WO2013109690A1 - Signatures pour le pronostic du cancer du sein - Google Patents

Signatures pour le pronostic du cancer du sein Download PDF

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Publication number
WO2013109690A1
WO2013109690A1 PCT/US2013/021839 US2013021839W WO2013109690A1 WO 2013109690 A1 WO2013109690 A1 WO 2013109690A1 US 2013021839 W US2013021839 W US 2013021839W WO 2013109690 A1 WO2013109690 A1 WO 2013109690A1
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genes
expression
panel
test
patient
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PCT/US2013/021839
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English (en)
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Susanne Wagner
Darl Flake
Jerry Lanchbury
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Myriad Genetics, Inc.
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Publication of WO2013109690A1 publication Critical patent/WO2013109690A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57415—Specifically defined cancers of breast
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/54—Determining the risk of relapse
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/56—Staging of a disease; Further complications associated with the disease

Definitions

  • the invention generally relates to a molecular classification of disease and particularly to molecular markers for breast cancer prognosis and methods of use thereof.
  • Cancer is a major public health problem, accounting for roughly 25% of all deaths in the United States.
  • American Cancer Society FACTS AND FIGURES 2010.
  • treatments have been devised for various cancers, these treatments often vary in severity of side effects. It is useful for clinicians to know how aggressive a patient's cancer is in order to determine how aggressively to treat the cancer.
  • the inventors have discovered new CCG panels that are similarly prognostic in cancer (e.g., Panels H, I, J, L, M, N & O in Tables 10, 11 & 25 below; "sub-panels" of Panel O in Tables 30 to 34 below). It has now been surprisingly discovered that the expression of certain additional genes, e.g., the ABCC5 and PGR genes, each alone or together, is also prognostic, and adds significant prediction power to CCG expression in the prognosis of breast cancer.
  • additional genes e.g., the ABCC5 and PGR genes
  • the present invention provides a method for determining gene expression in a tumor sample from a patient identified as having breast cancer.
  • the method includes at least the following steps: (1) obtaining one or more tumor samples from a patient identified as having breast cancer; (2) determining the expression of a panel of genes in said tumor sample(s) including at least 4 cell-cycle genes, ABCC5, and PGR; and (3) 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, wherein at least 50%, at least 75% or at least 90% of said plurality of test genes are cell-cycle genes (or wherein cell-cycle genes represent at least 50%, at least 75%) or at least 85% of the combined weight used to provide the test value).
  • one or both of ABCC5 and/or PGR is a test gene used to provide the test value. In some embodiments, neither ABCC5 and/or PGR is a test gene used to provide the
  • the present invention provides a method for determining the prognosis in a patient having breast cancer or the likelihood of breast cancer recurrence, which comprises: determining in a tumor sample from the patient the expression of the ABCC5 gene, and using the expression of the ABCC5 gene in determining the prognosis of the breast cancer in the patient, or predicting the breast cancer outcome, the likelihood of breast cancer recurrence or probability of post-surgery distant metastasis-free survival.
  • the method further comprises a step of determining in the same or a different tumor sample from the patient the expression of the PGR gene.
  • the patient is ER+ and node negative.
  • the patient is ER+ and node negative, has undergone surgery to remove some or all of the tumor in her breast, and is placed on hormone therapy.
  • the present invention provides a method for determining the prognosis in a patient having breast cancer or the likelihood of breast cancer recurrence, which comprises: determining in a tumor sample from the patient the expression of the PGR gene, and using the expression of the PGR gene in determining the prognosis of the breast cancer in the patient, or predicting the breast cancer outcome, the likelihood of breast cancer recurrence or probability of post-surgery distant metastasis-free survival.
  • the patient is ER+ and node negative.
  • the patient is ER+ and node negative, has undergone surgery to remove some or all of the tumor in her breast, and is placed on hormone therapy.
  • the method further comprises determining whether the patient has undergone hormonal therapy. In these embodiments, if the patient has undergone hormonal therapy, then the method further comprises correlating increased PGR expression to better prognosis.
  • the method further comprises correlating increased PGR expression to worse prognosis.
  • the method comprises correlating increased PGR expression to an increased likelihood of response to hormonal therapy.
  • the present invention provides a method for determining in a patient the prognosis of breast cancer or the likelihood of breast cancer recurrence, which comprises: determining in a sample from the patient the expression of a plurality of test genes comprising at least 6, 8, 10 or 15 or more cell-cycle genes, determining in the same or different sample from the patient the expression of the ABCC5 gene and/or the PGR gene, and using the expression of said plurality of test genes and the expression of the ABCC5 gene and/or the PGR gene in determining the prognosis of the breast cancer in the patient, or predicting the breast cancer outcome, the likelihood of breast cancer recurrence or probability of post- surgery distant metastasis- free survival.
  • the patient is ER+ and node negative.
  • the patient is ER+ and node negative, has undergone surgery to remove some or all of the tumor in her breast, and is placed on hormone therapy.
  • Clinical parameters may be combined with the information gained from CCG,
  • the present invention provides a method for determining in a patient the prognosis of breast cancer or the likelihood of breast cancer recurrence, which comprises: determining in a sample from the patient the expression of a plurality of test genes comprising at least 6, 8, 10 or 15 or more cell-cycle genes, determining in the same or different sample from the patient the expression of the ABCC5 gene and/or the PGR gene, determining at least one clinical parameter for the patient (e.g., age, tumor size, node status, tumor stage), and using the expression of said plurality of test genes and the expression of the ABCC5 gene and/or the PGR gene, and the clinical parameter(s), in determining the prognosis of the breast cancer in the patient, or predicting the breast cancer outcome, the likelihood of breast cancer recurrence or probability of post-surgery distant metastasis-free survival.
  • a clinical parameter for the patient e.g., age, tumor size, node status, tumor stage
  • the CCG, ABCC5, and/or PGR information and the clinical parameter information are combined to yield a quantitative (e.g., numerical) evaluation of the prognosis of the breast cancer in the patient, or the breast cancer outcome, the likelihood of breast cancer recurrence or probability of post- surgery distant metastasis-free survival.
  • the present invention provides a method for treating breast cancer, which comprises: determining in a tumor sample from a patient the expression of the ABCC5 and/or PGR gene, and recommending, prescribing or administering a particular treatment regimen (e.g. , a treatment regimen comprising chemotherapy, such as adjuvant or neoadjuvant
  • a treatment regimen comprising chemotherapy is recommended, prescribed or administered based at least in part on the determination that the tumor sample has an increased level of ABCC5 expression.
  • a treatment regimen comprising hormonal therapy is recommended, prescribed or administered based at least in part on the determination that the tumor sample has an increased level of PGR expression.
  • the method further comprises administering to the patient a regimen comprising chemotherapy (and/or radiation) and not comprising hormonal therapy.
  • the present invention provides a method for treating breast cancer in a patient, which comprises: determining in a sample from the patient the expression of a plurality of test genes comprising at least 6, 8, 10 or 15 or more cell-cycle genes, determining in the same or different sample from the patient the expression of the ABCC5 gene and/or the PGR gene, and recommending, prescribing or administering a particular treatment regimen (e.g. , a treatment regimen comprising chemotherapy) based in part on the determined expression of the plurality of test genes, as well as the determined ABCC5 and/or PGR expression.
  • a particular treatment regimen e.g. , a treatment regimen comprising chemotherapy
  • a treatment regimen comprising a non-hormonal therapy agent (e.g., chemotherapy) or radiotherapy is recommended, prescribed or administered based at least in part on any of (1) an increased level of the plurality of test genes, (2) an increased level of ABCC5 expression, and/or (3) decreased level of PGR expression.
  • a treatment regimen comprising a non-hormonal therapy agent (e.g., chemotherapy) or radiotherapy is recommended, prescribed or administered based at least in part on any two of (1) an increased level of the plurality of test genes, (2) an increased level of ABCC5 expression, and/or (3) decreased level of PGR expression.
  • a treatment regimen comprising a non-hormonal therapy agent (e.g., chemotherapy) or radiotherapy is recommended, prescribed or administered based at least in part on (1) an increased level of the plurality of test genes, (2) an increased level of ABCC5 expression, and (3) decreased level of PGR expression.
  • a treatment regimen comprising a non-hormonal therapy agent (e.g., chemotherapy) or radiotherapy, and not comprising hormonal therapy is recommended, prescribed or administered based at least in part on any of (1) an increased level of the plurality of test genes, (2) an increased level of ABCC5 expression, and/or (3) decreased level of PGR expression.
  • a treatment regimen comprising hormonal therapy is
  • the patient is ER+ and node negative. In some embodiments, the patient is ER+ and node negative, has undergone surgery to remove the tumor in her breast, and is placed on hormone therapy. In some embodiments of the methods described above, the patient is ER+ and node positive.
  • the plurality of test genes includes at least 8 cell-cycle genes, or at least 10, 15, 20, 25 or 30 cell-cycle genes. In some embodiments, at least 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 85%, 90%, 95%, or 99% of the plurality of test genes are cell-cycle genes. In some embodiments, all of the test genes are cell-cycle genes.
  • the step of determining the expression of the plurality of test genes in the tumor sample comprises measuring the amount of mR A in the tumor sample transcribed from each of from 6 to about 200 cell-cycle genes; and measuring the amount of mRNA of one or more housekeeping genes in the tumor sample.
  • the step of determining the expression of the plurality of test genes in the tumor sample comprises (1) determining in a tumor sample from a patient having breast cancer the expression of a panel of genes in said tumor sample including at least 4 or at least 8 cell-cycle genes; and (2) providing a "CCG score" 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 CCG score, wherein at least 50%>, at least 75% or at least 85% of the plurality of test genes are cell-cycle genes (or wherein cell-cycle genes represent at least 50%>, at least 75% or at least 85% of the combined weight used to provide the CCG score).
  • the prognosis method comprises (1) determining in a tumor sample from a patient having breast cancer the expression of a panel of genes including at least 4, 8, 10 or 15 cell-cycle genes; (2) providing a "CCG score" 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 CCG score, wherein at least 50%), at least 75% or at least 85% of the plurality of test genes are cell-cycle genes; (3) determining in a tumor sample from a patient having breast cancer the expression of the ABCC5 gene; and (4) providing a test value by weighting the determined CCG score and the determined expression of the ABCC5 gene with their respective predefined coefficients and combining the weighted expression.
