WO2017083716A2 - Détermination de partenaires létaux synthétiques de modifications spécifiques du cancer et procédés pour les utiliser - Google Patents

Détermination de partenaires létaux synthétiques de modifications spécifiques du cancer et procédés pour les utiliser Download PDF

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WO2017083716A2
WO2017083716A2 PCT/US2016/061623 US2016061623W WO2017083716A2 WO 2017083716 A2 WO2017083716 A2 WO 2017083716A2 US 2016061623 W US2016061623 W US 2016061623W WO 2017083716 A2 WO2017083716 A2 WO 2017083716A2
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gene
mutation
cancer
interest
genes
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WO2017083716A3 (fr
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Subarnarekha Sinha
David L. Dill
Ravindra Majeti
Daniel Thomas
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The Board Of Trustees Of The Leland Stanford Junior University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A61K31/33—Heterocyclic compounds
    • A61K31/335—Heterocyclic compounds having oxygen as the only ring hetero atom, e.g. fungichromin
    • A61K31/34—Heterocyclic compounds having oxygen as the only ring hetero atom, e.g. fungichromin having five-membered rings with one oxygen as the only ring hetero atom, e.g. isosorbide
    • A61K31/341—Heterocyclic compounds having oxygen as the only ring hetero atom, e.g. fungichromin having five-membered rings with one oxygen as the only ring hetero atom, e.g. isosorbide not condensed with another ring, e.g. ranitidine, furosemide, bufetolol, muscarine
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K45/00—Medicinal preparations containing active ingredients not provided for in groups A61K31/00 - A61K41/00
    • A61K45/06—Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation

Definitions

  • Genome sequencing projects such as The Cancer Genome Atlas (TCGA) project have generated an unprecedented amount of multidimensional data using high-resolution microarray and next-generation sequencing platforms. These data provide opportunities for mining data sets that can yield insights that would not be apparent from smaller, less diverse data sets. Obtaining the full value of these data requires the ability to find associations between heterogeneous data types.
  • TCGA Cancer Genome Atlas
  • a synthetic lethal interaction is a genetic interaction whereby a single gene defect leads to dependency on a second gene that is otherwise not essential.
  • Synthetic lethality is an attractive paradigm to identify targeted therapies for cancer-specific mutations as one can target the synthetic lethal partner of a given mutation and selectively kill cancer cells with the mutation.
  • Synthetic lethality has been successfully translated to the clinic in the targeting of BRCA1- and BRCA2-deficient tumor cells with PARP inhibitors.
  • a common key limitation of experimental screens is that they are usually performed in cell lines and can be negatively impacted by: (1) the limited representation in existing cell-lines of newly discovered mutations: for example, the CCLE collection of 1000 cell-lines contains no AML cell line with an oncogenic IDH1 mutation, even though the mutation is present in 10% of AML patients, and (2) the artificiality of in vitro screening conditions, which cannot fully capture in vivo tumor evolution in the tumor microenvironment.
  • DAISY used tumor genomic data along with shRNA data from existing cell-lines to predict synthetic lethal interactions. DAISY predicted a global network of potential SL interactions in human cells, and marks an important advance in computational methods for predicting SL interactions in cancer.
  • DAISY primarily utilizes a small number of inactivating (nonsense and frameshift) mutations and uses shRNA data from existing cell-lines as part of its inference strategy, which means it will miss synthetic lethal interactions that are false negatives in shRNA screens caused either by incomplete genetic knockdown or by inadequate representation of mutations in existing cell-lines.
  • SL synthetic lethal
  • the present invention provides analytic methods and systems for determining synthetic lethal (SL) partners of a gene of interest, where the gene of interest is mutated in cancer genomes. Identification of one or a set, where a set may include without limitation interacting pairs of SL partner genes, of candidate SL partners associated with a mutation in a gene of interest provides a target for drug screening, for repurposing of existing drugs, and for theranostic applications.
  • An individual with a cancer, identified as having a mutation in a gene of interest can be treated with an agent or agents that inhibit activity of the SL partner(s) of the gene of interest, alone or in combination with other therapeutic agents, e.g. chemotherapy, immunotherapy, surgery, radiation and the like.
  • the methods of the invention identify synthetic lethal partners of cancer-specific alterations by computational analysis of high-throughput primary tumor data, using a Boolean implication-based algorithm that analyzes large cancer patient datasets comprising data for gene mutation, gene copy number and gene expression.
  • alterations refers to a variety of genetic changes in cancer cell genomes, including without limitation nucleotide changes, gene overexpression, gene underexpression, gene fusions, clinical subtypes, co-occurring mutations, insertions or deletions, and the like.
  • the method assumes that across multiple cancers, synthetic lethal partners of a cancer-specific alteration will be amplified more frequently or deleted less frequently, with concordant changes in gene expression, in primary tumor samples harboring the mutation. Boolean implication mining identifies these relationships in a high throughput fashion.
  • the analytic methods start with a set of Boolean values for cancer patients, which values comprise mutation data, for which a cut-off value is typically defined with respect to recurrence; copy number data; and gene expression data.
  • the data is optionally filtered by type of cancer.
  • the algorithm performs tests for pairs of genes, which tests comprise: (i) a HILO Boolean implication, that an SL partner of an alteration of interest will not be deleted in tumor samples with an alteration in the gene of interest; and (ii) a LOLO Boolean implication, that an SL partner of an alteration of interest will be selectively amplified in tumor samples with an alteration in the gene of interest.
  • the resulting set of candidate SL partners is desirably filtered for those genes having concordant changes in expression, i.e. a deleted gene is not expressed, and an amplified gene is over-expressed.
  • the set of genes may then be filtered for high expression in cancer patients bearing the alteration of interest, and optionally filtered for the cancer type of interest.
  • the analytic methods start with a set of Boolean values for cancer patients, which values comprise mutation data, for which a cut-off value is typically defined with respect to recurrence; gene methylation data; and gene expression data.
