WO2016149366A1 - Méthodes et compositions pour le traitement du cancer - Google Patents

Méthodes et compositions pour le traitement du cancer Download PDF

Info

Publication number
WO2016149366A1
WO2016149366A1 PCT/US2016/022641 US2016022641W WO2016149366A1 WO 2016149366 A1 WO2016149366 A1 WO 2016149366A1 US 2016022641 W US2016022641 W US 2016022641W WO 2016149366 A1 WO2016149366 A1 WO 2016149366A1
Authority
WO
WIPO (PCT)
Prior art keywords
erv
cancer
aza
ervfcl
pcr
Prior art date
Application number
PCT/US2016/022641
Other languages
English (en)
Inventor
Katherine CHIAPPINELLI
Stephen B. Baylin
Reiner Strick
Pamela STRISSEL
Original Assignee
The Johns Hopkins University
University - Clinic Erlangen
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by The Johns Hopkins University, University - Clinic Erlangen filed Critical The Johns Hopkins University
Publication of WO2016149366A1 publication Critical patent/WO2016149366A1/fr

Links

Classifications

    • 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
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/70—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving virus or bacteriophage
    • C12Q1/701—Specific hybridization probes
    • C12Q1/702—Specific hybridization probes for retroviruses
    • 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

Definitions

  • the present invention relates to the field of epigenetics. More specifically, the present invention provides methods and compositions useful for predicting response to epigenetic drug therapy.
  • DNMTs non-small cell lung cancer
  • NSCLC non-small cell lung cancer
  • DNA methyltransf erase inhibitors such as 5-azacytidine (Aza) and 5-aza- 2' -deoxy cytidine (Dac) are increasingly being used in therapeutic strategies for cancer. They have been particularly effective in hematologic neoplasms where they are FDA approved for the pre-leukemic disorder, myelodysplasia (MDS). Despite such efficacy, the precise mechanisms accounting for the anti -tumor activities of these drugs are not certain. The major explored mode of action is their function as cytidine analogues which, when incorporated into DNA, serve as suicide inhibitors to block the catalytic actions of DNA methyltransferases (DNMTs) while also triggering degradation of these proteins.
  • DNMTs DNA methyltransf erase inhibitors
  • DNMTis the strongest clinical efficacy of DNMTis has emerged as doses of the drugs have actually been lowered. This may be because such doses avoid toxicities of the drugs that may be largely due to off- target effects such as global DNA damage.
  • transient exposure of cancer cells to nanomolar range levels of DNMTis can avoid early cytotoxicity due to DNA damage and subsequently, after drug withdrawal, allow cells to exhibit apparent reprogramming and blunting of tumorigenicity.
  • Candidate mechanisms for this effect include promoter DNA demethylation and re-expression of many genes, including some tumor suppressors, which are abnormally silenced in cancer. Concomitantly, there is restoration of more normal patterns for many signaling pathways in which abnormalities drive cancer initiation and progression including regulation of apoptosis, reduced cell cycle activity, and decrease in stem cell functions.
  • NSCLC non- small lung cancer
  • ERV epithelial ovarian cancer
  • NSCLC non-small cell lung cancer
  • a major mechanism underlying AZA triggering the above cascade of immune related events, and especially the interferon response is induction of a cytosolic double-stranded RNA (dsRNA) sensing system that serves as a viral defense mechanism for epithelial and other cell types and can induce antitumor responses.
  • dsRNA cytosolic double-stranded RNA
  • AZA activates the interferon response by upregulation of dsRNA.
  • a key contributor to the induction of the dsRNA interferon response is increased sense and antisense transcript expression of multiple DNA hypermethylated human endogenous retroviruses (ERVs).
  • ERPs DNA hypermethylated human endogenous retroviruses
  • ERV-High or “ERV-Low” that differentiates patients with a low immune and high immune signature and is regulated by epigenetic drugs such as demethylating drugs, histone deacetylase inhibitors.
  • epigenetic drugs such as demethylating drugs, histone deacetylase inhibitors.
  • patients with a high immune signature may benefit from epigenetic drugs such as demethylating drugs, histone deacetylase inhibitors.
  • immunotherapies such as anti PD1 or anti PDL1 antibodies or vaccines.
  • immunotherapies such as anti PD1 or anti PDL1 antibodies or vaccines.
  • patients with a low immune signature or low ERV would be patients who would then benefit from treatment with epigenetic drugs and then subsequent
  • Immunotherapy is emerging as one of the most exciting modality in solid tumors with recent identification of use of checkpoint therapy for melanomas, selected lung and renal cancers.
  • most solid cancers do not respond to immunotherapy.
  • common solid cancers such as colorectal, breast and ovarian cancers are not thought to be typically as immune responsive cancers.
  • We have now shown that these cancers can have an immune rich signature and others that are low in immune signal. Cancers that are high in this immune signature termed "ERV-High" would be candidates for immunotherapy.
  • ERV-Low cancers that are low in this immune signature termed “ERV-Low” would benefit from epigenetic drugs especially demethylating drugs, but also histone deacetylase inhibitors in increasing their immune stimulation and then treating with immunotherapy to treat these cancers.
  • epigenetic drugs especially demethylating drugs, but also histone deacetylase inhibitors in increasing their immune stimulation and then treating with immunotherapy to treat these cancers.
  • ERV -High high immune signature
  • ERV- Low a panel of ERV transcripts that identifies patients who have a low immune signature termed "ERV- Low” who would benefit from epigenetic therapy followed by immunotherapy for their cancer.
  • the present invention provides a panel termed "ERV,” a panel based on ERV expression in ovarian cancer. Patients are either low or high in this panel.
  • the baseline ERV panel can be used as a prognostic panel, for example, patients with high ERV expression may have better survival than patients with low ERV expression.
  • the baseline ERV panel can also be used to stratify patients who may benefit from epigenetic therapy. For example, patients with low ERV panel baseline may benefit from epigenetic therapy and/or immunotherapy and/or chemotherapy whereas patients with high ERV panel may do well with immunotherapy alone.
  • patients with a change in ERV panel may be the ones who are responding to therapy.
  • ERV expression scores for 20 patients would be divided into three equal groups, with the lowest in the “low” group, then the “medium” group, then the “high” group. There would be no comparison to the average for this subsetting.
  • a baseline expression for the ERVs can be established and used as an index or standard against which ERV expression data from subsequent patient could be compared.
  • the present invention provides a method for treating a patient having cancer comprising the steps of (a) obtaining a biological sample from the patient; (b) generating ERV expression data from the biological sample; (c) classifying the ERV expression data from the biological sample as ERV high or low based on a comparison to a control or reference; and (d) treating the cancer patient with immunotherapy if the ERV expression data from the biological sample is classified as ERV high or treating the cancer patient with epigenetic therapy followed by immunotherapy if the ERV expression data from the biological sample is classified as ERV low.
  • the epigenetic therapy comprises treatment with a DNA methyltransferase inhibitor (e.g., 5-azacitidine) and/or a histone deactytelase inhibitor (HDACi).
  • a DNA methyltransferase inhibitor e.g., 5-azacitidine
  • HDACi histone deactytelase inhibitor
  • Examples of DNMTi also include 5-aza-2'-deoxycytidine (Dac or decitabine); SGI-110 (Aztex); 5,6-dihydro-5-azacytidine; 5-fluoro-2'-deoxycytidine; zebularine.
  • HDACi examples include, but are not limited to, vorinostat, romidepsin, panobinostat (LBH589), valproic acid, belinostat (PXD101), mocetinostat (MGCD0103), abexinostat (PCI-24781), SB989, entinostat (MS0275), resminostat (4SC-201), givinostat (IF2357), quisinostat (JNJ-26481585), CUDC-101, AR-42, CHR-2845, CHR-3996, 4SC-202,
  • the immunotherapy comprises treatment with anti PD1 (e.g., Nivolumab) or anti PDL1 antibodies or vaccines.
  • Therapies can also include, but are not limited to, anti EGFR antibodies (e.g., Matuzumab), alemtuzumab (Campeth-1H), bevacizumab (Avastin), brentuximab vedotin, cetuximab (Erbitux), gemtuzumab ozogamicin, ibritumomab tiuxetan (Zevalin), ipilimumab (Yervoy), nimotuzumab, ofatumumab, panitumumab (Vectibix), rituximab, tositumomab, and trastuzumab.
  • anti EGFR antibodies e.g., Matuzumab
  • alemtuzumab Campeth-1H
  • bevacizumab Avastin
  • brentuximab vedotin cetuximab
  • a method for treating cancer in a patient comprises the step of administering epigenetic therapy followed by immunotherapy to a patient classified as having a low ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference.
  • a method for treating cancer in a patient comprises the step of administering immunotherapy to a patient classified as having a high ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference.
  • a method for treating cancer in a patient comprises the steps of (a) administering epigenetic therapy followed by immunotherapy to a patient classified as having a low ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference; or (b) administering immunotherapy to a patient classified as having a low ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference.
  • a method comprises the step of prescribing epigenetic therapy followed by immunotherapy to a patient classified as having a low ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference.
  • a method comprises the step of prescribing immunotherapy to a patient classified as having a high ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference.
  • a method comprises the steps of (a) prescribing epigenetic therapy followed by immunotherapy to a patient classified as having a low ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference; or (b) prescribing immunotherapy to a patient classified as having a low ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference.
  • a method comprises the steps of (a) ordering a diagnostic test that assays gene expression from a biological sample obtained from a patient and classifies the gene expression data from the biological sample as high or low ERV based on a comparison to a control or reference; and (b) administering or prescribing epigenetic therapy followed by immunotherapy to a patient classified as having a low ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference.
  • a method comprises the steps of (a) ordering a diagnostic test that assays gene expression from a biological sample obtained from a patient and classifies the gene expression data from the biological sample as high or low ERV based on a comparison to a control or reference; and (b) administering or prescribing immunotherapy to a patient classified as having a high ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference.
  • a method comprises the steps of (a) ordering a diagnostic test that assays gene expression from a biological sample obtained from a patient and classifies the gene expression data from the biological sample as high or low ERV based on a comparison to a control or reference; and (b) administering or prescribing either (i) epigenetic therapy followed by immunotherapy to a patient classified as having a low ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference or (ii) administering or prescribing immunotherapy to a patient classified as having a high ERV signature based on a comparison of gene expression data generated from a biological sample obtained from the patient to a control or reference.
  • the biological sample is a solid tumor sample.