  • the prognosis method comprises (1) determining in a tumor sample from a patient having breast cancer the expression of a panel of genes including at least 4 or at least 8 or 10 or 15 cell-cycle genes; (2) providing a "CCG score" 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 CCG score, wherein at least 50%, at least 75% or at least 85% of the plurality of test genes are cell-cycle genes; (3) determining in a tumor sample from a patient having breast cancer the expression of the PGR gene; and (4) providing a test value by weighting the determined CCG score and the determined expression of the PGR gene with their respective predefined coefficients and combining the weighted expression.
  • a method for determining gene expression in a tumor sample from a patient identified as having breast cancer, prostate cancer, lung cancer, bladder cancer, or brain cancer.
  • the method includes at least the following steps: (1) obtaining a tumor sample from a patient identified as having breast cancer, prostate cancer, lung cancer, bladder cancer, or brain cancer; (2) determining the expression of a panel of genes in said tumor sample including at least 2, 3, 4, 5 or 6 cell-cycle genes chosen from the group of genes in any of Panels H, I, J, L, M, N & O (including"sub-panels" of Panel O in Tables 30 to 34); and (3) 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, wherein at least 50%, at least 75% or at least 90%> of said plurality of test genes are cell-cycle genes.
  • the plurality of test genes includes at least 8 cell-cycle genes, or at least 10, 15, 20, or 25 cell-cycle genes from any of Panels H, I, J, L, M, N & O (at least 2, 3, 4, 5, or 6 of which are in a sub-panel of Panel O in Tables 30 to 34). In some embodiments, all of the test genes are cell-cycle genes. In some embodiments, the plurality of test genes includes at least 8, 10, 15, 20, or 25 genes from any of Panels H, I, J, L, M, N & O (at least 2, 3, 4, 5, or 6 of which are in a sub-panel of Panel O in Tables 30 to 34).
  • a method for determining the prognosis of breast cancer, prostate cancer, lung cancer, bladder cancer or brain cancer which comprises determining in a tumor sample from a patient diagnosed of breast cancer, prostate cancer, lung cancer, bladder cancer or brain cancer, the expression of at least 6, 8 or 10 cell- cycle genes in any of Panels H, I, J, L, M, N & O (at least 2, 3, 4, 5, or 6 of which are in a sub-panel of Panel O in Tables 30 to 34), wherein overexpression of said at least 6, 8 or 10 cell-cycle genes indicates a poor prognosis or an increased likelihood of recurrence of cancer in the patient.
  • the prognosis method comprises (1) determining in a tumor sample from a patient diagnosed of breast cancer, prostate cancer, lung cancer, bladder cancer or brain cancer, the expression of a panel of genes in said tumor sample including at least 4 or at least 8 cell-cycle genes in any of Panels H, I, J, L, M, N & O (at least 2, 3, 4, 5, or 6 of which are in a sub-panel of Panel O in Tables 30 to 34); and (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 at least 50%>, at least 75%> or at least 85%> of the plurality of test genes are cell-cycle genes in any of Panels H, I, J, L, M, N & O (including at least 2, 3, 4, 5, or 6 genes in a sub-panel of Panel O in Tables 30 to 34), and wherein an increased level of overall
  • the prognosis method further includes a step of comparing the test value provided in step (2) above to one or more reference values, and correlating the test value to a risk of cancer progression or risk of cancer recurrence. Optionally an increased likelihood of poor prognosis is indicated if the test value is greater than the reference value.
  • the present invention also provides a method of treating cancer in a patient identified as having breast cancer, prostate cancer, lung cancer, bladder cancer or brain cancer, comprising: (1) determining in a tumor sample from a patient diagnosed of breast cancer, prostate cancer, lung cancer, bladder cancer or brain cancer, the expression of a panel of genes in the tumor sample including at least 4 or at least 8 cell-cycle genes in any of Panels H, I, J, L, M, N & O (at least 2, 3, 4, 5, or 6 of which are in a sub-panel of Panel O in Tables 30 to 34); (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, wherein at least 50% or 75% or 85% of the plurality of test genes are cell-cycle genes, wherein an increased level of expression of the plurality of test genes indicates a poor prognosis, and an un-increased level
  • the present invention further provides a diagnostic kit for determining the prognosis of breast cancer in a patient, comprising, in a compartmentalized container, a plurality of oligonucleotides hybridizing to at least 8 test genes, wherein less than 10%, 30% or less than 40% of all of the at least 8 test genes are non-cell-cycle genes; one or more oligonucleotides hybridizing to the ABCC5 gene.
  • the kit further includes one or more
  • the kit may further include one or more
  • the kit consists essentially of, in a
  • the kit comprises one or more computer software programs for calculating a test value representing the expression of the test genes (either the overall expression of all test genes or of some subset) and the expression of the ABCC5 and/or PGR gene, and for comparing this test value to some reference value.
  • such computer software is programmed to weight the test genes such that cell-cycle genes are weighted to contribute at least 50%, at least 75% or at least 85% of the test value.
  • such computer software is programmed to communicate (e.g., display) that the patient has an increased likelihood of response to a treatment regimen comprising chemotherapy if the test value is greater than the reference value (e.g., by more than some predetermined amount).
  • the present invention also provides the use of (1) a plurality of
  • oligonucleotides hybridizing to at least 4 or at least 8 cell-cycle genes; and (2) one or more oligonucleotides hybridizing to the ABCC5 and/or PGR gene, for the manufacture of a diagnostic product for determining the expression of the test genes in a tumor sample from a patient having breast cancer, for the prognosis of breast cancer in the patient, wherein an increased level of the overall expression of the test genes indicates an increased likelihood, whereas no increase in the overall expression of the test genes indicates no increased likelihood.
  • the oligonucleotides are PCR primers suitable for PCR amplification of the test genes.
  • the oligonucleotides are probes hybridizing to the test genes under stringent conditions.
  • the plurality of oligonucleotides are probes for hybridization under stringent conditions to, or are suitable for PCR amplification of, from 4 to about 300 test genes, at least 50%, 70% or 80% or 90%> of the test genes being cell-cycle genes. In some other embodiments, the plurality of oligonucleotides are hybridization probes for, or are suitable for PCR amplification of, from 20 to about 300 test genes, at least 30%, 40%, 50%, 70% or 80% or 90% of the test genes being cell-cycle genes.
  • the present invention further provides a system for determining breast cancer prognosis in a patient, comprising: (1) a sample analyzer for determining the expression levels of a panel of genes in a tumor sample including at least 4 cell-cycle genes, and the expression levels of the ABCC5 and/or PGR genes, wherein the sample analyzer contains the tumor sample, mRNA molecules expressed from the panel of genes and extracted from the sample, or cDNA molecules from said mRNA molecules; (2) a first computer program for (a) receiving gene expression data on at least 4 test genes selected from the panel of genes, and the ABCC5 and/or PGR 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 test value, wherein at least 50%, at least at least 75% of at least 4 test genes are cell-cycle genes; and (3) a second computer program for comparing the test value to one or more reference values each associated with a predetermined likelihood of breast cancer recurrence or post
  • Figure 1 illustrates the relationship between PGR qPCR expression and PR
  • Figure 2 is a Kaplan Meier plot of distant metastasis-free survival for CCP score quartiles in the breast tumor samples assayed.
  • Figure 3 is an illustration of an example of a system useful in certain aspects and embodiments of the invention.
  • Figure 4 is a flowchart illustrating an example of a computer-implemented method of the invention.
  • Figure 5 is a Kaplan Meier plot of distant metastasis-free survival for PR IHC in the breast tumor samples assayed.
  • Figure 6 is a Kaplan Meier plot of distant metastasis-free survival for ABCC5 expression quartiles in the breast tumor samples assayed.
  • Figure 7 is a plot showing the relationship between CCP score and response to neoadjuvant therapy.
  • the present invention is based in part on the discovery that the expression levels of the ABCC5 and PGR genes in breast tumor sample from a breast cancer patient correlate with the prognosis of the breast cancer, and that they add significant prediction power when combined with cell-cycle genes ("CCGs").
  • CCG gene and “CCG” herein refer to a gene whose expression level closely tracks the progression of the cell through the cell-cycle. See, e.g., Whitfield et al., MOL. BIOL. CELL (2002) 13: 1977-2000.
  • the term "cell-cycle progression" or “CCP” will also be used in this application and will generally be interchangeable with CCG (i.e., a CCP gene is a CCG; a CCP score is a CCG score). More specifically, CCGs show periodic increases and decreases in expression that coincide with certain phases of the cell cycle— e.g., STK15 and PLK show peak expression at G2/M. Id.
  • CCGs have clear, recognized cell-cycle related function—e.g., in DNA synthesis or repair, in chromosome condensation, in cell-division, etc.
  • some CCGs 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.
  • UBE2S encodes a ubiquitin-conjugating enzyme, yet its expression closely tracks the cell-cycle.
  • a CCG according to the present invention need not have a recognized role in the cell-cycle.
  • Exemplary CCGs are listed in Tables 1, 2, 3, 5, 6, 7, 8 & 9. A fuller discussion of CCGs, including an extensive (though not exhaustive) list of CCGs, can be found in International
  • Whether a particular gene is a CCG may be determined by any technique known in the art, including those taught in Whitfield et al, MOL. BIOL. CELL (2002) 13: 1977-2000; Whitfield et al, MOL. CELL. BIOL. (2000) 20:4188-4198; WO/2010/080933 flj [0039]). All of the CCGs in Table 1 below form a panel of CCGs ("Panel A") useful in the invention. As will be shown detail throughout this document, individual CCGs (e.g., CCGs in Table 1) and subsets of these genes can also be used in the invention.
  • PAICS* 10606 Hs00272390_ml NM 001079525.1;
  • 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.
  • the ABCC5 gene (Entrez GenelD no. 10057) is also known as "ATP-binding cassette, sub-family C (CFTR/MRP), member 5.” Its expression can be determined by, e.g., using ABI Assay ID Hs00981085_ml .
