, SLC6A19, ICAM4,
ARHGEF12, and LOC10027183 indicates that the patient has
experienced or is at risk for carotid stenosis.
[0055] In a further aspect, the invention provides methods for
determining the occurrence of or the predisposition of a subject to
experience atrial fibrillation in a patient, the method comprising:
determining a level of expression of a plurality of ischemic
stroke-associated biomarkers in a biological sample from the
patient, wherein an increase or decrease of the level compared to a
control indicates that the patient has experienced or is at risk
for experiencing atrial fibrillation, wherein the plurality of
ischemic stroke-associated biomarkers is selected from the
biomarkers set forth in Table 15. In some embodiments, an increased
expression level of one or more or all ischemic stroke-associated
biomarkers selected from the group consisting of SMC1A, SNORA68,
GRLF1, SDC4, HIPK2, LOC100129034, CMTM1 and TTC7A indicates that
the patient has experienced or is at risk for atrial fibrillation.
In some embodiments, a decreased expression level of one or more or
all ischemic stroke-associated biomarkers selected from the group
consisting of LRRC43, MIF///SLC2A11, PER3, PPIE, COL13A1, DUSP16,
LOC100129034, BRUNOL6, GPR176, C6orf164 and MAP3K71P1 indicates
that the patient has experienced or is at risk for atrial
fibrillation. In some embodiments, an increased expression level of
1, 2, 3, 4, 5, 6, 7 or 8 ischemic stroke-associated biomarkers of
Table 15 selected from the group consisting of CMTM1, SDC4,
SNORA68, HIPK2, TTC7A, GRLF1, LOC100129034, SMC1A indicates that
the patient has experienced or is at risk for atrial fibrillation.
In some embodiments, a decreased expression level of 1, 2, 3, 4, 5,
6, 7, 8, 9 or 10 ischemic stroke-associated biomarkers of Table 15
selected from the group consisting of COL13A1, C6orf164, GPR176,
BRUNOL6, MIF///SLC2A11, DUSP16, PPIE, MAP3K71P1, PER3, LRRC43
indicates that the patient has experienced or is at risk for atrial
fibrillation.
[0056] With respect to embodiments of the methods for determination
of occurrence and/or cause of stroke, in some embodiments, the
level of expression of about 15-85, 20-70, 30-60 or 40-50 total
biomarkers are determined. In some embodiments, about 15, 20, 25,
30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or 100
biomarkers are determined. The levels of expression of the
plurality of biomarkers can be concurrently or sequentially
determined.
[0057] In some embodiments, the control level is the expression
level of a plurality of stably expressed endogenous reference
biomarkers. In some embodiments, the plurality of stably expressed
endogenous reference biomarkers are selected from the biomarkers
listed in Table 16. In some embodiments, the ischemic
stroke-associated biomarkers are overexpressed or underexpressed at
least about 1.2-fold, 1.3-fold, 1.4-fold, 1.5-fold, 1.6-fold,
1.7-fold, 1.8-fold, 1.9-fold, 2.0-fold, 2.1 fold, 2.2-fold,
2.3-fold, 2.4-fold, 2.5-fold, 2.6-fold, 2.7-fold, 2.8-fold,
2.9-fold, 3.0-fold, 3.1-fold, 3.2-fold, 3.3-fold, 3.4-fold or
3.5-fold, or more, in comparison to the expression levels of a
plurality of stably expressed endogenous reference biomarkers,
e.g., those listed in Table 16. In some embodiments, the expression
levels of 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, or all,
the endogenous reference biomarkers selected from the group
consisting of USP7, MAPRE2, CSNK1G2, SAFB2, PRKAR2A, PI4 KB, CRTC1,
HADHA, MAP1LC3B, KAT5, CDC2L1///CDC2L2, GTSE1, CDC2L1///CDC2L2,
TCF25, CHP, LRRC40, hCG 2003956/1/LYPLA2///LYPLA2P1, DAXX, UBE2NL,
EIF1, KCMF1, PRKRIP1, CHMP4A, TMEM184C, TINF2, PODNL1, FBXO42,
LOC441258, RRP1, C10orf104, ZDHHC5, C9orf23, LRRC45, NACC1,
LOC100133445///LOC115110, PEX16 are determined as a control.
[0058] In some embodiments, the control level is the expression
level of the same biomarker in a healthy individual, e.g. an
individual who has not experienced a vascular event and/or who is
not at risk of experiencing a vascular event (e.g., TIA, ischemic
stroke, myocardial infarction, peripheral vascular disease, or
venous thromboembolism). In some embodiments, the control is a
threshold level of expression, e.g., of the same ischemic
stoke-associated biomarker, optionally normalized to the expression
level of a stably expressed endogenous reference biomarker,
representative of a population of healthy individuals.
[0059] Methods for determining the occurrence or predisposition of
an ischemic event, may further comprise the step of determining
whether the patient has suffered a myocardial infarction or whether
the patient has vascular risk factors.
[0060] In some embodiments, the patient is asymptomatic. In some
embodiments, the patient is exhibiting symptoms of ischemic stroke,
e.g., of having experienced an ischemic event, of experiencing an
ischemic event, or of an imminent ischemic event. In some
embodiments, the patient has suffered an ischemic event. In some
embodiments, the determining step is performed at 3 or fewer hours
after the ischemic event. In some embodiments, the determining step
is performed 3 or more hours after the ischemic event.
[0061] In some embodiments, the methods further comprise the step
of recommending or providing a regime of treatment to the patient
appropriate to the determined cause of stroke. For example, in
patients diagnosed as experiencing or having a predisposition for
experiencing cardioembolic stroke, the methods further provide for
recommending or providing a regime of treatment or prevention for
cardioembolic stroke. In patients diagnosed as experiencing or
having a predisposition for experiencing carotid stenosis, the
methods further provide for recommending or providing a regime of
treatment or prevention for carotid stenosis. In patients diagnosed
as experiencing or having a predisposition for experiencing atrial
fibrillation, the methods further provide for recommending or
providing a regime of treatment or prevention for atrial
fibrillation.
[0062] With respect to embodiments for determination of the level
of expression of the biomarkers, in some embodiments, the level of
expression of the biomarker is determined at the transcriptional
level. For example, in some embodiments, the level of expression is
determined by detecting hybridization of an ischemic
stroke-associated gene probe to gene transcripts of the biomarkers
in the biological sample. In some embodiments, the hybridization
step is performed on a nucleic acid array chip. In some
embodiments, the hybridization step is performed in a microfluidics
assay plate. In some embodiments, the level of expression is
determined by amplification of gene transcripts of the biomarkers.
In some embodiments, the amplification reaction is a polymerase
chain reaction (PCR).
[0063] In some embodiments, the level of expression of the
biomarker is determined at the protein level.
[0064] In some embodiments, the methods further comprise obtaining
a biological sample from the patient. In some embodiments, the
biological sample is blood, serum or plasma.
[0065] In a further aspect, the invention provides a solid support
comprising a plurality of nucleic acids that hybridize to a
plurality of the genes set forth in Tables 7A, 7B, 13A, 13B, 14 and
15 (and optionally Table 16), wherein the plurality of nucleic
acids are attached to the solid support. The solid support may
optionally comprise a plurality of nucleic acids that hybridize to
a plurality of the genes set forth in Table 16. In various
embodiments, the solid support is a microarray. In various
embodiments, the solid support is attached to at least about 15,
20, 25, 30, 35, 40, 45, 50, 55, 60, 75, 80, 85, 90, 95 or 100, or
more or all, genes set forth in Tables 7A, 7B, 13A, 13B, 14, 15
and/or 16.
[0066] In one embodiment, the solid support comprises a plurality
of nucleic acids that hybridize to a plurality of the genes listed
in Table 7A (and 7B). For example, in one embodiment, the solid
support comprises 2, 5, 10, 15, 20, or more or all, nucleic acids
that hybridize to a plurality of stroke-associated biomarkers
selected from SNIP, BXDC5, FAT3, LECT2, THSD4,
CCDCl44C///LOC100134159, OVOL2, SPTLC3, CLEC4E, GLYATL1, RBMS3,
SPIB, DKFZP434L187, GADL1, SHOX, TBX5, UNC5B, PGM5, TIMP2, ELL2,
CXADR, and RNF141. In one embodiment, the solid support comprises
2, 3, 4, 5, 6, 7, 8, or 9, nucleic acids that hybridize to a
plurality of stroke-associated biomarkers selected from RPL22,
SNIP, SH3GL3, MCTP1, FAT3, SPTLC3, RBMS3, SNRPN, and TIMP2. In one
embodiment, the solid support comprises 2, 5, 10, 15, or more or
all, nucleic acids that hybridize to a plurality of
stroke-associated biomarkers selected from FGD4, F5, ABCA1,
LOC100290882, LTB4R, UBXN2B, CKLF, CLEC4E, PHTF1, ENTPD1, OSBPL1A,
RRAGD, CPEB2, CKLF, BST1, and CKLF. In one embodiment, the solid
support comprises 2, 5, 10, 15, 20, or more or all, nucleic acids
that hybridize to a plurality of stroke-associated biomarkers
selected from ZNF608, FCHO2, ST3GAL6, ABCA1, THBD, AMN1, QKI,
KIAA0319, MCTP1, VNN3, UBR5, FAR2, RBM25, CHMP1B, LAMP2, VAPA,
IFRD1, HNRNPH2, REM2, and GAB1. In one embodiment, the solid
support comprises 2, 5, 10, 15, 20, 25, 30, or more or all, nucleic
acids that hybridize to a plurality of stroke-associated biomarkers
selected from THSD4, SNRPN, ASTN2, SNIP, FAT3, TIMM8A,
CCDCl44C///LOC100134159, ANKRD28, TBX5, PGM5, SCD5, FCRL4, SHOX,
CCRL1, LECT2, PTPRD, CCDCl44A, LDB3, LOC729222///PPFIBP1, RBMS3,
P704P, GYPA, PRTG, GABRB2, HNRNPUL2, ELAVL2, SPTLC3, FOXA2, DLX6,
ALDOAP2, and FLJ35934. In one embodiment, the solid support
comprises 2, 5, 6, 7, or more or all, nucleic acids that hybridize
to a plurality of stroke-associated biomarkers selected from RPL22,
LOC100129488, LOC283027, LOC344595, THSD4, FAT3, and P704P.
[0067] In one embodiment, the solid support comprises 15, 20, 25,
30, 35, 40, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more or
all, nucleic acids that hybridize to a plurality of
stroke-associated biomarkers selected from SNIP, BXDC5, FAT3,
LECT2, THSD4, CCDCl44C///LOC100134159, OVOL2, SPTLC3, CLEC4E,
GLYATL1, RBMS3, SPIB, DKFZP434L187, GADL1, SHOX, TBX5, UNC5B, PGM5,
TIMP2, ELL2, CXADR, RNF141, RPL22, SH3GL3, MCTP1, SNRPN, FGD4, F5,
ABCA1, LOC100290882, LTB4R, UBXN2B, CKLF, PHTF1, ENTPD1, OSBPL1A,
RRAGD, CPEB2, CKLF, BST1, ZNF608, FCHO2, ST3GAL6, THBD, AMN1, QKI,
KIAA0319, MCTP1, VNN3, UBR5, FAR2, RBM25, CHMP1B, LAMP2, VAPA,
IFRD1, HNRNPH2, REM2, GAB1, ASTN2, TIMM8A, CCDCl44C///LOC100134159,
ANKRD28, SCD5, FCRL4, CCRL1, LECT2, PTPRD, CCDCl44A, LDB3,
LOC729222///PPFIBP1, P704P, GYPA, PRTG, GABRB2, HNRNPUL2, ELAVL2,
FOXA2, DLX6, ALDOAP2, FLJ35934, LOC100129488, LOC283027, and
LOC344595.
[0068] In one embodiment, the solid support comprises a plurality
of nucleic acids that hybridize to a plurality of the genes listed
in Table 13A (and 13B). In one embodiment, the solid support
comprises 2, 5, 10, 15, 20, 25, 30, 35, or more or all, nucleic
acids that hybridize to a plurality of cardioembolic
stroke-associated biomarkers selected from EBF1, GRM5, TSKS, ENPP2,
AP3S2, LRRC37A3, C16orf68, LOC284751, IRF6, LHFP, BANK1, ARHGEF5,
ZNF254, TFDP1, COL13A1, GSTK1, ADAMTSL4, P2RX5, LHFP, PIK3C2B,
CHURC1, EXT2, HLA-DOA, OOEP, ZNF185, TMEM19, FCRL1, FLJ40125,
ARHGEF12, CLEC18A, CD46, PTPN20A///PTPN20B, and C19orf28.
[0069] In one embodiment, the solid support comprises a plurality
of nucleic acids that hybridize to a plurality of the genes listed
in Table 14. In one embodiment, the solid support comprises 2, 5,
10, 15, 20, 25, 30, 35, or more or all, nucleic acids that
hybridize to a plurality of atrial fibrillation stroke-associated
biomarkers selected from EBF1, F1131945, C16orf68, SLC20A1, DOPEY2,
COL13A1, LHFP, LOC284751, GRM5, LOC100144603, MTBP, SHOX2, ARHGEF5,
RNF7, CLASP2, GIPC2, RANBP10, CMBL, LOC100127980, CYTH3, PROCR,
LOC146880, SLC6A19, ICAM4, C12orf42, ARHGEF12, PRSS35, NT5E,
LOC100271832, LHFP, NT5E and AKR1C3.
[0070] In one embodiment, the solid support comprises a plurality
of nucleic acids that hybridize to a plurality of the genes listed
in Table 15. In one embodiment, the solid support comprises 2, 5,
10, 15, 18, or more or all, nucleic acids that hybridize to a
plurality of atrial fibrillation stroke-associated biomarkers
selected from CMTM1, COL13A1, SDC4, C6orf164, GPR176, BRUNOL6,
SNORA68, MIF///SLC2A11, DUSP16, HIPK2, TTC7A, PPIE, GRLF1,
MAP3K71P1, LOC100129034, PER3, SMC1A, and LRRC43.
[0071] In various embodiments, the solid support further comprises
a plurality of nucleic acids that hybridize to a plurality of
endogenous reference genes selected from the group consisting of
USP7, MAPRE2, CSNK1G2, SAFB2, PRKAR2A, PI4 KB, CRTC1, HADHA,
MAP1LC3B, KAT5, CDC2L1///CDC2L2, GTSE1, TCF25, CHP, LRRC40, hCG
2003956/1/LYPLA2///LYPLA2P1, DAXX, UBE2NL, EIF1, KCMF1, PRKRIP1,
CHMP4A, TMEM184C, TINF2, PODNL1, FBXO42, LOC441258, RRP1,
C10orf104, ZDHHC5, C9orf23, LRRC45, NACC1,
LOC100133445///LOC115110, PEX16.
DEFINITIONS
[0072] Unless defined otherwise, all technical and scientific terms
used herein generally have the same meaning as commonly understood
by one of ordinary skill in the art to which this invention
belongs. Generally, the nomenclature used herein and the laboratory
procedures in cell culture, molecular genetics, organic chemistry
and nucleic acid chemistry and hybridization described below are
those well known and commonly employed in the art. Standard
techniques are used for nucleic acid and peptide synthesis.
Generally, enzymatic reactions and purification steps are performed
according to the manufacturer's specifications. The techniques and
procedures are generally performed according to conventional
methods in the art and various general references (see generally,
Sambrook et al. MOLECULAR CLONING: A LABORATORY MANUAL, 3rd ed.
(2001) Cold Spring Harbor Laboratory Press, Cold Spring Harbor,
N.Y. and Ausubel, et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY,
1990-2008, Wiley Interscience), which are provided throughout this
document. The nomenclature used herein and the laboratory
procedures in analytical chemistry, and organic synthetic described
below are those well known and commonly employed in the art.
Standard techniques, or modifications thereof, are used for
chemical syntheses and chemical analyses.
[0073] "Ischemia" or "ischemic event" as used herein refers to
diseases and disorders characterized by inadequate blood supply
(i.e., circulation) to a local area due to blockage of the blood
vessels to the area. Ischemia includes for example, strokes and
transient ischemic attacks. Strokes include, e.g., ischemic stroke
(including, but not limited to, cardioembolic strokes,
atheroembolic or atherothrombotic strokes, i.e., strokes caused by
atherosclerosis in the carotid, aorta, heart, and brain, small
vessel strokes (i.e., lacunar strokes), strokes caused by diseases
of the vessel wall, i.e., vasculitis, strokes caused by infection,
strokes caused by hematological disorders, strokes caused by
migraines, and strokes caused by medications such as hormone
therapy), hemorrhagic ischemic stroke, intracerebral hemorrhage,
and subarachnoid hemorrhage.
[0074] "Ischemia reference expression profile" refers to the
pattern of expression of a set of genes (e.g., a plurality of the
genes set forth in Tables 7A, 7B, 13A, 13B, 14 and 15)
differentially expressed (i.e., overexpressed or underexpressed) in
ischemia relative to a control (e.g., the expression level in an
individual free of an ischemic event or the expression level of a
stably expressed endogenous reference biomarker). A gene from
Tables 7A, 7B, 13A, 13B, 14 and 15 that is expressed at a level
that is at least about 1.2-, 1.3-, 1.4-, 1.5-, 1.6-, 1.7-, 1.8-,
1.9-, 2.0-, 2.1-, 2.2-, 2.3-, 2.4-, 2.5-, 2.6-, 2.7-, 2.8-, 2.9-,
3.0-, 3.1-, 3.2-, 3.3-, 3.4- or 3.5-fold higher than the level in a
control is a gene overexpressed in ischemia and a gene from Tables
7A, 7B, 13A, 13B, 14 and 15 that is expressed at a level that is at
least about 1.2-, 1.3-, 1.4-, 1.5-, 1.6-, 1.7-, 1.8-, 1.9-, 2.0-,
2.1-, 2.2-, 2.3-, 2.4-, 2.5-, 2.6-, 2.7-, 2.8-, 2.9-, 3.0-, 3.1-,
3.2-, 3.3-, 3.4- or 3.5-fold lower than the level in a control is a
gene underexpressed in ischemia. Alternately, genes that are
expressed at a level that is at least about 10%, 20%, 30%, 40%,
50%, 60%, 70%, 80%, 90%, or 100% higher than the level in a control
is a gene overexpressed in ischemia and a gene that is expressed at
a level that is at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%,
80%, 90%, or 100% lower than the level in a control is a gene
underexpressed in ischemia.
[0075] A "plurality" refers to two or more or all, for example, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23 or more (e.g., genes). In some embodiments, a plurality
refers to concurrent or sequential determination of about 15-85,
20-60 or 40-50 genes, for example, about 15, 20, 25, 30, 35, 40,
45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or 100, or more or all,
genes. In some embodiments, "plurality" refers to all genes listed
in one or more tables, e.g., all genes listed in Tables 7A, 7B,
13A, 13B, 14 and 15.
[0076] "Sample" or "biological sample" includes sections of tissues
such as biopsy and autopsy samples, and frozen sections taken for
histologic purposes. Such samples include blood, sputum, tissue,
lysed cells, brain biopsy, cultured cells, e.g., primary cultures,
explants, and transformed cells, stool, urine, etc. A biological
sample is typically obtained from a eukaryotic organism, most
preferably a mammal such as a primate, e.g., chimpanzee or human;
cow; dog; cat; a rodent, e.g., guinea pig, rat, mouse; rabbit; or a
bird; reptile; or fish.
[0077] "Array" as used herein refers to a solid support comprising
attached nucleic acid or peptide probes. Arrays typically comprise
a plurality of different nucleic acid or peptide probes that are
coupled to a surface of a substrate in different, known locations.
These arrays, also described as "microarrays" or colloquially
"chips" have been generally described in the art, for example, U.S.
Pat. Nos. 5,143,854, 5,445,934, 5,744,305, 5,677,195, 6,040,193,
5,424,186 and Fodor et al., Science, 251:767-777 (1991). These
arrays may generally be produced using mechanical synthesis methods
or light directed synthesis methods which incorporate a combination
of photolithographic methods and solid phase synthesis methods.
Techniques for the synthesis of these arrays using mechanical
synthesis methods are described in, e.g., U.S. Pat. No. 5,384,261.
Arrays may comprise a planar surface or may be nucleic acids or
peptides on beads, gels, polymeric surfaces, fibers such as fiber
optics, glass or any other appropriate substrate as described in,
e.g., U.S. Pat. Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193 and
5,800,992. Arrays may be packaged in such a manner as to allow for
diagnostics or other manipulation of an all inclusive device, as
described in, e.g., U.S. Pat. Nos. 5,856,174 and 5,922,591.
[0078] The term "gene" means the segment of DNA involved in
producing a polypeptide chain; it includes regions preceding and
following the coding region (leader and trailer) as well as
intervening sequences (introns) between individual coding segments
(exons).
[0079] The terms "nucleic acid" and "polynucleotide" are used
interchangeably herein to refer to deoxyribonucleotides or
ribonucleotides and polymers thereof in either single- or
double-stranded form. The term 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-O-methyl
ribonucleotides, peptide-nucleic acids (PNAs).
[0080] Unless otherwise indicated, a particular nucleic acid
sequence also encompasses conservatively modified variants thereof
(e.g., degenerate codon substitutions) and complementary sequences,
as well as the sequence explicitly indicated. Specifically,
degenerate codon substitutions may be achieved by generating
sequences in which the third position of one or more selected (or
all) codons is substituted with mixed-base and/or deoxyinosine
residues (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka
et al., J. Biol. Chem. 260:2605-2608 (1985); Rossolini et al., Mol.
Cell. Probes 8:91-98 (1994)). The term nucleic acid is used
interchangeably with gene, cDNA, mRNA, oligonucleotide, and
polynucleotide.
[0081] The phrase "stringent hybridization conditions" refers to
conditions under which a probe will hybridize to its target
subsequence, typically in a complex mixture of nucleic acid, but to
no other sequences. Stringent hybridization conditions are
sequence-dependent and will be different in different
circumstances. Longer sequences hybridize specifically at higher
temperatures. An extensive guide to the hybridization of nucleic
acids is found in Tijssen, Techniques in Biochemistry and Molecular
Biology--Hybridization with Nucleic Probes, "Overview of principles
of hybridization and the strategy of nucleic acid assays" (1993).
Generally, stringent hybridization conditions are selected to be
about 5-10.degree. C. lower than the thermal melting point for the
specific sequence at a defined ionic strength Ph. The T.sub.m is
the temperature (under defined ionic strength, Ph, and nucleic
concentration) at which 50% of the probes complementary to the
target hybridize to the target sequence at equilibrium (as the
target sequences are present in excess, at T.sub.m, 50% of the
probes are occupied at equilibrium). Stringent hybridization
conditions will be those in which the salt concentration is less
than about 1.0 M sodium ion, typically about 0.01 to 1.0 M sodium
ion concentration (or other salts) at Ph 7.0 to 8.3 and the
temperature is at least about 30.degree. C. for short probes (e.g.,
10 to 50 nucleotides) and at least about 60.degree. C. for long
probes (e.g., greater than 50 nucleotides). Stringent hybridization
conditions may also be achieved with the addition of destabilizing
agents such as formamide. For selective or specific hybridization,
a positive signal is at least two times background, optionally 10
times background hybridization. Exemplary stringent hybridization
conditions can be as following: 50% formamide, 5.times.SSC, and 1%
SDS, incubating at 42.degree. C., or, 5.times.SSC, 1% SDS,
incubating at 65.degree. C., with wash in 0.2.times.SSC, and 0.1%
SDS at 65.degree. C.
[0082] Nucleic acids that do not hybridize to each other under
stringent hybridization conditions are still substantially
identical if the polypeptides which they encode are substantially
identical. This occurs, for example, when a copy of a nucleic acid
is created using the maximum codon degeneracy permitted by the
genetic code. In such cases, the nucleic acids typically hybridize
under moderately stringent hybridization conditions. Exemplary
"moderately stringent hybridization conditions" include a
hybridization in a buffer of 40% formamide, 1 M NaCl, 1% SDS at
37.degree. C., and a wash in 1.times.SSC at 45.degree. C. A
positive hybridization is at least twice background. Those of
ordinary skill will readily recognize that alternative
hybridization and wash conditions can be utilized to provide
conditions of similar stringency.
[0083] The terms "isolated," "purified," or "biologically pure"
refer to material that is substantially or essentially free from
components that normally accompany it as found in its native state.
Purity and homogeneity are typically determined using analytical
chemistry techniques such as polyacrylamide gel electrophoresis or
high performance liquid chromatography. A protein that is the
predominant species present in a preparation is substantially
purified. The term "purified" denotes that a nucleic acid or
protein gives rise to essentially one band in an electrophoretic
gel. Particularly, it means that the nucleic acid or protein is at
least 85% pure, more preferably at least 95% pure, and most
preferably at least 99% pure.
[0084] The term "heterologous" when used with reference to portions
of a nucleic acid indicates that the nucleic acid comprises two or
more subsequences that are not found in the same relationship to
each other in nature. For instance, the nucleic acid is typically
recombinantly produced, having two or more sequences from unrelated
genes arranged to make a new functional nucleic acid, e.g., a
promoter from one source and a coding region from another source.
Similarly, a heterologous protein indicates that the protein
comprises two or more subsequences that are not found in the same
relationship to each other in nature (e.g., a fusion protein).
[0085] An "expression vector" is a nucleic acid construct,
generated recombinantly or synthetically, with a series of
specified nucleic acid elements that permit transcription of a
particular nucleic acid in a host cell. The expression vector can
be part of a plasmid, virus, or nucleic acid fragment. Typically,
the expression vector includes a nucleic acid to be transcribed
operably linked to a promoter.
[0086] The terms "polypeptide," "peptide" and "protein" are used
interchangeably herein to refer to a polymer of amino acid
residues. The terms apply to amino acid polymers in which one or
more amino acid residue is an artificial chemical mimetic of a
corresponding naturally occurring amino acid, as well as to
naturally occurring amino acid polymers and non-naturally occurring
amino acid polymer.
[0087] The term "amino acid" refers to naturally occurring and
synthetic amino acids, as well as amino acid analogs and amino acid
mimetics that function in a manner similar to the naturally
occurring amino acids. Naturally occurring amino acids are those
encoded by the genetic code, as well as those amino acids that are
later modified, e.g., hydroxyproline, .alpha.-carboxyglutamate, and
O-phosphoserine. "Amino acid analogs" refers to compounds that have
the same basic chemical structure as a naturally occurring amino
acid, i.e., an a carbon that is bound to a hydrogen, a carboxyl
group, an amino group, and an R group, e.g., homoserine,
norleucine, methionine sulfoxide, methionine methyl sulfonium. Such
analogs have modified R groups (e.g., norleucine) or modified
peptide backbones, but retain the same basic chemical structure as
a naturally occurring amino acid. "Amino acid mimetics" refers to
chemical compounds that have a structure that is different from the
general chemical structure of an amino acid, but that functions in
a manner similar to a naturally occurring amino acid.
[0088] Amino acids may be referred to herein by either their
commonly known three letter symbols or by the one-letter symbols
recommended by the IUPAC-IUB Biochemical Nomenclature Commission.
Nucleotides, likewise, may be referred to by their commonly
accepted single-letter codes.
[0089] "Conservatively modified variants" applies to both amino
acid and nucleic acid sequences. With respect to particular nucleic
acid sequences, conservatively modified variants refers to those
nucleic acids which encode identical or essentially identical amino
acid sequences, or where the nucleic acid does not encode an amino
acid sequence, to essentially identical sequences. Because of the
degeneracy of the genetic code, a large number of functionally
identical nucleic acids encode any given protein. For instance, the
codons GCA, GCC, GCG and GCU all encode the amino acid alanine.
Thus, at every position where an alanine is specified by a codon,
the codon can be altered to any of the corresponding codons
described without altering the encoded polypeptide. Such nucleic
acid variations are "silent variations," which are one species of
conservatively modified variations. Every nucleic acid sequence
herein which encodes a polypeptide also describes every possible
silent variation of the nucleic acid. One of skill will recognize
that each codon in a nucleic acid (except AUG, which is ordinarily
the only codon for methionine, and TGG, which is ordinarily the
only codon for tryptophan) can be modified to yield a functionally
identical molecule. Accordingly, each silent variation of a nucleic
acid which encodes a polypeptide is implicit in each described
sequence.
[0090] As to amino acid sequences, one of skill will recognize that
individual substitutions, deletions or additions to a nucleic acid,
peptide, polypeptide, or protein sequence which alters, adds or
deletes a single amino acid or a small percentage of amino acids in
the encoded sequence is a "conservatively modified variant" where
the alteration results in the substitution of an amino acid with a
chemically similar amino acid. Conservative substitution tables
providing functionally similar amino acids are well known in the
art. Such conservatively modified variants are in addition to and
do not exclude polymorphic variants, interspecies homologs, and
alleles of the invention.
[0091] The following eight groups each contain amino acids that are
conservative substitutions for one another: [0092] 1) Alanine (A),
Glycine (G); [0093] 2) Aspartic acid (D), Glutamic acid (E); [0094]
3) Asparagine (N), Glutamine (Q); [0095] 4) Arginine I, Lysine (K);
[0096] 5) Isoleucine (I), Leucine (L), Methionine (M), Valine (V);
[0097] 6) Phenylalanine (F), Tyrosine (Y), Tryptophan (W); [0098]
7) Serine (S), Threonine (T); and [0099] 8) Cysteine (C),
Methionine (M) [0100] (see, e.g., Creighton, Proteins (1984)).
[0101] The terms "identical" or percent "identity," in the context
of two or more nucleic acids or polypeptide sequences, refer to two
or more sequences or subsequences that are the same or have a
specified percentage of amino acid residues or nucleotides that are
the same (i.e., 60% identity, preferably 65%, 70%, 75%, 80%, 85%,
90%, or 95% identity over a specified region of an
ischemia-associated gene (e.g., a gene set forth in Tables 7A, 7B,
13A, 13B, 14 and 15), when compared and aligned for maximum
correspondence over a comparison window, or designated region as
measured using one of the following sequence comparison algorithms
or by manual alignment and visual inspection. Such sequences are
then said to be "substantially identical." This definition also
refers to the compliment of a test sequence. Preferably, the
identity exists over a region that is at least about 25 amino acids
or nucleotides in length, or more preferably over a region that is
50-100 amino acids or nucleotides in length.