  • the cancer is ovarian.
  • the cancer is colorectal, breast, melanoma or lung cancer.
  • the lung cancer is non- small cell lung cancer (NSCLC).
  • the cancer is a carcinoma.
  • the ERV expression data is generated using polymerase chain reaction (PCR).
  • the PCR is qRT-PCR.
  • the ERV expression data is generated using one or more primers from Table 1.
  • the ERV panel comprises one or more of ERVW-1, ERVFRD-1, ERVP(b), ERVR, ERVV-1, ERVV-2, ERVS71-1, ERVPABLB-1, HERV-K74261, HML- 2.HOM/K108, ERV-K17833 C19, HML-2 (K109), ERV-K113, ERV-K115, ERV-K102, ERVH-1, ERVH-2, ERVH-3, ERVE4-1, ERVFcl, ERVFCl-1, ERVFCl-2, ERVFCl-3, ERVFCl-4, ERVFCl-5, ERVW-2, ERVMER34-1, ERVH48-1, ERV9-1, ERV-F(XA34), and ERVW-5.
  • the ERV panel comprises one or more of the ERV panel comprises one or more of ERVW-1, ERVFRD-1, ERVP(b), ERVR, HERV-K74261, HML-2.HOM/K108, ERV-K17833 C19, HML-2 (K109), ERV-K113, ERV-K115, ERV- K102, ERVE4-1, ERVH-1, ERVH-2, ERVH-3, ERVFCl-1, ERVFCl-2, ERVFCl-3, ERVFCl-4, ERVFCl-5, ERVW-2, ERVW-5, ERVH48-1 and ERV9-1.
  • the ERV panel comprises one or more of ERVW-1, ERVP(b), ERV-3, ERVFcl, ERV-Fbl, and ERV9-1.
  • the present invention provides a method for treating a patient having cancer comprising the steps of (a) obtaining a biological sample from the patient; (b) generating ERV expression data from the biological sample; (c) classifying the ERV expression data from the biological sample as ERV high or low based on a comparison to a control or reference; and (d) treating the cancer patient with immunotherapy if the ERV expression data from the biological sample is classified as ERV high or treating the cancer patient with epigenetic therapy such as a DNA methyltransf erase inhibitor and/or a histone deacetylase inhibitor followed by immunotherapy if the ERV expression data from the biological sample is classified as ERV low.
  • epigenetic therapy such as a DNA methyltransf erase inhibitor and/or a histone deacetylase inhibitor
  • the cancer patient can be treated with chemotherapy if the ERV expression data from the biological sample is classified as ERV high.
  • the present invention provides a method for treating a patient having cancer comprising the steps of (a) obtaining a biological sample from the patient; (b) generating ERV expression data from the biological sample; (c) classifying the ERV expression data from the biological sample as ERV high or low based on a comparison to a control or reference; and (d) treating the cancer patient with immunotherapy such as anti PDl or anti PDLl antibodies or vaccines if the ERV expression data from the biological sample is classified as ERV high or treating the cancer patient with epigenetic therapy followed by immunotherapy such as anti PDl or anti PDLl antibodies or vaccines if the ERV expression data from the biological sample is classified as ERV low.
  • the methods can also comprise generating ERV expression data and indicating or recommending a treatment approach.
  • a low dose of DNMTi can be used, i.e., a lower dose than what it typically prescribed for patient. Lower doses can be more effective and less toxic in cancer. See Issa, J.P., 2 NATURE CLIN. PRACTICE ONCOLOGY S24- 239 (2005).
  • the low dose for Aza is about 10-85 mg/m 2 . In more specific embodiments, the low dose of Aza is about 10-85 mg/m 2 , 15-80 mg/m 2 , 20-80 mg/m 2 , 20-75 mg/m 2 , 25-80 mg/m 2 , 25-75 mg/m 2 , and so forth.
  • the low dose for Dac is about 5-85 mg/m 2 . In more specific embodiments, the low dose of Dac is about 5-80 mg/m 2 , 10-75 mg/m 2 , 10-80 mg/m 2 , 15-80 mg/m 2 , 15-75 mg/m 2 , and so forth.
  • Immunotherapy can be administered at typical levels.
  • doses of anti- CTLA-4 ipilimumab/Y ervoy/BMS
  • doses of anti-PD-1 are as follows: 200 mg, every 3 weeks.
  • patients receiving epigenetic therapy and immunotherapy can be treated as follows: zacitidine 40 mg/m 2 , subcutaneous days 1-6, 8-10 + Entinostat 8 mg Oral Days 3 & 10 for 28 Days times 2 cycles followed by Nivolumab 3 mg/kg every 2 weeks until progression.
  • zacitidine 40 mg/m 2 subcutaneous days 1-6, 8-10 + Entinostat 8 mg Oral Days 3 & 10 for 28 Days times 2 cycles followed by Nivolumab 3 mg/kg every 2 weeks until progression.
  • FIG. 1 DNMT inhibitors upregulate immune genes in ovarian cancer cell lines.
  • A) Levels of immune genes in four EOC cell lines and DKO colon cancer cell line (DNMTI-/-, DNMT3B-/-) relative to its parental HCT116 line. Y-axis log2 Aza/Mock fold change from microarray data. Dotted line denotes twofold change.
  • C-D qRT-PCR validation of interferon response genes in the A2780 (C) and TykNu (D) EOC lines treated with no drug (Mock), 500 nM Aza (Aza), or 100 nM Decitabine (Dac) for 3 days, and rested for 4 (Day 7) or 7 (Day 10) days before assaying.
  • Y-axis fold change over mock.
  • FIG. 2. DNMTis upregulate immune signaling through secreted interferon.
  • Aza induces immune signaling through dsRNA activation of secreted interferon.
  • A) Blocking IFNAR2 (aIFNAR2) or B) IFNP in TykNu cells treated vs. non- treated with Aza as in FIGS. 1C,D; parentheses U/mL of antibody.
  • DNA Cytoplasmic DNA
  • Cyto RNA Cytoplasmic RNA excluding ribosomal RNA.
  • FIG. 4 Aza activates dsRNA sensors to induce interferon signaling.
  • FIG. 5 Aza upregulates sense and antisense ERV transcripts.
  • RNA was isolated from cells at last (Day 3), one (Day 4), three (Day 7) and seven (Day 10) days after Mock or 500 nM Aza (Aza) for 3 days.
  • FIG. 6 Aza upregulates ERV transcripts, but not proteins, through DNA
  • FIG. 7 Aza-upregulated viral defense genes are significantly correlated with ERVs in primary tumors and correlate with sensitivity to immune therapy.
  • the (*) denotes that 8 of 10 high ERV tumors had significantly higher ISG expression compared to the low ERV tumors.
  • the term "antibody” is used in reference to any immunoglobulin molecule that reacts with a specific antigen. It is intended that the term encompass any immunoglobulin (e.g., IgG, IgM, IgA, IgE, IgD, etc.) obtained from any source (e.g., humans, rodents, non-human primates, caprines, bovines, equines, ovines, etc.).
  • antibodies include polyclonal, monoclonal, humanized, chimeric, human, or otherwise-human-suitable antibodies.
  • Antibodies also includes any fragment or derivative of any of the herein described antibodies.
  • the term "antigen” is generally used in reference to any substance that is capable of reacting with an antibody. More specifically, as used herein, the term “antigen” refers to a biomarker described herein. An antigen can also refer to a synthetic peptide, polypeptide, protein or fragment of a polypeptide or protein, or other molecule which elicits an antibody response in a subject, or is recognized and bound by an antibody.
  • biomarker refers to a molecule that is associated either quantitatively or qualitatively with a biological change.
  • biomarkers include polynucleotides, such as a gene product, RNA or RNA fragment; proteins, polypeptides, and fragments of a polypeptide or protein.
  • a biomarker means a molecule/compound that is differentially present (i.e., increased or decreased) in a biological sample as measured/compared against the same marker in another biological sample or control/reference.
  • a biomarker can be differentially present in a biological sample as measured/compared against the other markers in same or another biological sample or control/reference.
  • one or more biomarkers can be differentially present in a biological sample as measured/compared against other markers in the same or another biological sample or control/reference and against the same markers in another biological sample or control/reference.
  • a biomarker can be differentially present in a biological sample from a subj ect or a group of subjects having a first phenotype (e.g., having a disease or condition) as compared to a biological sample from a subject or group of subjects having a second phenotype (e.g., not having the disease or condition or having a less severe version of the disease or condition).
  • the one or more biomarkers can be generally present at a level that is increased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more; or is generally present at a level that is decreased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%,
  • a biomarker is preferably differentially present at a level that is statistically significant (e.g., a p-value less than 0.05 and/or a q-value of less than 0.10 as determined using, for example, either Welch's T-test or Wilcoxon's rank-sum Test). Biomarker levels can be used in conjunction with other parameters to assess a patient.
  • cancer means a type of hyperproliferative disease that includes a malignancy characterized by deregulated or uncontrolled cell growth. Cancers of virtually every tissue are known. Examples of cancer include, but are not limited to, carcinoma, lymphoma, blastoma, sarcoma, and leukemia or lymphoid malignancies.
  • squamous cell cancer e.g., epithelial squamous cell cancer
  • lung cancer including small-cell lung cancer, non-small cell lung cancer, adenocarcinoma of the lung and squamous carcinoma of the lung
  • cancer of the peritoneum hepatocellular cancer
  • gastric or stomach cancer including gastrointestinal cancer, pancreatic cancer, glioblastoma, cervical cancer, ovarian cancer, liver cancer, bladder cancer, hepatoma, breast cancer, colon cancer, rectal cancer, colorectal cancer, endometrial cancer, uterine carcinoma, salivary gland carcinoma, kidney or renal cancer, prostate cancer, thyroid cancer, hepatic carcinoma, as well as head and neck cancer.
  • cancer includes primary malignant cells or tumors (e.g., those whose cells have not migrated to sites in the subject's body other than the site of the original malignancy or tumor) and secondary malignant cells or tumors (e.g., those arising from metastasis, the migration of malignant cells or tumor cells to secondary sites that are different from the site of the original tumor).
  • primary malignant cells or tumors e.g., those whose cells have not migrated to sites in the subject's body other than the site of the original malignancy or tumor
  • secondary malignant cells or tumors e.g., those arising from metastasis, the migration of malignant cells or tumor cells to secondary sites that are different from the site of the original tumor.
  • abnormal cellular proliferation which can also be referred to as "excessive cellular proliferation or "cellular proliferative disease.”
  • diseases associated abnormal cellular proliferation include metastatic tumors, malignant tumors, benign tumors, cancers, precancers, hyperplasias, warts, and polyps, as well as non-cancerous conditions such as benign melanomas, benign chondroma, benign prostatic hyperplasia, moles, dysplastic nevi, dysplasia, hyperplasias, and other cellular growths occurring within the epidermal layers.
  • Classes of precancers include acquired small or microscopic precancers, acquired large lesions with nuclear atypia, precursor lesions occurring with inherited hyperplastic syndromes that progress to cancer, and acquired diffuse hyperplasias and diffuse metaplasias.
  • Examples of small or microscopic precancers include HGSIL (high grade squamous intraepithelial lesion of uterine cervix), AIN (anal intraepithelial neoplasia), dysplasia of vocal cord, aberrant crypts (of colon), PIN (prostatic intraepithelial neoplasia).
  • Examples of acquired large lesions with nuclear atypia include tubular adenoma, AILD
  • angioimmunoblastic lymphadenopathy with dysproteinemia atypical meningioma, gastric polyp, large plaque parapsoriasis, myelodysplasia, papillary transitional cell carcinoma in- situ, refractory anemia with excess blasts, and Schneiderian papilloma. It is understood that the methods and compositions of the present invention are applicable to cancer generally.
  • comparing refers to making an assessment of how the proportion, level or cellular localization of one or more biomarkers in a sample from a patient relates to the proportion, level or cellular localization of the corresponding one or more biomarkers in a standard, reference or control sample.