  • the PGR gene (Entrez GenelD no. 5241) is also known as "progesterone receptor gene” and its expression can be determined by, e.g., using ABI Assay ID Hs00172183_ml .
  • the present invention provides a method for determining the prognosis in a patient having breast cancer or the likelihood of breast cancer recurrence.
  • the method comprises: determining in a sample from the patient the expression of the ABCC5 gene, and using the expression of the ABCC5 gene in determining the prognosis of the breast cancer in the patient, or predicting the breast cancer outcome, the likelihood of breast cancer recurrence or probability of post-surgery distant metastasis-free survival.
  • the method comprises correlating an increased expression level of the ABCC5 gene to a poor prognosis, increased likelihood of cancer recurrence, and decreased probability of post- surgery distant metastasis-free survival.
  • the method further comprises determining in the same or different sample from the patient the expression of the PGR gene, and using the expression of the PGR gene in combination with that of the ABCC5 gene in determining the prognosis of the breast cancer in the patient, or predicting the breast cancer outcome, the likelihood of breast cancer recurrence or probability of post- surgery distant metastasis-free survival.
  • the present invention provides a method for determining the prognosis in a patient having breast cancer or the likelihood of breast cancer recurrence, which comprises: determining in a tumor sample from the patient the expression of the PGR gene, and using the expression of the PGR gene in determining the prognosis of the breast cancer in the patient, or predicting the breast cancer outcome, the likelihood of breast cancer recurrence or probability of post-surgery distant metastasis-free survival.
  • the method comprises correlating an increased expression level of the PGR gene, in patients who have received hormonal therapy, to a better prognosis, decreased likelihood of cancer recurrence, and increased probability of post-surgery distant metastasis-free survival.
  • the method comprises correlating an increased expression level of the PGR gene, in patients who have not received hormonal therapy, to a worse prognosis, increased likelihood of cancer recurrence, and decreased probability of post-surgery distant metastasis-free survival. Furthermore, in some embodiments the method comprises correlating an increased expression level of the PGR gene to an increased likelihood of response to hormonal treatment. In some embodiments the method comprises correlating a decreased expression level of the PGR gene to a decreased likelihood of response to hormonal treatment.
  • the present invention further provides a method for determining in a patient the prognosis of breast cancer or the likelihood of breast cancer recurrence, which comprises:
  • determining the expression of a plurality of test genes comprising (1) at least 2, 3, 5, 4, 6, 8, 10 or 15 or more cell-cycle genes (e.g., CCGs in Panel F in Table 9 or any of Panels H, I, J, L, M, N & O (including 2, 3, 4, 5, or 6 genes in a sub-panel of Panel O in Tables 30 to 34)), and/or (2) at least one of the ABCC5 gene and the PGR gene or both, together or separately in one or more samples from the patient, and using the expression of said plurality of test genes in determining the prognosis of the breast cancer in the patient, or predicting the breast cancer outcome, the likelihood of breast cancer recurrence or probability of post-surgery distant metastasis-free survival.
  • cell-cycle genes e.g., CCGs in Panel F in Table 9 or any of Panels H, I, J, L, M, N & O (including 2, 3, 4, 5, or 6 genes in a sub-panel of Panel O in Tables 30 to 34
  • the method comprises correlating an overall increased expression level of cell-cycle genes, i.e., CCGs, to poor prognosis of the breast cancer in the patient, poor breast cancer outcome, and increased likelihood of breast cancer recurrence, and decreased probability of post-surgery distant metastasis-free survival.
  • the method comprises correlating an increased level of ABCC5 gene expression to poor prognosis of the breast cancer in the patient, poor breast cancer outcome, and increased likelihood of breast cancer recurrence, and decreased probability of post-surgery distant metastasis-free survival.
  • the method comprises correlating an increased level of PGR gene expression, in patients who have received hormonal therapy, to better prognosis of the breast cancer in the patient, better breast cancer outcome, and decreased likelihood of breast cancer recurrence, and increased probability of post- surgery distant metastasis-free survival.
  • the method comprises correlating an increased level of PGR gene expression, in patients who have not received hormonal therapy, to better prognosis of the breast cancer in the patient, better breast cancer outcome, and decreased likelihood of breast cancer recurrence, and increased probability of post- surgery distant metastasis-free survival.
  • the patient is ER+ and node negative. In some embodiments, the patient is ER+ and node negative, has undergone surgery to remove the tumor in her breast, and is placed on hormone therapy. In some embodiments of the methods described above, the patient is ER+ and node positive.
  • the prognosis and treatment methods that involve determining a test value may further include a step of comparing the test value to one or more reference values, and correlating the test value to, e.g., a good or poor prognosis, an increased or decreased likelihood of recurrence, an increased or decreased likelihood of recurrence or metastasis-free survival, an increased or decreased likelihood of response to the particular treatment regimen, etc.
  • the CCP expression data are combined with ABCC5 and/or PGR expression data into one test value, which may then be compared against a reference value for the combined score.
  • the CCP expression data are used to provide a discrete CCP test value, which is then combined with ABCC5 and/or PGR expression data.
  • a test value greater than the reference value is correlated to an increased likelihood of response to treatment comprising chemotherapy.
  • the test value is correlated to an increased likelihood of response to treatment (e.g., treatment comprising chemotherapy), poor prognosis, an increased likelihood of recurrence, and/or a decreased likelihood of recurrence or metastasis-free survival if the test value exceeds the reference value by at least some amount (e.g., at least 0.5, 0.75, 0.85, 0.90, 0.95, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more fold or standard deviations).
  • the method of determining the likelihood of response to a particular treatment regimen comprises (1) determining in a tumor sample from a patient having breast cancer the expression of a panel of genes in said tumor sample 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; (3) determining in a tumor sample from the patient the expression of ABCC5 and/or PGR; and (4)(a) correlating a test value that is greater than some reference and/or ABCC5 expression that is greater than some reference and/or PGR expression that is greater than some reference to an increased likelihood of response to the particular treatment regimen (e.g.
  • a treatment regimen comprising chemotherapy a treatment regimen comprising hormonal therapy
  • a treatment regimen comprising hormonal therapy or (b) correlating a test value that is not greater than some reference and/or ABCC5 expression that is not greater than some reference and/or PGR expression that is not greater than some reference to no increased likelihood of response to the particular treatment regimen (e.g., a treatment regimen comprising chemotherapy, a treatment regimen comprising hormonal therapy).
  • the panel of genes includes at least 2, 4, 5, 6, 7, 8, 9,
  • test genes are weighted such that the cell-cycle genes are weighted to contribute at least 50%, at least 55%, at least 60%, at least 65%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 99% or 100% of the test value.
  • 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 75%, 80%, 85%, 90%, 95%, or at least 99% or 100% of the plurality of test genes are cell-cycle genes.
  • determining the status of a gene refers to determining the presence, absence, or extent/level of some physical, chemical, or genetic
  • Such characteristics include, but are not limited to, expression levels, activity levels, mutations, copy number, methylation status, etc.
  • CCGs in the context of CCGs as used to determine likelihood of response to a particular treatment regimen (e.g., a treatment regimen comprising chemotherapy), particularly useful characteristics include expression levels (e.g., mRNA, cDNA or protein levels) and activity levels. Characteristics may be assayed directly (e.g., by assaying a CCG's expression level) or determined indirectly (e.g., assaying the level of a gene or genes whose expression level is correlated to the expression level of the CCG).
  • expression levels e.g., mRNA, cDNA or protein levels
  • activity levels e.g., activity levels
  • Characteristics may be assayed directly (e.g., by assaying a CCG's expression level) or determined indirectly (e.g., assaying the level of a gene or genes whose expression level is correlated to the expression level of the CCG).
  • ABSORS means a marker's status in a particular sample differs from the status generally found in average samples (e.g., healthy samples, average diseased samples). Examples include mutated, elevated, decreased, present, absent, etc.
  • An "elevated status” means that one or more of the above characteristics (e.g., expression or mRNA level) is higher than normal levels. Generally this means an increase in the characteristic (e.g., expression or mRNA level) as compared to an index value as discussed below.
  • a “low status” means that one or more of the above characteristics (e.g., gene expression or mRNA level) is lower than normal levels. Generally this means a decrease in the characteristic (e.g. , expression) as compared to an index value as discussed below.
  • a "negative status” generally means the characteristic is absent or undetectable or, in the case of sequence analysis, there is a deleterious sequence variant (including full or partial gene deletion).
  • Gene expression can be determined either at the R A level (i.e., m NA or noncoding RNA (ncRNA)) (e.g., miRNA, tRNA, rRNA, snoRNA, siRNA and piRNA) or at the protein level. Measuring gene expression at the mRNA level includes measuring levels of cDNA corresponding to mRNA. Levels of proteins in a tumor sample can be determined by any known technique in the art, e.g., HPLC, mass spectrometry, or using antibodies specific to selected proteins (e.g., IHC, ELISA, etc.).
  • R A level i.e., m NA or noncoding RNA (ncRNA)
  • ncRNA noncoding RNA
  • Measuring gene expression at the mRNA level includes measuring levels of cDNA corresponding to mRNA.
  • Levels of proteins in a tumor sample can be determined by any known technique in the art, e.g., HPLC, mass spectrometry, or using antibodies specific to
  • the amount of RNA transcribed from the panel of genes including test genes is measured in the tumor sample.
  • the amount of RNA of one or more housekeeping genes in the tumor sample is also measured, and used to normalize or calibrate the expression of the test genes.
  • normalizing genes and “housekeeping genes” are defined herein below.
  • the plurality of test genes may include at least 2, 3 or 4 cell-cycle genes, which constitute at least 50%, 75%) or 80%o of the plurality of test genes, and preferably 100% of the plurality of test genes.
  • the plurality of test genes includes at least 5, 6, 7, or at least 8 cell-cycle genes, which constitute at least 20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80% or 90% of the plurality of test genes, and preferably 100% of the plurality of test genes.
  • a panel of genes is a plurality of genes. In some embodiments these genes are assayed together in one or more samples from a patient.
