U.S. patent application number 13/412696 was filed with the patent office on 2012-08-09 for genetic alterations in isocitrate dehydrogenase and other genes in malignant glioma.
This patent application is currently assigned to DUKE UNIVERSITY. Invention is credited to Philipp ANGENENDT, Darell BIGNER, Sian JONES, Rachel KARCHIN, Kenneth W. KINZLER, Chien-Tsun KUAN, Rebecca J. LEARY, Jimmy Cheng-Ho LIN, Nickolas PAPADOPOULOS, Giovanni PARMIGIANI, D. Williams PARSONS, Gregory J. RIGGINS, Victor VELCULESCU, Bert VOGELSTEIN, Hai YAN, Xiaosong ZHANG.
Application Number | 20120202207 13/412696 |
Document ID | / |
Family ID | 41228200 |
Filed Date | 2012-08-09 |
United States Patent
Application |
20120202207 |
Kind Code |
A1 |
VOGELSTEIN; Bert ; et
al. |
August 9, 2012 |
GENETIC ALTERATIONS IN ISOCITRATE DEHYDROGENASE AND OTHER GENES IN
MALIGNANT GLIOMA
Abstract
We found mutations of the R132 residue of isocitrate
dehydrogenase 1 (IDH1) in the majority of grade II and III
astrocytomas and oligodendrogliomas as well as in glioblastomas
that develop from these lower grade lesions. Those tumors without
mutations in IDH1 often had mutations at the analogous R172 residue
of the closely related IDH2 gene. These findings have important
implications for the pathogenesis and diagnosis of malignant
gliomas.
Inventors: |
VOGELSTEIN; Bert;
(Baltimore, MD) ; KINZLER; Kenneth W.; (Baltimore,
MD) ; PARSONS; D. Williams; (Bellaire, TX) ;
ZHANG; Xiaosong; (Baltimore, MD) ; LIN; Jimmy
Cheng-Ho; (Baltimore, MD) ; LEARY; Rebecca J.;
(Baltimore, MD) ; ANGENENDT; Philipp; (Hamburg,
DE) ; PAPADOPOULOS; Nickolas; (Towson, MD) ;
VELCULESCU; Victor; (Dayton, MD) ; PARMIGIANI;
Giovanni; (Boston, MA) ; KARCHIN; Rachel;
(Towson, MD) ; JONES; Sian; (Baltimore, MD)
; YAN; Hai; (Chapel Hill, NC) ; BIGNER;
Darell; (Mebane, NC) ; KUAN; Chien-Tsun;
(Cary, NC) ; RIGGINS; Gregory J.; (White Hall,
MD) |
Assignee: |
DUKE UNIVERSITY
Durham
SC
THE JOHNS HOPKINS UNIVERSITY
Baltimore
MD
|
Family ID: |
41228200 |
Appl. No.: |
13/412696 |
Filed: |
March 6, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13060191 |
Jun 7, 2011 |
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PCT/US2009/055803 |
Sep 3, 2009 |
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13412696 |
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61093739 |
Sep 3, 2008 |
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61110397 |
Oct 31, 2008 |
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61162737 |
Mar 24, 2009 |
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Current U.S.
Class: |
435/6.11 ;
435/6.12; 435/7.4; 530/387.3; 530/388.26; 530/389.8; 536/23.2 |
Current CPC
Class: |
A61P 35/00 20180101;
C12Q 2600/118 20130101; C12Q 2600/156 20130101; C12Q 1/6886
20130101; C12Q 2600/136 20130101; C12Q 2600/112 20130101; A61P
37/04 20180101 |
Class at
Publication: |
435/6.11 ;
435/6.12; 435/7.4; 530/389.8; 530/388.26; 530/387.3; 536/23.2 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68; C07K 16/40 20060101 C07K016/40; C07H 21/04 20060101
C07H021/04; G01N 33/574 20060101 G01N033/574 |
Goverment Interests
[0001] This application was made using funds from the United States
government. Therefore the U.S. government retains certain rights in
the invention under the terms of NIH grants CA 43460, CA 57345, CA
62924, R01CA118822, NS20023-21, R37CA11898-34, and CA 121113.
Claims
1. A method of detecting presence or absence of a somatic mutation
in codon 132 in isocitrate dehydrogenase 1 (IDH1) or codon 172 in
isocitrate dehydrogenase 2 (IDH2) in a sample from a human subject,
comprising: assaying the sample for the IDH1 or IDH2 gene or mRNA
transcribed from the gene in the human subject or protein
translated from the mRNA, to identify residue 132 or a codon for
residue 132 of IDH1 or residue 172 or a codon for residue 172 of
IDH2, wherein a mutation is identified if the residue or the codon
for the residue other than arginine is identified, and wherein if
the sample is from a colorectal tumor then the residue or codon for
the residue is not for a cysteine at residue 132 of IDH1.
2. The method of claim 1 wherein IDH1 gene is assayed and the
mutation is identified if a residue or a codon for a residue
selected from the group consisting of Histidine, Serine, Cysteine,
Leucine, and Glycine is identified in codon or residue 132 of
IDH1.
3. The method of claim 1 wherein the IDH2 gene is assayed and a
mutation is identified if a residue or a codon for a residue
selected from the group consisting of Lysine, Glycine, and
Methionine is identified in codon or residue 172 of IDH2.
4. The method of claim 2 wherein the somatic mutation is R132H in
IDH1.
5. The method of claim 2 wherein the somatic mutation is R132S in
IDH1.
6. The method of claim 2 wherein the somatic mutation is R132C in
IDH1.
7. The method of claim 2 wherein the somatic mutation is R132L in
IDH1
8. The method of claim 2 wherein the somatic mutation is R132G in
IDH1.
9. The method of claim 3 wherein the somatic mutation is R172M in
IDH2.
10. The method of claim 3 wherein the somatic mutation is R172K in
IDH2.
11. The method of claim 3 wherein the somatic mutation is R172G in
IDH2.
12. The method of claim 1 wherein the sample is selected from the
group consisting of tumor, blood, plasma, serum, cerebrospinal
fluid, urine, saliva, and lymph.
13. The method of claim 1 wherein the human subject has a tumor
selected from the group consisting of diffuse astrocytoma,
oligoastrocytoma, anaplastic astrocytoma, anaplastic
oligoastrocytoma, oligodendroglioma, glioblastoma multiforme, and
anaplastic oligodendroglioma.
14. The method of claim 1 wherein the human subject has a
glioblastoma multiforme (GBM), and the method further comprises the
step of: identifying the tumor as likely to be a secondary GBM when
the somatic mutation is present or a primary GBM when the somatic
mutation is absent.
15. The method of claim 13 further comprising the step of:
assigning a more favorable prognosis (longer life expectancy) when
the somatic mutation is present or a less favorable prognosis
(shorter life expectancy) when the somatic mutation is absent.
16. The method of claim 1 wherein the step of assaying comprises
amplifying at least a portion of: the IDH1 gene or cDNA of the IDH1
mRNA, said portion comprising codon 132 or nucleotide 394 or 395 of
IDH1 transcript; or the IDH2 gene or cDNA of the IDH2 mRNA, said
portion comprising codon 172 or nucleotide 515 of IDH2
transcript.
17. The method of claim 1 wherein the step of assaying employs an
antibody which specifically binds to isocitrate dehydrogenase 1
(IDH1), to isocitrate dehydrogenase 2 (IDH2), or to both IDH1 and
IDH2.
18. The method of claim 1 wherein the step of assaying employs an
antibody which preferentially binds to one or more of R132H, R132C,
R132S, R132L, and R132G, of isocitrate dehydrogenase 1 (IDH1) and
R172M, R172G, and R172K of IDH2, relative to R132 IDH1 or R172
IDH2.
19. The method of claim 1 wherein the step of assaying employs
hybridization of an oligonucleotide probe comprising: IDH1 codon
132 or nucleotide 394 or 395 of IDH1 transcript plus sufficient
adjacent nucleotides of IDH1 to achieve specific hybridization; or
IDH2 codon 172 or nucleotide 515 of IDH2 transcript plus sufficient
adjacent nucleotides of IDH2 to achieve specific hybridization.
20. The method of claim 19 wherein the codon is an arginine
codon.
21. The method of claim 19 wherein the codon is not an arginine
codon.
22. The method of claim 1 wherein the step of assaying comprises
using primer extension to generate a reaction product comprising at
least a portion of: IDH1 that includes codon 132 or nucleotide 394
or 395 of IDH1 transcript; or IDH2 that includes codon 172 or
nucleotide 515 of IDH2 transcript.
23. An isolated antibody which specifically binds to one or more of
the following: R132H IDH1, or R132C IDH1, or R132S IDH1, or R132L
IDH1, or R132G IDH1, but not R132 IDH1; R172M IDH2, R172G, or R172K
of IDH2, but not R172 IDH2; R132 IDH1; or R172 IDH2.
24. The antibody of claim 23 which is a monoclonal antibody.
25. The antibody of claim 23 which is a single chain variable
region molecule.
26. An isolated polynucleotide comprising at least 18 but less than
600 contiguous nucleotide residues of a coding sequence of a human
IDH1 or IDH2 protein found in a human tumor, said at least 18
contiguous amino acid residues comprising: nucleotides 394 and/or
395 of IDH1, wherein said nucleotides 394 and/or 395 are not C
and/or G, respectively; nucleotide 515 of IDH2, wherein said
nucleotide 515 is not a G.
27. The isolated polynucleotide of claim 26 wherein the coding
sequence is of a human IDH1 protein and the nucleotide 394 is
selected from the group consisting of T, A, and G or nucleotide 395
is selected from the group consisting of A and T.
28. The isolated polynucleotide of claim 26 wherein the coding
sequence is of a human IDH2 protein and the nucleotide 515 is
selected from the group consisting of T and A or nucleotide 514 is
a G.
1-95. (canceled)
Description
TECHNICAL FIELD OF THE INVENTION
[0002] This invention is related to the area of cancer diagnostics,
prognostics, drug screening, and therapeutics. In particular, it
relates to brain tumors in general, and glioblastoma multiforme, in
particular.
BACKGROUND OF THE INVENTION
[0003] Gliomas, the most common type of primary brain tumors, are
classified as Grade I to Grade IV using histopathological and
clinical criteria established by the World Health Organization
(WHO).sup.1. This group of tumors includes a number of specific
histologies, the most common of which are astrocytomas,
oligodendrogliomas, and ependymomas. Grade I gliomas, often
considered to be benign lesions, are generally curable with
complete surgical resection and rarely, if ever, evolve into
higher-grade lesions.sup.2. However, tumors of Grades II and III
are malignant tumors that grow invasively, progress to higher-grade
lesions, and carry a correspondingly poor prognosis. Grade IV
tumors (glioblastoma multiforme, GBM) are the most invasive form
and have a dismal prognosis.sup.3,4. Using histopathologic
criteria, it is impossible to distinguish a secondary GBM, defined
as one which occurs in a patient previously diagnosed with a lower
grade glioma, from a primary GBM which has no known antecedent
tumor.sup.5,6.
[0004] A number of genes are known to be genetically altered in
gliomas, including TP53, PTEN, CDKN2A, and EGFR.sup.7-12. These
alterations tend to occur in a defined order in the progression to
high grade tumors. TP53 mutation appears to be a relatively early
event during astrocytoma development, while loss or mutation of
PTEN and amplification of EGFR are characteristic of higher-grade
tumors.sup.6,13,14. In oligodendrogliomas, allelic losses of 1p and
19q occur in many Grade II tumors while losses of 9p21 are largely
confined to Grade III tumors.sup.15.
[0005] There is a continuing need in the art to identify the
causes, identifiers, and remedies for glioblastomas and other brain
tumors.
SUMMARY OF THE INVENTION
[0006] According to one aspect of the invention a method is
provided of characterizing a glioblastoma multiforme (GBM) tumor in
a human subject. A GBM tumor is analyzed to identify the presence
or absence of a somatic mutation at codon 132 in isocitrate
dehydrogenase 1 (IDH1) or at codon 172 in isocitrate dehydrogenase
2 (IDH2) in a GBM tumor of a human subject.
[0007] Also provided as another aspect of the invention is an
isolated antibody which specifically binds R132H IDH1, or R132C
IDH1, or R132S IDH1, or R132L IDH1, or R132G IDH1, but not R132
IDH1; or R172M IDH2, R172G IDH2, or R172K IDH2, but not R172; i.e.,
mutant forms of IDH1 or IDH2 which are found in GBM. Also provided
is an isolated antibody which specifically binds R132 IDH1 or R172
IDH2, i.e., wild-type active sites of IDH1 or IDH2.
[0008] Another aspect of the invention is a method of immunizing a
mammal. An IDH1 mutant polypolypeptide comprising at least 8
contiguous amino acid residues of a human IDH1 protein or an IDH2
mutant polypolypeptide comprising at least 8 contiguous amino acid
residues of a human IDH2 protein found in a human tumor is
administered to a mammal. The at least 8 contiguous amino acid
residues comprise residue 132 or IDH1 or residue 172 of IDH2.
Residue 132 or residue 172 is not arginine. Antibodies and/or T
cells which are immunoreactive with epitopes found on the IDH1 or
IDH2 mutant polypeptide but not found on normal IDH1 or IDH2 are
produced.
[0009] Also provided as another aspect of the invention is an IDH1
or IDH2 mutant polypeptide comprising at least 8 but less than 200
contiguous amino acid residues of a human IDH1 or IDH2 protein
found in a human tumor. The at least 8 contiguous amino acid
residues comprise residue 132 of IDH1 or residue 172 of IDH2.
Residues 132 or 172 are not R.
[0010] An additional aspect of the invention is an isolated
polynucleotide comprising at least 18 but less than 600 contiguous
nucleotide residues of a coding sequence of a human IDH1 or human
IDH2 protein found in a human tumor. The at least 18 contiguous
amino acid residues comprise nucleotides 394 and/or 395 of IDH1 or
nucleotide 515 or IDH2. Nucleotides 394 and/or 395 of IDH1 are not
C and/or G, respectively. Residue 515 of IDH2 is not G.
[0011] Another aspect of the invention is a method of immunizing a
mammal. An IDH1 polypeptide comprising at least 8 contiguous amino
acid residues of a human IDH 1 protein or an IDH2 polypeptide
comprising at least 8 contiguous amino acid residues of a human
IDH2 protein is administered to a mammal. The at least 8 contiguous
amino acid residues comprise residue 132 of IDH1 or residue 172 of
IDH2. Residue 132 or residue 172 is arginine. Antibodies and/or T
cells which are immunoreactive with epitopes found on the IDH1 or
IDH2 polypeptide are produced.
[0012] Also provided as another aspect of the invention is an IDH1
or IDH2 polypeptide comprising at least 8 but less than 200
contiguous amino acid residues of a human IDH1 or IDH2 protein. The
at least 8 contiguous amino acid residues comprise residue 132 of
IDH1 or residue 172 of IDH2. Residues 132 or 172 are R.
[0013] Still another aspect of the invention is a method of
detecting or diagnosing glioblastoma multiforme (GBM) or minimal
residual disease of GBM or molecular relapse of GBM in a human. A
somatic mutation in a gene or its encoded mRNA or protein is
determined in a test sample relative to a normal sample of the
human. The gene is selected from the group consisting of those
listed in FIG. 10, Table S7. The human is identified as likely to
have glioblastoma multiforme, minimal residual disease, or
molecular relapse of GBM when the somatic mutation is
determined.
[0014] Yet another aspect of the invention is a method of
characterizing a glioblastoma multiforme in a human. A CAN-gene
mutational signature for a glioblastoma multiforme is determined by
determining in a test sample relative to a normal sample of the
human, a somatic mutation in at least one gene or its encoded cDNA
or protein. The gene is selected from the group consisting of those
listed in FIG. 10, Table S7. The glioblastoma multiforme is
assigned to a first group of glioblastoma multiforme tumors that
have the CAN-gene mutational signature.
[0015] Another method provided by the invention is for
characterizing a glioblastoma multiforme tumor in a human. A
mutated pathway selected from the group consisting of TP53, RB1,
and PI3K/PTEN is identified in a glioblastoma multiforme tumor by
determining at least one somatic mutation in a test sample relative
to a normal sample of the human. The at least one somatic mutation
is in one or more genes selected from the group consisting of TP53,
MDM2, MDM4, RB1, CDK4, CDKN2A, PTEN, PIK3CA, PIK3R1, and IRS1. The
glioblastoma multiforme is assigned to a first group of
glioblastoma multiforme tumors that have a mutation in one of said
pathways. The first group is heterogeneous with respect to the
genes in the pathway that have a somatic mutation and homogeneous
with respect to the pathway that has a somatic mutation.
[0016] Also provided is a method to detect or diagnose glioblastoma
multiforme, or minimal residual disease of GBM or molecular relapse
of GBM in a human. Expression is determined in a clinical sample of
one or more genes listed in FIG. 10, Table S5 or S9 (brain
overexpressed genes from SAGE). The expression of the one or more
genes in the clinical sample is compared to expression of the one
or more genes in a corresponding sample of a control human or
control group of humans. A clinical sample with elevated expression
relative to a control is identified as likely to have glioblastoma
multiforme, or minimal residual disease of GBM or molecular relapse
of GBM in a human.
[0017] Another aspect of the invention is a method to monitor
glioblastoma multiforme burden. Expression in a clinical sample is
determined of one or more genes listed in FIG. 10, Table S5 or S9
(brain overexpressed genes from SAGE). The step of determining is
repeated one or more times. An increase, decrease or stable level
of expression over time is identified.
[0018] Yet another aspect of the invention is a method to monitor
glioblastoma multiforme burden. A somatic mutation is determined in
a clinical sample of one or more genes listed in FIG. 10, Table S7.
The step of determining is repeated one or more times. An increase,
decrease or stable level of said somatic mutation over time is
identified.
[0019] Still another aspect of the invention relates to a method to
detect or diagnose gliobastoma multiforme. Expression in a clinical
sample of one or more genes listed in FIG. 10, Table S6 (homozygous
deletions) is determined. Expression of the one or more genes in
the clinical sample is compared to expression of the one or more
genes in a corresponding sample of a control human or control group
of humans. A clinical sample with reduced expression relative to a
control is identified as likely to have gliobastoma multiforme.
[0020] A further aspect of the invention is a method to monitor
gliobastoma multiforme burden. Expression in a clinical sample of
one or more genes listed in FIG. 10, Table S6 (homozygous
deletions) is determined. The step of determining is repeated one
or more times. An increase, decrease or stable level of expression
over time is identified.