  • comparing may refer to assessing whether the proportion, level, or cellular localization of one or more biomarkers in a sample from a patient is the same as, more or less than, or different from the proportion, level, or cellular localization of the corresponding one or more biomarkers in standard, reference or control sample.
  • the term may refer to assessing whether the proportion, level, or cellular localization of one or more biomarkers in a sample from a patient is the same as, more or less than, different from or otherwise corresponds (or not) to the proportion, level, or cellular localization of predefined biomarker levels/ratios that correspond to, for example, high or low ERV level.
  • the term "comparing" refers to assessing whether the level of one or more biomarkers of the present invention in a sample from a patient is the same as, more or less than, different from other otherwise correspond (or not) to levels/ratios of the same biomarkers in a control sample (e.g., predefined levels/ratios that correlate to high or low ERV levels).
  • the term "comparing" refers to making an assessment of how the proportion, level or cellular localization of one or more biomarkers in a sample from a patient relates to the proportion, level or cellular localization of one or more biomarkers in the same sample. For example, a ratio of one biomarker to another (or more) from the same patient sample can be compared. Percentages or ratios of expression or levels of the biomarkers can be compared to other percentages or ratios in the same sample and/or to predefined reference or control percentages or ratios. Such comparison can be made to assess whether the patient's immune signature is ERV -high or ERV -low, which assessment can be used to direct therapy.
  • the ratio can include 1 -fold, 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-, 36-, 37-, 38-, 39-, 40-, 41 -, 42-, 43-, 44-, 45-, 46-, 47-, 48-, 49-, 50-, 51 -, 52-, 53-, 54-, 55-, 56-, 57-, 58-, 59-, 60-, 61 -, 62-, 63-, 64-, 65-, 66-, 67-, 68-, 69-, 70-, 71-, 72-, 73-,
  • the difference can include 0.9-fold, 0.8-fold, 0.7-fold, 0.7-fold, 0.6-fold, 0.5-fold, 0.4-fold, 0.3-fold, 0.2-fold, and 0.1 -fold (higher or lower) depending on context.
  • the foregoing can also be expressed in terms of a range (e.g., 1 -5 fold/times higher or lower) or a threshold (e.g., at least 2-fold/times higher or lower).
  • the evaluation of the relationship between one or more biomarkers in a sample can also be expressed in terms of a percentage including, but not limited to, 1 %, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 1 1 %, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21 %, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 6
  • the terms “identifies,” “indicates” or “correlates” (or “identifying,” “indicating” or “correlating,” or “identification,” “indication” or “correlation,” depending on the context) in reference to a parameter, e.g., a modulated proportion, level, or cellular localization in a sample from a patient, may mean that the patient has a particular immune signature, e.g., ERV high or ERV low.
  • the parameter may comprise the level (expression level or protein level) of one or more biomarkers of the present invention.
  • a particular set or pattern of the amounts of one or more biomarkers may identify the patient as having a particular immune signature, e.g., ERV high or ERV low
  • identifying may be by any linear or non-linear method of quantifying the relationship between levels/ratios of biomarkers to other biomarkers and/or standard, control or comparative value for the assessment of an immune signature.
  • patient refers to a mammal, particularly, a human.
  • the patient may have mild, intermediate or severe disease.
  • the patient may be treatment naive, responding to any form of treatment, or refractory.
  • the patient may be an individual in need of treatment or in need of diagnosis based on particular symptoms or family history.
  • the terms may refer to treatment in experimental animals, in veterinary application, and in the development of animal models for disease, including, but not limited to, rodents including mice, rats, and hamsters; and primates.
  • measuring and determining are used interchangeably throughout, and refer to methods which include obtaining a patient sample and/or detecting the level of a biomarker(s) in a sample. In one embodiment, the terms refer to obtaining a patient sample and detecting the level of one or more biomarkers in the sample. In another embodiment, the terms “measuring” and “determining” mean detecting the level of one or more biomarkers in a patient sample. Measuring can be accomplished by methods known in the art and those further described herein. The terms are also used interchangeably throughout with the term "detecting.”
  • sample encompasses a variety of sample types obtained from a patient, individual, or subject and can be used in a diagnostic or monitoring assay.
  • the patient sample may be obtained from a healthy subject, a diseased patient or a patient having associated symptoms of cancer.
  • a sample obtained from a patient can be divided and only a portion may be used for diagnosis. Further, the sample, or a portion thereof, can be stored under conditions to maintain sample for later analysis.
  • the definition specifically encompasses solid tissue samples such as a biopsy specimen or tissue cultures or cells derived therefrom and the progeny thereof.
  • sample includes blood and other liquid samples of biological origin (including, but not limited to, peripheral blood, serum, plasma, cerebrospinal fluid, urine, saliva, stool and synovial fluid).
  • a sample comprises a tumor sample.
  • sample also includes samples that have been manipulated in any way after their procurement, such as by centrifugation, filtration, precipitation, dialysis, chromatography, treatment with reagents, washed, or enriched for certain cell populations.
  • the terms further encompass a clinical sample, and also include cells in culture, cell supernatants, tissue samples, organs, and the like. Samples may also comprise fresh-frozen and/or formalin-fixed, paraffin-embedded tissue blocks, such as blocks prepared from clinical or pathological biopsies, prepared for pathological analysis or study by
  • a sample comprises an optimal cutting temperature (OCT)-embedded frozen tissue sample.
  • OCT optimal cutting temperature
  • the term "predetermined threshold value" of a biomarker refers to the level of the same biomarker in a corresponding control/normal sample or group of control/normal samples. Further, the term “altered level” of a biomarker in a sample refers to a level that is either below or above the predetermined threshold value for the same biomarker and thus encompasses either high (increased) or low (decreased) levels.
  • binding agent specific for or “binding agent that specifically binds” refers to an agent that binds to a biomarker and does not significantly bind to unrelated compounds.
  • binding agents that can be effectively employed in the disclosed methods include, but are not limited to, lectins, proteins and antibodies, such as monoclonal or polyclonal antibodies, or antigen-binding fragments thereof, aptamers, etc.
  • a binding agent binds a biomarker with an affinity constant of, for example, greater than or equal to about lxl 0 -6 M.
  • a binding agent can also comprise a probe or primer that specifically hybridizes a biomarker nucleic acid.
  • binding refers to that binding which occurs between such paired species as enzyme/substrate, receptor/agonist, antibody/antigen, nucleic acid/complement and lectin/carbohydrate which may be mediated by covalent or non-covalent interactions or a combination of covalent and non-covalent interactions.
  • the binding which occurs is typically electrostatic, hydrogen- bonding, or the result of lipophilic interactions. Accordingly, "specific binding" occurs between a paired species where there is interaction between the two which produces a bound complex having the characteristics of an antibody/antigen or enzyme/substrate interaction.
  • the specific binding is characterized by the binding of one member of a pair to a particular species and to no other species within the family of compounds to which the corresponding member of the binding member belongs.
  • an antibody typically binds to a single epitope and to no other epitope within the family of proteins.
  • specific binding between an antigen and an antibody will have a binding affinity of at least 10 -6 M. In other embodiments, the antigen and antibody will bind with affinities of at least 10 -7 M, 10 -8 M to 10 -9 M, 10 -10 M, 10 -11 M, or 10 -12 M.
  • Various methodologies of the instant invention include a step that involves comparing a value, level, feature, characteristic, property, etc. to a "suitable control,” referred to interchangeably herein as an “appropriate control,” a “control sample” or a “reference.”
  • a “suitable control,” “appropriate control,” “control sample” or a “reference” is any control or standard familiar to one of ordinary skill in the art useful for comparison purposes.
  • a "suitable control” or “appropriate control” is a value, level, feature, characteristic, property, etc., determined in a cell, organ, or patient, e.g., a control cell, organ, or patient, exhibiting, for example, a particular immune signature.
  • a "suitable control” or “appropriate control” is a value, level, feature, characteristic, property, ratio, etc. (e.g., biomarker levels that correlate to a particular immune signature) determined prior to performing a therapy (e.g., cancer treatment) on a patient.
  • a therapy e.g., cancer treatment
  • a transcription rate, mRNA level, translation rate, protein level/ratio, biological activity, cellular characteristic or property, genotype, phenotype, etc. can be determined prior to, during, or after administering a therapy into a cell, organ, or patient.
  • a "suitable control,” “appropriate control” or a “reference” is a predefined value, level, feature, characteristic, property, ratio, etc.
  • a "suitable control” can be a profile or pattern of levels/ratios of one or more biomarkers of the present invention that correlates to a particular immune signature, to which a patient sample can be compared. The patient sample can also be compared to a negative control.
  • control or reference levels may also be tailored to specific techniques that are used to measure levels of biomarkers in biological samples (e.g., LC-MS, GC-MS, ELISA, PCR, etc.), where the levels of biomarkers may differ based on the specific technique that is used.
  • a control or reference can be a profile or pattern of levels of one or more biomarkers that correlates to a particular immune signature, e.g., ERV high or ERV low.
  • biomarker nucleic acid is amplified prior to measurement.
  • the level of biomarker nucleic acid is measured during the amplification process.
  • the target nucleic acid is not amplified prior to measurement.
  • nucleic acid polymerization and amplification techniques include reverse transcription (RT), polymerase chain reaction (PCR), real-time PCR (quantitative PCR (q-PCR)), nucleic acid sequence-base amplification (NASBA), ligase chain reaction, multiplex ligatable probe amplification, invader technology (Third Wave), rolling circle amplification, in vitro transcription (IVT), strand displacement amplification, transcription-mediated amplification (TMA), RNA (Eberwine) amplification, and other methods that are known to persons skilled in the art.
  • more than one amplification method is used, such as reverse transcription followed by real time quantitative PCR (qRT-PCR). See, e.g., Chen et al, 33(20) NUCL. ACIDS RES. el 79 (2005).
  • a typical PCR reaction comprises multiple amplification steps or cycles that selectively amplify target nucleic acid species including a denaturing step in which a target nucleic acid is denatured; an annealing step in which a set of PCR primers (forward and reverse primers) anneal to complementary DNA strands; and an extension step in which a thermostable DNA polymerase extends the primers.
  • a DNA fragment is amplified to produce an amplicon, corresponding to the target DNA sequence.