  • the plurality of test genes includes at least 8, 10, 12,
  • cell-cycle genes which constitute at least 20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%), 80%) or 90%) of the plurality of test genes, and preferably 100% of the plurality of test genes.
  • tumor sample means any biological sample containing one or more tumor cells, or one or more tumor-derived RNA or protein, and obtained from a cancer patient.
  • a tissue sample obtained from a tumor tissue of a cancer patient is a useful tumor sample in the present invention.
  • the tissue sample can be an FFPE sample, or fresh frozen sample, and preferably contain largely tumor cells.
  • a single malignant cell from a cancer patient's tumor is also a useful tumor sample.
  • Such a malignant cell can be obtained directly from the patient's tumor, or purified from the patient's bodily fluid (e.g., blood, urine).
  • a bodily fluid such as blood, urine, sputum and saliva containing one or tumor cells, or tumor-derived RNA or proteins, can also be useful as a tumor sample for purposes of practicing the present invention.
  • the patient has (e.g., has been diagnosed with) a particular subtype of breast cancer.
  • the patient has (e.g., has been diagnosed with) a subtype of breast cancer chosen from the group consisting of: triple negative breast cancer (TNBC); ER+ breast cancer; node negative breast cancer; ER+/node negative breast cancer; invasive breast cancer; invasive ductal carcinoma; inflammatory breast cancer; medullary carcinoma; metaplastic breast cancer; Paget' s disease of the nipple; tubular carcinoma; ductal carcinoma; ductal carcinoma in situ (DCIS); papillary carcinoma; lobular carcinoma; invasive lobular carcinoma; and lobular carcinoma in situ (LCIS).
  • the patient has (e.g., has been diagnosed with) DCIS.
  • RNA transcribed from, or the protein encoded by, the gene can be measured as the level of the mRNA transcribed from, or the protein encoded by, the gene.
  • Useful techniques include, but are not limited to, microarray analysis ⁇ e.g., for assaying mRNA or microRNA expression, copy number, etc.), quantitative real-time PCRTM ("qRT-PCRTM", e.g., TaqManTM), immuno analysis ⁇ e.g., ELISA, immunohistochemistry), 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.
  • the invention provides any of the methods discussed above, wherein the activity level of a polypeptide encoded by the CCG is determined rather than or in addition to the expression level of the CCG.
  • the activity level of a polypeptide encoded by the CCG is determined rather than or in addition to the expression level of the CCG.
  • Those skilled in the art are familiar with techniques for measuring the activity of various such proteins, including those encoded by the genes listed in Exemplary CCGs are listed in Tables 1 , 2, 3, 5, 6, 7, 8 & 9, as well as those encoded by ABCC5 and PGR.
  • the methods of the invention may be practiced independent of the particular technique used.
  • the expression of one or more normalizing (often called “housekeeping”) genes is also obtained for use in normalizing the expression of test genes.
  • 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).
  • 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. The normalization ensures accurate comparison of expression of a test gene between different samples.
  • 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
  • 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.
  • housekeeper genes for use in the methods and compositions of the invention include those listed in Table A below.
  • RNA levels for the genes In the case of measuring RNA levels for the genes, one convenient and sensitive approach is real-time quantitative PCRTM (qPCR) assay, following a reverse transcription reaction.
  • qPCR real-time quantitative PCRTM
  • a cycle threshold C t is determined for each test gene and each normalizing gene, i.e., the number of cycles at which the fluorescence from a qPCR reaction above background is detectable.
  • 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 eaqually (straight addition or averaging) or by different predefined coefficients.
  • the normalizing value C can be the cycle threshold (Ct) of one single normalizing gene, or an average of the C t 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.
  • Cm (C t m + Ctm + "' CtHn) N.
  • the methods of the invention generally involve determining the level of expression of a panel of CCGs, and the ABCC5 and/or PGR genes and, optionally, deriving from these determined expression levels a score or value that can predict prognosis, therapy response, etc.
  • a score or value that can predict prognosis, therapy response, etc.
  • test genes comprising primarily CCGs and ABCC5 and/or PGR according to the present invention by combining the expression level values of the individual test genes to obtain a test value.
  • the CCP value provided in the present invention represents the overall expression level of the plurality of test genes composed substantially of (or weighted to be represented substantially by) cell-cycle genes.
  • the CCP 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.
  • CCP value (ACu + AC t2 + " + AC ta )/n.
  • this document discloses using the expression of a plurality of genes (e.g., "determining [in a tumor sample from the patient] the expression of a plurality of test genes” or “correlating increased expression of said plurality of test genes to an increased likelihood of response")
  • this includes in some embodiments using a test value representing or corresponding to the overall expression of this plurality of genes (e.g., "determining [in a tumor sample from the patient] a test value representing the expression of a plurality of test genes” or “correlating an increased test value [or a test value above some reference value] representing the expression of said plurality of test genes to an increased likelihood of response”).
  • the normalized expression for the ABCC5 gene and/or PGR gene can be combined with the CCP value described above to provide a test value. Same or different weight can be given to these three components using predefined coefficients.
  • CCGs do not correlate well with the mean (e.g., ABCC5 correlation to the mean is 0.097).
  • such genes may be grouped, tested, analyzed, etc. separately from those that correlate well. This is especially useful if these non- correlated genes are independently associated with the clinical feature of interest (e.g., prognosis, therapy response, etc.).
  • ABCC5 is a good example, as it does not correlate with the CCG mean at all but it correlates well with prognosis.
  • ABCC5 adds prognostic information beyond CCGs that correlate well with the mean (e.g., Panel F).
  • non-correlated genes are analyzed together with correlated genes.
  • a CCG is non-correlated if its correlation to the CCG mean is less than 0.5, 0.4, 0.3, 0.2, 0.10, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01 or less.
  • 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 & 12-20.
  • 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 6 below according to the predictive power of each individual gene.
  • the CCGs in Panel F can be similarly ranked as shown in Table 7 below.
  • 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 CCGs listed in Table 2, 3, 5, 6, or 7.
  • 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 genes: ASPM, BIRC5, BUB1B, CCNB2, CDC2, CDC20, CDCA8, CDKN3, CENPF, DLGAP5, FOXM1, KIAA0101, KIF11, KIF2C, KIF4A, MCM10, NUSAP1, PRC1, RACGAP1, and TPX2.
  • 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 genes: TPX2, CCNB2, KIF4A, KIF2C, BIRC5, RACGAP1, CDC2, PRC1, DLGAP5/DLG7, CEP55, CCNB1, TOP2A, CDC20, KIF20A, BUB1B, CDKN3, NUSAP1, CCNA2, KIF11, and CDCA8.
  • CCGs e.g., at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs
  • this plurality of CCGs comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, or 20 of the following genes: TPX2, CCNB2, KIF4A, KIF2C, BIRC
  • 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 2, 3, 5, 6, or 7.
  • 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 2, 3, 5, 6, or 7.
  • 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 2, 3, 5, 6, or 7.
  • 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 2, 3, 5, 6, or 7.
  • 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 2, 3, 5, 6, or 7.
  • the test value representing the overall expression of the plurality of test genes is compared to one or more reference values (or index values), and optionally correlated to breast cancer prognosis, or an increased or no increased likelihood of breast cancer recurrence or post-surgery metastasis-free survival. In some cases such values are called "scores," especially in the Examples below. In some embodiments a test value greater than the reference value(s) can be correlated to increased likelihood of poor prognosis or decreased probability of post-surgery metastasis-free survival.
  • the test value is deemed "greater than” the reference value (e.g., the threshold index value), and thus correlated to an increased likelihood of poor prognosis or decreased probability of post-surgery metastasis-free survival, if the test value exceeds the reference value by at least some amount (e.g., at least 0.5, 0.75, 0.85, 0.90, 0.95, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more fold or standard deviations).
  • the reference value e.g., the threshold index value
  • the index value may represent the gene expression levels found in a normal sample obtained from the patient of interest (including tissue surrounding the cancerous tissue in a biopsy), in which case an expression level in the tumor sample significantly higher than this index value would indicate, e.g., increased likelihood of response to a particular treatment regimen (e.g., a treatment regimen comprising chemotherapy).
  • a particular treatment regimen e.g., a treatment regimen comprising chemotherapy
  • the index value may represent the average expression level for a set of individuals from a diverse cancer 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 with cancer (e.g., breast cancer). This average expression level may be termed the "threshold index value”.
  • the index value may represent the average expression level of a particular gene or gene panel in a plurality of training patients (e.g. , breast cancer patients) with similar outcomes whose clinical and follow-up data are available and sufficient to define and categorize the patients by disease outcome. See, e.g., Examples, infra.
  • a "good prognosis index value” can be generated from a plurality of training cancer patients characterized as having "good prognosis” after breast cancer surgery and hormone deprivation therapy.
  • a “poor prognosis index value” can be generated from a plurality of training cancer patients defined as having "poor prognosis” breast cancer surgery and hormone deprivation therapy.
  • a good prognosis index value of a particular gene or gene panel may represent the average level of expression of the particular gene or gene panel in patients having a "good prognosis”
  • a poor prognosis index value of a particular gene or gene panel represents the average level of expression of the particular gene or gene panel in patients having a "poor prognosis.”
  • index values may be determined thusly:
  • a threshold value may be set for the cell cycle mean combined with the ABCC5 mean, and optionally PGR 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 combined 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.).
  • a panel of genes i.e., a plurality of genes.
  • a certain number e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 or more
  • a certain proportion e.g., 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%), 90%
  • 95% 99%
  • Panels of CCGs can accurately predict breast cancer prognosis. But addition of the ABCC5 and PGR genes significantly increases the prediction power.
  • the panel comprises at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 70, 80, 90, 100, 200, or more CCGs.
  • the panel comprises at least 10, 15, 20, or more CCGs.
  • the panel comprises between 5 and 100 CCGs, between 7 and 40 CCGs, between 5 and 25 CCGs, between 10 and 20 CCGs, or between 10 and 15 CCGs.
  • CCGs comprise at least a certain proportion of the panel.
  • the panel comprises at least 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 85%, 90%), 95%), 96%), 97%), 98%>, or 99% CCGs.