  • the data is optionally filtered by type of cancer.
  • the algorithm performs tests for pairs of genes, which tests comprise: (i) a HILO Boolean implication, that an SL partner of a mutation of interest will not be hypermethylated in tumor samples containing an alteration in the gene of interest; and (ii) a LOLO Boolean implication, that an SL partner of a mutation of interest will be selectively hypomethylated in tumor samples with a mutation in the gene of interest.
  • the resulting set of candidate SL partners is desirably filtered for those genes having concordant changes in expression, i.e. a hypermethylated gene is not expressed, and the hypomethylated gene is over-expressed.
  • the set of genes may then be filtered for high expression in cancer patients bearing the mutation of interest, and optionally filtered for the cancer type of interest.
  • the set of candidate SL partner genes is utilized to optimize therapy; drug selection; and patient selection for clinical trials.
  • the set of candidate SL partner genes is compared to a database of known therapeutic agents, where a known drug that inhibits an SL partner gene can be repurposed for treatment of cancers in which the mutation has been identified as active, e.g. cancers bearing a mutation in the gene that is synthetically lethal with the SL partner gene.
  • individuals in a clinical trial may be selected for treatment based on the presence of a mutation in the gene of interest for treatment with an agent that inhibits an SL partner gene.
  • Embodiments of the invention include computer software products, methods, and systems configured to perform the analysis described herein, which provide a user with the means to identify SL partners of a mutation of interest, which are optionally further defined by reference to a type of cancer of interest.
  • the software allows the user to interact with a database containing cancer cell genetic information, to identify one or a set of potential SL partners.
  • the methods comprise configuring a data processor to perform the analysis.
  • Embodiments of the user-provided application may include computer software which displays the SL partner data in various formats, styles, segments and filtering-depending modes.
  • the methods comprise screening or validation of the SL partner.
  • Such methods may comprise screening cancer cells comprising the mutation to verify the association, e.g. by treating the cancer cells with an inhibitor of the SL partner, and determining the effect of the agent on growth or viability of cancer cells bearing the mutation.
  • Such methods may also comprise screening for candidate therapeutic agents, e.g. by treating cancer cells bearing the mutation with a candidate agent, and determining the effect of the agent on cell viability or growth. Screening methods may alternatively rely on an initial screening process to determine if a candidate agent inhibits the expression or activity of the SL partner. Screening methods may also utilize databases of known pharmacologic agents to select agents that inhibit SL partners; which agents may be tested to determine the effect of the agent on cell viability or growth of cancer cells bearing a mutation in the gene of interest.
  • the methods of the invention may further comprise administering a therapeutic drug to a subject having a cancer with a recurrent mutation in a gene of interest, where an SL partner to the gene of interest has been identified by the methods of the invention.
  • the methods of the invention may comprise determining whether a patient cancer contains the mutation of interest, and if the mutation is present, treating the cancer with an inhibitor of the SL partner, alone or in combination with additional therapeutic agents, e.g. drugs, antibodies and derivatives thereof, radiation, etc.
  • the method may comprise modifying a therapeutic regimen.
  • the method further comprises selecting a therapeutic regimen based on the analysis.
  • the method further comprises determining a treatment course for the subject based on the analysis.
  • the identification of an SL partner for a gene of interest, and identification of a mutation in the gene of interest in a cancer provides information to guide clinical decision making, both in terms of institution of and escalation of therapy as well as in the selection of the therapeutic agent to which the patient is most likely to exhibit a robust response.
  • the information obtained by the methods of the invention for identification of SL partner genes can be used to (a) determine type and level of therapeutic intervention warranted (e.g. monotherapy versus combination therapy, type of combination therapy), and (b) to optimize the selection of therapeutic agents.
  • therapeutic regimens can be individualized and tailored, thereby providing a regimen that is individually appropriate.
  • an SL partner for the recurrent mutation in IDH1 is identified, which IDH1 mutation may be present in hematologic malignancies, such as acute myeloid leukemia (AML) cells or in other cancers, including without limitation low grade glioma, chondrosarcoma, osteosarcoma, low grade glioma, secondary glioblastoma, cholangiocarcinoma, etc. It is shown herein that ACACA is an SL partner to I DM , thereby providing a new target for intervention of cancer in which this mutation is present.
  • AML acute myeloid leukemia
  • an individual with a cancer in which an IDH 1 mutation is present is treated with a therapy comprising an effective dose of an inhibitor of ACACA.
  • the inhibitor is TOFA (5-(Tetradecyloxy) -2- furoic acid), or an analog or derivative thereof.
  • the genotype or phenotype of the cancer with respect to IDH1 activity or expression is determined prior to treatment.
  • treatment is combined with a mutation-specific inhibitor (such as IDH 1 mutation specific inhibitor AG-120) or a chemotherapeutic drug, with a biologic agent, e.g. an antibody specific for one or more markers present on the cancer cell, radiation therapy, imuno-oncology agent, targeted therapeutic such as a small molecule kinase inhibitor, IDH inhibitor, etc. and the like.
  • FIG. 1 panels a-d.
  • the MiSL Algorithm (a) Pipeline of MiSL algorithm: For a given mutation and a cancer of interest, the pipeline outputs a list of genes, which are the candidate synthetic lethal partners or MiSL candidates of the mutation in the cancer of interest, (b) Depiction of the various steps through the deletion pipeline and amplification pipeline in MiSL. (c) Fraction of recurrent mutations with synthetic lethal candidates in each of the 12 TCGA cancers, (d) Fraction of TCGA samples with MiSL-targetable mutations in each of the 12 TCGA cancers.
  • FIG. 2 panels a-h.
  • MiSL Predictions are Enriched for Mutation-Specific Synthetic Lethal Partners
  • a positive enrichment score meant that MiSL candidates were ranked near the top of the list for each mutation,
  • FIG. 3 panels a-i.