  • Typical PCR reactions include about 20 or more cycles of denaturation, annealing, and extension. In many cases, the annealing and extension steps can be performed concurrently, in which case the cycle contains only two steps.
  • a reverse transcription reaction (which produces a complementary cDNA sequence) may be performed prior to PCR reactions.
  • Reverse transcription reactions include the use of, e.g., a RNA-based DNA polymerase (reverse transcriptase) and a primer.
  • a set of primers is used for each target sequence.
  • the lengths of the primers depends on many factors, including, but not limited to, the desired hybridization temperature between the primers, the target nucleic acid sequence, and the complexity of the different target nucleic acid sequences to be amplified.
  • a primer is about 15 to about 35 nucleotides in length. In other embodiments, a primer is equal to or fewer than about 15, fewer than about 20, fewer than about 25, fewer than about 30, or fewer than about 35 nucleotides in length. In additional embodiments, a primer is at least about 35 nucleotides in length.
  • a forward primer can comprise at least one sequence that anneals to biomarker nucleic acid sequence and alternatively can comprise an additional 5' non-complementary region.
  • a reverse primer can be designed to anneal to the complement of a reverse transcribed mRNA.
  • the reverse primer may be independent of the biomarker nucleic acid sequence, and multiple biomarker nucleic acid sequences may be amplified using the same reverse primer.
  • a reverse primer may be specific for a biomarker nucleic acid.
  • two or more biomarker nucleic acid sequences are amplified in a single reaction volume.
  • One aspect includes multiplex q-PCR, such as qRT-PCR, which enables simultaneous amplification and quantification of at least two biomarker nucleic acid sequences of interest in one reaction volume by using more than one pair of primers and/or more than one probe.
  • the primer pairs comprise at least one amplification primer that uniquely binds each mRNA, and the probes are labeled such that they are distinguishable from one another, thus allowing simultaneous quantification of multiple biomarker nucleic acid sequences.
  • Multiplex qRT-PCR has research and diagnostic uses including, but not limited, to detection of biomarker nucleic acid sequences for diagnostic, prognostic, and therapeutic applications.
  • the qRT-PCR reaction may further be combined with the reverse transcription reaction by including both a reverse transcriptase and a DNA-based thermostable DNA polymerase.
  • a "hot start" approach may be used to maximize assay performance.
  • the components for a reverse transcriptase reaction and a PCR reaction may be sequestered using one or more thermoactivation methods or chemical alteration to improve polymerization efficiency. See U.S. Patents No. 6,403,341 ; No. 5,550,044; and No.
  • labels, dyes, or labeled probes and/or primers are used to detect amplified or unamplified biomarker nucleic acid sequence (mRNA/cDNA).
  • mRNA/cDNA amplified or unamplified biomarker nucleic acid sequence
  • detection methods are appropriate based on the sensitivity of the detection method and the abundance of the target. Depending on the sensitivity of the detection method and the abundance of the target, amplification may or may not be required prior to detection.
  • biomarker nucleic acid sequence amplification is preferred.
  • a probe or primer may include Watson-Crick bases or modified bases.
  • Modified bases include, but are not limited to, the AEGIS bases (from EraGen Biosciences, Inc.
  • bases are joined by a natural
  • phosphodiester bond or a different chemical linkage.
  • Different chemical linkages include, but are not limited to, a peptide bond or a Locked Nucleic Acid (LNA) linkage, which is described, e.g., in U.S. Patent No. 7,060,809.
  • LNA Locked Nucleic Acid
  • oligonucleotide probes or primers present in an amplification reaction are suitable for monitoring the amount of amplification product produced as a function of time.
  • probes having different single stranded versus double stranded character are used to detect the nucleic acid.
  • Probes include, but are not limited to, the 5'-exonuclease assay (e.g., TaqMan®) probes (see U.S. Patent No. 5,538,848), stem-loop molecular beacons (see, e.g., U.S. Patents No. 6,103,476 and No. 5,925,517), stemless or linear beacons (see, e.g., WO 9921881, U.S.
  • Patents No. 6,649,349 and No. 6,485,901 peptide nucleic acid (PNA) Molecular Beacons (see, e.g., U.S. Patents No. 6,593,091 and No. 6,355,421), linear PNA beacons (see, e.g., U.S. Patent No. 6,329,144), non-FRET probes (see, e.g., U.S. Patent No. 6,150,097), Sunrise®/Amplifluor® probes (see, e.g., U.S. Patent No. 6,548,250), stem-loop and duplex ScorpionTM probes (see, e.g., U.S. Patent No.
  • one or more of the primers in an amplification reaction can include a label.
  • different probes or primers comprise detectable labels that are distinguishable from one another.
  • a nucleic acid, such as the probe or primer may be labeled with two or more distinguishable labels.
  • a label is attached to one or more probes and has one or more of the following properties: (i) provides a detectable signal; (ii) interacts with a second label to modify the detectable signal provided by the second label, e.g. , FRET (Fluorescent
  • Biomarker nucleic acid sequences can be detected by direct or indirect methods.
  • a direct detection method one or more biomarker nucleic acid sequences are detected by a detectable label that is linked to a nucleic acid molecule.
  • the biomarker nucleic acid sequences may be labeled prior to binding to the probe. Therefore, binding is detected by screening for the labeled biomarker nucleic acid sequence that is bound to the probe.
  • the probe is optionally linked to a bead in the reaction volume.
  • nucleic acids are detected by direct binding with a labeled probe, and the probe is subsequently detected.
  • the nucleic acids such as amplified mRNA/cDNA
  • xMAP Microspheres Luminex Corp. (Austin, TX) conjugated with probes to capture the desired nucleic acids.
  • Some methods may involve detection with polynucleotide probes modified, for example, with fluorescent labels or branched DNA (bDNA) detection.
  • nucleic acids are detected by indirect detection methods.
  • a biotinylated probe may be combined with a stretavidin-conjugated dye to detect the bound nucleic acid.
  • the streptavidin molecule binds a biotin label on amplified nucleic acid, and the bound nucleic acid is detected by detecting the dye molecule attached to the streptavidin molecule.
  • the streptavi din-conjugated dye molecule comprises Phycolink® Streptavidin R-Phycoerythrin (ProZyme, Inc. (Heward, CA)). Other conjugated dye molecules are known to persons skilled in the art.
  • Labels include, but are not limited to, light-emitting, light-scattering, and light- absorbing compounds which generate or quench a detectable fluorescent, chemiluminescent, or bioluminescent signal. See, e.g., Garman A., Non-Radioactive Labeling, Academic Press (1997) and Kricka, L., Nonisotopic DNA Probe Techniques, Academic Press, San Diego (1992).
  • Fluorescent reporter dyes useful as labels include, but are not limited to, fluoresceins (see, e.g., U.S. Patents No. 6,020,481; No. 6,008,379; and No. 5,188,934), rhodamines (see, e.g., U.S. Patents No.
  • fluorescein dyes include, but are not limited to, 6-carboxyfluorescein; 2',4', 1,4,- tetrachlorofluorescein, and 2',4',5',7',1,4-hexachlorofluorescein.
  • the fluorescent label is selected from SYBR-Green, 6-carboxyfluorescein ("FAM”), TET, ROX, VICTM, and JOE.
  • FAM 6-carboxyfluorescein
  • TET 6-carboxyfluorescein
  • ROX ROX
  • VICTM VICTM
  • JOE JOE
  • labels are different fluorophores capable of emitting light at different, spectrally-resolvable wavelengths (e.g., 4-differently colored fluorophores); certain such labeled probes are known in the art and described above, and in U.S. Patent No.
  • a dual labeled fluorescent probe that includes a reporter fluorophore and a quencher fluorophore is used in some embodiments. It will be appreciated that pairs of fluorophores are chosen that have distinct emission spectra so that they can be easily distinguished.
  • labels are hybridization-stabilizing moieties which serve to enhance, stabilize, or influence hybridization of duplexes, e.g., intercalators and intercalating dyes (including, but not limited to, ethidium bromide and SYBR-Green), minor-groove binders, and cross-linking functional groups (see, e.g., Blackburn et al, eds. "DNA and RNA Structure” in Nucleic Acids in Chemistry and Biology (1996)).
  • intercalators and intercalating dyes including, but not limited to, ethidium bromide and SYBR-Green
  • minor-groove binders include, but not limited to, ethidium bromide and SYBR-Green
  • cross-linking functional groups see, e.g., Blackburn et al, eds. "DNA and RNA Structure” in Nucleic Acids in Chemistry and Biology (1996)).
  • methods relying on hybridization and/or ligation to quantify biomarker nucleic acid may be used including, but not limited to, oligonucleotide ligation (OLA) methods and methods that allow a distinguishable probe that hybridizes to the target nucleic acid sequence to be separated from an unbound probe.
  • OLA oligonucleotide ligation
  • HARP-like probes as disclosed in U. S. Patent Application Publication No. 2006/0078894 may be used to measure the quantity of target nucleic acid.
  • the probe after hybridization between a probe and the targeted nucleic acid, the probe is modified to distinguish the hybridized probe from the unhybridized probe. Thereafter, the probe may be amplified and/or detected.
  • a probe inactivation region comprises a subset of nucleotides within the target hybridization region of the probe.
  • a post-hybridization probe inactivation step is carried out using an agent which is able to distinguish between a HARP probe that is hybridized to its targeted nucleic acid sequence and the corresponding unhybridized HARP probe.
  • the agent is able to inactivate or modify the unhybridized HARP probe such that it cannot be amplified.
  • a probe ligation reaction may be used to quantify target biomarker nucleic acid.
  • a probe ligation reaction may be used to quantify target biomarker nucleic acid.
  • MLPA MLPA
  • pairs of probes which hybridize immediately adjacent to each other on the target nucleic acid are ligated to each other only in the presence of the target nucleic acid.
  • MLPA probes have flanking PCR primer binding sites. MLPA probes can only be amplified if they have been ligated, thus allowing for detection and quantification of biomarkers.
  • a sample may also be analyzed by means of a microarray.
  • Microarrays generally comprise solid substrates and have a generally planar surface, to which a capture reagent (also called an adsorbent or affinity reagent) is attached.
  • a capture reagent also called an adsorbent or affinity reagent
  • the surface of a microarray comprises a plurality of addressable locations, each of which has the capture reagent (e.g., miRNA probes specific for particular biomarkers) bound there.
  • Many microarrays are described in the art. These include, for example, biochips produced by Asuragen,. Inc. (Austin, TX); Affymetrix, Inc. (Santa Clara, CA); GenoSensor Corp. (Tempe, AZ); Invitrogen, Corp. (Carlsbad, CA); and Illumina, Inc. (San Diego, CA).
  • a method comprises the steps of (a) assaying gene expression levels of one or more ERV genes described herein (e.g., including a panel described herein) in a biological sample obtained from a patient; (b) calculating an immune signature value based on the assayed expression levels.
  • the assay step can comprise PCR amplification.
  • the method can further comprise generating a report summarizing the gene expression data and/or the immune signature values.
  • the method may further comprise recommending a particular treatment. For example, an immune signature that is determined to be low in comparison to other biomarkers/control levels indicates that the subject should be treated with epigenetic therapy followed immunotherapy, chemotherapy or some combination of therapy for the particular cancer.
