Note: Descriptions are shown in the official language in which they were submitted.
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BRCA DEFICIENCY AND METHODS OF USE
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001]
This application claims priority under 35 U.S.C. 119(e) to U.S. provisional
application Serial No. 61/388,692, filed October 1, 2010 , which is hereby
incorporated by
reference in its entirety.
FIELD OF THE INVENTION
[0002] The invention generally relates to a molecular
classification of disease and
particularly to methods and compositions for determining BRCA deficiency.
TABLES
[0003]
The instant application was filed with one (1) table (Table 1) under 37 C.F.R.
1.52(e)(1)(iii) & 1.58(b), submitted electronically as the following text
file: "3317-01-1P-2010-
10-01-TABLE1-BGJ.txt"; creation date: October 1, 2010; Size: 86,503 bytes.
This file and all its
contents are incorporated by reference herein in their entirety.
BACKGROUND OF THE INVENTION
[0004]
The breast and ovarian cancer susceptibility genes, BRCA _I and BRCA2, were
discovered in patients having a family history of breast or ovarian cancer.
Miki et at., SCIENCE
(1994) 266:66-71. The BRCA genes are tumor suppressors found deficient in a
large proportion
of solid tumors. For example, a significant proportion of sporadic breast and
ovarian cancers harbor
somatic BRCA mutations. Due to the critical role of BRCA deficiency in tumor
formation and
progression, identifying BRCA deficiency can be very important, inter alia, in
the individualized
clinical management of cancer patients (e.g., chemoselection). Thus, it is
desirable to identify new
markers and methods for detecting BRCA deficiency.
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SUMMARY OF THE INVENTION
[0005] It has been discovered that measuring expression of the
BRCA] and/or BRCA2
(referred to collectively as "BRCA") genes together with cell-cycle
progression ("CCP") gene
expression can effectively identifies tumors with BRCA deficiency.
Specifically, we determined
that tumors in which BRCA and CCP expression are anti-correlated represent a
subgroup of BRCA
deficient tumors. This subgroup is generally characterized by BRCA
hypermethylation. Thus the
invention generally provides compositions and methods for determining BRCA
status.
[0006] In one aspect the invention provides a method for
determining gene
expression comprising measuring the expression of BR CA] and/or BRCA2 (BRCA
expression) in a
sample and measuring the expression of a panel of CCP genes in the sample.
Some embodiments
further comprise determining whether BRCA expression is correlated to CCP
expression. Some
embodiments further comprise analyzing methylation in BRCA _1 and/or BRCA2 in
the sample.
[0007] As mentioned above, anti-correlation between BRCA and CCP
expression is
correlated with BRCA deficiency. Thus another aspect of the invention provides
a method for
determining whether a sample is BRCA deficient comprising measuring the
expression of BRCA]
and/or BR (BRCA expression) in said sample and measuring the expression of
a panel of CCP
genes in the sample. Some embodiments further comprise determining whether
BRCA expression is
correlated to CCP expression. In some embodiments, anti-correlation between
BRCA and CCP
expression indicates the sample is BRCA deficient. In some embodiments anti-
correlation between
BRCA and CCP expression indicates the sample has BRCA hypermethylation. Some
embodiments
further comprise analyzing methylation in BRCA] and/or BRCA2 in the sample.
[0008] In some embodiments the panel of CCP genes comprises at
least two (or five,
or six, or ten, or 15) CCP genes from any of Tables 1 to 5 or Panels A to G.
In some embodiments
the panel of CCP genes comprises the genes in any of Tables 1 to 5 or Panels A
to G.
[0009] In some embodiments, determining the expression of a panel
of genes
comprising CCP genes involves determining the expression of a plurality of
test genes comprising at
least 4, 6, 8, 10, 15 or more CCP genes and deriving a test value from the
determined expression,
wherein the CCP genes are weighted to contribute at least 50%, at least 75% or
at least 85% of the
test value. Thus, in some embodiments, the invention provides a method for
determining whether a
sample is BRCA deficient comprising (1) determining in a sample from a patient
(a) the expression
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of BRCA/ and/or BRCA2, and (b) the expression of a panel of genes including at
least 4 or at least 8
cell-cycle genes; (2) providing a test value by (a) weighting the determined
expression of each of a
plurality of test genes selected from the panel of genes with a predefined
coefficient, and (b)
combining the weighted expression to provide the test value, wherein the cell-
cycle genes are
weighted to contribute at least 50%, at least 75% or at least 85% of the test
value; and (3) comparing
the test value to the expression of BRCA 1 and/or BRCA2 to determine whether
these are correlated
or anti-correlated. In some embodiments the method further comprises (4)
correlating an anti-
correlation between the test value and BRCA1 and/or BRCA2 expression to BRCA
deficiency.
[0010] BRCA deficiency is associated with various characteristics
in tumors. Thus in
one aspect the invention provides a method of classifying a cancer comprising
measuring the
expression of BRCA1 and/or BRCA2 (BRCA expression) in said sample and
measuring the
expression of two or more CCP genes in the sample. Some embodiments further
comprise
determining whether BRCA expression is correlated to CCP expression. In some
embodiments,
anti-correlation between BRCA and CCP expression indicates any one of the
following: greater
likelihood of survival (e.g., progression-free survival, overall survival,
etc.), greater likelihood of
response to DNA damaging agents (e.g., platinum chemotherapy drugs, etc.),
greater likelihood of
response to drugs targeting the poly (ADP-ribose) polymerase (PARP) pathway,
etc. Some
embodiments further comprise determining whether BRCA1 and/or BRCA2 is
hypermethylated.
[0011] In some embodiments gene expression is determined using any
of the
following techniques: quantitative PCRTM (e.g., TaqManTm), microarray
hybridization analysis,
quantitative sequencing, etc. In some embodiments methylation is analyzed
using any of the
following techniques: Southern blotting, single nucleotide primer extension,
methylation-specific
polymerase chain reaction (MSPCR), restriction landmark genomic scanning for
methylation
(RLGS-M) and CpG island microarray, single nucleotide primer extension
(SNuPE), combined
bisulfite restriction analysis (COBRA), etc.
[0012] In another aspect the invention provides systems related to
the above methods
of the invention. In one embodiment the invention provides a system for
determining gene
expression in a tumor sample, comprising: (1) a sample analyzer for
determining the expression
levels of BRCA1 and/or BR and a panel of genes comprising at least two CCP
genes in a sample,
wherein the sample analyzer contains the sample, mRNA from the sample and
expressed from the
panel of genes, or cDNA synthesized from said mRNA; (2) a first computer
program for (a)
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receiving gene expression data on BRCA1 and/or BRCA2, (b) receiving gene
expression data on at
least two test genes selected from the panel of genes, (c) weighting the
determined expression of
each of the test genes with a predefined coefficient, and (d) combining the
weighted expression to
provide a CCP test value representing the expression level of the panel of
genes.
[0013] In some embodiments the above system further comprises a
computer
program for comparing the expression of BRCA/ and/or BRCA2 to the CCP test
value, wherein high
expression of BRCA1 and/or BRCA2 coupled with a high CCP test value indicates
BRCA and CCP
expression are correlated, wherein low expression of BRCA1 and/or BRCA2
coupled with a low CCP
test value indicates BRCA and CCP expression are correlated, wherein high
expression of BRCA1
and/or BR coupled with a low CCP test value indicates BRCA and CCP
expression are anti-
correlated, and wherein low expression of BRCA1 and/or BR
coupled with a high CCP test value
indicates BRCA and CCP expression are anti-correlated.
[0014] In some embodiments the above system further comprises a
computer
program for receiving data on the correlation between BRCA expression and CCP
expression in a
patient sample and concluding that the sample is BRCA deficient if BRCA
expression and CCP
expression are anti-correlated in the sample. In some embodiments the system
comprises a sample
analyzer for determining the methylation status of BRCA/ and/or BRCA2.
[0015] In yet another aspect the invention provides a kit for
practicing the methods
and for use in the systems of the present invention. The kit may include a
carrier for the various
components of the kit. The carrier can be a container or support, in the form
of, e.g., bag, box, tube,
rack, and is optionally compartmentalized. The carrier may define an enclosed
confinement for
safety purposes during shipment and storage.
[0016] The kit includes various components useful in determining
the expression of
BRCA1 and/or BRCA2, the expression of at least two CCP genes, and optionally
the expression of
one or more housekeeping gene markers and/or the methylation status of BRCA/
and/or BRCA2.
For example, the kit many include oligonucleotides specifically hybridizing
under high stringency to
mRNA or cDNA of BRCA1, BRCA2, or the genes in Tables 1 to 5 or Panels A to F.
Such
oligonucleotides can be used as PCR primers in RT-PCR reactions, or
hybridization probes.
[0017] Various techniques for determining BRCA status are known to
those skilled in
the art. In some embodiments the whole genome of one or more cells is
determined and the
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sequence of a BRCA gene found within that genome is analyzed for mutations. In
some
embodiments a BRCA gene is specifically sequenced, which may include exon
sequencing,
sequencing of exons along with at least some amount of flanking intronic
sequence, or sequencing of
the entire genomic region containing the BRCA gene of interest. Copy number
analysis may also be
used. In some embodiments large rearrangement analysis is used to determine
whether large
portions of the BRCA gene (or even the entire gene) have been deleted or
duplicated. In some
embodiments methylation analysis is used to determine BRCA status.
[0018] The foregoing and other advantages and features of the
invention, and the
manner in which the same are accomplished, will become more readily apparent
upon consideration
of the following detailed description of the invention taken in conjunction
with the accompanying
examples and drawings, which illustrate preferred and exemplary embodiments.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] Figure 1 illustrates how the predictive power of CCP gene
signatures varies
with the number of CCP genes.
[0020] Figure 2 illustrates the relationship between BRCA1 and cell-
cycle expression.
[0021] Figure 3 illustrates embodiments of computer systems of the
invention.
[0022] Figure 4 illustrates embodiments of computer-implemented
methods of the
invention.
[0023] Figure 5 illustrates the correlation between BRCA-CCP
expression anti-
correlation and BRCA1 hypermethylation.
[0024] Figure 6 shows the pairwise relationships between BRCA _1
qPCR assays.
Correlations are given in the upper panels.
[0025] Figure 7 a histogram of BR CA] expression as measured by
qPCR.
[0026] Figure 8 shows the relationship between each of the cell-
cycle genes and the
CCP score.
[0027] Figure 9 shows CCP score and BRCA1 expression.
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[0028] Figure 10 shows CCP score and BRCA1 expression separated by
ER/PR/HER2 subtype as determined by IHC.
[0029] Figure 11 shows the relationship between BRCA1 promoter
methylation and
BRCA _I expression.
[0030] Figure 12 shows the relationship between CCP score and BRCA1
expression
in samples with BRCA1 methylation data. The size of the points represents the
degree of BRCA1
methylation. Each point is colored by tumor subtype as identified by IHC
DETAILED DESCRIPTION OF THE INVENTION
[0031] It has been discovered that measuring BRCA expression
together with cell-
cycle progression ("CCP") gene expression can effectively identify tumors with
BRCA deficiency
(Example 2). Specifically, we determined that tumors in which BRCA and CCP
expression are anti-
correlated represent a subgroup of BRCA deficient tumors (id.). This subgroup
is generally
characterized by BRCA hypermethylation (id.). Thus determining BRCA and CCP
expression
levels can effectively identify BRCA deficient tumors better than BRCA
expression alone.
Accordingly the invention generally provides compositions and methods for
determining BRCA
status.
[0032] In one aspect the invention provides a method for
determining gene
expression comprising measuring the expression of BRCA1 and/or BRCA2 (BRCA
expression) in a
sample and measuring the expression of a panel of CCP genes in the sample.
Some embodiments
further comprise determining whether BRCA expression is correlated to CCP
expression. Some
embodiments further comprise analyzing methylation in BRCA] and/or BRCA2 in
the sample.
[0033] As mentioned above, anti-correlation between BRCA and CCP
expression is
correlated with BRCA deficiency. Thus another aspect of the invention provides
a method for
determining whether a sample is BRCA deficient comprising measuring the
expression of BRCA1
and/or BR (BRCA expression) in said sample and measuring the expression of
a panel of CCP
genes in the sample. "BRCA deficient" and "BRCA deficiency" mean attenuated
cellular activity of
BRCA1 and/or BRCA2 protein. This can include deletion of part or all of the
BRCA1 and/or
BRCA2 gene, lowered transcription and/or stability of BRCA / and/or BRCA2 mRNA
(e.g., as
caused by hypermethylation), lowered translation of BRCA1 and/or BRCA2
protein, or mutation(s)
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in the BRCA1 and/or BRCA2 gene or transcripts leading to a protein with
lowered biochemical
activity.
[0034] "Cell-cycle progression gene" and "CCP gene" herein refer to
a gene whose
expression level closely tracks the progression of the cell through the cell-
cycle. See, e.g., Whitfield
et at., MOL. BIOL. CELL (2002) 13:1977-2000. More specifically, CCP genes show
periodic
increases and decreases in expression that coincide with certain phases of the
cell cycle¨e.g.,
STK 15 and PLK show peak expression at G2/M. Id. Often CCP genes have clear,
recognized cell-
cycle related function ¨e.g., in DNA synthesis or repair, in chromosome
condensation, in cell-
division, etc. However, some CCP genes have expression levels that track the
cell-cycle without
having an obvious, direct role in the cell-cycle¨e.g., UBE2S encodes a
ubiquitin-conjugating
enzyme, yet its expression closely tracks the cell-cycle. Thus a CCP gene
according to the present
invention need not have a recognized role in the cell-cycle. Exemplary CCP
genes (and panels of
CCP genes) are listed in Tables 1 (Table 1 as shown in U.S. provisional
application Serial No.