  • MiSL Identifies a Novel Therapeutic Target for the IDH1 Mutation in Acute Myeloid Leukemia
  • MiSL candidate genes were filtered for druggability using the DGIdb database. 17 compounds were obtained that were predicted to antagonize/ inhibit the products of these genes,
  • (b) 17 candidate MiSL drugs were tested for activity in the absence (-Dox) or the presence (+Dox) of IDH1 R132H mutation and IC50s were calculated based on cell viability after 72 h.
  • FACS purified primary IDH1 mutant AML blasts were plated in duplicate in low serum OPTIMEM with myeloid growth factors for 72 h in the presence of either 10 ⁇ TOFA or 10 ⁇ AG-120 or DMSO vehicle as indicated. Cell viability was measured as above, two independent experiments were performed.
  • FIG. 5 panels a-g. IDH 1 mutant solid tumors are sensitive to ACACA inhibition in vivo
  • Fibrosarcoma IDH1 mutant R132C HT1080 cells were transduced with non-targeting shRNA or shRNA specific for ACACA and cultured in low serum (0.5%) or high serum (10%) for 3 days and cell growth was measured using PrestoBlue fluorescence. The growth defect by ACACA shRNA #1 could be partially rescued by addition of extra serum-bound lipid
  • IDH1 wildtype U87 glioblastoma cells does not show significant growth disadvantage in vivo after ACACA targeting with shRNA #1 or #2 compared to non-targeting shRNA.
  • IDH1 wildtype U118 glioblastoma cells does not show significant growth disadvantage in vivo after ACACA targeting with shRNA #1 or #2 compared to non-targeting shRNA.
  • FIG. 5 panels a-f. MiSL Identifies Predictive Genetic Biomarkers for Existing Targeted Therapies, (a) Pipeline showing the use of MiSL to identify predictive biomarkers for a drug. The predictive biomarkers could be gene-specific mutations or copy number alterations in a particular cancer, (b) Validation of MiSL biomarker predictions using pharmacologic data for CCLE cell-lines. For a given target family (such as MEK or HDAC), cell-lines from the CCLE that harbor the biomarkers identified by MiSL were predicted to be sensitive, and cell-lines in the first quartile (based on ICso values) were considered to be pharmacologically sensitive.
  • a target family such as MEK or HDAC
  • PIK3CA mutant MCF-7, T47D, CAL-148, CAL-51
  • PIK3CA wild-type CAL- 120, HCC-1806, HCC-38, SKBR-7
  • FIG. 6 panels a-f. Primary Tumor Samples and Cancer Types Used in MiSL.
  • the green barplot shown the number of MiSL-targetable mutations that have at least one common MiSL candidate in different number of cancers.
  • the red barplot shows the number of synthetic lethal interactions (i.e., a ⁇ mutation, MiSL candidate ⁇ pair) that are common across different numbers of cancers.
  • FIG. 7 panels a-h. Validation of MiSL.
  • FIG. 8 panels a-b. Pathway Analysis of MiSL Candidates for Recurrent Mutations and Recurrent MiSL Candidates,
  • MiSL candidates are said to be enriched in the same pathway as the mutated gene if (i) the MiSL candidates of the mutation are enriched for a pathway where overlap is measured using Fisher's exact test (ii) the mutation belongs to the same pathway. Only statistically significant results (p ⁇ 0.05) are reported,
  • FIG. 9 panels a-m. Drug Screen for MiSL candidates of IDH1 mutation in AML.
  • IDH1 R132 pTRIPZ THP- 1 cells treated with TOFA (4 mM) and/or AG120 (1 mM) as shown and cell growth measured at 10 days in 0.5 % serum, bars represent average of 2 replicates, shown is representative of 2 independent experiments . P-values indicate unpaired t-test in all cases.
  • FIG. 10 Panel 10, panels a-f. Validation of Predictive Biomarker Analysis using MiSL.
  • the horizontal line shows the ICso threshold used to determine sensitivity to the drug - lower than threshold implies the cell line is sensitive.
  • the MiSL-predicted sensitive cell lines are marked in black, (d) MiSL analysis steps illustrated for AKT1 - (i) mutual exclusion of PIK3CA mutation and gene deletion across cancers (HI-LO Boolean implication), (ii) deletion of gene concordant with lower expression of gene (p ⁇ 0.05), (iii) expression of gene is higher in P I K3C A-mutated breast cancer (p ⁇ 0.05) (e) Breast cancer cells were seeded in the presence of increasing concentrations of MK-2206 with concentrations as indicated.
  • FIG 11 panels a-c. DAISY Comparison with shRNA Data, (a) Schematic for comparing DAISY candidates for IDH1 mutation with synthetic lethal partners as per DECIPHER library screen generated using a doxycycline-inducible IDH 1 R132H THP-1 cells, (b) Contingency table showing overlap between DAISY candidates for IDH1 mutation and shRNA synthetic lethals as per DECIPHER screen with drop-out ratio of 0.8 for 2 or more shRNAs for calling synthetic lethals in shRNA data.
  • An individual is not limited to a human being but may also be other organisms including, but not limited to, mammals, plants, bacteria, or cells derived from any of the above.
  • Computer software products of the invention typically include, for instance, computer readable medium having computer-executable instructions for performing the logic steps of the method of the invention.
  • Suitable computer readable medium include, but are not limited to, a floppy disk, CD-ROM/DVD/DVD-ROM, hard-disk drive, flash memory, ROM/RAM, magnetic tapes, and others that may be developed.
  • the computer executable instructions may be written in a suitable computer language or combination of several computer languages.
  • the invention may also make use of various computer program products and software for a variety of purposes, such as management of data, analysis, and instrument operation.
  • purposes such as management of data, analysis, and instrument operation.
  • the invention encompasses embodiments that may include methods for providing genetic information over networks such as the internet.
  • TCGA Cancer Genome Atlas
  • NCI National Cancer Institute
  • NHGRI National Human Genome Research Institute
  • TCGA tissue collection comprises matched tumor and normal tissues from 11 ,000 patients, allowing for the comprehensive characterization of 33 cancer types and subtypes, including 10 rare cancers.