[0021] These and other embodiments which will be apparent to those
of skill in the art upon reading the specification provide the art
with new tools for analyzing, detecting, stratifying and treating
GBM.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] FIG. 1. Sequence alterations in IDH1. Representative
examples of somatic mutations at codon 132 of the IDH1 gene. The
top sequence chromatogram was obtained from analysis of DNA from
normal tissue while the lower chromatograms were obtained from the
indicated GBM samples. Arrows indicate the location of the
recurrent heterozygous missense mutations C394A (in tumor Br104X)
and G395A (in tumor Br129X) resulting in the indicated amino acid
changes.
[0023] FIG. 2. Structure of the active site of IDH1. The crystal
structure of the human cytosolic NADP(+)-dependent IDH is shown in
ribbon format (PDBID: 1TOL) (42). The active cleft of IDH1 consists
of a NADP-binding site and the isocitrate-metal ion-binding site.
The alpha-carboxylate oxygen and the hydroxyl group of isocitrate
chelate the Ca2+ ion. NADP is colored in orange, isocitrate in
purple and Ca2+ in blue. The Arg132 residue, displayed in yellow,
forms hydrophilic interactions, shown in red, with the
alpha-carboxylate of isocitrate.
[0024] FIG. 3. Overall survival among patients<45 years old
according to IDH1 mutation status. The hazard ratio for death among
patients with mutated IDH1, as compared to those with wildtype
IDH1, was 0.19 (95 percent confidence interval, 0.08 to 0.49;
P<0.001). The median survival was 3.8 years for patients with
mutated IDH 1, as compared to 1.5 years for patients with wildtype
IDH1.
[0025] FIG. 4A-4B. IDH1 and IDH2 mutations in human gliomas. FIG.
4A. Schematic diagram of mutations at codon R132 in IDH1 (bottom)
and R172 in IDH2 (top) identified in human gliomas. Codons 130 to
134 of IDH1 and 170 to 174 of IDH2 are shown. The number of
patients with each mutation (n) is listed at the right of the
figure. FIG. 4B. Number and frequency of IDH1 and IDH2 mutations in
human gliomas and other tumor types. The non-CNS cancers included
35 lung cancers, 57 gastric cancers, 27 ovarian cancers, 96 breast
cancers, 114 colorectal cancers, 95 pancreatic cancers, seven
prostate cancers, and peripheral blood specimens from 4 chronic
myelogenous leukemias, 7 chronic lymphocytic leukemias, seven acute
lymphoblastic leukemias, and 45 acute myelogenous leukemias.
[0026] FIG. 5A-5B. Survival for patients with malignant gliomas
according to IDH1 and IDH2 mutation status. For patients with
anaplastic astrocytomas (FIG. 5A), the median survival was 65
months for patients with mutated IDH1 or IDH2, as compared to 19
months for patients with wildtype IDH1 and IDH2. For patients with
GBM (FIG. 5B), the median survival was 39 months for patients with
mutated IDH1 or IDH2, as compared to 13.5 months for patients with
wildtype IDH1 and IDH2.
[0027] FIG. 6. Model of malignant glioma development. For each
tumor type common genetic alterations (IDH1/IDH2 mutation, TP53
mutation, 1p 19q loss, and CDKN2A loss) are indicated. Detailed
frequencies of genetic alterations are contained in Table 1 and 2
or reference.sup.1. In general, tumors on the right acquire IDH
alterations, while those on the left do not.
[0028] FIG. 7. Sequence alterations in IDH1 and IDH2.
Representative examples of somatic mutations at codon 132 of the
IDH1 gene (top) and codon 172 of the IDH2 gene (bottom). In each
case, the top sequence chromatogram was obtained from analysis of
DNA from normal tissue while the lower chromatograms were obtained
from the indicated tumor samples. Arrows indicate the location of
the missense mutations and resulting amino acid changes in IDH1 in
tumor TB2604 (anaplastic astrocytoma), 640 (anaplastic
astrocytoma), and 1088 (anaplastic oligodendroglioma), and in IDH2
in tumor H883 (anaplastic astrocytoma) and H476 (anaplastic
oligodendroglioma).
[0029] FIG. 8. Sequence alterations in IDH1 in progressive gliomas.
Representative examples of somatic mutations at codon 132 of the
IDH1 are indicated in three representative cases. The top sequence
chromatogram was obtained from analysis of DNA from normal tissue
while the lower chromatograms were obtained from the indicated
brain tumor samples. Arrows indicate the location of the mutations
and the resulting amino acid changes in IDH1. In all cases, the
identical IDH1 mutations were found in the lower- and higher-grade
tumors from each patient.
[0030] FIG. 9A-9B. Age distribution of glioma patients with mutated
and wild-type IDH. Age distribution of oligodendroglioma (0),
anaplastica oligodendroglioma (AO), diffuse astrocytoma (DA),
anaplastic astrocytoma (AA), and glioblastoma multiforme (GBM) in
patients with wild-type IDH genes (FIG. 9A) or mutated IDH genes
(FIG. 9 B).
[0031] FIG. 10. Compendium of Tables S3-S10. Table S3 (somatic
mutations identified in GBM discovery screen). Tables S4 (somatic
mutations in prevalence screen), Table S5 (amplified genes), Table
S6 (homozygously deleted genes), Table S7 (top CAN-candidate
genes), Table S8 (candidate gene sets enriched for genetic
alterations in GBM), Table S9 (overexpressed genes in SAGE), and
Table S10 (extracellular subset of overexpressed genes in
SAGE).
[0032] FIG. 11. Summary of genetic and clinical characteristics of
brain tumors.
[0033] FIG. 12. Evaluation of frequency of common genetic
alterations in IDH1/IDH2 mutated and wildtype gliomas.
[0034] A sequence listing is part of this application.
DETAILED DESCRIPTION OF THE INVENTION
[0035] In a genome-wide analysis of GBMs, we identified somatic
mutations of codon 132 of the isocitrate dehydrogenase 1 gene
(IDH1) in .about.12% of GBMs analyzed.sup.16. These mutations were
found at higher frequency in secondary GBMs (5 of 6 patients
evaluated). One interpretation of these data is that IDH 1
mutations occur in a subset of lower-grade gliomas, driving them to
progress to GBMs. To evaluate this possibility, we have analyzed a
large number of gliomas of various types. Remarkably, we found IDH1
mutations in the majority of early malignant gliomas. Furthermore,
many of the gliomas without IDH1 mutations had analogous mutations
in the closely related IDH2 gene. These results suggest that IDH
mutations play an early and essential role in malignant glioma
development.
[0036] Somatic mutations are mutations which occur in a particular
clone of somatic cells during the lifetime of the individual
organism. The mutation is thus not inherited or passed on. The
mutation will appear as a difference relative to other cells,
tissues, organs. When testing for a somatic mutation in a brain
tissue suspected of being cancerous, a comparison can be made to
normal brain tissue that appears to be non-neoplastic, or to a
non-brain sample, such as blood cells, or to a sample from an
unaffected individual.
[0037] The common amino acid at codon 132 of IDH1 and codon 172 of
IDH2 in healthy tissues is arginine (R). Mutant codons have been
found with substitutions of histidine (H), serine (S), and cysteine
(C), leucine (L), and glycine (G) of IDH1 codon 132 and of
methionine (M), lysine (K), and glycine (G) of codon 172 of IDH2.
The mutations at codon 132 and codon 172 can be detected using any
means known in the art, including at the DNA, mRNA, or protein
levels. Antibodies which specifically bind to the arginine-132 form
of the enzyme, the histidine-132 form of the enzyme, the serine-132
form of the enzyme, leucine-132 form of the enzyme, glycine-132
form of the enzyme, or the cysteine-132 form of the enzyme can be
used in assays for mutation detection. Likewise antibodies which
specifically bind to the arginine-172, methionine-172, lysine-172,
or glycine-172 forms of IDH2 can be used in assays for mutation
detection. Similarly, probes which contain codons for these amino
acid residues in the context of the coding sequence of IDH1 or IDH2
can be used for detecting the gene or mRNA of the different forms.
Primers which contain all or part of these codons can also be used
for allele-specific amplification or extension. Primers hybridizing
to regions surrounding these codons can be used to amplify the
codons, followed by subsequent analysis of the amplified region
containing codon 132 of IDH1 or codon 172 of IDH2.
[0038] Interestingly, the codon 132 mutations of IDH1 and codon 172
mutations of IDH2 have been found to be strongly associated with
secondary GBM and with a favorable prognosis. Drugs can be tested
against groups of glioblastoma patients that are stratified with
regard to the 132.sup.nd amino acid residue of IDH1 and/or the
172.sup.nd amino acid residue of IDH2. The groups may comprise
wild-type (arginine) and variants (combined) or variants (each
separately). Drug sensitivity can be determined for each group to
identify drugs which will or will not be efficacious relative to a
particular mutation or wild-type (arginine). Both sensitivity and
resistance information are useful to guide treatment decisions.
[0039] Once a codon 132 or 172 mutation is identified in a tumor,
inhibitors of IDH 1 or IDH2 may be used therapeutically. Such
inhibitors may be specific for a mutation in the tumor or may
simply be an inhibitor of IDH1 or IDH2. Small molecule inhibitors
as well as antibodies and antibody-derivatives can be used. Such
antibodies include monoclonal and polyclonal antibodies, ScFv
antibodies, and other constructs which comprise one or more
antibody Fv moieties. Antibodies can be humanized, human, or
chimeric, for example. Antibodies may be armed or unarmed. Armed
antibodies may be conjugated to toxins or radioactive moieties, for
example. Unarmed antibodies may function to bind to tumor cells and
participate in host immunological processes, such as
antibody-dependent cell-medicated cytotoxicity. Antibodies may
preferentially bind to mutant versus wild-type IDH1 or IDH2,
specifically bind to mutant versus wild-type IDH1 or IDH2, or bind
equally to both mutant and wild-type IDH1 or IDH2. Preferably the
antibodies will bind to an epitope in the active site which may
include codon 132 or codon 172. Epitopes may be continuous or
discontinuous along the primary sequence of the protein. Inhibitors
may include alpha-methyl isocitrate, aluminum ions, or oxalomalate.
Other inhibitors may be used and optionally identified using enzyme
assays known in the art, including spectrophotometric assays
(Kornberg, A., 1955) and bioluminescent assays (Raunio, R. et al.,
1985). Inhibitors may be alternatively identified by binding tests,
for example by in vitro or in vivo binding assays. Peptides and
proteins which bind to IDH1 or IDH2 may also be used as
inhibitors.
[0040] Inhibitory RNA molecules may be used to inhibit expression.
These may be, for example, siRNA, microRNA or antisense
oligonucleotides or constructs. These can be used to inhibit the
expression of IDH1 or IDH2 as appropriate in a human.
[0041] Potential therapeutic efficacy can be tested for an
antibody, polynucleotide, protein, small molecule, or antibody by
contacting with cells, tissues, whole animals, or proteins.
Indications of efficacy include modulation of enzyme activity,
inhibition of cancer cell growth, prolongation of life expectancy,
inhibition of cancer cell proliferation, stimulation of cancer cell
apoptosis, and inhibition or retardation of tumor growth. Any
assays known in the art can be used, without limitation.
Combinations of candidates and combinations of candidates with
known agents can be assessed as well. Known agents may include, for
example, chemotherapeutic anti-cancer agents, biological
anti-cancer agents, such as antibodies and hormones, radiation.
[0042] In order to raise or increase an immune response to a
glioblastoma in a person or mammal with a tumor, in a person with a
likelihood of developing a tumor, or in an apparently healthy
individual, a polypeptide can be administered to the person or
mammal. The polypeptide will typically comprise at least 6, at
least 8, at least 10, at least 12, or at least 14 contiguous amino
acid residues of human IDH1 protein including residue 132 or IDH2
including residue 172. Typically but not always, the polypeptide
will contain a residue other than arginine at residue 132 of IDH1
or residue 172 or IDH2. In the situation where the person or mammal
already has a tumor, the amino acid at residue 132 can be matched
to the residue in the tumor. The polypeptide may comprise the whole
of IDH1, but can comprise less than 200, less than 150, less than
100, less than 50, less than 30 amino acid residues. Although
applicants do not wish to be bound by any mechanism of action, the
polypeptide immunization may act though an antibody and/or T cell
response. Polypeptides can be administered with immune adjuvants or
conjugated to moieties which stimulate an immune response. These
are well known in the art, and can be used as appropriate.
[0043] Antibodies which specifically bind to an epitope on IDH1 or
IDH2 do so with a higher avidity or a higher association rate than
they bind to other proteins. Preferably the higher avidity or rate
of association is at least about 2-fold, 5-fold, 7-fold, or 10-fold
relative to other proteins that do not contain the epitope.
[0044] An isolated polynucleotide can be used to encode and deliver
the polypeptide for immunization. The polynucleotide can be used to
manufacture the polypeptide in a host cell in culture, or may be
used in a gene therapy context to raise an immune response in vivo
upon expression in the vaccine recipient. Polynucleotides can also
be used as primers or probes, which may or may not be labeled with
a detectable label. Primers can be used for primer extension, for
example, using a primer that is complementary to nucleotides
adjacent to but not including either nt 394 or nt 395 of IDH1 or
nucleotide 515 of IDH2. Products can be detected and distinguished
using labeled nucleotides as reagents. Different labels may be used
on different nucleotides so that the identity of the analyte can be
readily determined. Typically the polynucleotide for use as a
primer or probe will comprise at least 10, at least 12, at least
14, at least 16, at least 18, at least 20 contiguous nucleotides of
IDH1 or IDH2 coding sequence. Typically the polynucleotide will
comprise less than 600, less than 500, less than 400, less than
300, less than 200, less than 100 nucleotides of IDH1 or IDH2
coding sequence.
[0045] Our data identified IDH1 as a major target of genetic
alteration in patients with GBM. All mutations in this gene
resulted in amino acid substitutions at position 132, an
evolutionarily conserved residue located within the isocitrate
binding site (42). In addition, the only previously-reported
mutation of IDH1 was another missense mutation affecting this same
residue in a colorectal cancer patient (10). The functional effect
of these IDH1 mutations is unclear. The recurrent nature of the
mutations is reminiscent of activating alterations in other
oncogenes such as BRAF, KRAS, and PIK3CA. The prediction that this
mutation would be activating is strengthened by the lack of
observed inactivating changes (i.e. frameshift or stop mutations,
splice site alterations), the lack of alterations in other key
residues of the active site, and by the fact that all mutations
observed to date were heterozygous (without any evidence of loss of
the second allele through LOH). Interestingly, enzymatic studies
have shown that substitution of arginine at residue 132 with
glutamate results in a catalytically inactive enzyme suggesting
that this residue plays a critical role in IDH1 activity (46).
However, the nature of the substitutions observed in GBMs is
qualitatively different, with arginine changed to histidine or
serine. Histidine forms hydrogen bonding interactions, with
carboxylate as part of the catalytic activity of many enzymes (47),
and could serve an analogous function to the known interaction of
Arg132 and the .alpha.-carboxylate of isocitrate. It is conceivable
that R132H alterations may lead to higher overall catalytic
activity. Increased activity of IDH1 would be expected to result in
higher levels of NADPH, providing additional cellular defenses
against reactive oxygen species, preventing apoptosis and
increasing cellular survival and tumor growth. Further biochemical
and molecular analyses will be needed to determine the effect of
alterations of IDH1 on enzymatic activity and cellular
phenotypes.
[0046] Regardless of the specific molecular consequences of IDH1
and IDH2 alterations, it is clear that detection of mutations in
IDH1 and IDH2 will be clinically useful. Although significant
effort has focused on the identification of characteristic genetic
lesions in primary and secondary GBMs, the altered genes identified
to date are far from perfect for this purpose. For example, in
comparing primary versus secondary GBMs, TP53 is mutated in
.about.30% vs. 65%, respectively, EFGR amplification is present in
.about.35% vs. 5-10%, and PTEN mutation is present in .about.25%
vs. .about.5% (5). Our study revealed IDH1 mutation to be a novel
and significantly more specific marker for secondary GBM, with 5 of
the 6 (83%) secondary GBM samples analyzed having a mutation in
this gene, while only 7 of 99 (7%) primary GBM patients had such
alterations (P<0.001, binomial test). The sole secondary GBM
patient sample that did not have an IDH1 mutation was both
genetically and clinically unusual, harboring mutations of PTEN but
not TP53, and occurring in an older patient (age 56 years) with a
prior diagnosis of ganglioglioma (which is rarely known to undergo
malignant transformation) (48). It is possible that this patient
had two distinct CNS tumors which were completely unrelated, and
that the GBM in this case was actually a primary tumor.
[0047] One intriguing hypothesis is that IDH1 alterations identify
a biologically-specific subgroup of GBM patients, including both
patients who would be classified as having secondary GBMs as well
as a subpopulation of primary GBM patients with a similar tumor
biology and more protracted clinical course (Table 4).
Interestingly, patients with IDH1 mutations had a very high
frequency of TP53 mutation and a very low frequency of mutations in
other commonly-altered GBM genes. For example, such patients had
TP53 mutation without any detected mutation of EGFR, PTEN, RB1, or
NF1 in 83% of cases (10 of 12 patients); in contrast, only 12% of
patients with wildtype IDH1 (11 of 93) had the same mutation
pattern (FIG. 12) (P<0.001, binomial test). In addition to this
relative genetic uniformity, the patients with mutated IDH1 had
distinct clinical characteristics, including younger age and a
significantly improved clinical prognosis (Table 4) even after
adjustment for age and TP53 mutation status (both of which are
associated with improved survival). Perhaps most surprisingly, they
all shared mutation of a single amino acid residue of IDH1, a
protein that previously had no genetic link to GBMs or other
cancers. This unforeseen result clearly validates the utility of
genome-wide screening for genetic alterations in the study of human
cancers.
[0048] Mutations that have been found in GBM tumors are shown in
FIG. 10, Table S7. These mutations can be detected in test samples,
such as suspected tumor tissue samples, blood, CSF, urine, saliva,
lymph etc. A somatic mutation is typically determined by comparing
a sequence in the test sample to a sequence in a normal control
sample, such as from healthy brain tissue. One or more mutations
can be used for this purpose. If the patient has undergone surgery,
detection of the mutation in tumor margin or remaining tissue can
be used to detect minimal residual disease or molecular relapse. If
GBM has been previously undiagnosed, the mutation may serve to help
diagnose, for example in conjunction with other physical findings
or laboratory results, including but not limited to biochemical
markers and radiological findings.
[0049] CAN-gene signatures can be determined in order to
characterize a GBM. A signature is a set of one or more somatic
mutations in a CAN gene. The CAN genes for GBM are listed in FIG.