  • an immune signature that is determined to be high in comparison to other biomarkers/control levels indicates that the subject can be treated with immunotherapy (and optionally chemotherapy or some combination of therapy for the particular cancer).
  • immunotherapy and optionally chemotherapy or some combination of therapy for the particular cancer.
  • the methods listed above include all embodiments of the ERV panels described herein.
  • ERV expression can be measured using a microfluidic card (e.g., TaqMan® Array Microfluidic Card (Applied Biosystems).
  • the biomarkers of the present invention can be used in diagnostic tests to assess, determine, and/or qualify (used interchangeably herein) immune signature status in a patient and therefore, direct treatment of the patient.
  • immune signature status includes a high immune signature (ERV high) and a low immune signature (ERV low). Based on this status, further procedures may be indicated, including additional diagnostic tests or therapeutic procedures or regimens.
  • biomarkers are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc., of these biomarkers are disclosed that while specific reference of each various individual and collective combinations and permutation of these compounds may not be explicitly disclosed, each is specifically contemplated and described herein. Thus, if a panel of biomarkers A, B, and C are disclosed as well as a class of biomarkers D, E, and F and an example of a combination panel A-D is disclosed, then even if each is not individually recited each is individually and collectively contemplated meaning combinations, A-E, A-F, B-D, B-E, B-F, C-D, C-E, and C-F are considered disclosed.
  • any subset or combination of these is also disclosed.
  • the sub-group of A-E, B-F, and C-E would be considered disclosed.
  • This concept applies to all aspects of this application including, but not limited to, steps in methods of using the disclosed biomarkers.
  • steps in methods of using the disclosed biomarkers are a variety of additional steps that can be performed, it is understood that each of these additional steps can be performed with any specific embodiment or combination of embodiments of the disclosed methods.
  • the ERV panel comprises one or more of ERVW-1, ERVFRD-1, ERVP(b), ERVR, ERVV-1, ERVV-2, ERVS71-1, ERVPABLB-1, HERV- K74261, HML-2.HOM/K108, ERV-K17833 C19, HML-2 (K109), ERV-K113, ERV-K115, ERV-K102, ERVH-1, ERVH-2, ERVH-3, ERVE4-1, ERVFcl, ERVFCl-1, ERVFCl-2, ERVFCl-3, ERVFCl-4, ERVFCl-5, ERVW-2, ERVMER34-1, ERVH48-1, ERV9-1, ERV- F(XA34), and ERVW-5.
  • the foregoing includes, for example, combinations of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, and 31 biomarkers.
  • the foregoing combinations are common in any of breast, colorectal and ovarian cancer.
  • the ERV panel comprises one or more of ERVW-1, ERVFRD-1, ERVP(b), ERVR, HERV-K74261, HML-2.HOM/K108, ERV-K17833 C19, HML-2 (K109), ERV-K113, ERV-K115, ERV-K102, ERVE4-1, ERVH-1, ERVH-2, ERVH- 3, ERVFCl-1, ERVFCl-2, ERVFCl-3, ERVFCl-4, ERVFCl-5, ERVW-2, ERVW-5, ERVH48-1 and ERV9-1.
  • the foregoing includes, for example, combinations of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23 and 24 biomarkers.
  • the ERV panel comprises one or more of ERVW-1, ERVP(b), ERV-3, ERV-Fcl, ERV-Fbl, and ERV9-1.
  • the foregoing includes, for example, combinations of at least 2, at least 3, at least 4, at least 5, and 6 biomarkers.
  • expression of all the ERVs can be measured and the expression of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, and 31 of the biomarkers can be used to classify ERV high, ERV intermediate and/or ERV low.
  • the power of a diagnostic test to correctly predict status is commonly measured as the sensitivity of the assay, the specificity of the assay or the area under a receiver operated characteristic ("ROC") curve.
  • Sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative.
  • An ROC curve provides the sensitivity of a test as a function of 1- specificity. The greater the area under the ROC curve, the more powerful the predictive value of the test.
  • Other useful measures of the utility of a test are positive predictive value and negative predictive value. Positive predictive value is the percentage of people who test positive that are actually positive. Negative predictive value is the percentage of people who test negative that are actually negative.
  • the biomarker panels of the present invention may show a statistical difference in different immune signature statuses of at least p ⁇ 0.05, p ⁇ 10 -2 , p ⁇ 10- 3 , p ⁇ 10 -4 or p ⁇ 10 -5 . Diagnostic tests that use these biomarkers may show an ROC of at least 0.6, at least about 0.7, at least about 0.8, or at least about 0.9.
  • the biomarkers are measured in a patient sample using the methods described herein and an immune signature status is calculated.
  • the measurement(s) may then be compared with a relevant diagnostic amount(s), cut-off(s), or multivariate model scores that distinguish a high immune signature (ERV high) status from a low immune signature (ERV low) status.
  • the diagnostic amount(s) represents a measured amount of a biomarker(s) above which or below which a patient is classified as having a particular immune signature status.
  • the particular diagnostic cut-off can be determined, for example, by measuring the amount of biomarkers in a statistically significant number of samples from patients with different immune signature statuses, and drawing the cut-off to suit the desired levels of specificity and sensitivity.
  • the values measured for markers of a biomarker panel are mathematically combined and the combined value is correlated to the underlying diagnostic question of high or low ERV immune signature.
  • Biomarker values may be combined by any appropriate state of the art mathematical method.
  • DA discriminant analysis
  • DFA Discriminant Functional Analysis
  • Kernel Methods e.g., SVM
  • Multidimensional Scaling MDS
  • Nonparametric Methods e.g., k-Nearest-Neighbor Classifiers
  • PLS Partial Least Squares
  • Tree-Based Methods e.g., Logic Regression, CART, Random Forest Methods, Boosting/Bagging Methods
  • Generalized Linear Models e.g., Logistic
  • the method used in a correlating a biomarker combination of the present invention is selected from DA (e.g., Linear-, Quadratic-, Regularized Discriminant Analysis), DFA, Kernel Methods (e.g., SVM), MDS, Nonparametric Methods (e.g., k-Nearest-Neighbor Classifiers), PLS (Partial Least Squares), Tree-Based Methods (e.g., Logic Regression, CART, Random Forest Methods, Boosting Methods), or Generalized Linear Models (e.g., Logistic Regression), and Principal Components Analysis. Details relating to these statistical methods are found in the following references: Ruczinski et al.,12 J.
  • data that are generated using samples such as "known samples” can then be used to "train” a classification model.
  • a "known sample” is a sample that has been pre-classified.
  • the data that are used to form the classification model can be referred to as a "training data set.”
  • the training data set that is used to form the classification model may comprise raw data or pre-processed data.
  • the classification model can recognize patterns in data generated using unknown samples.
  • the classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition.
  • Classification models can be formed using any suitable statistical classification or learning method that attempts to segregate bodies of data into classes based on objective parameters present in the data. Classification methods may be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, "Statistical Pattern Recognition: A Review", IEEE Transactions on Pattem Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000, the teachings of which are incorporated by reference.
  • supervised classification training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the known classes. New data may then be applied to the learning mechanism, which then classifies the new data using the learned relationships.
  • supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).
  • linear regression processes e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)
  • binary decision trees e.g., recursive partitioning processes such as CART
  • artificial neural networks such as back propagation networks
  • discriminant analyses e.g., Bayesian classifier or Fischer analysis
  • Another supervised classification method is a recursive partitioning process.
  • Recursive partitioning processes use recursive partitioning trees to classify data derived from unknown samples. Further details about recursive partitioning processes are provided in U.S. Patent Application No. 2002 0138208 Al to Paulse et al., "Method for analyzing mass spectra.”
  • the classification models that are created can be formed using unsupervised learning methods.
  • Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre-classifying the spectra from which the training data set was derived.
  • Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into "clusters" or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other.
  • Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self-Organizing Map algorithm.
  • the classification models can be formed on and used on any suitable digital computer.
  • Suitable digital computers include micro, mini, or large computers using any standard or specialized operating system, such as a Unix, Windows® or LinuxTM based operating system.
  • the digital computer that is used may be physically separate from the mass spectrometer that is used to create the spectra of interest, or it may be coupled to the mass spectrometer.
  • the training data set and the classification models according to embodiments of the invention can be embodied by computer code that is executed or used by a digital computer.
  • the computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, etc., and can be written in any suitable computer
  • the learning algorithms described above are useful both for developing classification algorithms for the biomarkers already discovered, and for finding new biomarker biomarkers.
  • the classification algorithms form the base for diagnostic tests by providing diagnostic values (e.g., cut-off points) for biomarkers used singly or in combination.
  • kits for qualifying immune signature status which kits are used to detect the biomarkers described herein.
  • the kit is provided as a PCR kit comprising primers that specifically bind to one or more of the biomarkers described herein.
  • primers the specifically bind and amplify the target biomarkers can further comprise substrates and other reagents necessary for conducting PCR (e.g., quantitative real-time PCR).
  • the kit can be configured to conduct singleplex or multiplex PCR.
  • the kit can further comprise instructions for carrying out the PCR reaction(s).
  • the biological sample obtained from a subject may be manipulated to extract nucleic acid.
  • the nucleic acids are contacted with primers that specifically bind the target biomarkers to form a primer: biomarker complex.
  • the complexes can then be amplified and detected/quantified/measured to determine the levels of one or more biomarkers.
  • the subject can then be identified as having a particular immune signature (e.g., ERV high or ERV low) based on a comparison of the measured levels of one or more biomarkers to one or more reference controls and/or a comparison of one set of biomarkers to another set of biomarkers.
  • a particular immune signature e.g., ERV high or ERV low
  • the kit comprises primers/probes for
  • ERVW-1 amplifying/detecting/measuring one or more of ERVW-1, ERVFRD-1, ERVP(b), ERVR, ERVV-1, ERVV-2, ERVS71-1, ERVPABLB-1, HERV-K74261, HML-2.HOM/K108, ERV- K17833 CI 9, HML-2 (K109), ERV-K113, ERV-K115, ERV-K102, ERVH-1, ERVH-2, ERVH-3, ERVE4-1, ERVFcl, ERVFCl-1, ERVFCl-2, ERVFCl-3, ERVFCl-4, ERVFCl-5, ERVW-2, ERVMER34-1, ERVH48-1, ERV9-1, ERV-F(XA34) and ERVW-5, and combinations of all of the foregoing.
  • the kit comprises primers/probes for amplifying/detecting/measuring one or more of ERVW-1, ERVFRD-1, ERVP(b), ERVR, HERV-K74261, HML-2.HOM/K108, ERV-K17833 C19, HML-2 (K109), ERV-K113, ERV- K115, ERV-K102, ERVE4-1, ERVH-1, ERVH-2, ERVH-3, ERVFCl-1, ERVFCl-2, ERVFCl-3, ERVFCl-4, ERVFCl-5, ERVW-2, ERVW-5, ERVH48-1 and ERV9-1, and combinations of all of the foregoing.