  • publicly available databases are utilized as a source of information about cancer cell genes, although the data may be generated de novo or obtained from other sources as desired for a specific analysis.
  • cancer refers to cells which exhibit autonomous, unregulated growth, such that they exhibit an aberrant growth phenotype characterized by a significant loss of control over cell proliferation.
  • Cells of interest for detection, analysis, or treatment in the present application may include, but are not limited to, precancerous (e.g., benign), malignant, pre-metastatic, metastatic, and non- metastatic cells. Cancers of virtually every tissue are known.
  • precancerous e.g., benign
  • malignant pre-metastatic, metastatic, and non- metastatic cells.
  • cancers of virtually every tissue are known.
  • the phrase “cancer burden” may refer to the quantum of cancer cells or cancer volume in a subject. Reducing cancer burden accordingly may refer to reducing the number of cancer cells or the cancer volume in a subject.
  • cancer cell may refer to any cell that is a cancer cell or is derived from a cancer cell, e.g. clone of a cancer cell.
  • cancers include solid tumors such as carcinomas, sarcomas, glioblastomas, melanomas, lymphomas, myelomas, etc., and circulating cancers such as leukemias.
  • tumor cells include but are not limited to AML, ALL, CML, adrenal cortical cancer, anal cancer, aplastic anemia, bile duct cancer, bladder cancer, bone cancer, bone metastasis, brain cancers, central nervous system (CNS) cancers, peripheral nervous system (PNS) cancers, breast cancer, cervical cancer, childhood Non-Hodgkin's lymphoma, colon and rectum cancer, endometrial cancer, esophagus cancer, Ewing's family of tumors (e.g.
  • Ewing's sarcoma eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors, gestational trophoblastic disease, Hodgkin's lymphoma, Kaposi's sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, liver cancer, lung cancer, lung carcinoid tumors, Non-Hodgkin's lymphoma, male breast cancer, malignant mesothelioma, multiple myeloma, myelodysplasia syndrome, myeloproliferative disorders, nasal cavity and paranasal cancer, nasopharyngeal cancer, neuroblastoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumor, prostate cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcomas, mela
  • uterine sarcoma transitional cell carcinoma, vaginal cancer, vulvar cancer, mesothelioma, squamous cell or epidermoid carcinoma, bronchial adenoma, choriocarinoma, head and neck cancers, teratocarcinoma, Waldenstrom's macroglobulinemia, chondrosarcoma, osteosarcoma, low grade glioma, secondary glioblastoma, cholangiocarcinoma, etc.
  • the "pathology" of cancer may include, but it not limited to, all phenomena that compromise the well-being of the patient. This includes, without limitation, abnormal or uncontrollable cell growth, metastasis, interference with the normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression or aggravation of inflammatory or immunological response, neoplasia, premalignancy, malignancy, invasion of surrounding or distant tissues or organs, such as lymph nodes, etc.
  • cancer recurrence and “tumor recurrence,” and grammatical variants thereof, may refer to further growth of neoplastic or cancerous cells after diagnosis of cancer.
  • Tumor spread similarly, may occur when the cells of a tumor disseminate into local or distant tissues and organs; therefore tumor spread may encompass tumor metastasis.
  • Tuor invasion may occur when the tumor growth spreads out locally to compromise the function of involved tissues by compression, destruction, and/or prevention of normal organ function.
  • Metastasis may refer to the growth of a cancerous tumor in an organ or body part, which is not directly connected to the organ of the original cancerous tumor. Metastasis may include micrometastasis, which is the presence of an undetectable amount of cancerous cells in an organ or body part which is not directly connected to the organ of the original cancerous tumor. Metastasis can also be defined as several steps of a process, such as the departure of cancer cells from an original tumor site, and migration and/or invasion of cancer cells to other parts of the body.
  • DNA encompasses any type of nucleic acid (e.g., DNA, RNA, DNA/RNA hybrids, and analogues thereof).
  • RNA complementary DNA
  • the methods may further comprise reverse transcription of the RNA to produce a complementary DNA (cDNA) or DNA copy.
  • Mutation may refer to a genetic alteration in the genome of an organism.
  • mutations of interest are typically changes relative to the germline sequence, e.g. cancer cell specific changes.
  • Mutations may include single nucleotide variants (SNV), copy number variants (CNV), insertions, deletions and rearrangements (e.g., fusions).
  • SNV single nucleotide variants
  • CNV copy number variants
  • a mutation is in a gene of interest, and causes a change in the activity of the gene.
  • the term “mutations” may also be used to broadly refer to a number of genetic changes in cancer cell genomes, including without limitation gene overexpression, gene underexpression, gene fusions, clinical subtypes, co- occurring mutations, and the like.
  • Tumor genomes contain from tens to thousands of somatic mutations. However, only a few of them "drive” tumorigenesis by affecting genes referred to as drivers, which upon alteration confer selective growth advantage to tumor cells. Mutations of interest include, without limitation, those in driver genes. Determination of whether a gene of interest comprises a driver mutation for a cancer of interest may utilize any of the art-known methods for determining such, for example: Abbott et al. (2015) The Candidate Cancer Gene Database: a database of cancer driver genes from forward genetic screens in mice. Nucleic Acids Res., D1 , D844-D848; Tamborero et al. (2013) Scientific Reports 3, Article number: 2650; etc., herein incorporated by reference.
  • a genomic region, or a gene within a genomic region may comprise a recurrent mutation.
  • a recurrently mutated region may be characterized by a "Recurrence Index" (Rl).
  • Rl generally refers to the number of individual subjects (e.g., cancer patients) with a mutation that occurs within a given kilobase of genomic sequence (e.g., number of patients with mutations/genomic region length in kb), or within a gene of interest.