  • the panel comprises at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 70, 80, 90, 100, 200, or more CCGs, and such CCGs constitute of 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 panel.
  • the panel of CCGs comprises the genes in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G. In some embodiments the 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 Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G.
  • the invention provides a method of determining the prognosis in a breast cancer patient comprising determining the status of the CCGs in any one of Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G, determining the status of the ABCC5 gene or the PGR gene or both, and using the combined expression to determine the prognosis of the breast cancer.
  • Panels A, B, C, D, E, F, G, H, I, J, L, M, N & O are useful in this regard.
  • ESRl is optional and is analyzed primarily as a confirmation of the tumor's ER+ status.
  • Panel J lacks ESRl .
  • ESRl is optional and is analyzed primarily as a confirmation of the tumor's ER+ status.
  • Panel N lacks ESRl .
  • Table 25 the rankings of each gene in Panel O of Example 3 according to correlation to the mean (Table 25) and p-value (Table 26) were used to derive two different combination rankings.
  • CCG signatures the particular CCGs assayed is often not as important as the total number of CCGs.
  • the number of CCGs assayed 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 CCGs assayed in a panel according to the invention is, as a general matter, advantageous because, e.g. , a larger pool of mR As to be assayed means less "noise" caused by outliers and less chance of an assay error throwing off the overall predictive power of the test.
  • cost and other considerations will generally limit this number and finding the optimal number of CCGs for a signature is desirable.
  • Tables 30 to 34 further illustrate this feature of the invention by showing the predictive power (both univariate and multivariate p-value) of numerous sub-panels chosen from Panel O.
  • each 2-gene and 3 -gene sub-panel chosen from Panel O is significantly predictive of breast cancer prognosis and recurrence in the cohort described in Examples 1 & 3.
  • the panel of genes comprises a sub-panel of any of Tables 30 to 34.
  • the invention provides a method of determining the prognosis of a patient having breast cancer or the likelihood of cancer recurrence in said patient, comprising: (1) obtaining a sample from said patient; (2) determining the expression levels of a panel of genes in said sample, wherein said panel comprises a sub-panel of Panel O chosen from any of Tables 30 to 34 and optionally includes ABCC5 or PGR or both; (3) providing a test value by (i) weighting the determined expression of each of a plurality of test genes selected from said panel of genes with a predefined coefficient, and (ii) combining the weighted expression to provide said test value, wherein the genes of said sub-panel are weighted to contribute at least 25% of the test value and wherein said plurality of test genes optionally includes ABCC5 or PGR
  • the optimal number of CCGs in a signature (no) can be found wherever the following is true
  • P is the predictive power (i.e., P Sil is the predictive power of a signature with n genes and P Sil+i 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.
  • a graph of predictive power as a function of gene number may be plotted 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. It has been shown that p-values ceased to improve significantly between about 10 and about 15 CCGs, thus indicating that an optimal number of CCGs in a prognostic panel is from about 10 to about 15.
  • the panel comprises between about 10 and about 15 CCGs and the CCGs constitute at least 80%> of the panel (or are weighted to contribute at least 75%).
  • the panel comprises CCGs plus one or more additional markers that significantly increase the predictive power of the panel (i.e., make the predictive power significantly better than if the panel consisted of only the CCGs).
  • CCGs including any of those listed in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G
  • Any other combination of CCGs can be used to practice the invention.
  • the panel comprises at least 3, 4, 5, 6, 7, 8, 9, 10, 15,
  • the panel comprises between 5 and 100 CCGs, between 7 and 40 CCGs, between 5 and 25 CCGs, between 10 and 20 CCGs, or between 10 and 15 CCGs.
  • CCGs comprise at least a certain proportion of the panel.
  • the panel comprises at least 25%, 30%, 40%, 50%, 60%>, 70%>, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% CCGs.
  • the CCGs are any of the genes listed in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G. In some
  • the panel comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more genes in any of Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G. In some embodiments the panel comprises all of the genes in any of Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G.
  • 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 from Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J, L, M, N or O (including sub-panesl of Panel O in Tables 30 to 34)) and this plurality of CCGs comprises the top 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40 or more CCGs listed in Table 2, 3, 5, 6, 7, or 12-20.
  • CCGs e.g., at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs from Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J, L, M, N or O (including sub-panesl of Panel O in Tables 30 to 34
  • 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 from Tables 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panels A, B, C, D, E, F, G, H, I, J, L, M, N or O (including sub-panels of Panel O in Tables 30 to 34)) and this plurality of CCGs comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, or 20 of the following genes: ASPM, BIRC5, BUB IB, CCNB2, CDC2, CDC20, CDCA8, CDKN3, CENPF, DLGAP5, FOXM1, KIAA0101, KIF11, KIF2C, KIF4A, MCM10, NUSAP1, PRC1, RACGAP1, and TPX2.
  • CCGs e.g., at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or
  • 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 from Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J, L, M, N or O (including sub-panels of Panel O in Tables 30 to 34)) 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 2, 3, 5, 6, 7, or 12-20.
  • CCGs ⁇ e.g., at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs from Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G,
  • 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 from Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J, L, M, N or O (including sub-panels of Panel O in Tables 30 to 34)) 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 2, 3, 5, 6, 7, or 12-20.
  • CCGs ⁇ e.g., at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs from Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J,
  • 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 from Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J, L, M, N or O (including sub-panels of Panel O in Tables 30 to 34)) 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 2, 3, 5, 6, 7, or 12-20.
  • CCGs e.g., at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs from Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J, L, M, N or
  • 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 from Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J, L, M, N or O (including sub-panels of Panel O in Tables 30 to 34)) 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 2, 3, 5, 6, 7, or 12-20.
  • 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 from Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J, L, M, N or O (including sub-panels of Panel O in Tables 30 to 34)) 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 2, 3, 5, 6, 7, or 12-20.
  • the present invention provides a method for treating breast cancer, which comprises determining the prognosis of breast cancer in a patient as described herein, and recommending, prescribing or administering a particular treatment regimen (e.g., a treatment regimen comprising chemotherapy) based at least in part on the determined prognosis.
  • a particular treatment regimen e.g., a treatment regimen comprising chemotherapy
  • Neoadjuvant chemotherapy can cure some patients but the toxic drugs can be debilitating and expensive, making the decision whether to undertake neoadjuvant chemotherapy difficult.
  • aggressive treatment comprising neoadjuvant chemotherapy may be provided. See Example 2, below.
  • the present invention provides a method for treating breast cancer, which comprises determining the prognosis of breast cancer in a patient who has not yet had surgical resection of the tumor as described herein, and recommending, prescribing or administering a treatment regimen comprising neoadjuvant chemotherapy based at least in part on the determined prognosis.
  • chemotherapy as used herein means adjuvant and/or neoadjuvant chemotherapy.
  • the breast cancer treatment method includes: determining in a tumor sample from the patient the expression of a plurality of test genes comprising at least 6, 8, 10 or 15 or more cell-cycle genes, determining in the same or different sample from the patient the expression of the ABCC5 gene or the PGR gene or both, and recommending, prescribing or administering a particular treatment regimen (e.g., a treatment regimen comprising chemotherapy) based in part on the determined expression of the plurality of test genes, as well as the determined ABCC5 and/or PGR expression.
  • the method further comprises administering to the patient a non-hormone-blocking therapy agent or radiotherapy.
  • “Hormone-blocking therapy” as generally understood in the art means drugs that block the estrogen receptor, e.g. tamoxifen, or block the production of estrogen, e.g., using aromatase inhibitors such as anastrozole (Arimidex) or letrozole (Femara).
  • aromatase inhibitors such as anastrozole (Arimidex) or letrozole (Femara).
  • Non-hormone-blocking therapy agents suitable for breast cancer adjuvant therapy are known in the art and may include, e.g., cyclophosphamide, doxorubicin (Adriamycin), taxane, methotrexate, fluorouracil, and monoclonal antibodies such as Trastuzumab.
  • a patient has an "increased likelihood" of some clinical feature or outcome (e.g. , response) 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 cancer recurrence after surgery in the general breast cancer patient population (or some specific subpopulation) 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 response.
  • a threshold or reference value may be determined and a particular patient's probability of response may be compared to that threshold or reference. Because predicting outcome is a prognostic endeavor, "predicting prognosis” will sometimes be used herein to refer to predicting recurrence or survival.
  • 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.
  • a transmittable 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.
  • 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.
  • 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.
  • the information and data on a test result can be produced anywhere in the world and transmitted to a different location.
  • the 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
  • 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 a product of such a method.
  • Several embodiments of the invention described herein involve a step of correlating high CCP gene expression according to the present invention (e.g., high expression of a panel of CCP genes as described in various embodiments throughout this application; a test value derived from or reflecting high expression; etc.) to a particular clinical feature (e.g., a poor prognosis; an increased likelihood of breast cancer recurrence; etc.) if the CCP gene expression is greater than some reference (or optionally to another feature if the expression is less than some reference).
  • high CCP gene expression e.g., high expression of a panel of CCP genes as described in various embodiments throughout this application; a test value derived from or reflecting high expression; etc.
  • a particular clinical feature e.g., a poor prognosis; an increased likelihood of breast cancer recurrence; etc.
  • a further, related embodiment of the invention may involve, in addition to or instead of a correlating step, one or both of the following steps: (a) concluding that the patient has (or classifying the patient as having) the clinical feature based at least in part on high CCP expression (or a test value derived from or reflecting such); or (b) communicating that the patient has the clinical feature based at least in part on high CCP expression (or a test value derived from or reflecting such).
  • one embodiment described in this document is a method for determining in a patient the prognosis of breast cancer or the likelihood of breast cancer recurrence, comprising: (1) determining the expression of a plurality of test genes comprising (a) at least 4, 6, 8, 10 or 15 or more cell-cycle genes (e.g., CCGs in Panel F in Table 9; in any of Panels H, I, J, L, M, N & O; or in any sub-panel of Panel O in any of Tables 30 through 34; etc.), and/or (b) at least one of the ABCC5 gene and the PGR gene or both, together or separately in one or more samples from the patient, and (2) correlating high expression of said plurality of test genes to poor prognosis of the breast cancer in the patient, increased likelihood of poor breast cancer outcome, an increased likelihood of breast cancer recurrence, and/or a decreased probability of post- surgery distant metastasis-free survival.