61/388,692), 2, 3, 4, and 5 and Panels A, B, C, D, E, and F.
[0035] Whether a particular gene is a CCP gene may be determined by
any technique
known in the art, including that taught in Whitfield et at., MOL. BIOL. CELL
(2002) 13:1977-2000.
For example, a sample of cells, e.g., HeLa cells, can be synchronized such
that they all progress
through the different phases of the cell cycle at the same time. Generally
this is done by arresting
the cells in each phase¨e.g., cells may be arrested in S phase by using a
double thymidine block or
in mitosis with a thymidine-nocodazole block. See, e.g., Whitfield et at.,
MOL. CELL. BIOL. (2000)
20:4188-4198. RNA is extracted from the cells after arrest in each phase and
gene expression is
quantitated using any suitable technique¨e.g., expression microarray (genome-
wide or specific
genes of interest), real-time quantitative PCRTM (RTQ-PCR). Finally,
statistical analysis (e.g.,
Fourier Transform) is applied to determine which genes show peak expression
during particular cell-
cycle phases. Genes may be ranked according to a periodicity score describing
how closely the
gene's expression tracks the cell-cycle¨e.g., a high score indicates a gene
very closely tracks the
cell cycle. Finally, those genes whose periodicity score exceeds a defined
threshold level (see
Whitfield et at., MOL. BIOL. CELL (2002) 13:1977-2000) may be designated CCP
genes. A large, but
not exhaustive, list of nucleic acids associated with CCP genes (e.g., genes,
ESTs, cDNA clones,
etc.) is given in Table 1. See Whitfield et at., MOL. BIOL. CELL (2002)
13:1977-2000. All of the
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CCP genes in Table 2 below form a panel of CCP genes ("Panel A") useful in the
methods of the
invention.
Table 2
Entrez RefSeq Accession
Gene Symbol ABI Assay ID
GeneID Nos.
APOBEC3B* 9582 Hs00358981 ml NM
004900.3
ASF1B* 55723 Hs00216780 ml NM
018154.2
ASPM* 259266 Hs00411505 ml NM
018136.4
ATAD2* 29028 Hs00204205 ml NM
014109.3
NM 001012271.1;
BIRC5* 332 Hs00153353-m1; NM
001012270.1;
Hs03043576 ml
NM 001168.2
BLM* 641 Hs00172060 ml NM
000057.2
RUB] 699 Hs00177821 ml NM
004336.3
BUB1B* 701 Hs01084828 ml NM
001211.5
C12orf48* 55010 Hs00215575 ml NM
017915.2
NM 145060.3;
C18orf24* 220134 Hs00536843 ml
NM 001039535.2
Clorf135* 79000 Hs00225211 ml NM
024037.1
C21orf45* 54069 Hs00219050 ml NM
018944.2
CCDC99* 54908 Hs00215019 ml
NMO17785.4
CCNA2* 890 Hs00153138 ml NM
001237.3
CCNB1* 891 Hs00259126 ml NM
031966.2
CCNB2* 9133 Hs00270424 ml NM
004701.2
NM 001238.1;
CCNE1* 898 Hs01026536 ml
NM 057182.1
NM 033379.3;
CDC2* 983 Hs00364293 ml NM
001130829.1;
NM 001786.3
CDC20* 991 Hs03004916 gl NM
001255.2
CDC45L* 8318 Hs00185895 ml NM
003504.3
CDC6* 990 Hs00154374 ml NM
001254.3
CDCA3* 83461 Hs00229905 ml NM
031299.4
CDCA8* 55143 Hs00983655 ml NM
018101.2
NM 001130851.1;
CDKN3* 1033 Hs00193192 ml
NM 005192.3
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CDT1* 81620 Hs00368864 ml NM
030928.3
NM 001042426.1;
CENPA 1058 Hs00156455 ml
NM 001809.3
CENPE* 1062 Hs00156507 ml NM 001813.2
CENPF* 1063 Hs00193201 ml NMO16343.3
CENPI* 2491 Hs00198791 ml NM 006733.2
CENPM* 79019 Hs00608780 ml NM
024053.3
NMO18455.4;
CENPN* 55839 Hs00218401 ml NM
001100624.1;
NM 001100625.1
NM 018131.4;
CEP55* 55165 Hs00216688 ml
NM 001127182.1
NM 001114121.1;
CHEK1* 1111 Hs00967506 ml NM
001114122.1;
NM 001274.4
NM 018204.3;
CKAP2* 26586 Hs00217068 ml
NM 001098525.1
CKS1B* 1163 Hs01029137 gl NM 001826.2
CKS2* 1164 Hs01048812 gl NM 001827.1
CTPS* 1503 Hs01041851 ml NM 001905.2
CTSL2* 1515 Hs00952036 ml NM 001333.2
DBF4* 10926 Hs00272696 ml NM
006716.3
DDX39* 10212 Hs00271794 ml NM
005804.2
DLGAP5/DLG7* 9787 Hs00207323 ml
NMO14750.3
DONSON* 29980 Hs00375083 ml NM
017613.2
DSN1* 79980 Hs00227760 ml NM
024918.2
DTL* 51514 Hs00978565 ml NM
016448.2
E2F8* 79733 Hs00226635 ml NM
024680.2
ECT2* 1894 Hs00216455 ml NM 018098.4
ESPL1* 9700 Hs00202246 ml NM 012291.4
NM 130398.2;
EX01* 9156 Hs00243513 ml NM 003686.3;
NM 006027.3
NM 152998.1;
EZH2* 2146 Hs00544830 ml
NM 004456.3
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NM 018193.2;
FANCI* 55215 Hs00289551 ml
NM 001113378.1
NM 001142522.1;
FBX05* 26271 Hs03070834 ml
NMO12177.3
NM 202003.1;
FOX11-1* 2305 Hs01073586 ml NM 202002.1;
NM 021953.2
GINS1* 9837 Hs00221421 ml NM 021067.3
GMPS* 8833 Hs00269500 ml NM 003875.2
GPSM2* 29899 Hs00203271 ml NM 013296.4
GTSE1* 51512 Hs00212681 ml NM 016426.5
H2AFX* 3014 Hs00266783 sl NM 002105.2
NM 001142556.1;
NM 001142557.1;
HMMR* 3161 Hs00234864 ml
NM 012484.2;
NM 012485.2
NM 001002033.1;
HN1* 51155 Hs00602957 ml NM 001002032.1;
NMO16185.2
KIAA0101* 9768 Hs00207134 ml NM 014736.4
KIF11* 3832 Hs00189698 ml NM 004523.3
KIF15* 56992 Hs00173349 ml NM 020242.2
KIF18A* 81930 Hs01015428 ml NM 031217.3
KIF20A* 10112 Hs00993573 ml NM 005733.2
KIF20B/MPHOSPH1* 9585 Hs01027505 ml NM 016195.2
NM 138555.1;
K1F23* 9493 Hs00370852 ml
NM 004856.4
KIF2C* 11004 Hs00199232 ml NM 006845.3
KIF4A* 24137 Hs01020169 ml NM 012310.3
KIFC1* 3833 Hs00954801 ml NM 002263.3
KPNA2 3838 Hs00818252 gl NM 002266.2
LMNB2* 84823 Hs00383326 ml NM 032737.2
MAD2L/ 4085 Hs01554513 gl NM 002358.3
MCAM* 4162 Hs00174838 ml NM 006500.2
NM 018518.3;
MCM10* 55388 Hs00960349 ml
NM 182751.1
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MCM2* 4171 Hs00170472 ml NM 004526.2
NM 005914.2;
MCM4* 4173 Hs00381539 ml
NM 182746.1
MCM6* 4175 Hs00195504 ml NM 005915.4
NM 005916.3;
MCM7* 4176 Hs01097212 ml
NM 182776.1
MELK 9833 Hs00207681 ml NM 014791.2
MK167* 4288 Hs00606991 ml NM 002417.3
MYBL2* 4605 Hs00231158 ml NM 002466.2
NCAPD2* 9918 Hs00274505 ml NM 014865.3
NCAPG* 64151 Hs00254617 ml NM 022346.3
NCAPG2* 54892 Hs00375141 ml NM 017760.5
NCAPH* 23397 Hs01010752 ml NM 015341.3
NDC80* 10403 Hs00196101 ml NM 006101.2
NEK2* 4751 Hs00601227 mH NM 002497.2
NMO18454.6;
NUSAP1* 51203 Hs01006195 ml NM 001129897.1;
NMO16359.3
01P5* 11339 Hs00299079 ml NM 007280.1
ORC6L* 23594 Hs00204876 ml NM 014321.2
NM 001079524.1;
PAICS* 10606 Hs00272390 ml NM 001079525.1;
NM 006452.3
PBK* 55872 Hs00218544 ml NM 018492.2
NM 182649.1;
PCNA* 5111 Hs00427214 gl
NM 002592.2
PDSS1* 23590 Hs00372008 ml NM 014317.3
PLK1* 5347 Hs00153444 ml NM 005030.3
PLK4* 10733 Hs00179514 ml NM 014264.3
POLE2* 5427 Hs00160277 ml NM 002692.2
NM 199413.1;
PRO* 9055 Hs00187740 ml NM 199414.1;
NM 003981.2
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PSMA7* 5688 Hs00895424 ml NM 002792.2
NM 032636.6;
NM 001005290.2;
PSRC1* 84722 Hs00364137 ml
NM 001032290.1;
NM 001032291.1
PTTG1* 9232 Hs00851754 ul NM 004219.2
RACGAP1* 29127 Hs00374747 ml NM 013277.3
NM 133487.2;
RAD51* 5888 Hs00153418 ml
NM 002875.3
NM 001130862.1;
RAD51AP1* 10635 Hs01548891 ml
NM 006479.4
RAD54B* 25788 Hs00610716 ml NM 012415.2
NM 001142548.1;
RAD54L* 8438 Hs00269177 ml
NM 003579.3
NM 181471.1;
RFC2* 5982 Hs00945948 ml
NM 002914.3
NM 181573.2;
RFC4* 5984 Hs00427469 ml
NM 002916.3
NM 181578.2;
NM 001130112.1;
RFC5* 5985 Hs00738859 ml
NM 001130113.1;
NM 007370.4
RNASEH2A* 10535 Hs00197370 ml NM 006397.2
RRM2* 6241 Hs00357247 gl NM 001034.2
SHCBP1* 79801 Hs00226915 ml NM 024745.4
NM 001042550.1;
SMC2* 10592 Hs00197593 ml NM 001042551.1;
NM 006444.2
SPAG5* 10615 Hs00197708 ml NM 006461.3
SPC25* 57405 Hs00221100 ml NM 020675.3
NM 001048166.1;
STIL* 6491 Hs00161700 ml
NM 003035.2
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STMN1* 3925 Hs00606370 ml=' NM 005563.3;
¨
Hs01033129 ml NM 203399.1
TACC3* 10460 Hs00170751 ml NM 006342.1
TIMELESS* 8914 Hs01086966 ml NM 003920.2
TK1* 7083 Hs01062125 ml NM 003258.4
TOP2A* 7153 Hs00172214 ml NM 001067.2
TPX2* 22974 Hs00201616 ml NM 012112.4
TRIP13* 9319 Hs01020073 ml NM 004237.2
TTK* 7272 Hs00177412 ml NM 003318.3
TUBA1C* 84790 Hs00733770 ml NM 032704.3
TYMS* 7298 Hs00426591 ml NM 001071.2
NM 181799.1;
NM 181800.1;
NM 181801.1;
UBE2C 11065 Hs00964100 gl
NM 181802.1;
NM 181803.1;
NM 007019.2
UBE2S 27338 Hs00819350 ml NM 014501.2
VRK1* 7443 Hs00177470 ml NM 003384.2
NM 017975.3;
ZWILCH* 55055 Hs01555249 ml
NR 003105.1
NM 032997.2;
ZWINT* 11130 Hs00199952 ml NM 001005413.1;
NM 007057.3
* 124-gene subset of CCP genes useful in the invention ("Panel B"). ABI Assay
ID means the
catalogue ID number for the gene expression assay commercially available from
Applied
Biosystems Inc. (Foster City, CA) for the particular gene.
[0036] Additional CCP gene panels useful in the invention are as
follows:
Table 3: "Panel C"
Gene Entrez Gene Entrez Gene
Entrez
Symbol GeneID Symbol GeneID Symbol
GeneID
*
AURKA 6790 DTL 51514 PRC1* 9055
BUB1* 699 FOXM1* 2305 PTTG1* 9232
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CCNB 1* 891 HMMR* 3161 RRM2* 6241
CCNB2* 9133 KIF23* 9493 TIMELESS* 8914
CDC2* 983 KPNA2 3838 TPX2* 22974
CDC20* 991 MAD2L 1* 4085 TRIP 13*
9319
CDC45L* 8318 MELK 9833 TTK 7272
CDCA8* 55143 MYBL2* 4605 UBE2C 11065
CENPA 1058 NUSAP 1* 51203 UBE2S*
27338
CKS2* 1164 PBK 55872 ZWINT 11130
DLG7* 9787
*
These genes are useful as a 26-gene subset panel ("Panel D").