  • a genomic region may also be characterized by the number of patients with a mutation per exon. Thresholds for each metric (e.g. Rl and patients per exon or genomic region) may be selected to statistically enrich for a gene of interest for therapeutic intervention.
  • Thresholds can be selected by arbitrarily choosing a cut-off, e.g. where the Rl for a gene of interest at least about 1 % of individuals with the cancer type of interest, at least about 2%, at least about 3%, at least about 4%, at least about 5%, at least about 6%, at least about 7%, at least about 8%, at least about 9%, at least about 10%, at least about 15%, at least about 20%, at least about 25% or more.
  • a cut-off e.g. where the Rl for a gene of interest at least about 1 % of individuals with the cancer type of interest, at least about 2%, at least about 3%, at least about 4%, at least about 5%, at least about 6%, at least about 7%, at least about 8%, at least about 9%, at least about 10%, at least about 15%, at least about 20%, at least about 25% or more.
  • Synthetic Lethal Partner A synthetic lethal interaction is a genetic interaction whereby a mutation, usually a mutation resulting in a loss of activity of a gene of interest, leads to dependency on a second gene that is otherwise not essential. The second gene is the synthetic lethal partner of the mutation. Synthetic lethality is an attractive paradigm to identify targeted therapies for cancer-specific mutations as one can target the synthetic lethal partner of a given mutation and selectively kill cancer cells with the mutation.
  • Copy number Humans (being normally diploid) ordinarily have two copies of each autosomal region of genetic material, one per chromosome. Thus, the germline copy number for an autosomal gene is generally two, and the germline copy number for certain genes on X or Y chromosomes may be one.
  • Copy number variations may either be inherited or caused by de novo mutation. CNVs can be caused by genomic rearrangements such as deletions, duplications, inversions, and translocations.
  • a copy number variation (CNV) is a segment of DNA in which copy number differences have been found by comparison of two or more genomes. The segment may range from one kilobase to several megabases in size.
  • Gene expression data refers to information regarding the relative or absolute level of expression of a gene or set of genes in a cell or group of cells.
  • the level of expression of a gene may be determined based on the level of RNA, such as mRNA, encoded by the gene. Alternatively, the level of expression may be determined based on the level of a polypeptide or fragment thereof encoded by the gene.
  • Gene expression data may be acquired for an individual cell, or for a group of cells such as a tumor or biopsy sample.
  • microarray array
  • array or “chip” refers to a plurality of nucleic acid probes coupled to the surface of a substrate in different known locations.
  • Microarrays have been generally described in the art in, for example, U.S. Patent Nos. 5, 143,854 (Pirrung), 5,424,186 (Fodor), 5,445,934 (Fodor), 5,677,195 (Winkler), 5,744,305 (Fodor), 5,800,992 (Fodor), 6,040, 193 (Winkler), and Fodor et al. 1991. Light-directed, spatially addressable parallel chemical synthesis. Science, 251 :767- 777. Each of these references is incorporated by reference herein in their entirety.
  • Boolean values In the methods of the present invention, information about cancer cell genetics, i.e. mutation data, copy number data and gene expression data, can be converted into Boolean values.
  • the conversion of the data into Boolean variables is specific to each data type.
  • a large database may be utilized for the data, e.g. from at least about 50 cancer samples, from at least about 100 cancer samples, from at least about 500 cancer samples, from at least about 1000 cancer samples, from at least about 5000 cancer samples, from at least about 10,000 cancer samples or more.
  • the mutation data specify the mutated genes and the mutation type on a per sample basis.
  • a Boolean variable is introduced for each mutated gene.
  • Boolean variables were also introduced for each type of mutated gene (such as frame shift deletions, missense mutations, nonsense mutations, and splice site mutations). For each sample, the Boolean variable associated with a given mutation is set to high if the mutation associated with the variable was present and to low otherwise.
  • the data for copy number alterations contain the segmented copy number data for the tumor and normal samples.
  • the tumor-specific alterations can be determined by removing regions that had more than 50% overlap with altered regions in the corresponding normal sample.
  • the segmented data is provided, for example, in the hg18 human genome assembly.
  • the liftover program (see Hinrichs et al. (2006) Nucleic Acids Research 1 (34): D590-598) can be used to convert the tumor-specific regions to hg19; identified regions in hg19 used to find, in each tumor sample, the genes affected by a copy number alteration.
  • Two Boolean variables can be introduced - one for gene amplification and another for gene deletion.
  • the Boolean variables for amplification or deletion are high if the gene was amplified or deleted.
  • a cut-off value for deciding amplification and deletion can be set at from about 0.1 to 0.5, for example at about 0.3.
  • the Boolean variables for amplified broad CNAs are set to high or low depending on whether or not the magnitude of the change in copy number is about 0.5 for each sample.
  • a threshold of -0.5 for the change in copy number can be used.
  • DNA Methylation The methylation status of the lllumina Golden Gate assay, the Infinium Human Methylation 27k BeadChip assay and the Infinium Human Methylation 450K BeadChip assay can be measured as beta-values, which is how the TCGA reported it. For each specific CpG site, the beta-value was calculated from the intensity of the methylated and unmethylated alleles, as the ratio of the fluorescent signals:
  • the beta-value is a continuous variable between 0 (absent methylation) and 1 (completely methylated) representing the ratio of the methylated allele to the combined locus intensity. It can be interpreted as an approximation of the percentage of methylation for a given CpG site in the sample.
  • StepMiner was used to derive thresholds that divide the data into low and high levels of methylation. For each methylation probe, the associated Boolean variable can be set to high if the value of the probe was above the threshold.
  • a gene was said to be hypermethylated in a sample when the associated Boolean variable of any probe belonging to the gene had a high value in the same sample.
  • a gene was said to be hypomethylated in a sample when the associated Boolean variable of any probe belonging to the gene had a low value in the same sample.