[0102] For sequence comparison, typically one sequence acts as a
reference sequence, to which test sequences are compared. When
using a sequence comparison algorithm, test and reference sequences
are entered into a computer, subsequence coordinates are
designated, if necessary, and sequence algorithm program parameters
are designated. Default program parameters can be used, or
alternative parameters can be designated. The sequence comparison
algorithm then calculates the percent sequence identities for the
test sequences relative to the reference sequence, based on the
program parameters. For sequence comparison of nucleic acids and
proteins to ischemia-associated nucleic acids and proteins, the
BLAST and BLAST 2.0 algorithms and the default parameters discussed
below are used.
[0103] A "comparison window", as used herein, includes reference to
a segment of any one of the number of contiguous positions selected
from the group consisting of from 20 to 600, usually about 50 to
about 200, more usually about 100 to about 150 in which a sequence
may be compared to a reference sequence of the same number of
contiguous positions after the two sequences are optimally aligned.
Methods of alignment of sequences for comparison are well-known in
the art. Optimal alignment of sequences for comparison can be
conducted, e.g., by the local homology algorithm of Smith &
Waterman, Adv. Appl. Math. 2:482 (1981), by the homology alignment
algorithm of Needleman & Wunsch, J. Mol. Biol. 48:443 (1970),
by the search for similarity method of Pearson & Lipman, Proc.
Nat'l. Acad. Sci. USA 85:2444 (1988), by computerized
implementations of these algorithms (GAP, BESTFIT, FASTA, and
TFASTA in the Wisconsin Genetics Software Package, Genetics
Computer Group, 575 Science Dr., Madison, Wis.), or by manual
alignment and visual inspection (see, e.g., Current Protocols in
Molecular Biology (Ausubel et al., eds. 1995 supplement)).
[0104] A preferred example of algorithm that is suitable for
determining percent sequence identity and sequence similarity are
the BLAST and BLAST 2.0 algorithms, which are described in Altschul
et al., Nuc. Acids Res. 25:3389-3402 (1977) and Altschul et al., J.
Mol. Biol. 215:403-410 (1990), respectively. BLAST and BLAST 2.0
are used, with the parameters described herein, to determine
percent sequence identity for the nucleic acids and proteins of the
invention. Software for performing BLAST analyses is publicly
available through the National Center for Biotechnology Information
(http://www.ncbi.nlm.nih.gov/). This algorithm involves first
identifying high scoring sequence pairs (HSPs) by identifying short
words of length W in the query sequence, which either match or
satisfy some positive-valued threshold score T when aligned with a
word of the same length in a database sequence. T is referred to as
the neighborhood word score threshold (Altschul et al., supra).
These initial neighborhood word hits act as seeds for initiating
searches to find longer HSPs containing them. The word hits are
extended in both directions along each sequence for as far as the
cumulative alignment score can be increased. Cumulative scores are
calculated using, for nucleotide sequences, the parameters M
(reward score for a pair of matching residues; always >0) and N
(penalty score for mismatching residues; always <0). For amino
acid sequences, a scoring matrix is used to calculate the
cumulative score. Extension of the word hits in each direction are
halted when: the cumulative alignment score falls off by the
quantity X from its maximum achieved value; the cumulative score
goes to zero or below, due to the accumulation of one or more
negative-scoring residue alignments; or the end of either sequence
is reached. The BLAST algorithm parameters W, T, and X determine
the sensitivity and speed of the alignment. The BLASTN program (for
nucleotide sequences) uses as defaults a word length (W) of 11, an
expectation (E) of 10, M=5, N=-4 and a comparison of both strands.
For amino acid sequences, the BLASTP program uses as defaults a
word length of 3, and expectation (E) of 10, and the BLOSUM62
scoring matrix (see Henikoff & Henikoff, Proc. Natl. Acad. Sci.
USA 89:10915 (1989)) alignments (B) of 50, expectation (E) of 10,
M=5, N=-4, and a comparison of both strands.
[0105] The BLAST algorithm also performs a statistical analysis of
the similarity between two sequences (see, e.g., Karlin &
Altschul, Proc. Nat'l. Acad. Sci. USA 90:5873-5787 (1993)). One
measure of similarity provided by the BLAST algorithm is the
smallest sum probability (P(N)), which provides an indication of
the probability by which a match between two nucleotide or amino
acid sequences would occur by chance. For example, a nucleic acid
is considered similar to a reference sequence if the smallest sum
probability in a comparison of the test nucleic acid to the
reference nucleic acid is less than about 0.2, more preferably less
than about 0.01, and most preferably less than about 0.001.
[0106] An indication that two nucleic acid sequences or
polypeptides are substantially identical is that the polypeptide
encoded by the first nucleic acid is immunologically cross reactive
with the antibodies raised against the polypeptide encoded by the
second nucleic acid, as described below. Thus, a polypeptide is
typically substantially identical to a second polypeptide, for
example, where the two peptides differ only by conservative
substitutions. Another indication that two nucleic acid sequences
are substantially identical is that the two molecules or their
complements hybridize to each other under stringent conditions, as
described below. Yet another indication that two nucleic acid
sequences are substantially identical is that the same primers can
be used to amplify the sequence.
[0107] The phrase "selectively (or specifically) hybridizes to"
refers to the binding, duplexing, or hybridizing of a molecule only
to a particular nucleotide sequence under stringent hybridization
conditions when that sequence is present in a complex mixture
(e.g., total cellular or library DNA or RNA).
[0108] By "host cell" is meant a cell that contains an expression
vector and supports the replication or expression of the expression
vector. Host cells may be, for example, prokaryotic cells such as
E. coli or eukaryotic cells such as yeast cells or mammalian cells
such as CHO cells.
[0109] "Inhibitors," "activators," and "modulators" of expression
or of activity are used to refer to inhibitory, activating, or
modulating molecules, respectively, identified using in vitro and
in vivo assays for expression or activity, e.g., ligands, agonists,
antagonists, and their homologs and mimetics. The term "modulator"
includes inhibitors and activators Inhibitors are agents that,
e.g., inhibit expression of a polypeptide or polynucleotide of the
invention or bind to, partially or totally block stimulation or
enzymatic activity, decrease, prevent, delay activation,
inactivate, desensitize, or down regulate the activity of a
polypeptide or polynucleotide of the invention, e.g., antagonists.
Activators are agents that, e.g., induce or activate the expression
of a polypeptide or polynucleotide of the invention or bind to,
stimulate, increase, open, activate, facilitate, enhance activation
or enzymatic activity, sensitize or up regulate the activity of a
polypeptide or polynucleotide of the invention, e.g., agonists.
Modulators include naturally occurring and synthetic ligands,
antagonists, agonists, small chemical molecules and the like.
Assays to identify inhibitors and activators include, e.g.,
applying putative modulator compounds to cells, in the presence or
absence of a polypeptide or polynucleotide of the invention and
then determining the functional effects on a polypeptide or
polynucleotide of the invention activity. Samples or assays
comprising a polypeptide or polynucleotide of the invention that
are treated with a potential activator, inhibitor, or modulator are
compared to control samples without the inhibitor, activator, or
modulator to examine the extent of effect. Control samples
(untreated with modulators) are assigned a relative activity value
of 100% Inhibition is achieved when the activity value of a
polypeptide or polynucleotide of the invention relative to the
control is about 80%, optionally 50% or 25-1%. Activation is
achieved when the activity value of a polypeptide or polynucleotide
of the invention relative to the control is 110%, optionally 150%,
optionally 200-500%, or 1000-3000% higher.
[0110] The term "test compound" or "drug candidate" or "modulator"
or grammatical equivalents as used herein describes any molecule,
either naturally occurring or synthetic, e.g., protein,
oligopeptide (e.g., from about 5 to about 25 amino acids in length,
preferably from about 10 to 20 or 12 to 18 amino acids in length,
preferably 12, 15, or 18 amino acids in length), small organic
molecule, polysaccharide, lipid, fatty acid, polynucleotide, RNAi,
oligonucleotide, etc. The test compound can be in the form of a
library of test compounds, such as a combinatorial or randomized
library that provides a sufficient range of diversity. Test
compounds are optionally linked to a fusion partner, e.g.,
targeting compounds, rescue compounds, dimerization compounds,
stabilizing compounds, addressable compounds, and other functional
moieties. Conventionally, new chemical entities with useful
properties are generated by identifying a test compound (called a
"lead compound") with some desirable property or activity, e.g.,
inhibiting activity, creating variants of the lead compound, and
evaluating the property and activity of those variant compounds.
Often, high throughput screening (HTS) methods are employed for
such an analysis.
[0111] A "small organic molecule" refers to an organic molecule,
either naturally occurring or synthetic, that has a molecular
weight of more than about 50 Daltons and less than about 2500
Daltons, preferably less than about 2000 Daltons, preferably
between about 100 to about 1000 Daltons, more preferably between
about 200 to about 500 Daltons.
BRIEF DESCRIPTION OF THE DRAWINGS
[0112] FIG. 1. PAM prediction accuracy of IS and Healthy controls
using the set of 29 gene predictors of IS from Tang et al, 2006.
The Prediction Analysis of Microarrays (PAM) algorithm (K-NN,
number of neighbors n=10) was trained on the expression values of a
first random half of IS (n=35, 100 samples) and healthy (n=19)
subjects from the current study using the 29 IS predictors from
Tang et al, 2006. Then, these 29 IS predictors were used to predict
the class of the second half of the samples (IS n=35, 99 samples;
and healthy n=19, Test Set) and calculate the prediction accuracy.
The X-axis represents the patient sample number and the Y-axis
represents the Test Set probability of diagnosis. A sample is
considered misclassified if the predicted class does not match the
known class with a probability greater than 0.5.
[0113] FIGS. 2A-C. PAM prediction accuracy of IS predictors in the
current study. Prediction accuracy of the Test Set using PAM.
Prediction Analysis of Microarrays (PAM) was used to perform the
predictions (K-NN, neighbors n=10; threshold=0). For panels A, B
and C the X-axis represents the patient sample number and the
Y-axis represents Test Set probabilities. A sample is considered
miss-classified if its correct class predicted probability is less
than 0.5. The numbers of subjects in the Training Set were: 3 h IS
n=34; 24 h IS n=33; SAVVY vascular controls n=26; and MI n=9. The
numbers of subjects in the Test Set were: 3 h IS n=33; 24 h IS
n=33; SAVVY n=26; and MI n=8. A. 3 h IS predictors. The 60-probe
set predictors for 3 h IS (combined from comparisons of 3 h IS
samples to healthy, MI and SAVVY samples from the Training Set)
were put into PAM to predict the class of the Test Set subject
samples by calculating the probability that they were in a given
class. B. 24 h IS predictors. The 46-probe set predictors for 24 h
IS (combined from comparisons of 24 h IS samples to healthy, MI and
SAVVY samples from the Training Set) were put into PAM to predict
the class of the Test Set subject samples by calculating the
probability that they were in a given class. C. Combined 3 h and 24
h IS predictors. The 97-probe set predictors for 3 h IS and 24 h IS
(combined from comparisons of 3 h IS and 24 h IS samples to
healthy, MI and SAVVY samples from the Training Set) were put into
PAM to predict the class of the Test Set subject samples by
calculating the probability that they were in a given class.
[0114] FIG. 3. Diagram of the analysis work flow for the
identification of IS predictors.
[0115] FIG. 4. PAM prediction accuracy of IS and healthy using the
29 probe set predictors of IS from Tang et al, 2006. The internal
gene normalized expression values of all IS (n=70, 199 samples) and
healthy (n=38) for the 29 IS predictors from Tang et al, 2006 were
used as input in PAM. K-NN (number of neighbors n=10) threshold=0
(including all 29 predictors, and a 10-fold cross-validation was
used to estimate prediction accuracy. X-axis represents sample
number and the Y-axis represents cross-validated probability of
diagnosis. A sample is considered misclassified if the predicted
class does not match the known class with a probability greater
than 0.5.
[0116] FIG. 5. PAM 3 h vs. Healthy test set+test set confusion
matrix
[0117] FIG. 6. PAM 3 h vs. MI CV+CV confusion matrix
[0118] FIG. 7. PAM 3 h vs. SAVVY test set+test set confusion
matrix
[0119] FIG. 8. PAM 24 h vs. healthy test set+test set confusion
matrix
[0120] FIG. 9. PAM 24 h vs. MI CV+CV confusion matrix
[0121] FIG. 10. PAM 24 h vs. SAVVY test set+test set confusion
matrix
[0122] FIGS. 11A-C. PAM on Combined 3 h, 24 h and 3+24 h IS
predictors. CV Probabilities. FIG. 11A. 3 h IS predictors. Combined
60-probe set predictors from combined analysis on 3 h IS vs all
controls (healthy, MI and SAVVY) were input in PAM. FIG. 11B. 24 h
IS predictors. Combined 46-probe set predictors from combined
analysis on 24 h IS vs all controls (healthy, MI and SAVVY) were
input in PAM. FIG. 11C. Combined 3 h and 24 h IS predictors.
Combined 97-probe set predictors from combined analysis on 3 h IS
and 24 h IS vs all controls (healthy, MI and SAVVY) were input in
PAM.
[0123] FIGS. 12A-B A. Hierarchical cluster plot of the 40 genes
found to differentiate cardioembolic stroke from large vessel
stroke. Genes are shown on the y-axis and subjects are shown on the
x-axis. Red indicates a high level of gene expression and blue
indicates a low level of gene expression. Subjects can be observed
to cluster by diagnosis. A group of genes have a high level of
expression in cardioembolic stroke and a low level of expression in
large vessel stroke. A separate group of genes have a low level of
expression in cardioembolic stroke and a high level of expression
in large vessel stroke. The cardioembolic group appears to cluster
into two subgroups. B. Principal Component Analysis (PCA) of the 40
genes found to differentiate cardioembolic stroke from large vessel
stroke. Each sphere represents a single subject. The ellipsoid
surrounding the spheres represents two standard deviations from the
group mean.
[0124] FIG. 13. Leave one out cross-validation prediction analysis
of the 40 total genes found to differentiate cardioembolic stroke
from large vessel stroke. The probability of the predicted
diagnosis in shown on the y-axis. The actual diagnosis of is shown
on the x-axis. Subjects with cardioembolic stroke were predicted to
have cardioembolic stroke for 69 out of 69 samples (100% correct
prediction). Subjects with large vessel stroke were predicted to
have large vessel stroke for 29 out of 30 samples (96.7% correct
prediction). A sample is considered misclassified if the predicted
class does not match the known class with a probability greater
than 0.5.
[0125] FIG. 14. Venn diagram of genes identified from the
comparison of cardioembolic to controls, and large vessel stroke to
control (p<0.005, FC>|1.2|). A total of 503 genes were found
to be unique to cardioembolic stroke, 554 genes unique to large
vessel stroke and 228 genes were common to stroke subtypes. These
gene lists were used for functional analyses shown in Tables
9-11.
[0126] FIG. 15A-B. A. Hierarchical cluster analysis of the 37 genes
found to differentiate cardioembolic stroke due to atrial
fibrillation from non-atrial fibrillation causes. Genes are shown
on the y-axis and subjects are shown on the x-axis. Red indicates a
high level of gene expression and blue indicates a low level of
gene expression. Subjects can be observed to cluster by diagnosis.
A group of genes have a high level of expression in cardioembolic
stroke due to atrial fibrillation and a low level of expression in
non-atrial fibrillation causes. A group of genes have a low level
of expression in cardioembolic stroke due to atrial fibrillation
and a high level of expression in non-atrial fibrillation causes.
B. Principal Component Analysis of the 37 genes found to
differentiate cardioembolic stroke due to atrial fibrillation from
non-atrial fibrillation causes. Each sphere represents a single
subject. The ellipsoid surrounding the spheres represents two
standard deviations from the group mean.
[0127] FIG. 16. Leave one out cross-validation prediction analysis
of the 37 genes found to differentiate cardioembolic stroke due to
atrial fibrillation from non-atrial fibrillation causes. The
probability of the predicted diagnosis in shown on the y-axis. The
actual diagnosis of is shown on the x-axis. Subjects with
cardioembolic stroke due to atrial fibrillation were predicted to
have atrial fibrillation as a cause of stroke in 30 out of 30
samples (100% correct prediction). Subjects with cardioembolic
stroke due to non-atrial fibrillation causes were correctly
predicted in 22 out of 24 samples (91.7% correct prediction). A
sample is considered misclassified if the predicted class does not
match the known class with a probability greater than 0.5.
[0128] FIG. 17. Hierarchical cluster plots and PCAs of the 40 genes
found to differentiate cardioembolic stroke from large vessel
stroke at 3 hours, 5 hours and 24 hours following stroke onset. The
hierarchical clusters show that separation by the 40 genes of
cardioembolic stroke from large vessel stroke is achieved at 3
hours, 5 hours and 24 hours following onset of ischemic stroke.
This is confirmed by the PCAs which show that subjects with
cardioembolic stroke are separated by greater than two standard
deviations from large vessel stroke.
[0129] FIG. 18. Hierarchical cluster plots and PCAs of the 37 genes
found differentiate cardioembolic stroke due to atrial fibrillation
from non-atrial fibrillation causes at 3 hours, 5 hours and 24
hours following the stroke onset. The hierarchical clusters show
the 37 genes can separate cardioembolic stroke due atrial
fibrillation non-atrial fibrillation causes at 3 hours, 5 hours and
24 hours following onset of ischemic stroke. This is confirmed by
the PCA analyses which show that subjects with cardioembolic stroke
due to atrial fibrillation are separated by greater than two
standard deviations from non-atrial fibrillation causes.
DETAILED DESCRIPTION
1. Introduction
[0130] The present invention provides biomarkers for diagnosing the
occurrence and risk of stroke in a patient, and further biomarkers
for determining the cause of stroke in an individual diagnosed as
experiencing a stroke or with a predisposition for experiencing a
stroke. Evaluation of the expression levels of combined biomarkers,
e.g., in a sample of blood, serum or plasma, allows the rapid
diagnosis of the occurrence and cause of stroke in a patient who
has experienced a suspected stroke event or who is experiencing
symptoms indicative of a risk of stroke. By simultaneously
determining whether a stroke has occurred, and the underlying cause
of the stroke, appropriate medical treatment or intervention
regimes are delivered to the patient as rapidly as possible. It is
particularly desirable to be able to diagnose and treat a patient
within 3 hours of a suspected stroke event. The present invention
makes this possible, e.g., using available microarray
technologies.
[0131] The biomarkers described herein for the diagnosis of the
occurrence and risk of stroke can be used together, e.g., on a
single microarray or in a single assay procedure. The biomarkers
also find use independently for the diagnosis of the occurrence of
stroke, e.g., in conjunction with alternative methods for
determining the cause of stroke, and for determining the cause of
stroke, e.g., in conjunction with alternative methods for
determining whether a stroke has occurred.
2. Patients Who can Benefit from the Present Methods
[0132] Individuals who will benefit from the present methods may be
exhibiting symptoms of ischemic stroke. In some embodiments, the
subject has experienced an ischemic event (e.g., TIA, ischemic
stroke, myocardial infarction, peripheral vascular disease, or
venous thromboembolism). Alternatively, the subject may be
suspected of having experienced an ischemic event. In some
embodiments, the subject has not experienced and/or is not at risk
of having an intracerebral hemorrhage or hemorrhagic stroke. In
some embodiments, the subject has been diagnosed as having not
experienced and/or not at risk of having an intracerebral
hemorrhage or hemorrhagic stroke.
[0133] In some embodiments, the levels of expression of the panel
of biomarkers are determined within 3 hours of a suspected ischemic
event. In some embodiments, the levels of expression of the panel
of biomarkers are determined at 3 or more hours after a suspected
ischemic event. In some embodiments, the levels of expression of
the panel of biomarkers are determined within 6, 12, 18, 24, 36, 48
hours of a suspected ischemic event.
[0134] In some cases, the subject is asymptomatic, but may have a
risk or predisposition to experiencing ischemic stroke, e.g., based
on genetics, a related disease condition, environment or lifestyle.
In some embodiments, the patient has one or more vascular risk
factors, e.g., hypertension, diabetes mellitus, hyperlipidemia, or
tobacco smoking.
3. Biomarkers Useful for the Prediction or Diagnosis of Stroke
[0135] Biomarkers useful for the prediction, diagnosis or
confirmation of the occurrence of ischemic stroke are listed in
Tables 7A and 7B. Determination of the expression levels of a
plurality of the biomarkers of Table 7A can be performed for the
prediction, diagnosis or confirmation of the occurrence of stroke
in conjunction with other biomarkers known in the art for the
prediction, diagnosis or confirmation of the occurrence of stroke,
in conjunction with other methods known in the art for the
diagnosis of stroke, in conjunction with biomarkers described
herein and known in the art useful for determining the cause of
stroke (e.g., as described herein) and/or in conjunction with
methods known in the art for determining the cause of stroke.
[0136] Determination of the expression levels of a plurality of the
biomarkers of Table 7A can be performed for the prediction,
diagnosis or confirmation of the occurrence of stroke can also be
performed independently, e.g., to diagnose that a stroke has
occurred or determine the risk that a patient may suffer a stroke,
independently of its cause.
[0137] In some embodiments, the expression levels of at least about
3, 5, 10, 15, 20, 25, 30, 40, 50, 60 or more biomarkers from Table
7A (and Table 7B) are determined. In some embodiments, the
expression levels of a plurality of biomarkers in Table 7A and a
plurality of biomarkers in Table 7B are determined.
[0138] In some embodiments, the level of expression of biomarkers
indicative of the occurrence of stroke is determined within 3 hours
of a suspected ischemic event. In an otherwise healthy individual
(i.e., no myocardial infarction, no vascular risk factors), an
increased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of FAT3, GADL1, CXADR, RNF141, CLEC4E, TIMP2, ANKRD28,
TIMM8A, PTPRD, CCRL1, FCRL4, DLX6, GABRB2, GYPA, PHTF1, CKLF, CKLF,
RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B, ENTPD1,
BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3, AMN1, LAMP2,
FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6, HNRNPH2, GAB1,
UBR5, VAPA, MCTP1 and SH3GL3 indicates that the patient suffers
from or is at risk of developing ischemic stroke. In an otherwise
healthy individual (i.e., no myocardial infarction, no vascular
risk factors), a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX,
TBX5, SPTLC3, SNIP, RBMS3, P704P, THSD4, SNRPN, GLYATL1,
DKRZP434L187, OVOL2, SPIB, BXDC5, UNC5B, ASTN2, FLJ35934, CCDCl44A,
ALDOAP2, LDB3, LOC729222///PPFIBP1, HNRNPUL2, ELAVL2, PRTG, FOXA2,
SCD5, LOC283027, LOC344595, RPL22, LOC100129488 and RPL22 indicates
that the patient suffers from or is at risk of developing ischemic
stroke.
[0139] In various embodiments, in an individual presenting with
myocardial infarction within 3 hours of a suspected ischemic event,
an increased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of CLEC4E, TIMP2, FGD4, CPEB2, LTB4R and VNN3 indicates
that the individual suffers from or is at risk of developing
ischemic stroke. In various embodiments, in an individual
presenting with myocardial infarction within 3 hours of a suspected
ischemic event, a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX,
TBX5, SPTLC3, SNIP, RBMS3, P704P, THSD4, FAT3, SNRPN, GLYATL1,
GADL1, CXADR, OVOL2, RNF141, SPIB, BXDC5, UNC5B, ASTN2, FLJ35934,
ANKRD28, CCDCl44A, TIMM8A, ALDOAP2, LDB3, PTPRD,
LOC729222///PPFIBP1, CCRL1, HNRNPUL2, FCRL4, ELAVL2, PRTG, DLX6,
FOXA2, SCD5, GABRB2, GYPA, PHTF1, CKLF, CKLF, RRAGD, CKLF,
LOC100290882, UBXN2B, ENTPD1, BST1, F5, IFRD1, KIAA0319, CHMP1B,
MCTP1, AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6,
HNRNPH2, GAB1, UBR5, VAPA, LOC283027, LOC344595, RPL22,
LOC100129488, RPL22, MCTP1 and SH3GL3 indicates that the individual
suffers from or is at risk of developing ischemic stroke.
[0140] In various embodiments, in an individual presenting with one
or more vascular risk factors (e.g., hypertension, diabetes
mellitus, hyperlipidemia, or tobacco smoking) within 3 hours of a
suspected ischemic event, an increased expression level of one or
more or all ischemic stroke-associated biomarkers of Table 7A
selected from the group consisting of RNF141, CLEC4E, TIMP2, PHTF1,
CKLF, CKLF, RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B,
ENTPD1, BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3,
AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6,
HNRNPH2, GAB1, UBR5, VAPA and MCTP1 indicates that the individual
suffers from or is at risk of developing ischemic stroke. In
various embodiments, in an individual presenting with one or more
vascular risk factors within 3 hours of a suspected ischemic event,
a decreased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX, TBX5,
SPTLC3, SNIP, RBMS3, P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1,
CXADR, OVOL2, SPIB, BXDC5, UNC5B, ASTN2, FLJ35934, ANKRD28,
CCDCl44A, TIMM8A, ALDOAP2, LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1,
HNRNPUL2, FCRL4, ELAVL2, PRTG, DLX6, FOXA2, SCD5, GABRB2, GYPA,
LOC283027, LOC344595, RPL22, LOC100129488, RPL22 and SH3GL3
indicates that the individual suffers from or is at risk of
developing ischemic stroke.
[0141] In some embodiments, the level of expression of biomarkers
indicative of the occurrence of stroke is determined 3 or more
hours after a suspected ischemic event. In an otherwise healthy
individual (i.e., no myocardial infarction, no vascular risk
factors), an increased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of PGM5, CCDCl44C///LOC100134159, LECT2, SHOX,
TBX5, SNIP, RBMS3, P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1,
CXADR, OVOL2, RNF141, CLEC4E, BXDC5, UNC5B, TIMP2, ASTN2, FLJ35934,
ANKRD28, CCDCl44A, TIMM8A, ALDOAP2, LDB3, PTPRD,
LOC729222///PPFIBP1, CCRL1, FCRL4, ELAVL2, PRTG, DLX6, SCD5,
GABRB2, GYPA, PHTF1, CKLF, CKLF, RRAGD, CLEC4E, CKLF, FGD4, CPEB2,
LOC100290882, UBXN2B, ENTPD1, BST1, LTB4R, F5, IFRD1, KIAA0319,
CHMP1B, MCTP1, VNN3, AMN1, LAMP2, FCHO2, ZNF608, REM2, QKI, RBM25,
FAR2, ST3GAL6, HNRNPH2, GAB1, UBR5, VAPA, LOC283027, LOC344595,
RPL22, LOC100129488 and MCTP1 indicates that the patient suffers
from or is at risk of developing ischemic stroke. In an otherwise
healthy individual (i.e., no myocardial infarction, no vascular
risk factors), a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of SPTLC3, DKRZP434L187, SPIB, HNRNPUL2, FOXA2,
RPL22 and SH3GL3 indicates that the patient suffers from or is at
risk of developing ischemic stroke.
[0142] In various embodiments, in an individual presenting with
myocardial infarction 3 or more hours after a suspected ischemic
event, an increased expression level of one or more or all ischemic
stroke-associated biomarkers of Table 7A selected from the group
consisting of RNF141, CLEC4E, TIMP2, HNRNPUL2, PHTF1, CKLF, CKLF,
RRAGD, CLEC4E, CKLF, FGD4, CPEB2, UBXN2B, BST1, LTB4R, F5, IFRD1,
KIAA0319, MCTP1, VNN3, AMN1, LAMP2, ZNF608, FAR2, GAB1, VAPA and
MCTP1 indicates that the individual suffers from or is at risk of
developing ischemic stroke. In various embodiments, in an
individual presenting with myocardial infarction 3 or more hours
after a suspected ischemic event, a decreased expression level of
one or more or all ischemic stroke-associated biomarkers of Table
7A selected from the group consisting of PGM5,
CCDCl44C///LOC100134159, LECT2, SHOX, TBX5, SPTLC3, SNIP, RBMS3,
P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1, CXADR, OVOL2, SPIB,
BXDC5, UNC5B, ASTN2, FLJ35934, ANKRD28, CCDCl44A, TIMM8A, ALDOAP2,
LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1, FCRL4, ELAVL2, PRTG, DLX6,
FOXA2, SCD5, GABRB2, GYPA, LOC100290882, ENTPD1, CHMP1B, FCHO2,
LOC283027, REM2, QKI, RBM25, ST3GAL6, HNRNPH2, UBR5, LOC344595,
RPL22, LOC100129488, RPL22 and SH3GL3 indicates that the individual
suffers from or is at risk of developing ischemic stroke.
[0143] In various embodiments, in an individual presenting with one
or more vascular risk factors 3 or more hours after a suspected
ischemic event, an increased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 7A selected from the
group consisting of RNF141, CLEC4E, TIMP2, PHTF1, CKLF, CKLF,
RRAGD, CLEC4E, CKLF, FGD4, CPEB2, LOC100290882, UBXN2B, ENTPD1,
BST1, LTB4R, F5, IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3, AMN1, LAMP2,
FCHO2, ZNF608, REM2, QKI, RBM25, FAR2, ST3GAL6, HNRNPH2, GAB1,
UBR5, VAPA and MCTP1 indicates that the individual suffers from or
is at risk of developing ischemic stroke. In various embodiments,
in an individual presenting with one or more vascular risk factors
3 or more hours after a suspected ischemic event, a decreased
expression level of one or more or all ischemic stroke-associated
biomarkers of Table 7A selected from the group consisting of PGM5,
CCDCl44C///LOC100134159, LECT2, SHOX, TBX5, SPTLC3, SNIP, RBMS3,
P704P, THSD4, FAT3, SNRPN, GLYATL1, GADL1, CXADR, OVOL2, SPIB,
BXDC5, UNC5B, ASTN2, FLJ35934, ANKRD28, CCDCl44A, TIMM8A, ALDOAP2,
LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1, HNRNPUL2, FCRL4, ELAVL2,
PRTG, DLX6, FOXA2, SCD5, GABRB2, GYPA, LOC283027, LOC344595, RPL22,
LOC100129488, RPL22 and SH3GL3 indicates that the individual
suffers from or is at risk of developing ischemic stroke.