Table 4: "Panel E"
Gene Entrez Gene Entrez Gene Entrez
Symbol GeneID Symbol GeneID Symbol GeneID
ASF 1B* 55723 CENPM* 79019 ORC6L*
23594
ASPM* 259266 CEP55* 55165 PBK*
55872
BIRC5* 332 DLGAP5 9787 PLK1* 5347
BUB 1B* 701 DTL 51514 PRC1* 9055
C 1 8orf24* 220134 FOXM1* 2305 PTTG1* 9232
CDC2* 983 KIAA0101* 9768 RAD51* 5888
CDC20* 991 KIF 11* 3832 RAD54L*
8438
CDCA3* 83461 KIF20A* 10112 RRM2*
6241
CDCA8* 55143 KIF4A 24137 TK1* 7083
CDKN3* 1033 MCM/ 0* 55388 TOP2A*
7153
CENPF* 1063 NUSAP 1* 51203
*
These genes are useful as a 31-gene subset panel ("Panel F").
Table 5: "Panel G"
Gene Entrez Entrez
Gene Entrez
Symbol GeneID Gene SymbolGeneID Symbol GeneID
AURKA 6790 DLG7/DLGAP5 9787 PBK 55872
RUB] 699 DTL 51514 PRC1 9055
CCNB 1 891 FOXM1 2305 PTTG1
9232
CCNB2 9133 HMMR 3161 RRM2 6241
CDC2/CDK1 983 KIF23
9493 TPX2 22974
CDC20 991 MAD2L1 4085 TRIP
13 9319
CDC45L 8318 MELK 9833 TTK 7272
CDCA8 55143 MYBL2 4605
UBE2C 11065
CENPA 1058 NUSAP 1 51203
ZWINT 11130
CKS2 1164
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[0037] Various embodiments of the invention involve determining the
expression of
genes (e.g., BRCA1 , BRCA2, CCP genes, etc.) in a sample. In the context of an
individual test gene,
"expression level" means the amount (normalized or absolute) of an analyte
associated with that
gene in a sample. For example, the level of BR CA] expression can be the
amount of BRCA/
transcript (or cDNA reverse transcribed from such transcript) or protein in a
sample.
[0038] Those skilled in the art are familiar with various
techniques for determining
the expression level of a gene or protein in a tissue or cell sample. Gene
expression can be
determined either at the RNA level (i.e., noncoding RNA (ncRNA), mRNA, miRNA,
tRNA, rRNA,
snoRNA, siRNA and piRNA) or at the protein level. Expression analysis at the
RNA level can be
done using, e.g., microarray analysis (e.g., for assaying mRNA or microRNA
expression, copy
number, etc.), quantitative real-time PCRTM ("qRT-PCRTm", e.g., TaqManTm),
etc. Levels of
proteins in a tumor sample can be determined by any known techniques in the
art, e.g., HPLC, mass
spectrometry, or using antibodies specific to selected proteins (e.g., IHC,
ELISA, etc.). The activity
level of a polypeptide encoded by a gene may be used in much the same way as
the expression level
of the gene or polypeptide. Often higher activity levels indicate higher
expression levels while lower
activity levels indicate lower expression levels. Thus, in some embodiments,
the activity level of a
polypeptide encoded by a gene is determined rather than or in addition to the
expression level of the
gene. Those skilled in the art are familiar with techniques for measuring the
activity of various such
proteins, including BRCA1, BRCA2, and those encoded by the genes listed in
Tables 1 to 5. The
methods of the invention may be practiced independent of the particular
technique used.
[0039] In some embodiments, the expression of one or more
normalizing genes is
also obtained for use in normalizing the expression of test genes. As used
herein, "normalizing
genes" referred to the genes whose expression is used to calibrate or
normalize the measured
expression of the gene of interest (e.g., test genes). Importantly, the
expression of normalizing genes
should be independent of cancer outcome/prognosis, and the expression of the
normalizing genes is
very similar among all the tumor samples. Normalization ensures accurate
comparison of expression
of a test gene between different samples. For this purpose, housekeeping genes
known in the art can
be used. Housekeeping genes are well known in the art, with examples
including, but are not limited
to, GUSB (glucuronidase, beta), HMBS (hydroxymethylbilane synthase), SDHA
(succinate
dehydrogenase complex, subunit A, flavoprotein), UBC (ubiquitin C) and YWHAZ
(tyrosine 3-
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monooxygenase/tryptophan 5-monooxygenase activation protein, zeta
polypeptide). One or more
housekeeping genes can be used. Preferably, at least 2, 5, 10 or 15
housekeeping genes are used to
provide a combined normalizing gene set. The amount of gene expression of such
normalizing
genes can be averaged, combined together by straight additions or by a defined
algorithm. Some
examples of particularly useful housekeeper genes for use in the methods and
compositions of the
invention include those listed in Table A below.
Table A
Gene Entrez Applied Biosystems
RefSeq Accession Nos.
Symbol GeneID Assay ID
CLTC* 1213 Hs00191535 ml NM 004859.3
GUSB 2990 Hs99999908 ml NM 000181.2
HMBS 3145 Hs00609297 ml NM 000190.3
MMADHC* 27249 Hs00739517 gl NM 015702.2
MRFAP1* 93621 Hs00738144 gl NM 033296.1
PPP2CA* 5515 Hs00427259 ml NM 002715.2
PSMA1* 5682 Hs00267631 ml
PSMC1* 5700 Hs02386942 gl NM 002802.2
RPL13A* 23521 Hs03043885 gl NM 012423.2
RPL37* 6167 Hs02340038 gl NM 000997.4
RPL38* 6169 Hs00605263 gl NM 000999.3
RPL4* 6124 Hs03044647 gl NM 000968.2
RPL8* 6132 Hs00361285 gl NM 033301.1; NM 000973.3
RP529* 6235 Hs03004310 gl NM 001030001.1; NM
001032.3
SDHA 6389 Hs00188166 ml NM 004168.2
5LC25A3* 6515 Hs00358082 ml NM 213611.1; NM 002635.2;
NM 005888.2
TXNL1* 9352 Hs00355488 ml NR 024546.1; NM 004786.2
UBA52* 7311 Hs03004332 gl NM 001033930.1; NM
003333.3
UBC 7316 Hs00824723 ml NM 021009.4
YWHAZ 7534 Hs00237047 ml NM 003406.3
* Subset of useful housekeeping genes.
[0040] In
the case of measuring RNA levels for the genes, one convenient and
sensitive approach is the real-time quantitative PCRTM (qPCRTM) assay,
following a reverse
transcription reaction. Typically, a cycle threshold (Ct) is determined for
each test gene and each
normalizing gene, i.e., the number of cycles at which the fluoescence from a
qPCR reaction above
background is detectable.
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[0041] The overall expression of the one or more normalizing genes
can be
represented by a "normalizing value" which can be generated by combining the
expression of all
normalizing genes, either weighted equally (straight addition or averaging) or
by different
predefined coefficients. In one simple example, the normalizing value CtH can
be the cycle threshold
(Ct) of one single normalizing gene, or an average of the Ct values of 2 or
more, preferably 10 or
more, or 15 or more normalizing genes, in which case, the predefined
coefficient is 1/N, where N is
the total number of normalizing genes used. Thus, CtH = (CtH1 + CtH2 ***
Ctx0/N. As will be
apparent to skilled artisans, depending on the normalizing genes used, and the
weight desired to be
given to each normalizing gene, any coefficients (from 0/N to N/N) can be
given to the normalizing
genes in weighting the expression of such normalizing genes. That is, CtH =
XCtH1 + YCtH2 + ***
wherein x + y + === + z = 1.
[0042] As discussed above, the methods of the invention generally
involve
determining the level of expression of a panel of CCP genes. With modern high-
throughput
techniques, it is often possible to determine the expression level of tens,
hundreds or thousands of
genes. Indeed, it is possible to determine the level of expression of the
entire transcriptome (i.e.,
each transcribed gene in the genome). Once such a global assay has been
performed, one may then
informatically analyze one or more subsets (i.e., panels) of genes. For
example, one may analyze the
expression of a panel comprising primarily CCP genes according to the present
invention by
combining the expression level values of the individual test genes to obtain a
test value.
[0043] As will be apparent to a skilled artisan, such a test value
represents the overall
expression level of the panel of test genes (e.g., a panel composed of
substantially CCP genes). In
one embodiment, to provide a test value in the methods of the invention, the
normalized expression
for a test gene can be obtained by normalizing the measured Ct for the test
gene against the Cal, i.e.,
ACti= (Ct1 ¨ CtH). Thus, the test value representing the overall expression of
the plurality of test
genes can be provided by combining the normalized expression of all test
genes, either by straight
addition or averaging (i.e., weighted equally) or by a different predefined
coefficient. For example,
the simplest approach is averaging the normalized expression of all test
genes: test value = (ACti +
ACt2 + ***+ AC)/n. As will be apparent to skilled artisans, depending on the
test genes used,
different weight can also be given to different test genes in the present
invention.
[0044] Thus in methods of the invention described herein comprising
determining the
expression of a panel of CCP genes, such determining step may comprise: (1)
determining the
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expression of a panel of genes in the sample comprising at least two CCP
genes; and (2) providing a
test value by (a) weighting the determined expression of each of a plurality
of test genes selected
from said panel of genes with a predefined coefficient, and (b) combining the
weighted expression to
provide said test value. This test value represents the level of expression of
the panel of genes in the
sample. In embodiments involving comparison or analysis of CCP expression, the
test value will
often be compared to BRCA expression in order to determine whether the two are
correlated or anti-
correlated. In some embodiments, anti-correlation indicates BRCA deficiency.
[0045] In some embodiments the methods of the invention comprise
determining the
status of a panel (i.e., a plurality) of test genes comprising a plurality of
CCP genes (e.g., to provide
a test value representing the average expression of the test genes). For
example, increased
expression in a panel of test genes may refer to the average expression level
of all panel genes in a
particular patient being higher than the average expression level of these
genes in normal patients (or
higher than some index value that has been determined to represent the normal
average expression
level). Alternatively, increased expression in a panel of test genes may refer
to increased expression
in at least a certain number (e.g., 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 15, 20, 25,
30 or more) or at least a
certain proportion (e.g., 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%,
99%, 100%) of
the genes in the panel as compared to the average normal expression level.
[0046] In some embodiments the plurality of test genes (which may
itself be a sub-
panel analyzed informatically) comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10,
15, 20, 25, 30, 35, 40, 45,
50, 70, 80, 90, 100, 200, or more CCP genes. In some embodiments the plurality
of test genes
comprises at least 10, 15, 20, or more CCP genes. In some embodiments the
plurality of test genes
comprises between 5 and 100 CCP genes, between 7 and 40 CCP genes, between 5
and 25 CCP
genes, between 10 and 20 CCP genes, or between 10 and 15 CCP genes. In some
embodiments CCP
genes comprise at least a certain proportion of the plurality of test genes
used to provide a test value.
Thus in some embodiments the plurality of test genes comprises at least 25%,
30%, 40%, 50%, 60%,
70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% CCP genes. In some
preferred
embodiments the plurality of test genes comprises at least 10, 15, 20, 25, 30,
35, 40, 45, 50, 70, 80,
90, 100, 200, or more CCP genes, and such CCP genes constitute at least 50%,
60%, 70%,
preferably at least 75%, 80%, 85%, more preferably at least 90%, 95%, 96%,
97%, 98%, or 99% or
more of the total number of genes in the plurality of test genes.
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[0047] In some embodiments the CCP genes are the genes in any one of Table
1 and
Panels A through G. In some embodiments the test panel comprises at least 2,
3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 20, 25, 30, or more of the genes in any of Tables 1 to 5
and Panels A to F. In
some embodiments the invention provides methods comprising determining (e.g.,
in a sample) the
expression of the genes in any one of Tables 1 to 5 and Panels A to F.
[0048] It has been determined that, once the CCP phenomenon reported herein
is
appreciated, the choice of individual CCGs for a test panel can, in some
embodiments, be somewhat
arbitrary. In other words, many CCGs have been found to be very good
surrogates for each other.
Thus any CCG (or panel of CCGs) can be used in the various embodiments of the
invention. In
other embodiments of the invention, optimized CCGs are used. One way of
assessing whether
particular CCGs will serve well in the methods and compositions of the
invention is by assessing
their correlation with the mean expression of CCGs (e.g., all known CCGs, a
specific set of CCGs,
etc.). Those CCGs that correlate particularly well with the mean are expected
to perform well in
assays of the invention, e.g., because these will reduce noise in the assay.
[0049] 126 CCGs and 47 housekeeping genes had their expression compared to
the
CCG and housekeeping mean in order to determine preferred genes for use in
some embodiments of
the invention. Rankings of select CCGs according to their correlation with the
mean CCG
expression as well as their ranking according to predictive value are given in
Tables 2, 3, 5, 6, & 7.
[0050] Assays of 126 CCGs and 47 HK (housekeeping) genes were run against
96
commercially obtained, anonymous prostate tumor FFPE samples without outcome
or other clinical
data. The working hypothesis was that the assays would measure with varying
degrees of accuracy
the same underlying phenomenon (cell cycle proliferation within the tumor for
the CCGs, and
sample concentration for the HK genes). Assays were ranked by the Pearson's
correlation
coefficient between the individual gene and the mean of all the candidate
genes, that being the best
available estimate of biological activity. Rankings for these 126 CCGs
according to their correlation
to the overall CCG mean are reported in Table 6.
Table 6
Correl. Correl.
Correl.