  • the kit comprises primers/probes for amplifying/detecting/measuring one or more of ERVW-1, ERVFRD-1, ERVP(b), ERVR, HERV-K74261, HML
  • kits comprises the primers encoded by SEQ ID NOS: 1-62.
  • the kit comprises the primers encoded by SEQ ID NOS: l-8, 17-52, 55-58 and 61-62.
  • the kits comprise the primers encoded by SEQ ID NOS: 1-2, 5-6, 7-8, 39-40, and 55-58.
  • the kits can further comprise reagents and instructions for performing PCR.
  • the PCR is qRT- PCR.
  • DNA methyltransf erase inhibitors such as 5-azacytidine (Aza) and 5-aza- 2'-deoxycytidine (Dac) are effective cancer therapies in hematologic neoplasms (Tsai et al, 2012) (Matei et al, 2012) and are FDA approved for the pre-leukemic disorder
  • MDS myelodysplasia
  • DNMTs DNA methyltransferases
  • tumorigenicity (Tsai et al, 2012).
  • Mechanisms can include reversal of abnormal promoter DNA methylation, re-expression of silenced genes including tumor suppressors (Baylin and Jones, 2011), and changes to cancer signaling pathways including apoptosis, cell cycle activity, and stem cell functions (Tsai et al, 2012).
  • a long recognized activity of DNMTis described by others (Karpf et al, 2004; Karpf et al, 1999), and our group (Li et al, 2014; Wrangle et al, 2013), is induction of immune responses in cancer cells.
  • NSCLC non-small cell lung cancer
  • Aza induces interferon signaling and concordant upregulation of surface antigens and their assembly proteins, viral defense pathways, and transcript and surface protein levels of PD- Ll, the key checkpoint ligand targeted in the above immunotherapy (Li et al, 2014; Wrangle et al, 2013).
  • Aza- Induced iMmune genes AIM (Li et al, 2014) for which activation is greatest for epithelial ovarian cancer (EOC) and NSCLC (Li et al, 2014).
  • dsRNA cytosolic double-stranded RNA
  • TCGA Cancer Genome Atlas
  • the viral defense gene expression separates primary EOC and other cancers into high and low expression and high tumor expression strongly associates with clinical benefit in a trial of immune checkpoint therapy for advanced melanoma.
  • Aza sensitizes to immune checkpoint blockade in a pre-clinical model of melanoma.
  • Cell lines were treated with 500 nM Aza, 100 nM Dac, or 500 nM- 3 ⁇ carboplatin (Sigma, St. Louis, Missouri) for 72 hours, and DNA and RNA were isolated using standard methods at 1, 3, or 7 days following removal of drug.
  • 2 ⁇ ruxolitinib Invivogen #tlrl-rux
  • 0.625- 5 U/mL of anti-IFNAR2 antibody PBL Interferon Source #21385-1
  • 0.625-2.5 U/mL of anti- IFNB antibody PBL Interferon Source #31400- 1
  • 1.25-5 U/mL of anti-ILlORB antibody Abeam # ab89884 were added during DNMTi treatment.
  • Ribosomal RNA was depleted using the Ribominus kit (Invitrogen), and PolyA+ and Poly A- RNA were isolated using the Oligotex Direct mRNA Mini Kit (Invitrogen). Nucleic acids were treated with 1 U ⁇ g of RNase III (Ambion), 10 U ⁇ g of RNaseH (Invitrogen), or 3 U/ 1 ⁇ g calf intestine alkaline phosphatase (New England Biolabs) according to manufacturer's instructions and 400 ng of each nucleic acid was transfected into HT29 cells.
  • DNA Methylation Analysis DNA was bisulfite converted and subjected to
  • Methylation-Specific PCR (Herman et al, 1996) for IRF7 and Fc2, and COBRA (Xiong and Laird, 1997) for the Fc2 locus on chromosome 1 1.
  • Western blot analyses employed antibodies against ERV-3 (1 : 1000, Everest), B-Actin (1 :5000, Sigma), MDA5 (1 : 1000, Cell Signaling #5321), PARP (#9542, 1 : 1000; Cell Signaling Technology, Inc.), RIG-I (1 : 1000, Cell Signaling #4200), STING (1 : 1000, Abeam #ab82960), Syncytin-1 (1 :350, Dr. Herve Perron, Geneuro, Geneva
  • IFN ⁇ ELISA utilized the Verikine- HSTM Human Interferon Beta Serum ELISA kit (PBL Interferon Source) and IFNL ELISA the DuoSet ELISA for Human IL-29/IL28-B (IFNL 1/3) kit (R & D Systems).
  • Syncytin-1, ERV-3 and ERV-W2 env, ER, and E-GFP vectors and siRNAs targeting Syncytin-1 , ERV-3, or a scrambled control were transfected using the JetPei or Hyperfect transfection reagents, respectively.
  • TLR3, MAVS, and STING shRNA were performed according to established methods (Stewart et al, 2003).
  • RNAseq Expression Analysis o f Tumors from Anti-CTLA-4-Treated Patients . Patients were described previously (Snyder et al, 2014) and samples were obtained with written informed consent per approved institutional review board (IRB) protocols. Expression data were obtained using RNASeq with all data deposited at GEO (accession number pending).
  • mice were subcutaneously injected with 1x105 B16-F10 tumor cells. On days 4, 8, 1 1 , 14, 18, mice were treated
  • mice received two cycles of intraperitoneal injection of 0.1 to 0.75 mg/kg Aza in PBS for 5 consecutive days followed by 7 days off treatment, starting at day 8 after developing palpable tumors, with control groups receiving corresponding doses of non-specific isotype antibody control and PBS intraperitoneally.
  • Tumor surface was measured with a caliper using the ellipse surface formula (Length*Width*n)/400.
  • Consensus hierarchical clustering was performed with the ConsensusClusterPlus R-package (Wilkerson and Hayes, 2010) and data analyzed by the Fisher exact p value test for association between clusters (p ⁇ 0.05 *; p ⁇ 0.0l **;p ⁇ 0.001 ***).
  • Colorectal cell lines HCT116 and HT29 were obtained from the American Type Tissue Collection and were cultured under recommended conditions.
  • Ovarian cancer cell lines were obtained from the laboratory of Dr. Dennis Slamon and included A2780 Hey, Kuramochi, and TykNu. These cell lines were cultured according to ATCC recommended conditions.
  • Cell lines were treated with 500 nM of Aza, 100 nM of Dac, or 500 nM- 3 ⁇ of carboplatin (Sigma; St. Louis, Missouri) for 72 hours while in log-growth phase, changing the media and drug every 24 hours for drug treatment.
  • carboplatin Sigma; St. Louis, Missouri
  • ruxolitinib (Invivogen #tlrlrux), 0.625-5 U/mL of anti-IFNAR2 antibody (PBL Interferon Source #21385-1), 0.625-2.5 U/mL of anti-IFNB antibody (PBL Interferon Source #31400- 1), or 1.25-5 U/mL of anti-ILlORB antibody (Abeam # ab89884) were added during DNMTi treatment.
  • Cells were harvested at 1, 3, or 7 days following initial application of drug. DNA and RNA were obtained using standard protocols (Tsai et al, 2012) RNA from cell lines was sent for the Agilent 44K Expression Array (Li et al., 2014).
  • DNA bisulfite conversion was performed using a commercially available kit (EZ DNA Methylation GoldTM Kit, Zymo Research, Orange, CA).
  • Methylation-Specific PCR was performed as previously described (Herman et al, 1996) for the IRF7 and Fc2 genes. Primers are listed in Table 6.
  • COBRA Combined Bisulfite Restriction Analysis
  • Primers are listed in Table 6. Bands and restriction fragments were resolved on 2% agarose gels, stained with ethidium bromide, and photoimaged with a UV camera.
  • RNAseq Expression Analysis of Tumors from Anti-CTLA-4- Treated Patients RNAseq Expression Analysis of Tumors from Anti-CTLA-4- Treated Patients .
  • RNA Sequencing was performed at New York Genome Center.
  • Raw reads in the fastq format were aligned to Human Genome HG19 using RNA-seq STAR aligner (Dobin et al, 2013).
  • Mapped reads for each sample were counted for each gene in annotation files in GTF format (gencode.vl9.annotation.gtf available for download from GENECODE website
  • qRT-PCR results are expressed as mean +/- SEM, with p values ⁇ 0.05 considered statistically significant using the program IBM SPSS version 21. Different groups were compared by Mann-Whitney U test or Student /-test as appropriate, and 2-tailed p values are reported. Normalized, level 3 Agilent gene expression array data for high-grade serous ovarian cancer was downloaded from The Cancer Genome Atlas (TCGA) data portal.
  • TCGA Cancer Genome Atlas
  • RNA concentration was determined using the Nanodrop machine and software (Thermo Fisher Scientific, Rockville, Maryland). 1 ⁇ g total RNA was used to generate cDNA with the QuantiTect Reverse Transcription Kit (Qiagen, Venlo, The Netherlands).
  • Quantitative reverse transcription PCR of DDX41, DDX58, IFI16, IFI27, IFI44, IFI44L, IFI6, IFNB1, IFNL1, IFNL2, IFNL3, IFNLR1, IL-1B, IL-10RB, IL-15, IRF7, MAGEB2, MAYS, MB21D1, MX1, OASL, STAT1, and TMEM173 mRNA was performed using TaqMan assays or Custom Taqman Gene Expression Array Cards (Life Technologies, Carlsbad, California) and the Applied Biosystems 7500 Fast real-time PCR system and software. TBP and GAPDH were used as reference genes. The AACT method was used to calculate relative expression levels. All qRT-PCR assays were carried out in triplicate and then repeated with new cDNA synthesis. Reverse transcriptase negative cDNA synthesis reactions were performed for at least one sample per plate.
  • Protein was isolated from cell lines and measured by BCA (Pierce Biotechnology. Protein extracts were subjected to polyacrylamide gel electrophoresis using the 4%-12% NuPAGE gel system (Invitrogen), transferred to PVDF (Millipore) membranes, and immunoblotted using antibodies that specifically recognize ERV-3, MDA5, cleaved PARP, RIG-I, STING (TMEM173), Syncytin-1, and TLR3. ⁇ -actin (Sigma #5441, 1 :5000) was used as a loading control.
  • Antibodies used were as follows: polyclonal goat anti-ERV-3 (1 : 1000, Everest), mouse anti- ⁇ -Actin (1:5000, Sigma), goat anti-mouse IgG-HRP (sc-2005, 1:5,000; Santa Cruz Biotechnology, Inc.), rabbit anti-MDA5 (1: 1000, Cell Signaling #5321), rabbit anti-PARP (#9542, 1: 1000; Cell Signaling Technology, Inc.), rabbit anti -RIG-I (1 : 1000, Cell Signaling #4200), rabbit anti-STING (TMEM173) (ab92605, 1: 1000, Abeam), Syncytin-1 monoclonal mouse antibody 14A5 against the SU (1:350) (kind gift from Dr.
  • Cytoplasmic RNA was depleted of ribosomal RNA using the Ribominus kit (Invitrogen), according to the manufacturer's instructions. PolyA+ and Poly A- RNA were isolated using the Oligotex Direct mRNA Mini Kit (Invitrogen), according to the manufacturer's instructions. Nucleic acids were treated with 1 U ⁇ g of RNase III (Ambion), 10 U ⁇ g of RNaseH (Invitrogen) or 3 U ⁇ g calf intestine alkaline phosphatase (New England Biolabs) according to the
  • RNA concentration was measured with aNanodrop, and 400 ng of each nucleic acid or 1 ⁇ g/mL PolyLC (Invivogen) was transfected into HT29 recipient cells.
  • IFNB ELISA was performed with the Verikine-HSTM Human Interferon Beta Serum ELISA kit (PBL Interferon Source) and IFNL ELISA was performed with the DuoSet ELISA for Human IL- 29/IL28-B (IFNL 1/3) kit (R & D Systems).
  • TLR3, MAVS, STING and GFP knockdowns were created in A2780, Hey, and TykNu cell lines. Virus production and infections were carried out according to established methods (Stewart et al, 2003).
  • the short hairpin sequences used were:
  • PCR cDNA amplification was performed and fragments cloned into TopoTA vectors (Invitrogen) for all new ERV genes not previously analyzed as stated above (Strissel et al, 2012) (Tables 3 and 4).
  • qPCR analyses of all cloned env and other ERV genes with a known copy number was used as an external standard to generate a standard curve with a cycle threshold (CT) value against the log of amount of standard. Importantly, a similar PCR efficiency (over 97 %) between all env genes was needed for comparison. Similar standard curves of all env genes were obtained for the SYBR-Green based qPCR with the following slopes and calculations (Strissel et al, 2012) and Tables 3 and 4).
  • ERV env genes were amplified by qPCR from 40 ng of cell line cDNA with SYBR- green technology and analyzed with an ABI7300 (ABI, Darmstadt, Germany) according to Strissel et al, 2012). Expression values were calculated as molecules (mol) per ng total RNA using a standard curve of each cloned env gene determined by real time PCR and calculated as mean +/- standard deviation of the mean (S.E.M.) according to (Ruebner et al, 2013) (Ruebner et al, 2012).
  • ERV-W1 URE/ 5'LTR without the U5 region (URE-U3-R) (-613 to +133 nt) on chromosome 7q21.2 was amplified with PCR from human control placenta and cloned via Kpnl and Hind III into pGL3-basic (Promega) (Ruebner et al. 2013).