10, Table S7. Once such a signature has been determined, a GBM can
be assigned to a group of GBMs sharing the signature. The group can
be used to assign a prognosis, to assign to a clinical trial group,
to assign to a treatment regimen, and/or to assign for further
characterization and studies. In a clinical trial group, drugs can
be assessed for the ability to differentially affect GBMs with and
without the signature. Once a differential effect is determined,
the signature can be used to assign patients to drug regimens, or
to avoid unnecessarily treating patients in whom the drug will not
have a beneficial effect. The drug in a clinical trial can be one
which is previously known for another purpose, previously known for
treating GBM, or previously unknown as a therapeutic. A CAN-gene
signature may comprise at least 1, at least 2, at least 3, at least
4, at least 5, at least 6, at least 7, at least 8, at least 9. at
least 10 genes. The number of genes or mutations in a particular
signature may vary depending on the identity of the CAN genes in
the signature. Standard statistical analyses can be used to achieve
desired sensitivity and specificity of a CAN gene signature.
[0050] Analysis of the mutated genes in the analyzed GBM tumors has
revealed interesting involvement of pathways. Certain pathways
frequently carry mutations in GBMs. A single gene mutation appears
to exclude the presence of a mutation in another gene in that
pathway in a particular tumor. Frequently mutated pathways in GBMs
are the TP53, RB1, PI3K/PTEN pathways. Pathways can be defined
using any of the standard reference databases, such as MetaCore
Gene Ontology (GO) database, MetaCore canonical gene pathway maps
(MA) database, MetaCore GeneGo (GG) database, Panther, TRMP, KEGG,
and SPAD databases. Groups can be formed based on the presence or
absence of a mutation in a certain pathway. Such groups will be
heterogeneous with respect to mutated gene but homogeneous with
respect to mutated pathway. As with CAN gene signatures, these
groups can be used to characterize a GBM. Once a mutation in a
pathway has been determined, a GBM can be assigned to a group of
GBMs sharing the mutated pathway. The group can be used to assign a
prognosis, to assign to a clinical trial group, to assign to a
treatment regimen, and/or to assign for further characterization
and studies. In a clinical trial group, drugs can be assessed for
the ability to differentially affect GBMs with and without the
mutated pathway. Once a differential effect is determined, the
pathway can be used to assign patients to drug regimens, or to
avoid unnecessarily treating patients in whom the drug will not
have a beneficial effect. The drug in a clinical trial can be one
which is previously known for another purpose, previously known for
treating GBM, or previously unknown as a therapeutic. Among the
genes in the pathways which may be found mutant are: TP53, MDM2,
MDM4, RB1, CDK4, CDKN2A, PTEN, PIK3CA, PIK3RI, and IRS 1. This list
is not necessarily exhaustive.
[0051] Expression levels can be determined and overexpression may
be indicative of a new GBM tumor, molecular relapse, or minimal
residual disease of GBM. Highly increased expression found in GBM
tumors are shown in FIG. 10, Table S5 and FIG. 10, Table S9. These
overexpressed genes can be detected in test samples, such as
suspected tumor tissue samples, blood, CSF, urine, saliva, lymph
etc. Elevated expression is typically determined by comparing
expression of a gene in the test sample to expression of a gene in
a normal control sample, such as from healthy brain tissue.
Elevated expression of one or more genes can be used for this
purpose. If the patient has undergone surgery, detection of the
elevated expression in tumor margin or remaining tissue can be used
to detect minimal residual disease or molecular relapse. If GBM has
been previously undiagnosed, the elevated expression may serve to
help diagnose, for example in conjunction with other physical
findings or laboratory results, including but not limited to
biochemical markers and radiological findings. For these purposes,
any means known in the art for quantitating expression can be used,
including SAGE or microarrays for detecting elevated mRNA, and
antibodies used in various assay formats for detecting elevated
protein expression. For detecting protein expression, the genes
listed in FIG. 10, Table S10 are particularly useful.
[0052] Tumor burden can be monitored using the mutations listed in
FIG. 10, Table S7. This may be used in a watchful waiting mode, or
during therapy to monitor efficacy, for example. Using a somatic
mutation as a marker and assaying for level of detectable DNA,
mRNA, or protein over time, can indicate tumor burden. The level of
the mutation in a sample may increase, decrease or remain stable
over the time of analysis. Therapeutic treatments and timing may be
guided by such monitoring.
[0053] Analysis of the GBMs revealed certain genes which are
homozygously deleted. These are listed in FIG. 10, Table S6.
Determining loss of expression of one or more of these genes can be
used as a marker of GBM. This may be done in a sample of blood or
lymph node or in a brain tissue sample. Expression of one or more
of these genes may be tested. Techniques such as ELISA or IHC may
be used to detect diminution or loss of protein expression in a
sample. Similarly the homozygously deleted genes listed in FIG. 10,
Table S6 may be used to monitor tumor burden over time. Expression
can be repeatedly monitored so that in increase, decrease, or
stable level of expression can be ascertained.
[0054] The data resulting from this integrated analysis of
mutations and copy number alterations have provided a novel view of
the genetic landscape of glioblastomas. The combination of
different types of genetic data, including point mutations,
amplifications, and deletions allows for identification of
individual CAN-genes as well as groups of genes that may be
preferentially affected in complex cellular pathways and processes
in GBMs. Identification of virtually all genes previously shown to
be affected in GBMs by mutation, amplification, or deletion
validates the comprehensive genomic approach we have employed.
[0055] It should be noted, however, that our approach, like all
genome-wide studies, has limitations. First we did not assess
chromosomal translocations, which is one type of genetic alteration
that could play an important role in tumorigenesis. However,
observations of recurrent chromosomal translocations have only
rarely been reported in cyotogenetic studies of GBM. We also did
not assess epigenetic alterations, though our large scale
expression studies should have identified any genes that were
differentially expressed through this mechanism (FIG. 10, Table
S9). Additionally, for copy number changes we focused on regions
that were truly amplified or homozygously deleted as these have
historically been most useful in identifying cancer genes. The SNP
array data we have generated for these samples, however, contains
information that can be analyzed to determine loss of
heterozygosity (LOH) or small copy number gains due to duplications
rather than true amplification events. Analysis of such data for
known cancer genes, such as CDKN2A or NF1, identified additional
tumors that had LOH in these regions, but given the substantial
fraction of the genome that undergoes LOH in GBMs, such
observations are in general not likely to be helpful in pinpointing
new candidate cancer genes. Finally, the primary tumors used in our
analysis contained small amounts of contaminating normal tissue, as
is the rule for this sample type, which limited our ability to
detect homozygous deletions and to a lesser extent, somatic
mutations, in those specific tumors. This was true even though we
carefully selected these tumors to contain a minimal stromal
component by histological and molecular biologic criteria. This
observation serves as an important reminder of the value of early
passage xenografts and cell lines for such large scale genomic
studies.
[0056] Despite these limitations, our studies provide a number of
important genetic and clinical insights into GBMs. The first of
these is that the pathways known to be altered in GBMs affect a
larger fraction of gene members and patients than previously
anticipated. A majority of the tumors analyzed had alterations in
members of each of the TP53, RB1, and P13K pathways. The fact that
all but one of the cancers with mutations in members of a pathway
did not have alterations in other members of the same pathway is
significant and suggests that such alterations are functionally
equivalent in tumorigenesis. These observations also point to
distinct opportunities for potential therapeutic intervention in
these pathways in GBMs. The second observation is that a variety of
new genes and pathways not previously implicated in GBMs were
identified. Among the new pathways detected, a number of these
appear to be involved in brain specific ion transport and signaling
processes and represent interesting and potentially useful aspects
of GBM biology.
[0057] These data immediately raise questions with important
implications for the treatment and counseling of patients with GBMs
as well as those with lower-grade gliomas. For example, are
mutations in IDH also present in a subset of patients diagnosed
with lower-grade gliomas (WHO grades I-III)? If IDH1 mutations are
indeed found to be a relatively early genetic event in glioma
progression, are these patients at increased risk of progression to
GBM? Given the significant clinical difficulty of deciding which
low grade glioma patients will receive adjuvant radiation therapy
or chemotherapy (and how aggressive treatment should be), the
knowledge that a patient is at increased risk for malignant
progression would significantly alter the risk-benefit analysis of
such treatment decisions. For pediatric patients, in whom radiation
therapy can have particularly devastating effects on neurocognitive
development and function, these decisions are particularly
difficult and any additional risk-classification would be
especially useful. IDH mutations may also provide one biological
explanation for the occasional long-term GBM survivor, and could
help to identify patients that would receive particular benefit
from specific currently-available therapies. The utility of IDH as
a clinical marker is likely to be enhanced by the fact that only a
single codon of the gene needs to be examined to determine mutation
status. Finally, it is conceivable that new treatments may be
designed to take advantage of these IDH alterations, either as
monotherapy or in combination with other agents. Along these lines,
inhibition of mitochondrial IDH2 has recently been shown to result
in increased sensitivity of tumor cells to a variety of
chemotherapeutic agents (49). In summary, this finding of IDH
mutations in a subset of GBM patients and in at least one other
cancer type opens a new avenue of research that could illuminate a
previously unappreciated aspect of human tumorigenesis.
[0058] The above disclosure generally describes the present
invention. All references disclosed herein are expressly
incorporated by reference. A more complete understanding can be
obtained by reference to the following specific examples which are
provided herein for purposes of illustration only, and are not
intended to limit the scope of the invention.
EXAMPLE 1
Materials and Methods
[0059] DNA was extracted from primary tumor and xenograft samples
and patient-matched normal blood lymphocytes obtained from the
Tissue Bank at the Preston Robert Tisch Brain Tumor Center at Duke
and collaborating centers, as previously described.sup.17. All
brain tumors analyzed were subjected to consensus review by two
neuropathologists. The panel of brain tumors consisted of 21
pilocytic astrocytomas and 2 subependymal giant cell gliomas (WHO
Grade I); 31 diffuse astrocytomas, 51 oligodendrogliomas, three
oligoastrocytomas, 30 ependymomas, and seven pleomorphic
xanthoastrocytomas (WHO Grade II); 43 anaplastic astrocytomas, 36
anaplastic oligodendrogliomas, and seven anaplastic
oligoastrocytomas (WHO Grade III); 178 GBMs and 55 medulloblastomas
(WHO Grade IV). The GBM samples included 165 primary and 13
secondary cases. Fifteen of the GBMs were from patients<20 years
old). Secondary GBMs were defined as those that were resected>1
year after a prior diagnosis of a lower grade glioma (WHO Grades
I-III). Sixty-six of the 178 GBMs, but none of the lower grade
tumors, had been analyzed in our prior genome-wide mutation
analysis of GBMs.sup.16. In addition to the brain tumors, 494
non-CNS cancers were examined: 35 lung cancers, 57 gastric cancers,
27 ovarian cancers, 96 breast cancers, 114 colorectal cancers, 95
pancreatic cancers, seven prostate cancers, 4 chronic myelogenous
leukemias, 7 chronic lymphocytic leukemias, 7 acute lymphoblastic
leukemias, and 45 acute myelogenous leukemias. All samples were
obtained in accordance with the Health Insurance Portability and
Accountability Act. Acquisition of tissue specimens was approved by
the Duke University Health System Institutional Review Board and
the corresponding IRBs at collaborating institutions.
[0060] Exon 4 of the IDH1 gene was PCR-amplified and sequenced in
the matched tumor and normal DNAs for each patient as previously
described.sup.16. In selected patients without an R132 IDH1
mutation (those with Grade II or III lesions or secondary GBM), the
remaining seven exons of IDH1 and all 11 exons of IDH2 were
sequenced and analyzed for mutations. All coding exons of TP53 and
PTEN were also sequenced in the panel of oligodendrogliomas,
anaplastic oligodendrogliomas, anaplastic astrocytomas, and GBMs.
EGFR amplification and CDKN2A/CDKN2B deletion were analyzed by
quantitative real-time PCR in the same tumors.sup.18.
Oligodendroglioma and anaplastic oligodendroglioma samples were
evaluated for loss of heterozygosity (LOH) at 1p and 19q as
previously described .sup.15,19.
[0061] Clinical information included date of birth, date the study
sample was obtained, date of pathologic diagnosis, date and
pathology of any preceding diagnosis of a lower grade glioma,
administration of radiation therapy and/or chemotherapy prior to
the date that the study sample was obtained, date of last patient
contact, and patient status at last contact Clinical information
for survival analysis was available for all 482 primary brain tumor
patients. Kaplan-Meier survival curves were plotted and the
survival distributions were compared by the Mantel Cox log-rank
test and the Wilcoxon test. Overall survival was calculated by
using date of GBM diagnosis and date of death or last patient
contact. The correlations between the occurrence of IDH1/IDH2
mutations and other genetic alterations were examined using
Fisher's exact test.
EXAMPLE 2
High Frequency Alterations of IDH1 in Young GBM Patients
[0062] The top CAN-gene list included a number of individual genes
which had not previously been linked to GBMs. The most frequently
mutated of these genes, IDH1, encodes isocitrate dehydrogenase 1,
which catalyzes the oxidative carboxylation of isocitrate to
.alpha.-ketoglutarate, resulting in the production of NADPH. Five
isocitrate dehydrogenase genes are encoded in the human genome,
with the products of three (IDH3 alpha, IDH3 beta, IDH3 gamma)
forming a heterotetramer (.alpha..sub.2.beta..gamma. in the
mitochondria and utilizing NAD(+) as an electron acceptor to
catalyze the rate-limiting step of the tricarboxylic acid cycle.
The fourth isocitrate dehydrogenase (IDH2) is also localized to the
mitochondria, but like IDH1, uses NADP(+) as an electron acceptor.
The IDH1 product, unlike the rest of the IDH proteins, is contained
within the cytoplasm and peroxisomes (41). The protein forms an
asymmetric homodimer (42), and is thought to function to regenerate
NADPH and .alpha.-ketoglutarate for intraperoxisomal and
cytoplasmic biosynthetic processes. The production of cytoplasmic
NADPH by IDH1 appears to play a significant role in cellular
control of oxidative damage (43) (44). None of the other IDH genes,
other genes involved in the tricarboxylic acid cycle, or other
peroxisomal proteins were found to be genetically altered in our
analysis.
[0063] IDH1 was found to be somatically mutated in five GBM tumors
in the Discovery Screen. Surprisingly, all five had the same
heterozygous point mutation, a change of a guanine to an adenine at
position 395 of the IDH1 transcript (G395A), leading to a
replacement of an arginine with a histidine at amino acid residue
132 of the protein (R132H). In our prior study of colorectal
cancers, this same codon had been found to be mutated in a single
case through alteration of the adjacent nucleotide, resulting in a
R132C amino acid change (10). Five additional GBMs evaluated in our
Prevalence Screen were found to have heterozygous R132H mutations,
and an additional two tumors had a third distinct mutation
affecting the same amino acid residue, R132S (FIG. 1; Table 4). The
R132 residue is conserved in all known species and is localized to
the substrate binding site, forming hydrophilic interaction with
the alpha-carboxylate of isocitrate (FIG. 2) (42, 45).
[0064] Several important observations were made about IDH1
mutations and their potential clinical significance. First,
mutations in IDH1 preferentially occurred in younger GBM patients,
with a mean age of 33 years for IDH1-mutated patients, as opposed
to 53 years for patients with wildtype IDH1 (P<0.001, t-test,
Table 4. In patients under 35 years of age, nearly 50% (9 of 19)
had mutations in IDH1. Second, mutations in IDH1 were found in
nearly all of the patients with secondary GBMs (mutations in 5 of 6
secondary GBM patients, as compared to 7 of 99 patients with
primary GBMs, P<0.001, binomial test), including all five
secondary GBM patients under 35 years of age. Third, patients with
IDH1 mutations had a significantly improved prognosis, with a
median overall survival of 3.8 years as compared to 1.1 years for
patients with wildtype IDH1 (P<0.001, log-rank test). Although
younger age and mutated TP53 are known to be positive prognostic
factors for GBM patients, this association between IDH1 mutation
and improved survival was noted even in patients<45 years old
(FIG. 3, P<0.001, log-rank test), as well as in the subgroup of
young patients with TP53 mutations (P<0.02, log-rank test).
EXAMPLE 3
Glioblastoma Multiforme (GBM) DNA Samples
[0065] Tumor DNA was obtained from GBM xenografts and primary
tumors, with matched normal DNA for each case obtained from
peripheral blood samples, as previously described (1). All samples
were given the histologic diagnosis of glioblastoma multiforme
(GBM; World Health Organization Grade IV), except for two Discovery
Screen samples who were recorded as "high grade glioma, not
otherwise specified". Samples were classified as recurrent for
patients in whom a GBM had been diagnosed at least 3 months prior
to the surgery when the study GBM sample was obtained. There were 3
recurrent GBMs in the Discovery Screen, and 15 in the Prevalence
Screen. Samples were classified as secondary for patients in whom a
lower grade glioma (WHO grade I-III) had been histologically
confirmed at least 1 year prior to the surgery when the study GBM
sample was obtained. One Discovery Screen sample and 5 Prevalence
Screen samples were classified as secondary.
[0066] Pertinent clinical information, including date of birth,
date study GBM sample obtained, date of original GBM diagnosis (if
different than the date that the GBM sample was obtained, as in the
case of recurrent GBMs), date and pathology of preceding diagnosis
of lower grade glioma (in cases of secondary GBMs), the
administration of radiation therapy and/or chemotherapy prior to
the date that the GBM sample was obtained, date of last patient
contact, and patient status at last contact. All samples were
obtained in accordance with the Health Insurance Portability and
Accountability Act (HIPAA). All samples were obtained in accordance
with the Health Insurance Portability and Accountability Act
(HIPAA). As previously described, tumor-normal pair matching was
confirmed by typing nine STR loci using the PowerPlex 2.1 System
(Promega, Madison, Wis.) and sample identities checked throughout
the Discovery and Prevalence screens by sequencing exon 3 of the
HLA-A gene. PCR and sequencing was carried out as described in
(1).
EXAMPLE 4
Statistical Analysis of Clinical Data
[0067] Paired normal and malignant tissue from 105 GBM patients
were used for genetic analysis. Complete clinical information (i.e.
all pertinent clinical information such as date of initial GBM
diagnosis, date of death or last contact) was available for 91 of
the 105 patients. Of these 91 patients, five (all IDH1-wildtype)
died within the first month after surgery and were excluded from
analysis (Br308T, Br246T, Br23X, Br301 T, Br139X), as was a single
patient (Br119X) with a presumed surgical cure (also IDH1-wildtype)
who was alive at last contact .about.10 years after diagnosis.