Gene Gene Gene Gene Gene
w/ w/ Gene Symbol w/
# Symbol # Symbol #
Mean Mean
Mean
1 TPX2 0.931 44 PBK 0.805 87 KIF18A 0.6987
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2 CCNB2 0.9287 45 ESPL1 0.805 88 DONSON 0.688
3 KIF4A 0.9163 46 MK167 0.7993 89 MCM4 0.686
4 KIF2C 0.9147 47 SPAG5 0.7993 90 RAD54B 0.679
BIRC5 0.9077 48
MCM10 0.7963 91 RNASEH2A 0.6733
6 BIRC5 0.9077 49 MCM6 0.7957 92 TUBA1C 0.6697
7 RACGAP1 0.9073 50 01P5 0.7943
93 Cl8orf24 0.6697
8 CDC2 0.906 51 CDC45L 0.7937 94 SMC2 0.6697
9 PRC1 0.9053 52 K1F23 0.7927 95 CENPI 0.6697
DLGAP5/
0.9033 53 EZH2 0.789 96 GMPS 0.6683
DLG7
11 CEP55 0.903 54 SPC25 0.7887 97 DDX39 0.6673
12 CCNB1 0.9 55 STIL 0.7843 98 POLE2 0.6583
13 TOP2A 0.8967 56 CENPN 0.783 99 APOBEC3B 0.6513
14 CDC20 0.8953 57 GTSE1 0.7793 100 RFC2 0.648
KIF20A 0.8927 58 RAD5] 0.779 101 PSMA7 0.6473
16
BUB1B 0.8927 59 CDCA3 0.7783 102 PM HOSPH1/ 0.6457
kif2. Ob
17 CDKN3 0.8887 60 TACC3 0.778 103 CDT] 0.645
18 NUSAP1 0.8873 61 PLK4 0.7753 104 H2AFX 0.6387
19 CCNA2 0.8853 62 ASF1B 0.7733 105 ORC6L 0.634
KIF11 0.8723 63 DTL 0.769 106 Clorf135 0.6333
21 CDCA8 0.8713 64 CHEK1 0.7673 107 PSRC1 0.633
22 NCAPG 0.8707 65 NCAPG2 0.7667 108 VRK1 0.6323
23 ASPM 0.8703 66 PLK1 0.7657 109 CKAP2 0.6307
24 FOXM1 0.87 67 TIMELESS 0.762 110 CCDC99
0.6303
NEK2 0.869 68 E2F8 0.7587 111 CCNE1 0.6283
26 ZWINT 0.8683 69 EX01 0.758 112 LMNB2 0.625
27 PTTG1 0.8647 70 ECT2 0.744 113 GPSM2 0.625
28 RRM2 0.8557 71 STMN1 0.737 114 PAICS 0.6243
29 TTK 0.8483 72 STMN1 0.737 115 MCAM 0.6227
TRIP13 0.841 73 RFC4 0.737 116 DSN1 0.622
31 GINS] 0.841 74 CDC6 0.7363 117 NCAPD2 0.6213
32 CENPF 0.8397 75 CENPM 0.7267 118 RAD54L 0.6213
33 HMMR 0.8367 76 MYBL2 0.725 119 PDSS1 0.6203
34 NCAPH 0.8353 77 SHCBP1 0.723 120 HN1 0.62
NDC80 0.8313 78 ATAD2 0.723 121 C2 lorf45
0.6193
36 KIF15 0.8307 79 KIFC1 0.7183 122 CTSL2 0.619
37 CENPE 0.8287 80 DBF4 0.718 123 CTPS 0.6183
38 TYMS 0.8283 81 CKS1B 0.712 124 MCM7 0.618
39 KIAA0101 0.8203 82 PCNA 0.7103 125
ZWILCH 0.618
FANCI 0.813 83 FBX05 0.7053 126 RFC5 0.6177
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41 RAD51AP1 0.8107 84 C12orf48 0.7027
42 CKS2 0.81 85 TK1 0.7017
43 MCM2 0.8063 86 BLM 0.701
[0051]
After excluding CCGs with low average expression, assays that produced
sample failures, CCGs with correlations less than 0.58, and HK genes with
correlations less than
0.95, a subset of 56 CCGs (Panel H) and 36 HK candidate genes were left.
Correlation coefficients
were recalculated on these subsets, with the rankings shown in Tables 7 and 8,
respectively.
Table 7 ("Panel H")
Correl. Correl.
Correl.
Gene Gene Gene Gene Gene Gene
w/ CCG w/ CCG
w/ CCG
# Symbol # Symbol # Symbol
mean mean
mean
1 FOXM1 0.908 20 Cl8orf24 0.817 39 FANCI
0.702
2 CDC20 0.907 21 RAD54L 0.816 40 KIF15
0.701
3 CDKN3 0.9 22 PTTG1
0.814 41 PLK4 0.688
4 CDC2 0.899 23 KIF4A 0.814 42 APOBEC3B 0.67
KIF11 0.898 24 CDCA3 0.811 43 NCAPG 0.667
6 KIAA0101 0.89 25 MCM10 0.802 44 TRIP13
0.653
7 NUSAP1 0.887 26 PRC1 0.79 45 KIF23
0.652
8 CENPF 0.882 27 DTL 0.788 46 NCAPH
0.649
9 ASPM 0.879 28 CEP55 0.787 47 TYMS
0.648
BUB1B 0.879 29 RADS] 0.783 48 GINS] 0.639
11 RRM2 0.876 30 CENPM 0.781 49 STMN1
0.63
12 DLGAP5 0.875 31 CDCA8 0.774 50 ZWINT
0.621
13 BIRC5 0.864 32 01P5 0.773 51 BLM
0.62
14 KIF20A 0.86 33 SHCBP1 0.762 52 TTK
0.62
PLK1 0.86 34 ORC6L 0.736
53 CDC6 0.619
16 TOP2A 0.851 35 CCNB1 0.727 54 KIF2C
0.596
17 TK1
0.837 36 CHEK1 0.723 55 RAD51AP1 0.567
18 PBK 0.831 37 TACC3 0.722 56 NCAPG2
0.535
19 ASF1B 0.827 38 MCM4 0.703
Table 8
Correlation
Gene Gene
with HK
# Symbol
Mean
1 RPL38 0.989
2 UBA52 0.986
3 PSMC1 0.985
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4 RPL4 0.984
RPL37 0.983
6 RPS29 0.983
7 SLC25A3 0.982
8 CLTC 0.981
9 TXNL1 0.98
PSMA/ 0.98
11 RPL8 0.98
12 MMADHC 0.979
13 RPL13A;
L00728658 0.979
14 PPP2CA 0.978
MRFAP1 0.978
[0052] The CCGs in Panel F were likewise ranked according to
correlation to the
CCG mean as shown in Table 9 below.
Table 9
Correl. Correl. Correl.
w/
Gene Gene Gene Gene Gene Gene
w/ CCG w/ CCG CCG
# Symbol # Symbol # Symbol
mean mean mean
1 DLGAP5 0.931 12 Cl8orf24 0.885 22 TOP2A
0.852
2 ASPM 0.931 13 PLK1 0.879 23 KIF20A
0.851
3 K1F11 0.926 14 CDKN3 0.874 24 KIAA0101
0.839
4 BIRC5 0.916 15 RRM2 0.871 25 CDCA3
0.835
5 CDCA8 0.902 16 RADS] 0.864 26 ASF1B
0.797
6 CDC20 0.9 17 CEP55 0.862 27 CENPM
0.786
7 MCM10 0.899 18 ORC6L 0.86 28 TK1
0.783
8 PRC1 0.895 19 RAD54L 0.86 29 PBK
0.775
9 BUB1B 0.892 20 CDC2 0.858 30 PTTG1
0.751
10 FOX111 0.889 21 CENPF 0.855 31 DTL
0.737
11 NUSAP1 0.888
[0053] When choosing specific CCGs for inclusion in any embodiment
of the
invention, the individual predictive power of each gene may be used to rank
them in importance.
The inventors have determined that the CCGs in Panel C can be ranked as shown
in Table 10 below
according to the predictive power of each individual gene. The CCGs in Panel F
can be similarly
ranked as shown in Table 11 below.
Table 10
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Gene Gene Gene
Gene p-value Gene p-value
Gene p-value
1 NUSAP1 2.8E-07 12 RUB] 8.3E-05 23 KPNA2 2.0E-02
2 DLG7 5.9E-07 13 PBK 1.2E-04 24 UBE2C 2.2E-02
3 CDC2 6.0E-07 14 TTK 3.2E-04 25 MELK 2.5E-02
4 FOXM1 1.1E-06 15 CDC45L 7.7E-04 26 CENPA
2.9E-02
MYBL2 1.1E-06 16 PRC1 1.2E-03 27 CKS2
5.7E-02
6 CDCA8 3.3E-06 17 DTL 1.4E-03 28 MAD2L1 1.7E-01
7 CDC20 3.8E-06 18 CCNB1 1.5E-03 29 UBE2S 2.0E-01
8 RRM2 7.2E-06 19 TPX2 1.9E-03 30 AURKA 4.8E-01
9 PTTG1 1.8E-05 20 ZWINT 9.3E-03 31 TIMELESS 4.8E-01
CCNB2 5.2E-05 21 KIF23 1.1E-02
11 HMMR 5.2E-05 22 TRIP13 1.7E-02
Table 11
Gene Gene Gene Gene Gene Gene
p-value p-value p-
value
# Symbol # Symbol # Symbol
1 MCM10 8.60E-10 12 BUB 1B 1.10E-05 23 C18o1124
0.0011
2 ASPM 2.30E-09 13 RAD54L 1.40E-05 24 BIRC5 0.00118
3 DLGAP5 1.20E-08 14 CEP55 2.60E-05 25 RRM2 0.00255
4 CENPF 1.40E-08 15 CDCA8 3.10E-05 26 CENPM 0.0027
5 CDC20 2.10E-08 16 TK1 3.30E-05 27 RAD51
0.0028
6 FOXM1 3.40E-07 17 DTL
3.60E-05 28 KIAA0101 0.00348
7 TOP2A 4.30E-07 18 PRC1 3.90E-05 29 CDCA3 0.00863
8 NUSAP1 4.70E-07 19 PTTG1 4.10E-05 30 PBK
0.00923
9 CDKN3 5.50E-07 20 CDC2 0.00013 31 ASF1B 0.00936
10 KIF11 6.30E-06 21 ORC6L 0.00017
11 KIF20A 6.50E-06 22 PLK1 0.0005
[0054] Thus, in some embodiments of each of the various aspects of
the invention the
plurality of test genes comprises the top 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12,
13, 14, 15, 20, 25, 30, 35, 40
or more genes listed in Table 6, 7, 9, 10, or 11. In some embodiments the
plurality of test genes
comprises at least some number of CCGs (e.g., at least 3, 4, 5, 6, 7, 8, 9,
10, 15, 20, 25, 30, 35, 40,
45, 50 or more CCGs) and this plurality of CCGs comprises at least 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 15, or
of the following genes: ASPM, BIRC5, BUB1B, CCNB2, CDC2, CDC20, CDCA8, CDKN3,
CENPF, DLGAP5, FOXM1 , KIAA0101, KIF11, KIF2C, KIF4A, MCM10, NUSAP1, PRC1,
RACGAP1, and TPX2. In some embodiments the plurality of test genes comprises
at least some
number of CCGs (e.g., at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35,
40, 45, 50 or more CCGs)
and this plurality of CCGs comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
15, or 20 of the following
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genes: TPX2, CCNB2, KIF4A, KIF2C, BIRC5, RACGAP1, CDC2, PRC1, DLGAP5/DLG7,
CEP55,
CCNB1, TOP2A, CDC20, KIF20A, BUB1B, CDKN3, NUSAP1, CCNA2, KIF11, and CDCA8. In
some embodiments the plurality of test genes comprises at least some number of
CCGs (e.g., at least
3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs) and this
plurality of CCGs
comprises any one, two, three, four, five, six, seven, eight, nine, or ten or
all of gene numbers 1 & 2,
1 to 3, 1 to 4, 1 to 5, 1 to 6, 1 to 7, 1 to 8, 1 to 9, or 1 to 10 of any of
Table 6, 7, 9, 10, or 11. In some
embodiments the plurality of test genes comprises at least some number of CCGs
(e.g., at least 3, 4,
5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs) and this
plurality of CCGs comprises
any one, two, three, four, five, six, seven, eight, or nine or all of gene
numbers 2 & 3, 2 to 4, 2 to 5, 2
to 6,2 to 7,2 to 8,2 to 9, or 2 to 10 of any of Table 6, 7, 9, 10, or 11. In
some embodiments the
plurality of test genes comprises at least some number of CCGs (e.g., at least
3, 4, 5, 6, 7, 8, 9, 10,
15, 20, 25, 30, 35, 40, 45, 50 or more CCGs) and this plurality of CCGs
comprises any one, two,
three, four, five, six, seven, or eight or all of gene numbers 3 & 4, 3 to 5,
3 to 6, 3 to 7, 3 to 8, 3 to 9,
or 3 to 10 of any of Table 6, 7, 9, 10, or 11. In some embodiments the
plurality of test genes
comprises at least some number of CCGs (e.g., at least 3, 4, 5, 6, 7, 8, 9,
10, 15, 20, 25, 30, 35, 40,
45, 50 or more CCGs) and this plurality of CCGs comprises any one, two, three,
four, five, six, or
seven or all of gene numbers 4 & 5,4 to 6,4 to 7,4 to 8,4 to 9, or 4 to 10 of
any of Table 6, 7, 9, 10,
or 11. In some embodiments the plurality of test genes comprises at least some
number of CCGs
(e.g., at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or
more CCGs) and this plurality of
CCGs comprises any one, two, three, four, five, six, seven, eight, nine, 10,
11, 12, 13, 14, or 15 or all
of gene numbers 1 & 2,1 to 3,1 to 4, 1 to 5,1 to 6,1 to 7,1 to 8,1 to 9,1 to
10, 1 to 11, 1 to 12, 1 to
13, 1 to 14, or 1 to 15 of any of Table 6, 7, 9, 10, or 11.