[0144] Overexpression or underexpression of a plurality of
biomarkers from Table 7A (and Table 7B) that is at least about
1.2-fold, 1.3-fold, 1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold,
1.8-fold, 1.9-fold, 2.0-fold, 2.1 fold, 2.2-fold, 2.3-fold,
2.4-fold, 2.5-fold, 2.6-fold, 2.7-fold, 2.8-fold, 2.9-fold,
3.0-fold, 3.1-fold, 3.2-fold, 3.3-fold, 3.4-fold or 3.5-fold, or
more, in comparison to the expression levels of a plurality of
stably expressed endogenous reference biomarkers, e.g., those
listed in Table 16 indicates that the subject has experienced or is
at risk of experiencing an ischemic stroke. Overexpression or
underexpression of a plurality of biomarkers from Table 7A (and
Table 7B) that is at least about 1.2-fold, 1.3-fold, 1.4-fold,
1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold, 2.0-fold, 2.1
fold, 2.2-fold, 2.3-fold, 2.4-fold, 2.5-fold, 2.6-fold, 2.7-fold,
2.8-fold, 2.9-fold, 3.0-fold, 3.1-fold, 3.2-fold, 3.3-fold,
3.4-fold or 3.5-fold, or more, in comparison to the expression
level of the same biomarker in an individual or a population of
individuals who have not experienced a vascular event indicates
that the subject has experienced or is at risk of experiencing an
ischemic stroke.
4. Biomarkers Useful for the Diagnosis of Cause of Stroke
[0145] Biomarkers useful for the determination and diagnosis of the
cause of stroke are listed in Tables 13A, 13B, 14 and 15.
Determination of the expression levels of a plurality of the
biomarkers of Tables 13A, 13B, 14 and 15 independently can be
performed for the determination of the cause of stroke in
conjunction with biomarkers described herein and known in the art
for the prediction, diagnosis or confirmation of the occurrence of
stroke, in conjunction with other methods known in the art for the
diagnosis of stroke, in conjunction with other biomarkers known in
the art useful for determining the cause of stroke (e.g., as
described herein) and/or in conjunction with methods known in the
art for determining the cause of stroke. Classification of stroke
subtypes is known in the art and reviewed in, e.g., in Amarenco, et
al., Cerebrovasc Dis (2009) 27:493-501.
[0146] Determination of the expression levels of a plurality of the
biomarkers of Tables 13A, 14 and 15 can be performed for the
determination of the cause of stroke can also be performed
independently, e.g., to diagnose the cause of a stroke when it is
already known that a stroke has occurred or that the patient has a
predisposition to experience ischemic stroke.
[0147] In some embodiments, the expression levels of at least about
3, 5, 10, 15, 20, 25, 30, 40, 50, 60 or more biomarkers from Tables
13A (and Table 13B) are independently determined. In some
embodiments, the expression levels of at least about 3, 5, 10, 15,
20, 25, 30, 40, 50, 60 or more biomarkers from Table 14 are
independently determined. In some embodiments, the expression
levels of at least about 3, 5, 10, 15, 20, 25, 30, 40, 50, 60 or
more biomarkers from Table 15 are independently determined. In some
embodiments, the expression levels of a plurality of biomarkers in
Table 13A and a plurality of biomarkers in Table 13B are
determined. In some embodiments, the expression levels of a
plurality of biomarkers in Table 14 are determined. In some
embodiments, the expression levels of a plurality of biomarkers in
Table 15 are determined.
[0148] The biomarkers in Tables 13A and 13B find use in the
determination of whether a patient has experienced or has a
predisposition to experience cardioembolic stroke (a.k.a, cardiac
embolism, cardioembolism emboligenic heart disease). A
cardioembolic stroke occurs when a thrombus (clot) dislodges from
the heart, travels through the cardiovascular system and lodges in
the brain, first cutting off the blood supply and then often
causing a hemorrhagic bleed. In some embodiments an increased
expression level of one or more or all ischemic stroke-associated
biomarkers of Table 13A selected from the group consisting of IRF6,
ZNF254, GRM5, EXT2, AP3S2, PIK3C2B, ARHGEF5, COL13A1,
PTPN20A///PTPN20B, LHFP, BANK1, HLA-DOA, EBF1, TMEM19, LHFP, FCRL1,
OOEP and LRRC37A3 indicates that the patient has experienced or is
at risk for cardioembolic stroke. In some embodiments, a decreased
expression level of one or more or all ischemic stroke-associated
biomarkers of Table 13A selected from the group consisting of
LOC284751, CD46, ENPP2, C19orf28, TSKS, CHURC1, ADAMTSL4, F1140125,
CLEC18A, ARHGEF12, C16orf68, TFDP1 and GSTK1 indicates that the
patient has experienced or is at risk for cardioembolic stroke.
[0149] Overexpression or underexpression of a plurality of
biomarkers from Table 13A (and Table 13B) that is at least about
1.2-fold, 1.3-fold, 1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold,
1.8-fold, 1.9-fold, 2.0-fold, 2.1 fold, 2.2-fold, 2.3-fold,
2.4-fold, 2.5-fold, 2.6-fold, 2.7-fold, 2.8-fold, 2.9-fold,
3.0-fold, 3.1-fold, 3.2-fold, 3.3-fold, 3.4-fold or 3.5-fold, or
more, in comparison to the expression levels of a plurality of
stably expressed endogenous reference biomarkers, e.g., those
listed in Table 16 indicates that the subject has experienced or is
at risk of experiencing cardioembolic stroke. Overexpression or
underexpression of a plurality of biomarkers from Table 13A (and
Table 13B) that is at least about 1.2-fold, 1.3-fold, 1.4-fold,
1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold, 2.0-fold, 2.1
fold, 2.2-fold, 2.3-fold, 2.4-fold, 2.5-fold, 2.6-fold, 2.7-fold,
2.8-fold, 2.9-fold, 3.0-fold, 3.1-fold, 3.2-fold, 3.3-fold,
3.4-fold or 3.5-fold, or more, in comparison to the expression
level of the same biomarker in an individual or a population of
individuals who have not experienced a vascular event indicates
that the subject has experienced or is at risk of experiencing
cardioembolic stroke.
[0150] The biomarkers in Table 14 find use in the determination of
whether a patient has experienced or has a predisposition to
experience carotid stenosis. Carotid stenosis is a narrowing or
constriction of the inner surface (lumen) of the carotid artery,
usually caused by atherosclerosis. An inflammatory buildup of
plaque can narrow the carotid artery and can be a source of
embolization. Emboli break off from the plaque and travel through
the circulation to blood vessels in the brain, causing ischemia
that can either be temporary (e.g., a transient ischemic attack),
or permanent resulting in a thromboembolic stroke (a.k.a.,
atherothrombosis, large-artery atherosclerosis, atherosclerosis
with stenosis). In some embodiments, an increased expression level
of one or more or all ischemic stroke-associated biomarkers of
Table 14 selected from the group consisting of NT5E, CLASP2, GRM5,
PROCR, ARHGEF5, AKR1C3, COL13A1, LHFP, RNF7, CYTH3, EBF1, RANBP10,
PRSS35, C12orf42 and LOC100127980 indicates that the patient has
experienced or is at risk for carotid stenosis. In some
embodiments, a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 14 selected from the
group consisting of F1131945, LOC284751, LOC100271832, MTBP, ICAM4,
SHOX2, DOPEY2, CMBL, LOC146880, SLC20A1, SLC6A19, ARHGEF12,
C16orf68, GIPC2 and LOC100144603 indicates that the patient has
experienced or is at risk for carotid stenosis.
[0151] Overexpression or underexpression of a plurality of
biomarkers from Table 14 that is at least about 1.2-fold, 1.3-fold,
1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold,
2.0-fold, 2.1 fold, 2.2-fold, 2.3-fold, 2.4-fold, 2.5-fold,
2.6-fold, 2.7-fold, 2.8-fold, 2.9-fold, 3.0-fold, 3.1-fold,
3.2-fold, 3.3-fold, 3.4-fold or 3.5-fold, or more, in comparison to
the expression levels of a plurality of stably expressed endogenous
reference biomarkers, e.g., those listed in Table 16 indicates that
the subject has experienced or is at risk of experiencing carotid
stenosis. Overexpression or underexpression of a plurality of
biomarkers from Table 14 that is at least about 1.2-fold, 1.3-fold,
1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold,
2.0-fold, 2.1 fold, 2.2-fold, 2.3-fold, 2.4-fold, 2.5-fold,
2.6-fold, 2.7-fold, 2.8-fold, 2.9-fold, 3.0-fold, 3.1-fold,
3.2-fold, 3.3-fold, 3.4-fold or 3.5-fold, or more, in comparison to
the expression level of the same biomarker in an individual or a
population of individuals who have not experienced a vascular event
indicates that the subject has experienced or is at risk of
experiencing carotid stenosis.
[0152] The biomarkers in Table 15 find use in the determination of
whether a patient has experienced or has a predisposition to
experience atrial fibrillation. Atrial fibrillation (AF or A-fib)
is the most common cardiac arrhythmia and involves the two upper
chambers (atria) of the heart fibrillating (i.e., quivering)
instead of a coordinated contraction. In some instances,
cardioembolic stroke can occur as a result of atrial fibrillation.
Cardioembolic stroke can be a downstream result of atrial
fibrillation in that stagnant blood in the fibrillating atrium can
form a thrombus that then embolises to the cerebral circulation,
blocking arterial blood flow and causing ischaemic injury. In some
embodiments, an increased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 15 selected from the
group consisting of SMC1A, SNORA68, GRLF1, SDC4, HIPK2,
LOC100129034, CMTM1 and TTC7A indicates that the patient has
experienced or is at risk for atrial fibrillation. In some
embodiments, a decreased expression level of one or more or all
ischemic stroke-associated biomarkers of Table 15 selected from the
group consisting of LRRC43, MIF///SLC2A11, PER3, PPIE, COL13A1,
DUSP16, LOC100129034, BRUNOL6, GPR176, C6orf164 and MAP3K71P1
indicates that the patient has experienced or is at risk for atrial
fibrillation. Overexpression or underexpression of a plurality of
biomarkers from Table 15 that is at least about 1.2-fold, 1.3-fold,
1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold,
2.0-fold, 2.1 fold, 2.2-fold, 2.3-fold, 2.4-fold, 2.5-fold,
2.6-fold, 2.7-fold, 2.8-fold, 2.9-fold, 3.0-fold, 3.1-fold,
3.2-fold, 3.3-fold, 3.4-fold or 3.5-fold, or more, in comparison to
the expression levels of a plurality of stably expressed endogenous
reference biomarkers, e.g., those listed in Table 16 indicates that
the subject has experienced or is at risk of experiencing atrial
fibrillation. Overexpression or underexpression of a plurality of
biomarkers from Table 15 that is at least about 1.2-fold, 1.3-fold,
1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold,
2.0-fold, 2.1 fold, 2.2-fold, 2.3-fold, 2.4-fold, 2.5-fold,
2.6-fold, 2.7-fold, 2.8-fold, 2.9-fold, 3.0-fold, 3.1-fold,
3.2-fold, 3.3-fold, 3.4-fold or 3.5-fold, or more, in comparison to
the expression level of the same biomarker in an individual or a
population of individuals who have not experienced a vascular event
indicates that the subject has experienced or is at risk of
experiencing atrial fibrillation.
5. Comparison to a Control Level of Expression
[0153] The expression of the ischemic stroke-associated biomarkers
are compared to a control ischemic stroke level of expression. As
appropriate, the control level of expression can be the expression
level of the same ischemic stroke-associated biomarker in an
otherwise healthy individual (e.g., in an individual who has not
experienced and/or is not at risk of experiencing TIA). In some
embodiments, the control level of expression is the expression
level of a plurality of stably expressed endogenous reference
biomarkers, as described herein or known in the art. In some
embodiments, the control level of expression is a predetermined
threshold level of expression of the same ischemic
stroke-associated biomarker, e.g., based on the expression level of
the biomarker in a population of otherwise healthy individuals. In
some embodiments, the expression level of the ischemic
stroke-associated biomarker and the ischemic stroke-associated
biomarker in an otherwise healthy individual are normalized to
(i.e., divided by), e.g., the expression levels of a plurality of
stably expressed endogenous reference biomarkers.
[0154] In some embodiments, the overexpression or underexpression
of a ischemic stroke-associated biomarker is determined with
reference to the expression of the same ischemic stroke associated
biomarker in an otherwise healthy individual. For example, a
healthy or normal control individual has not experienced and/or is
not at risk of experiencing ischemic stroke. The healthy or normal
control individual generally has not experienced a vascular event
(e.g., TIA, ischemic stroke, myocardial infarction, peripheral
vascular disease, or venous thromboembolism). The healthy or normal
control individual generally does not have one or more vascular
risk factors (e.g., hypertension, diabetes mellitus,
hyperlipidemia, or tobacco smoking). As appropriate, the expression
levels of the target ischemic stroke-associated biomarker in the
healthy or normal control individual can be normalized (i.e.,
divided by) the expression levels of a plurality of stably
expressed endogenous reference biomarkers.
[0155] In some embodiments, the overexpression or underexpression
of a ischemic stroke-associated biomarker is determined with
reference to one or more stably expressed endogenous reference
biomarkers. Internal control biomarkers or endogenous reference
biomarkers are expressed at the same or nearly the same expression
levels in the blood of patients with stroke or TIAs as compared to
control patients. Target biomarkers are expressed at higher or
lower levels in the blood of the stroke or TIA patients. The
expression levels of the target biomarker to the reference
biomarker are normalized by dividing the expression level of the
target biomarker to the expression levels of a plurality of
endogenous reference biomarkers. The normalized expression level of
a target biomarker can be used to predict the occurrence or lack
thereof of stroke or TIA, and/or the cause of stroke or TIA.
[0156] In some embodiments, the expression level of the ischemic
stroke-associated biomarker from a patient suspected of having or
experiencing ischemic stroke and from a control patient are
normalized with respect to the expression levels of a plurality of
stably expressed endogenous. The expression levels of the
normalized expression of the ischemic stroke-associated biomarker
is compared to the expression levels of the normalized expression
of the same ischemic stroke-associated biomarker in a control
patient. The determined fold change in expression=normalized
expression of target biomarker in ischemic stroke
patient/normalized expression of target biomarker in control
patient. Overexpression or underexpression of the normalized
ischemic stroke-associated biomarker in the ischemic stroke patient
by at least about 1.2-fold, 1.3-fold, 1.4-fold, 1.5-fold, 1.6-fold,
1.7-fold, 1.8-fold, 1.9-fold, 2.0-fold, 2.1 fold, 2.2-fold,
2.3-fold, 2.4-fold, 2.5-fold, 2.6-fold, 2.7-fold, 2.8-fold,
2.9-fold, 3.0-fold, 3.1-fold, 3.2-fold, 3.3-fold, 3.4-fold or
3.5-fold, or more, in comparison to the expression levels of the
normalized ischemic stroke-associated biomarker in a healthy
control patient indicates that the ischemic stroke patient has
experienced or is at risk of experiencing ischemic stroke.
[0157] In some embodiments, the control level of expression is a
predetermined threshold level. The threshold level can correspond
to the level of expression of the same ischemic stroke-associated
biomarker in an otherwise healthy individual or a population of
otherwise healthy individuals, optionally normalized to the
expression levels of a plurality of endogenous reference
biomarkers. After expression levels and normalized expression
levels of the ischemic stroke-associated biomarkers are determined
in a representative number of otherwise healthy individuals and
individuals predisposed to experiencing ischemic stroke, normal and
ischemic stroke expression levels of the ischemic stroke-associated
biomarkers can be maintained in a database, allowing for
determination of threshold expression levels indicative of the
presence or absence of risk to experience ischemic stroke or the
occurrence of ischemic stroke. If the predetermined threshold level
of expression is with respect to a population of normal control
patients, then overexpression or underexpression of the ischemic
stroke-associated biomarker (usually normalized) in the ischemic
stroke patient by at least about 1.2-fold, 1.3-fold, 1.4-fold,
1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold, 1.9-fold, 2.0-fold, 2.1
fold, 2.2-fold, 2.3-fold, 2.4-fold, 2.5-fold, 2.6-fold, 2.7-fold,
2.8-fold, 2.9-fold, 3.0-fold, 3.1-fold, 3.2-fold, 3.3-fold,
3.4-fold or 3.5-fold, or more, in comparison to the threshold level
indicates that the ischemic stroke patient has experienced or is at
risk of experiencing ischemic stroke. If the predetermined
threshold level of expression is with respect to a population of
patients known to have experienced ischemic stroke or known to be
at risk for experiencing ischemic stroke, then an expression level
in the patient suspected of experiencing ischemic stroke that is
approximately equal to the threshold level (or overexpressed or
underexpressed greater than the threshold level of expression),
indicates that the ischemic stroke patient has experienced or is at
risk of experiencing ischemic stroke.
[0158] With respect to the endogenous reference biomarkers used for
comparison, preferably, Exemplary endogenous reference biomarkers
that find use are listed in Table 16, below. Further suitable
endogenous reference biomarkers are published, e.g., in Stamova, et
al., BMC Medical Genomics (2009) 2:49. In some embodiments, the
expression levels of a plurality of endogenous reference biomarkers
are determined as a control. In some embodiments, the expression
levels of at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25,
30, 35, or more or all, endogenous reference biomarkers, e.g., as
listed in Table 16 or known in the art, are determined as a
control.
[0159] In some embodiments, the expression levels of the endogenous
reference biomarkers GAPDH, ACTB, B2M, HMBS and PPIB are determined
as a control. In some embodiments, the expression levels of 2, 3,
4, 5, 6, 7, 8, 9, 10, 15, 20, 25, or more or all, endogenous
reference biomarkers selected from the group consisting of USP7,
MAPRE2, CSNK1G2, SAFB2, PRKAR2A, PI4 KB, CRTC1, HADHA, MAP1LC3B,
KAT5, CDC2L1///CDC2L2, GTSE1, CDC2L1///CDC2L2, TCF25, CHP, LRRC40,
hCG 2003956///LYPLA2///LYPLA2P1, DAXX, UBE2NL, EIF1, KCMF1,
PRKRIP1, CHMP4A, TMEM184C, TINF2, PODNL1, FBXO42, LOC441258, RRP1,
C10orf104, ZDHHC5, C9orf23, LRRC45, NACC1,
LOC100133445///LOC115110, PEX16 are determined as a control.
[0160] Biomarkers indicative of stroke or a particular cause of
stroke have levels of expression that are at least about 1.2-fold,
1.3-fold, 1.4-fold, 1.5-fold, 1.6-fold, 1.7-fold, 1.8-fold,
1.9-fold, 2.0-fold, 2.1 fold, 2.2-fold, 2.3-fold, 2.4-fold,
2.5-fold, 2.6-fold, 2.7-fold, 2.8-fold, 2.9-fold, 3.0-fold,
3.1-fold, 3.2-fold, 3.3-fold, 3.4-fold or 3.5-fold, or more, in
comparison to the expression levels of a plurality of stably
expressed endogenous reference biomarkers, e.g., the geometric
average expression level of the evaluated endogenous reference
biomarkers, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35,
or more biomarkers listed in Table 16.
6. Methods of Detecting Biomarkers
[0161] Gene expression may be measured using any method known in
the art. One of skill in the art will appreciate that the means of
measuring gene expression is not a critical aspect of the
invention. The expression levels of the biomarkers can be detected
at the transcriptional or translational (i.e., protein) level.
[0162] In some embodiments, the expression levels of the biomarkers
are detected at the transcriptional level. A variety of methods of
specific DNA and RNA measurement using nucleic acid hybridization
techniques are known to those of skill in the art (see, Sambrook,
supra and Ausubel, supra) and may be used to detect the expression
of the genes set forth in Tables 7A, 7B, 13A, 13B, 14 and 15. Some
methods involve an electrophoretic separation (e.g., Southern blot
for detecting DNA, and Northern blot for detecting RNA), but
measurement of DNA and RNA can also be carried out in the absence
of electrophoretic separation (e.g., by dot blot). Southern blot of
genomic DNA (e.g., from a human) can be used for screening for
restriction fragment length polymorphism (RFLP) to detect the
presence of a genetic disorder affecting a polypeptide of the
invention. All forms of RNA can be detected, including, e.g.,
message RNA (mRNA), microRNA (miRNA), ribosomal RNA (rRNA) and
transfer RNA (tRNA).
[0163] The selection of a nucleic acid hybridization format is not
critical. A variety of nucleic acid hybridization formats are known
to those skilled in the art. For example, common formats include
sandwich assays and competition or displacement assays.
Hybridization techniques are generally described in Hames and
Higgins Nucleic Acid Hybridization, A Practical Approach, IRL Press
(1985); Gall and Pardue, Proc. Natl. Acad. Sci. U.S.A., 63:378-383
(1969); and John et al. Nature, 223:582-587 (1969).
[0164] Detection of a hybridization complex may require the binding
of a signal-generating complex to a duplex of target and probe
polynucleotides or nucleic acids. Typically, such binding occurs
through ligand and anti-ligand interactions as between a
ligand-conjugated probe and an anti-ligand conjugated with a
signal. The binding of the signal generation complex is also
readily amenable to accelerations by exposure to ultrasonic
energy.
[0165] The label may also allow indirect detection of the
hybridization complex. For example, where the label is a hapten or
antigen, the sample can be detected by using antibodies. In these
systems, a signal is generated by attaching fluorescent or enzyme
molecules to the antibodies or in some cases, by attachment to a
radioactive label (see, e.g., Tijssen, "Practice and Theory of
Enzyme Immunoassays," Laboratory Techniques in Biochemistry and
Molecular Biology, Burdon and van Knippenberg Eds., Elsevier
(1985), pp. 9-20).
[0166] The probes are typically labeled either directly, as with
isotopes, chromophores, lumiphores, chromogens, or indirectly, such
as with biotin, to which a streptavidin complex may later bind.
Thus, the detectable labels used in the assays of the present
invention can be primary labels (where the label comprises an
element that is detected directly or that produces a directly
detectable element) or secondary labels (where the detected label
binds to a primary label, e.g., as is common in immunological
labeling). Typically, labeled signal nucleic acids are used to
detect hybridization. Complementary nucleic acids or signal nucleic
acids may be labeled by any one of several methods typically used
to detect the presence of hybridized polynucleotides. The most
common method of detection is the use of autoradiography with
.sup.3H, .sup.125I, .sup.35S, .sup.14C, or .sup.32P-labeled probes
or the like.
[0167] Other labels include, e.g., ligands that bind to labeled
antibodies, fluorophores, chemiluminescent agents, enzymes, and
antibodies which can serve as specific binding pair members for a
labeled ligand. An introduction to labels, labeling procedures and
detection of labels is found in Polak and Van Noorden Introduction
to Immunocytochemistry, 2nd ed., Springer Verlag, N.Y. (1997); and
in Haugland Handbook of Fluorescent Probes and Research Chemicals,
a combined handbook and catalogue Published by Molecular Probes,
Inc. (1996).
[0168] In general, a detector which monitors a particular probe or
probe combination is used to detect the detection reagent label.
Typical detectors include spectrophotometers, phototubes and
photodiodes, microscopes, scintillation counters, cameras, film and
the like, as well as combinations thereof. Examples of suitable
detectors are widely available from a variety of commercial sources
known to persons of skill in the art. Commonly, an optical image of
a substrate comprising bound labeling moieties is digitized for
subsequent computer analysis.
[0169] Most typically, the amount of RNA is measured by quantifying
the amount of label fixed to the solid support by binding of the
detection reagent. Typically, the presence of a modulator during
incubation will increase or decrease the amount of label fixed to
the solid support relative to a control incubation which does not
comprise the modulator, or as compared to a baseline established
for a particular reaction type. Means of detecting and quantifying
labels are well known to those of skill in the art.
[0170] In preferred embodiments, the target nucleic acid or the
probe is immobilized on a solid support. Solid supports suitable
for use in the assays of the invention are known to those of skill
in the art. As used herein, a solid support is a matrix of material
in a substantially fixed arrangement.
[0171] For example, in one embodiment of the invention, microarrays
are used to detect the pattern of gene expression. Microarrays
provide one method for the simultaneous measurement of the
expression levels of large numbers of genes. Each array consists of
a reproducible pattern of a plurality of nucleic acids (e.g., a
plurality of nucleic acids that hybridize to a plurality of the
genes set forth in Tables 7A, 7B, 13A, 13B, 14 and 15) attached to
a solid support. In one embodiment, the array contains a plurality
of nucleic acids that hybridize to a plurality of the genes listed
in Table 7A (and 7B). In one embodiment, the array contains a
plurality of nucleic acids that hybridize to a plurality of the
genes listed in Table 13A (and 13B). In one embodiment, the array
contains a plurality of nucleic acids that hybridize to a plurality
of the genes listed in Table 14. In one embodiment, the array
contains a plurality of nucleic acids that hybridize to a plurality
of the genes listed in Table 15. Labeled RNA or DNA is hybridized
to complementary probes on the array and then detected by laser
scanning Hybridization intensities for each probe on the array are
determined and converted to a quantitative read-out of relative
gene expression levels in ischemia (e.g., stroke or transient
ischemic attacks).
[0172] In some embodiments, a sample is obtained from a subject,
total mRNA is isolated from the sample and is converted to labeled
cRNA and then hybridized to an array. Relative transcript levels
are calculated by reference to appropriate controls present on the
array and in the sample. See Mahadevappa and Warrington, Nat.
Biotechnol. 17, 1134-1136 (1999).
[0173] A variety of automated solid-phase assay techniques are also
appropriate. For instance, very large scale immobilized polymer
arrays (VLSIPSTM), available from Affymetrix, Inc. (Santa Clara,
Calif.) can be used to detect changes in expression levels of a
plurality of genes involved in the same regulatory pathways
simultaneously. See, Tijssen, supra., Fodor et al. (1991) Science,
251: 767-777; Sheldon et al. (1993) Clinical Chemistry 39(4):
718-719, and Kozal et al. (1996) Nature Medicine 2(7): 753-759.
Integrated microfluidic systems and other point-of-care diagnostic
devices available in the art also find use. See, e.g., Liu and
Mathies, Trends Biotechnol. (2009) 27(10):572-81 and Tothill, Semin
Cell Dev Biol (2009) 20(1):55-62. Microfluidics systems for use in
detecting levels of expression of a plurality of nucleic acids are
available, e.g., from NanoString Technologies, on the internet at
nanostring.com.
[0174] Detection can be accomplished, for example, by using a
labeled detection moiety that binds specifically to duplex nucleic
acids (e.g., an antibody that is specific for RNA-DNA duplexes).
One preferred example uses an antibody that recognizes DNA-RNA
heteroduplexes in which the antibody is linked to an enzyme
(typically by recombinant or covalent chemical bonding). The
antibody is detected when the enzyme reacts with its substrate,
producing a detectable product. Coutlee et al. (1989) Analytical
Biochemistry 181:153-162; Bogulayski (1986) et al. J. Immunol.
Methods 89:123-130; Prooijen-Knegt (1982) Exp. Cell Res.
141:397-407; Rudkin (1976) Nature 265:472-473, Stollar (1970) Proc.
Nat'l Acad. Sci. USA 65:993-1000; Ballard (1982) Mol. Immunol.
19:793-799; Pisetsky and Caster (1982) Mol. Immunol. 19:645-650;
Viscidi et al. (1988) J. Clin. Microbial. 41:199-209; and Kiney et
al. (1989) J. Clin. Microbiol. 27:6-12 describe antibodies to RNA
duplexes, including homo and heteroduplexes. Kits comprising
antibodies specific for DNA:RNA hybrids are available, e.g., from
Digene Diagnostics, Inc. (Beltsville, Md.).
[0175] In addition to available antibodies, one of skill in the art
can easily make antibodies specific for nucleic acid duplexes using
existing techniques, or modify those antibodies that are
commercially or publicly available. In addition to the art
referenced above, general methods for producing polyclonal and
monoclonal antibodies are known to those of skill in the art (see,
e.g., Paul (3rd ed.) Fundamental Immunology Raven Press, Ltd., NY
(1993); Coligan, et al., Current Protocols in Immunology, Wiley
Interscience (1991-2008); Harlow and Lane, Antibodies: A Laboratory
Manual Cold Spring Harbor Press, NY (1988); Harlow and Lane, Using
Antibodies, Cold Spring Harbor Press, NY (1999); Stites et al.
(eds.) Basic and Clinical Immunology (4th ed.) Lange Medical
Publications, Los Altos, Calif., and references cited therein;
Goding Monoclonal Antibodies: Principles and Practice (2d ed.)
Academic Press, New York, N.Y., (1986); and Kohler and Milstein
Nature 256: 495-497 (1975)). Other suitable techniques for antibody
preparation include selection of libraries of recombinant
antibodies in phage or similar vectors (see, Huse et al. Science
246:1275-1281 (1989); and Ward et al. Nature 341:544-546 (1989)).
Specific monoclonal and polyclonal antibodies and antisera will
usually bind with a K.sub.D of at least about 0.1 .mu.M, preferably
at least about 0.01 .mu.M or better, and most typically and
preferably, 0.001 .mu.M or better.
[0176] The nucleic acids used in this invention can be either
positive or negative probes. Positive probes bind to their targets
and the presence of duplex formation is evidence of the presence of
the target. Negative probes fail to bind to the suspect target and
the absence of duplex formation is evidence of the presence of the
target. For example, the use of a wild type specific nucleic acid
probe or PCR primers may serve as a negative probe in an assay
sample where only the nucleotide sequence of interest is
present.
[0177] The sensitivity of the hybridization assays may be enhanced
through use of a nucleic acid amplification system that multiplies
the target nucleic acid being detected. Examples of such systems
include the polymerase chain reaction (PCR) system, in particular
RT-PCR or real time PCR, and the ligase chain reaction (LCR)
system. Other methods recently described in the art are the nucleic
acid sequence based amplification (NASBA, Cangene, Mississauga,
Ontario) and Q Beta Replicase systems. These systems can be used to
directly identify mutants where the PCR or LCR primers are designed
to be extended or ligated only when a selected sequence is present.
Alternatively, the selected sequences can be generally amplified
using, for example, nonspecific PCR primers and the amplified
target region later probed for a specific sequence indicative of a
mutation. High throughput multiplex nucleic acid sequencing or
"deep sequencing" to detect captured expressed biomarker genes also
finds use. High throughput sequencing techniques are known in the
art (e.g., 454 Sequencing on the internet at 454.com).
[0178] An alternative means for determining the level of expression
of the nucleic acids of the present invention is in situ
hybridization. In situ hybridization assays are well known and are
generally described in Angerer et al., Methods Enzymol. 152:649-660
(1987). In an in situ hybridization assay, cells, preferentially
human cells, e.g., blood cells, are fixed to a solid support,
typically a glass slide. If DNA is to be probed, the cells are
denatured with heat or alkali. The cells are then contacted with a
hybridization solution at a moderate temperature to permit
annealing of specific probes that are labeled. The probes are
preferably labeled with radioisotopes or fluorescent reporters.