  • a plurality of test genes comprising (a) at least 4, 6, 8, 10 or 15 or
  • this description of this embodiment is understood to include a description of two further, related embodiments, i.e., a method for determining in a patient the prognosis of breast cancer or the likelihood of breast cancer recurrence, comprising: (1) determining the expression of a plurality of test genes comprising (a) at least 2, 3, 4, 5, 6, 8, 10 or 15 or more cell-cycle genes (e.g., CCGs in Panel F in Table 9 or any of Panels H, I, J, L, M, N & O (at least 2, 3, 4, 5, or 6 of which are in a sub-panel of Panel O in Tables 30 to 34)), and/or (b) at least one of the ABCC5 gene and the PGR gene or both, together or separately in one or more samples from the patient, and (2)(a) concluding that said patient has a poor prognosis of the breast cancer, an increased likelihood of poor breast cancer outcome, an increased likelihood of breast cancer recurrence, and/or a decreased probability of post-sur
  • correlating a particular assay or analysis output ⁇ e.g., high CCP gene expression, etc.
  • some likelihood e.g., increased, not increased, decreased, etc.
  • some clinical feature e.g., poor prognosis, breast cancer recurrence, etc.
  • concluding or communicating may comprise assigning a risk or likelihood of the clinical feature occurring based at least in part on the particular assay or analysis output.
  • such risk is a percentage probability of the event or outcome occurring (e.g., at least 5%, 10%, 15%, 20%>, 25%, 30%, 35%, 40%, 45%, 50% 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%
  • the patient is assigned to a risk group ⁇ e.g., low risk, intermediate risk, high risk, etc.).
  • low risk is any percentage probability below 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50%.
  • low risk is any percentage probability below 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50%.
  • intermediate risk is any percentage probability above 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% and below 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%).
  • high risk is any percentage probability above 25%, 30%>, 35%, 40%>, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%.
  • communicating means to make such information known to another person or to transfer such information to a thing (e.g., a computer).
  • a patient's prognosis or likelihood of recurrence or response to a particular treatment is communicated.
  • the information used to arrive at such a prognosis or response prediction e.g., high CCP gene expression according to the present invention, etc.
  • This communication may be auditory (e.g., verbal), visual (e.g., written), electronic (e.g., data transferred from one computer system to another), etc.
  • communicating a cancer classification comprises generating a report that communicates the cancer
  • the report is a paper report, an auditory report, or an electronic record. In some embodiments the report is displayed and/or stored on a computing device (e.g., handheld device, desktop computer, smart device, website, etc.).
  • the cancer classification is communicated to a physician (e.g., a report communicating the classification is provided to the physician). In some embodiments the cancer classification is communicated to a patient (e.g., a report communicating the classification is provided to the patient). Communicating a cancer classification can also be accomplished by transferring information (e.g.
  • data embodying the classification to a server computer and allowing an intermediary or end-user to access such information (e.g., by viewing the information as displayed from the server, by downloading the information in the form of one or more files transferred from the server to the intermediary or end- user's device, etc.).
  • this may include in some embodiments a computer program concluding such fact, typically after performing an algorithm that applies information on CCP gene expression according to the present invention.
  • the present invention encompasses a further, related embodiment involving a test value or score (e.g., CCP score, etc.) derived from, incorporating, and/or, at least to some degree, reflecting such expression levels.
  • a test value or score e.g., CCP score, etc.
  • the bare CCP gene expressions data or levels need not be used in the various methods, systems, etc. of the invention; a test value or score derived from such numbers or lengths may be used.
  • test value will be compared to a reference value (as described at length in this document) and the method will end by correlating a high test value (or a test value derived from, incorporating, and/or, at least to some degree, reflecting high CCP gene expression) to a poor prognosis.
  • a high test value or a test value derived from, incorporating, and/or, at least to some degree, reflecting high CCP gene expression
  • the invention encompasses, mutatis mutandis, corresponding embodiments where the test value or score is used to determine the patient's prognosis, the patient's likelihood of response to a particular treatment regimen, the patient's or patient's sample's likelihood of having a breast cancer recurrence, etc.
  • the present invention further provides a system for determining gene expression in a tumor sample, comprising: (1) a sample analyzer for determining the expression levels of a panel of genes in a sample (e.g., a tumor sample) including at least 2, 4, 6, 8 or 10 cell- cycle genes, wherein the sample analyzer contains the sample which is from a patient having breast cancer, or mR A molecules from the patient sample or cDNA molecules from mR A expressed from the panel of genes; (2) a first computer program for (a) receiving gene expression data on at least 4 test genes selected from the panel of genes, (b) weighting the determined expression of each of the test genes, and (c) combining the weighted expression to provide a test value, wherein at least 20%, 50%), at least 75% or at least 90%> of the test genes are cell-cycle genes (or wherein the cell- cycle genes are weighted to contribute at least 50%, 60%, 70%, 80%, 90%, 95% or 100% of the test value), and wherein the test genes include ABCC5 or
  • the system further comprises a display module displaying the comparison between the test value to the one or more reference values, or displaying a result of the comparing step.
  • the amount of R A transcribed from the panel of genes including test genes is measured in the sample.
  • 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.
  • the plurality of test genes includes at least 2, 3 or 4 cell-cycle genes, which constitute at least 50%, 75% or 80%> of the plurality of test genes, and preferably 100% of the plurality of test genes.
  • the plurality of test genes includes at least 5, 6 or 7, or at least 8 cell-cycle genes, which constitute at least 20%>, 25%, 30%>, 40%, 50%, 60%, 70%, 75%, 80% or 90% of the plurality of test genes, and preferably 100% of the plurality of test genes.
  • the plurality of test genes includes at least 8, 10,
  • cell-cycle genes which constitute at least 20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80% or 90% of the plurality of test genes, and preferably 100% of the plurality of test genes.
  • the sample analyzer can be any instrument useful in determining gene expression, including, e.g., a sequencing machine, a real-time PCR machine, and a microarray instrument.
  • 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.
  • the application can also be written for the MacintoshTM, SUNTM, UNIX or LINUX environment.
  • the functional steps can also be implemented using a universal or platform-independent programming language.
  • 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.
  • Java - or JavaScript -enabled browsers such as HotJavaTM, MicrosoftTM ExplorerTM, or NetscapeTM can be used.
  • active content web pages may include JavaTM applets or ActiveXTM controls or other active content technologies.
  • 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 status analysis. These computer program 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.
  • the system comprises (1) computer program for receiving, storing, and/or retrieving a patient's CCG status data (e.g., expression level, activity level, variants), ABCC5 status data, PGR status data, and optionally clinical parameter data ⁇ e.g., age, tumor size, node status); (2) computer program for querying this patient data; (3) computer program for concluding whether there is an increased likelihood of recurrence based on this patient data; and optionally (4) computer program for outputting/displaying this conclusion.
  • this means for outputting the conclusion may comprise a computer program for informing a health care professional of the conclusion.
  • Computer system [400] may include at least one input module [430] for entering patient data into the computer system [400] .
  • the computer system [400] may include at least one output module [424] for indicating whether a patient has an increased or decreased likelihood of response and/or indicating suggested treatments determined by the computer system
  • Computer system [400] may include at least one memory module [406] in communication with the at least one input module [430] and the at least one output module [424] .
  • the at least one memory module [406] may include, e.g., a removable storage drive [408], which can be in various forms, including but not limited to, a magnetic tape drive, a floppy disk drive, a VCD drive, a DVD drive, an optical disk drive, etc.
  • the removable storage drive [408] may be compatible with a removable storage unit [410] such that it can read from and/or write to the removable storage unit [410].
  • Removable storage unit [410] may include a computer usable storage medium having stored therein computer-readable program codes or instructions and/or computer readable data.
  • removable storage unit [410] may store patient data.
  • Example of removable storage unit [410] 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 [406] may also include a hard disk drive [412], which can be used to store computer readable program codes or instructions, and/or computer readable data.
  • the at least one memory module [406] may further include an interface [414] and a removable storage unit [416] that is compatible with interface [414] such that software, computer readable codes or instructions can be transferred from the removable storage unit [416] into computer system [400] .
  • interface [414] and removable storage unit [416] 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 [400] may also include a secondary memory module [418], such as random access memory (RAM).
  • RAM random access memory
  • Computer system [400] may include at least one processor module [402] . It should be understood that the at least one processor module [402] may consist of any number of devices.
  • the at least one processor module [402] may include a data processing device, such as a microprocessor or microcontroller or a central processing unit.
  • the at least one processor module [402] 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.
  • the at least one processor module [402] may include any other type of analog or digital circuitry that is designed to perform the processing functions described herein.
  • the at least one memory module [404], the at least one processor module [402], and secondary memory module [418] are all operably linked together through communication infrastructure [420] , which may be a
  • Input interface [426] may operably connect the at least one input module [426] to the communication infrastructure [420] .
  • output interface [422] may operably connect the at least one output module [424] to the communication infrastructure [420] .
  • the at least one input module [430] may include, for example, a keyboard, mouse, touch screen, scanner, and other input devices known in the art.
  • the at least one output module [424] 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 [430]; a printer; and audio speakers.
  • Computer system [400] may also include, modems, communication ports, network cards such as Ethernet cards, and newly developed devices for accessing intranets or the internet.
  • the at least one memory module [406] may be configured for storing patient data entered via the at least one input module [430] and processed via the at least one processor module [402] .
  • Patient data relevant to the present invention may include expression level, activity level, copy number and/or sequence information for ABCC5, PGR, and/or a CCG.
  • Patient data relevant to the present invention may also include clinical parameters relevant to the patient's disease (e.g., age, tumor size, node status, tumor stage). 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
  • the at least one memory module [406] may include a computer-implemented method stored therein.
  • the at least one processor module [402] 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.
  • the computer-implemented method may be configured to identify a patient 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 an increased likelihood of recurrence. Alternatively or 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.
  • Figure 3 illustrates one embodiment of a computer-implemented method
  • [300] of the invention that may be implemented with the computer system [400] of the invention.