[0055] In CCP signatures the particular CCP genes analyzed is often
not as important
as the total number of CCP genes. The number of CCP genes analyzed can vary
depending on many
factors, e.g., technical constraints, cost considerations, the classification
being made, the cancer
being tested, the desired level of predictive power, etc. Increasing the
number of CCP genes
analyzed in a panel according to the invention is, as a general matter,
advantageous because, e.g., a
larger pool of genes to be analyzed means less "noise" caused by outliers and
less chance of an error
in measurement or analysis throwing off the overall predictive power of the
test. However, cost and
other considerations will sometimes limit this number and finding the optimal
number of CCP genes
for a signature is desirable.
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[0056] It has been discovered that the predictive power of a CCP
signature often
ceases to increase significantly beyond a certain number of CCP genes (see
FIG.1; Example 1).
More specifically, the optimal number of CCP genes in a signature (no) can be
found wherever the
following is true
(Pn+1 ¨ PO < Co,
wherein P is the predictive power (i.e., Pn is the predictive power of a
signature with n genes and
Pn+1 is the predictive power of a signature with n genes plus one) and Co is
some optimization
constant. Predictive power can be defined in many ways known to those skilled
in the art including,
but not limited to, the signature's p-value. Co can be chosen by the artisan
based on his or her
specific constraints. For example, if cost is not a critical factor and
extremely high levels of
sensitivity and specificity are desired, Co can be set very low such that only
trivial increases in
predictive power are disregarded. On the other hand, if cost is decisive and
moderate levels of
sensitivity and specificity are acceptable, Co can be set higher such that
only significant increases in
predictive power warrant increasing the number of genes in the signature.
[0057] Alternatively, a graph of predictive power as a function of
gene number may
be plotted (as in FIG.1) and the second derivative of this plot taken. The
point at which the second
derivative decreases to some predetermined value (Co') may be the optimal
number of genes in the
signature.
[0058] Example 1 and FIG.1 illustrate the empirical determination
of optimal
numbers of CCP genes in CCP panels of the invention. Randomly selected subsets
of the 31 CCP
genes listed in Table 3 were tested as distinct CCP signatures and predictive
power (i.e., p-value) for
predicting prostate cancer recurrence was determined for each. As FIG.1 shows,
p-values ceased to
improve significantly beyond about 10 to 15 CCP genes, thus indicating that a
preferred number of
CCP genes in a diagnostic or prognostic panel is from about 10 to about 15.
Thus some
embodiments of the invention provide methods comprising determining the
expression of a panel of
genes, wherein the panel comprises between about 10 and about 15 CCP genes. In
some
embodiments the panel comprises between about 10 and about 15 CCP genes and
the CCP genes
constitute at least 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%,
96%, 97%, 98%,
99%, or 100% of the panel. Any other combination of CCP genes (including any
of those listed in
Table 1 or Panels A through G) can be used to practice the invention.
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[0059] Determining expression levels can be, to varying degrees,
quantitative,
qualitative, or both. For example, when determining the BRCA1 mRNA transcript
levels in a
sample, the absolute number of transcripts can be determined. Alternatively,
the absolute number of
transcripts may be normalized against some standard as discussed above to
yield a relative rather
than absolute expression level. When determining protein expression levels,
more qualitative
analysis is common. For example, tissue samples may be stained with an
antibody against BRCA1
protein and the level of staining in tumor cells can be assigned certain semi-
quantitative numbers
(e.g., ¨1, 0, +1). Assigning particular expression levels in this way will
often be based on an internal
control (e.g., surrounding non-tumor cells) or an external control (e.g.,
unrelated BRCA-intact cells).
[0060] Those skilled in the art are familiar with various ways of
determining the
expression of a panel (plurality) of genes (e.g., CCP genes). One may
determine the expression of a
panel of genes by determining the average (e.g., mean, median, weighted
average, etc.) expression
level, normalized or absolute, of panel genes in a sample obtained from a
particular patient (either
throughout the sample or in a subset of cells from the sample or in a single
cell). Increased
expression in this context will mean the average expression is higher than the
average expression
level of these genes in normal patients (or higher than some index value,
e.g., a value that has been
determined to represent the average expression level in a reference population
(e.g., patients with
cancer or patients with the same cancer)). Alternatively, one may determine
the expression of a
panel of genes by determining the average expression level (normalized or
absolute) of at least a
certain number (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 or more)
or at least a certain
proportion (e.g., 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%)
of the genes
in the panel. Alternatively, one may determine the expression of a panel of
genes by determining the
absolute copy number of the mRNA (or protein) of all the genes in the panel
and either total or
average these across the genes.
[0061] In preferred embodiments, the test value representing the
expression level of a
test gene (e.g., BRCA _I) or a plurality of test genes (e.g., a panel of CCP
genes) is compared to one or
more reference values (or index values) to determine if expression of the test
gene(s) is high, low,
average, etc. Once BRCA and CCP expression have thus been determined as high,
low, etc., one
can, according to the methods of the present invention, determine whether BRCA
and CCP
expression are correlated or anti-correlated.
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[0062] Those skilled in the art are familiar with various ways of
deriving and using
index values. For example, the index value may represent the gene expression
levels found in a
normal sample obtained from the patient of interest, in which case an
expression level (e.g., test
value) in the test sample significantly above this index value would indicate
high expression in the
sample.
[0063] Alternatively, the index value may represent the average
expression level for a
set of individuals from a diverse population or a subset of the population.
For example, one may
determine the average expression level of a gene or gene panel in a random
sampling of patients.
This average expression level may be termed the "threshold index value." In
some embodiments of
the invention the methods comprise determining whether the expression of one
or more test genes is
"increased" or "high." In the context of the invention, "increased" or "high"
expression of a test
gene means the patient's expression level is either elevated over a normal
index value or a threshold
index (e.g., by at least some threshold amount (e.g., a standard deviation))
or within the range of
expression that has been determined in patients to be high (e.g., top quartile
of reference patients).
[0064] Alternative index values may be derived by dividing patients
into groups
based on expression level. For example, one may determine the level of
expression of the test
gene(s) for a set of patients and group the patients into terciles, quartiles,
quintiles, etc. A threshold
may be set at the boundary of each group, with test patients being placed into
a group (e.g., quartile)
depending on which threshold(s) their determined expression exceeds.
[0065] Alternatively index values may be determined thusly: In
order to assign
patients to risk groups (e.g., high likelihood of having cancer, high
likelihood of
recurrence/progression), a threshold value will be set for the cell cycle
mean. The optimal threshold
value is selected based on the receiver operating characteristic (ROC) curve,
which plots sensitivity
vs (1 ¨ specificity). For each increment of the cell cycle mean, the
sensitivity and specificity of the
test is calculated using that value as a threshold. The actual threshold will
be the value that
optimizes these metrics according to the artisan's requirements (e.g., what
degree of sensitivity or
specificity is desired, etc.).
[0066] As mentioned above, anti-correlation between BRCA and CCP
expression
indicates BRCA deficiency. Thus in one aspect the invention provides a method
for determining
whether a sample is BRCA deficient comprising measuring the expression of
BRCA1 and/or BRCA2
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(BRCA expression) in said sample, measuring the expression of a panel of CCP
genes in the sample,
and determining whether BRCA expression is correlated to CCP expression. In
this context, BRCA
and CCP expression are "correlated" in a sample if BRCA and CCP expression are
both high, low,
or intermediate in the sample. Conversely, BRCA and CCP expression are "anti-
correlated" in a
sample if one is low while the other is high or if one is either high or low
and the other is
intermediate in the sample. In a preferred embodiment BRCA and CCP expression
are anti-
correlated if BRCA (especially BR CA]) expression is low and CCP expression
(especially
expression of one of the panels in Tables 1 to 5 (e.g., Panels A to F)) is
high.
[0067] In some embodiments the sample is from a patient having (or
suspected of
having) ovarian cancer, breast cancer, lung cancer, colon cancer, or prostate
cancer, or any
combination of these. In some embodiments, the sample is a tumor tissue
sample, a blood or blood
derivative (e.g., serum, plasma) sample, a urine sample, or any other sample
derived from the body
of a patient. In some embodiments the sample used to determine expression
levels is some
derivative of these bodily samples (e.g., an isolate of the RNA, DNA, protein,
etc. from a bodily
sample).
[0068] In some embodiments, the invention provides a method for
determining
whether a sample is BRCA deficient comprising measuring the expression of
BRCA1 and/or BRCA2
(BRCA expression) in said sample, measuring the expression of a panel of CCP
genes in the sample,
and determining whether BRCA expression is correlated to CCP expression,
wherein anti-correlation
between BRCA and CCP expression indicates the sample is BRCA deficient.
[0069] In some embodiments anti-correlation between BRCA and CCP
expression
indicates the sample has BRCA hypermethylation. Some embodiments further
comprise
determining the methylation status and level of a gene or panel of genes
(preferably the BRCA _I
and/or BRCA2 gene) in the sample. As used herein, "methylation status" is used
to indicate the
presence or absence or the level or extent of methyl group modification in the
polynucleotide of at
least one gene. As used herein, "methylation level" is used to indicate the
quantitative measurement
of methylated DNA for a given gene, defined as the percentage of total DNA
copies of that gene that
are determined to be methylated, based on quantitative methylation-specific
PCR.
[0070] Any assay that can be employed to determine the methylation
status of the
gene or gene panel should suffice for the purposes of the present invention.
In general, assays are
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designed to assess the methylation status of individual genes, or portions
thereof Examples of types
of assays used to assess the methylation pattern include, but are not limited
to, Southern blotting,
single nucleotide primer extension, methylation-specific polymerase chain
reaction (MSPCR),
restriction landmark genomic scanning for methylation (RLGS-M) and CpG island
microarray,
single nucleotide primer extension (SNuPE), and combined bisulfite restriction
analysis (COBRA).
The COBRA technique is disclosed in Xiong & Laird, NUCLEIC ACIDS RES. (1997)
25:2532-2534,
which is incorporated by reference. In addition, methylation arrays may also
be employed to
determine the methylation status of a gene or panel of genes. Methylation
arrays are disclosed in
Beier et at., ADV. BIOCHEM. ENG. BIOTECHNOL. (2007) 104:1-11, which is
incorporated by
reference. For example, a method for determining the methylation state of
nucleic acids is described
in U.S. Pat. No. 6,017,704 which is incorporated by reference. Determining the
methylation state of
the nucleic acid includes amplifying the nucleic acid by means of
oligonucleotide primers that
distinguishes between methylated and unmethylated nucleic acids.
[0071] In some embodiments the panel of CCP genes comprises at
least two (or five,
or six, or ten, or 15, or more) CCP genes from any of Tables 1 to 5. In some
embodiments the panel
of CCP genes comprises at least two (or five, or six, or ten, or 15, or more)
CCP genes from any of
Tables 1 to 5. In some embodiments the panel of CCP genes comprises the genes
listed in Table 4.
In some embodiments the panel of CCP genes comprises the genes in Panel F. In
some
embodiments the panel of CCP genes comprises the genes listed in Table 5.
[0072] BRCA deficiency has been found to be correlated with, inter
alia,
progression-free survival (Example 2). Specifically, BRCA deficient patients
show a significantly
longer progression-free survival than non-BRCA-deficient patients. Thus in one
aspect the
invention provides a method of classifying a cancer comprising measuring the
expression of BRCA1
and/or BRCA2 (BRCA expression) in said sample and measuring the expression of
two or more CCP
genes in the sample. Some embodiments further comprise determining whether
BRCA expression is
correlated to CCP expression. In some embodiments, anti-correlation between
BRCA and CCP
expression indicates any one of the following: greater likelihood of survival
(e.g., progression-free
survival, overall survival, etc.), greater likelihood of response to DNA
damaging agents (e.g.,
platinum chemotherapy drugs, etc.), greater likelihood of response to drugs
targeting the poly (ADP-
ribose) polymerase (PARP) pathway, etc.
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[0073] As used herein, a patient has an "increased likelihood" of
some clinical
feature or outcome (e.g., recurrence, progression, response to a particular
therapeutic regimen, etc.)
if the probability of the patient having the feature or outcome exceeds some
reference probability or
value. The reference probability may be the probability of the feature or
outcome across the general
relevant patient population. For example, if the probability of recurrence in
the general breast cancer
population is X% and a particular patient has been determined by the methods
of the present
invention to have a probability of recurrence of Y%, and if Y > X, then the
patient has an "increased
likelihood" of recurrence. Alternatively, as discussed above, a threshold or
reference value may be
determined and a particular patient's probability of recurrence may be
compared to that threshold or
reference.
[0074] Those skilled in the art are familiar with various
techniques for determining
gene expression and any technique that determines gene expression can be used
in the methods of
the invention. In some embodiments gene expression is determined using any of
the following
techniques: quantitative PCRTM (e.g., TaqManTm), microarray hybridization
analysis, quantitative
sequencing, etc.
[0075] The results of any analyses according to the invention will
often be
communicated to physicians, genetic counselors and/or patients (or other
interested parties such as
researchers) in a transmittable form that can be communicated or transmitted
to any of the above
parties. Such a form can vary and can be tangible or intangible. The results
can be embodied in
descriptive statements, diagrams, photographs, charts, images or any other
visual forms. For
example, graphs showing expression or activity level or sequence variation
information for various
genes can be used in explaining the results. Diagrams showing such information
for additional
target gene(s) are also useful in indicating some testing results. The
statements and visual forms can
be recorded on a tangible medium such as papers, computer readable media such
as floppy disks,
compact disks, etc., or on an intangible medium, e.g., an electronic medium in
the form of email or
website on internet or intranet. In addition, results can also be recorded in
a sound form and
transmitted through any suitable medium, e.g., analog or digital cable lines,
fiber optic cables, etc.,
via telephone, facsimile, wireless mobile phone, internet phone and the like.