  • the new pGL3-LTR vector was incubated with Nco I and Xba 1 deleting the luciferase gene and Syncytin-1 (1617 bp) was cloned into the Nco I and Xba 1 sites of pGL3-basic without luciferase and sequenced (5' LTR-Syn-l-pGL3).
  • the vector of ERV-W2 env (phCMV-Xq22.3 env FL) was a kind gift from Dr. K. Ruprecht, Charite, University Medicine, Berlin and represents the env-W2 cDNA but only has a partial codogenic env due to a stop codon (Table 2) (Roebke et al, 2010).
  • the p3Xfiag-CMV-14 vector contains the ERV-3 env full length cDNA and was contributed by Dr. Neal Rote, Case Western University.
  • the pEGFPNl vector was purchased (CloneTech Laboratories, Inc.).
  • a pCMV-Tag vector (Stratagene) contains the human wild type ERa cDNA and is called HEGO-CMV-ERa and was a kind gift from Dr. R.X. Song, University of Virginia, USA. Following transfection of HEGO-CMV-ERa into an ER minus carcinoma cell line an ERa full length 66 KD protein was observed (Strissel et al Oncotarget, 2012).
  • ERV siRNA Knockdown Experiments. A2780, Hey and TykNu ovarian cancer cell lines were treated with 500 nM of Aza (Sigma; St. Louis, Missouri) for 72 hours while in log- growth phase, changing the media and drug every 24 hours. The next day (Day 4) following Aza treatment, the cells were transfected using Hyperfect reagent (Qiagen) according to manufacturer's instructions with siAlexa scrambled (80nM) (Allstars Neg siRNA AF488, Qiagen) to achieve transfection efficiency above 85% and also to represent a negative mock control.
  • Hyperfect reagent Qiagen
  • siAlexa scrambled 80nM
  • Allstars Neg siRNA AF488, Qiagen Allstars Neg siRNA AF488, Qiagen
  • RNA was used for ⁇ -actin with all cell lines; Syncytin-1 (400ng for A2780, Hey, TykNu, HCT116 and 200ng for DKO) and env-Fc2, [A2780 (200ng), Hey and TykNu (400ng), HCT116 (200ng), DKO (100ng)].
  • ⁇ ⁇ of a gene specific primer ligated to a TAG- sequence not specific for the human genome (GSP sense/antisense (RT) TAG was implemented in the reaction. RNA and primers were preheated at 65°C for 5min.
  • the GSP-TAG, 0.5mM dNTP, 5mM MgC12, lOmM DTT, 40U RNaseOUT, 100U SuperScriptlll® RT (life technologies, Germany) and 240ng Actinomycin D (Sigma, Germany) were added with the RNA for a 20 ⁇ 1 reaction. Synthesis was performed at 50°C for 50 min and terminated at 85°C for 5 min. RT with extremely low intrinsic RNase H activity (for cleavage of RNA from RNA/DNA duplexes) and Actinomycin D was added to prevent second strand cDNA RT resulting in antisense artifacts (PMID 17897965).
  • 20-50mg of tissue was demembranated and the RNA isolated and cDNA synthesized according to (Strissel et al, 2012). All cDNAs were quantified for 22 ERV env genes to determine molecules / ng RNA (identical env genes as in FIG. 5 but not including gag-W5, env-MER34, ERV-FXA34 and ERV9-1).
  • mice Bl 6-F10 Melanoma Mouse Model. All mouse procedures were performed in accordance with the institutional protocol guidelines at the Memorial Sloan Kettering Cancer Center (MSKCC; New York, NY). C57BL/6J mice (6-8- week-old females) were obtained from The Jackson Laboratory. C57BL/6J mice were subcutaneously injected with 1x105 B16-F10 tumor cells. On days 4, 8, 11, 14, 18, the mice were treated intraperitoneally with anti-ctla-4 (100 ⁇ g in 100 ⁇ ). For Aza treatment, mice received two cycles of
  • DNMTis trigger viral defense and type I interferon signaling.
  • Induction of AIM in a previous study of 23 EOC cell lines included, in addition to previously reported DNA hypermethylated cancer testis antigens (MAGEA4, MAGEA9, NY-ESO-1) (James et al, 2013; Karpf et al., 2009; Karpf et al, 2004; Odunsi et al, 2014), interferon/viral defense, antigen processing and presentation, and host immune cell attraction genes (FIG. la).
  • TMEM173/STING TMEM173/STING
  • RNA DDX41 and DDX58
  • FIG. 1C D, FIG. SI A, B
  • IFNL1 IL28A
  • IFNL3 IL29
  • IFN III receptor IFNLR1 IFN III receptor IFNLR1
  • RNA sensing proteins include TLR3 on the endosomal membrane and MDA5, PKR, and RIG-I in the cytoplasm (FIG. 2A). These induce IRF3, IRF7, and NF-KB to translocate to the nucleus and activate transcription of IFN ⁇ 1 (Ivashkiv and Donlin, 2014). IRF7 is frequently promoter DNA hypermethylated in cancer and the associated low basal expression can be reversed by Aza in squamous NSCLC (Wrangle et al, 2013). Among 23 EOC lines examined, IRF7 was hypermethylated in only one, A2780 (Li et al, 2014) (FIG.
  • Type III IFN signaling can be activated by viral infection (Robek et al, 2005) (Ding and Robek, 2014). However, even though we observed upregulation of Type III ligand transcripts IFNL1 (IL28A) and IFNL3 (IL29) (FIG. SIC), secreted Type III interferon proteins are undetectable by ELISA (FIG. S2B).
  • IFN ⁇ binding to IFNAR2 also may contribute to late, Aza induced apoptosis that peaks at 4-7 days after Aza withdrawal, since anti-IFNAR2 leads to a lower ratio of cleaved/total PARP (FIG. 3A,C). (FIGS. 3C, S2F).
  • DNMTis trigger viral defense through induction ofdsRNA.
  • Aza-induced viral defense genes and IFN ⁇ 1 are not generally DNA methylated at promoter regions (Li et al, 2014), thus Aza may activate the pathway upstream of these genes.
  • Aza-induced human endogenous retrovirus (ERV) transcripts can activate viral de fense responses in EOC.
  • the above data suggests Aza might activate endogenous retroviral sequences (ERVs) that constitute more than 8% of the human genome, can activate cytosolic RNA sensors, and are silenced in normal somatic cells by promoter DNA methylation (Bannert and Kurth, 2004) (Tristem, 2000) (Hurst and Magiorkinis, 2014) (Mankan et al, 2014).
  • Aza can induce specific ERV transcripts in melanoma, choriocarcinoma, and endometrial cancer cells (Laska et al, 2013; Ruebner et al, 2013; Stengel et al, 2010) (Strissel et al., 2012). Indeed, in initial testing, the ERVK subfamily (Wang-Johanning et al, 2003) transcripts increased 2.5-fold in the A2780 cell line upon Aza treatment (data not shown).
  • Upregulation of individual ERVs (22 full length env, 6 partial coding env, one full length gag, and two partial coding poh) (Tables Sl- S2), in PCR assays for non-repeat sequences, occur especially, at Day 7, coinciding with ISG expression, in three EOC lines following Aza and Dac treatment (FIG. 5A,B, FIG. S5A,B,C).
  • ERV env genes like Syncytin-1, ERVS, env-K and env-H (Blond et al, 1999; Lower et al, 1993; Mi et al, 2000; Rote et al, 2004) and at especially high levels, env-Fc2, a less well characterized gene (Benit et al, 2003).
  • DKO as well as Aza treated A2780 and TykNu cells, loss of env-Fc2 promoter methylation correlates with increased Fc2 expression (FIGS. 5B, 6A-C, S6A) but not in Hey cells (FIG. S6A).
  • bidirectional transcription producing sense and anti-sense transcripts occurred for Syncytin-1 and five env- Fc2 gene loci, but not ⁇ -actin, in three EOC lines and HCT 116 and DKO cells (FIG. 5C, Tables 3 and 4), analyzed by the TAG-aided sense/antisense transcript detection (TASA-TD) technique (Henke et al, 2015).
  • TASA-TD TAG-aided sense/antisense transcript detection
  • ERV transcripts seem directly involved in the Aza responses in that, first, although druginduced upregulation of ERV transcripts begins early after Aza, both ERVs and viral defense gene increases generally peak by day 7 (FIGS. 5, S5). Second, ERV env proteins such as Synl and ERV-3 are not increased after Aza treatment, supporting a dominant role for viral defense signaling via RNA transcripts (FIG. 6D, FIG. S6B, and C). Third, overexpression of ERVS, EnvW2, and Syncytin-1 in TykNu (FIG. 6e-j), A2780 (FIG. S6S), and Hey cells (FIG.
  • ER V transcripts a driving role for ER V transcripts in triggering Aza-induced viral defense gene responses is evidenced by a high correlation of basal levels of both in 19 primary EOC.
  • Viral defense gene levels divide human tumors into high and low expression groups that track with responses to immune checkpoint therapy.
  • Human cancers can evolve immune evasion to become less responsive to immune modulation (Drake et al, 2006) (Schreiber et al., 2011).
  • basal transcript levels for the Aza-induced viral defense genes group primary EOC, breast, colon, and lung cancers, and melanoma from The Cancer Genome Atlas (TCGA) studies into high and low groups (FIG. 7B, FIG. S7C-F).
  • TCGA Cancer Genome Atlas
  • this basal expression divides tumors into high, medium, and low expression groups and the former two encompass virtually all of the TCGA (Verhaak et al 2013) immune reactive (IMR), good prognosis tumors.
  • the Low group encompasses the PRO (high proliferative), poor prognosis subtype (p ⁇ 0.001 to .0001 -FIGS. 7B, S7B)).
  • CIMP DNA hypermethylation frequency phenotype
  • Aza treatment potentiates immune checkpoint therapy in a mouse model of melanoma.
  • multiple combinations of low dose Aza directly enhance tumor responses to anti-CTLA4 immune checkpoint therapy (FIGS. 7E,F, S7G).
  • B16 cells treated in vitro with Aza, then injected into mice and treated with anti- CTLA-4, are even cleared completely (data not shown).
  • DNMTis can potentiate the anti -tumor effects of immune checkpoint inhibitors.
  • DNMTis induce a complex set of immune pathway responses in tumor cells (Li et al., 2014; Wrangle et al, 2013).
  • DNMTis trigger cytoplasmic dsRNA sensing, central to cellular viral defense responses, and activate interferon in EOC and colon cancer cells by disrupting DNMTs. This activation could induce tumor attraction of lymphocytes (Ivashkiv and Donlin, 2014).
  • ERVs A major trigger of the Aza-induced viral defense response appears to be bidirectional transcription of ERVs that are known to fold into dsRNA secondary structures.
  • ERVs representing more than 8% of the human genome (Bannert and Kurth, 2004; Tristem, 2000), integrated into the genome of mammals between 0.1 and 40 million years ago via exogenous retroviral infections of germ cells (Egan et al, 2004; Turner et al, 2001).
  • ERV genes are non-functional due to DNA recombination, mutations and deletions, but some produce functional proteins including group-specific antigen (gag), polymerase (pol) with reverse transcriptase (RT) and the envelope (env) surface unit (SU) with a transmembrane immunosuppressive-like peptide (Mi et al, 2000) (Blaise et al, 2005; de Parseval et al, 2003; Villesen et al, 2004).
  • the env gene of ERVW-1 chromosome 7q21.2
  • Syncytin- 1 has an essential role in placentogenesis (Blond et al, 1999; Mi et al, 2000).
  • Syncytin-1 is epigenetically regulated throughout placentogenesis (Matouskova et al, 2006).
  • Some tumors have ERV demethylation and increased expression such as the ERV-K (HML- 2) 5'LTR-UTR in melanoma (Stengel et al, 2010) and the 5'-LTR region of several ERVs in testicular cancer (Gimenez et al., 2010).
  • HML- 2 HML- 2
  • 5'LTR-UTR in melanoma
  • Gimenez et al., 2010 A 20% overall mean demethylation of single CpGs in the ERVW-1 5' LTR regulating Syncytin-1 correlates with increased expression in endometrial cancer (Strissel et al, 2012).
  • ERVs can be targeted as tumor-associated antigens on melanoma cells (Cooper et al, 2015).
  • individual ERVs can maintain full or partial promoter DNA methylation and low expression and DNMTis can induce ERV demethylation and viral defense signaling in human embryonic stem cells (Grow et al., 2015).
  • Noncoding RNAs could contribute to the Aza-induced immune response, such as repetitive Alu elements (Tarallo et al, 2012). UV light can damage small nucleolar RNA and activate an interferon response via TLR3 (Bernard et al.,
  • Dac can induce an interferon response, apoptosis, increased ERVs and repetitive satellite RNAs in p53-null mouse fibroblasts (Leonova et al,
  • ERV-K env proteins have been shown to increase immunotherapeutic potential of melanoma, breast, and ovarian cancer patients (Rycaj et al, 2014; Wang-Johanning et al, 2012) (Cooper et al, 2015).
  • our hypotheses that drugs like Aza might sensitize patients with multiple cancer types to immune checkpoint blockade and other immunotherapies are further strengthened by the data in our pre-clinical melanoma model.
  • immune checkpoint therapy in addition to the functional significance of our data, a potential biomarker strategy is suggested by our findings in a melanoma trial.