  • the method [300] begins with one of three queries ([310], [311], [312]), either sequentially or substantially simultaneously. If the answer to/result for any of these queries is "Yes” [320], the method concludes [330] that the patient has an increased likelihood of recurrence or of response to a particular treatment regimen (e.g., treatment comprising chemotherapy). If the answer to/result for all of these queries is "No" [321], the method concludes [331] that the patient does not have an increased likelihood of recurrence or of response to a particular treatment regimen (e.g., treatment comprising chemotherapy). The method [300] may then proceed with more queries, make a particular treatment recommendation ([340], [341]), or simply end.
  • the queries may be made in the order suggested by Figure 3 or in any other order. Whether subsequent queries are made can also be dependent on the results/answers for preceding queries.
  • the method asks about CCP [311] first and, if the patient has increased CCP gene expression then the method concludes such [330] or optionally confirms by ABCC5 status [311], and/or PGR status [312] .
  • the method may query clinical parameters (e.g., tumor size, age, tumor stage) before or after querying any of the molecular characteristics of CCP [310], ABCC5 [311], and/or PGR [312] .
  • the preceding order of queries may be modified.
  • an answer of "yes" to one query e.g.,
  • [310]) prompts one or more of the remaining queries to confirm that the patient has, e.g., increased risk of recurrence.
  • the apparent first step [310] in Figure 3 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 [300] 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 (e.g., "patient should/should not undergo adjuvant chemotherapy”); additional queries about additional biomarkers, clinical parameters (e.g., age, tumor size, node status, tumor stage), or other useful patient information (e.g., age at diagnosis, general patient health, etc.).
  • additional biomarkers e.g., clinical parameters, age, tumor size, node status, tumor stage
  • other useful patient information e.g., age at diagnosis, general patient health, etc.
  • the answers to the queries may be determined by the method instituting a search of patient data for the answer.
  • patient data may be searched for CCG status (e.g., CCG expression level data), ABCC5 status, PGR status (and optionally clinical parameters, e.g., age, tumor size, node status, tumor stage, nomogram score, etc.). If such a comparison has not already been performed, the method may compare these data to some reference in order to determine if the patient has an abnormal (e.g., elevated, low, negative) status.
  • the method may present one or more of the queries ([310], [311],
  • [311], [312]) may be presented via an output module [424] .
  • the user may then answer "Yes” or “No” or provide some other value (e.g., numerical or qualitative value representing CCG status) via an input module [430] .
  • the method may then proceed based upon the answer received.
  • the conclusions [330, 331] may be presented to a user of the computer-implemented method via an output module [424] .
  • the invention provides a method comprising: accessing information on a patient's CCG status and the status of ABCC5 or PGR or both stored in a computer-readable medium; querying this information to determine whether a sample obtained from the patient shows increased expression of a plurality of test genes comprising at least 2 CCGs (e.g., a test value representing the expression of this plurality of test genes that is weighted such that CCGs contribute at least 50% to the test value, such test value being higher than some reference value), ABCC5 and/or PGR; outputting [or displaying] the quantitative or qualitative (e.g., "increased") likelihood that the patient will respond to a particular treatment regimen.
  • "displaying" 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's genotype.
  • 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 ah, INTRODUCTION TO COMPUTATIONAL BIOLOGY METHODS (PWS Publishing Company, Boston, 1997); Salzberg et al.
  • BIOINFORMATICS A PRACTICAL GUIDE FOR 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
  • 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;
  • 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.
  • the invention provides a system for determining a patient's prognosis and/or whether a patient will respond to a particular treatment regimen, comprising:
  • sample analyzer for determining the expression levels in a sample of a plurality of test genes including at least 4 CCGs, and ABCC5 or PGR or both, wherein the sample analyzer contains the sample, RNA from the sample and expressed from the panel of genes, or DNA synthesized from said RNA;
  • a second computer program for comparing the test value to one or more reference values each associated with a predetermined likelihood of recurrence or progression or a predetermined likelihood of response to a particular treatment regimen.
  • At least 20%>, 50%>, 75%, or 90%> of said plurality of test genes are CCGs.
  • the sample analyzer contains reagents for determining the expression levels in the sample of said panel of genes including at least 4 CCGs and ABCC5 or PGR or both.
  • the invention provides a system for determining gene expression in a sample (e.g., tumor sample), comprising: (1) a sample analyzer for determining the expression levels of a panel of genes in a sample including at least 4 CCGs and ABCC5 or PGR or both, wherein the sample analyzer contains the sample which is from a patient having breast cancer, R A from the sample and expressed from the panel of genes, or DNA synthesized from said R A; (2) a first computer program for (a) receiving gene expression data on at least 4 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 test value, wherein the combined weight given to said at least 4 CCGs is at least 40% (or 50%, 60%, 70%, 80%, 90%) of the total weight given to the expression of all of said plurality of test genes; and (3) a second computer program for comparing the test value to one or more reference values each associated with a pre
  • the system comprises a computer program for determining the patient's prognosis and/or determining (including quantifying) the patient's degree of risk of cancer recurrence or progression based at least in part on the comparison of the test value with said one or more reference values.
  • 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, or displaying the patient's prognosis and/or degree of risk of cancer recurrence or progression.
  • the amount of RNA transcribed from the panel of genes including test genes (and/or DNA reverse transcribed therefrom) is measured in the sample.
  • the amount of RNA of one or more housekeeping genes in the sample (and/or DNA reverse transcribed therefrom) is also measured, and used to normalize or calibrate the expression of the test genes, as described above.
  • the plurality of test genes includes at least 2, 3 or 4
  • the plurality of test genes includes at least 5, 6 or 7, or at least 8 CCGs and ABCC5 or PGR or both, which constitute at least 20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80% or 90% of the plurality of test genes.
  • the plurality of test genes comprises in addition to ABCC5 or PGR or both, 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 the top 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40 or more CCGs listed in Table 2, 3, 5, 6, or 7.
  • the plurality of test genes comprises in addition to ABCC5 or PGR or both, 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 genes: ASPM, BIRC5, BUBIB, CCNB2, CDC2, CDC20, CDCA8, CDKN3, CENPF, DLGAP5, FOXMl, KIAAOlOl, KIFll, KIF2C, KIF4A, MCM10, NUSAP1, PRC1, RACGAP1, and TPX2.
  • the plurality of test genes comprises beside ABCC5 or PGR or both, 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 2, 3, 5, 6, or 7.
  • the plurality of test genes comprises in addition to ABCC5 or PGR or both, 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 2, 3, 5, 6, or 7.
  • the plurality of test genes comprises in addition to ABCC5 or PGR or both, 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 2, 3, 5, 6, or 7.
  • the plurality of test genes comprises in addition to ABCC5 or PGR or both, 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 2, 3, 5, 6, or 7.
  • the plurality of test genes comprises in addition to ABCC5 or PGR or both, 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 2, 3, 5, 6, or 7. [00133] In some other embodiments, the plurality of test genes includes in addition to
  • ABCC5 or PGR or both at least 8, 10, 12, 15, 20, 25 or 30 CCGs, which constitute at least 20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80% or 90% of the plurality of test genes, and preferably 100%) of the plurality of test genes.
  • the sample analyzer can be any instrument useful in determining gene expression, including, e.g., a sequencing machine (e.g., Illumina HiSeqTM, Ion Torrent PGM, ABI SOLiDTM sequencer, PacBio RS, Helicos HeliscopeTM, etc.), a real-time PCR machine (e.g., ABI 7900, Fluidigm BioMarkTM, etc.), a microarray instrument, etc.
  • a sequencing machine e.g., Illumina HiSeqTM, Ion Torrent PGM, ABI SOLiDTM sequencer, PacBio RS, Helicos HeliscopeTM, etc.
  • a real-time PCR machine e.g., ABI 7900, Fluidigm BioMarkTM, etc.
  • microarray instrument e.g., a microarray instrument, etc.
  • the present invention provides methods of treating a cancer patient comprising obtaining CCG status information (e.g., the CCGs in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G), and recommending, prescribing or administering a treatment for the cancer patient based on the CCG status.
  • CCG status information e.g., the CCGs in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G
  • the invention provides a method of treating a cancer patient comprising:
  • step (2) based at least in part on the determination in step (1), recommending, prescribing or administering either
  • a treatment regimen comprising chemotherapy (e.g., adjuvant chemotherapy) if the patient has increased expression of the plurality of test genes (e.g., and CCGs are weighted to contribute at least 50% to the determination of increased expression of the plurality of test genes), or
  • a treatment regimen not comprising chemotherapy if the patient does not have increased expression of the plurality of test genes (e.g., and CCGs are weighted to contribute at least 50% to the determination of increased expression of the plurality of test genes).
  • compositions for use in the above methods include, but are not limited to, nucleic acid probes hybridizing to, ABCC5 or PGR or both, and a CCG including but not limited to a CCG listed in any of Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J, L, M, N or O (or to any nucleic acids encoded thereby or complementary thereto); nucleic acid primers and primer pairs suitable for seletively amplifying all or a portion of ABCC5 or PGR or both, and the CCG or any nucleic acids encoded thereby; antibodies binding immunologically to a polypeptide encoded by ABCC5 or PGR or both, and by CCG; probe sets comprising a plurality of said nucleic acid probes, nucleic acid primers, antibodies, and/or polypeptides; microarrays comprising any of these; kits comprising any of these; etc.
  • the invention provides a probe comprising an isolated oligonucleotide capable of selectively hybridizing to ABCC5 or PGR or both, and at least one of the genes in any of Table 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 or 25 or Panel A, B, C, D, E, F, G, H, I, J, L, M, N or O.
  • probe and "oligonucleotide” (also “oligo"), when used in the context of nucleic acids, interchangeably refer to a relatively short nucleic acid fragment or sequence.
  • the invention also provides primers useful in the methods of the invention.
  • Primers are probes capable, under the right conditions and with the right companion reagents, of selectively amplifying a target nucleic acid (e.g., a target gene).
  • target nucleic acid e.g., a target gene
  • probe is used herein to encompass “primer” since primers can generally also serve as probes.