Kaplan Meier survival curves were compared using the Mantel Cox
log-rank test. Hazard ratios were computed using the
Mantel-Haenszel method. The following definitions were used in the
GBM patient grouping and survival analysis computations: 1) Patient
age refered to the age at which the patient GBM sample was
obtained. 2) Recurrent GBM designates a GBM which was resected
>3 months after a prior diagnosis of GBM. 3) Secondary GBM
designates a GBM which was resected>1 year after a prior
diagnosis of a lower grade glioma (WHO I-III) Overall survival was
calculated using date of GBM diagnosis and date of death or last
patient contact. All confidence intervals were calculated at the
95% level.
EXAMPLE 5
IDH1 and IDH2 Mutations
[0068] Sequence analysis of IDH1 in 976 tumor samples revealed a
total of 167 somatic mutations at residue R132, including R132H
(148 tumors), R132C (8 tumors), R132S (2 tumors), R132L (8 tumors)
and R132G (1 tumor) (FIG. 4A, FIG. 7). Tumors with somatic R132
mutations included 25 of 31 (81%) diffuse astrocytomas (WHO Grade
II), 41 of 51 (80%) oligodendrogliomas (WHO Grade II), 3 of 3
(100%) oligoastrocytomas (WHO Grade II), one of 7 (14%) pleomorphic
xanthoastrocytomas (WHO Grade II), 41 of 61 (67%) anaplastic
astrocytomas (WHO Grade III), 31 of 36 (86%) anaplastic
oligodendrogliomas (WHO Grade III), 7 of 7 (100%) anaplastic
oligoastrocytomas (WHO grade III), 11 of 13 (85%) secondary GBMs,
and 7 of 165 (4%) primary GBMs (FIG. 1B, FIG. 11). In contrast, no
R132 mutations were observed in 21 pilocytic astrocytomas (WHO
Grade I), two subependymal giant cell astrocytomas (WHO Grade I),
30 ependymomas (WHO Grade II), 55 medulloblastomas, or in any of
the 494 non-CNS tumor samples. Sequence analysis of the remaining
IDH1 exons revealed no other somatic mutations of IDH1 in the
R132-negative tumors.
[0069] If IDH1 were critical to the development or progression of
oligodendrogliomas and astrocytomas, we reasoned that alterations
in other genes with similar functions to IDH1 might be found in
those in those tumors without IDH1 mutations. We therefore analyzed
the IDH2 gene, which encodes the only human protein homologous to
IDH1 that utilizes NADP+ as an electron acceptor. Sequence
evaluation of all IDH2 exons in these samples, revealed eight
somatic mutations, all at residue R172: R172M in three tumors,
R172K in three tumors, and R172G in two tumors (FIG. 1A, FIG. 7).
The R172 residue in IDH2 is the exact analogue of the R132 residue
of IDH1, which is located in the active site of the enzyme and
forms hydrogen bonds with the isocitrate substrate.
[0070] To further evaluate the timing of IDH alterations in glioma
progression, we assessed IDH1 mutations in seven patients with
progressive gliomas in which both low- and high-grade tumor samples
were available. Sequence analysis identified IDH1 mutations in both
the low and high-grade tumors in all seven cases (FIG. 8, Table 4).
These results unambiguously demonstrate that IDH1 alterations occur
in low-grade tumors and that subsequent cancers in such patients
are directly derived from these early lesions.
[0071] We also examined the oligodendrogliomas, anaplastic
oligodendrogliomas, anaplastic astrocytomas, and a subset of GBMs
for mutations of TP53 and PTEN, amplification of EGFR, deletion of
CDKN2A/CDKN2B, and LOH of 1p/19q (FIG. 12). TP53 mutations were
much more common in anaplastic astrocytomas (63%) and secondary
GBMs (60%) than in oligodendrogliomas (16%) or anaplastic
oligodendrogliomas (10%) (p<0.001, Fisher's exact test).
Conversely, deletions of 1p and 19q were found more often in
oligodendrocytic than astrocytic tumors, as expected 15.
[0072] Comparison of these alterations with those in IDH1 and IDH2
revealed several striking correlations. Nearly all of the
anaplastic astrocytomas and GBMs with mutated IDH1/IDH2 also had
mutation of TP53 (82%), but only 5% had any alteration of PTEN,
EGFR, or CDKN2A/CDKN2B (FIG. 12). Conversely, anaplastic
astrocytomas and GBMs with wild-type IDH1 had few TP53 mutations
(21%) and more frequent alterations of PTEN, EGFR, or CDKN2A/CDKN2B
(40%) (p<0.001, Fisher's exact test). Loss of 1p/19q was
observed in 85% (45/53) of the oligodendrocytic tumors with mutated
IDH1 or IDH2 but in none (0/9) of the patients with wild-type IDH
genes (p<0.001, Fisher's exact test).
[0073] Patients with anaplastic astrocytomas and GBMs with IDH1 or
IDH2 mutations were significantly younger than those with wild-type
IDH 1 and IDH2 genes (median age of 34 years vs. 58 years,
p<0.001, Student's t-test). Interestingly, despite the lower
median age of patients with IDH1 or IDH2 mutations, no mutations
were identified in GBM from patients who were less than 20 years
old (0 of 18 patients, FIG. 9). hi patients with oligodendrogliomas
and anaplastic oligodendrogliomas, the median age of the patients
with IDH1 or IDH2 mutation was 39 years, with IDH1 mutations
identified in two teenagers (14 and 16 yrs), but not in younger
patients (0 of 4).
[0074] Our prior observation of improved prognosis for GBM patients
with mutated IDH1 16 was confirmed in this larger data set and
extended to include patients with mutations in IDH2. Patients with
IDH1 or IDH2 mutations had a median overall survival of 39 months,
significantly longer than the 13.5 month survival in patients with
wild-type IDH1 (FIG. 5, p<0.001, log-rank test). Mutations of
IDH genes were also associated with improved prognosis in patients
with anaplastic astrocytomas (WHO Grade III), with median overall
survival of 65 months for patients with mutations and 19 months for
those without (p<0.001, log-rank test). Differential survival
analyses could not be performed in patients with diffuse
astrocytomas, oligodendrogliomas, or anaplastic oligodendrogliomas
because there were so few tumors of these types without IDH gene
mutations.
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EXAMPLE 6
Sequencing Strategy
[0142] We extended our previously-developed sequencing strategy for
identification of somatic mutations to include 23,219 transcripts
from 20,583 genes. These included 2783 additional genes from the
Ensembl databases that were not present in the CCDS or RefSeq
databases analyzed in previous studies (10, 11). In addition, we
redesigned PCR primers for regions of the genome that (i) were
difficult to PCR amplify and had been sub-optimally analyzed in
prior studies; or (ii) were found to share significant identity
with other human or mouse sequences. The combination of these new,
redesigned, and existing primers sequences resulted in a total of
208,311 primer pairs (table S1; available on-line at Science 26
Sep. 2008: Vol. 321. no. 5897, pp. 1807-1812) that were
successfully used for sequence analysis of the coding exons of
these genes.
[0143] Twenty-two GBM samples (FIG. 10, Table S2) were selected for
PCR sequence analysis, consisting of 7 samples extracted directly
from patient tumors and 15 tumor samples passaged in nude mice as
xenografts. One tumor (Br27P) was a secondary GBM obtained from a
patient who had previously been treated with both radiation therapy
and chemotherapy, including temozolomide. All other tumors were
categorized as primary GBMs and had not received tumor-directed
treatment prior to the acquisition of the studied tumor sample.
[0144] In the first stage of this analysis, called the Discovery
Screen, the primer pairs were used to amplify and sequence 175,471
coding exons and adjacent intronic splice donor and acceptor
sequences in the 22 GBM samples and in one matched normal sample.
The data were assembled for each amplified region and evaluated
using stringent quality criteria, resulting in successful
amplification and sequencing of 95.0% of targeted amplicons and
93.0% of targeted bases in the 22 tumors. A total of 689 Mb of
sequence data were generated through this approach. The amplicon
traces were analyzed using automated approaches to identify changes
in the tumor sequences that were not present in the reference
sequences of each gene, then alterations present in the normal
control sample and in single nucleotide polymorphism (SNP)
databases were removed from further analyses. The remaining
sequence traces of potential alterations were visually inspected to
remove false-positive mutation calls generated through our
automated software. All exons containing putative mutations were
then re-amplified and sequenced in the affected tumor and matched
normal DNA samples. This process allowed confirmation of the
mutation in the tumor sample and determined whether the alteration
was somatic (i.e. tumor-specific) or was present in the germline.
All putative somatic mutations were examined computationally and
experimentally to confirm that the alterations did not arise
through the aberrant co-amplification of related gene sequences
(12).
TABLE-US-00001 TABLE 1 Summary of genomic analyses of GBM
Sequencing analysis Number of genes analyzed 20,583 Number of
transcripts analyzed 23,781 Number of exons analyzed 184,292 Primer
pairs designed for amplification 219,229 Fraction of passing
amplicons* 95.0% Total number of nucleotides sequenced 689,071,123
Fraction of passing amplicon sequences 98.4% successfully
analyzed.sup.# Fraction of targeted bases sucessfully
analyzed.sup.# 93.0% Number of somatic mutations identified 2,328
(n = 22 samples) Number of somatic mutations (excluding Br27P) 996
Missense 870 Nonsense 43 Insertion 3 Deletion 46 Duplication 7
Splice site or UTR 27 Average number of sequence alterations 47.4
per sample Copy number analysis Total number of SNP loci assessed
for copy 1,069,688 number changes Number of copy number alterations
identified 281 (n = 22 samples) Amplifications 147 Homozygous
deletions 134 Average number of amplifications per sample 6.7
Average number of homozygous deletions 6.1 per sample *Passing
amplicons were defined as having PHRED20 scores or better over 90%
of the target sequence in 75% of samples analyzed. .sup.#Fraction
of nucleotides having PHRED20 scores or better (see Supporting
Online Materials for additional information).
EXAMPLE 7
Analysis of Sequence Alterations
[0145] We found that 2043 genes (10% of the 20,661 genes analyzed)
contained at least one somatic mutation that would be expected to
alter the protein sequence. The vast majority of these alterations
were single-base substitutions (94%), while the others were small
insertions, deletions, or duplications. The tumor sample Br27P
obtained from the patient previously treated with radiation therapy
and chemotherapy (including temozolomide), had 1332 total somatic
mutations, 17-fold higher than any of the other 21 patients (FIG.
10, Table S3). The mutation spectrum of this sample, comprising an
excess of C>T transitions in the 5' cytosine of CpC
dinucleotides, was dramatically different from those of the other
GBM patients, but was consistent with previous observations of a
hypermutation phenotype in glioma samples of patients treated with
temozolomide (13, 14). In the previously-reported patients, the
hypermutability was thought to occur due to prolonged exposure of
an akylating agent in the presence of MSH6 mismatch repair
deficiency; however, in BR27P, no somatic alterations were observed
in MSH6 or in any of the other mismatch repair genes (MSH2, MLH1,
MLH3, PMS 1, PMS2). In contrast to BR27P, none of the other 21
tumor samples analyzed in the Discovery Screen were known to have
received prior radiation or chemotherapeutic treatment, and none
had the characteristic CpC mutation spectrum that has been found in
such pre-treated tumors.
[0146] After removing Br27P from consideration, the remaining 993
mutations were observed to be distributed relatively evenly among
the 21 remaining tumors (FIG. 10, Table S3). The number of somatic
mutations identified in each tumor ranged between 17 and 79 with a
mean of 47 mutations per tumor, or 1.51 mutations per Mb of GBM
tumor genome sequenced. Six DNA samples extracted from primary
tumors had somewhat smaller numbers of mutations than those
obtained from xenografts, likely because of the masking effect of
non-neoplastic cells in the former. It has previously been shown
that cell lines and xenografts provide the optimal template DNA for
cancer genome sequencing analyses (15) and that they faithfully
represent the alterations present in primary tumors (16).
[0147] Both the total number and frequency of sequence alterations
in GBMs were substantially smaller than the number and frequency of
such alterations observed in cancers of the colon or breast, and
slightly less than in pancreas (10, 11, 17). The most likely
explanation for this difference is the reduced number of cell
generations in glial cells prior to the onset of neoplasia. It has
been suggested that up to half of the somatic mutations observed in
colorectal cancers occur in epithelial stem cells during the normal
cell renewal processes (16). As normal glial stem cells turn over
much less frequently than mammary or colon epithelial cells, they
would be expected to contain many fewer mutations when the
tumor-initiating mutation occurred (18).
[0148] We further evaluated a set of 20 mutated genes identified in
the Discovery Screen in a second screen, called a Prevalence
Screen, comprising an additional 83 GBMs with well-documented
clinical histories (table S2, available on line at Science 26 Sep.
2008: Vol. 321. no. 5897, pp. 1807-1812). These genes were mutated
in at least two tumors and had mutation frequencies>10 mutations
per Mb of tumor DNA sequenced. Nonsilent somatic mutations were
identified in 15 of these 20 genes in the additional tumor samples
(FIG. 10, Table S4). The mutation frequency of all analyzed genes
in the Prevalence Screen was 24 mutations per Mb of tumor DNA,
markedly increased from the overall mutation frequency in the
Discovery Screen of 1.5 mutations per Mb (p<0.001, binomial
test). Additionally, the observed ratio of nonsilent to silent
mutations (NS:S) among mutations in the Prevalence Screen was
14.8:1, substantially higher than the 3.1:1 ratio that was observed
in the Discovery Screen (P<0.001, binomial test). The increased
mutation frequency and higher number of nonsilent mutations
suggested that genes mutated in the Prevalence Screens were
enriched for genes that actively contributed to tumorigenesis.
[0149] In addition to the frequency of mutations in a gene, the
type of mutation can provide information useful for evaluating its
potential role in disease (19). Nonsense mutations, out-of-frame
insertions or deletions, and splice site changes generally lead to
inactivation of the protein products. The likely effect of missense
mutations can be assessed through evaluation of the mutated residue
by evolutionary or structural means. To evaluate missense
mutations, we developed a new algorithm that employs machine
learning of 56 predictive features based on the physical-chemical
properties of amino acids involved in the substation and their
evolutionary conservation at equivalent positions of conserved
proteins (12). Approximately 15% of the missense mutations
identified in this study were predicted to have a statistically
significant effect on protein function when assessed by this method
(FIG. 10, Table S3). We were also able to make structural models of
244 of the 870 missense mutations identified in this study (20). In
each case, the model was based on x-ray crystallography or nuclear
magnetic resonance spectroscopy of the normal protein or a closely
related homolog. This analysis showed that 35 of the missense
mutations were located close to a domain interface or
substrate-binding site and were likely to impact function (links to
structural models available in (12)).
EXAMPLE 8
Analysis of Copy Number Changes
[0150] The same tumors were then evaluated for copy number
alterations through genomic hybridization of DNA samples to
Illumina high density oligonucleotide arrays containing .about.1
million SNP loci probes (21). We have recently developed a
sensitive and specific approach for the identification of focal
amplifications resulting in 12 or more copies per nucleus (6-fold
or greater amplification compared to the diploid genome) as well as
deletions of both copies of a gene (homozygous deletions) using
such arrays (22). Such focused alterations can be used to identify
underlying candidate genes in these regions. It is impossible to
reliably identify such candidate genes in regions with larger
chromosomal aberrations, such as those involving gains or losses of
entire chromosomal arms, which occur frequently in tumors and are
of unknown significance.
[0151] We identified a total of 147 amplifications (FIG. 10, Table
S5) and 134 homozygous deletions (FIG. 10, Table S6) in the 22
samples used in the Discovery Screen, with 0 to 34 amplifications
and 0 to 14 deletions found per tumor sample. Although the number
of amplifications was similar between primary samples and those
tumors that had been passaged as xenografts, the latter samples
allowed detection of a larger number of homozygous deletions
(average of 8.0 deletions per xenograft versus 2.2 per primary
sample). These observations are consistent with previous reports
documenting the difficulty of identifying homozygous deletions in
samples containing contaminating normal DNA (23) and highlight the
importance of using purified human tumor cells, such as those
present in xenografts or cell lines, for genomic analyses.
EXAMPLE 9
Integration of Sequencing, Copy Number and Expression Analyses
[0152] Mutations that arise during tumorigenesis may provide a
selective advantage to the tumor cell (driver mutations) or have no
net effect on tumor growth (passenger mutations). The mutational
data obtained from sequencing and analysis of copy number
alterations were integrated in order to identify GBM candidate
cancer genes (CAN-genes) that would be most likely to be drivers
and therefore worthy of further investigation. The bioinformatic
approach employed to determine if a gene was likely to harbor
driver mutations involved comparison of the number and type of
mutations observed in each gene to the number that would be
expected due to the passenger mutation rates. For sequence
alterations, we calculated upper and lower bounds of passenger
rates. The upper bound was conservatively calculated as the total
number of observed alterations minus those mutations occurring in
known cancer genes divided by the amount of tumor DNA sequenced,
while the lower bound was determined on the basis of the observed
silent mutations and estimates of expected NS:S ratios (12). For
copy number changes, we made the very conservative assumption that
all amplifications and deletions were passengers when determining
the background rate. For analysis of each gene, all types of
alterations (sequence changes, amplifications and homozygous
deletions), were then combined to estimate the passenger
probability for that gene (see (12) for a more detailed description
of the statistical methods).
[0153] The top-ranked CAN-genes, together with their passenger
probabilities, are listed in FIG. 10, Table S7. The CAN-genes
included a number of genes that had been well established with
respect to their involvement in gliomas, including TP53, PTEN,
CDKN2A, RB1, EGFR, NF1, PIK3CA and PIK3R1 (24-34). The most
frequently altered of these genes in our analyses included CDKN2A
(altered in 50% of GBMs), TP53, EGFR, and PTEN (altered in 30-40%),
NF1, CDK4 and RB1 (altered in 12-15%), and PIK3CA and PIK3R1
(altered in 8-10%). Overall, these frequencies, which are similar
to or in some cases higher than those previously reported, validate
the sensitivity of our approach for detection of somatic
alterations.