[0179] In other embodiments, quantitative RT-PCR is used to detect
the expression of a plurality of the genes set forth in Tables 7A,
7B, 13A, 13B, 14 and 15. In one embodiment, quantitative RT-PCR is
used to detect a plurality of the genes listed in Table 7A (and
7B). In one embodiment, quantitative RT-PCR is used to detect a
plurality of the genes listed in Table 13A (and 13B). In one
embodiment, quantitative RT-PCR is used to detect a plurality of
the genes listed in Table 14. In one embodiment, quantitative
RT-PCR is used to detect a plurality of the genes listed in Table
15. A general overview of the applicable technology can be found,
for example, in A-Z of Quantitative PCR, Bustin, ed., 2004,
International University Line; Quantitative PCR Protocols,
Kochanowski and Reischl, eds., 1999, Humana Press; Clinical
Applications of PCR, Lo, ed., 2006, Humana Press; PCR Protocols: A
Guide to Methods and Applications (Innis et al. eds. (1990)) and
PCR Technology: Principles and Applications for DNA Amplification
(Erlich, ed. (1992)). In addition, amplification technology is
described in U.S. Pat. Nos. 4,683,195 and 4,683,202. Methods for
multiplex PCR, known in the art, are applicable to the present
invention.
[0180] Accordingly, in one embodiment of the invention provides a
reaction mixture comprising a plurality of polynucleotides which
specifically hybridize (e.g., primers) to a plurality of nucleic
acid sequences of the genes set forth in Tables 7A, 7B, 13A, 13B,
14 and 15. In some embodiments, the invention provides a reaction
mixture comprising a plurality of polynucleotides which
specifically hybridize (e.g., primers) to a plurality of nucleic
acid sequences of the genes set forth in Table 7A (and 7B). In some
embodiments, the invention provides a reaction mixture comprising a
plurality of polynucleotides which specifically hybridize (e.g.,
primers) to a plurality of nucleic acid sequences of the genes set
forth in Table 13A (and 13B). In some embodiments, the invention
provides a reaction mixture comprising a plurality of
polynucleotides which specifically hybridize (e.g., primers) to a
plurality of nucleic acid sequences of the genes set forth in Table
14. In some embodiments, the invention provides a reaction mixture
comprising a plurality of polynucleotides which specifically
hybridize (e.g., primers) to a plurality of nucleic acid sequences
of the genes set forth in Table 15. In some embodiments, the
reaction mixture is a PCR mixture, for example, a multiplex PCR
mixture.
[0181] This invention relies on routine techniques in the field of
recombinant genetics. Generally, the nomenclature and the
laboratory procedures in recombinant DNA technology described below
are those well known and commonly employed in the art. Standard
techniques are used for cloning, DNA and RNA isolation,
amplification and purification. Generally enzymatic reactions
involving DNA ligase, DNA polymerase, restriction endonucleases and
the like are performed according to the manufacturer's
specifications. Basic texts disclosing the general methods of use
in this invention include Sambrook et al., Molecular Cloning, A
Laboratory Manual (3rd ed. 2001); Kriegler, Gene Transfer and
Expression: A Laboratory Manual (1990); and Current Protocols in
Molecular Biology (Ausubel et al., eds., 1994-2008, Wiley
Interscience)).
[0182] For nucleic acids, sizes are given in either kilobases (kb)
or base pairs (bp). These are estimates derived from agarose or
acrylamide gel electrophoresis, from sequenced nucleic acids, or
from published DNA sequences. For proteins, sizes are given in
kilodaltons (kDa) or amino acid residue numbers. Proteins sizes are
estimated from gel electrophoresis, from sequenced proteins, from
derived amino acid sequences, or from published protein
sequences.
[0183] Oligonucleotides that are not commercially available can be
chemically synthesized according to the solid phase phosphoramidite
triester method first described by Beaucage & Caruthers,
Tetrahedron Letts. 22:1859-1862 (1981), using an automated
synthesizer, as described in Van Devanter et. al., Nucleic Acids
Res. 12:6159-6168 (1984). Purification of oligonucleotides is by
either native acrylamide gel electrophoresis or by anion-exchange
HPLC as described in Pearson & Reanier, J. Chrom. 255:137-149
(1983).
[0184] In some embodiments, the expression level of the biomarkers
described herein are detected at the translational or protein
level. Detection of proteins is well known in the art, and methods
for protein detection known in the art find use. Exemplary assays
for determining the expression levels of a plurality of proteins
include, e.g., ELISA, flow cytometry, mass spectrometry (e.g.,
MALDI or SELDI), surface plasmon resonance (e.g., BiaCore),
microfluidics and other biosensor technologies. See, e.g., Tothill,
Semin Cell Dev Biol (2009) 20(1):55-62.
7. Ischemic Stroke Reference Profiles
[0185] The invention also provides ischemia reference profiles. The
reference profiles comprise information correlating the expression
levels of a plurality of ischemia-associated genes (i.e., a
plurality of the genes set forth in Tables 7A, 7B, 13A, 13B, 14 and
15) to particular types of ischemia. In one embodiment, the
ischemia reference profile correlates the expression levels of a
plurality of the genes listed in Tables 7A (and 7B) to the
occurrence or risk of ischemia. In one embodiment, the ischemia
reference profile correlates the expression levels of a plurality
of the genes listed in Tables 13A (and 13B) to the occurrence or
risk of cardioembolic stroke. In one embodiment, the ischemia
reference profile correlates the expression levels of a plurality
of the genes listed in Table 14 to the occurrence or risk of
carotid stenosis. In one embodiment, the ischemia reference profile
correlates the expression levels of a plurality of the genes listed
in Table 15 to the occurrence or risk of atrial fibrillation. The
profiles can conveniently be used to diagnose, monitor and prognose
ischemia.
[0186] One embodiment of the invention provides an ischemia
reference profile for subjects who have experienced or are at risk
for experiencing stroke, regardless of cause. Accordingly, the
ischemia reference profile correlates the expression levels of a
plurality of the genes selected from Table 7A (and Table 7B). For
example, an expression profile exhibiting at least about a 1.2-fold
increase in expression of a plurality of the following genes: PGM5,
CCDC 144C///LOC100134159, LECT2, SHOX, TBX5, SNIP, RBMS3, P704P,
THSD4, FAT3, SNRPN, GLYATL1, GADL1, CXADR, OVOL2, RNF141, CLEC4E,
BXDC5, UNC5B, TIMP2, ASTN2, F1135934, ANKRD28, CCDCl44A, TIMM8A,
ALDOAP2, LDB3, PTPRD, LOC729222///PPFIBP1, CCRL1, FCRL4, ELAVL2,
PRTG, DLX6, SCD5, GABRB2, GYPA, PHTF1, CKLF, CKLF, RRAGD, CLEC4E,
CKLF, FGD4, CPEB2, LOC100290882, UBXN2B, ENTPD1, BST1, LTB4R, F5,
IFRD1, KIAA0319, CHMP1B, MCTP1, VNN3, AMN1, LAMP2, FCHO2, ZNF608,
REM2, QKI, RBM25, FAR2, ST3GAL6, HNRNPH2, GAB1, UBR5, VAPA,
LOC283027, LOC344595, RPL22, LOC100129488 and MCTP1 when compared
to the control level, and at least about a 1.2-fold decrease in
expression of a plurality of the following genes: SPTLC3,
DKRZP434L187, SPIB, HNRNPUL2, FOXA2, RPL22 and SH3GL3 when compared
to the control level is a reference profile for a subject who has
experienced or is at risk for stroke.
[0187] One embodiment of the invention provides an ischemia
reference profile for subjects who have experienced or are at risk
for experiencing cardioembolic stroke. Accordingly, the ischemia
reference profile correlates the expression levels of a plurality
of the genes selected from Table 13A (and Table 13B). For example,
an expression profile exhibiting at least about a 1.2-fold increase
in expression of a plurality of the following genes: IRF6, ZNF254,
GRM5, EXT2, AP3S2, PIK3C2B, ARHGEF5, COL13A1, PTPN20A///PTPN20B,
LHFP, BANK1, HLA-DOA, EBF1, TMEM19, LHFP, FCRL1, OOEP and LRRC37A3
when compared to the control level, and at least about a 1.2-fold
decrease in expression of a plurality of the following genes:
LOC284751, CD46, ENPP2, C19orf28, TSKS, CHURC1, ADAMTSL4, FLJ40125,
CLEC18A, ARHGEF12, C16orf68, TFDP1 and GSTK1 when compared to the
control level is a reference profile for a subject who has
experienced or is at risk for a cardioembolic stroke.
[0188] One embodiment of the invention provides an ischemia
reference profile for subjects who have experienced or are at risk
for experiencing carotid stenosis and atherosclerotic stroke.
Accordingly, the ischemia reference profile correlates the
expression levels of a plurality of the genes selected from Table
14. For example, an expression profile exhibiting at least about a
1.2-fold increase in expression of a plurality of the following
genes: NT5E, CLASP2, GRM5, PROCR, ARHGEF5, AKR1C3, COL13A1, LHFP,
RNF7, CYTH3, EBF1, RANBP10, PRSS35, C12orf42 and LOC100127980 when
compared to the control level, and at least about a 1.2-fold
decrease in expression of a plurality of the following genes:
F1131945, LOC284751, LOC100271832, MTBP, ICAM4, SHOX2, DOPEY2,
CMBL, LOC146880, SLC20A1, SLC6A19, ARHGEF12, C16orf68, GIPC2 when
compared to the control level is a reference profile for a subject
who has experienced or is at risk for carotid stenosis and
atherothrombotic stroke.
[0189] One embodiment of the invention provides an ischemia
reference profile for subjects who have experienced or are at risk
for experiencing atrial fibrillation. Accordingly, the ischemia
reference profile correlates the expression levels of a plurality
of the genes selected from Table 15. For example, an expression
profile exhibiting at least about a 1.2-fold increase in expression
of a plurality of the following genes: SMC1A, SNORA68, GRLF1, SDC4,
HIPK2, LOC100129034, CMTM1 and TTC7A when compared to the control
level, and at least about a 1.2-fold decrease in expression of a
plurality of the following genes: LRRC43, MIF///SLC2A11, PER3,
PPIE, COL13A1, DUSP16, LOC100129034, BRUNOL6, GPR176, C6orf164 and
MAP3K71P1 when compared to the control level is a reference profile
for a subject who has experienced or is at risk for atrial
fibrillation.
[0190] The reference profiles can be entered into a database, e.g.,
a relational database comprising data fitted into predefined
categories. Each table, or relation, contains one or more data
categories in columns. Each row contains a unique instance of data
for the categories defined by the columns. For example, a typical
database for the invention would include a table that describes a
sample with columns for age, gender, reproductive status,
expression profile and so forth. Another table would describe a
disease: symptoms, level, sample identification, expression profile
and so forth. In one embodiment, the invention matches the
experimental sample to a database of reference samples. The
database is assembled with a plurality of different samples to be
used as reference samples. An individual reference sample in one
embodiment will be obtained from a patient during a visit to a
medical professional. Information about the physiological, disease
and/or pharmacological status of the sample will also be obtained
through any method available. This may include, but is not limited
to, expression profile analysis, clinical analysis, medical history
and/or patient interview. For example, the patient could be
interviewed to determine age, sex, ethnic origin, symptoms or past
diagnosis of disease, and the identity of any therapies the patient
is currently undergoing. A plurality of these reference samples
will be taken. A single individual may contribute a single
reference sample or more than one sample over time. One skilled in
the art will recognize that confidence levels in predictions based
on comparison to a database increase as the number of reference
samples in the database increases.
[0191] The database is organized into groups of reference samples.
Each reference sample contains information about physiological,
pharmacological and/or disease status. In one aspect the database
is a relational database with data organized in three data tables,
one where the samples are grouped primarily by physiological
status, one where the samples are grouped primarily by disease
status and one where the samples are grouped primarily by
pharmacological status. Within each table the samples can be
further grouped according to the two remaining categories. For
example the physiological status table could be further categorized
according to disease and pharmacological status.
[0192] As will be appreciated by one of skill in the art, the
present invention may be embodied as a method, data processing
system or program products. Accordingly, the present invention may
take the form of data analysis systems, methods, analysis software,
etc. Software written according to the present invention is to be
stored in some form of computer readable medium, such as memory,
hard-drive, DVD ROM or CD ROM, or transmitted over a network, and
executed by a processor. The present invention also provides a
computer system for analyzing physiological states, levels of
disease states and/or therapeutic efficacy. The computer system
comprises a processor, and memory coupled to said processor which
encodes one or more programs. The programs encoded in memory cause
the processor to perform the steps of the above methods wherein the
expression profiles and information about physiological,
pharmacological and disease states are received by the computer
system as input. Computer systems may be used to execute the
software of an embodiment of the invention (see, e.g., U.S. Pat.
No. 5,733,729).
8. Providing Appropriate Treatment and Prevention Regimes to
Patient
[0193] Upon a positive determination or confirmation that a patient
has experienced a stroke, and a determination of the cause of
stroke, e.g., using the biomarkers provided herein, the methods
further provide for the step of prescribing, providing or
administering a regime for the prophylaxis or treatment of ischemic
stroke. By diagnosing the occurrence and/or the cause of stroke
using the biomarkers described herein, a patient can rapidly
receive treatment that is tailored to and appropriate for the type
of stroke that has been experienced, or that the patient is at risk
of experiencing.
[0194] If the expression levels of the plurality of biomarkers
evaluated from Table 7A (and 7B) indicate the occurrence or risk of
stroke, a positive diagnosis of stroke can be confirmed using
methods known in the art. For example, the patient can be subject
to MRI imaging of brain and vessels, additional blood tests, EKG,
and/or echocardiogram.
[0195] If the expression levels of the plurality of biomarkers
evaluated from Table 13A (and 13B) indicate the occurrence or risk
of cardioembolic stroke, the patient can be prescribed or
administered a regime of an anticoagulant. Exemplary anticoagulants
include aspirin, heparin, warfarin, and dabigatran.
[0196] If the expression levels of the plurality of biomarkers
evaluated from Table 14 indicate the occurrence or risk of carotid
stenosis, the patient can be prescribed or administered a regime of
an anti-platelet drug. The most frequently used anti-platelet
medication is aspirin. An alternative to aspirin is the
anti-platelet drug clopidogrel (Plavix). Some studies indicate that
aspirin is most effective in combination with another anti-platelet
drug. In some embodiments, the patient is prescribed a combination
of low-dose aspirin and the anti-platelet drug dipyridamole
(Aggrenox), to reduce blood clotting. Ticlopidine (Ticlid) is
another anti-platelet medication that finds use. Patients having a
moderately or severely narrowed neck (carotid) artery, may require
or benefit from carotid endarterectomy. This preventive surgery
clears carotid arteries of fatty deposits (atherosclerotic plaques)
to prevent a first or subsequent strokes. In some embodiments, the
patient may require or benefit from carotid angioplasty, or
stenting. Carotid angioplasty involves using a balloon-like device
to open a clogged artery and placing a small wire tube (stent) into
the artery to keep it open.
[0197] If the expression levels of the plurality of biomarkers
evaluated from Table 15 indicate the occurrence or risk of atrial
fibrillation, the patient can be prescribed a regime of an
anti-coagulant (to prevent stroke) and/or a pharmacological agent
to achieve rate control. Exemplary anticoagulants include aspirin,
heparin, warfarin, and dabigatran. Exemplary rate control drugs
include beta blockers (e.g., metoprolol, atenolol, bisoprolol),
non-dihydropyridine calcium channel blockers (e.g., diltiazem or
verapamil), and cardiac glycosides (e.g., digoxin).
9. Solid Supports and Kits
[0198] The invention further provides a solid supports comprising a
plurality of nucleic acid probes that hybridize to a plurality
(e.g., two or more, or all) of the genes set forth in Tables 7A,
7B, 13A, 13B, 14, 15, and optionally 16. For example, the solid
support can be a microarray attached to a plurality of nucleic acid
probes that hybridize to a plurality (e.g., two or more, or all) of
the genes set forth in Table 7A, and optionally Table 16. For
example, the solid support can be a microarray attached to a
plurality of nucleic acid probes that hybridize to a plurality
(e.g., two or more, or all) of the genes set forth in Table 13A,
and optionally Table 16. For example, the solid support can be a
microarray attached to a plurality of nucleic acid probes that
hybridize to a plurality (e.g., two or more, or all) of the genes
set forth in Table 14, and optionally Table 16. For example, the
solid support can be a microarray attached to a plurality of
nucleic acid probes that hybridize to a plurality (e.g., two or
more, or all) of the genes set forth in Table 15, and optionally
Table 16.
[0199] In various embodiments, the solid supports are configured to
exclude genes not associated with or useful to the diagnosis,
prediction or confirmation of a stroke or the causes of stroke. For
example, genes which are overexpressed or underexpressed less than
1.2-fold in subjects having or suspected of having stroke,
regardless of cause, in comparison to a control level of expression
can be excluded from the present solid supports. In some
embodiments, genes that are overexpressed or underexpressed less
than 1.2-fold in subjects with ischemic stroke, including
cardioembolic stroke, atherothrombotic stroke, and stroke
subsequent to atrial fibrillation, in comparison to a control level
of expression can be excluded from the present solid supports. The
solid support can comprise a plurality of nucleic acid probes that
hybridize to a plurality (e.g., two or more, or all) of the genes
useful for the diagnosis of ischemic stroke, cardioembolic stroke,
carotid stenosis, and/or atrial fibrillation, as described herein.
As appropriate, nucleic acid probes that hybridize to a plurality
(e.g., two or more, or all) of the genes useful for the diagnosis
of ischemic stroke, cardioembolic stroke, carotid stenosis, and/or
atrial fibrillation can be arranged in a predetermined array on the
solid support. In various embodiments, nucleic acids not
specifically identified and/or not relating to the diagnosis of
and/or not associated with the diagnosis of ischemic stroke,
cardioembolic stroke, carotid stenosis, and/or atrial fibrillation
are not attached to the solid support. The solid support may be a
component in a kit.
[0200] The invention also provides kits for diagnosing ischemia or
a predisposition for developing ischemia. For example, the
invention provides kits that include one or more reaction vessels
that have aliquots of some or all of the reaction components of the
invention in them. Aliquots can be in liquid or dried form.
Reaction vessels can include sample processing cartridges or other
vessels that allow for the containment, processing and/or
amplification of samples in the same vessel. The kits may comprise
a plurality of nucleic acid probes that hybridize to a plurality
the genes set forth in Tables 7A, 7B, 13A, 13B, 14 and 15. In one
embodiment, the kits comprise a plurality of nucleic acid probes
that hybridize to a plurality of the genes set forth in Table 7A
(and 7B). In one embodiment, the kits comprise a plurality of
nucleic acid probes that hybridize to a plurality of the genes set
forth in Table 13A (and 13B). In one embodiment, the kits comprise
a plurality of nucleic acid probes that hybridize to a plurality of
the genes set forth in Table 14. In one embodiment, the kits
comprise a plurality of nucleic acid probes that hybridize to a
plurality of the genes set forth in Table15. The probes may be
immobilized on an array as described herein.
[0201] In some embodiments, the kits comprise a solid support
comprising a plurality of nucleic acid probes that hybridize to a
plurality the genes set forth in Tables 7A, 7B, 13A, 13B, 14 and
15, and optionally Table 16. For example, the solid support can be
a microarray attached to a plurality of nucleic acid probes that
hybridize to a plurality the genes set forth in Tables 7A, 7B, 13A,
13B, 14 and 15, and optionally Table 16.
[0202] In addition, the kit can comprise appropriate buffers, salts
and other reagents to facilitate amplification and/or detection
reactions (e.g., primers, labels) for determining the expression
levels of a plurality of the genes set forth in Tables 7A, 7B, 13A,
13B, 14 and 15. In one embodiment, the kit comprises appropriate
buffers, salts and other reagents to facilitate amplification
and/or detection reactions (e.g., primers, labels) for determining
the expression levels of a plurality of the genes set forth in
Table 7A (and 7B). In one embodiment, the kit comprises appropriate
buffers, salts and other reagents to facilitate amplification
and/or detection reactions (e.g., primers) for determining the
expression levels of a plurality of the genes set forth in Table
13A (and 13B). In one embodiment, the kit comprises appropriate
buffers, salts and other reagents to facilitate amplification
and/or detection reactions (e.g., primers) for determining the
expression levels of a plurality of the genes set forth in Table
14. In one embodiment, the kit comprises appropriate buffers, salts
and other reagents to facilitate amplification and/or detection
reactions (e.g., primers) for determining the expression levels of
a plurality of the genes set forth in Table 15. The kits can also
include written instructions for the use of the kit.
[0203] In one embodiment, the kits comprise a plurality of
antibodies that bind to a plurality of the biomarkers set forth in
Tables 7A, 7B, 13A, 13B, 14 and 15. The antibodies may or may not
be immobilized on a solid support, e.g., an ELISA plate.
EXAMPLES
[0204] The following examples are offered to illustrate, but not to
limit the claimed invention.
Example 1
Biomarkers for the Diagnosis of the Occurrence and/or Risk of
Ischemic Stroke
Materials and Methods
[0205] The study had two objectives: (1) Demonstrate that the
previously identified 29 probes distinguish IS from healthy
controls [Tang Y et al., J Cereb Blood Flow Metab., 26:1089-1102
(2006)] in a new cohort; and (2) Identify additional genes that
discriminate IS from vascular risk factor (SAVVY) controls and
myocardial infarction (MI) controls. Whole blood was drawn from IS
patients (n=70, 199 samples) at .ltoreq.3, 5 and 24 hours (3 h IS,
5 h IS, 24 IS) as part of the CLEAR trial [Pancioli A M et al.,
Stroke, 39:3268-3276 (2008)] (NCT00250991 at Clinical-Trials.gov).
IS subjects were treated with r-tPA with or without eptifibatide
after the 3 h blood sample was obtained. Controls included healthy
subjects (n=38), subjects with acute myocardial infarction (MI,
n=17) and subjects with at least one cardiovascular risk factor
(hypertension, diabetes mellitus, hyperlipidemia, or tobacco
smoking) recruited from the SAVVY (Sex, Age and Variation in
Vascular functionalitY) study (n=52). The institutional review
board at each site approved the study, and each patient provided
informed consent. Blood samples were collected in PAXgene tubes
(PreAnlytix, Germany). Isolated RNA was processed using Ovation
Whole Blood reagents (Nugen Technologies, San Carlos, Calif.) and
hybridized onto Affymetrix Genome U133 Plus 2 GeneChips (Affymetrix
Santa Clara, Calif.). Data was normalized using Robust Multichip
Averaging (RMA) [Bolstad B M et al., Bioinformatics, 19:185-193
(2003)] and our internal-gene normalization approach. [Stamova B S
et al., BMC Med Genomics, 2:49 (2009)]
[0206] Objective 1: The predictive ability of the 29 previously
identified genes was determined using k-nearest neighbor in PAM
(Prediction Analysis of Microarrays) [Tibshirani R et al., Proc
Natl Acad Sci USA, 99:6567-6572 (2002)]. IS and healthy subjects
were randomly split in half, stratified by Group and Time-Point
(for the IS samples) into a Training Set to develop the prediction
algorithm and an independent Test (Validation) Set for evaluating
the accuracy of the prediction algorithm.
[0207] Objective 2: To identify genes able to discriminate between
IS and all controls groups, an ANCOVA adjusted for age, gender and
microarray batch effect was used. The numbers of predictive genes
were minimized using the nearest-shrunken centroids algorithm
(PAM). The ability of the identified genes to predict IS from
controls was assessed using (1) 10-fold cross-validation (CV), and
(2) assessed in a second (independent) Test (Validation) Set using
several prediction algorithms (k-nearest neighbor (K-NN), support
vector machine (SVM), linear discriminant analysis (LDA), and
quadratic discriminant analysis (QDA)). Only the 3 h IS (not
treated) and 24 h IS samples were analyzed for objective 2 since
they were considered most clinically relevant. See supplementary
materials and methods for details of the prediction and
cross-validation analyses for Objectives 1 and 2.
Study Participants
Ischemic Stroke (IS) Patients
[0208] Participants with acute IS (n=68) were recruited from the
CLEAR trial, a multicenter, randomized double blind safety study of
recombinant tissue-plasminogen activator (r-tPA) and eptifibatide
as previously described [Pancioli A M et al., Stroke, 39:3268-3276
(2008)] (NCT00250991 at Clinical-Trials.gov). Blood samples were
collected at .ltoreq.3 hours (3 h IS), 5 hours (5 hr IS) and 24
hours (24 IS) following ischemic stroke onset. r-tPA, with or
without eptifibatide, was administered following the 3 h blood
draw. IS was diagnosed by a stroke neurologist with access to all
clinical and diagnostic tests including neurovascular imaging
data.
Control Groups
Vascular Risk Factor Subjects (SAVVY)
[0209] Subjects with at least one cardiovascular risk factor
(hypertension, diabetes mellitus, hyperlipidemia, or tobacco
smoking) were recruited from the SAVVY (Sex, Age and Variation in
Vascular functionalitY) study (n=52). These subjects are referred
to as vascular risk factor
[0210] SAVVY Controls in the current study. Exclusion criteria were
past history of cardiovascular disease (including stroke, coronary
artery disease, peripheral artery disease or deep vein thrombosis),
BMI >46 kg/m.sup.2, history of cancer, chronic infection,
autoimmune disease or blood dyscrasias.
Patients with Myocardial Infarction (MI)
[0211] Subjects with MI (n=16) were recruited from the University
of California Davis Medical Center. The average time since the
event was 58.0 h (range 19.3-176.5). All were treated acutely with
anti-platelet drugs and an anticoagulant prior to the blood draw.
Angioplasty (n=8) or CABG (n=1) were performed in some of the
patients prior to the blood draw. No MI patient received r-tPA.
Healthy Controls
[0212] Healthy controls were recruited from the University of
Cincinnati (n=15), UC Davis (n=3) and Stanford (n=20). These
subjects had never been hospitalized, were on no medications, and
had no known major medical, surgical or psychiatric diseases.
[0213] Baseline demographic data were compared between the previous
[Tang Y et al., J Cereb Blood Flow Metab., 26:1089-1102 (2006)] and
current study as well as between current IS and control subjects
using Student's 2-tail t-test for continuous variables (age) and a
.chi..sup.2 or Fisher Exact tests for categorical variables
(gender, race).
Probe-Level Data Analysis
[0214] Raw expression values of each probe from the Affymetrix U133
Plus 2.0 expression arrays were collapsed into probe set level data
using Robust Multichip Averaging (RMA) normalization [Bolstad B M
et al., Bioinformatics, 19:185-193 (2003)], as well as by modified
internal-gene normalization (manuscript in preparation) to a subset
of stably expressed internal genes [Stamova B S et al., BMC Med
Genomics, 2:49 (2009)]. This involved Median Polishing
summarization step, division of each individual gene expression
value by the geometric mean of the reference genes, and
log.sub.2-transformation. For the analysis in Objective 1, both RMA
and Internal control gene normalized values were used. For all the
analysis of Objective 2, the derivation of the discriminatory genes
was performed using the internal control gene normalized values.
The same values were used in developing the Classifiers.
Batch Correction
[0215] Due to the unbalanced nature of the batches, bias is
introduced when batch is used as a factor in an ANCOVA model.
However, it is still desirable to account for the existing
technical variation. This was accomplished by selecting genes that
were common to the ANCOVA output sets with and without batch as a
factor. While this technique introduced strict criteria for the
selection of discriminating genes, it was intended to improve the
chance of validation of the results upon subsequent studies and to
achieve greater generalization, which can be translated into IS
predictive clinical test.
Identification of Discriminatory Genes
[0216] Analysis of each comparison (IS per time-point (3 h and 24
h) vs Healthy, MI and SAVVY, respectively) was performed
individually. The samples were randomly split, stratified by Group,
in order to perform a split-sample analysis, where the Prediction
Algorithms are trained on half of the samples (Training Set), and
the performance of the Classifiers is tested on the second half of
the samples (Test Set). The Analysis Workflow Chart is shown in
FIG. 3. The feature selection for the derivation of the
discriminatory genes between Healthy and IS at 3 h and IS at 24 h,
respectively, involved finding common probe sets from four
different ANCOVA analysis, referred to here as Models 1-4. All
factors used in the analysis were common to all models (Group, Age,
Gender) with the exception of Batch, which was only factored in
Model 1 and 3. Models 1 and 2 were applied to a randomly selected
one-half of the samples stratified by Group and time-point (for the
IS samples) named here 1.sup.st random half, whereas Models 3 and 4
were applied to the complete data sets. Overlap of models with and
without batch was performed due to the unbalanced nature of batches
in an attempt to select more reliable probe sets. Overlap of
complete-set and split-set models was performed to achieve greater
generalization compared to the split set model which can be
translated into IS predictive clinical test.
[0217] Gene lists satisfying the following criteria were developed:
FDR-corrected p-value (Group).ltoreq.0.05 and fold-change
.ltoreq.-1.5 or .gtoreq.1.5, as well as being not-significant for
the rest of the factors (uncorrected p (Age)>0.5 and uncorrected
p (Gender)>0.05 and, for the models including Batch, uncorrected
p (Batch)>0.05). The goal is to find genes whose expressions are
not affected by significant technical (batch), gender, or age
effects.
[0218] Exception to Flow Chart Analysis for IS at 24 h vs Healthy
was at Model 1, where the uncorrected p (Group)<0.01 was used to
generate a larger gene list. Analysis of SAVVY vs IS at 3 h and IS
at 24 h, respectively, included only Models 2 and 4, since Batch
could not be factored in, due to the complete confounding of the
batches. Analysis of MI vs IS at 3 h and IS at 24 h, respectively,
included only Models 3 and 4, since the sample size of the MI
patients was very small (n=17). In this case a 10-fold
cross-validation procedure was used to determine the performance of
the Classification Algorithms. If the number of the probe sets at
the feature selection step was large, we proceeded with excluding
probe sets not annotated, annotated as chromosomal segments,
annotated as hypothetical proteins, probe sets which per Affimetrix
annotation may potentially detect more than one unique gene
(*_x_at, *_a_at, *_s_at), and exclusion of duplicates.