  • Isocitrate dehydrogenase 1 (NADP+), soluble is an enzyme that in humans is encoded by the IDH 1 gene. Isocitrate dehydrogenases catalyze the oxidative decarboxylation of isocitrate to 2-oxoglutarate. The protein encoded by this gene is the NADP+-dependent isocitrate dehydrogenase found in the cytoplasm and peroxisomes. It contains the PTS-1 peroxisomal targeting signal sequence.
  • the presence of this enzyme in peroxisomes suggests roles in the regeneration of NADPH for intraperoxisomal reductions, such as the conversion of 2,4-dienoyl-CoAs to 3-enoyl-CoAs, as well as in peroxisomal reactions that consume 2- oxoglutarate, namely the alpha-hydroxylation of phytanic acid.
  • the cytoplasmic enzyme serves a significant role in cytoplasmic NADPH production.
  • IDH1 mutations are heterozygous, typically involving an amino acid substitution in the active site of the enzyme in codon 132.
  • the mutation results in a loss of normal enzymatic function and the abnormal production of 2-hydroxyglutarate (2-HG).
  • 2-HG has been found to inhibit enzymatic function of many alpha-ketoglutarate dependent dioxygenases, including histone and DNA demethylases, causing widespread changes in histone and DNA methylation and promoting tumorigenesis.
  • Mutations in IDH 1 are implicated in cancer. Mutations in IDH1 and its homologue IDH2 are among the most frequent mutations in diffuse gliomas, including diffuse astrocytoma, anaplastic astrocytoma, oligodendroglioma, anaplastic oligodendroglioma, oligoastrocytoma, anaplastic oligoastrocytoma, and secondary glioblastoma. In addition to being mutated in diffuse gliomas, IDH1 has also been shown to harbor mutations in human acute myeloid leukemia.
  • Acetyl-CoA carboxylase 1 also known as ACC-alpha or ACCa is an enzyme that in humans is encoded by the ACACA gene.
  • Acetyl-CoA carboxylase (ACC) is a complex multifunctional enzyme system.
  • ACC is a biotin-containing enzyme which catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, the rate-limiting step in fatty acid synthesis.
  • ACC-alpha is highly enriched in lipogenic tissues. The enzyme is under long term control at the transcriptional and translational levels and under short term regulation by the phosphorylation/dephosphorylation of targeted serine residues and by allosteric transformation by citrate or palmitoyl-CoA.
  • the terms "recipient”, “individual”, “subject”, “host”, and “patient”, are used interchangeably herein and refer to any mammalian subject for whom diagnosis, treatment, or therapy is desired, particularly humans.
  • "Mammal” for purposes of treatment refers to any animal classified as a mammal, including humans, domestic and farm animals, and zoo, sports, or pet animals, such as dogs, horses, cats, cows, sheep, goats, pigs, etc.
  • the mammal is human.
  • sample with respect to a patient encompasses blood and other liquid samples of biological origin, solid tissue samples such as a biopsy specimen or tissue cultures or cells derived or isolated therefrom and the progeny thereof.
  • the definition also includes samples that have been manipulated in any way after their procurement, such as by treatment with reagents; washed; or enrichment for certain cell populations, such as cancer cells.
  • the definition also includes samples that have been enriched for particular types of molecules, e.g., nucleic acids, polypeptides, etc.
  • biological sample encompasses a clinical sample, and also includes tissue obtained by surgical resection, tissue obtained by biopsy, cells in culture, cell supernatants, cell lysates, tissue samples, organs, bone marrow, blood, plasma, serum, and the like.
  • a “biological sample” includes a sample comprising target cells or normal control cells or suspected of comprising such cells or biological fluids derived therefrom (e.g., cancerous cell, infected cell, etc.), e.g., a sample comprising polynucleotides and/or polypeptides that is obtained from such cells (e.g., a cell lysate or other cell extract comprising polynucleotides and/or polypeptides).
  • a biological sample comprising an inflicted cell from a patient can also include non-inflicted cells.
  • treatment used herein to generally refer to obtaining a desired pharmacologic and/or physiologic effect.
  • the effect can be prophylactic in terms of completely or partially preventing a disease or symptom(s) thereof and/or may be therapeutic in terms of a partial or complete stabilization or cure for a disease and/or adverse effect attributable to the disease.
  • treatment encompasses any treatment of a disease in a mammal, particularly a human, and includes: (a) preventing the disease and/or symptom(s) from occurring in a subject who may be predisposed to the disease or symptom but has not yet been diagnosed as having it; (b) inhibiting the disease and/or symptom(s), i.e., arresting their development; or (c) relieving the disease symptom(s), i.e., causing regression of the disease and/or symptom(s).
  • Those in need of treatment include those already inflicted (e.g., those with cancer, those with an infection, etc.) as well as those in which prevention is desired (e.g., those with increased susceptibility to cancer, those with an increased likelihood of infection, those suspected of having cancer, those suspected of harboring an infection, etc.).
  • a therapeutic treatment is one in which the subject is inflicted prior to administration and a prophylactic treatment is one in which the subject is not inflicted prior to administration.
  • the subject has an increased likelihood of becoming inflicted or is suspected of being inflicted prior to treatment.
  • the subject is suspected of having an increased likelihood of becoming inflicted.
  • the invention provides methods for reducing growth of cancer cells.
  • the methods provide for decreasing the number of cancer cells bearing a specific mutation of interest and SL partner by decreasing the level of and/or decreasing an activity of the SL partner in a cancer cell bearing the relevant mutation.
  • the methods comprise contacting a cancer cell with a therapeutic agent, e.g. an inhibitory drug or small molecule, an antibody or ligand specific for a SL partner or combination of SL partners, and anti-sense or RNAi agent specific for the SL partner, and the like as provided herein.
  • Reducing growth of cancer cells includes, but is not limited to, reducing proliferation of cancer cells, and reducing the incidence of a non-cancerous cell becoming a cancerous cell. Whether a reduction in cancer cell growth has been achieved can be readily determined using any known assay, including, but not limited to, [ 3 H]-thymidine incorporation; counting cell number over a period of time; detecting and/or measuring a marker associated with the cancer, etc.