TABLE-US-00002 TABLE 2 Most frequently altered GBM CAN-genes Point
mutations.sup.{circumflex over ( )} Amplifications.sup.&
Homozygous deletions.sup.& Fraction of Number of Fraction of
Number of Fraction of Number of Fraction of tumors with Passenger
Gene tumors tumors tumors tumors tumors tumors any alteration
Probability* CDKN2A 0/22 0% 0/22 0% 11/22 50% 50% 0.00 TP53 37/105
35% 0/22 0% 1/22 5% 40% 0.00 EGFR 15/105 14% 5/22 23% 0/22 0% 37%
0.00 PTEN 27/105 26% 0/22 0% 1/22 5% 30% 0.00 NF1 16/105 15% 0/22
0% 0/22 0% 15% 0.04 CDK4 0/22 0% 3/22 14% 0/22 0% 14% 0.00 RB1
8/105 8% 0/22 0% 1/22 5% 12% 0.01 IDH1 12/105 11% 0/22 0% 0/22 0%
11% 0.00 PIK3CA 10/105 10% 0/22 0% 0/22 0% 10% 0.10 PIK3R1 8/105 8%
0/22 0% 0/22 0% 8% 0.14 The most frequently-altered CAN-genes are
listed; all CAN-genes are listed in Table S7. .sup.{circumflex over
( )}Fraction of tumors with point mutations indicates the fraction
of mutated GBMs out of the 105 samples in the Discovery and
Prevalence Screens. CDKN2A and CDK4 were not analyzed for point
mutations in the Prevalence Screen because no sequence alterations
were detected in these genes in the Discovery Screen.
.sup.&Fraction of tumors with amplifications and deletions
indicates the number of tumors with these types of alterations in
the 22 Discovery Screen samples. *Passenger probability indicates
the Passenger probability--Mid (12).
[0154] Analysis of additional gene members within pathways affected
by these genes identified alterations of critical genes in the TP53
pathway (TP53, MDM2, MDM4), the RB1 pathway (RB1, CDK4, CDKN2A),
and the PI3K/PTEN pathway (PIK3CA, PIK3R1, PTEN, IRS1). These
alterations resulted in aberrant pathways in a majority of tumors
(64%, 68%, and 50%, respectively) and in all cases but one,
mutations within each tumor affected only a single member of each
pathway in a mutually exclusive manner (P<0.05) (Table 3).
Systematic analyses of functional gene groups and pathways
contained within the well-annotated MetaCore database (35)
identified enrichment of mutated genes in additional members of the
TP53 and PI3K/PTEN pathways as well as in a variety of other
cellular processes, including those regulating cell adhesion as
well as brain specific cellular pathways such those involving
synaptic transmission, transmission of nerve impulses, and channels
involved in transport of sodium, potassium and calcium ions (FIG.
10, Table S8). Interestingly, none of these latter pathways were
observed as being enriched in large-scale studies on pancreatic
cancers (17) and may represent a subversion of normal glial cell
processes to promote dysregulated growth and invasion. Many members
of the detected pathways had not been appreciated to have any role
in GBMs or any other human cancer, and substantial effort will be
required to determine their role in tumorigenesis.
TABLE-US-00003 TABLE 3 Mutations of the TP53, PI3K, and RB1
pathways in GBM TP53 PI3K Pathway RB1 All All All Tumor sample TP53
MDM2 MDM4 genes PTEN PIK3CA PIK3R1 IRS1 genes RB1 CDK4 CDKN2A genes
Br02 Del Alt Mu Alt Del Alt Br03 Mu Alt Mu Alt Br04 Mu Alt Mu Alt
Mu Alt Br05 Amp Alt Mu Alt Del Alt Br06 Del Alt Br07 Mu Alt Mu Alt
Del Alt Br08 Del Alt Br09P Mu Alt Amp Alt Br10P Mu Alt Br11P Mu Alt
Br12P Mu Alt Mu Alt Br13 Mu Alt Del Alt Br14 Mu Alt Del Alt Br15 Mu
Del Alt Br16 Amp Alt Amp Alt Br17 Mu Alt Del Alt Br20P Br23 Mu Alt
Del Alt Br25 Mu Alt Del Alt Br26 Mu Alt Del Alt Br27P Mu Alt Amp
Alt Br29P Mu Alt Fraction of tumors 0.55 0.05 0.05 0.64 0.27 0.09
0.09 0.05 0.50 0.14 0.14 0.45 0.68 with altered gene/ pathway.sup.#
* Mut, mutated; Amp, amplified; Del, deleted; Alt, altered
.sup.#Fraction of affected tumors in 22 Discovery Screen
samples
[0155] Gene expression patterns can inform the analysis of pathways
because they can reflect epigenetic alterations not detectable by
sequencing or copy number analyses. They can also point to
downstream effects on gene expression resulting from the altered
pathways described above. To analyze the transcriptome of GBMs, we
performed SAGE (serial analysis of gene expression) (36) on all GBM
samples used for mutation analysis for which RNA was available
(total of 18 samples) as well as two independent normal brain RNA
controls. When combined with massively parallel
sequencing-by-synthesis methods (37-40), SAGE provides a highly
quantitative and sensitive measure of gene expression.
[0156] The transcript analysis was first used to help identify
target genes from the amplified and deleted regions that were
identified in this study. Though some of these regions contained a
known tumor suppressor gene or oncogene, many contained several
genes that had not previously been implicated in cancer. In tables
S5 and S6, a candidate target gene could be identified within
several of these regions through the use of the mutational as well
as transcriptional data.
[0157] Second, we attempted to identify genes that were
differentially expressed in GBMs compared to normal brain. There
was a high number (143) of genes that were expressed at an average
10-fold higher level in 18 GBMs analyzed (compared to normal brain
samples). Among the 143 over-expressed genes, there were 16 that
were secreted or expressed on the cell surface. Many of these were
over expressed in the xenografts as well as in the primary brain
tumors, suggesting new opportunities for diagnostic and therapeutic
applications.
EXAMPLE 10
High Frequency Alterations of IDH1 in Young GBM Patients
[0158] The top CAN-gene list (FIG. 10, Table S7) included a number
of individual genes which had not previously been linked to GBMs.
The most frequently mutated of these genes, IDH1, encodes
isocitrate dehydrogenase 1, which catalyzes the oxidative
carboxylation of isocitrate to .alpha.-ketoglutarate, resulting in
the production of NADPH. Five isocitrate dehydrogenase genes are
encoded in the human genome, with the products of three (IDH3
alpha, IDH3 beta, IDH3 gamma) forming a heterotetramer (2 in the
mitochondria and utilizing NAD(+) as an electron acceptor to
catalyze the rate-limiting step of the tricarboxylic acid cycle.
The fourth isocitrate dehydrogenase (IDH2) is also localized to the
mitochondria, but like IDH1, uses NADP(+) as an electron acceptor.
The IDH1 product, unlike the rest of the IDH proteins, is contained
within the cytoplasm and peroxisomes (41). The protein forms an
asymmetric homodimer (42), and is thought to function to regenerate
NADPH and -ketoglutarate for intraperoxisomal and cytoplasmic
biosynthetic processes. The production of cytoplasmic NADPH by IDH1
appears to play a significant role in cellular control of oxidative
damage (43) (44). None of the other IDH genes, other genes involved
in the tricarboxylic acid cycle, or other peroxisomal proteins were
found to be genetically altered in our analysis.
[0159] was found to be somatically mutated in five GBM tumors in
the Discovery Screen. Surprisingly, all five had the same
heterozygous point mutation, a change of a guanine to an adenine at
position 395 of the IDH1 transcript (G395A), leading to a
replacement of an arginine with a histidine at amino acid residue
132 of the protein (R132H). In our prior study of colorectal
cancers, this same codon had been found to be mutated in a single
case through alteration of the adjacent nucleotide, resulting in a
R132C amino acid change (10). Five additional GBMs evaluated in our
Prevalence Screen were found to have heterozygous R132H mutations,
and an additional two tumors had a third distinct mutation
affecting the same amino acid residue, R132S (FIG. 1; Table 4). The
R132 residue is conserved in all known species and is localized to
the substrate binding site, forming hydrophilic interaction with
the alpha-carboxylate of isocitrate (FIG. 2) (42, 45).
TABLE-US-00004 TABLE 4 Characteristics of GBM patients with IDH1
mutations Overall Patient age Recurrent Secondary survival IDH1
Mutation Mutation Mutation of PTEN, Patient ID (years)* Sex
GBM.sup.# GBM.sup.{circumflex over ( )} (years).sup.&
Nucleotide Amino acid of TP53 RB1, EGFR, or NF1 Br10P 30 F No No
2.2 G395A R132H Yes No Br11P 32 M No No 4.1 G395A R132H Yes No
Br12P 31 M No No 1.6 G395A R132H Yes No Br104X 29 F No No 4.0 C394A
R132S Yes No Br106X 36 M No No 3.8 G395A R132H Yes No Br122X 53 M
No No 7.8 G395A R132H No No Br123X 34 M No Yes 4.9 G395A R132H Yes
No Br237T 26 M No Yes 2.6 G395A R132H Yes No Br211T 28 F No Yes 0.3
G395A R132H Yes No Br27P 32 M Yes Yes 1.2 G395A R132H Yes No Br129X
25 M Yes Yes 3.2 C394A R132S No No Br29P 42 F Yes Unknown Unknown
G395A R132H Yes No IDH1 mutant 33.2 67% M 25% 42% 3.8 100% 100% 83%
0% patients (n = 12) IDH1 wildtype 53.3 65% M 16% 1% 1.5 0% 0% 27%
60% patients (n = 93) *Patient age refers to age at which patient
GBM sample was obtained. .sup.#Recurrent GBM designates a GBM which
was resected >3 months after a prior diagnosis of GBM.
.sup.{circumflex over ( )}Secondary GBM designates a GBM which was
resected >1 year after a prior diagnosis of a lower grade glioma
(WHO I-III). .sup.&Overall survival was calculated using date
of GBM diagnosis and date of death or last patient contact:
patients Br10P and Br11P were alive at last contact. Median
survival for IDH1 mutant patients and IDH1 wildtype patients was
calculated using logrank test. Previous pathologic diagnoses in
secondary GBM patients were oligodendroglioma (WHO grade II) in
Br123X, low grade glioma (WHO grade I-II) in Br237T and Br211T,
anaplastic astrocytoma (WHO grade III) in Br27P, and anaplastic
oligodendroglioma (WHO grade III) in Br129X, Abbreviations: GBM
(glioblastoma multiforme, WHO grade IV), WHO (World Health
Organization). M (male), F (female), mut (mutant). Mean age and
median survival are listed for the groups of IDH1-mutated and
IDH1-wildtype patients.
[0160] Several important observations were made about IDH1
mutations and their potential clinical significance. First,
mutations in IDH1 preferentially occurred in younger GBM patients,
with a mean age of 33 years for IDH1-mutated patients, as opposed
to 53 years for patients with wildtype IDH1 (P<0.001, t-test,
Table 4). In patients under 35 years of age, nearly 50% (9 of 19)
had mutations in IDH 1. Second, mutations in IDH1 were found in
nearly all of the patients with secondary GBMs (mutations in 5 of 6
secondary GBM patients, as compared to 7 of 99 patients with
primary GBMs, P<0.001, binomial test), including all five
secondary GBM patients under 35 years of age. Third, patients with
IDH1 mutations had a significantly improved prognosis, with a
median overall survival of 3.8 years as compared to 1.1 years for
patients with wildtype IDH1 (P<0.001, log-rank test). Although
younger age and mutated TP53 are known to be positive prognostic
factors for GBM patients, this association between IDH1 mutation
and improved survival was noted even in patients<45 years old
(FIG. 3, P<0.001, log-rank test), as well as in the subgroup of
young patients with TP53 mutations (P<0.02, log-rank test).
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EXAMPLE 11
Materials and Methods
Gene Selection
[0211] The protein coding exons from 23,781 transcripts
representing 20,735 unique genes were targeted for sequencing. This
set comprised 14,554 transcripts from the highly curated Consensus
Coding Sequence (CCDS) database
(http://www.ncbi.nlm.nih.gov/CCDS/), a further 6,019 transcripts
from the Reference Sequence (RefSeq) database
(http://www.ncbi.nlm.nih.gov/projects/RefSeq/) and an additional
3,208 transcripts with intact open reading frames from the Ensembl
database (http://www.ensembl.org/). We excluded transcripts from
genes that were located on the Y chromosome or were precisely
duplicated within the genome. As detailed below, 23,219 transcripts
representing 20,661 genes were successfully sequenced.
Bioinformatic Resources
[0212] Consensus Coding Sequence (Release 1), RefSeq (release 16,
March 2006) and Ensembl (release 31) gene coordinates and sequences
were acquired from the UCSC Santa Cruz Genome Bioinformatics Site
(http://genome.ucsc.edu). The positions listed in the Supplementary
Tables correspond to UCSC Santa Cruz hg17, build 35.1. The single
nucleotide polymorphisms used to filter-out known SNPs were those
present in dbSNP (release 125) that had been validated by the
HapMap project. BLAT and In Silico PCR
(http://genome.ucsc.edu/cgi-bin/hgPcr) were used to perform
homology searches in the human and mouse genomes.
Primer Design
[0213] Primer 3 software
(http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi) was used
to generate primers no closer than 50 by to the target boundaries,
producing products of 300 to 600 by. Exons exceeding 350 by were
divided into several overlapping amplicons. In silico PCR and BLAT
were used to select primer pairs yielding a single PCR product from
a unique genomic position. Primer pairs for duplicated regions
giving multiple in silico PCR or BLAT hits were redesigned at
positions that were maximally different between the target and
duplicated sequences. A universal primer (M13F,
5'-GTAAAACGACGGCCAGT-3'; SEQ ID NO: 136) was added to the 5' end of
the primer with the smallest number of mono- or dinucleotide
repeats between itself and the target region. The primer sequences
used in this study are listed in table Si available on line at
Science 26 Sep. 2008: Vol. 321. no. 5897, pp. 1807-1812.
Glioblastoma Multiforme (GBM) DNA Samples
[0214] Tumor DNA was obtained from GBM xenografts and primary
tumors, with matched normal DNA for each case obtained from
peripheral blood samples, as previously described (1). The
Discovery Screen consisted of 22 tumor samples (15 xenografts and 7
primary tumors), with the Prevalence screen including another 83
samples (53 xenografts and 30 primary tumors). Additional clinical
information regarding Discovery and Prevalence Screen samples is
available in table S2, available on line at Science 26 Sep. 2008:
Vol. 321. no. 5897, pp. 1807-1812. All samples were given the
histologic diagnosis of glioblastoma multiforme (GBM; World Health
Organization Grade IV), except for two Discovery Screen samples who
were recorded as "high grade glioma, not otherwise specified".
Samples were classified as recurrent for patients in whom a GBM had
been diagnosed at least 3 months prior to the surgery when the
study GBM sample was obtained. There were 3 recurrent GBMs in the
Discovery Screen, and 15 in the Prevalence Screen. Samples were
classified as secondary for patients in whom a lower grade glioma
(WHO grade I-III) had been histologically confirmed at least 1 year
prior to the surgery when the study GBM sample was obtained. One
Discovery Screen sample and 5 Prevalence Screen samples were
classified as secondary.
TABLE-US-00005 TABLE 5 Overview of GBM samples used in the
Prevalence and Discovery Screens: Discovery Validation Total Number
of samples 22 83 105 Patient age Mean age (years) 48.6 51.7 51.0
Median age (years) 45 53 52 Patient sex Male 14 55 69 Female 8 28
36 Sample source Xenograft 15 53 68 Primary tumor 7 30 37 GBM
subclasses Recurrent 3 15 18 Recurrent with prior chemotherapy 1 10
11 Secondary 1 5 6
[0215] Pertinent clinical information, including date of birth,
date study GBM sample obtained, date of original GBM diagnosis (if
different than the date that the GBM sample was obtained, as in the
case of recurrent GBMs), date and pathology of preceding diagnosis
of lower grade glioma (in cases of secondary GBMs), the
administration of radiation therapy and/or chemotherapy prior to
the date that the GBM sample was obtained, date of last patient
contact, and patient status at last contact. All samples were
obtained in accordance with the Health Insurance Portability and
Accountability Act (HIPAA). All samples were obtained in accordance
with the Health Insurance Portability and Accountability Act
(HIPAA). As previously described, tumor-normal pair matching was
confirmed by typing nine STR loci using the PowerPlex 2.1 System
(Promega, Madison, Wis.) and sample identities checked throughout
the Discovery and Prevalence screens by sequencing exon 3 of the
HLA-A gene. PCR and sequencing was carried out as described in
(1).
Statistical Analysis of Clinical Data
[0216] Paired normal and malignant tissue from 105 GBM patients
were used for genetic analysis. Complete clinical information (i.e.
all pertinent clinical information such as date of initial GBM
diagnosis, date of death or last contact) was available for 91 of
the 105 patients. Of these 91 patients, five (all IDH1-wildtype)
died within the first month after surgery and were excluded from
analysis (Br308T, Br246T, Br23X, Br301T, Br139X), as was a single
patient (Br119X) with a presumed surgical cure (also IDH
1-wildtype) who was alive at last contact .about.10 years after
diagnosis. Kaplan Meier survival curves were compared using the
Mantel Cox log-rank test. Hazard ratios were computed using the
Mantel-Haenszel method. The following definitions were used in the
GBM patient grouping and survival analysis computations: 1) Patient
age refered to the age at which the patient GBM sample was
obtained. 2) Recurrent GBM designates a GBM which was resected>3
months after a prior diagnosis of GBM. 3) Secondary GBM designates
a GBM which was resected>1 year after a prior diagnosis of a
lower grade glioma (WHO I-III) Overall survival was calculated
using date of GBM diagnosis and date of death or last patient
contact. All confidence intervals were calculated at the 95%
level.
Mutation Discovery Screen
[0217] CCDS, RefSeq and Ensembl genes were amplified in 22 GBM
samples and one control samples from normal tissues of one of the
GBM patients. All coding sequences and the flanking 4 by were
analyzed using Mutations Surveyor (Softgenetics, State College,
Pa.) coupled to a relational database (Microsoft SQL Server). For
an amplicon to be further analyzed, at least three quarters of the
tumors were required to have 90% or more of bases in the region of
interest with a Phred quality score of 20. In the amplicons that
passed this quality control, mutations identical to those observed
in the normal sample as well as known single nucleotide
polymorphisms were removed. The sequencing chromatogram of each
detected mutation was then visually inspected to remove false
positive calls by the software. Every putative mutation was
re-amplified and sequenced in tumor DNA to eliminate artifacts. DNA
from normal tissues of the same patient in which the mutation was
identified was amplified and sequenced to determine whether the
mutations were somatic. When a mutation was found, BLAT was used to
search the human and mouse genomes for related exons to ensure that
putative mutations were the result of amplification of homologous
sequences. When there was a similar sequence with 90% identity over
90% of the target region, additional steps were performed.