Predictions/Classification
[0219] Different prediction algorithms were used. Prediction
Analysis of Microarrays (PAM) uses the K-nearest neighbor as a
classification engine (default k=10) as well as nearest shrunken
centroid as a feature-selection method [Tibshirani R et al., Proc
Natl Acad Sci USA, 99:6567-6572 (2002)]. The differentially
expressed genes that passed the criteria outlined above were input
into PAM and the minimum numbers of genes with the optimal
classification accuracy were selected. In addition, multiple other
classification methods were evaluated in the analysis of the
combined 3 h IS predictors, 24 h IS predictors and 3 h plus 24 h IS
predictors in order to find an optimal model and to produce an
unbiased estimate of prediction accuracy (analysis performed in
Partek Genomics Suite, Partek Inc., St. Louis, Mich., USA). A
combination of the ANCOVA models and nearest-shrunken centroids for
our feature reduction step was used. In addition to PAM, the
classification models used in this study were K-Nearest Neighbor
(K-NN) with k=1, 3, 5, 7, and 9 number of neighbors with Euclidian
Distance similarity measure; Nearest-Centroid (NC) with equal and
proportional prior probabilities; Quadratic Discriminant Analysis
(QDA) with equal and proportional prior probabilities, Linear
Discriminant Analysis (LDA) with equal and proportional prior
probabilities, and Support Vector Machine, constituting a 121-model
space. For overview of these methods, see [Asyali M H et al.,
Current Bioinformatics, 1:55-73 (2006); Jain A K et al.,
Statistical pattern recognition: A review, IEEE Transactions On
Pattern Analysis and Machine Intelligence., 22:4-37 (2000)].
2-level nested cross-validation (CV) was performed to generate a
less biased estimate of classification success (reported as
accuracy (normalized) estimate). In this approach, an "outer"
cross-validation is performed in order to produce an unbiased
estimate of prediction error (by holding out samples as an
independent test set). To select the optimal model to be applied to
the held out test sample, additional "inner" cross-validation is
performed on the training data (which is the data not held out as
test data by the "outer" cross-validation). Full leave-one-out
cross validation (CV) was used in cases where the complete set was
used to train and CV the prediction accuracy.
[0220] For Table 4 in the Results section, the following parameters
were used: *Accuracy (normalized) estimate of 121-Model Space=91.2%
(80.3/88). Best Model: SVM (shrink=yes, cost=101, nu=0.5, to
1=0.001, kern rbf deg=3, radial basis function (gamma)=0.01,
coef=0.0). Kappa=0.83. .dagger.Accuracy (normalized) estimate of
121-Model Space=87.9% (76.4/87). Best Model: SVM (shrink=yes,
cost=101, nu=0.5, to 1=0.001, kern rbf deg=3, radial basis function
(gamma)=0.0001, coef=0.0). Kappa=0.83. .dagger-dbl.Accuracy
(normalized) estimate of 121-Model Space=91.2% (110/121). Best
Model: SVM (shrink=yes, cost=701, nu=0.5, to 1=0.001, kern rbf
deg=3, radial basis function (gamma)=0.00001, coef=0.0).
.parallel.Correct classification at 3 h=76%, at 24 h=97%. #Correct
classification at 3 h=94%, at 24 h=97%.
Gene Enrichment Analysis of Discriminatory Genes to Identify
Biological Themes in the Combined 3 h and 24 h IS Predictors
[0221] Ingenuity Pathway Analysis (IPA 8.0, Ingenuity.RTM. Systems)
was used for identifying over-represented biological functions in
the combined 97 probe set list of 3 h and 24 h predictors. A
Fisher's exact test (p<0.1) was used to determine whether there
was over representation of the 97 probe sets/genes in any given
biological function. Gene ontology of the stroke predictors was
extracted from Affymetrix NetAffix website (on the internet at
affymetrix.com/user/login.jsp?toURL=-/analysis/netaffx/index.affx).
Results
Subject Demographics
[0222] Demographic information is presented in Table 1 (Objective
1) and Table 2 (Objective 2). Age was significantly different
between IS and control groups (p<0.05) (Tables 1 and 2). Gender
was significantly different (p<0.05) between IS and healthy
subjects in the Tang et al, 2006 study [Tang Y et al., J Cereb
Blood Flow Metab., 26:1089-1102 (2006)] and the current study
(Table 1), as well as between IS and Vascular Risk Factor (SAVVY)
Control subjects from the current study (Table 2). Race was
significantly different between IS compared to Healthy and MI
controls (Table 2). Hypertension and diabetes were not
significantly different between the groups.
TABLE-US-00001 TABLE 1 Demographic Summary of Subjects from our
previous Tang et al. 2006 Study [Tang Y et al., J Cereb Blood Flow
Metab., 26: 1089-1102 (2006)] and our Current Study of Ischemic
Stroke (IS) and Healthy Controls. N = number of subjects. Healthy
IS Healthy Controls Tang et. Tang et. al, IS Current al, 2006 2006
Current Study Study N 15 15 70.sup..dagger. 38 Mean Age, 64 .+-. 14
49 .+-. 11 66.8 .+-. 12.7 45.0 .+-. 19.8 years (SD) Gender, % Male
73.3 87.5* 57.1 47.4* Female 26.7 12.5* 42.9 52.6* Race, %
Caucasian 80.0 75.0 80.0 55.3 African 20.0 0.0 20.0 15.8 American
Other 0.0 25.0 0.0 28.9 NIH Stroke Scale 1.sup.st Blood 15 .+-. 7
N/A 14 .+-. 7 N/A Draw (3 h) 2.sup.nd Blood 12 .+-. 8 11 .+-. 8
Draw (5 h) 3.sup.rd Blood 9 .+-. 7 10 .+-. 8 Draw (24 h)
.sup..dagger.N = 67 at 3 h, 66 at 5 h, 66 at 24 h. 61 subjects had
all three time points. *Gender distribution significantly different
(p < 0.05) between healthy subjects in the current study
compared to the Tang et al. 20061 study and marginally different
between IS subjects in the current study compared to the Tang et
al. 2006 [Tang Y et al., J Cereb Blood Flow Metab., 26: 1089-1102
(2006)] study.
TABLE-US-00002 TABLE 2 Demographic Summary of Current Study
Participants. Vascular Healthy MI SAVVY IS Controls Controls
Controls N 70.sup..dagger. 38 17 52 Mean Age, years (SD) 66.8 .+-.
12.7 45.0 .+-. 19.8 59.6 .+-. 12.2 56.2 .+-. 5.4 Gender, % Male
57.1 47.4 70.6 32.7 Female 42.9 52.6 29.4 67.3* Race, % Caucasian
80.0 55.3 47.1 86.5 African American 20.0 15.8 17.6 11.5 Other 0.0
28.9** 35.3** 2.0 NIH Stroke Scale 1.sup.st Blood Draw (3 h) 14
.+-. 7 N/A N/A N/A 2.sup.nd Blood Draw (5 h) 11 .+-. 8 3.sup.rd
Blood Draw (24 h) 10 .+-. 8 .sup..dagger.N = 67 at 3 h, 66 at 5 h,
66 at 24 h. 61 subjects had all three time points; *Gender
distribution significantly different (p < 0.05) between Ischemic
Stroke (IS) and Vascular Risk Factor (SAVVY) controls. MI =
myocardial infarction. N = number of subjects. **Race significantly
different (p < 0.05) between IS compared to healthy and MI.
1) Replication of Tang et al, 2006 [Tang Y et al., J Cereb Blood
Flow Metab., 26:1089-1102 (2006)] IS Predictors in a Larger
Cohort
[0223] Due to the different array processing protocols in the study
by Tang et al, 2006 [Tang Y et al., J Cereb Blood Flow Metab.,
26:1089-1102 (2006)] and the current studies, the following
analyses were performed: (1) the prediction algorithm was retrained
on the first random half of the new samples (Training Set) and the
performance of the 29 probe sets evaluated in the second half
(Test/Validation Set); and (2) the samples used in the Tang et al,
2006 study [Tang Y et al., Cereb Blood Flow Metab., 26:1089-1102
(2006)] and the current study were internal gene normalized.
Overall, 92.9% sensitivity for IS and 94.7% specificity for healthy
controls with high Test Set probabilities were achieved (FIG. 1,
Table 3). The results are similar to the ability of these
predictors to classify the previously published patients [Tang Y et
al., J Cereb Blood Flow Metab., 26:1089-1102 (2006)], with 88.9%
sensitivity for IS and 100% specificity for healthy controls (Table
3). In addition, for comparison purposes to the previous study
[Tang Y et al., J Cereb Blood Flow Metab., 26:1089-1102 (2006)],
RMA normalization and Cross-Validation (used in the previous study
[Tang Y et al., J Cereb Blood Flow Metab., 26:1089-1102 (2006)]) on
our complete set of IS and healthy samples was performed. Similar
results were obtained (Table 5 and FIG. 4).
TABLE-US-00003 TABLE 3 Validation of the of the 29 probe sets from
the Tang et al. 2006 study [Tang Y et al., J Cereb Blood Flow
Metab., 26: 1089-1102 (2006)]. These probe sets were trained on the
first half (Training Set) of the subjects in this study (n = 35 IS,
n = 19 Healthy), and then used predict the Test Set probabilities
on a second half of the ischemic stroke (IS) subjects (Test Set) (n
= 35, 99 samples) and Healthy subjects (n = 19, 19 samples) in the
Current Study. In addition, the same probe sets were used to
predict the Test set probabilities on the original subjects in the
Tang et al. 2006 study. All Time Class Prediction Study 3 h 5 h 24
h Points IS, Sensitivity, % Tang et al, 2006 73.3 93.3 100 88.9
Current Study 84.8 97.0 97.0 92.9 Healthy, Specificity, % Tang et
al, 2006 N/A N/A N/A 100 Current Study N/A N/A N/A 94.7 Sensitivity
= % correct classification of IS samples Specificity = % correct
classification of healthy samples
TABLE-US-00004 TABLE 4 Classification Accuracy (%) of 3 h and 24 h
Ischemic Stroke (IS) Predictors. 60 probe sets 46 probe sets 97
probe sets 3 h IS vs Controls 24 h IS vs Controls 3 h and 24 h IS
Combined vs (Healthy, MI, SAVVY) (Healthy, MI, SAVVY) Controls
(Healthy, MI, SAVVY) Group PAM SVM* PAM SVM.sup..dagger. PAM
SVM.sup..dagger-dbl. IS 85 94 91 94 86.sup..parallel. .sup.
95.sup.# SAVVY 92 96 92 96 96 96 MI 88 88 63 50 75 75 Healthy 84 68
89 84 84 68 .sup..parallel.Correct classification at 3 h = 76%, at
24 h = 97%. .sup.#Correct classification at 3 h = 94%, at 24 h =
97%. Half of the subjects (training set) were used to derive the IS
Predictors. For the Test Set prediction accuracy estimate on the
second half of the subjects, there were 3 h IS (n = 33), 24 h IS (n
= 33), healthy (n = 19), Vascular Risk Factor (SAVVY) (n = 26) and
MI (n = 8). The 60-probe set 3 h IS predictors represented the sum
of the 3 h IS comparison to the three control groups: Healthy(17),
SAVVY(22) and MI(31), of which 10 were common to the 3 h IS vs MI
and 3 h IS vs SAVVY predictors, yielding 60 probe sets. The
46-probe set 24 h IS predictors represented the sum of the 24 h IS
comparison to the three control groups: Healthy(20), SAVVY(9) and
MI(17). The 3 h and 24 h IS Combined predictors represent the sum
of the 3 h IS predictors (60) and 24 h IS predictors(46) of which 9
were common, yielding 97 probe sets.
TABLE-US-00005 TABLE 5 Validation of the of the 29 probe sets from
the Tang et al, 2006 study [Tang Y et al., J Cereb Blood Flow
Metab., 26: 1089-1102 (2006)]. Cross-validated Probabilities.
Trained and cross-validated on current study samples (IS: n = 70,
199 samples) and Healthy (n = 38, 38 samples). Normal- All ization
Class Time Method Prediction Study 3 h 5 h 24 h Points RMA IS, Tang
et al, 2006 66.7 86.7 100 84.4 Sensitivity, Current Study 86.6 98.5
89.4 91.5 % Healthy, Tang et al, 2006 N/A N/A N/A 100 Specificity,
Current Study N/A N/A N/A 84.2 % Internal IS, Tang et al, 2006 73.3
93.3 100 88.9 Genes Sensitivity, Current Study 86.6 98.5 95.5 93.5
% Healthy, Tang et al, 2006 N/A N/A N/A 100 Specificity, Current
Study N/A N/A N/A 89.5 % Sensitivity = % correct classification of
IS samples Specificity = % correct classification of healthy
samples
2) Refinement of Prediction of IS Against Several Different Control
Groups
[0224] Differentiation of IS Patients from Controls
[0225] Predictive gene expression signatures were derived
individually for each comparison. To discriminate the 3 h IS group
from the healthy (training set), MI (Cross Validation set, due to
small sample size for MI), and SAVVY (training set) control groups,
the PAM classification algorithm derived 17, 31, and 22 predictor
probesets/genes, respectively. Putting these genes into PAM to
predict the class of the subjects in the test groups yielded
87.9/94.7%, 98.5/82.4%, and 100/96.2% sensitivity/specificity for 3
h IS compared to healthy, MI and SAVVY control samples,
respectively (FIGS. 5, 6 and 7, respectively).
[0226] To discriminate the 24 h IS group from the healthy (training
set), MI (CV set, due to small sample size for MI), and SAVVY
(training set) control groups, the PAM classification algorithm
derived 20, 19, and 9 predictor probesets/genes, respectively.
Putting these genes into PAM to predict the class of the subjects
in the test groups yielded 90.9/94.7%, 93.9/88.2%, and 97/100%
sensitivity/specificity for 24 h IS compared to healthy, MI and
SAVVY control samples, respectively (FIGS. 8, 9, and 10,
respectively).
Prediction Accuracy of 3 h IS Predictors on 3 h IS, Healthy, MI and
SAVVY Subjects
[0227] Combining the lists of the 3 h predictors from the
individual comparison analyses yielded 60 unique probe sets
representing 56 annotated genes. Their prediction probability using
PAM on the Test Set is presented in FIG. 2A. The percent correctly
predicted samples from PAM as well as the best performing
prediction model (SVM) are presented in Table 4. Overall
(normalized) accuracy was 91.2%. With SVM the sensitivity was 94%
and specificities were 96% for SAVVY, 88% for MI, and 68% for
healthy. Analysis in PAM produced lower sensitivity for IS but
higher specificity for healthy subjects compared to SVM (Table 4).
In addition to the split sample analysis, a 10-fold Cross
Validation was performed which is a preferred method for developing
and evaluating prediction algorithms for small sample sizes. This
produced the expected better prediction results (Table 6 and FIG.
11A).
TABLE-US-00006 TABLE 6 Classification Accuracy (% correct
classification) of 3 h and 24 h Ischemic Stroke (IS) Predictors. 97
probe sets 60 probe sets 46 probe sets 3 h and 24 h IS 3 h IS vs 24
h IS vs Combined vs Controls Controls Controls (Healthy, (Healthy,
(Healthy, MI, SAVVY) MI, SAVVY) MI, SAVVY) Group PAM SVM* PAM
SVM.sup..dagger. PAM SVM.sup..dagger-dbl. IS 90 91 88 91 90.sup.||
96 SAVVY 94 98 98 98 94 98 MI 71 88 65 82 71 82 Healthy 82 84 79 84
79 76 Sample sizes used for Cross-Validation were n = 67 at 3 h IS,
n = 66 at 24 h IS, n = 52 for SAVVY, n = 17 for MI. Sample sizes
used for split-sample prediction performance estimate on the test
set were n = 33 at 3 h IS, n = 33 at 24 h IS, n = 26 for SAVVY, n =
8 for MI. The 60-probe set 3 h IS predictors represented the sum of
the 3 h IS comparison to the three control groups: Healthy (17
probe sets), SAVVY controls (22 probe sets) and MI (31 probe sets).
The 46-probe set 24 h IS predictors represented the sum of the 24 h
IS comparison to the three control groups: Healthy (20 probe sets),
SAVVY controls (9 probe sets) and MI (17 probe sets). The 3 h and
24 h IS Combined predictors represent the sum of the 3 h IS
predictors (60 probe sets) and 24 h IS predictors (n = 46) of which
9 were common, thus yielding 97 probe sets. *Accuracy (normalized)
estimate of 121-Model Space = 86.4% (150/174). Best Model: SVM
shrink = yes, cost = 201, nu = 0.5, tol = 0.001, kern rbf deg = 3,
radial basis function (gamma) = 0.001, coef = 0.0).
.dagger.Accuracy (normalized) estimate of 121-Model Space = 89.2%
(154/173). Best Model: SVM (shrink = yes, cost = 201, nu = 0.5, tol
= 0.001, kern rbf deg = 3, radial basis function (gamma) = 0.0001,
coef = 0.0). .dagger-dbl.Accuracy (normalized) estimate of
121-Model Space = 88.2% (212/240). Best Model: SVM (shrink = yes,
cost = 101, nu = 0.5, tol = 0.001, kern rbf deg = 3, radial basis
function (gamma) = 0.01, coef = 0.0). ||Correct classification at 3
h = 87%, at 24 h = 96%
Prediction Accuracy of 24 h IS Predictors on 24 h IS, Healthy, MI
and SAVVY Subjects
[0228] Combining the lists of the 24 h predictors from the
individual comparison analyses yielded 46 unique probe sets
representing 32 annotated genes. Their prediction probability using
PAM on the Test Set is presented in FIG. 2B. The percent correctly
predicted samples from PAM as well as SVM (best performing
prediction model) are presented in Table 4. Overall (normalized)
accuracy was 89.2%. With SVM the sensitivity was 94% and
specificities were 96% for SAVVY, 50% for MI and 84% for healthy.
Better results were again obtained using a 10-fold cross validation
(Table 6 and FIG. 11B).
Prediction Accuracy of combined 3 h and 24 IS Predictors on 3 h and
24 h IS, Healthy, MI and SAVVY Subjects
[0229] Combining the lists of the 3 h and 24 h predictors from the
individual comparison analyses yielded 97 unique probe sets
representing 79 annotated genes. Their prediction probability using
PAM on the Test Set is presented in FIG. 2C. The percent correctly
predicted samples from PAM and SVM (best performing prediction
model) are presented in Table 4. Overall (normalized) accuracy was
91.2%. With SVM the sensitivity was 95% and specificities were 96%
for SAVVY, 75% for MI, and 68% for healthy. Analysis in PAM
produced lower sensitivity for IS but higher specificity for
healthy subjects compared to SVM (Table 4). Similarly, due to the
small sample numbers of MI subjects, 10-fold cross-validation was
performed which yielded somewhat better results (Table 6 and FIG.
11C).
IV. Main Biological Function of Biomarkers Described
[0230] Using Ingenuity Pathway analysis software (see Supplementary
Materials) the coagulation system was the only statistically
over-represented bio-function in the combined 97-probe set list of
3 h and 24 h IS predictors. The coagulation genes included
coagulation factor V (proaccelerin, labile factor) (F5) and
thrombomodulin (THBD). G0 annotations and the complete list of
predictors are presented in Tables 7A-C. Less stringent criteria
yielded large numbers of genes with many more regulated
pathways.
TABLE-US-00007 TABLE 7A Combined 3 h and 24 h IS predictors -
Identification of Genes Table 7A. Biomarkers Useful to Predict the
Occurrence of Stroke Probe Set ID Gene Symbol Gene Title GenBank ID
UniGene ID RefSeq Transcript ID RefSeq Protein ID 1554560_at PGM5
phosphoglucomutase 5 BC033073.1 Hs.307835 NM_021965 NP_068800
1561271_at CCDC144C coiled-coil domain containing BC036241.1
Hs.652797 NR_023380 /// XP_001718313 /// 144C /// similar to
Coiled-coil XM_001718261 LOC100134159 domain containing 144B
207409_at LECT2 leukocyte cell-derived NM_002302.1 Hs.512580
NM_002302 NP_002293 chemotaxin 2 207570_at SHOX short stature
homeobox NM_000451.2 Hs.105932 NM_000451 /// NP_000442 ///
NM_006883 NP_006874 240715_at TBX5 T-box 5 AW269421 Hs.381715
NM_000192 /// NP_000183 /// NM_080717 /// NP_542448 /// NM_080718
/// NP_542449 /// NM_181486 NP_852259 220456_at SPTLC3 serine
palmitoyltransferase, NM_018327.1 Hs.425023 NM_018327 NP_060797
long chain base subunit 3 232547_at SNIP SNAP25-interacting protein
BF062187 Hs.448872 NM_025248 NP_079524 238447_at RBMS3 RNA binding
motif, single AA428240 Hs.696468 NM_001003792 /// NP_001003792 ///
stranded interacting protein NM_001003793 /// NP_001003793 ///
NM_014483 NP_055298 242912_at P704P prostate-specific P704P
AI041215 Hs.654289 NM_001145442 NP_001138914 222835_at THSD4
thrombospondin, type I, BG163478 Hs.387057 NM_024817 NP_079093
domain containing 4 236029_at FAT3 FAT tumor suppressor AI283093
Hs.98523 NM_001008781 NP_001008781 homolog 3 (Drosphila) 1559545_at
SNRPN small nuclear AI371649 Hs.632166 NM_003097 /// NP_003088 ///
ribonucleoprotein NM_022805 /// NP_073716 /// polypeptide N
NM_022806 /// NP_073717 /// NM_022807 /// NP_073718 /// NM_022808
NP_073719 1562089_at GLYATL1 glycine-N-acyltransferase- BC013929.1
Hs.616909 NM_080661 NP_542392 like 1 1563533_at GADL1 glutamate
decarboxylase-like 1 AL832766.1 Hs.657052 NM_207359 NP_997242
203917_at CXADR coxsackie virus and NM_001338.1 Hs.634837 NM_001338
NP_001329 adenovirus receptor 206048_at OVOL2 ovo-like 2
(Drosphila) NM_021220.1 Hs.661013 NM_021220 NP_067043 219104_at
RNF141 ring finger protein 141 NM_016422.1 Hs.44685 NM_016422
NP_057506 219859_at CLEC4E C-type lectin domain family NM_014358.1
Hs.236516 NM_014358 NP_055173 4, member E 232739_at SPIB Spi-B
transcription factor AK025419.1 Hs.437905 NM_003121 NP_003112
(Spi-1/PU.1 related) 234243_at BXDC5 brix domain containing 5
AL359584.1 Hs.481202 NM_025065 NP_079341 226899_at UNC5B unc-5
homolog B (C. elegans) AK022859.1 Hs.522997 NM_170744 NP_734465
203167_at TIMP2 TIMP metallopeptidase NM_003255.2 Hs.633514
NM_003255 NP_003246 inhibitor 2 1554816_at ASTN2 astrotactin 2
BC010680.1 Hs.601562 NM_014010 /// NP_054729 /// NM_198186 ///
NP_937829 /// NM_198187 /// NP_937830 /// NM_198188 NP_937831
1557895_at FLJ35934 FLJ35934 protein BC033201.1 Hs.375092 XR_041166
-- 1561079_at ANKRD28 ankyrin repeat domain 28 BC035170.1 Hs.335239
NM_015199 NP_056014 1561477_at CCDC144A coiled-coil domain
containing BC034617.1 -- NM_014695 NP_055510 144A 210800_at TIMM8A
translocase of inner BC005236.1 Hs.447877 NM_001145951 ///
NP_001139423 /// mitochondrial membrane 8 NM_004085 NP_004076
homolog A (yeast) 211617_at ALDOAP2 aldolase A, fructose- M21191.1
Hs.652473 -- -- bisphosphate pseudogene 2 213371_at LDB3 LIM domain
binding 3 AI803302 Hs.657271 NM_001080114 /// NP_001073583 ///
NM_001080115 /// NP_001073584 /// NM_001080116 /// NP_001073585 ///
NM_007078 NP_009009 214043_at PTPRD protein tyrosine BF062299
Hs.446083 NM_001040712 /// NP_001035802 /// phosphatase, receptor
type, D NM_002839 /// NP_002830 /// NM_130391 /// NP_569075 ///
NM_130392 /// NP_569076 /// NM_130393 NP_569077 214375_at LOC729222
similar to PTPRF interacting AI962377 Hs.172445 NM_003622 ///
NP_003613 /// /// PPFIBP1 protein binding protein 1 /// NM_177444
/// NP_803193 PTPRF interacting protein, XR_015484 /// binding
protein 1 (liprin beta XR_037707 /// 1) XR_037871 220351_at CCRL1
chemokine (C-C motif) NM_016557.1 Hs.310512 NM_016557 /// NP_057641
/// receptor-like 1 NM_178445 NP_848540 222264_at HNRNPUL2
heterogeneous nuclear BG167570 Hs.714969 NM_001079559 NP_001073027
ribonucleoprotein U-like 2 224403_at FCRL4 Fc receptor-like 4
AF343661.1 Hs.120260 NM_031282 NP_112572 228260_at ELAVL2 ELAV
(embryonic lethal, AL161628 Hs.166109 NM_004432 NP_004423 abnormal
vision, Drosphila)-like 2 (Hu antigen B) 229073_at PRTG protogenin
homolog (Gallus AA912476 Hs.130957 NM_173814 NP_776175 gallus)
239309_at DLX6 distal-less homeobox 6 T65128 Hs.249196 NM_005222
NP_005213 40284_at FOXA2 forkhead box A2 AB028021 Hs.155651
NM_021784 /// NP_068556 /// NM_153675 /// NP_710141 ///
XM_002345401 XP_002345442 220232_at SCD5 stearoyl-CoA desaturase 5
NM_024906.1 Hs.379191 NM_001037582 /// NP_001032671 /// NM_024906
NP_079182 242344_at GABRB2 gamma-aminobutyric acid AA772920
Hs.303527 NM_000813 /// NP_000804 /// (GABA) A receptor, beta 2
NM_021911 NP_068711 1559520_at GYPA Glycophorin A (MNS blood
AL833104.1 Hs.434973 NM_002099 NP_002090 group) 215285_s_at PHTF1
putative homeodomain AA927671 Hs.655824 NM_006608 NP_006599
transcription factor 1 219161_s_at CKLF chemokine-like factor
NM_016951.2 Hs.15159 NM_001040138 /// NP_001035228 /// NM_016326
/// NP_057410 /// NM_016951 /// NP_058647 /// NM_181640 ///
NP_857591 /// NM_181641 NP_857592 221058_s_at CKLF chemokine-like
factor NM_016326.2 Hs.15159 NM_001040138 /// NP_001035228 ///
NM_016326 /// NP_057410 /// NM_016951 /// NP_058647 /// NM_181640
/// NP_857591 /// NM_181641 NP_857592 221524_s_at RRAGD Ras-related
GTP binding D AF272036.1 Hs.31712 NM_021244 NP_067067 222934_s_at
CLEC4E C-type lectin domain family BC000715.1 Hs.236516 NM_014358
NP_055173 4, member E 223451_s_at CKLF chemokine-like factor
AF096895.2 Hs.15159 NM_001040138 /// NP_001035228 /// NM_016326 ///
NP_057410 /// NM_016951 /// NP_058647 /// NM_181640 /// NP_857591
/// NM_181641 NP_857592 227948_at FGD4 FYVE, RhoGEF and PH AI949549
Hs.117835 NM_139241 NP_640334 domain containing 4 235479_at CPEB2
cytoplasmic polyadenylation AI948598 Hs.656937 NM_182485 ///
NP_872291 /// element binding protein 2 NM_182646 NP_872587
236297_at -- -- AI420817 Hs.585479 -- -- 236898_at LOC100290882
similar to hCG1994130 AW242604 -- XM_002347794 XP_002347835
238903_at UBXN2B UBX domain protein 2B AI636090 Hs.155572
NM_001077619 NP_001071087 207691_x_at ENTPD1 ectonucleoside
triphosphate NM_001776.1 Hs.719076 NM_001098175 /// NP_001091645
/// diphosphohydrolase 1 NM_001164178 /// NP_001157650 ///
NM_001164179 /// NP_001157651 /// NM_001164181 /// NP_001157653 ///
NM_001164182 /// NP_001157654 /// NM_001164183 /// NP_001157655 ///
NM_001776 NP_001767 205715_at BST1 bone marrow stromal cell
NM_004334.1 -- NM_004334 NP_004325 antigen 1 236172_at LTB4R
leukotriene B4 receptor AW206817 Hs.567248 NM_001143919 ///
NP_001137391 /// NM_181657 NP_858043 231029_at F5 coagulation
factor V AI740541 Hs.30054 NM_000130 NP_000121 (proaccelerin,
labile factor) 202146_at IFRD1 interferon-related AA747426 Hs.7879
NM_001007245 /// NP_001007246 /// developmental regulator 1
NM_001550 NP_001541 206017_at KIAA0319 KIAA0319 NM_014809.1
Hs.26441 NM_014809 NP_055624 218177_at CHMP1B chromatin modifying
protein AA293502 Hs.656244 NM_020412 NP_065145 1B 220122_at MCTP1
multiple C2 domains, NM_024717.1 Hs.655087 NM_001002796 ///
NP_001002796 /// transmembrane 1 NM_024717 NP_078993 220528_at VNN3
vanin 3 NM_018399.1 Hs.183656 NM_001024460 /// NP_001019631 ///
NM_018399 /// NP_060869 /// NM_078625 /// NP_523239 NR_028290 ///
NR_028291 226258_at AMN1 antagonist of mitotic exit BG031897
Hs.591146 NM_001113402 /// NP_001106873 network 1 homolog (S.