[0076] Thus, the information and data on a test result can be
produced anywhere in
the world and transmitted to a different location. As an illustrative example,
when an expression
level, activity level, or sequencing (or genotyping) assay is conducted
outside the United States, the
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information and data on a test result may be generated, cast in a
transmittable form as described
above, and then imported into the United States. Accordingly, the present
invention also
encompasses a method for producing a transmittable form of information on at
least one of (a)
expression level or (b) activity level for at least one patient sample. The
method comprises the steps
of (1) determining at least one of (a) or (b) above according to methods of
the present invention; and
(2) embodying the result of the determining step in a transmittable form. The
transmittable form is
the product of such a method.
[0077] Techniques for analyzing such expression, activity, and/or
sequence data
(indeed any data obtained according to the invention) will often be
implemented using hardware,
software or a combination thereof in one or more computer systems or other
processing systems
capable of effectuating such analysis.
[0078] Thus one aspect of the present invention provides systems
related to the above
methods of the invention. In one embodiment the invention provides a system
for determining gene
expression in a tumor sample, comprising: (1) a sample analyzer for
determining the expression
levels of BRCA1 and/or BR and a panel of genes comprising at least two CCP
genes in a sample,
wherein the sample analyzer contains the sample, mRNA from the sample and
expressed from the
panel of genes, or cDNA synthesized from said mRNA; (2) a first computer
program means for (a)
receiving gene expression data on BRCA _I and/or BRCA2, (b) receiving gene
expression data on at
least two test genes selected from the panel of genes, (b) weighting the
determined expression of
each of the test genes with a predefined coefficient, and (c) combining the
weighted expression to
provide a CCP test value representing the expression level of the panel of
genes.
[0079] As with the methods of the invention, the systems of the
invention may be
used to determine whether BRCA and/or CCP expression in a sample are high,
low, etc. Thus in
some embodiments the above system further comprises a computer program means
of comparing the
expression of BRCA1 and/or BRCA2 to a reference value, wherein expression of
BRCA1 and/or
BRCA2 above this reference value indicates said BR CA _I and/or BRCA2
expression is high. In some
embodiments the above system further comprises a computer program means of
comparing the CCP
test value to a reference value, wherein a CCP test value above this reference
value indicates CCP
expression is high.
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[0080] As with the methods of the invention, the systems of the
invention may be
used to determine whether BRCA and CCP expression are correlated in a sample.
Thus in some
embodiments the above system further comprises a computer program means of
comparing the
expression of BRCA1 and/or BRCA2 to the CCP test value, wherein high
expression of BRCA /
and/or BR coupled with a high CCP test value indicates BRCA and CCP
expression are
correlated, wherein low expression of BRCA/ and/or BRCA2 coupled with a low
CCP test value
indicates BRCA and CCP expression are correlated, wherein high expression of
BRCA/ and/or
BRCA2 coupled with a low CCP test value indicates BRCA and CCP expression are
anti-correlated,
and wherein low expression of BRCA1 and/or BRCA2 coupled with a high CCP test
value indicates
BRCA and CCP expression are anti-correlated.
[0081] As with the methods of the invention, the systems of the
invention may be
used to determine whether the sample is BRCA deficient. Thus in some
embodiments the above
system further comprises a computer program means of receiving data on the
correlation between
BRCA expression and CCP expression in a patient sample and concluding that the
sample is BRCA
deficient if BRCA expression and CCP expression are anti-correlated in the
sample.
[0082] In some embodiments the system comprises a sample analyzer
for
determining the methylation status of BRCA1 and/or BRCA2. In some embodiments
this sample
analyzer is the same as the sample analyzer for determining gene expression.
[0083] In the systems of the invention, as with the methods of the
invention described
above, the test genes may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15,
20, 25, 30, 35, 40, 45, 50, 70,
80, 90, 100, 200, or more CCP genes. In some embodiments the test genes
comprise at least 10, 15,
20, or more CCP genes. In some embodiments the test gene comprises between 5
and 100 CCP
genes, between 7 and 40 CCP genes, between 5 and 25 CCP genes, between 10 and
20 CCP genes,
or between 10 and 15 CCP genes. In some embodiments CCP genes comprise at
least a certain
proportion of the test genes used to provide a test value. Thus in some
embodiments the test genes
comprise at least 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 96%,
97%, 98%,
99%, or 100% CCP genes. In some preferred embodiments the test genes comprise
at least 10, 15,
20, 25, 30, 35, 40, 45, 50, 70, 80, 90, 100, 200, or more CCP genes, and such
CCP genes constitute
at least 50%, 60%, 70%, preferably at least 75%, 80%, 85%, more preferably at
least 90%, 95%,
96%, 97%, 98%, or 99% or more of the total number of test genes.
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[0084] In some embodiments, the system further comprises a display
module
displaying the comparison between the test value and the one or more reference
values, or displaying
a result of the comparing step.
[0085] In a preferred embodiment, the amount of RNA transcribed
from the panel of
genes including test genes is measured in the sample. In addition, the amount
of RNA of one or
more housekeeping genes in the sample is also measured, and used to normalize
or calibrate the
expression of the test genes, as described above.
[0086] The sample analyzer can be any instrument useful in
determining gene
expression, including, e.g., a sequencing machine, a real-time PCR machine, a
microarray
instrument, etc. In embodiments comprising a sample analyzer for determining
methylation status,
such a sample analyzer can be any instrument useful in determining methylation
status.
[0087] The computer-based analysis function can be implemented in
any suitable
language and/or browsers. For example, it may be implemented with C language
and preferably
using object-oriented high-level programming languages such as Visual Basic,
SmallTalk, C++, and
the like. The application can be written to suit environments such as the
Microsoft WindowsTM
environment including WindowsTM 98, WindowsTM 2000, WindowsTM NT, and the
like. In addition,
the application can also be written for the MacIntoshTM, SUNTM, UNIX or LINUX
environment. In
addition, the functional steps can also be implemented using a universal or
platform-independent
programming language. Examples of such multi-platform programming languages
include, but are
not limited to, hypertext markup language (HTML), JAVATM, JavaScriptTM, Flash
programming
language, common gateway interface/structured query language (CGI/SQL),
practical extraction
report language (PERL), AppleScriptTM and other system script languages,
programming
language/structured query language (PL/SQL), and the like. JavaTM- or
JavaScriptTm-enabled
browsers such as HotJavaTM, MicrosoftTM ExplorerTM, or NetscapeTM can be used.
When active
content web pages are used, they may include JavaTM applets or ActiveXTM
controls or other active
content technologies.
[0088] The analysis function can also be embodied in computer
program products
and used in the systems described above or other computer- or internet-based
systems. Accordingly,
another aspect of the present invention relates to a computer program product
comprising a
computer-usable medium having computer-readable program codes or instructions
embodied
thereon for enabling a processor to carry out gene expression analysis. These
computer program
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instructions may be loaded onto a computer or other programmable apparatus to
produce a machine,
such that the instructions which execute on the computer or other programmable
apparatus create
means for implementing the functions or steps described above. These computer
program
instructions may also be stored in a computer-readable memory or medium that
can direct a
computer or other programmable apparatus to function in a particular manner,
such that the
instructions stored in the computer-readable memory or medium produce an
article of manufacture
including instruction means which implement the analysis. The computer program
instructions may
also be loaded onto a computer or other programmable apparatus to cause a
series of operational
steps to be performed on the computer or other programmable apparatus to
produce a computer
implemented process such that the instructions which execute on the computer
or other
programmable apparatus provide steps for implementing the functions or steps
described above.
[0089] Some embodiments of the present invention provide a system
for determining
whether a patient sample is BRCA deficient. Generally speaking, the system
comprises (1)
computer program means for receiving, storing, and/or retrieving data on the
correlation between
BRCA and CCP expression in a patient sample; (2) computer program means for
querying this
patient data; (3) computer program means for concluding whether there is or is
not a correlation; and
optionally (4) computer program means for outputting/displaying this
conclusion. In some
embodiments this means for outputting the conclusion may comprise a computer
program means for
informing a health care professional of the conclusion. In some embodiments
the system further
comprises a computer program means for receiving, storing, and/or retrieving
data on BRCA and
CCP expression in a patient sample and a computer program means for
determining if BRCA and
CCP expression are correlated in such sample.
[0090] One example of such a computer system is the computer system
[300]
illustrated in FIG.3. Computer system [300] may include at least one input
module [330] for
entering patient data into the computer system [300]. The computer system
[300] may include at
least one output module [324] for indicating whether a patient has an
increased or decreased
likelihood of response and/or indicating suggested treatments determined by
the computer system
[300]. Computer system [300] may include at least one memory module [306] in
communication
with the at least one input module [330] and the at least one output module
[324].
[0091] The at least one memory module [306] may include, e.g., a
removable storage
drive [308], which can be in various forms, including but not limited to, a
magnetic tape drive, a
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floppy disk drive, a VCD drive, a DVD drive, an optical disk drive, etc. The
removable storage
drive [308] may be compatible with a removable storage unit [310] such that it
can read from and/or
write to the removable storage unit [310]. Removable storage unit [310] may
include a computer
usable storage medium having stored therein computer-readable program codes or
instructions
and/or computer readable data. For example, removable storage unit [310] may
store patient data.
Example of removable storage unit [310] are well known in the art, including,
but not limited to,
floppy disks, magnetic tapes, optical disks, and the like. The at least one
memory module [306] may
also include a hard disk drive [312], which can be used to store computer
readable program codes or
instructions, and/or computer readable data.
[0092] In addition, as shown in FIG.3, the at least one memory
module [306] may
further include an interface [314] and a removable storage unit [316] that is
compatible with
interface [314] such that software, computer readable codes or instructions
can be transferred from
the removable storage unit [316] into computer system [300]. Examples of
interface [314] and
removable storage unit [316] pairs include, e.g., removable memory chips
(e.g., EPROMs or
PROMs) and sockets associated therewith, program cartridges and cartridge
interface, and the like.
Computer system [300] may also include a secondary memory module [318], such
as random access
memory (RAM).
[0093] Computer system [300] may include at least one processor
module [302]. It
should be understood that the at least one processor module [302] may consist
of any number of
devices. The at least one processor module [302] may include a data processing
device, such as a
microprocessor or microcontroller or a central processing unit. The at least
one processor module
[302] may include another logic device such as a DMA (Direct Memory Access)
processor, an
integrated communication processor device, a custom VLSI (Very Large Scale
Integration) device or
an ASIC (Application Specific Integrated Circuit) device. In addition, the at
least one processor
module [302] may include any other type of analog or digital circuitry that is
designed to perform
the processing functions described herein.
[0094] As shown in FIG.3, in computer system [300], the at least
one memory
module [306], the at least one processor module [302], and secondary memory
module [318] are all
operably linked together through communication infrastructure [320], which may
be a
communications bus, system board, cross-bar, etc.). Through the communication
infrastructure
[320], computer program codes or instructions or computer readable data can be
transferred and
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exchanged. Input interface [326] may operably connect the at least one input
module [326] to the
communication infrastructure [320]. Likewise, output interface [322] may
operably connect the at
least one output module [324] to the communication infrastructure [320].
[0095] The at least one input module [330] may include, for
example, a keyboard,
mouse, touch screen, scanner, and other input devices known in the art. The at
least one output
module [324] may include, for example, a display screen, such as a computer
monitor, TV monitor,
or the touch screen of the at least one input module [330]; a printer; and
audio speakers. Computer
system [300] may also include, modems, communication ports, network cards such
as Ethernet
cards, and newly developed devices for accessing intranets or the internet.
[0096] The at least one memory module [306] may be configured for
storing patient
data entered via the at least one input module [330] and processed via the at
least one processor
module [302]. Patient data relevant to the present invention may include
expression level, activity
level, copy number and/or sequence information for a CCP and optionally PTEN.
Patient data
relevant to the present invention may also include clinical parameters
relevant to the patient's
disease. Any other patient data a physician might find useful in making
treatment
decisions/recommendations may also be entered into the system, including but
not limited to age,
gender, and race/ethnicity and lifestyle data such as diet information. Other
possible types of patient
data include symptoms currently or previously experienced, patient's history
of illnesses,
medications, and medical procedures.
[0097] The at least one memory module [306] may include a computer-
implemented
method stored therein. The at least one processor module [302] may be used to
execute software or
computer-readable instruction codes of the computer-implemented method. The
computer-
implemented method may be configured to, based upon the patient data, indicate
whether the patient
has an increased likelihood of recurrence, progression or response to any
particular treatment,
generate a list of possible treatments, etc.
[0098] In certain embodiments, the computer-implemented method may
be
configured to identify a patient as having or not having cancer or as having
or not having an
increased likelihood of recurrence or progression. For example, the computer-
implemented method
may be configured to inform a physician that a particular patient has cancer,
has a quantified
probability of having cancer, has an increased likelihood of recurrence, etc.
Alternatively or
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additionally, the computer-implemented method may be configured to actually
suggest a particular
course of treatment based on the answers to/results for various queries.