Mutations potentially arising from human duplications were
re-amplified using primers designed to distinguish between the two
sequences. Mutations not observed using the new primer pair were
excluded. The remainder were included as long as the mutant base
was not present in the homologous sequence identified by BLAT.
Mutations originally observed in mouse xenografts were re-amplified
in DNA from primary tumors and included either if the mutation was
present in the primary tumors or if the mutant was not identified
in the homologous mouse sequence identified by BLAT.
Mutation Prevalence Screen
[0218] We further evaluated a set of 20 mutated genes that had been
identified in the Discover Screen in a second (Prevalence) screen,
which included an additional 83 GBMs (tabl S2). The genes selected
were mutated in at least two tumors and had mutatio
frequencies>10 mutations per Mb of tumor DNA sequenced. The
primers used (table S1, available on line at Science 26 Sep. 2008:
Vol. 321. no. 5897, pp. 1807-1812) and methods of analysis and
duration of potential mutations were the same as in the Discovery
screen. All somatic mutations observed in the Prevalence screen are
reported in FIG. 10, Table S4.
Copy Number Analysis
[0219] The Illumina Infinium II Whole Genome Genotyping Assay
employing the BeadChip platform was used to analyze tumor samples
at 1,072,820 (1M) SNP loci. All SNP positions were based on the
hg18 (NCBI Build 36, March 2006) version of the human genome
reference sequence. The genotyping assay begins with hybridization
to a 50 nucleotide oligo, followed by a two-color fluorescent
single base extension. Fluorescence intensity image files were
processed using Illumina BeadStation software to provide normalized
intensity values (R) for each SNP position. For each SNP, the
normalized experimental intensity value (R) was compared to the
intensity values for that SNP from a training set of normal samples
and represented as a ratio (called the "Log R Ratio") of
log2(Rexperimental/Rtraining set).
[0220] The SNP array data were analyzed using modifications of a
previously described method (2). Homozygous deletions (HDs) were
defined as three or more consecutive SNPs with a Log R Ratio value
of -2. The first and last SNPs of the HD region were considered to
be the boundaries of the alteration for subsequent analyses. To
eliminate chip artifacts and potential copy number polymorphisms,
we removed all HDs that were included in copy number polymorphism
databases. Adjacent homozygous deletions separated by three or
fewer SNPs were considered to be part of the same deletion, as were
HDs within 100,000 by of each other. To identify the target genes
affected by HDs, we compared the location of coding exons in the
RefSeq, CCDS and Ensembl databases with the genomic coordinates of
the observed HDs. Any gene with a portion of its coding region
contained within a homozygous deletion was considered to be
affected by the deletion.
[0221] As outlined in (2), amplifications were defined by regions
containing three SNPs with an average LogR ratio 0.9, with at least
one SNP having a LogR ratio 1.4. As with HDs, we excluded all
putative amplifications that had identical boundaries in multiple
samples. As focal amplifications are more likely to be useful in
identifying specific target genes, a second set of criteria were
used to remove complex amplifications, large chromosomal regions or
entire chromosomes that showed copy number gains.
Amplifications>3 Mb in size and groups of nearby amplifications
(within 1 Mb) that were also >3 Mb in size were considered
complex. Amplifications or groups of amplifications that occurred
at a frequency of 4 distinct amplifications in a 10 Mb region or 5
amplifications per chromosome were deemed to be complex. The
amplifications remaining after these filtering steps were
considered to be focal amplifications and were the only ones
included in subsequent statistical analyses. To identify protein
coding genes affected by amplifications, we compared the location
of the start and stop positions of each gene within the RefSeq,
CCDS and Ensmbl databases with the genomic coordinates of the
observed amplifications. As amplifications containing only a
fraction of a gene are less likely to have a functional
consequence, we only considered genes whose entire coding regions
were included in the observed amplifications.
Estimation of Passenger Mutation Rates
[0222] From the synonymous mutations observed in the Discovery
Screen, we estimated a lower bound of the passenger rate. The lower
bound was defined as the product of the synonymous mutation rate
and the NS:S ratio (1.02) observed in the HapMap database of human
polymorphisms. The calculated rate of 0.38 mutations/Mb
successfully sequenced is likely an underestimate because selection
against nonsynonymous mutations may be more stringent in the
germline than in somatic cells. An upper bound was calculated from
the total observed number of non-synonymous mutations/Mb after
excluding the most highly mutated genes known to be drivers from
previous studies (TP53, PTEN, and RB1). The resultant passenger
mutation rate of 1.02 non-synonymous mutations/Mb represents an
over-estimate of the background rate as some of the mutations in
genes other than TP53, PTEN, and RB1 were likely to be drivers. A
`Mid" measure of 0.70 mutations/Mb was obtained from the average of
the lower and upper bound rates. For comparisons of the number and
type of somatic mutations identified in the Discovery and
Prevalence Screens, two sample t-tests between percents were
used.
Expression analysis
[0223] SAGE tags were generated using a Digital Gene Expression-Tag
Profiling preparation kit (Illumina, San Diego, Calif.) as
recommended by the manufacturer. In brief, RNA was purified using
guianidine isothiocyanate and reverse transcription with oligo-dT
magnetic beads was performed on .about.1 ug of total RNA from each
sample. Second strand synthesis was accomplished through RNAse H
nicking and DNA polymerase I extension. The double-stranded cDNA
was digested with the restriction enonuclease Nla III and ligated
to an adapter containing a Mme I restriction site. After Mme I
digestion, a second adapter was ligated, and the adapter-ligated
cDNA construct was enriched by 18 cycles of PCR and fragments of 85
by were purified from a polyacrylamide gel. The library size was
estimated using real-time PCR and the tags sequenced on a Genome
Analyzer System (Illumina, San Diego, Calif.).
Statistical Analysis
Overview of Statistical Analysis
[0224] The statistical analyses focused on quantifying the evidence
that the mutations in a gene or a biologically defined set of genes
reflect an underlying mutation rate that is higher than the
passenger rate. In both cases, the analysis integrates data on
point mutations with data on copy number alterations (CNA). The
methodology for the analysis of point mutations is based on that
described in (3) while the methodology for integration across point
mutations and CNA's is based on (2). We provide a self-contained
summary herein, as several modifications to the previously
described methods were required.
Statistical Analyses of CAN-Genes
[0225] The mutation profile of a gene refers to the number of each
of the twenty-five context-specific types of mutations defined
earlier (3). The evidence on mutation profiles is evaluated using
an Empirical Bayes analysis (4) comparing the experimental results
to a reference distribution representing a genome composed only of
passenger genes. This is obtained by simulating mutations at the
passenger rate in a way that precisely replicates the experimental
plan. Specifically, we consider each gene in turn and simulate the
number of mutations of each type from a binomial distribution with
success probability equal to the context-specific passenger rate.
The number of available nucleotides in each context is the number
of successfully sequenced nucleotides for that particular context
and gene in the samples studied. When considering nonsynonymous
mutations other than indels, we focus on nucleotides at risk, as
defined previously (3).
[0226] Using these simulated datasets, we evaluated the passenger
probabilities for each of the genes that were analyzed in this
study. These passenger probabilities represent statements about
specific genes rather than about groups of genes. Each passenger
probability is obtained via a logic related to that of likelihood
ratios: the likelihood of observing a particular score in a gene if
that gene is a passenger is compared to the likelihood of observing
it in the real data. The gene-specific score used in our analysis
is based on the Likelihood Ratio Test (LRT) for the null hypothesis
that, for the gene under consideration, the mutation rate is the
same as the passenger mutation rate. To obtain a score, we simply
transform the LRT to s=log(LRT). Higher scores indicate evidence of
mutation rates above the passenger rates. This general approach for
evaluating passenger probabilities follows that described by Efron
and Tibshirani (4). Specifically, for any given score s, F(s)
represents the proportion of simulated genes with scores higher
than s in the experimental data, F0 is the corresponding proportion
in the simulated data, and p0 is the estimated overall proportion
of passenger genes (discussed below). The variation across
simulations is small but nonetheless we generated and collated 100
datasets to estimate FO. We then numerically estimated the density
functions f and f0 corresponding to F and FO and calculated, for
each score s, the ratio p0f0(s)/f(s), also known as "local false
discovery rate" (4). Density estimation was performed using the
function "density" in the R statistical programming language with
default settings. The passenger probability calculations depend on
an estimate of p0, the proportion of true passengers. Our
implementation seeks to give an upper bound to p0 and thus provide
conservatively high estimates of the passenger probability. To this
end we set p0=1. We also constrained the passenger probability to
change monotonically with the score by starting with the lowest
values and recursively setting values that decrease in the next
value to their right. We similarly constrain passenger
probabilities to change monotonically with the passenger rate.
[0227] An open source package for performing these calculations in
the R statistical environment, named CancerMutationAnalysis, is
available at
http://astor.som.jhmi.edu/.about.gp/software/CancerMutationAnalysis/cma.h-
tm. A detailed mathematical account of our specific implementation
is provided in (5) and general analytic issues are discussed in
(6).
[0228] Statistical Analysis of CNA. For each of the genes involved
in amplifications or deletions, we further quantified the strength
of the evidence that they drive tumorigenesis through estimations
of their passenger probabilities. In each case, we obtain the
passenger probability as an a posteriori probability that
integrates information from the somatic mutation analysis of (3)
with the data presented in this article. The passenger
probabilities derived from the point mutation analysis serve as a
priori probabilities. These are available for three different
scenarios of passenger mutation rates and results are presented
separately for each in FIG. 10, Table S3. Then, a likelihood ratio
for "driver" versus "passenger" was evaluated using as evidence the
number of samples in which a gene was found to be amplified (or
deleted). The passenger term is the probability that the gene in
question is amplified (or deleted) at the frequency observed. For
each sample, we begin by computing the probability that the
observed amplifications (and deletions) will include the gene in
question by chance. Inclusion of all available SNPs is required for
amplification, while any overlap of SNPs is sufficient for
deletions. Specifically, if in a specific sample N SNPs are typed,
and K amplifications are found, whose sizes, in terms of SNPs
involved, are A1 . . . AK, a gene with G SNPs will be included at
random with probability (A 1-G+1)/N+ . . . +(AK-G+1)/N for
amplifications and (A 1+G-1)/N+ . . . +(AK+G-1)/N for deletions. We
then compute the probability of the observed number of
amplifications (or deletions) assuming that the samples are
independent but not identically distributed Bernoulli random
variables, using the Thomas and Traub algorithm (7). Our approach
to evaluating the likelihood under the null hypothesis is highly
conservative, as it assumes that all the deletions and
amplifications observed only include passengers. The driver term of
the likelihood ratio was approximated as for the passenger term,
after multiplying the sample-specific passenger rates above by a
gene-specific factor reflecting the increase (alternative
hypothesis) of interest. This increase is estimated by the ratio
between the empirical deletion rate of the gene and the overall
deletion rate.
[0229] This combination approach makes an approximating assumption
of independence of amplifications and deletions. In reality,
amplified genes cannot be deleted, so independence is technically
violated. However, because of the relatively small number of
amplification and deletion events, this assumption is tenable for
the purposes of our analysis. Inspection of the likelihood, in a
logarithmic scale, suggests that it is roughly linear in the
overall number of events, supporting the validity of this
approximation as a scoring system.
Analysis of Mutated Gene Pathways and Groups
[0230] Four types of data were obtained from the MetaCore database
(GeneGo, Inc., St. Joseph, Mich.): pathway maps, Gene Ontology (GO)
processes, GeneGo process networks, and protein-protein
interactions. The memberships of each of the 23,781 transcripts in
these categories were retrieved from the databases using RefSeq
identifiers. In GeneGo pathway maps, 22,622 relations were
identified, involving 4,175 transcripts and 509 pathways. For Gene
Ontology processes, a total of 66,397 pairwise relations were
identified, involving 12,373 transcripts and 4,426 GO groups. For
GeneGo process networks, a total of 23,356 pairwise relationships,
involving 6,158 transcripts and 127 processes, were identified. The
predicted protein products of each mutated gene were also evaluated
with respect to their physical interactions with proteins encoded
by other mutated genes as inferred from the MetaCore database.
[0231] For each of the gene sets considered, we quantified the
strength of the evidence that they included a higher-than-average
proportion of drivers of carcinogenesis after consideration of set
size. For this purpose, we sorted the genes by a score based on the
combined passenger probability described above (taking into account
mutations, homozygous deletions, and amplifications). We compared
the ranking of the genes contained in the set with the ranking of
those outside, using the Wilcoxon test, as implemented by the Limma
package in Bioconductor (8), then corrected for multiplicity by the
q-value method with an alpha of 0.2 (9).
Bioinformatic Analysis
Overview of Bioinformatic Analysis
[0232] We have developed a novel bioinformatics software pipeline
(depicted below) to compute: (1) a score for ranking somatic
missense mutations by the likelihood that they are passengers
(LSMUT). The scores are based on properties derived from protein
sequences, amino acid residue changes and positions within the
proteins; and (2) qualitative annotations of each mutation, based
on protein structure homology models.
Mutation Scores
[0233] We tested several supervised machine learning algorithms to
identify one that would reliably distinguish between presumably
neutral polymorphisms and cancer-associated mutations. The best
algorithm was a Random Forest (12), which we trained on 2,840
cancer-associated mutations and 19,503 polymorphisms from the
SwissProt Variant Pages (13) using parallel Random Forest software
(PARF) [http://www.irb.hrien/cir/projects/info/parf].
Cancer-associated mutations were identified by parsing for the
keywords "cancer", "carcinoma", "sarcoma", "blastoma", "melanoma",
"lymphoma", "adenoma" and "glioma". For each mutation or
polymorphism, we computed 58 numerical and categorical features
(see table below). Two mutations present in the GBM tumor samples
were found in the SwissProt Variant Pages and removed from the
training data. Because the training set contained .about.7 times as
many polymorphisms as cancer- associated mutations, we used class
weights to upweight the minority class (cancer-associated mutation
weight was 5.0 and polymorphism weight was 1.0). The mtry parameter
was set to 8 and the forest size to 500 trees. Missing feature
values were filled in using the Random Forest proximity-based
imputation algorithm (12) with six iterations. Full parameter
settings and all data used to build the Random Forest are available
upon request.
[0234] We then applied the trained forest to 594 GBM missense
mutations and to a control set of 142 randomly generated missense
mutations in transcripts of 78 genes that were found to be
non-mutated in 11 colorectal cancers (5). For each mutation, the 58
predictive features were computed as described above and the
trained forest was used to compute a predictive score for ranking
the mutations. Specifically, the scores used are the fraction of
trees that voted in favor of the "Polymorphic" class for each
mutation.
[0235] To test the hypothesis that the scores of missense mutations
in top-ranked CAN-genes were distributed differently than random
missense mutations, we applied a modified Kolmogorov-Smirnov (KS)
test, in which ties are broken by adding a very small random number
to each score. The scores of missense mutations in the top 13 CAN
genes were found to be significantly different from the mutations
in the control set (P<0.001).
[0236] We estimate that mutations with scores<0.7 (.about.15% of
the missense mutations) are unlikely to be passengers. The
threshold is based on the putative similarity of passengers to the
neutral polymorphisms in the SwissProt Variant set, of which only
.about.2% have scores<0.7. Scores of SwissProt Variants were
obtained by randomly partitioning them into two folds, training a
Random Forest on each (as described above) and then scoring each
fold with the Random Forest trained on the other one.
Homology Models
[0237] The protein translations of mRNA transcripts found to have
somatic missense mutations were input into ModPipe 1.0/MODELLER 9.1
homology model building software (14, 15). For each mutation, we
identified all models that included the mutated position. If more
than one model was produced for a mutation, we selected the model
having the highest sequence identity with its template structure.
The resulting model was used to compute the solvent accessibility
of the wild type residue at the mutated position, using DSSP
software (16). Accessibility values were normalized by dividing by
the maximum residue solvent accessibility for each side chain type
in a Gly-X-Gly tri-peptide (17). Solvent accessibilities greater
than 36% were considered to be "exposed", those between 9% and 35%
were considered "intermediate", and those <9% were considered
"buried". DSSP was also used to compute the secondary structure of
the mutated position. We used the LigBase (18) and PiBase (19)
databases to identify mutated residue positions in the homology
models that were close to ligands or domain interfaces in the
equivalent positions of their template structures. Finally, for
each mutation, we generated an image of the mutation mapped onto
its homology model with UCSF Chimera (20). The images and
associated information for each mutation are available at
http://karchinlab.org/Mutants/CAN-genes/pancreatic/Pancreatic_cancer.html-
). Model coordinates are available on request.
TABLE-US-00006 TABLE 6 The 58 numerical and categorical features
used to train the Random Forest # Feature Description 1 Net residue
charge The change in formal charge resulting change from the
mutation. 2 Net residue volume The change in residue volume
resulting change from the mutation (18). 3 Net residue The change
in residue hydrophobicity hydrophobicity resulting from the
substitution (19). change 4 Positional Hidden This feature is
calculated based on the Markov model degree of conservation of the
residue (HMM) estimated from a multiple sequence conservation score
alignment built with SAM-T2K software (20), using the protein in
which the mutation occurred as the seed sequence (21). The SAM-T2K
alignments are large, superfamily- level alignments that include
distantly related homologs (as well as close homologs and
orthologs) of the protein of interest. 5 Entropy of HMM The Shannon
entropy calculated for alignment the column of the SAM-T2K multiple
sequence alignment, corresponding to the location of the mutation
(21). 6 Relative entropy of Difference in Shannon entropy HMM
alignment calculated for the column of the SAM-T2K multiple
sequence alignment (corresponding to the location of the mutation)
and that of a background distribution of amino acid residues
computed from a large sample of multiple sequence alignments (21).