cerevisiae) NR_004854 226671_at LAMP2 lysosomal-associated AI150000
Hs.496684 NM_001122606 /// NP_001116078 /// membrane protein 2
NM_002294 /// NP_002285 /// NM_013995 NP_054701 228220_at FCHO2 FCH
domain only 2 AI627666 Hs.719247 NM_001146032 /// NP_001139504 ///
NM_138782 NP_620137 229817_at ZNF608 zinc finger protein 608
AI452715 Hs.266616 NM_020747 NP_065798 235699_at REM2 RAS (RAD and
GEM)-like H19232 Hs.444911 NM_173527 NP_775798 GTP binding 2
236154_at QKI Quaking homolog, KH R41907 Hs.593520 NM_006775 ///
NP_006766 /// domain RNA binding NM_206853 /// NP_996735 ///
(mouse) NM_206854 /// NP_996736 /// NM_206855 NP_996737 236613_at
RBM25 RNA binding motif protein 25 BE466195 Hs.531106 NM_021239
NP_067062 239108_at FAR2 Fatty acyl CoA reductase 2 H16791
Hs.719237 NM_018099 NP_060569 213355_at ST3GAL6 ST3
beta-galactoside alpha- AI989567 Hs.148716 NM_006100 NP_006091
2,3-sialyltransferase 6 243201_at HNRNPH2 Heterogeneous nuclear
BF061744 Hs.632828 NM_001032393 /// NP_001027565 ///
ribonucleoprotein H2 (H') NM_019597 NP_062543 214987_at GAB1
GRB2-associated binding AL049449.1 Hs.80720 NM_002039 /// NP_002030
/// protein 1 NM_207123 NP_997006 208883_at UBR5 ubiquitin protein
ligase E3 BF515424 Hs.591856 NM_015902 NP_056986 component
n-recognin 5 228480_at VAPA VAMP (vesicle-associated AW296039
Hs.699980 NM_003574 /// NP_003565 /// membrane protein)- NM_194434
NP_919415 associated protein A, 33 kDa 1556834_at -- -- BC042986.1
Hs.562766 -- -- 1561754_at -- -- AF086134.1 Hs.671185 -- --
1561856_at -- -- BC030088.1 Hs.398148 -- -- 1562084_at -- --
BC042866.1 Hs.571857 -- -- 1562527_at LOC283027 hypothetical
protein AF519622.1 Hs.710809 -- -- LOC283027
1569539_at -- -- BC037935.1 Hs.650514 -- -- 1569664_at -- --
BC035915.1 Hs.622886 -- -- 230959_at -- -- AW072078 Hs.656184 -- --
231597_x_at -- -- AI371550 -- -- -- 231598_x_at -- -- AI379823 --
-- -- 235606_at LOC344595 hypothetical LOC344595 AA417117 Hs.655735
NR_028301 /// XP_001128525 /// NR_028302 /// XP_002345727 ///
XM_001128525 /// XP_948634 XM_002345686 /// XM_943541 238370_x_at
RPL22 Ribosomal protein L22 AI252081 Hs.554762 NM_000983 NP_000974
243489_at -- -- BF514098 Hs.678608 -- -- 244723_at LOC100129488
hypothetical protein BF510430 Hs.656497 XM_001724110 ///
XP_001724162 /// LOC100129488 XM_001724617 XP_001724669 240331_at
-- -- AI820961 Hs.658892 -- -- 238375_at RPL22 Ribosomal protein
L22 AI820887 Hs.554762 NM_000983 NP_000974 1554730_at MCTP1
multiple C2 domains, BC030005.1 Hs.655087 NM_001002796 ///
NP_001002796 /// transmembrane 1 NM_024717 NP_078993 211565_at
SH3GL3 SH3-domain GRB2-like 3 AF036272.1 Hs.666365 NM_003027 ///
NP_003018 NR_026799
TABLE-US-00008 TABLE 7B Combined 3 h and 24 h IS predictors -
Identification of Additional Genes Gene RefSeq RefSeq Probe Set ID
Symbol Gene Title GenBank ID UniGene ID Transcript ID Protein ID
203505_at ABCA1 ATP-binding cassette, sub- AF285167.1 Hs.719214
NM_005502 NP_005493 family A (ABC1), member 1 1569476_at
DKFZP434L187 hypothetical LOC26082 BC033224.1 Hs.652128 NR_026771
-- 226982_at ELL2 elongation factor, RNA AI745624 Hs.192221
NM_012081 NP_036213 polymerase II, 2 208158_s_at OSBPL1A oxysterol
binding protein-like NM_018030.1 Hs.370725 NM_018030 /// NP_060500
/// 1A NM_080597 NP_542164 237252_at THBD thrombomodulin AW119113
Hs.2030 NM_000361 NP_000352
TABLE-US-00009 TABLE 7C Combined 3 h and 24 h IS predictors - Fold
Change in Expression Fold Fold Fold Change Change Fold Change Fold
Fold (Stroke_3 h (Stroke_24 h Change (Stroke_24 h Change Change vs
vs (Stroke_3 h vs (Stroke_3 h (Stroke_24 h Vascular Vascular Gene
Symbol GenBank ID vs Healthy) Healthy) vs MI) vs MI) RF) RF) ABCA1
AF285167.1 2.07119 2.2176 1.09826 1.31812 2.53649 2.82053 PGM5
BC033073.1 -1.02257 1.0524 -2.95407 -1.70705 -2.71273 -2.09609
CCDC144C /// BC036241.1 -1.10901 1.33794 -3.34373 -1.89213 -3.74796
-2.67466 LOC100134159 LECT2 NM_002302.1 -1.02083 1.37036 -2.59881
-1.71369 -4.29623 -2.98873 SHOX NM_000451.2 -1.14389 1.11225 -2.767
-1.72507 -3.06805 -2.39484 TBX5 AW269421 -1.03714 1.19129 -3.0574
-2.27761 -3.06348 -2.47297 SPTLC3 NM_018327.1 -1.14707 -1.03685
-2.19854 -1.83648 -3.30052 -2.75386 SNIP BF062187 -1.17632 1.08103
-3.82871 -3.06017 -5.6754 -4.47024 RBMS3 AA428240 -1.12009 1.02386
-2.45005 -1.70022 -3.16789 -2.60285 P704P AI041215 -1.05604 1.00723
-2.43469 -2.22274 -2.01936 -1.86268 THSD4 BG163478 -1.08685 1.12498
-4.69322 -2.63617 -3.79344 -3.11379 FAT3 AI283093 1.01071 1.24955
-3.79842 -2.32779 -4.30382 -3.5885 SNRPN AI371649 -1.08724 1.34813
-4.11683 -2.74804 -3.4562 -2.56379 GLYATL1 BC013929.1 -1.02659
1.15432 -2.02763 -1.33612 -3.17767 -2.50921 GADL1 AL832766.1
1.08821 1.07407 -2.11355 -1.52718 -3.08374 -2.61596 DKFZP434L187
BC033224.1 -1.51544 -1.14093 -2.31467 -1.58952 -3.11353 -2.53751
CXADR NM_001338.1 1.0038 1.24866 -2.17672 -1.37969 -2.59518
-1.96103 OVOL2 NM_021220.1 -1.00245 1.2244 -2.6229 -1.90303
-3.73623 -3.11322 RNF141 NM_016422.1 1.41652 1.76732 -1.2503 1.0572
2.50809 2.88426 CLEC4E NM_014358.1 2.20581 1.74528 1.48355 1.23558
3.20009 2.54377 ELL2 AI745624 1.55833 1.55667 -1.1485 1.01227
2.61216 2.75015 SPIB AK025419.1 -1.49579 -1.1927 -1.30814 -1.21513
-3.13558 -2.57627 BXDC5 AL359584.1 -1.12459 1.0218 -3.37543
-2.16061 -4.34359 -3.47716 UNC5B AK022859.1 -1.06322 1.08493
-1.82774 -1.59757 -2.97215 -2.38636 TIMP2 NM_003255.2 1.28723
1.29348 1.17202 1.27056 2.65311 2.63656 ASTN2 BC010680.1 -1.03518
1.32302 -3.91463 -2.04726 -2.20417 -1.67438 FLJ35934 BC033201.1
-1.06005 1.08555 -1.7712 -1.53152 -2.90353 -2.69988 ANKRD28
BC035170.1 1.02142 1.52112 -3.23102 -1.72253 -3.75956 -2.19095
CCDC144A BC034617.1 -1.04089 1.40116 -2.58087 -1.9595 -2.94944
-2.15249 TIMM8A BC005236.1 1.05795 1.1857 -3.61689 -2.51479 -3.9091
-3.38927 ALDOAP2 M21191.1 -1.00693 1.18023 -2.05999 -1.4251
-2.14058 -1.77344 LDB3 AI803302 -1.02467 1.31867 -2.4936 -1.58633
-2.80843 -2.24309 PTPRD BF062299 1.13646 1.22444 -2.58721 -2.91802
-2.00587 -1.70565 LOC729222 /// AI962377 -1.01017 1.07748 -2.45679
-2.23759 -3.62343 -2.8743 PPFIBP1 CCRL1 NM_016557.1 1.06414 1.45982
-2.599 -1.47982 -2.2814 -1.54199 HNRNPUL2 BG167570 -1.1163 -1.0024
2.22518 2.22789 -1.33616 -1.19626 FCRL4 AF343661.1 1.07525 1.21448
-2.78886 -1.89115 -2.49879 -2.24799 ELAVL2 AL161628 -1.01813
1.21927 -2.20416 -1.49881 -1.86331 -1.56998 PRTG AA912476 -1.04494
1.0727 -2.3894 -1.62453 -2.99348 -2.50635 DLX6 T65128 1.00215
1.1532 -2.11674 -1.46511 -1.95595 -1.70623 FOXA2 AB028021 -1.0925
-1.03101 -2.1882 -1.81159 -1.99843 -1.7749 SCD5 NM_024906.1
-1.03966 1.02948 -2.87609 -1.91564 -2.53071 -2.01041 GABRB2
AA772920 1.04696 1.32248 -2.28193 -1.33566 -1.78517 -1.39109 GYPA
AL833104.1 1.04745 1.30732 -2.41685 -1.59859 -1.67937 -1.39989
OSBPL1A NM_018030.1 1.69237 2.0161 1.00357 1.22861 1.80349 2.07533
PHTF1 AA927671 1.72132 1.82978 -1.11534 1.20764 1.90827 2.12932
CKLF NM_016951.2 1.49288 1.67299 -1.083 1.05107 1.77407 1.85927
CKLF NM_016326.2 1.74019 1.92981 -1.09886 1.052 1.87206 1.89616
RRAGD AF272036.1 1.64361 1.82266 -1.00147 1.30328 1.59064 1.74087
CLEC4E BC000715.1 1.72855 1.61958 1.25076 1.17072 1.52696 1.35715
CKLF AF096895.2 1.59532 1.72012 -1.10928 1.00131 1.64747 1.65053
FGD4 AI949549 2.18122 1.77306 1.22089 1.20979 2.35983 1.917 CPEB2
AI948598 1.60268 1.66275 1.15005 1.40965 1.7109 1.90031 -- AI420817
1.55624 1.60124 -1.28503 1.01626 1.77952 2.0373 LOC100290882
AW242604 1.92911 2.33974 -1.89614 -1.13965 2.98199 3.56684 UBXN2B
AI636090 1.74169 1.9441 -1.11509 1.21234 1.70517 1.89673 ENTPD1
NM_001776.1 1.71167 1.76733 -1.21194 -1.07299 1.73969 1.87582 BST1
NM_004334.1 1.53532 1.62794 -1.06879 1.06228 1.26326 1.34039 LTB4R
AW206817 1.80645 1.74035 1.15737 1.21072 1.71929 1.59047 F5
AI740541 2.14346 2.2038 -1.11679 1.43859 2.20136 2.347 IFRD1
AA747426 1.47432 1.73008 -1.34744 1.06181 1.31407 1.4635 KIAA0319
NM_014809.1 1.63362 1.97771 -1.05306 1.43649 1.46648 1.73293 CHMP1B
AA293502 1.40135 1.80505 -1.3705 -1.09633 1.18349 1.53651 MCTP1
NM_024717.1 1.58307 1.95238 -1.1366 1.10386 1.53504 1.96499 VNN3
NM_018399.1 1.99343 1.93901 1.07486 1.19707 1.96766 1.8831 AMN1
BG031897 1.7461 2.0847 -1.39916 1.07802 1.95345 2.38444 LAMP2
AI150000 1.55435 1.79845 -1.13336 1.11498 1.66826 1.84881 FCHO2
AI627666 1.63852 2.28796 -2.06562 -1.09689 1.6281 2.30861 ZNF608
AI452715 1.83637 4.23691 -1.64571 1.60438 1.757 4.09293 REM2 H19232
1.51731 1.67729 -1.41824 -1.10004 1.24838 1.35338 QKI R41907
1.71321 2.0559 -1.66896 -1.18819 1.49767 1.98251 RBM25 BE466195
1.61161 1.82166 -1.39912 -1.08457 1.43618 1.81597 FAR2 H16791
1.31592 1.85091 -1.43408 1.12536 1.15805 1.86642 ST3GAL6 AI989567
1.38604 2.22775 -1.83273 -1.10403 1.27982 2.05433 HNRNPH2 BF061744
1.5212 1.69734 -1.34191 -1.10041 1.42575 1.58788 GAB1 AL049449.1
1.20963 1.46575 -1.09271 1.28122 1.60125 1.95743 UBR5 BF515424
1.48189 1.85612 -1.61547 -1.08548 1.34557 1.71885 VAPA AW296039
1.63693 1.7318 -1.19576 1.07236 1.6695 1.83796 THBD AW119113
1.59427 2.17248 -1.25924 1.19969 2.00552 2.50204 -- BC042986.1
1.01757 1.24549 -3.49838 -2.38168 -3.50237 -2.86054 -- AF086134.1
-1.11298 1.08518 -4.34061 -3.27948 -7.60167 -5.23956 -- BC030088.1
-1.05299 1.36405 -5.35944 -2.82975 -5.70757 -4.37802 -- BC042866.1
1.02169 1.23725 -6.2228 -3.34786 -5.66994 -4.21957 LOC283027
AF519622.1 -1.02208 1.17339 -4.87073 -3.15597 -3.09193 -2.23434 --
BC037935.1 -1.18515 -1.10026 -4.36438 -3.29567 -6.29072 -4.67834 --
BC035915.1 -1.16002 -1.00454 -3.4789 -2.32404 -5.06879 -3.9396 --
AW072078 -1.13183 -1.03534 -2.5817 -2.45432 -2.97149 -2.42805 --
AI371550 -1.2797 -1.00935 -5.39252 -3.15227 -6.53552 -4.91151 --
AI379823 -1.43222 -1.11788 -7.45042 -4.9032 -8.79656 -6.41832
LOC344595 AA417117 -1.12772 -1.04434 -3.36106 -2.70588 -4.60892
-3.38362 RPL22 AI252081 -1.29183 1.00176 -10.6787 -5.52024 -13.1426
-9.07862 -- BF514098 -1.3354 -1.14637 -7.15023 -4.63994 -7.02866
-5.73683 LOC100129488 BF510430 -1.29144 1.03103 -5.98942 -3.38109
-10.5201 -8.12763 -- AI820961 -1.03836 1.07016 -4.96218 -3.43895
-4.54929 -3.90039 RPL22 AI820887 -1.41083 -1.1041 -10.3524 -5.84457
-7.0436 -5.09597 MCTP1 BC030005.1 1.65623 2.02722 -1.04349 1.29446
2.71543 3.28561 SH3GL3 AF036272.1 1.01474 -1.0085 -5.88871 -3.48898
-6.30725 -4.45099
Discussion
[0231] Diagnosis of ischemic stroke is based on clinical impression
combined with brain imaging. However, in the acute setting, brain
imaging is not always readily accessible, and clinical evaluation
by persons experienced in stroke is not always readily available.
In such patients, a blood test could be of use to diagnose ischemic
stroke (IS). Several protein biomarkers have been associated with
IS, but in the acute setting these have not yet shown sufficient
sensitivity nor specificity to be clinically useful [Whiteley W et
al., Stroke, 39:2902-2909 (2008); Foerch C et al., Neurology,
73:393-399 (2009); Jensen M B et al., Expert Rev Cardiovasc Ther.,
7:389-393 (2009)]. In this study we show that gene expression
profiles can be used as biomarkers of IS, replicated our previous
findings, and refined the gene expression signature of IS by
including more relevant control groups.
[0232] A 29-probe set profile was previously reported that
distinguished IS from healthy controls [Tang Y et al., J Cereb
Blood Flow Metab., 26:1089-1102 (2006)]. When this profile was used
to predict a larger cohort of patients in this study, it
distinguished IS from healthy subjects with a sensitivity of 92.9%
and specificity of 94.7%. This is important in that it represents a
validation of the concept that gene expression profiles can
identify patients with stroke. Replication of gene expression
profiles has been a challenge in the field, in large part due to
false discovery associated with performing multiple comparisons.
Robust biological responses and careful analyses made it possible
to validate this 29-probe set profile in this study.
[0233] To obtain more biologically useful predictors of IS, gene
profiles that distinguish IS from patients with vascular risk
factors (RF) and myocardial infarction (MI) were identified. Using
the individual group comparisons, the diagnosis of IS compared to
the vascular risk factor group with over 95% sensitivity and
specificity was predicted. Using the individual group comparisons,
patients with IS from MI with over 90% sensitivity and over 80%
specificity were differentiated. Biologically, this suggests at
least some differences in the immune responses to infarction in
brain and heart.
[0234] The 3 hour time point was a focus of most comparisons
because this represents the critical time when decisions are made
regarding acute therapy such as thrombolysis. Thus, for the
development of a point-of-care test, this time period is when gene
expression profiles could be of greatest use. With the 60-probe set
signature, at the 3 hour time point, correct classification rates
of 85-94%, 92-96%, 88% and 68-84% for IS, vascular risk factor, MI
and healthy controls, respectively, was achieved. These are
approaching clinical useful ranges.
[0235] Though RNA profiles were the focus in this study, the
identified genes could be used as a guide in the evaluation of
protein biomarkers for ischemic stroke. Genes for Factor 5 and
throbomodulin were both identified as differentially expressed in
IS compared to controls. Both of these molecules have also been
identified as proteins associated with IS [Tang Y et al., J Cereb
Blood Flow Metab., 26:1089-1102 (2006); Moore D F et al.,
Circulation, 111:212-221 2005; Kozuka K et al., Atherosclerosis,
161:161-168 (2002)].
[0236] The goal of this study was not to identify all
differentially expressed genes between IS and controls, but rather
identify sets of genes whose patterns of expression may be useful
for stroke prediction. As a result, these analyses have excluded
large numbers of differentially expressed genes that are
biologically relevant in IS. These will be the subject of future
studies. Limitations of this study include (1) lack of stroke
"mimics" in the control groups (2) lack of validation by qRT-PCR
which would likely be used for clinical applications (3) the
confounding treatment effects in the 5 h and 24 h blood samples
from IS patients (4) race was not factored in due to different
distributions with zero subjects in some of the race categories and
(5) age is a confounder that was addressed by factoring it in
ANCOVA models and by selecting control groups with close age
distribution to the IS patients.
Example 2
Biomarkers for the Diagnosis of the Cause of Ischemic Stroke
1. Study Patients
[0237] Patients with acute ischemic stroke were enrolled from the
CLEAR trial, a multicenter, randomized double-blind safety study of
recombinant tissue-plasminogen activator (rt-PA) and eptifibatide
as previously described [Pancioli A M et al., Stroke, 39:3268-3276
(2008)] (NCT00250991 at Clinical-Trials.gov). The institutional
review board of each site approved the study protocol and written
informed consent was obtained from each patient prior to study
entry. Eligible patients had a diagnosis of acute ischemic stroke,
therapy initiated within 3 hours of stroke onset, a National
Institutes of Health Stroke Scale (NIHSS)>5, and were 18-80
years of age. All patients had standardized clinical evaluations,
including NIHSS, and brain imaging. Blood samples were drawn into
PAXgene tubes (PreAnalytiX, Hilden, Germany) at <3 hours, 5
hours, and 24 hours after stroke onset for use in gene expression
analysis. A total of 194 samples were obtained from 76
patients.
[0238] Etiology of ischemic stroke was classified according to
TOAST [Adams H P, Jr., et al., Stroke, 24:35-41 (1993)]. Patients
with cardioembolic stroke, large vessel stroke and cryptogenic
stroke (undetermined etiology) were included for study.
Cardioembolic stroke required at least one source of cardiac
embolus to be identified and the exclusion of large vessel or small
vessel causes of stroke. Large vessel stroke required stenosis
greater than 50% of ipsilateral extracranial or intracranial artery
and the exclusion of cardioembolic and small vessel causes of
stroke. Cause of stroke was determined using medical history, blood
tests, brain imaging, Doppler and vascular angiography, and cardiac
investigations. Patients with atrial fibrillation were identified
using electrocardiogram, echocardiogram and 24-48 hour cardiac
monitoring. Control blood samples were drawn from 23 control
subjects similar in age, gender and race to stroke subjects. These
subjects had no history of ischemic stroke or cardiovascular
disease, no recent infection and no hematological disease.
2. Sample Processing
[0239] Whole blood was collected from the antecubital vein into
PAXgene tubes (PreAnalytiX, Germany). PAXgene tubes were frozen at
-80.degree. C. after 2 hours at room temperature. All samples were
processed in the same laboratory. Total RNA was isolated according
to the manufacturer's protocol (PAXgene blood RNA kit;
Pre-AnalytiX). RNA was analyzed using Agilent 2100 Bioanalyzer for
quality and Nano-prop (Thermo Fisher) for concentration. Samples
required A260/A280 absorbance ratios of purified RNA .gtoreq.2.0
and 28S/18S rRNA ratios .gtoreq.1.8. Reverse transcription,
amplification, and sample labeling were carried out using Nugen's
Ovation Whole Blood Solution (Nugen Technologies, San Carlos,
Calif.). Each RNA sample was hybridized according to manufacturer's
protocol onto Affymetrix Human U133 Plus 2.0 GeneChips (Affymetrix
Santa Clara, Calif.), which contain 54,697 probe sets. The arrays
were washed and processed on a Fluidics Station 450 and then
scanned on a Genechip Scanner 3000. Samples were randomly assigned
to microarray batch stratified by cause of stroke.
3. Gene Expression Profile Analyses
[0240] Raw expression values (probe level data) were imported into
Partek software (Partek Inc., St. Louis, Mo.). They were log
transformed and normalized using RMA (Robust Multichip Average) and
our previously reported internal gene normalization method [Stamova
B S et al., BMC Med Genomics, 2:49 (2009)]. Statistical analysis,
principal components analysis, and hierarchical unsupervised
clustering analysis were performed with Partek Genomics Suite 6.04.
The fidelity of genetic biomarker subsets as class prediction tools
was established using k-nearest neighbor and 10-fold leave-one-out
cross-validation in PAM (Prediction Analysis of Microarrays)
[Tibshirani R J and Efron B., Stat Appl Genet Mol. Biol., 1:Article
1 (2002)]. Leave-one-out cross-validation provides a relatively
unbiased estimate of the generalization ability of the genetic
classifier. A model is generated on 90% of the samples and used to
predict the remaining 10% of samples. The procedure is repeated 10
times to compute the overall error in the model. Ingenuity Pathway
Analysis (IPA, Ingenuity Systems.RTM., www.ingenuity.com) was used
to determine whether the numbers of genes regulated within given
pathways or cell functions were greater than expected by chance
(Fisher's exact test).
4. Statistical Analyses
[0241] Differences in demographic data between groups were analyzed
using Fisher's exact test and a two-tailed t-test where
appropriate. All data are presented as mean.+-.standard error. To
identify the gene expression profiles that distinguish
cardioembolic stroke from large vessel stroke, repeated measures
analysis of variance (ANOVA) was used including stroke etiology,
time, stroke etiology & time interaction, and the within
subject variance in the model. Unsupervised hierarchical clustering
and principal components analysis (PCA) were used to evaluate
relationships between cardioembolic stroke and large vessel stroke.
Gene probes with a p value .ltoreq.0.005 and a fold change
.gtoreq.|1.2| were considered significant.
[0242] A similar analysis was used to identify the gene expression
profiles that distinguish cardioembolic stroke due to atrial
fibrillation from non-atrial fibrillation causes. A repeated
measures ANOVA was used including cardioembolic stroke etiology,
time, and within subject variance in the model. Unsupervised
hierarchical clustering and PCA were used to evaluate relationships
between cardioembolic stroke caused by atrial fibrillation and
non-atrial fibrillation. Gene probes with a p value .ltoreq.0.005
and a fold change .gtoreq.|1.2| were considered significant.
[0243] Functional analysis was performed by comparing subjects with
cardioembolic stroke and large vessel stroke to control subjects. A
one-way ANCOVA was used adjusting for age and gender. Gene probes
with a p value .ltoreq.0.005 and a fold change .gtoreq.|1.2| were
considered significant and analyzed in IPA.
Results
Cardioembolic Versus Large Vessel Ischemic Stroke
[0244] Demographic and clinical characteristics of subjects used
for the comparison of cardioembolic stroke to large vessel stroke
are shown in Table 8. Atrial fibrillation was the only variable
significantly different between groups (p<0.05). There were 69
samples with cardioembolic stroke and 30 samples with large vessel
stroke.
[0245] Initially the ability of the previously published 77 gene
list to distinguish cardioembolic stroke from large vessel stroke
was evaluated [Xu H et al., J Cereb Blood Flow Metab., 28:1320-1328
(2008)]. This gene list was based on the first 11 patients enrolled
in the CLEAR trial, 7 with cardioembolic stroke and 4 with large
vessel stroke. Using a k-nearest neighbor prediction model, the
preliminary 77 gene list was used to predict the completed CLEAR
trial patient population. Cardioembolic stroke was correctly
predicted in 82.6% of samples, and large vessel stroke was
correctly predicted in 80.0% of samples. However, on 10-fold leave
one out cross-validation, 56.5% were correctly predicted as
cardioembolic stroke and 60% were correctly predicted as large
vessel stroke, with the probability of predicted diagnosis being
below 90% in most samples. These results suggests that gene
expression profiles in blood can distinguish cause of stroke,
though further refinement is required to better generalize genomic
predictors to a larger patient population.
[0246] Analysis of the complete CLEAR trial patients was thus
performed. A repeated measures ANOVA identified 40 genes
significantly different between cardioembolic stroke and large
vessel stroke at all three time points (Table 13). A hierarchical
cluster plot of the 40 genes is shown in FIG. 12a, and a Principal
Component Analysis (PCA) in FIG. 12b. The 40 genes separate
cardioembolic stroke from large vessel stroke by at least 2
standard deviations (FIG. 12b). The hierarchical cluster plot
demonstrates a group of genes that are up-regulated in
cardioembolic stroke and down-regulated in large vessel stroke.
There is also a group of genes that are down-regulated in
cardioembolic stroke and up-regulated in large vessel stroke. The
40 genes separate cardioembolic from large vessel stroke at <3
hours, 5 hours and 24 hours following ischemic stroke as shown in
FIG. 17.
Prediction of Cardioembolic and Large Vessel Stroke
[0247] The ability of the 40 genes to predict cardioembolic stroke
from large vessel stroke was evaluated using 10-fold leave one out
cross-validation model in PAM. Of the 99 samples, 100% of the 69
samples with cardioembolic stroke were correctly predicted, and
96.7% of the 30 samples with large vessel stroke were correctly
predicted (FIG. 13). The probability of predicted diagnosis was
>90% for the majority of samples (FIG. 13). To further evaluate
the 40 gene list, it was applied to a separate group of patients
with known cardioembolic stroke. Of the 10 samples, 90% ( 9/10)
were correctly predicted as cardioembolic stroke.
[0248] The 40 gene list was subsequently used to predict the cause
of stroke in patients with cryptogenic stroke. There were 36
patients (85 samples) with cryptogenic stroke. To be considered
classified by the prediction model, all samples from each patient
were required to have a >90% probability of the same predicted
diagnosis. A total of 15 patients (41%) were predicted to have a
profile similar to cardioembolic stroke with a probability >90%,
and a total of 6 patients (17%) were predicted to have a profile
similar to large vessel stroke with a probability >90%. This
represents a potential reclassification of 58% of cryptogenic
stroke to either cardioembolic or large vessel stroke.
Functional Analysis
[0249] To determine the functional pathways associated with
cardioembolic and large vessel stroke, the subjects with
cardioembolic and large vessel stroke were compared to controls.
There were 731 genes significantly different between cardioembolic
stroke subjects and controls, and 782 genes that were significantly
different between large vessel stroke and controls (p.ltoreq.0.005,
fold change .gtoreq.|1.2|). These two gene lists are shown in a
Venn diagram in FIG. 14. There were 503 genes unique to
cardioembolic stroke, 554 genes unique to large vessel stroke and
228 genes common to cardioembolic stroke and large vessel stroke.
The top Canonical and molecular functions of these respective gene
lists are shown in Tables 9-11.
[0250] Of the 503 cardioembolic stroke genes, specific genes that
have been previously associated with three of the main cardiac
diseases include: atrial fibrillation genes--CREM, SLC8A1, KNCH7,
KCNE1; myocardial infarction genes--PDE4B, TLR2; and heart failure
genes--MAPK1, HTT, GNAQ, CD52, PDE4B, RAFT, CFLAR, and MDM2 (Table
9). Cardioembolic stroke was associated with development of
lymphocytes, inflammatory disorder, cardiomyocyte cell death, and
phosphatidylinositiol 4-phosphate modification. Top canonical
pathways included renin-angiotensin signaling, thrombopoietin
signaling, NF-.kappa.B activation, cardiac hypertrophy, and B cell
receptor signaling (Table 9).
[0251] Of the 554 large vessel stroke genes, specific genes that
have been previously associated with atherosclerotic lesion and
atherosclerotic plaque include MMP9, FASLG, CX3R1, RAG1, TNF,
IRAG1, CX3CR, and THBS1 (Table 10). Large vessel stroke was
associated with T cell and leukocyte development, inflammation, and
invasion. Top canonical pathways include T cell activation and
regulation, CCR5 signaling in macrophages, relaxin signaling, and
corticotropin releasing hormone signaling (Table 10).
[0252] A total of 228 genes were common to cardioembolic stroke and
large vessel stroke, representing ischemic stroke (FIG. 14). They
were associated with leukocyte and phagocyte development and
movement, cardiovascular processes, NF-.kappa.B response element
expression, and oxidative stress (Table 11). Top canonical pathways
include p38 MAPK signaling, toll-like receptor signaling, IL-6 and
IL-10 signaling, NK-.kappa.B signaling, B-cell receptor signaling,
and NRF-mediated oxidative stress (Table 11).