  • the probe can generally be of any suitable size/length. In some embodiments the probe has a length from about 8 to 200, 15 to 150, 15 to 100, 15 to 75, 15 to 60, or 20 to 55 bases in length. They can be labeled with detectable markers with any suitable detection marker including but not limited to, radioactive isotopes, fluorophores, biotin, enzymes (e.g., alkaline phosphatase), enzyme substrates, ligands and antibodies, etc. See Jablonski et al., NUCLEIC ACIDS RES. (1986) 14:6115-6128; Nguyen et al, BIOTECHNIQUES (1992) 13: 116-123; Rigby et al, J. MOL. BIOL. (1977) 113 :237-251. Indeed, probes may be modified in any conventional manner for various molecular biological applications. Techniques for producing and using such oligonucleotide probes are conventional in the art.
  • Probes according to the invention can be used in the hybridization/ amplification/ detection techniques discussed above.
  • some embodiments of the invention comprise probe sets suitable for use in a microarray in detecting, amplifying and/or quantitating ABCC5 or PGR or both, and a plurality of CCGs.
  • the probe sets have a certain proportion of their probes directed to CCGs— e.g., a probe set consisting of 10%, 20%, 30%>, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% probes specific for CCGs.
  • the probe set comprises probes directed to at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 40, 45, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 250, 300, 350, 400, 450, 500, 600, 700, or 800 or more, or all, of the genes in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G.
  • probe sets can be incorporated into high-density arrays comprising 5,000, 10,000, 20,000, 50,000, 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, or 1,000,000 or more different probes.
  • the probe sets comprise primers (e.g., primer pairs) for amplifying nucleic acids comprising at least a portion of one or more of the CCGs in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G.
  • kits for practicing the prognosis 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.
  • the kit includes various components useful in determining the status of ABCC5 or PGR or both, and one or more CCGs and one or more housekeeping gene markers, using the above-discussed detection techniques.
  • the kit many include oligonucleotides specifically hybridizing under high stringency to mRNA or cDNA of the genes in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G.
  • oligonucleotides can be used as PCR primers in RT- PCR reactions, or hybridization probes.
  • 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% CCGs (e.g., CCGs in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G).
  • 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 CCGs (e.g., CCGs in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G).
  • CCGs e.g., CCGs in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G.
  • 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 ah, Nucleic Acids Res., 14:6115-6128 (1986); Nguyen et al, Biotechniques, 13: 116-123 (1992); Rigby et al, J. Mol. Biol, 113:237-251 (1977).
  • 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.
  • the detection kit contains one or more antibodies selectively immunoreactive with one or more proteins encoded by ABCC5 or PGR or both, and by one or more CCGs or optionally any additional markers.
  • antibodies that bind immunologically to a protein encoded by a gene in Table 1, 2, 3, 5, 6, 7, 8, or 9 or Panel A, B, C, D, E, F, or G. Methods for producing and using such antibodies are well-known in the art.
  • the detection kit of this invention 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 limited to, Taq polymerase, deoxyribonucleotides, dideoxyribonucleotides, other primers suitable for the amplification of a target DNA sequence, RNase A, and the like.
  • the detection kit preferably includes instructions on using the kit for practice the prognosis method of the present invention using human samples.
  • Panel L The expression of the genes in Panel L was evaluated for its ability to determine prognosis of breast cancer patients.
  • Panel L includes 24 CCP genes (Panel M), ESRI, ABCC5, and PGR (which together with Panel M form Panel N (note that ESRI is optional and in this study was analyzed primarily to confirm ER+ status)), and 14 housekeeping genes (Panel K). Table 11 illustrates these panels and how they interact.
  • DMFS distant metastasis-free survival
  • the CCP score was calculated from RNA expression of 24 CCP genes (Panel
  • the CCP score is the unweighted mean of C T values for CCP gene expression, normalized by the unweighted mean of the HK genes so that higher values indicate higher expression. One unit is equivalent to a two-fold change in expression.
  • the CCP scores were centered by the mean value, again determined in the training set.
  • RNA yield was determined on a Nanodrop spectrophotometer.
  • RNA was converted to cDNA using the high capacity cDNA archive kit (Applied Biosystems). Newly synthesized cDNA served as template for replicate pre-amplification reactions. Each of the reactions contained 3 ⁇ 1 cDNA and a pool of TaqmanTM assays for all 38 genes in the signature (14 housekeeping genes, 24 cell cycle genes).
  • Preamplification was run for 14 cycles to generate sufficient total copies even from a low copy sample to inoculate individual PCR reactions for 38 genes.
  • Preamplification reactions were diluted 1 :20 before loading on TaqmanTM low density arrays (TLDA, Applied Biosystems).
  • Raw data for the calculation of the CCP score were the C t values of the 46 genes from the TLDA arrays.
  • the CCP score was the unweighted mean of C t values for cell cycle gene expression, normalized by the unweighted mean of the house keeper genes so that higher values indicate higher expression. One unit is equivalent to a two-fold change in expression.
  • the CCP scores were centered by the mean value determined in the commercial training set.
  • CCP scores were unusable for 36 samples: 21 for too many missing housekeeper genes (12 were required), 14 for too many missing CCP genes (18 were required), and 1 because the standard deviation of the by-card CCP scores was greater than 0.5. Therefore, 498 (93%) samples received passing CCP scores.
  • Hs00174860_ml Both had the same number of missing values and their measurements correlated well. The expression for the two assays was averaged and 513 patients had acceptable values.
  • Figure 1 shows the relationship between PR IHC and PGR qPCR expression.
  • Table 22 summarizes the results of univariate prediction of DMFS for each variable.
  • Figure 2 illustrates the relationship between CCP score and DMFS.
  • the HR for PGR is 0.76 (0.68, 0.86) in patients treated with hormone therapy and 1.18 (0.92, 1.52) in patients that did not receive hormone therapy.
  • the HR for PR is 0.82 (0.74, 0.9) in patients treated with hormone therapy and 1.22 (0.91, 1.64) in patients that did not receive hormone therapy.
  • a multivariate model for DMFS was fit with the following predictors: age of patient, grade of tumor, size of tumor, interaction of hormone therapy status and PR IHC expression, CCP score, and squared CCP score.
  • ABCC5 qPCR expression was added to the model to assess its significance.
  • CCP, PR IHC and ABCC5 qPCR expression remained significant in the expanded model.
  • Example 1 The samples analyzed in Example 1 were combined with 181 additional samples from patients with positive nodes. This combined cohort was analyzed as described in Example 1 with the following distinctions and as further noted below: (a) use of hormone therapy as a time dependent covariate was introduced and (b) expression was measured for an altered gene panel ("Panel O", as shown in Tables 25 & 26 below, together with an altered list of housekeeper genes, as shown in Table 27). Two assays were selected to measure the expression of each of ABCC5 (Assay ID nos. Hs00981085_ml and Hs00981087_ml) and PGR (Assay ID nos.
  • hormone therapy was included as a time dependent covariate instead of a binary indicator of treatment.
  • the effect of hormone therapy was only estimated in recipients during the time while it was being administered. When the exact dates of the beginning and end of therapy were unknown it was assumed that the patient received hormone therapy for the first five years after surgery (which is the standard of care).
  • Tables 30 through 34 below illustrate further embodiments of the invention involving 2-gene, 3-gene, 4-gene, 5-gene and 6-gene sub-panels chosen from Panel O.
  • Table 30 Predictive power (both univariate and multivariate) for all 2-gene sub-panels chosen from Panel O.
  • CDK1 1.25E-06 1.33E-02 174 CDK1, KIAAOlOl 4.31E-04 1.64E-01
  • Table 31 Predictive power (both univariate and multivariate) for all 3-gene sub-panels chosen from Panel O.
  • CDKN3, SKAl, CDCA3 1.43E-05 1.04E-02 48 CDKN3, KIF20A, PRC1 6.62E-08 1.46E-03
  • CDK1 130 CDKN3, PLKl, MCMIO 1.04E-07 4.63E-03
  • CDKN3 CDKl, 180 1.37E-05 4.06E-02
  • CDKN3, PTTG1, RAD54L CDKN3, PTTG1, RAD54L

Abstract

La présente invention concerne la classification moléculaire d'une maladie et, en particulier, des marqueurs moléculaires pour le pronostic du cancer du sein ainsi que des procédés et des systèmes d'utilisation associés.
PCT/US2013/021839 2012-01-17 2013-01-17 Signatures pour le pronostic du cancer du sein WO2013109690A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3134548A4 (fr) * 2014-04-23 2018-02-28 Myriad Genetics, Inc. Signatures de pronostic du cancer
CN113785076A (zh) * 2019-05-03 2021-12-10 株式会社递希真 预测癌症预后的方法及其组合物

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008077165A1 (fr) * 2006-12-22 2008-07-03 Austrian Research Centers Gmbh - Arc Ensemble de marqueurs tumoraux
US20110129833A1 (en) * 2004-04-09 2011-06-02 Baker Joffre B Gene Expression Markers for Predicting Response to Chemotherapy
US20110178374A1 (en) * 2004-11-05 2011-07-21 Baker Joffre B Predicting Response to Chemotherapy Using Gene Expression Markers

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110129833A1 (en) * 2004-04-09 2011-06-02 Baker Joffre B Gene Expression Markers for Predicting Response to Chemotherapy
US20110178374A1 (en) * 2004-11-05 2011-07-21 Baker Joffre B Predicting Response to Chemotherapy Using Gene Expression Markers
WO2008077165A1 (fr) * 2006-12-22 2008-07-03 Austrian Research Centers Gmbh - Arc Ensemble de marqueurs tumoraux

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
TOILLON ET AL.: "Estrogens decrease gamma-ray -induced senescence and maintain cell cycle progression in breast cancer cells independently of p53", INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY, BIOLOGY, PHYSICS, vol. 67, no. 4, 15 March 2007 (2007-03-15), pages 1187 - 1200, XP005914338 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3134548A4 (fr) * 2014-04-23 2018-02-28 Myriad Genetics, Inc. Signatures de pronostic du cancer
CN113785076A (zh) * 2019-05-03 2021-12-10 株式会社递希真 预测癌症预后的方法及其组合物

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