7 Compatibility score These multiple sequence alignments for amino
acid are calculated using groups of substitution in the orthologous
proteins from the OMA column of a multiple database (22), which are
aligned sequence alignment with T-Coffee software (23). The of
orthologs. compatibility score for the mutation in the column of
interest is computed as: (P(most frequent residue in the column) -
2*P(wild type) + P(mutant) + P(Deletion) - 1)/ (5 * number of
unique amino acid residues in the column) 8 Grantham score The
Grantham substitution score for the wild type => mutant
transition (24). 9-11 Predicted residue These features consist of
the solvent probability of the wild type residue accessibility
being buried, intermediate or exposed as predicted by a neural
network trained with Predict-2nd software (20) on a set of 1763
proteins with high resolution X-ray crystal structures sharing less
than 30% homology (25). 12-14 Predicted These features consist of
the contribution probability that the wild type residue to protein
contributes to overall protein stability stability in a manner that
is highly stabilizing, average or destabilizing, as predicted by a
neural network trained with Predict-2nd software (20) on a set of
1763 proteins with less than 30% homology. Stability estimates for
the neural net training data were calculated using the FoldX force
field (26). 15-17 Predicted These features consist of the
flexibility probability that the wild type residue (Bfactor)
backbone is stiff, intermediate or flexible as predicted by a
neural network trained with Predict-2nd software (20) on a set of
1763 proteins with less than 30% homology. Flexibilities for the
neural net training data were estimated based on normalized
temperature factors, computed using the method of (27) from the
X-ray crystal structure files. 18-20 Predicted These features
consist of the secondary probability that the secondary structure
structure of the region in which the wild type residue exists is
helix, loop or strand as predicted by a neural net trained with
Predict-2nd software (20) on a set of 1763 proteins with crystal
structures and with less than 30% homology. 21 Change in Change in
residue hydrophobicity hydrophobicity due to the wild type .fwdarw.
mutant . transition 22 Change in Change in residue volume due to
volume the wildtype .fwdarw. mutant transition. 23 Change in Change
in residue formal charge charge due to the wild type .fwdarw.
mutant transition. 24 Change in Change in residue polarity due
polarity to the wildtype .fwdarw. mutant transition. 25 EX
substitution Amino acid substitution score score from the EX matrix
(28) 26 PAM250 Amino acid substitution score substitution score
from the PAM250 matrix (29) 27 BLOSUM 62 Amino acid substitution
score substitution score from the BLOSUM 62 matrix (30) 28 MJ
substitution Amino acid substitution score score from the
Miyazawa-Jernigan contact energy matrix (28, 31) 29 HGMD2003 Number
of times that the wild mutation count type .fwdarw. mutant
substitution occurs in the Human Gene Mutation Database, 2003
version (28, 32). 30 VB mutation Amino acid substitution score
count from the VB (Venkatarajan and Braun) matrix (28, 33) 31-34
Probability of seeing Calculated by joint frequencies the wild type
residue of amino acid triples in in the first, middle, human
proteins found in or last position of an UniProtKB (11) amino acid
triple 35-37 Probability of seeing Calculated by joint frequencies
the mutant residue in of amino acid triples in human the first,
middle, or proteins found in UniProtKB last position of an (11)
amino acid triple 38-40 Difference in Calculated by joint
frequencies probability of seeing of amino acid triples in human
the wildtype vs. the proteins found in UniProtKB mutant residue in
the (11) first, middle, or last position of an amino acid triple 41
Probability of seeing Calculated by a Markov chain the wildtype at
the of amino acid quintuples in center of a window of human
proteins found in 5 amino acid residues UniProtKB (11) 42
Probability of seeing Calculated by a Markov chain the mutant at
the of amino acid quintuples in center of a window of human
proteins found in 5 amino acid residues UniProtKB (11) 43-56 Binary
categorical These features give annotations, features from the
curated from the literature, of UniProt general binding sites,
general KnowledgeBase active sites, lipid, metal, feature table for
the carbohydrate, DNA, phosphate protein product of and calcium
binding sites, the transcript disulfides, seleno-cysteines,
modified residues, propeptide residues, signal peptide residues,
known mutagenic sites, transmembrane regions, compositionally
biased regions, repeat regions, known motifs, and zinc fingers. The
integer 1 indicates that a feature is present and the integer 0
indicates that it is absent at a mutated position.
References for Example 11
[0238] 1. T. Sjoblom et al., Science 314, 268 (2006). [0239] 2. R.
J. Leary et al., Submitted (2008). [0240] 3. L. D. Wood et al.,
Science 318, 1108 (2007). [0241] 4. B. Efron, R. Tibshirani, Genet
Epidemiol 23, 70 (2002). [0242] 5. G. Parmigiani et al.,
"Statistical Methods for the Analysis of Cancer Genome Sequencing
Data" (Johns Hopkins University, 2006). [0243] 6. G. Parmigiani et
al., Genomics in press (2008). [0244] 7. M. A. Thomas, A. E. Taub,
Journal of Statistical Computation and Simulation 14, 125 (1982).
[0245] 8. G. K. Smyth, in Bioinformatics and Computational Biology
Solutions using R and
[0246] Bioconductor V. Gentleman, S. Carey, R. Dudoit, W. H.
Irizarry, Eds. (Springer, New York, 2005) pp. 397-420. [0247] 9. Y.
Benjamini, Y. Hochberg, Journal of the Royal Statistical Society.
Series B (Methodological) 57 289-300 (1995). [0248] 10. L. Breiman,
Machine Learning, 5 (2001). [0249] 11. C. H. Wu et al., Nucleic
Acids Res 34, D187 (2006). [0250] 12. R. Karchin et al.,
Bioinformatics 21, 2814 (2005). [0251] 13. A. Sali, T. L. Blundell,
Journal of Molecular Biology 234, 779 (1993). [0252] 14. G. D.
Rose, A. R. Geselowitz, G. J. Lesser, R. H. Lee, M. H. Zehfus,
Science 229, 834 (1985). [0253] 15. A. C. Stuart, V. A. Ilyin, A.
Sali, Bioinformatics 18, 200 (2002). [0254] 16. F. P. Davis, A.
Sali, Bioinformatics 21, 1901 (2005). [0255] 17. E. F. Pettersen et
al., J Comput Chem 25, 1605 (2004). [0256] 18. A. A. Zamyatnin,
Prog Biophys Mol Biol, 107 (1972). [0257] 19. D. M. Engelman, T. A.
Steitz, A. Goldman, Annu Rev Biophys Biophys Chem 15, 321 (1986).
[0258] 20. K. Karplus et al., Proteins Suppl 5, 86 (2001). [0259]
21. S. Kullback, Information theory and statistics (Wiley, New
York, 1959), pp. [0260] 22. A. Schneider, C. Dessimoz, G. H.
Gonnet, Bioinformatics 23, 2180 (2007). [0261] 23. C Notredame, D.
G. Higgins, J. Heringa, J Mol Biol 302, 205 (2000). [0262] 24. R.
Grantham, Science 185, 862 (1974). [0263] 25. G. Wang, R. L.
Dunbrack, Jr., Bioinformatics 19, 1589 (2003). [0264] 26. J.
Schymkowitz et al., Nucleic Acids Res 33, W382 (2005). [0265] 27.
D. K. Smith, P. Radivojac, Z. Obradovic, A. K. Dunker, G. Zhu,
Protein Sci 12, 1060 (2003). [0266] 28. L. Y. Yampolsky, A.
Stoltzfus, Pac Symp Biocomput, 433 (2005). [0267] 29. R. M.
Schwartz, M. 0. Dayhoff, Science 199, 395 (1978). [0268] 30. S.
Henikoff, J. G. Henikoff, Proc Natl Acad Sci USA 89, 10915 (1992).
[0269] 31. S. Miyazawa, and Jernigan, R. L., Macromolecules, 534
(1985). [0270] 32. P. D. Stenson et al., Hum Mutat 21, 577 (2003).
[0271] 33. M. S. Venkatarajan, and Braun, W., Journal of Molecular
Modeling, 445 (2001).
Sequence CWU 1
1
136115PRTHomo sapiens 1Lys Pro Ile Ile Ile Gly Arg His Ala Tyr Gly
Asp Gln Tyr Arg1 5 10 15215PRTHomo sapiens 2Lys Pro Ile Ile Ile Gly
His His Ala Tyr Gly Asp Gln Tyr Arg1 5 10 15315PRTHomo sapiens 3Lys
Pro Ile Ile Ile Gly Cys His Ala Tyr Gly Asp Gln Tyr Arg1 5 10
15415PRTHomo sapiens 4Lys Pro Ile Ile Ile Gly Leu His Ala Tyr Gly
Asp Gln Tyr Arg1 5 10 15515PRTHomo sapiens 5Lys Pro Ile Ile Ile Gly
Ser His Ala Tyr Gly Asp Gln Tyr Arg1 5 10 15615PRTHomo sapiens 6Lys
Pro Ile Ile Ile Gly Gly His Ala Tyr Gly Asp Gln Tyr Arg1 5 10
15715PRTHomo sapiens 7Lys Pro Ile Thr Ile Gly Arg His Ala His Gly
Asp Gln Tyr Lys1 5 10 15815PRTHomo sapiens 8Lys Pro Ile Thr Ile Gly
Met His Ala His Gly Asp Gln Tyr Lys1 5 10 15915PRTHomo sapiens 9Lys
Pro Ile Thr Ile Gly Lys His Ala His Gly Asp Gln Tyr Lys1 5 10
151027PRTHomo sapiens 10Ser Gly Trp Val Lys Pro Ile Ile Ile Gly His
His Ala Tyr Gly Asp1 5 10 15Gln Tyr Arg Ala Thr Asp Phe Val Val Pro
Gly 20 251115PRTHomo sapiens 11Gly Trp Val Lys Pro Ile Ile Ile Gly
His His Ala Tyr Gly Asp1 5 10 151215PRTHomo sapiens 12Ile Gly His
His Ala Tyr Gly Asp Gln Tyr Arg Ala Thr Asp Phe1 5 10 151315PRTHomo
sapiens 13Ser Gly Trp Val Lys Pro Ile Ile Ile Gly His His Ala Tyr
Gly1 5 10 151415PRTHomo sapiens 14Lys Pro Ile Ile Ile Gly His His
Ala Tyr Gly Asp Gln Tyr Arg1 5 10 151515PRTHomo sapiens 15Gly Trp
Val Lys Pro Ile Ile Ile Gly His His Ala Tyr Gly Asp1 5 10
151615PRTHomo sapiens 16Val Lys Pro Ile Ile Ile Gly His His Ala Tyr
Gly Asp Gln Tyr1 5 10 151715PRTHomo sapiens 17Pro Ile Ile Ile Gly
His His Ala Tyr Gly Asp Gln Tyr Arg Ala1 5 10 15188PRTHomo sapiens
18Gly His His Ala Tyr Gly Asp Gln1 5199PRTHomo sapiens 19Lys Pro
Ile Ile Ile Gly His His Ala1 5209PRTHomo sapiens 20Lys Pro Ile Ile
Ile Gly His His Ala1 5219PRTHomo sapiens 21Pro Ile Ile Ile Gly His
His Ala Tyr1 5229PRTHomo sapiens 22Gly His His Ala Tyr Gly Asp Gln
Tyr1 52310PRTHomo sapiens 23Lys Pro Ile Ile Ile Gly His His Ala
Tyr1 5 102410PRTHomo sapiens 24Ile Gly His His Ala Tyr Gly Asp Gln
Tyr1 5 102511PRTHomo sapiens 25Val Lys Pro Ile Ile Ile Gly His His
Ala Tyr1 5 102611PRTHomo sapiens 26Ile Ile Gly His His Ala Tyr Gly
Asp Gln Tyr1 5 10279PRTHomo sapiens 27Ile Ile Ile Gly His His Ala
Tyr Gly1 5289PRTHomo sapiens 28Ile Ile Gly His His Ala Tyr Gly Asp1
5299PRTHomo sapiens 29Pro Ile Ile Ile Gly His His Ala Tyr1
5309PRTHomo sapiens 30Trp Val Lys Pro Ile Ile Ile Gly His1
53110PRTHomo sapiens 31Ile Ile Ile Gly His His Ala Tyr Gly Asp1 5
103210PRTHomo sapiens 32Ile Ile Gly His His Ala Tyr Gly Asp Gln1 5
103310PRTHomo sapiens 33Val Lys Pro Ile Ile Ile Gly His His Ala1 5
103410PRTHomo sapiens 34His His Ala Tyr Gly Asp Gln Tyr Arg Ala1 5
10359PRTHomo sapiens 35Pro Ile Ile Ile Gly His His Ala Tyr1
5369PRTHomo sapiens 36Trp Val Lys Pro Ile Ile Ile Gly His1
5379PRTHomo sapiens 37Ile Ile Ile Gly His His Ala Tyr Gly1
5389PRTHomo sapiens 38Gly His His Ala Tyr Gly Asp Gln Tyr1
5399PRTHomo sapiens 39Ile Ile Gly His His Ala Tyr Gly Asp1
5409PRTHomo sapiens 40Lys Pro Ile Ile Ile Gly His His Ala1
54110PRTHomo sapiens 41Trp Val Lys Pro Ile Ile Ile Gly His His1 5
104210PRTHomo sapiens 42Ile Ile Ile Gly His His Ala Tyr Gly Asp1 5
104310PRTHomo sapiens 43Pro Ile Ile Ile Gly His His Ala Tyr Gly1 5
104410PRTHomo sapiens 44Lys Pro Ile Ile Ile Gly His His Ala Tyr1 5
104510PRTHomo sapiens 45Ile Gly His His Ala Tyr Gly Asp Gln Tyr1 5
104610PRTHomo sapiens 46Ile Ile Gly His His Ala Tyr Gly Asp Gln1 5
10479PRTHomo sapiens 47Trp Val Lys Pro Ile Ile Ile Gly His1
54810PRTHomo sapiens 48Trp Val Lys Pro Ile Ile Ile Gly His His1 5
104910PRTHomo sapiens 49Gly His His Ala Tyr Gly Asp Gln Tyr Arg1 5
10509PRTHomo sapiens 50Pro Ile Ile Ile Gly His His Ala Tyr1
5519PRTHomo sapiens 51Trp Val Lys Pro Ile Ile Ile Gly His1
5529PRTHomo sapiens 52Gly His His Ala Tyr Gly Asp Gln Tyr1
55310PRTHomo sapiens 53Trp Val Lys Pro Ile Ile Ile Gly His His1 5
105410PRTHomo sapiens 54Lys Pro Ile Ile Ile Gly His His Ala Tyr1 5
105510PRTHomo sapiens 55Ile Gly His His Ala Tyr Gly Asp Gln Tyr1 5
10569PRTHomo sapiens 56Trp Val Lys Pro Ile Ile Ile Gly His1
5579PRTHomo sapiens 57Ile Ile Ile Gly His His Ala Tyr Gly1
5589PRTHomo sapiens 58His His Ala Tyr Gly Asp Gln Tyr Arg1
55910PRTHomo sapiens 59Pro Ile Ile Ile Gly His His Ala Tyr Gly1 5
10609PRTHomo sapiens 60Lys Pro Ile Ile Ile Gly His His Ala1
56110PRTHomo sapiens 61Lys Pro Ile Ile Ile Gly His His Ala Tyr1 5
10629PRTHomo sapiens 62Trp Val Lys Pro Ile Ile Ile Gly His1
5639PRTHomo sapiens 63Pro Ile Ile Ile Gly His His Ala Tyr1
5649PRTHomo sapiens 64Trp Val Lys Pro Ile Ile Ile Gly His1
5659PRTHomo sapiens 65Gly His His Ala Tyr Gly Asp Gln Tyr1
56610PRTHomo sapiens 66Lys Pro Ile Ile Ile Gly His His Ala Tyr1 5
106710PRTHomo sapiens 67Ile Gly His His Ala Tyr Gly Asp Gln Tyr1 5
106810PRTHomo sapiens 68Trp Val Lys Pro Ile Ile Ile Gly His His1 5
10699PRTHomo sapiens 69His His Ala Tyr Gly Asp Gln Tyr Arg1
5709PRTHomo sapiens 70Gly His His Ala Tyr Gly Asp Gln Tyr1
5719PRTHomo sapiens 71Pro Ile Ile Ile Gly His His Ala Tyr1
5729PRTHomo sapiens 72Gly His His Ala Tyr Gly Asp Gln Tyr1
5738PRTHomo sapiens 73Ile Ile Ile Gly His His Ala Tyr1 5748PRTHomo
sapiens 74His His Ala Tyr Gly Asp Gln Tyr1 5759PRTHomo sapiens
75Trp Val Lys Pro Ile Ile Ile Gly His1 5769PRTHomo sapiens 76His
His Ala Tyr Gly Asp Gln Tyr Arg1 5779PRTHomo sapiens 77Val Lys Pro
Ile Ile Ile Gly His His1 5789PRTHomo sapiens 78Pro Ile Ile Ile Gly
His His Ala Tyr1 5799PRTHomo sapiens 79Gly His His Ala Tyr Gly Asp