Atrial Fibrillation Versus Non-Atrial Fibrillation Cardioembolic
Stroke
[0253] There were 23 subjects with cardioembolic stroke, 10 with
atrial fibrillation and 13 with no atrial fibrillation identified
on routine investigation. Subjects in the non-atrial fibrillation
group who are more likely to have paroxysmal atrial fibrillation
were excluded. To do this, the patients with stroke due to atrial
fibrillation were initially compared to the 10 patients with large
vessel stroke. Repeated measures ANOVA identified a 39 gene profile
of atrial fibrillation. This profile was then used to predict which
of the 13 cardioembolic stroke subjects without atrial fibrillation
identified on routine investigation had the highest probability of
being similar to atrial fibrillation. There were 5 subjects who
fell within 4 standard deviations of the mean predicted probability
of patients with known atrial fibrillation. These patients were
considered more likely to have paroxysmal atrial fibrillation and
thus were excluded from further analysis, as a conservative method
to reduce the possibility of paroxysmal atrial fibrillation being
present in the non-atrial fibrillation group. The remaining 8
non-atrial fibrillation patients were compared to the 10 patients
with atrial fibrillation. The demographic and clinical
characteristics of are shown in Table 12. Atrial fibrillation was
the only variable significantly different between the two groups
(p<0.05). A repeated measures ANOVA identified 37 genes that
were significantly different between atrial fibrillation and
non-atrial fibrillation causes of cardioembolic stroke (Table 14).
A hierarchical cluster plot of the 37 genes is shown in FIG. 15a,
and a PCA in FIG. 15b. The 37 genes clearly separate atrial
fibrillation from non-atrial fibrillation (FIG. 15). The 37 genes
can separate atrial fibrillation from non-atrial fibrillation
cardioembolic stroke at .ltoreq.3 hours, 5 hours and 24 hours
following ischemic stroke (FIG. 18). The 37 genes were applied to
the 5 subjects excluded from analysis, with 2 being predicted to be
atrial fibrillation, 2 being indeterminate, and 1 being predicted
to be non-atrial fibrillation cardioembolic stroke.
Prediction of Atrial Fibrillation and Non-Atrial Fibrillation
Cardioembolic Stroke
[0254] The ability of the 37 genes to predict atrial fibrillation
from non-atrial fibrillation causes of cardioembolic stroke was
evaluated using a 10-fold leave one out cross-validation model in
PAM. In the 60 samples, 100% of the 30 samples with atrial
fibrillation cardioembolic stroke were correctly predicted, and
91.7% of the 30 samples with non-atrial fibrillation cardioembolic
stroke were correctly predicted (FIG. 16). Additionally, the
probability of predicted diagnosis was >90% for most
samples.
[0255] The 37 gene list was used to predict a test set of 10
samples with cardioembolic stroke who did not have atrial
fibrillation identified on routine testing. Of these 10 samples, 3
(30%) were predicted to have paroxysmal atrial fibrillation with
>90% probability when compared to the gene expression profile of
subjects with known symptomatic atrial fibrillation. The 37 gene
list was also used to predict the cause of stroke in patients with
cryptogenic stroke. There were 11 patients with cryptogenic stroke
who were predicted to have cardioembolic stroke based on the 40
gene profile. Of these 11 patients, 3 patients (27%) were predicted
to have paroxysmal atrial fibrillation with a probability >90%
based on a gene expression profile that was similar to subjects
with known atrial fibrillation stroke.
Discussion
[0256] Determining the cause of ischemic stroke is of paramount
importance as it guides management decisions such as whether to
initiate antiplatelet or anticoagulation treatment. However,
identifying the cause of stroke remains a challenge in many
patients as exemplified by cryptogenic stroke. Given that
cryptogenic stroke accounts for approximately 30% of ischemic
strokes, better classification tools are required. The use of gene
expression profiles in blood to distinguish cardioembolic stroke
from large vessel stroke on a molecular level are described herein.
A 40 gene expression profile can distinguish cardioembolic stroke
from large vessel stroke, and a 37 gene expression profile can
distinguish cardioembolic stroke due to atrial fibrillation from
non-atrial fibrillation causes. When applied to cryptogenic stroke,
58% of subjects can be reclassified with a probability >90% as
being either cardioembolic or large vessel stroke.
[0257] Limitations of large-scale gene expression profiling have
been well described [Schulze A and Downward J., Nat Cell Biol.,
3:E190-195 (2001)]. However, comparable approaches have applied in
patients with human malignancies and that have translated to PCR
based arrays for diagnostic purposes [Hedenfalk I et al., N Engl J
Med., 344:539-548 (2001); Valk P J et al., N Engl J Med.,
350:1617-1628 (2004)]. Unlike human malignancy with distinct
histological criteria, ischemic stroke subtypes are heterogeneous
and rely on a combination of clinical and investigational criteria.
With strict patient selection, molecular classification of ischemic
stroke subtypes into clinically relevant subgroups with biomarkers
appears to be feasible. Indeed, several prothrombotic and
inflammatory biomarkers in the blood are different in each subtype
of ischemic stroke [Laskowitz D T et al., Stroke, 40:77-85 (2009);
Shibazaki K et al., Intern Med., 48:259-264 (2009); Montaner J et
al., Stroke, 39:2280-2287 (2008); Hassan A et al., Brain,
126:424-432 (2003); Xu H et al., J Cereb Blood Flow Metab.,
28:1320-1328 (2008)].
Cardioembolic and Large Vessel Atherosclerotic Stroke
[0258] A gene expression profile able to differentiate
cardioembolic stroke from large vessel stroke was identified. This
distinction is clinically important as treatment and diagnostic
testing are different between the two subtypes. In general,
cardioembolic stroke benefit from anticoagulation, whereas large
vessel stroke benefit from antiplatelet therapy and vascular
surgery. Determining the etiology of stroke and thus the
preventative treatments to be initiated relies on diagnostic tests.
In fact, the TOAST criteria require that other causes of stroke be
ruled out to make a probable diagnosis of cause [Adams H P, Jr., et
al., Stroke, 24:35-41 (1993)]. As a result, patients with ischemic
stroke frequently undergo extensive testing to image the
vasculature and evaluate cardiac function. Diagnostic testing to
determine the cause of stroke can be better focused by using gene
expression profiles, particularly in cryptogenic stroke. In this
manner, costly resources can be targeted to subjects where they
will be of highest yield.
Cardioembolic Stroke
[0259] Currently, the selection of which patients with ischemic
stroke require cardiac investigations such as Holter monitor and
echocardiogram is based on clinical judgment combined with brain
imaging. However, determining which ischemic stroke patients should
be screened by transthoracic and transesophageal echocardiography
is challenging. Though age <50 years is associated with higher
diagnostic yield, many stroke patients are older than 50 years.
Gene expression profiles in combination with clinical impression
serve as a guide to direct echocardiography.
[0260] Cardiac monitoring for arrhythmias is also commonly
performed following ischemic stroke. Identifying atrial
fibrillation is important, as anticoagulation reduces recurrent
embolic events. However, cardiac monitoring for 24 to 48 hours
often misses paroxysmal atrial fibrillation [Tayal A H et al.,
Neurology, 71:1696-1701 (2008); Ziegler P D et al., Stroke,
41:256-260]. A gene expression profile suggesting a patient has a
high probability of atrial fibrillation may be an additional tool
to aid in preventing such missed treatment opportunities. In a
group of 10 cardioembolic strokes who did not have atrial
fibrillation identified on routine investigation, it is shown that
a gene expression profile can predict 3 subjects (30%) to have
paroxysmal atrial fibrillation with greater than 90% probability.
This is consistent with previous studies of cardioembolic stroke
without atrial fibrillation on routine investigation, where an
additional 23-28% cases of paroxysmal atrial fibrillation can be
identified using long term cardiac monitoring [Tayal A H et al.,
Neurology, 71:1696-1701 (2008), Ziegler P D et al., Stroke,
41:256-260]. Subjects who appear to have atrial fibrillation by
gene expression profiles could be a target group for such prolonged
cardiac recording.
Large Vessel Stroke
[0261] Gene expression profiles may also aid in the diagnosis of
large vessel stroke. Evaluation of large vessel atherosclerotic
disease includes imaging of extracranial and intracranial vessels
using magnetic resonance angiography (MRA), computed tomography
angiography (CTA), ultrasound and conventional angiography.
Inconsistencies in the results of vascular imaging do occur. For
example, the degree of carotid stenosis by ultrasound may not agree
with the degree of stenosis by MRA or CTA. Supplementing imaging
with a gene expression profile suggestive of symptomatic
atherosclerotic disease could add confidence to the diagnosis of
large vessel atherosclerotic disease. The presence of large vessel
disease is large based on a single factor, the degree of vascular
stenosis. In the TOAST criteria, a stenosis less than 50% is
considered negative [Adams H P, Jr., et al., Stroke, 24:35-41
(1993)]. Gene expression profiles provide an additional measure of
factors associated with symptomatic atherosclerotic disease,
particularly inflammation. This is similar in concept to MRI
methods to determine atheroma inflammation [Tang T Y et al.,
Arterioscler Thromb Vasc Biol., 29:1001-1008 (2009)]. These
proposed applications of gene expression profiles require further
investigation. However, they show promise as methods to better
target investigations and treatments to patients with ischemic
stroke.
Cryptogenic Stroke
[0262] Cryptogenic stroke is a heterogeneous group of patients
where better diagnostic tools are required. The gene expression
profiles described herein were applied to the cryptogenic stroke
group and predicted 41% to have cardioembolic stroke. Of these
patients, 27% were suggested to have atrial fibrillation.
Cryptogenic stroke patients with a molecular signature similar to
cardioembolic stroke may represent a group where long term cardiac
monitoring can be focused, and potentially a subgroup where a trial
of anticoagulation could be performed [Tayal A H et al., Neurology,
71:1696-1701 (2008); Harloff A et al., Stroke, 37:859-864 (2006);
Sacco R L et al., Cerebrovasc Dis., 22:4-12 (2006); Mohr J P et
al., N Engl J Med., 345:1444-1451 (2001)]. 17% of the cryptogenic
group were also predicted to have large vessel stroke. This finding
may represent a symptomatic stenosis <50%, though further study
with thorough vascular imaging is required.
Functional Analysis
[0263] The rationale for changes in gene expression in blood of
patients with ischemic stroke rests largely in differences in
patterns of inflammation. The major source of RNA in the blood is
immune cells including leukocytes, neutrophils, and monocytes [Du X
et al., Genomics, 87:693-703 (2006)]. Immune cells provide an
indirect reflection of a patient's disease state and subsequent
response, such as the immune response to ischemic brain tissue and
immune response to disease mediated by vascular risk factors. The
majority of these responses remain unclear, though it appears there
are differences in the ways these responses are orchestrated
between subjects with cardioembolic stroke and large vessel stroke.
This is evidenced by the 40 gene profile for cardioembolic stroke
and large vessel stroke, and the 37 gene profile for cardioembolic
stroke due to atrial fibrillation and non-atrial fibrillation. The
fact that different genes are associated with stroke of large
vessel, cardioembolic, and atrial fibrillation origin suggests
specific immune responses in each condition. The precise cause for
these differences, including immune cell-endothelial interactions,
remain unknown and should become clearer as each condition and
cause is studied.
[0264] In conclusion, the present invention provides gene
expression signatures can distinguish between cardioembolic and
large vessel subtypes of ischemic stroke. Gene expression profiles
find use for the development of blood tests to aid in the
classification of ischemic stroke, target stroke investigation and
treatment, and determine the causes of cryptogenic stroke.
Tables
TABLE-US-00010 [0265] TABLE 8 Demographic variables for subjects
with cardioembolic stroke and large vessel stroke. Cardioembolic
Large Vessel Variables (n = 23) (n = 10) p value Mean Age (years)
71.7 .+-. 1.6 66.9 .+-. 2.9 0.14 Sex, male (%) 12 (52.2%) 8 (80%)
0.25 Race, Caucasian (%) 15 (65.2%) 8 (80%) 0.68 Hypertension (%)
16 (69.6%) 8 (80%) 0.55 Mean Systolic BP 158.3 .+-. 6.1 163.5 .+-.
8.0 0.63 Mean Diastolic BP 80.6 .+-. 3.6 88 .+-. 6.7 0.30 Diabetes
(%) 4 (17.4%) 4 (40%) 0.21 Hyperlipidemia (%) 6 (26.1%) 3 (30%)
1.00 Mean Weight (kg) 81.9 .+-. 4.5 89.6 .+-. 6.2 0.34 Atrial
Fibrillation (%) 10 (43.4%) 0 (0%) 0.03 Myocardial Infarction (%) 4
(17.3%) 2 (20%) 1.00 Congestive Heart Failure 8 (34.8%) 2 (20%)
0.68 Coronary Artery Bypass 5 (21.7%) 1 (10%) 0.64 Carotid
Endarterectomy 0 (0%) 2 (20%) 0.08 Femoral Popliteal Bypass 0 (0%)
1 (10%) 0.30 Prior Stroke 7 (30.4%) 1 (10%) 0.38 Mean NIHSS 3 hours
11.9 .+-. 1.7 12.7 .+-. 1.0 0.69 Mean NIHSS 24 hours 11.2 .+-. 1.8
13.9 .+-. 3.0 0.44 Mean NIHSS 5 days 10.3 .+-. 2.1 12.1 .+-. 4.7
0.69 p-values represent comparisons of subjects with cardioembolic
to large vessel stroke using Fisher's exact test or two-tailed
t-test where appropriate. (BP, blood pressure; CABG, coronary
artery bypass graft)
TABLE-US-00011 TABLE 9 Functional analysis of 503 genes found to be
unique to Cardioembolic strokes when compared to controls (p <
0.005, FC > |1.2|) CE Genes p-value Canonical Renin-angiotensin
ADCY4, GNAQ, 2.6 .times. 10.sup.-4 Pathways signaling PAPK1,
MAPK14, PIK3C3, PIK3C2B, PRKARIA, PPKCZ, RAF1 Thrombopoietin MAPK1,
PIK3C3, 5.8 .times. 10.sup.-4 signaling PIK3C2B, PRKCZ, RAF1,
STAT5B* CXCR5, ITGAL, MAPK1, PIK3C3, PIK3C2B, PRKCZ, RAF1 NF-kB
activation MAPK1, MDM2, 6.0 .times. 10.sup.-4 PIK3C3, PIK3C2B,
PRKCZ, PTEN*, RAF1, TFDP1 Cardiac ADCY4, GNAQ, 9.9 .times.
10.sup.-4 Hypertropthy role of MAPK1, MAPK14, NFAT MEF2A, PIK3C3,
PIK3C2B, PRKARIA, PRKCZ, RAF1, SLC8A1 B cell receptor BCL6, FCGR2C,
2.4 .times. 10.sup.-3 Signaling MAP3K2, MAPK1, MAPK14, PIK3C3,
PIK2C2B, PTEN*, RAF1 Molecular Lymphocyte APC, BCL6, CARD11, 1.4
.times. 10.sup.-5 Functions development CD55, CFLAR, CXCR5, DTX1,
GATA3, HIST1H1C, HLA-DOA, IFNGR1, IL13RA1, IL27RA, ITGAL, KLF13,
MAP3K2, MAPK1, MAPK14, MBP, MDM2, PRKCZ, PTEN, RAF1, RBPJ, SEMA4A,
SMARCA4, SRGN, STAT5B, STK17B, TXN, XRCC5 Cardiomyocytes cell CREM,
GHRL, GNAQ, 1.8 .times. 10.sup.-4 death MAPK1, MAPK14, MDM2, NAMPT,
PTEN, RAF1, SLC8A1, SOD2 T lymphocyte APC, BCL6, CARD11, 2.8
.times. 10.sup.-4 development CD55, CFLAR, DTX1, GATA3, HIST1H1C,
HLA-DOA, IFNGR1, IL27RA, KLF13, MAP3K2, MAPK1, MAPK14, MBP, MDM2,
PRKCZ, PTEN, SEMA4A, SMARCA4, SRGN, STAT5B, STK17B, XRCC5
Inflammatory ABCB4, AHNAK, 4.1 .times. 10.sup.-4 disorder AKAP13,
ANXA3, AQP9, ARF1, ASPH, B4GALT1, BCL6, CARD11, CASC4, CD55, CDH26,
CFLAR, CLEC4D, CMIP, CR1, CREM, CXCR5, DHX37, DIS3L2, DYNC1LI1,
ENG, ENTPD1, ETV5, EXOC6, FBXL13, FGGY, GATA3, GEMIN5, GLG1, H3F3B,
HCG27, HDGFRP3, HLA-DOA, HTT, IFNGR1, IL18RAP, IL27RA, ITGAL,
KALRN, KIF13A, KLF13, LHX2, LYST, MAP4, MAPK14, MBP, MDM2, MED24,
MEF2A, MTHFS, NAMPT, NAT10, NBAS, NCOA2, NUMB, NUP62, OSBPL1A,
PDE4B, PHF15, PHRF1, PRKCZ, PTEN, RAF1, S100A9, S100A12, SBF2,
SLC22A4, SLC7A11, SLC8A1, SOD2, SOX6, STAT5B, STK17B, TPST1, TRRAP,
TSPAN2, TTYH2, TXN, USP11, USP15, VIM, VSIG4, YEATS2, ZFHX3,
ZNF230, ZNF831 Phosphatidylinositol PI4KA, PIK3C3, 4.4 .times.
10.sup.-4 4-phosphate PIK3C2B, PTEN modification
TABLE-US-00012 TABLE 10 Functional analysis of the 554 genes unique
to large vessel atherosclerotic stroke when compared to controls (p
< 0.005, FC > |1.2|). LV Genes p-value Canonical Cytotoxic T
APAF1, CD247, FADD, 1.3 .times. 10.sup.-5 Pathways lymphocyte
FASLG, TRA@, TRD@ target cell apoptosis CCR5 CD247, FASLG, GNAI1,
1.3 .times. 10.sup.-4 signaling in GNG*, MAPK13*, PRKCZ,
macrophages TRA@, TRD@ Relaxin GNAI1, GNG2, GUCY2D, 1.5 .times.
10.sup.-4 signaling MAPK1, MMP9, NF-.kappa.B IE, NPR2, PDE2A,
PIK3R3, PRKACA, PRKCZ Corticotropin FASLG, GNAI1, GUCY2D, 1.0
.times. 10.sup.-3 releasing MAPK1, MAPK13, NPR2, hormone NR4A1
signaling T lymphocyte CD247, NR4A1, PRKCZ, 1.3 .times. 10.sup.-3
regulation TRA@, TRD@, ZAP70* Molecular T lymphocyte ADA, TRA@,
ZAP70 1.4 .times. 10.sup.-3 Functions differentiation Leukocyte
ADA, APAF1, CCR8, CD83, 1.5 .times. 10.sup.-3 development, CD247,
CSF1, CSF2RA, morphology CXCL12, EZH2, F5, FADD, FASLG, HIVEP2,
IL12RB1, IL21R, ITGB7, MAPK1, NR4A1, PDIA3, PRKCZ, RAG1, RNASEL,
SMAD7, THBS1, TRA@, XIAP, ZAP70 Invasion of MMP9, PLAUR 1.7 .times.
10.sup.-3 cells Inflammatory ADA, ADORA3, ADRB2, 2.8 .times.
10.sup.-3 disorder ALOX5AP, APAF1, ARHGDIB, ARHGEF17, ASPH, ATP4B,
C20ORF43, CA4, CA13, CAPN10, CD83, CD247, CDK6, CLCN6, COL9A3,
COLQ, CORIN, CORO2A, CSF1, CSF2RA, CX3CR1, CXCL12, EGFL8, F5,
FAM101B, FASLG, FBF1, GNG2, GRB10, HIC1, HIVEP1, HP, KIAA1908,
LIMD1, LTB4R, MAPK13, MDC1, MMP9, MPHOSPH9, MSRA, MYH3, NFIA,
NR4A1, NUMA1, OLAH, PACSIN2, PADI4, PCNX, PDE2A, PDIA3, PER1,
PFTK1, PGLYRP1, PGM1, PHF19, PIK3R3, PITPNA, PLAUR, PMF1, PPARGC1B,
PRKCZ, PRR5L, PTGDR, PXK, RAB7A, RAG1, RAPH1, RARG, ROPN1L, SAMSN1,
SERPINE2, SEZ6L, SLC25A15, SLC26A8, SLC8A1, SLCO4C1, SPRED1,
SPTLC2, SRPK2, STK36, TAF7L, TBC1D1, TGFBR3, THBS1, TKT, TNFSF8,
TNIK, TRA@, TTC7A, TUBA4, TUBA4A, VARS2, ZEB1 T cell ADA, APAF1,
CCR8, CD83, 5.2 .times. 10.sup.-3 development CD247, CXCL12, F5,
FADD, FASLG, HIVEP2, IL12RB1, IL21R, ITGB7, MAPK1, NR4A1, PDIA3,
PRKCZ, RAG1, RNASEL, SMAD7, THBS1, TRA@, XIAP, ZAP70
TABLE-US-00013 TABLE 11 Functional analysis of 228 genes common to
cardioembolic and large vessel atherosclerotic stroke when compared
to controls (p < 0.005, FC > |1.2|). LV-CE Common Genes
p-value Canonical p38 MAPK Signaling DUSP1*, IL1R2, 3.6 .times.
10.sup.-6 Pathways IRAK3*, MAP2K6, MAPK14, MAX, MKNK1, TNF
Toll-like receptor IRAK3*, MAP2K6, 1.2 .times. 10.sup.-4 signaling
MAPK14, TLR2, TOLLIP IL-6 signaling ABCB1, IL1R2*, 2.4 .times.
10.sup.-4 MAP2K6, MAPK14, SOS2, TNF NF-.kappa.B Signaling GSK3B,
IL1R2, IRAK3, 4.5 .times. 10.sup.-4 MAP2K6, MAP3K3, TLR2, TNF B
Cell Receptor GSK3B, MAP2K6, 5.6 .times. 10.sup.-3 Signaling
MAP3K3, MAPK14, NFATC2, PTEN, SOS2 Role of Macrophages CEBPD*,
GSK3B, 2.8 .times. 10.sup.-3 fibroblasts and IL1R2*, IRAK3*,
endothelial cells in MAP2K6, MAPK14, RA NFATC2, TLR2, TNF IL-10
Signaling IL1R2, MAP2K6, 3.1 .times. 10.sup.-3 MAPK14, TNF
NRF2-mediated DNAJC3, FKBP5, 6.6 .times. 10.sup.-3 Oxidative Stress
GSK3B, MAP2K6, MAPK14, TXN Molecular Neutrophil/ CAMP, CD55,
CSF2RA, 1.7 .times. 10.sup.-5 Functions Phagocyte/Leukocyte DUSP1,
FCAR*, movement LILRA6, MAPK14, PTEN, SLPI, TLR2, TNF Leukocyte
BST1, CAMP, CD55, 5.4 .times. 10.sup.-5 development, CD59, CEBPD,
CFLAR, activation CSF2RA, F5, GATA3, GSK3B, IL2RB, LILRA6, MAPK14,
MLL, NFATC2, PRKDC, PTEN, RGL4, TLR2, TNF, TXN Cardiovascular BMX,
GSK3B, IL18BP, 1.2 .times. 10.sup.-4 process MAP3K3, MAPK14, TLR2
NF-kappa B response GSK3B, MAPK14, 2.5 .times. 10.sup.-4 element
expression TLR2, TNF Leukocyte CAMP, CD59, CFLAR, 2.9 .times.
10.sup.-4 proliferation CSF2RA, GATA3, IL2RB, IRS2, MAPK14, MLL,
NFATC2, PCYT1A, PTEN, SLPI, TLR2, TNF, TXN Oxidative stress TNF,
TXN 3.0 .times. 10.sup.-4
TABLE-US-00014 TABLE 12 Demographic variables for subjects with
cardioembolic stroke due to atrial fibrillation and non-atrial
fibrillation causes. p-values represent comparisons of subjects
with atrial fibrillation to those with non-atrial fibrillation
using Fisher's exact test or two-tailed t-test where appropriate.
(BP, blood pressure; CABG, coronary artery bypass graft) Atrial
Fibrillation Non-Atrial Variables (n = 10) (n = 8) p value Mean Age
(years) 72.9 .+-. 2.3 68.5 .+-. 3.1 0.26 Sex, male (%) 4 (40%) 6
(75%) 0.19 Race, Caucasian (%) 6 (60%) 5 (62%) 0.65 Hypertension
(%) 8 (80%) 6 (75%) 1.00 Mean Systolic BP 158.3 .+-. 9.3 160.4 .+-.
10.7 0.88 Mean Diastolic BP 80.8 .+-. 4.7 86.1 .+-. 8.0 0.56
Diabetes (%) 1 (10%) 2 (25%) 0.56 Hyperlipidemia (%) 3 (30%) 3
(30%) 1.00 Mean Weight (kg) 86.9 .+-. 8.2 84.9 .+-. 5.7 0.85
Myocardial Infarction (%) 1 (10%) 3 (37%) 0.28 Congestive Heart
Failure 5 (50%) 3 (37%) 0.28 Coronary Artery Bypass 1 (10%) 1 (10%)
1.00 Carotid Endarterectomy 0 (0%) 0 (0%) -- Femoral Popliteal
Bypass 0 (0%) 0 (0%) -- Prior Stroke 3 (30%) 3 (37%) 1.00 Mean
NIHSS 3 hours 17.3 .+-. 3.1 13.6 .+-. 2.4 0.39 Mean NIHSS 24 hours
11.6 .+-. 3.5 10.6 .+-. 3.0 0.83 Mean NIHSS 5 days 9.9 .+-. 3.5
10.0 .+-. 4.0 0.98
TABLE-US-00015 TABLE 13A A list of 40 genes that differentiate
cardioembolic stroke from large vessel stroke (p < 0.005, fold
change >|1.2|). Table 13A. Biomarkers that differentiate
cardioembolic stroke from large vessel stroke Fold- Change (Large
Vessel vs. Probe Set Cardio- Gene GenBank Entrez UniGene RefSeq
RefSeq ID embolic) Symbol Gene Title ID Gene ID ID Transcript ID
Protein ID 1552477_a_at 1.26832 IRF6 interferon regulatory
BC014852.1 3664 Hs.719361 NM_006147 NP_006138 factor 6 1556896_at
-2.02233 LOC284751 hypothetical AK090605.1 284751 Hs.282325
NM_001025463 NP_001020634 LOC284751 1557542_at -1.33949 -- --
AW069144 -- Hs.353829 -- -- 1559449_a_at 1.34719 ZNF254 Zinc finger
protein 254 BF679633 9534 Hs.434406 NM_203282 NP_975011
1565389_s_at 1.45568 GRM5 glutamate receptor, S64316.1 2915
Hs.147361 NM_000842 /// NP_000833 /// metabotropic 5 NM_001143831
NP_001137303 202012_s_at 1.19814 EXT2 exostoses (multiple) 2
AA196245 2132 Hs.368404 NM_000401 /// NP_000392 /// NM_207122
NP_997005 202399_s_at 1.20871 AP3S2 adaptor-related protein
NM_005829.1 10239 Hs.632161 NM_005829 /// NP_005820 complex 3,
sigma 2 NR_023361 subunit 204484_at 1.32829 PIK3C2B
phosphoinositide-3- NM_002646.1 5287 Hs.497487 NM_002646 NP_002637
kinase, class 2, beta polypeptide 204765_at 1.22689 ARHGEF5 Rho
guanine NM_005435.1 7984 Hs.334 NM_005435 NP_005426 nucleotide
exchange factor (GEF) 5 207549_x_at -1.23817 CD46 CD46 molecule,
NM_002389.1 4179 Hs.510402 NM_002389 /// NP_002380 /// complement
regulatory NM_153826 /// NP_722548 /// protein NM_172350 ///
NP_758860 /// NM_172351 /// NP_758861 /// NM_172352 /// NP_758862
/// NM_172353 /// NP_758863 /// NM_172354 /// NP_758864 ///
NM_172355 /// NP_758865 /// NM_172356 /// NP_758866 /// NM_172357
/// NP_758867 /// NM_172358 /// NP_758868 /// NM_172359 ///
NP_758869 /// NM_172360 /// NP_758870 /// NM_172361 NP_758871
210839_s_at -1.35639 ENPP2 ectonucleotide D45421.1 5168 Hs.190977
NM_001040092 /// NP_001035181 /// pyrophosphatase/ NM_001130863 ///
NP_001124335 /// phosphodiesterase 2 NM_006209 NP_006200
211343_s_at 1.84752 COL13A1 collagen, type XIII, M33653.1 1305
Hs.695934 NM_001130103 /// NP_001123575 /// alpha 1 NM_005203 ///
NP_005194 /// NM_080798 /// NP_542988 /// NM_080799 /// NP_542989
/// NM_080800 /// NP_542990 /// NM_080801 /// NP_542991 ///
NM_080802 /// NP_542992 /// NM_080803 /// NP_542993 /// NM_080804
/// NP_542994 /// NM_080805 /// NP_542995 /// NM_080806 ///
NP_542996 /// NM_080807 /// NP_542997 /// NM_080808 /// NP_542998
/// NM_080809 /// NP_542999 /// NM_080810 /// NP_543000 ///
NM_080811 /// NP_543001 /// NM_080812 /// NP_543002 /// NM_080813
/// NP_543003 /// NM_080814 /// NP_543004 /// NM_080815 NP_543005
215172_at 1.46054 PTPN20A protein tyrosine AL050040.1 26095 ///
Hs.440733 NM_001042357 /// NP_001035816 /// /// phosphatase, non-
653129 NM_001042358 /// NP_001035817 /// PTPN20B receptor type 20A
/// NM_001042359 /// NP_001035818 /// protein tyrosine NM_001042360
/// NP_001035819 /// phosphatase, non- NM_001042361 ///
NP_001035820 /// receptor type 20B NM_001042362 /// NP_001035821
/// NM_001042363 /// NP_001035822 /// NM_001042364 /// NP_001035823
/// NM_001042365 /// NP_001035824 /// NM_001042387 /// NP_001035846
/// NM_001042389 /// NP_001035848 /// NM_001042390 /// NP_001035849
/// NM_001042391 /// NP_001035850 /// NM_001042392 /// NP_001035851
/// NM_001042393 /// NP_001035852 /// NM_001042394 /// NP_001035853
/// NM_001042395 /// NP_001035854 /// NM_001042396 /// NP_001035855
/// NM_001042397 /// NP_001035856 /// NM_015605 NP_056420
218656_s_at 1.51991 LHFP lipoma HMGIC fusion NM_005780.1 10186
Hs.507798 NM_005780 NP_005771 partner 220178_at -1.22676 C19orf28
chromosome 19 open NM_021731.1 126321 Hs.656901 NM_001042680 ///
NP_001036145 /// reading frame 28 NM_021731 /// NP_068377 ///
NM_174983 NP_778148 220545_s_at -1.41349 TSKS testis-specific
serine NM_021733.1 60385 Hs.515858 NM_021733 NP_068379 kinase
substrate 222915_s_at 1.52098 BANK1 B-cell scaffold protein
AA811540 55024 Hs.480400 NM_001083907 /// NP_001077376 /// with
ankyrin repeats 1 NM_001127507 /// NP_001120979 /// NM_017935
NP_060405 223210_at -1.30772 CHURC1 churchill domain AF060510.1
91612 Hs.325531 NM_145165 NP_660148 containing 1 226071_at -1.30444
ADAMTSL4 ADAMTS-like 4 AF217974.1 54507 Hs.516243 NM_019032 ///
NP_061905 /// NM_025008 NP_079284 226878_at 1.47827 HLA-DOA major
AL581873 3111 Hs.631991 NM_002119 NP_002110 histocompatibility
complex, class II, DO alpha 229487_at 1.81657 EBF1 early B-cell
factor 1 W73890 1879 Hs.573143 NM_024007 NP_076870 229559_at
-1.29088 FLJ40125 protein phosphatase BE732320 147699 Hs.532872
NM_001080401 NP_001073870 1B-like 230022_at -1.5369 CLEC18A C-type
lectin domain BF057185 348174 Hs.592064 NM_001136214 ///
NP_001129686 /// family 18, member A NM_182619 NP_872425
230676_s_at 1.23715 TMEM19 transmembrane protein AW663887 55266
Hs.688627 NM_018279 NP_060749 19 231411_at 1.51995 LHFP Lipoma
HMGIC BE674089 10186 Hs.507798 NM_005780 NP_005771 fusion partner
233016_at -1.2455 -- -- AK022893.1 -- Hs.288478 -- -- 233621_s_at
-1.72591 ARHGEF12 Rho guanine AL137456.1 23365 Hs.24598 NM_015313
NP_056128 nucleotide exchange factor (GEF) 12 233742_at -1.30295
C16orf68 Chromosome 16 open AK000114.1 79091 Hs.306380 NM_024109
NP_077014 reading frame 68 235982_at 1.53186 FCRL1 Fc receptor-like
1 AA677057 115350 Hs.656112 NM_001159397 /// NP_001152869 ///
NM_001159398 /// NP_001152870 /// NM_052938 NP_443170 236592_at
-1.27563 -- -- AI791859 -- Hs.658362 -- -- 238218_at 1.3701 OOEP
oocyte expressed AW206656 441161 Hs.671212 NM_001080507
NP_001073976 protein homolog (dog) 239591_at 3.94309 LRRC37A3
leucine rich repeat BF433269 374819 Hs.551962 NM_199340 NP_955372
containing 37, member A3 239724_at -1.32678 -- -- AI653368 --
Hs.658979 -- -- 242939_at -1.37839 TFDP1 transcription factor
AI950069 7027 Hs.79353 NM_007111 /// NP_009042 Dp-1 NR_026580
243185_at 1.89601 -- -- AA804267 -- Hs.438315 -- -- 243325_at
-1.21991 GSTK1 Glutathione S- AV722006 373156 Hs.390667
NM_001143679 /// NP_001137151 /// transferase kappa 1 NM_001143680
/// NP_001137152 /// NM_001143681 /// NP_001137153 /// NM_015917
NP_057001 243467_at 1.37052 -- -- AW406163 -- Hs.435736 -- --
244181_at -1.81372 -- -- AA018968 -- -- -- --
TABLE-US-00016 TABLE 13B Additional genes that differentiate
cardioembolic stroke from large vessel stroke (p < 0.005, fold
change >|1.2|). Table 13B. Biomarkers that differentiate
cardioembolic stroke from large vessel stroke Fold- Change (Large
Probe Set Vessel vs. Gene GenBank Entrez UniGene RefSeq RefSeq ID
Cardioembolic) Symbol Gene Title ID Gene ID ID Transcript ID
Protein ID 203585_at 1.31961 ZNF185 zinc finger protein 185
NM_007150.1 7739 Hs.16622 NM_007150 NP_009081 (LIM domain)
210448_s_at 1.51809 P2RX5 purinergic receptor U49396.1 5026
Hs.408615 NM_002561 /// NP_002552 /// P2X, ligand-gated ion
NM_175080 /// NP_778255 /// channel, 5 NM_175081 NP_778256
221211_s_at C21orf7 chromosome 21 open 56911 reading frame 7
226085_at CBX5 chromobox homolog 5 23468 (HP1 alpha homolog,
Drosphila) 207979_s_at CD8B CD8b molecule 926 201280_s_at DAB2
disabled homolog 2, 1601 mitogen-responsive phosphoprotein
(Drosphila) 219863_at HERC5 hect domain and RLD 5 51191 205821_at
KLRK1 killer cell lectin-like 22914 receptor subfamily K, member 1
1558882_at LOC401233 similar to HIV TAT 401233 specific factor 1;
cofactor required for Tat activation of HI 236930_at NUMB (clone
S171) 8650 215175_at PCNX pecanex homolog 22990 (Drosphila)
214146_s_at PPBP pro-platelet basic 5473 protein (chemokine (C- X-C
motif) ligand 7) 204507_s_at PPP3R1 /// protein phosphatase 3 5534
WDR92 (formerly 2B), regulatory subunit B, alpha isoform /// WD
232078_at PVRL2 poliovirus receptor- 5819 related 2 (herpesvirus
entry mediator B) 232079_s_at PVRL2 poliovirus receptor- 5819
related 2 (herpesvirus entry mediator B) 225418_at PVRL2 poliovirus
receptor- 5819 related 2 (herpesvirus entry mediator B) 228996_at
RC3H1 ring finger and CCCH- 149041 type zinc finger domains 1
202131_s_at RIOK3 RIO kinase 3 (yeast) 8780 212589_at RRAS2 related
RAS viral (r- 22800 ras) oncogene homolog 2 206108_s_at SFRS6
splicing factor, 6431 arginine/serine-rich 6 239084_at SNAP29
synaptosomal- 9342 associated protein, 29 kDa 217104_at ST20
suppressor of 400410 tumorigenicity 20 206366_x_at XCL1 chemokine
(C motif) 6375 ligand 1 214567_s_at XCL1 /// chemokine (C motif)
6375 /// XCL2 ligand 1 /// chemokine 6846 (C motif) ligand 2
TABLE-US-00017 TABLE 14 Genes that differentiate carotid stenosis
from atrial fibrillation (p < 0.005, fold change >|1.2|).