[0099] FIG.4 illustrates one embodiment of a computer-implemented
method [400]
of the invention that may be implemented with the computer system [300] of the
invention. The
method [400] begins with a query ([410]), either sequentially or substantially
simultaneously. If the
answer to/result for this query is "Yes" [420], the method concludes [430]
that the sample is BRCA
deficient. If the answer to/result for this query is "No" [421], the method
concludes [431] that the
sample is not necessarily BRCA deficient. The method [400] may then proceed
with more queries,
make a particular treatment recommendation ([440], [441]), or simply end.
[00100] In some embodiments, the computer-implemented method of the
invention
[400] is open-ended. In other words, the apparent first step [410] in FIG.4
may actually form part of
a larger process and, within this larger process, need not be the first
step/query. Additional steps
may also be added onto the core methods discussed above. These additional
steps include, but are
not limited to, informing a health care professional (or the patient itself)
of the conclusion reached;
combining the conclusion reached by the illustrated method [400] with other
facts or conclusions to
reach some additional or refined conclusion regarding the patient's diagnosis,
prognosis, treatment,
etc.; making a recommendation for treatment; additional queries about
additional biomarkers,
clinical parameters, or other useful patient information (e.g., age at
diagnosis, general patient health,
etc.).
[00101] Regarding the above computer-implemented method [400], the
answers to
queries may be determined by the method instituting a search of patient data
for the answer. For
example, to answer the query [410], patient data may be searched for BRCA and
CCP expression
data. If such a comparison has not already been performed, the method may
compare these data to
some reference in order to determine if the respective expressions are high,
low, average, etc. The
method may also compare the respective expressions to determine if BRCA and
CCP expression are
correlated. Additionally or alternatively, the method may present one or more
of the queries (e.g.,
[410]) to a user (e.g., a physician) of the computer system [300]. For
example, the query [410] may
be presented via an output module [324]. The user may then answer "Yes" or
"No" via an input
module [330]. The method may then proceed based upon the answer received.
Likewise, the
conclusions [430, 431, 440, 441] may be presented to a user of the computer-
implemented method
via an output module [324].
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[00102] As used herein in the context of computer-implemented
embodiments of the
invention, "displaying" means communicating any information by any sensory
means. Examples
include, but are not limited to, visual displays, e.g., on a computer screen
or on a sheet of paper
printed at the command of the computer, and auditory displays, e.g., computer
generated or recorded
auditory expression of a patient sample's BRCA status.
[00103] The practice of the present invention may also employ
conventional biology
methods, software and systems. Computer software products of the invention
typically include
computer readable media having computer-executable instructions for performing
the logic steps of
the method of the invention. Suitable computer readable medium include floppy
disk, CD-
ROM/DVD/DVD-ROM, hard-disk drive, flash memory, ROM/RAM, magnetic tapes and
etc. Basic
computational biology methods are described in, for example, Setubal et at.,
INTRODUCTION TO
COMPUTATIONAL BIOLOGY METHODS (PWS Publishing Company, Boston, 1997); Salzberg
et at.
(Ed.), COMPUTATIONAL METHODS IN MOLECULAR BIOLOGY, (Elsevier, Amsterdam,
1998); Rashidi
& Buehler, BIOINFORMATICS BASICS: APPLICATION IN BIOLOGICAL SCIENCE AND
MEDICINE (CRC
Press, London, 2000); and Ouelette & Bzevanis, BIOINFORMATICS: A PRACTICAL
GUIDE FOR
ANALYSIS OF GENE AND PROTEINS (Wiley & Sons, Inc., 2nd ed., 2001); see also,
U.S. Pat. No.
6,420,108.
[00104] The present invention may also make use of various computer
program
products and software for a variety of purposes, such as probe design,
management of data, analysis,
and instrument operation. See U.S. Pat. Nos. 5,593,839; 5,795,716; 5,733,729;
5,974,164;
6,066,454; 6,090,555; 6,185,561; 6,188,783; 6,223,127; 6,229,911 and
6,308,170. Additionally, the
present invention may have embodiments that include methods for providing
genetic information
over networks such as the Internet as shown in U.S. Ser. Nos. 10/197,621 (U.S.
Pub. No.
20030097222); 10/063,559 (U.S. Pub. No. 20020183936), 10/065,856 (U.S. Pub.
No.
20030100995); 10/065,868 (U.S. Pub. No. 20030120432); 10/423,403 (U.S. Pub.
No.
20040049354).
[00105] In one aspect, the present invention provides methods of
treating a cancer
patient comprising determining whether BRCA and CCP expression are correlated
in a sample from
the patient and (1) recommending, prescribing, or administering a particular
treatment regimen if
BRCA and CCP expression are anti-correlated in the sample or (2) recommending,
prescribing, or
administering a particular treatment regimen if BRCA and CCP expression are
correlated in the
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sample. In some embodiments, the particular treatment regimen comprises a DNA-
damaging agent
(e.g., platinum) chemotherapy if BRCA and CCP expression are anti-correlated
in the sample. In
some embodiments, the particular treatment regimen comprises PARP-inhibitor
drugs if BRCA and
CCP expression are anti-correlated in the sample. In some embodiments, if BRCA
and CCP
expression are correlated in the sample the particular treatment regimen
comprises a regimen chosen
from the group consisting of AC, FEC, FAC, FEC-T, Epirubicin-CMF, TAC, AC-
Paclitaxel, AT,
TC, T-Carboplatin, Lapatinib, Trastuzumab, Bevacizumab, Sunitinib, Docetaxel,
Paclitaxel, Nano
Paclitaxel, Docetaxel/capecitabine, Paclitaxel/gemcitabine,
Docetaxel/gemcitabine, Gemcitabine,
Trastuzumab/Docetaxel, Trastuzumab/Paclitaxel, Capecitabine,
Lapatinib/Capecitabine,
Ixabepilone, and Toco-P.
[00106] The methods of the invention are useful, inter alia, in
identifying individuals
who may benefit from germline BRCA testing but who may not meet the commonly
applied criteria
for identifying such individuals. For instance, commonly used criteria include
personal history of
cancer and significant family history of cancer. As used herein, "personal
history of cancer" has its
conventional meaning in the art (e.g., a previous cancer in the individual in
question). As used
herein, "significant family history of cancer" also has its conventional
meaning in the art. Various
guidelines have been devised and are used by healthcare professionals to
determine whether an
individual has a "significant family history of cancer." These include
guidelines of American
Gastroenterological Association; American Society of Breast Surgeons; American
Society of
Clinical Oncology; American Society of Colon & Rectal Surgeons; Oncology
Nursing Society;
Society of Gynecologic Oncologists (e.g., women with breast cancer at <40
years, women with
bilateral breast cancer (particularly if the first cancer was at <50 years);
women with breast cancer at
<50 years and a close relativet with breast cancer at <50 years; women of
Ashkenazi Jewish
ancestry with breast cancer at <50 years; women with breast or ovarian cancer
at any age and two or
more close relatives with breast cancer at any age (particularly if at least
one breast cancer was at
<50 years); unaffected women with a first or second degree relative that meets
one of the above
criteria), etc. Other widely accepted criteria include individuals with a
personal or family history of
breast cancer before age 50 or ovarian cancer at any age; individuals with two
or more primary
diagnoses of breast and/or ovarian cancer; individuals of Ashkenazi Jewish
descent with a personal
or family history of breast cancer before age 50 or ovarian cancer at any age;
male breast cancer
patients. A patient lacks a "significant family history of cancer" when one or
more of these criteria
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are not met (usually all). Thus in some embodiments the patient to be assessed
by the methods of
the invention has a significant family history of cancer. In some embodiments
the patient has a
personal history of cancer.
[00107] In another aspect of the present invention, a kit is
provided for practicing the
methods and for use in the systems of the present invention. The kit may
include a carrier for the
various components of the kit. The carrier can be a container or support, in
the form of, e.g., bag,
box, tube, rack, and is optionally compartmentalized. The carrier may define
an enclosed
confinement for safety purposes during shipment and storage.
[00108] The kit includes various components useful in determining
the expression of
BRCA _I and/or BRCA2, the expression of at least two CCP genes, and optionally
the expression of
one or more housekeeping gene markers and/or the methylation status of BR CA 1
and/or BRCA2.
For example, the kit many include oligonucleotides specifically hybridizing
under high stringency to
mRNA or cDNA of BRCA1, BRCA2, or the genes in Tables 1 to 5 or Panels A to F.
Such
oligonucleotides can be used as PCR primers in RT-PCR reactions, or
hybridization probes. In some
embodiments the kit comprises reagents (e.g., probes, primers, and or
antibodies) for determining the
expression level of a panel of genes, where said panel comprises at least 25%,
30%, 40%, 50%,
60%, 75%, 80%, 90%, 95%, 99%, or 100% CCP genes (e.g., CCP genes in Tables 1
to 5 or Panels A
to F). In some embodiments the kit consists of reagents (e.g., probes,
primers, and or antibodies) for
determining the expression level of no more than 2500 genes, wherein at least
5, 10, 15, 20, 30, 40,
50, 60, 70, 80, 90, 100, 120, 150, 200, 250, or more of these genes are CCP
genes (e.g., Tables 1 to 5
or Panels A to F).
[00109] The oligonucleotides in the detection kit can be labeled
with any suitable
detection marker including but not limited to, radioactive isotopes,
fluorephores, biotin, enzymes
(e.g., alkaline phosphatase), enzyme substrates, ligands and antibodies, etc.
See Jablonski et at.,
Nucleic Acids Res., 14:6115-6128 (1986); Nguyen et al., Biotechniques, 13:116-
123 (1992); Rigby
et at., J. Mot. Biol., 113:237-251 (1977). Alternatively, the oligonucleotides
included in the kit are
not labeled, and instead, one or more markers are provided in the kit so that
users may label the
oligonucleotides at the time of use.
[00110] Various other components useful in the detection techniques
may also be
included in the detection kit of this invention. Examples of such components
include, but are not
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limited to, Taq polymerase, deoxyribonucleotides, dideoxyribonucleotides,
other primers suitable for
the amplification of a target DNA sequence, RNase A, and the like. In
addition, the detection kit
preferably includes instructions on using the kit for practice the prognosis
method of the present
invention using human samples.
EXAMPLE 1
[00111] The following example illustrates the validation of a CCP
gene panel in
predicting predicting time to chemical recurrence after radical prostatectomy
in prostate cancer
patients. The following CCP gene panel was tested:
Table 12
31-CCP Gene Cancer Recurrence
Signature
AURKA DTL PTTG1
RUB] FOXM1 RRM2
CCNB1 HMMR TIMELESS
CCNB2 KIF23 TPX2
CDC2 KPNA2 TRIP13
CDC20 MAD2L1 TTK
CDC45L MELK UBE2C
CDCA8 MYBL2 UBE2S
CENPA NUSAP1 ZWINT
CKS2 PBK
DLG7 PRC1
[00112] Mean mRNA expression for the above 31 CCP genes was tested
on 440
prostate tumor FFPE samples using a Cox Proportional Hazard model in Splus 7.1
(Insightful, Inc.,
Seattle WA). The p-value for the likelihood ratio test was 3.98 x 10-5. The
mean of CCP expression
is robust to measurement error and individual variation between genes.
[00113] The study further aimed at determining the optimal number of
CCP genes to
include in a CCP panel. As mentioned above, CCP expression levels are
correlated to each other so
it was possible that measuring a small number of genes would be sufficient,
e.g., to predict prostate
cancer outcome. In order to determine the optimal number of CCP genes for the
signature, the
predictive power of the mean was tested for randomly selected sets of from 1
to 30 of the CCP genes
listed above. To evaluate how smaller subsets of the larger CCP set (i.e.,
smaller CCP panels)
performed, the study also compared how well the signature predicted outcome as
a function of the
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number of CCP genes included in the signature (FIG.1). Time to chemical
recurrence after prostate
surgery was regressed on the CCP mean adjusted by the post-RP nomogram score.
Data consist of
TLDA assays expressed as deltaCT for 199 FFPE prostate tumor samples and 26
CCP genes and
were analyzed by a CoxPH multivariate model. P-values are for the likelihood
ratio test of the full
model (nomogram + cell cycle mean including interaction) vs the reduced model
(nomogram only).
As shown in Table 13 below and FIG.1, small CCP signatures (e.g., 2, 3, 4, 5,
6 CCP genes, etc.)
add significantly to the Kattan-Stephenson nomogram:
Table 13
# of CCP Mean of log10 (p-
genes value)*
1 -3.579
2 -4.279
3 -5.049
4 -5.473
-5.877
6 -6.228
* For 1000 randomly drawn subsets, size 1 through 6, of cell cycle genes.
[00114] This simulation showed that there is a threshold range of
CCP genes in a panel
that provides significantly improved predictive power (FIG.1).
EXAMPLE 2
Patient Characteristics
[00115] Unselected human ovarian cancer tissues (235) were obtained
under
Institutional Review Board (IRB)-approved protocols. Table 9 shows the
patient/cancer
characteristics.
RNA/DNA Extraction from Frozen Cancers
[00116] 10[Lm thick sections from frozen cancer blocks in Tissue-Tek
OCT (Qiagen,
Valencia, CA) were homogenized using a TissueRuptor (Qiagen) after adding
QIAzol lysis reagent,
followed by RNA isolation using a QIAgen miRNAeasy Mini Kit per manufacturers
protocol. A
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QIAamp DNA Mini Kit (QIAgen) was used to isolate DNA per the manufacturer's
protocol with
overnight incubation at 56 C and RNaseA treatment.
Quantitative-PCR ¨ BRCA1
[00117] Reverse transcription was performed using a High-Capacity
cDNA Reverse
Transcription Kit (Applied Biosystems, Inc.) per manufacturer instructions.