  • Whether a substance, or a specific amount of the substance, is effective in treating cancer can be assessed using any of a variety of known diagnostic assays for cancer, including, but not limited to biopsy, contrast radiographic studies, CAT scan, and detection of a tumor marker associated with cancer in the blood of the individual.
  • the substance can be administered systemically or locally, usually systemically.
  • Therapeutic agents that target an SL partner identified by the methods of the invention can be formulated using any convenient excipients, reagents and methods.
  • Compositions are provided in formulation with a pharmaceutically acceptable excipient(s).
  • a wide variety of pharmaceutically acceptable excipients are known in the art and need not be discussed in detail herein.
  • Pharmaceutically acceptable excipients have been amply described in a variety of publications, including, for example, A. Gennaro (2000) "Remington: The Science and Practice of Pharmacy," 20th edition, Lippincott, Williams, & Wilkins; Pharmaceutical Dosage Forms and Drug Delivery Systems (1999) H.C.
  • Agents that act to reduce cellular proliferation and that can be combined with an SL partner inhibitor are known in the art and widely used.
  • Agents of interest in the present invention include, without limitation, agents that are affected by transporter-mediated multidrug resistance. Such agents may include vinca alkyloids, taxanes, epipodophyllotoxins, anthracyclines, actinomycin, etc.
  • Anthracyclines are a class of chemotherapeutic agents based upon samine and tetra- hydro-naphthacene-dione. These compounds are used to treat a wide range of cancers, including (but not limited to) leukemias, lymphomas, and breast, uterine, ovarian, and lung cancers.
  • Useful agents include daunorubicin hydrochloride (daunomycin, rubidomycin, cerubidine), doxorubicin, epirubicin, idarubicin, and mitoxantrone.
  • Vinca alkyloids are a class of drugs originally derived from the Vinca plant, and include vinblastine, vincristine, vindesine, vinorelbine. These agents bind tubulin, thereby inhibiting the assembly of microtubules.
  • Taxanes are diterpenes produced by the plants of the genus Taxus, and derivatives thereof.
  • the principal mechanism of the taxane class of drugs is the disruption of microtubule function. It does this by stabilizing GDP-bound tubulin in the microtubule.
  • the class includes paclitaxel and docetaxel.
  • Epipodophyllotoxins are naturally occurring alkaloids, and derivatives thereof.
  • Epipodophyllotoxin derivatives currently used in the treatment of cancer include etoposide, teniposide.
  • Quinoline alkaloids are another class of interest. This class includes camptothecin, SN- 38, DX-8951f, topotecan, 9-aminocamptothecin, BN 80915, irinotecan, DB 67, BNP 1350, exatecan, lurtotecan, ST 1481 , and CKD 602.
  • Other natural products include azathioprine; brequinar; phenoxizone biscyclopeptides, e.g. dactinomycin; basic glycopeptides, e.g. bleomycin; anthraquinone glycosides, e.g. plicamycin (mithrmycin); anthracenediones, e.g. mitoxantrone; azirinopyrrolo indolediones, e.g. mitomycin; macrocyclic immunosuppressants, e.g. cyclosporine, FK-506 (tacrolimus, prograf), rapamycin, etc; and the like.
  • azathioprine brequinar
  • phenoxizone biscyclopeptides e.g. dactinomycin
  • basic glycopeptides e.g. bleomycin
  • anthraquinone glycosides e.g. plicamycin (mithrmycin)
  • chemotherapeutic agents include metal complexes, e.g. cisplatin (cis-DDP), carboplatin, etc.; ureas, e.g. hydroxyurea; and hydrazines, e.g. N-methylhydrazine.
  • Retinoids e.g. vitamin A, 13-cis-retinoic acid, trans-retinoic acid, isotretinoin, etc.
  • carotenoids e.g. beta- carotene, vitamin D, etc.
  • Retinoids regulate epithelial cell differentiation and proliferation, and are used in both treatment and prophylaxis of epithelial hyperproliferative disorders.
  • combination therapies include administration with cell-specific antibodies, for example antibodies selective for tumor cell markers, radiation, surgery, and/or hormone deprivation ⁇ Kwon et al., Proc. Natl. Acad. Sci U.S.A., 96: 15074-9, 1999).
  • Angiogenesis inhibitors can also be combined with the methods of the invention.
  • a number of antibodies are currently in clinical use for the treatment of cancer, and others are in varying stages of clinical development.
  • antigens and corresponding monoclonal antibodies for the treatment of B cell malignancies.
  • One target antigen is CD20.
  • Rituximab is a chimeric unconjugated monoclonal antibody directed at the CD20 antigen.
  • CD20 has an important functional role in B cell activation, proliferation, and differentiation.
  • the CD52 antigen is targeted by the monoclonal antibody alemtuzumab, which is indicated for treatment of chronic lymphocytic leukemia.
  • CD22 is targeted by a number of antibodies, and has recently demonstrated efficacy combined with toxin in chemotherapy-resistant hairy cell leukemia.
  • Monoclonal antibodies useful in the methods of the invention that have been used in solid tumors include, without limitation, edrecolomab and trastuzumab (herceptin).
  • Edrecolomab targets the 17-1A antigen seen in colon and rectal cancer, and has been approved for use in Europe for these indications.
  • Trastuzumab targets the HER-2/neu antigen. This antigen is seen on 25% to 35% of breast cancers.
  • Cetuximab (Erbitux) is also of interest for use in the methods of the invention.
  • the antibody binds to the EGF receptor (EGFR), and has been used in the treatment of solid tumors including colon cancer and squamous cell carcinoma of the head and neck (SCCHN).
  • EGFR EGF receptor
  • compositions such as vehicles, adjuvants, carriers or diluents
  • pharmaceutically acceptable auxiliary substances such as pH adjusting and buffering agents, tonicity adjusting agents, stabilizers, wetting agents and the like, are readily available to the public.