Gln Tyr1 5808PRTHomo sapiens 80Val Lys Pro Ile Ile Ile Gly His1
5819PRTHomo sapiens 81His His Ala Tyr Gly Asp Gln Tyr Arg1
5829PRTHomo sapiens 82Gly His His Ala Tyr Gly Asp Gln Tyr1
5839PRTHomo sapiens 83Val Lys Pro Ile Ile Ile Gly His His1
5849PRTHomo sapiens 84Lys Pro Ile Ile Ile Gly His His Ala1
5859PRTHomo sapiens 85Pro Ile Ile Ile Gly His His Ala Tyr1
5869PRTHomo sapiens 86Gly His His Ala Tyr Gly Asp Gln Tyr1
58710PRTHomo sapiens 87Lys Pro Ile Ile Ile Gly His His Ala Tyr1 5
108810PRTHomo sapiens 88Ile Gly His His Ala Tyr Gly Asp Gln Tyr1 5
10899PRTHomo sapiens 89Lys Pro Ile Ile Ile Gly His His Ala1
5909PRTHomo sapiens 90Lys Pro Ile Ile Ile Gly His His Ala1
5919PRTHomo sapiens 91Lys Pro Ile Ile Ile Gly His His Ala1
5928PRTHomo sapiens 92Lys Pro Ile Ile Ile Gly His His1 59315PRTHomo
sapiens 93Gly Trp Val Lys Pro Ile Ile Ile Gly His His Ala Tyr Gly
Asp1 5 10 159415PRTHomo sapiens 94Lys Pro Ile Ile Ile Gly His His
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Ala Tyr Gly Asp Gln Tyr Arg Ala Thr Asp Phe Val Val1 5 10
159615PRTHomo sapiens 96Ser Gly Trp Val Lys Pro Ile Ile Ile Gly His
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98Trp Val Lys Pro Ile Ile Ile Gly His His Ala Tyr Gly Asp Gln1 5 10
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Ile Gly His His Ala Tyr Gly Asp Gln Tyr1 5 10 1510115PRTHomo
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Gln1 5 10 1510215PRTHomo sapiens 102Lys Pro Ile Ile Ile Gly His His
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His Ala Tyr Gly Asp Gln Tyr Arg Ala Thr Asp Phe Val1 5 10
1510415PRTHomo sapiens 104Pro Ile Ile Ile Gly His His Ala Tyr Gly
Asp Gln Tyr Arg Ala1 5 10 1510515PRTHomo sapiens 105His His Ala Tyr
Gly Asp Gln Tyr Arg Ala Thr Asp Phe Val Val1 5 10 1510615PRTHomo
sapiens 106Ser Gly Trp Val Lys Pro Ile Ile Ile Gly His His Ala Tyr
Gly1 5 10 1510715PRTHomo sapiens 107Val Lys Pro Ile Ile Ile Gly His
His Ala Tyr Gly Asp Gln Tyr1 5 10 1510815PRTHomo sapiens 108Pro Ile
Ile Ile Gly His His Ala Tyr Gly Asp Gln Tyr Arg Ala1 5 10
1510915PRTHomo sapiens 109Gly Trp Val Lys Pro Ile Ile Ile Gly His
His Ala Tyr Gly Asp1 5 10 1511015PRTHomo sapiens 110Trp Val Lys Pro
Ile Ile Ile Gly His His Ala Tyr Gly Asp Gln1 5 10 1511115PRTHomo
sapiens 111Ile Gly His His Ala Tyr Gly Asp Gln Tyr Arg Ala Thr Asp
Phe1 5 10 1511215PRTHomo sapiens 112Gly His His Ala Tyr Gly Asp Gln
Tyr Arg Ala Thr Asp Phe Val1 5 10 1511315PRTHomo sapiens 113Ser Gly
Trp Val Lys Pro Ile Ile Ile Gly His His Ala Tyr Gly1 5 10
1511415PRTHomo sapiens 114Trp Val Lys Pro Ile Ile Ile Gly His His
Ala Tyr Gly Asp Gln1 5 10 1511515PRTHomo sapiens 115Ile Ile Ile Gly
His His Ala Tyr Gly Asp Gln Tyr Arg Ala Thr1 5 10 1511615PRTHomo
sapiens 116Gly Trp Val Lys Pro Ile Ile Ile Gly His His Ala Tyr Gly
Asp1 5 10 1511715PRTHomo sapiens 117His His Ala Tyr Gly Asp Gln Tyr
Arg Ala Thr Asp Phe Val Val1 5 10 1511815PRTHomo sapiens 118Val Lys
Pro Ile Ile Ile Gly His His Ala Tyr Gly Asp Gln Tyr1 5 10
1511915PRTHomo sapiens 119His His Ala Tyr Gly Asp Gln Tyr Arg Ala
Thr Asp Phe Val Val1 5 10 1512015PRTHomo sapiens 120Trp Val Lys Pro
Ile Ile Ile Gly His His Ala Tyr Gly Asp Gln1 5 10 1512115PRTHomo
sapiens 121Ser Gly Trp Val Lys Pro Ile Ile Ile Gly His His Ala Tyr
Gly1 5 10 1512215PRTHomo sapiens 122Gly Trp Val Lys Pro Ile Ile Ile
Gly His His Ala Tyr Gly Asp1 5 10 1512315PRTHomo sapiens 123Ser Gly
Trp Val Lys Pro Ile Ile Ile Gly His His Ala Tyr Gly1 5 10
1512415PRTHomo sapiens 124Val Lys Pro Ile Ile Ile Gly His His Ala
Tyr Gly Asp Gln Tyr1 5 10 1512515PRTHomo sapiens 125Lys Pro Ile Ile
Ile Gly His His Ala Tyr Gly Asp Gln Tyr Arg1 5 10 1512615PRTHomo
sapiens 126Pro Ile Ile Ile Gly His His Ala Tyr Gly Asp Gln Tyr Arg
Ala1 5 10 1512715PRTHomo sapiens 127Ile Ile Gly His His Ala Tyr Gly
Asp Gln Tyr Arg Ala Thr Asp1 5 10 151289PRTHomo sapiens 128Gly His
His Ala Tyr Gly Asp Gln Tyr1 51299PRTHomo sapiens 129Pro Ile Ile
Ile Gly His His Ala Tyr1 5130414PRTHomo sapiens 130Met Ser Lys Lys
Ile Ser Gly Gly Ser Val Val Glu Met Gln Gly Asp1 5 10 15Glu Met Thr
Arg Ile Ile Trp Glu Leu Ile Lys Glu Lys Leu Ile Phe 20 25 30Pro Tyr
Val Glu Leu Asp Leu His Ser Tyr Asp Leu Gly Ile Glu Asn 35 40 45Arg
Asp Ala Thr Asn Asp Gln Val Thr Lys Asp Ala Ala Glu Ala Ile 50 55
60Lys Lys His Asn Val Gly Val Lys Cys Ala Thr Ile Thr Pro Asp Glu65
70 75 80Lys Arg Val Glu Glu Phe Lys Leu Lys Gln Met Trp Lys Ser Pro
Asn 85 90 95Gly Thr Ile Arg Asn Ile Leu Gly Gly Thr Val Phe Arg Glu
Ala Ile 100 105 110Ile Cys Lys Asn Ile Pro Arg Leu Val Ser Gly Trp
Val Lys Pro Ile 115 120 125Ile Ile Gly Arg His Ala Tyr Gly Asp Gln
Tyr Arg Ala Thr Asp Phe 130 135 140Val Val Pro Gly Pro Gly Lys Val
Glu Ile Thr Tyr Thr Pro Ser Asp145 150 155 160Gly Thr Gln Lys Val
Thr Tyr Leu Val His Asn Phe Glu Glu Gly Gly 165 170 175Gly Val Ala
Met Gly Met Tyr Asn Gln Asp Lys Ser Ile Glu Asp Phe 180 185 190Ala
His Ser Ser Phe Gln Met Ala Leu Ser Lys Gly Trp Pro Leu Tyr 195 200
205Leu Ser Thr Lys Asn Thr Ile Leu Lys Lys Tyr Asp Gly Arg Phe Lys
210 215 220Asp Ile Phe Gln Glu Ile Tyr Asp Lys Gln Tyr Lys Ser Gln
Phe Glu225 230 235 240Ala Gln Lys Ile Trp Tyr Glu His Arg Leu Ile
Asp Asp Met Val Ala 245 250 255Gln Ala Met Lys Ser Glu Gly Gly Phe
Ile Trp Ala Cys Lys Asn Tyr 260 265 270Asp Gly Asp Val Gln Ser Asp
Ser Val Ala Gln Gly Tyr Gly Ser Leu 275 280 285Gly Met Met Thr Ser
Val Leu Val Cys Pro Asp Gly Lys Thr Val Glu 290 295 300Ala Glu Ala
Ala His Gly Thr Val Thr Arg His Tyr Arg Met Tyr Gln305 310 315
320Lys Gly Gln Glu Thr Ser Thr Asn Pro Ile Ala Ser Ile Phe Ala Trp
325 330 335Thr Arg Gly Leu Ala His Arg Ala Lys Leu Asp Asn Asn Lys
Glu Leu 340 345 350Ala Phe Phe Ala Asn Ala Leu Glu Glu Val Ser Ile
Glu Thr Ile Glu 355 360 365Ala Gly Phe Met Thr Lys Asp Leu Ala Ala
Cys Ile Lys Gly Leu Pro 370 375 380Asn Val Gln Arg Ser Asp Tyr Leu
Asn Thr Phe Glu Phe Met Asp Lys385 390 395 400Leu Gly Glu Asn Leu
Lys Ile Lys Leu Ala Gln Ala Lys Leu 405 410131452PRTHomo sapiens
131Met Ala Gly Tyr Leu Arg Val Val Arg Ser Leu Cys Arg Ala Ser Gly1
5 10 15Ser Arg Pro Ala Trp Ala Pro Ala Ala Leu Thr Ala Pro Thr Ser
Gln 20 25 30Glu Gln Pro Arg Arg His Tyr Ala Asp Lys Arg Ile Lys Val
Ala Lys 35 40 45Pro Val Val Glu Met Asp Gly Asp Glu Met Thr Arg Ile
Ile Trp Gln 50 55 60Phe Ile Lys Glu Lys Leu Ile Leu Pro His Val Asp
Ile Gln Leu Lys65 70 75 80Tyr Phe Asp Leu Gly Leu Pro Asn Arg Asp
Gln Thr Asp Asp Gln Val 85 90 95Thr Ile Asp Ser Ala Leu Ala Thr Gln
Lys Tyr Ser Val Ala Val Lys 100 105 110Cys Ala Thr Ile Thr Pro Asp
Glu Ala Arg Val Glu Glu Phe Lys Leu 115 120 125Lys Lys Met Trp Lys
Ser Pro Asn Gly Thr Ile Arg Asn Ile Leu Gly 130 135 140Gly Thr Val
Phe Arg Glu Pro Ile Ile Cys Lys Asn Ile Pro Arg Leu145 150 155
160Val Pro Gly Trp Thr Lys Pro Ile Thr Ile Gly Arg His Ala His Gly
165 170 175Asp Gln Tyr Lys Ala Thr Asp Phe Val Ala Asp Arg
Ala Gly Thr Phe 180 185 190Lys Met Val Phe Thr Pro Lys Asp Gly Ser
Gly Val Lys Glu Trp Glu 195 200 205Val Tyr Asn Phe Pro Ala Gly Gly
Val Gly Met Gly Met Tyr Asn Thr 210 215 220Asp Glu Ser Ile Ser Gly
Phe Ala His Ser Cys Phe Gln Tyr Ala Ile225 230 235 240Gln Lys Lys
Trp Pro Leu Tyr Met Ser Thr Lys Asn Thr Ile Leu Lys 245 250 255Ala
Tyr Asp Gly Arg Phe Lys Asp Ile Phe Gln Glu Ile Phe Asp Lys 260 265
270His Tyr Lys Thr Asp Phe Asp Lys Asn Lys Ile Trp Tyr Glu His Arg
275 280 285Leu Ile Asp Asp Met Val Ala Gln Val Leu Lys Ser Ser Gly
Gly Phe 290 295 300Val Trp Ala Cys Lys Asn Tyr Asp Gly Asp Val Gln
Ser Asp Ile Leu305 310 315 320Ala Gln Gly Phe Gly Ser Leu Gly Leu
Met Thr Ser Val Leu Val Cys 325 330 335Pro Asp Gly Lys Thr Ile Glu
Ala Glu Ala Ala His Gly Thr Val Thr 340 345 350Arg His Tyr Arg Glu
His Gln Lys Gly Arg Pro Thr Ser Thr Asn Pro 355 360 365Ile Ala Ser
Ile Phe Ala Trp Thr Arg Gly Leu Glu His Arg Gly Lys 370 375 380Leu
Asp Gly Asn Gln Asp Leu Ile Arg Phe Ala Gln Met Leu Glu Lys385 390
395 400Val Cys Val Glu Thr Val Glu Ser Gly Ala Met Thr Lys Asp Leu
Ala 405 410 415Gly Cys Ile His Gly Leu Ser Asn Val Lys Leu Asn Glu
His Phe Leu 420 425 430Asn Thr Thr Asp Phe Leu Asp Thr Ile Lys Ser
Asn Leu Asp Arg Ala 435 440 445Leu Gly Arg Gln 45013245DNAHomo
sapiens 132aaacctatca tcataggtcg tcatgcttat ggggatcaat acaga
4513345DNAHomo sapiens 133aagcccatca ccattggcag gcacgcccat
ggcgaccagt acaag 451342339DNAHomo sapiens 134cctgtggtcc cgggtttctg
cagagtctac ttcagaagcg gaggcactgg gagtccggtt 60tgggattgcc aggctgtggt
tgtgagtctg agcttgtgag cggctgtggc gccccaactc 120ttcgccagca
tatcatcccg gcaggcgata aactacattc agttgagtct gcaagactgg
180gaggaactgg ggtgataaga aatctattca ctgtcaaggt ttattgaagt
caaaatgtcc 240aaaaaaatca gtggcggttc tgtggtagag atgcaaggag
atgaaatgac acgaatcatt 300tgggaattga ttaaagagaa actcattttt
ccctacgtgg aattggatct acatagctat 360gatttaggca tagagaatcg
tgatgccacc aacgaccaag tcaccaagga tgctgcagaa 420gctataaaga
agcataatgt tggcgtcaaa tgtgccacta tcactcctga tgagaagagg
480gttgaggagt tcaagttgaa acaaatgtgg aaatcaccaa atggcaccat
acgaaatatt 540ctgggtggca cggtcttcag agaagccatt atctgcaaaa
atatcccccg gcttgtgagt 600ggatgggtaa aacctatcat cataggtcgt
catgcttatg gggatcaata cagagcaact 660gattttgttg ttcctgggcc
tggaaaagta gagataacct acacaccaag tgacggaacc 720caaaaggtga
catacctggt acataacttt gaagaaggtg gtggtgttgc catggggatg
780tataatcaag ataagtcaat tgaagatttt gcacacagtt ccttccaaat
ggctctgtct 840aagggttggc ctttgtatct gagcaccaaa aacactattc
tgaagaaata tgatgggcgt 900tttaaagaca tctttcagga gatatatgac
aagcagtaca agtcccagtt tgaagctcaa 960aagatctggt atgagcatag
gctcatcgac gacatggtgg cccaagctat gaaatcagag 1020ggaggcttca
tctgggcctg taaaaactat gatggtgacg tgcagtcgga ctctgtggcc
1080caagggtatg gctctctcgg catgatgacc agcgtgctgg tttgtccaga
tggcaagaca 1140gtagaagcag aggctgccca cgggactgta acccgtcact
accgcatgta ccagaaagga 1200caggagacgt ccaccaatcc cattgcttcc
atttttgcct ggaccagagg gttagcccac 1260agagcaaagc ttgataacaa
taaagagctt gccttctttg caaatgcttt ggaagaagtc 1320tctattgaga
caattgaggc tggcttcatg accaaggact tggctgcttg cattaaaggt
1380ttacccaatg tgcaacgttc tgactacttg aatacatttg agttcatgga
taaacttgga 1440gaaaacttga agatcaaact agctcaggcc aaactttaag
ttcatacctg agctaagaag 1500gataattgtc ttttggtaac taggtctaca
ggtttacatt tttctgtgtt acactcaagg 1560ataaaggcaa aatcaatttt
gtaatttgtt tagaagccag agtttatctt ttctataagt 1620ttacagcctt
tttcttatat atacagttat tgccaccttt gtgaacatgg caagggactt
1680ttttacaatt tttattttat tttctagtac cagcctagga attcggttag
tactcatttg 1740tattcactgt cactttttct catgttctaa ttataaatga
ccaaaatcaa gattgctcaa 1800aagggtaaat gatagccaca gtattgctcc
ctaaaatatg cataaagtag aaattcactg 1860ccttcccctc ctgtccatga
ccttgggcac agggaagttc tggtgtcata gatatcccgt 1920tttgtgaggt
agagctgtgc attaaacttg cacatgactg gaacgaagta tgagtgcaac
1980tcaaatgtgt tgaagatact gcagtcattt ttgtaaagac cttgctgaat
gtttccaata 2040gactaaatac tgtttaggcc gcaggagagt ttggaatccg
gaataaatac tacctggagg 2100tttgtcctct ccatttttct ctttctcctc
ctggcctggc ctgaatatta tactactcta 2160aatagcatat ttcatccaag
tgcaataatg taagctgaat cttttttgga cttctgctgg 2220cctgttttat
ttcttttata taaatgtgat ttctcagaaa ttgatattaa acactatctt
2280atcttctcct gaactgttga ttttaattaa aattaagtgc taattaccaa
aaaaaaaaa 23391351740DNAHomo sapiens 135ccagcgttag cccgcggcca
ggcagccggg aggagcggcg cgcgctcgga cctctcccgc 60cctgctcgtt cgctctccag
cttgggatgg ccggctacct gcgggtcgtg cgctcgctct 120gcagagcctc
aggctcgcgg ccggcctggg cgccggcggc cctgacagcc cccacctcgc
180aagagcagcc gcggcgccac tatgccgaca aaaggatcaa ggtggcgaag
cccgtggtgg 240agatggatgg tgatgagatg acccgtatta tctggcagtt
catcaaggag aagctcatcc 300tgccccacgt ggacatccag ctaaagtatt
ttgacctcgg gctcccaaac cgtgaccaga 360ctgatgacca ggtcaccatt
gactctgcac tggccaccca gaagtacagt gtggctgtca 420agtgtgccac
catcacccct gatgaggccc gtgtggaaga gttcaagctg aagaagatgt
480ggaaaagtcc caatggaact atccggaaca tcctgggggg gactgtcttc
cgggagccca 540tcatctgcaa aaacatccca cgcctagtcc ctggctggac
caagcccatc accattggca 600ggcacgccca tggcgaccag tacaaggcca
cagactttgt ggcagaccgg gccggcactt 660tcaaaatggt cttcacccca
aaagatggca gtggtgtcaa ggagtgggaa gtgtacaact 720tccccgcagg
cggcgtgggc atgggcatgt acaacaccga cgagtccatc tcaggttttg
780cgcacagctg cttccagtat gccatccaga agaaatggcc gctgtacatg
agcaccaaga 840acaccatact gaaagcctac gatgggcgtt tcaaggacat
cttccaggag atctttgaca 900agcactataa gaccgacttc gacaagaata
agatctggta tgagcaccgg ctcattgatg 960acatggtggc tcaggtcctc
aagtcttcgg gtggctttgt gtgggcctgc aagaactatg 1020acggagatgt
gcagtcagac atcctggccc agggctttgg ctcccttggc ctgatgacgt
1080ccgtcctggt ctgccctgat gggaagacga ttgaggctga ggccgctcat
gggaccgtca 1140cccgccacta tcgggagcac cagaagggcc ggcccaccag
caccaacccc atcgccagca 1200tctttgcctg gacacgtggc ctggagcacc
gggggaagct ggatgggaac caagacctca 1260tcaggtttgc ccagatgctg
gagaaggtgt gcgtggagac ggtggagagt ggagccatga 1320ccaaggacct
ggcgggctgc attcacggcc tcagcaatgt gaagctgaac gagcacttcc
1380tgaacaccac ggacttcctc gacaccatca agagcaacct ggacagagcc
ctgggcaggc 1440agtaggggga ggcgccaccc atggctgcag tggaggggcc
agggctgagc cggcgggtcc 1500tcctgagcgc ggcagagggt gagcctcaca
gcccctctct ggaggccttt ctaggggatg 1560tttttttata agccagatgt
ttttaaaagc atatgtgtgt ttcccctcat ggtgacgtga 1620ggcaggagca
gtgcgtttta cctcagccag tcagtatgtt ttgcatactg taatttatat
1680tgcccttgga acacatggtg ccatatttag ctactaaaaa gctcttcaca
aaaaaaaaaa 174013617DNAM13 virus 136gtaaaacgac ggccagt 17
* * * * *
References