For pre-amplification, a
0.2x probe mix was made by combining 1[LL of 91 20X gene expression assays
from Applied
Biosystems Inc. and 94, of low-EDTA TE. Pre-amplification was performed using
2.54, of 2x
TaqMan PreAmp Master Mix (Applied Biosystems, Inc), 1.254, of 0.2x probe mix,
and 1.254,
cDNA. Applied Biosystems TaqMan assays (BRCA 1:
Hs00173233 ml/Hs00173237 ml/Hs01556190 ml/Hs01556191 ml; BRCA2: Hs00609060 ml;
housekeepers: Hs99999908 ml (GUSB)/Hs00188166 ml (SDHA)/Hs00237047 ml
(YWHAZ)/Hs00824723 ml (UBC)/Hs00609297 ml (HMBS)) were used for pre-
amplification and
qPCR on a Fluidigm (South San Francisco, CA) BioMark instrument. Cycle
conditions were 95 C
for 10 minutes, 17 cycles of 95 C for 15 seconds and 60 C for 4 minutes. The
PCR products were
diluted 1:5 with low- EDTA TE. Samples were assessed on gene expression M48
dynamic arrays
(Fluidigm) per manufacturer's protocol.
Quantitative PCR ¨ CCP Score
[00118] 50Ong-ln of RNA was treated with Amplification Grade
Deoxyribonuclease
I (Sigma-Aldrich Inc.) in a 101AL reaction at room temperature for 30 minutes.
1[LL of Stop Solution
is then added and heated to 70 C for 10 minutes. 14[iLs of RNase-free water is
added to make lug
of RNA in 25[LLs to be used in a 50[LL reverse transcription reaction using
High-Capacity cDNA
Reverse Transcription Kit (Applied Biosystems, Inc.)
[00119] Pre-Amplification was done using a 0.2x probe mix made
combining 1[LL of
the 48 individual 20X gene expression assays from Applied Biosystems, Inc. and
521iLs of low-
EDTA TE. Pre-amplification was performed using 2.5[LLs of TaqMan0 PreAmp
Master Mix (2X)
(Applied Biosystems, Inc.), 1.25[LLs of the 0.2x probe mix, and 1.250_, cDNA.
[00120] The range of expression of the genes involved in the
calculation of CCP score
was too large to allow accurate quantification under uniform conditions. Two
pre-amplifications
were run independently at each of the two cycle conditions, 8 and 18 cycles.
Cycle conditions were
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95 C for 10 minutes and 8/18 cycles of 95 C X 15 seconds and 60 C X 4 minutes.
The products
were then diluted 1:5 using low-EDTA TE. Samples were run versus the 48 assays
(Table 10) on
the Fluidigm Gene Expression 48.48 Dynamic Arrays per manufacturers' protocol.
qPCR Analysis
[00121] The comparative CT method was used to calculate relative
gene expression
using the CT for the BRCA2 assay, the average Cis from the BRCA1 assays, and
the average Cis
from housekeeper genes. qPCR was performed in 220 cancers where high quality
RNA was
obtained.
BRCA1 Methylation Assay
[00122] MeAH-011E Methyl-ProfilerTM DNA Methylation PCR Assay Human
Breast Cancer, Signature Panel (24-Genes, 385-Well Plates) was used per
manufacturers' protocol
for the 4-sample format. 125 ng RNase treated genomic DNA was used per
restriction enzyme
digestion, for a total of 500 ng. Incubation of digestion reactions was
performed at 37 C for 6 hours.
Data Analysis
Calculation of CCP Score
[00123] CCP scores were calculated for each sample in the following
manner. CT
values less than 8 were considered to be above the limit of detection and were
removed from the
analysis. Data from the two pre-amplification cycling conditions were
normalized by subtracting off
the average of the CT values of the genes that were not missing any values and
whose CT were
between 8 and 23 under both conditions. These centered CT values were averaged
for each gene
with at least two CT values whose standard deviation was less than or equal to
3. ACT was calculated
as the difference in centered CT values between the gene of interest and the
average of the
housekeeper genes. ACT was then centered for each gene by the average ACT on
all the samples that
were not missing ACT for any gene. The negative of the average of the centered
ACT across the cell-
cycle genes is the CCP score.
Abnormal BRCA1 Expression
[00124] FIG.2 shows the relationship between BRCA1 and cell-cycle
gene (as
measured by the CCP score) expression. The samples where BRCA1 and cell-cycle
gene expression
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are correlated (circles, correlation = 0.65) are considered to have normal
expression. The samples
with high CCP scores but low expression of BR CA] are considered to have
abnormal expression
(i.e., anti-correlation; X's). FIG.5 shows that, upon further analysis, the
samples with anti-
correlation between BRCA1 and CCP expression (those within the shaded circle)
generally turned
out to have BRCA _1 hypermethylation (larger points indicate higher extent of
methylation). An
iterative method was used to identify these samples. First, a linear model was
fit with BRCA1
expression as the response and CCP score as the only predictor. Next, the
differences between the
observed and fitted BRCA 1 expression from the previous step were separated
into two clusters using
k-means clustering. Last, the lower cluster was removed and the process was
repeated until the
cluster membership did not change from one iteration to the next.
BRCA Deficiency
[00125] A patient sample was considered BRCA deficient (79 out of
242 tested) if it
had a mutation in BRCA1/2 (41 out of 227 tested), abnormal expression of BRCA1
(47/239), or more
than 10% methylation of BRCA/ (9 out of 53 tested).
Association between PFS and BRCA Deficiency
[00126] The association between progression free survival (PFS) and
BRCA
deficiency was tested using the partial likelihood ratio test from a Cox's
proportional hazards model
with PFS as the response and BRCA deficiency as the only predictor. The hazard
ratio (HR) for
deficient patients versus non-deficient patients was 0.66 (p-value = 0.014, n
= 193, 16% censoring),
indicating decreased risk of disease progression in deficient patients.
Table 14
Total Number
235
of Patients
Range 23-92
Age at
Median 60
Diagnosis
Unknown 20 (8.5%)
19 ¨ 6141
Range
Follow-up days
Time Median 1071 days
Unknown 8 (3.5%)
1 11(5%)
Sta 2 14(6%)
ge
3 156 (66%)
4 33 (14%)
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Unknown 21(9%)
Serous 186 (79%)
Non-serous 13 (6%)
Histology
Mixed 13 (6%)
Unknown 22 (95)
1 13 (5.5%)
2 19(8%)
Grade
3 180 (76.5%)
Unknown 23 (10%)
0 12(5)
Residual
<1 cm 126 (53.5%)
Disease after
>1cm 60 (25.5%)
Surgery
Unknown 37 (16%)
Yes 230 (98%)
Surgery No 5 (2%)
Unknown 0
No chemotherapy 9 (3.8%)
Unknown 33 (14%)
Platinum (cis or
carboplatin)-based 17 (7.2%)
Chemotherapy (no taxane)
Platinum plus
taxane (paclitaxel
176 (74.9%)
or docetaxel)-
based
Table 15
CCP Entrez Housekeeper Entrez
Genes GeneId Genes GeneId
ASFIB 55723 CLTC 1213
ASPM 259266 MMADHC 27249
BIRC5 332 MRFAP1 93621
BUBJB 701 PPP2CA 5515
CThorf24 220134 PSMA1 5682
CDC20 983 PSMC1 5700
CDC2 991 RPL13A 23521
CDCA3 83461 RPL37 6167
CDCA8 55143 RPL38 6169
CDKN3 1033 RPL4 6124
CENPF 1063 RPL8 6132
CENPM 79019 RPS29 6235
CEP55 55165 SLC25A3 5250
DLGAP5 9787 TXNL/ 9352
DTL 51514 UBA52 7311
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FOXM1 2305
KIAA0101 9768
KIF11 3832
KIF20A 10112
MCM10 55388
NUSAP1 51203
ORC6L 23594
PBK 55872
PLK1 5347
PRC1 9055
PTTG1 9232
RADS] 5888
RAD54L 8438
RRM2 6241
TK1 7083
TOP2A 7153
EXAMPLE 3
Description of Clinical Data
[00127] The samples in this study consisted of 216 fresh frozen
breast tumors from 4
commercial sources. All but one had ER, PR, and HER2 status. Unless stated
otherwise, all assay
and statistical details for this study were as described in Example 2 above.
ER/PR/HER2 Subtype Classification
[00128] Three ER-patients were PR+. As such, each sample was
assigned one of three
subtypes based on ER status first and then on HER2 status in the ER-tumors:
113 ER+, 64 triple
negative, and 38 ER-/HER2+. One ER- patient was missing HER2 status. As a
result her tumor
subtype could not be assigned.
BRCA1 Expression
[00129] BRCA1 expression was measured and calculated for 215
patients' tumors.
Three qPCR assays for BRCA1 (Hs00173233 ml (BRCA1), Hs00173237 ml (BRCA1(2)),
and
Hs01556190 ml (BRCA1(3))) and three housekeeper genes (MMADHC, RPS23, and
SDHA) were
used to measure BRCA1 expression on these samples. Each sample was
preamplified with all the
assays 4 times: twice for 12 cycles and twice for 18 cycles. CT was determined
for each assay-
sample-preamp. For each sample, the genes with CT between 8 and 23 on all
preamps were
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identified as centering genes. They were averaged for each preamp. This
quantity was subtracted
from the CT of each measurement to put the CT from different numbers of cycles
of preamp on the
same scale. All replicates with CT greater than 8 were averaged for each
assay. ACT was calculated
for each BRCA _I assay by subtracting the average of the three housekeeper
genes. The pairwise
relationships between the normalized expression for the BRCA1 assays are shown
in Figure 6.
[00130] As the correlation of the three BRCA1 assays was high, BRCA
_1 expression
was calculated as the average -ACT of the three assays. Figure 7 is a
histogram of the final BRCA1
expression values.
CCP Score
[00131] Cell-cycle gene expression was measured and calculated for
215 patients'
samples in the same manner as BR CA _I expression, with a few exceptions.
First, the ProAssay04 set
of assays, which consists of 31 cell-cycle genes and 15 housekeepers (Table 15
above), was used
instead of 3 housekeepers and 3 assays for the gene of interest. Second, 8 and
18 cycles of preamp
were used instead of 12 and 18. Lastly, before averaging all the genes, each
gene was centered by
the average expression of that gene in the samples where all the cell-cycle
genes performed well.
[00132] The correlation between each of the cell-cycle genes and the
CCP score is
shown in Figure 8.
Abnormal BRCA1 expression
[00133] Figure 9 is a plot of CCP score and BRCA1 expression. Figure
10 is a plot of
CCP score and BRCA 1 expression colored by ER/PR/HER2 subtype as determined by
IHC.
BRCA1 Methylation
[00134] Methylation of the BRCA _1 promoter region was measured in
199 tumors.
Figure 11 shows the relationship between BRCA1 methylation and expression.
Figure 12 shows the
relationship between BRCA _I expression, CCP score, and BRCA _I methylation. A
distinct subset of
samples with anti-correlated CCP and BRCA _I expression can be seen in the
lower right quadrant of
Figure 7 (shaded circle). Most of these samples show high CCP expression
paired with average to
low BR CA _I expression. It is further notable that such samples generally
showed hypermethylation.
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* * * * * * * * * *
[00135] It is specifically contemplated that any embodiment of any
method or
composition of the invention may be used with respect to any other method or
composition of the
invention.
[00136] In the context of genes and gene products, the name of the
gene is generally
italicized herein following convention. In such cases, the italicized gene
name is generally to be
understood to refer to the gene (i.e., genomic), its mRNA (or cDNA) product,
and/or its protein
product. Generally, though not always, a non-italicized gene name refers to
the gene's protein
product.
[00137] The use of the term "or" in the claims is used to mean
"and/or" unless
explicitly indicated to refer to alternatives only or the alternative are
mutually exclusive, although
the disclosure supports a definition that refers to only alternatives and
"and/or."
[00138] Throughout this application, the term "about" is used to
indicate that a value
includes the standard deviation of error for the device or method being
employed to determine the
value.
[00139] Following long-standing patent law, the words "a" and "an,"
when used in
conjunction with the word "comprising" in the claims or specification, denotes
one or more, unless
specifically noted.
[00140] Other objects, features and advantages of the present
invention will become
apparent from the following detailed description. It should be understood,
however, that the detailed
description and the specific examples, while indicating specific embodiments
of the invention, are
given by way of illustration only, since various changes and modifications
within the spirit and
scope of the invention will become apparent to those skilled in the art from
this detailed description.
* * * * * * * * * *
[00141] All of the compositions and methods disclosed and claimed
herein can be
made and executed without undue experimentation in light of the present
disclosure. While the
compositions and methods of this invention have been described in terms of
preferred embodiments,
it will be apparent to those of skill in the art that variations may be
applied to the compositions and
methods and in the steps or in the sequence of steps of the method described
herein without
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departing from the concept, spirit and scope of the invention. More
specifically, it will be apparent
that certain agents that are both chemically and physiologically related may
be substituted for the
agents described herein while the same or similar results would be achieved.
All such similar
substitutes and modifications apparent to those skilled in the art are deemed
to be within the spirit,
scope and concept of the invention as defined by the appended claims.
[00142] Unless otherwise defined, all technical and scientific terms
used herein have
the same meaning as commonly understood by one of ordinary skill in the art to
which this invention
pertains. In case of conflict, the present specification, including
definitions, will control. In
addition, the materials, methods, and examples are illustrative only and not
intended to be limiting.
[00143] Other features and advantages of the invention will be
apparent from the
preceding detailed description and from the following claims