Abstract

La présente invention se rapporte au domaine de l'épigénétique. En particulier, la présente invention concerne des méthodes et des compositions utiles pour prédire la réponse à un traitement utilisant des médicaments épigénétiques. L'invention a permis d'identifier une signature unique appelée EVR qui différencie les patients ayant une signature immunitaire faible de ceux ayant une signature immunitaire forte, et qui est régulée par des médicaments épigénétiques tels que des médicaments déméthylants et des inhibiteurs de l'histone désacétylase. Selon certains modes de réalisation, pour les patients présentant une forte signature immunitaire, les immunothérapies telles que des vaccins ou des anticorps anti-PD1 ou anti-PDL1 seraient bénéfiques. Selon d'autres modes de réalisation, pour les patients présentant une faible signature immunitaire ou un faible ERV, un traitement utilisant des médicaments épigénétiques, puis une immunothérapie ultérieure, seraient bénéfiques.
PCT/US2016/022641 2015-03-16 2016-03-16 Méthodes et compositions pour le traitement du cancer WO2016149366A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201562133713P 2015-03-16 2015-03-16
US62/133,713 2015-03-16
US201562190426P 2015-07-09 2015-07-09
US62/190,426 2015-07-09

Publications (1)

Publication Number Publication Date
WO2016149366A1 true WO2016149366A1 (fr) 2016-09-22

Family

ID=56919464

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2016/022641 WO2016149366A1 (fr) 2015-03-16 2016-03-16 Méthodes et compositions pour le traitement du cancer

Country Status (1)

Country Link
WO (1) WO2016149366A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10287353B2 (en) 2016-05-11 2019-05-14 Huya Bioscience International, Llc Combination therapies of HDAC inhibitors and PD-1 inhibitors
US10385131B2 (en) 2016-05-11 2019-08-20 Huya Bioscience International, Llc Combination therapies of HDAC inhibitors and PD-L1 inhibitors
WO2019173456A1 (fr) * 2018-03-06 2019-09-12 Board Of Regents, The University Of Texas System Biomarqueurs de réponse au stress de réplication pour réponse d'immunothérapie
US20190365836A1 (en) * 2017-01-13 2019-12-05 Mayo Foundation For Medical Education And Research Materials and methods for treating cancer
WO2023069943A1 (fr) * 2021-10-18 2023-04-27 Dana-Farber Cancer Institute, Inc. Polythérapie à base de decitabine et d'inhibiteur de mps1 pour amorcer l'immunogénicité du cancer

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120321637A1 (en) * 2011-06-20 2012-12-20 The Board Of Regents Of The University Of Texas System Combination cancer therapy with herv inhibition

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120321637A1 (en) * 2011-06-20 2012-12-20 The Board Of Regents Of The University Of Texas System Combination cancer therapy with herv inhibition

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
ANDERSSON ET AL.: "ERV3 and related sequences in humans: structure and RNA expression", JOURNAL OF VIROLOGY, vol. 79, no. 14, 2005, pages 9270 - 9284, XP055311165 *
RUEBNER ET AL.: "Reduced syncyt in-1 expression levels in placental syndromes correlates with epigenetic hypermethylation of the ERVW-1 promoter region", PLOS ONE, vol. 8, no. Issue No.2, 2013, pages 1 - 10, XP055311168 *
STRISSEL ET AL.: "Reactivation of codogenic endogenous retroviral (ERV) envelope genes in human endometrial carcinoma and prestages: Emergence of new molecular targets", ONCOTARGET, vol. 3, no. 10, 2012, pages 1204 - 1219, XP055311160 *
TRISTEM: "Identification and characterization of novel human endogenous retrovirus families by phylogenetic screening of the human genome mapping project database", JOURNAL OF VIROLOGY, vol. 74, no. 8, 2000, pages 3715 - 3730, XP002996133 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10287353B2 (en) 2016-05-11 2019-05-14 Huya Bioscience International, Llc Combination therapies of HDAC inhibitors and PD-1 inhibitors
US10385131B2 (en) 2016-05-11 2019-08-20 Huya Bioscience International, Llc Combination therapies of HDAC inhibitors and PD-L1 inhibitors
US10385130B2 (en) 2016-05-11 2019-08-20 Huya Bioscience International, Llc Combination therapies of HDAC inhibitors and PD-1 inhibitors
US11535670B2 (en) 2016-05-11 2022-12-27 Huyabio International, Llc Combination therapies of HDAC inhibitors and PD-L1 inhibitors
US20190365836A1 (en) * 2017-01-13 2019-12-05 Mayo Foundation For Medical Education And Research Materials and methods for treating cancer
US11865150B2 (en) * 2017-01-13 2024-01-09 Mayo Foundation For Medical Education And Research Materials and methods for treating cancer
WO2019173456A1 (fr) * 2018-03-06 2019-09-12 Board Of Regents, The University Of Texas System Biomarqueurs de réponse au stress de réplication pour réponse d'immunothérapie
US11851712B2 (en) 2018-03-06 2023-12-26 Board Of Regents, The University Of Texas System Replication stress response biomarkers for immunotherapy response
WO2023069943A1 (fr) * 2021-10-18 2023-04-27 Dana-Farber Cancer Institute, Inc. Polythérapie à base de decitabine et d'inhibiteur de mps1 pour amorcer l'immunogénicité du cancer

Similar Documents

Publication Publication Date Title
Lim et al. Thioredoxin and thioredoxin-interacting protein as prognostic markers for gastric cancer recurrence
Tang et al. Circulation long non-coding RNAs act as biomarkers for predicting tumorigenesis and metastasis in hepatocellular carcinoma
US20220073995A1 (en) Method for quantification of pd-l1 expression
Debernardi et al. Noninvasive urinary miRNA biomarkers for early detection of pancreatic adenocarcinoma
US10619210B2 (en) Predicting response to epigenetic drug therapy
Patnaik et al. MiR-205 and MiR-375 microRNA assays to distinguish squamous cell carcinoma from adenocarcinoma in lung cancer biopsies
US20130337449A1 (en) Marker for predicting gastric cancer prognosis and method for predicting gastric cancer prognosis using the same
US20140302042A1 (en) Methods of predicting prognosis in cancer
WO2016149366A1 (fr) Méthodes et compositions pour le traitement du cancer
EP3082840A1 (fr) Méthodes et dosages biologiques se rapportant à des cellules tumorales circulantes
US20150191793A1 (en) Molecular markers for prognostically predicting prostate cancer, method and kit thereof
Zhong et al. LncRNA AC099850. 3 promotes hepatocellular carcinoma proliferation and invasion through PRR11/PI3K/AKT axis and is associated with patients prognosis
US20110071032A1 (en) Novel tumor marker determination
Zhang et al. Identification of long non-coding RNA expression profiles and co-expression genes in thyroid carcinoma based on the cancer genome atlas (TCGA) database
Kloth et al. Elevated expression of SerpinA1 and SerpinA3 in HLA‐positive cervical carcinoma
Sun et al. Hedgehog interacting protein 1 is a prognostic marker and suppresses cell metastasis in gastric cancer
Chen et al. MicroRNA-183 correlates cancer prognosis, regulates cancer proliferation and bufalin sensitivity in epithelial ovarian caner
Llorca-Cardeñosa et al. Epigenetic changes in localized gastric cancer: the role of RUNX3 in tumor progression and the immune microenvironment
US20120201750A1 (en) Serum biomarkers for melanoma metastasis
Niu et al. Promoter methylation of protocadherin8 is an independent prognostic factor for biochemical recurrence of early-stage prostate cancer
Lotan et al. Urine-Based Markers for Detection of Urothelial Cancer and for the Management of Non–muscle-Invasive Bladder Cancer
WO2020175903A1 (fr) Marqueur de méthylation d'adn pour prédire la récurrence d'un cancer du foie et utilisation associée
Ye et al. Expression of protein TARBP1 in human hepatocellular carcinoma and its prognostic significance
WO2018183603A1 (fr) Méthode et kit permettant diagnostiquer un cancer du pancréas de stade précoce
Lorenzon et al. Down‐regulated miRs specifically correlate with non‐cardial gastric cancers and Lauren's classification system

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16765654

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 16765654

Country of ref document: EP

Kind code of ref document: A1