Table 14. Biomarkers that differentiate carotid stenosis from
atrial fibrillation Fold- Change Probe Set (Carotid Gene GenBank ID
vs. Afib) Symbol Gene Title ID 1553994_at 1.42712 NT5E
5'-nucleotidase, BC015940.1 ecto (CD73) 1555469_a_at 1.20537 CLASP2
cytoplasmic linker BC029035.1 associated protein 2 1556578_a_at
-1.20603 FLJ31945 hypothetical protein AI911996 LOC440137
1556896_at -2.01983 LOC284751 hypothetical AK090605.1 LOC284751
1556999_at -1.29018 LOC100271832 hypothetical BC035107.1
LOC100271832 1557542_at -1.36504 -- -- AW069144 1563614_at -1.34743
MTBP Mdm2, transformed AL832671.1 3T3 cell double minute 2, p53
binding protein (mouse) binding protein, 104 kDa 1565389_s_at
1.64476 GRM5 glutamate receptor, S64316.1 metabotropic 5
1565862_a_at -1.28258 -- -- H65800 203650_at 1.4381 PROCR protein C
receptor, NM_006404.1 endothelial (EPCR) 203939_at 1.73974 NT5E
5'-nucleotidase, NM_002526.1 ecto (CD73) 204765_at 1.27552 ARHGEF5
Rho guanine NM_005435.1 nucleotide exchange factor (GEF) 5
207194_s_at -1.77347 ICAM4 intercellular NM_001544.2 adhesion
molecule 4 (Landsteiner- Wiener blood group) 208443_x_at -1.20918
SHOX2 short stature NM_006884.1 homeobox 2 209160_at 1.59131 AKR1C3
aldo-keto reductase AB018580.1 family 1, member C3 (3-alpha
hydroxysteroid dehydrogenase, type II) 211343_s_at 2.22751 COL13A1
collagen, type XIII, M33653.1 alpha 1 215219_at -1.46668 DOPEY2
dopey family AK025095.1 member 2 218656_s_at 1.57558 LHFP lipoma
HMGIC NM_005780.1 fusion partner 224394_at 1.20322 RNF7 ring finger
protein 7 AF312226.1 225147_at 1.29667 CYTH3 cytohesin 3 AL521959
227522_at -3.08723 CMBL carboxymethylenebutenolidase AA209487
homolog (Pseudomonas) 228779_at -1.24601 LOC146880 hypothetical
AA524743 LOC146880 228818_at 1.4946 -- -- BF110792 229487_at
1.88689 EBF1 early B-cell factor 1 W73890 230494_at -1.20819
SLC20A1 Solute carrier family AI671885 20 (phosphate transporter),
member 1 230710_at -1.20654 -- -- W05495 231021_at -1.20317 SLC6A19
solute carrier family AI627358 6 (neutral amino acid transporter),
member 19 231411_at 1.55003 LHFP Lipoma HMGIC BE674089 fusion
partner 232329_at 1.40571 RANBP10 RAN binding protein AV717059 10
233621_s_at -2.0246 ARHGEF12 Rho guanine AL137456.1 nucleotide
exchange factor (GEF) 12 233742_at -1.29936 C16orf68 Chromosome 16
AK000114.1 open reading frame 68 235874_at 1.2001 PRSS35 protease,
serine, 35 AL574912 236548_at -1.25287 GIPC2 GIPC PDZ domain
AL044570 containing family, member 2 236963_at 1.70511 -- --
AV700946 238360_s_at -1.28837 -- -- AI885665 238557_at -1.25224
LOC100144603 hypothetical R58282 transcript 238827_at -1.36024 --
-- BE843544 239977_at 1.26675 C12orf42 chromosome 12 AI638494 open
reading frame 42 242462_at 1.23474 LOC100127980 hypothetical
protein BE218570 LOC100127980 Probe Set Entrez UniGene RefSeq
RefSeq Protein ID Gene ID ID Transcript ID ID 1553994_at 4907
Hs.153952 NM_002526 NP_002517 1555469_a_at 23122 Hs.108614
NM_015097 NP_055912 1556578_a_at 440137 Hs.183953 XM_001714983 ///
XP_001715035 /// XM_001716811 /// XP_001716863 /// XM_001718431
XP_001718483 1556896_at 284751 Hs.282325 NM_001025463 NP_001020634
1556999_at 100271832 -- NR_027097 -- 1557542_at -- Hs.353829 -- --
1563614_at 27085 Hs.657656 NM_022045 NP_071328 1565389_s_at 2915
Hs.147361 NM_000842 /// NP_000833 /// NM_001143831 NP_001137303
1565862_a_at -- Hs.658642 -- -- 203650_at 10544 Hs.647450 NM_006404
NP_006395 203939_at 4907 Hs.153952 NM_002526 NP_002517 204765_at
7984 Hs.334 NM_005435 NP_005426 207194_s_at 3386 Hs.706750
NM_001039132 /// NP_001034221 /// NM_001544 /// NP_001535 ///
NM_022377 NP_071772 208443_x_at 6474 Hs.55967 NM_001163678 ///
NP_001157150 /// NM_003030 /// NP_003021 /// NM_006884 NP_006875
209160_at 8644 Hs.78183 NM_003739 NP_003730 211343_s_at 1305
Hs.695934 NM_001130103 /// NP_001123575 /// NM_005203 /// NP_005194
/// NM_080798 /// NP_542988 /// NM_080799 /// NP_542989 ///
NM_080800 /// NP_542990 /// NM_080801 /// NP_542991 /// NM_080802
/// NP_542992 /// NM_080803 /// NP_542993 /// NM_080804 ///
NP_542994 /// NM_080805 /// NP_542995 /// NM_080806 /// NP_542996
/// NM_080807 /// NP_542997 /// NM_080808 /// NP_542998 ///
NM_080809 /// NP_542999 /// NM_080810 /// NP_543000 /// NM_080811
/// NP_543001 /// NM_080812 /// NP_543002 /// NM_080813 ///
NP_543003 /// NM_080814 /// NP_543004 /// NM_080815 NP_543005
215219_at 9980 Hs.204575 NM_005128 NP_005119 218656_s_at 10186
Hs.507798 NM_005780 NP_005771 224394_at 9616 Hs.134623 NM_014245
/// NP_055060 /// NM_183237 NP_899060 225147_at 9265 Hs.487479
NM_004227 NP_004218 227522_at 134147 Hs.192586 NM_138809 NP_620164
228779_at 146880 Hs.117853 NR_026899 /// -- NR_027487 228818_at --
Hs.661673 -- -- 229487_at 1879 Hs.573143 NM_024007 NP_076870
230494_at 6574 Hs.187946 NM_005415 NP_005406 230710_at -- Hs.446388
-- -- 231021_at 340024 Hs.481478 NM_001003841 NP_001003841
231411_at 10186 Hs.507798 NM_005780 NP_005771 232329_at 57610
Hs.368569 NM_020850 NP_065901 233621_s_at 23365 Hs.24598 NM_015313
NP_056128 233742_at 79091 Hs.306380 NM_024109 NP_077014 235874_at
167681 Hs.98381 NM_153362 NP_699193 236548_at 54810 Hs.659356
NM_017655 NP_060125 236963_at -- Hs.432337 -- -- 238360_s_at --
Hs.634043 -- -- 238557_at 100144603 Hs.657275 NR_021492 --
238827_at -- Hs.666833 -- -- 239977_at 374470 Hs.534649
NM_001099336 /// NP_001092806 /// NM_198521 NP_940923 242462_at
100127980 Hs.595153 XM_001720119 /// XP_001720171 /// XM_001722650
XP_001722702
TABLE-US-00018 TABLE 15 A list of 40 genes that differentiate
atrial fibrillation from non-atrial fibrillation (p < 0.005,
fold change >|1.2|). Table 15. Biomarkers that differentiate
atrial fibrillation from non-atrial fibrillation Fold- Change (Afib
vs. Gene Entrez Probe Set ID NonAfib) Symbol Gene Title GenBank ID
Gene ID 1553730_x_at -1.20376 LRRC43 leucine rich repeat
NM_152759.1 254050 containing 43 1555677_s_at 1.21973 SMC1A
structural BC046147.1 8243 maintenance of chromosomes 1A
1558540_s_at -1.25589 MIF /// macrophage AK055523.1 4282 ///
SLC2A11 migration 66035 inhibitory factor (glycosylation-
inhibiting factor) /// solute carrier family 2 (facilitated glucose
transporter), member 11 1560550_at -1.2744 -- -- BC037972.1 --
1561741_at -1.29835 -- -- BC042016.1 -- 1562254_at 1.45766 -- --
AK024394.1 -- 1566402_at 1.79094 SNORA68 small nucleolar Y11162.1
26780 RNA, H/ACA box 68 1569609_at -1.55035 -- -- BC028185.1 --
1569701_at -1.32035 PER3 Period homolog 3 BC036937.1 8863
(Drosphila) 202046_s_at 1.28563 GRLF1 glucocorticoid NM_004491.1
2909 receptor DNA binding factor 1 202071_at 1.43001 SDC4 syndecan
4 NM_002999.1 6385 202494_at -1.21636 PPIE peptidylprolyl
NM_006112.1 10450 isomerase E (cyclophilin E) 211343_s_at -1.90743
COL13A1 collagen, type XIII, M33653.1 1305 alpha 1 213747_at
1.25775 -- -- AA047234 -- 214964_at 1.26326 -- -- AA554430 --
224336_s_at -1.24201 DUSP16 dual specificity AB052156.1 80824
phosphatase 16 225097_at 1.25832 HIPK2 homeodomain BF594155 28996
interacting protein kinase 2 225214_at 1.20457 LOC100129034
hypothetical AI762915 100129034 protein LOC100129034 227775_at
-1.45039 BRUNOL6 bruno-like 6, RNA BE467313 60677 binding protein
(Drosphila) 227846_at -1.23421 GPR176 G protein-coupled AA526584
11245 receptor 176 229074_at 1.25524 -- -- AI692267 -- 229189_s_at
-1.59286 -- -- BF672306 -- 229190_at -1.67118 -- -- BF672306 --
230506_at -1.494 C6orf164 chromosome 6 NM_022084.1 63914 open
reading frame 164 231219_at 1.42855 CMTM1 CKLF-like AI825627 113540
MARVEL transmembrane domain containing 1 234142_at -1.20672 -- --
AK025053.1 -- 235480_at -1.36329 MAP3K7IP1 Mitogen-activated
AA063633 10454 protein kinase kinase kinase 7 interacting protein 1
235843_at 1.46959 -- -- BF448158 -- 236963_at -2.00452 -- --
AV700946 -- 237075_at 1.95707 -- -- AI191591 -- 237816_at 1.38498
-- -- AA702582 -- 239069_s_at -1.39094 -- -- BF691045 -- 239718_at
-1.31179 -- -- R42552 -- 240369_at 1.35893 TTC7A Tetratricopeptide
AW195569 57217 repeat domain 7A 241797_at 1.21912 -- -- AI904095 --
243603_at 1.21948 -- -- AI973041 -- 244646_at -1.2136 -- --
AW972881 -- RefSeq RefSeq Protein Probe Set ID UniGene ID
Transcript ID ID 1553730_x_at Hs.374856 NM_001098519 ///
NP_001091989 /// NM_152759 NP_689972 1555677_s_at Hs.211602
NM_006306 NP_006297 1558540_s_at Hs.407995 NM_001024938 ///
NP_001020109 /// NM_001024939 /// NP_001020110 /// NM_002415 ///
NP_002406 /// NM_030807 NP_110434 1560550_at Hs.589927 -- --
1561741_at Hs.639369 -- -- 1562254_at -- -- -- 1566402_at Hs.684118
NR_000012 -- 1569609_at Hs.621293 -- -- 1569701_at Hs.162200
NM_016831 NP_058515 202046_s_at Hs.509447 NM_004491 NP_004482
202071_at Hs.632267 NM_002999 NP_002990 202494_at Hs.524690
NM_006112 /// NP_006103 /// NM_203456 /// NP_982281 /// NM_203457
NP_982282 211343_s_at Hs.695934 NM_001130103 /// NP_001123575 ///
NM_005203 /// NP_005194 /// NM_080798 /// NP_542988 /// NM_080799
/// NP_542989 /// NM_080800 /// NP_542990 /// NM_080801 ///
NP_542991 /// NM_080802 /// NP_542992 /// NM_080803 /// NP_542993
/// NM_080804 /// NP_542994 /// NM_080805 /// NP_542995 ///
NM_080806 /// NP_542996 /// NM_080807 /// NP_542997 /// NM_080808
/// NP_542998 /// NM_080809 /// NP_542999 /// NM_080810 ///
NP_543000 /// NM_080811 /// NP_543001 /// NM_080812 /// NP_543002
/// NM_080813 /// NP_543003 /// NM_080814 /// NP_543004 ///
NM_080815 NP_543005 213747_at -- -- -- 214964_at Hs.661763 -- --
224336_s_at Hs.536535 NM_030640 NP_085143 225097_at Hs.397465
NM_001113239 /// NP_001106710 /// NM_022740 /// NP_073577 ///
XM_001716827 /// XP_001716879 /// XM_925800 XP_930893 225214_at
Hs.654980 NR_027406 /// -- XR_079577 227775_at Hs.348342 NM_052840
NP_443072 227846_at Hs.37196 NM_007223 NP_009154 229074_at
Hs.598990 -- -- 229189_s_at Hs.438950 -- -- 229190_at Hs.438950 --
-- 230506_at Hs.645177 NR_026784 -- 231219_at Hs.15159 NM_052999
/// NP_443725 /// NM_181268 /// NP_851785 /// NM_181269 ///
NP_851786 /// NM_181270 /// NP_851787 /// NM_181271 /// NP_851788
/// NM_181272 /// NP_851789 /// NM_181283 /// NP_851800 ///
NM_181296 NP_851813 234142_at Hs.612895 -- -- 235480_at Hs.507681
NM_006116 /// NP_006107 /// NM_153497 NP_705717 235843_at Hs.710512
-- -- 236963_at Hs.432337 -- -- 237075_at -- -- -- 237816_at
Hs.687470 -- -- 239069_s_at Hs.649155 -- -- 239718_at Hs.718467 --
-- 240369_at Hs.370603 NM_020458 NP_065191 241797_at Hs.687709 --
-- 243603_at Hs.672035 -- -- 244646_at Hs.663316 -- --
TABLE-US-00019 TABLE 16 The 38 endogenous reference biomarkers
stably expressed in blood for use in normalization and as control
levels. Table 16. Stably expressed endogenous reference biomarkers
UniGene RefSeq RefSeq Protein Probe Set ID Gene Symbol Gene Title
GenBank ID ID Transcript ID ID 201499_s_at USP7 ubiquitin specific
peptidase NM_003470.1 Hs.706830 NM_003470 NP_003461 7 (herpes
virus- associated) 202501_at MAPRE2 microtubule-associated
NM_014268.1 Hs.532824 NM_001143826 /// NP_001137298 /// protein,
RP/EB family, NM_001143827 /// NP_001137299 /// member 2 NM_014268
/// NP_055083 NR_026570 202573_at CSNK1G2 casein kinase 1, gamma 2
AL530441 Hs.651905 NM_001319 NP_001310 203280_at SAFB2 scaffold
attachment factor NM_014649.1 Hs.655392 NM_014649 NP_055464 B2
204842_x_at PRKAR2A protein kinase, cAMP- BC002763.1 Hs.631923
NM_004157 NP_004148 dependent, regulatory, type II, alpha
206138_s_at PI4KB phosphatidylinositol 4- NM_002651.1 Hs.632465
NM_002651 NP_002642 kinase, catalytic, beta 207159_x_at CRTC1 CREB
regulated NM_025021.1 Hs.371096 NM_001098482 /// NP_001091952 ///
transcription coactivator 1 NM_015321 NP_056136 208630_at HADHA
hydroxyacyl-Coenzyme A AI972144 Hs.516032 NM_000182 NP_000173
dehydrogenase/3- ketoacyl-Coenzyme A thiolase/enoyl-Coenzyme A
hydratase (trifunctional protein), alpha subunit 208786_s_at
MAP1LC3B microtubule-associated AF183417.1 Hs.356061 NM_022818
NP_073729 protein 1 light chain 3 beta 209192_x_at KAT5 K(lysine)
acetyltransferase 5 BC000166.2 Hs.397010 NM_006388 /// NP_006379
/// NM_182709 /// NP_874368 /// NM_182710 NP_874369 210474_s_at
CDC2L1 /// cell division cycle 2-like 1 U04819.1 Hs.651228
NM_024011 /// NP_076916 /// CDC2L2 (PITSLRE proteins) /// cell
NM_033486 /// NP_277021 /// division cycle 2-like 2 NM_033487 ///
NP_277022 /// (PITSLRE proteins) NM_033488 /// NP_277023 ///
NM_033489 /// NP_277024 /// NM_033492 /// NP_277027 /// NM_033493
/// NP_277028 /// NM_033529 NP_277071 211040_x_at GTSE1 G-2 and
S-phase BC006325.1 Hs.386189 NM_016426 NP_057510 expressed 1
211289_x_at CDC2L1 /// cell division cycle 2-like 1 AF067524.1
Hs.651228 NM_024011 /// NP_076916 /// CDC2L2 (PITSLRE proteins) ///
cell NM_033486 /// NP_277021 /// division cycle 2-like 2 NM_033487
/// NP_277022 /// (PITSLRE proteins) NM_033488 /// NP_277023 ///
NM_033489 /// NP_277024 /// NM_033492 /// NP_277027 /// NM_033493
/// NP_277028 /// NM_033529 NP_277071 213311_s_at TCF25
transcription factor 25 BF000251 Hs.415342 NM_014972 NP_055787
(basic helix-loop-helix) 214665_s_at CHP calcium binding protein
AK000095.1 Hs.406234 NM_007236 NP_009167 P22 215063_x_at LRRC40
leucine rich repeat AL390149.1 Hs.147836 NM_017768 NP_060238
containing 40 215200_x_at -- -- AK022362.1 Hs.663419 -- --
215568_x_at hCG_2003956 hCG2003956 /// AL031295 Hs.533479 NM_007260
/// NP_009191 /// LYPLA2 /// lysophospholipase II /// NR_001444
LYPLA2P1 lysophospholipase II pseudogene 1 216038_x_at DAXX
death-domain associated BE965715 Hs.336916 NM_001141969 ///
NP_001135441 /// protein NM_001141970 /// NP_001135442 ///
NM_001350 /// NP_001341 NR_024517 217393_x_at UBE2NL
ubiquitin-conjugating AL109622 Hs.585177 NM_001012989 NP_001013007
enzyme E2N-like 217549_at -- -- AW574933 Hs.527860 -- --
217672_x_at EIF1 eukaryotic translation BF114906 Hs.150580
NM_005801 NP_005792 initiation factor 1 217938_s_at KCMF1 potassium
channel NM_020122.1 Hs.654968 NM_020122 NP_064507 modulatory factor
1 218378_s_at PRKRIP1 PRKR interacting protein 1 NM_024653.1
Hs.406395 NM_024653 NP_078929 (IL11 inducible) 218571_s_at CHMP4A
chromatin modifying NM_014169.1 Hs.279761 NM_014169 NP_054888
protein 4A 219074_at TMEM184C transmembrane protein NM_018241.1
Hs.203896 NM_018241 NP_060711 184C 220052_s_at TINF2 TERF1
(TRF1)-interacting NM_012461.1 Hs.496191 NM_001099274 ///
NP_001092744 /// nuclear factor 2 NM_012461 NP_036593 220411_x_at
PODNL1 podocan-like 1 NM_024825.1 Hs.448497 NM_001146254 ///
NP_001139726 /// NM_001146255 /// NP_001139727 /// NM_024825
NP_079101 221813_at FBXO42 F-box protein 42 AI129395 Hs.522384
NM_018994 NP_061867 222207_x_at LOC441258 Williams Beuren syndrome
AK024602.1 Hs.711232 -- -- chromosome region 19 pseudogene
222733_x_at RRP1 ribosomal RNA processing BC000380.1 Hs.110757
NM_003683 NP_003674 1 homolog (S. cerevisiae) 224667_x_at C10orf104
chromosome 10 open AK023981.1 Hs.426296 NM_173473 NP_775744 reading
frame 104 224858_at ZDHHC5 zinc finger, DHHC-type AK023130.1
Hs.27239 NM_015457 NP_056272 containing 5 225403_at C9orf23
chromosome 9 open AL528391 Hs.15961 NM_148178 /// NP_680544 ///
reading frame 23 NM_148179 NP_680545 226253_at LRRC45 leucine rich
repeat BE965418 Hs.143774 NM_144999 NP_659436 containing 45
227651_at NACC1 nucleus accumbens AI498126 Hs.531614 NM_052876
NP_443108 associated 1, BEN and BTB (POZ) domain containing
232190_x_at LOC100133445 hypothetical AI393958 Hs.132272 NR_026927
/// -- /// LOC115110 LOC100133445 /// XR_036887 /// hypothetical
protein XR_038144 LOC115110 49878_at PEX16 peroxisomal biogenesis
AA523441 Hs.100915 NM_004813 /// NP_004804 /// factor 16 NM_057174
NP_476515
Example 3
Exemplary Flow Outline of Using Gene Expression Analysis for the
Diagnosis of the Occurrence of Ischemic Stroke and the Cause of
Ischemic Stroke
[0266] The following example provides an exemplary outline of using
the biomarkers described herein for the diagnosis of the occurrence
and cause of stroke in a patient suspected of having a stroke.
[0267] (1) Detection of biomarkers can be performed using a
microarray, e.g., a microfluidics approach. cDNA from the patient's
RNA in a blood sample is prepared and labeled (e.g., with a
fluorophore). The labled cDNA is hybridized to probes on the array
within the microfluidics device. The fluoresence of the bound cDNA
is measured to provide a quantitative measure of the amount of RNA
for each gene expressed in the blood of the patient.
[0268] (2) The amount of RNA for at least about 15 target genes is
first measured in the blood sample. The amount of RNA for at least
about 30 endogenous reference biomarkers is measured in the blood
sample. The amounts of RNA for each target gene is normalized to
the reference genes (geometric average) and a normalized expression
value obtained for each target gene. The expression of all of the
target genes (15 or more) is then used as input into a predictive
equation (support vector machine--for example) that then determines
whether the gene expression profile for the subject is most similar
to that for stroke or control, and whether the gene expression
profile for the subjects is most similar to cardioembolic stroke,
atheroembolic stroke, or neither.
[0269] (3) Based upon the results of the testing for the above
biomarkers, a regime for the prevention and/or treatment of stroke
is prescribed and/or administered to the patient.
(a) Patients with a positive diagnosis of stroke, based on the
biomarkers of Table 7A can be subject to further confirmatory
diagnostic testing, e.g., MRI imaging of brain and vessels, blood
tests, EKG, echocardiogram, others. (b) Patients with a negative
diagnosis of stroke, based on the biomarkers of Table 7A can be
sent home, or subject to diagnostic analysis and/or testing for a
differenct condition. (c) Cryptogenic stroke--if determined to be
cardioembolic, e.g., based on the biomarkers of Tables 13A and 15,
an anticoagulant may be prescribed or administered. (d) Cryptogenic
stroke--if determined to be atherosclerotic, e.g., based on the
biomarkers of Table 14, the patient can be subject to vascular
imaging to image carotid and other brain vessels; an anti-platelet
agent may be prescribed or administered. (e) If a diagnosis of
cardioembolic stroke, e.g., based on the biomarkers of Tables 13A
and 15, an anticoagulant may be prescribed or administered. (f) If
a diagnosis of large vessel atheroembolic stroke, e.g., based on
the biomarkers of Table 14, the patient can be subject to vascular
imaging to image carotid and other brain vessels. An anti-platelet
agent may be prescribed or administered, e.g., if stenosis <50%
or if intracranial or aortic atherosclerosis. Recommend or perform
carotid surgery if stenosis >50%.
[0270] It is understood that the examples and embodiments described
herein are for illustrative purposes only and that various
modifications or changes in light thereof will be suggested to
persons skilled in the art and are to be included within the spirit
and purview of this application and scope of the appended claims.
All publications, sequence accession numbers, patents, and patent
applications cited herein are hereby incorporated by reference in
their entirety for all purposes.
* * * * *
References