  • the agent is formulated in an aqueous buffer.
  • Suitable aqueous buffers include, but are not limited to, acetate, succinate, citrate, and phosphate buffers varying in strengths from 5mM to 100mM.
  • the aqueous buffer includes reagents that provide for an isotonic solution. Such reagents include, but are not limited to, sodium chloride; and sugars e.g., mannitol, dextrose, sucrose, and the like.
  • the aqueous buffer further includes a non-ionic surfactant such as polysorbate 20 or 80.
  • the formulations may further include a preservative.
  • Suitable preservatives include, but are not limited to, a benzyl alcohol, phenol, chlorobutanol, benzalkonium chloride, and the like.
  • the formulation is stored at about 4°C. In some cases, the formulation is stored at -20 °C.
  • Formulations may also be lyophilized, in which case they generally include cryoprotectants such as sucrose, trehalose, lactose, maltose, mannitol, and the like. Lyophilized formulations can be stored over extended periods of time, even at ambient temperatures.
  • the agent is administered to individuals in a formulation with a pharmaceutically acceptable excipient(s).
  • the molecules, as well as additional therapeutic agents as described herein for combination therapies can be administered orally, topically, subcutaneously, intramuscularly, parenterally, by inhalation, IV, IP or other routes.
  • the subject complexes and additional therapeutic agents may be administered by the same route of administration or by different routes of administration.
  • the therapeutic agents can be administered by any suitable means including, but not limited to, for example, oral, rectal, nasal, topical (including transdermal, aerosol, buccal and sublingual), ocular, vaginal, parenteral (including subcutaneous, intramuscular, intravenous and intradermal), intravesical or injection into an affected organ.
  • the agent may be administered in a unit dosage form and may be prepared by any methods well known in the art. Such methods include combining the subject compound with a pharmaceutically acceptable carrier or diluent which constitutes one or more accessory ingredients.
  • a pharmaceutically acceptable carrier is selected on the basis of the chosen route of administration and standard pharmaceutical practice. Each carrier must be "pharmaceutically acceptable” in the sense of being compatible with the other ingredients of the formulation and not injurious to the subject. This carrier can be a solid or liquid and the type is generally chosen based on the type of administration being used.
  • suitable solid carriers include lactose, sucrose, gelatin, agar and bulk powders.
  • suitable liquid carriers include water, pharmaceutically acceptable fats and oils, alcohols or other organic solvents, including esters, emulsions, syrups or elixirs, suspensions, solutions and/or suspensions, and solution and or suspensions reconstituted from non-effervescent granules and effervescent preparations reconstituted from effervescent granules.
  • Such liquid carriers may contain, for example, suitable solvents, preservatives, emulsifying agents, suspending agents, diluents, sweeteners, thickeners, and melting agents.
  • Preferred carriers are edible oils, for example, corn or canola oils. Polyethylene glycols, e.g. PEG, are also good carriers.
  • Any drug delivery device or system that provides for the dosing regimen of the instant disclosure can be used.
  • a wide variety of delivery devices and systems are known to those skilled in the art.
  • a therapeutically effective amount of a compound in this context can be regarded as an amount that is effective in reducing the incidence (e.g., the likelihood that an individual will develop) of a disorder by at least about 10%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, or at least about 80%, or more, compared to an untreated individual, or to a placebo- treated individual.
  • a computational pipeline method is provided to identify synthetic lethal partners of a specific mutation using high-throughput cancer data sets, which may be from one or more different types of cancer.
  • a flow chart of the pathway is provided in Figure 2.
  • Boolean implications are used to find pairwise associations in heterogeneous cancer data sets. Boolean implications are if-then rules. There are four Boolean implications: (1) A-low ⁇ B-low (LOLO), (2) A-high -*B-low (HILO), (3) A-low ⁇ B-high (LOHI), (4) A-high ⁇ B-high (HIHI). Boolean implications can also be interpreted according to set theory.
  • Boolean implication A-high B-high means that "the set of samples where A is high is a subset of the set of samples where B is high”.
  • the implication A-high ⁇ B-low means that "the set of samples where A is high is mutually exclusive with the set of samples where B is high”.
  • Boolean implications are useful in the context of mining heterogeneous cancer data sets because (1) they can expose subset and mutual exclusion relationships, both of which have L-shaped scatterplots between related variable pairs; and (2) they provide a common and unified framework to expose relationships between categorical and continuous data.
  • variables for mutation, copy number and gene expression are assigned “low” and “high” levels, depending either on presence or absence, or a threshold.
  • Each point in the scatterplot can be used to represent the values of two variables in a tumor sample.
  • a Boolean implication exists between two variables when one quadrant is very sparse. Boolean implications can capture L-shaped relationships as well as linear relationships (in which case the two opposite quadrants are sparse), revealing associations not found by other methods.
  • gene methylation data; and gene expression data are input as Boolean variables.
  • a gene of interest is selected for having a recurrent mutation in cancer cells, where the threshold for recurrence is set as described in the definitions.
  • the recurrence index may be higher in certain cancer types, and one or more types of cancer may be selected for analysis.
  • genes which are candidate genes for being an SL partner to the gene of interest, are identified that have more copies in the presence of a mutation as determined by (i) preferred amplification in the presence of the mutation, or (ii) deletion only in the absence of the mutation.
  • This corresponds to a search for two types of Boolean implications: (i) if gene B is amplified, then mutation A is present - which is a subset relationship or a HIHI Boolean implication; (ii) if mutation A is present, then gene B is not deleted - which is a mutual exclusion relationship or a HILO Boolean implication.
  • the analysis is restricted to those Boolean implications with a false discovery rate (FDR) ⁇ 0.05.
  • genes which are candidate genes for being an SL partner to the gene of interest, are identified that have more copies in the presence of a mutation as determined by (i) a HILO Boolean implication, that an SL partner of a mutation of interest will not be present in tumor samples with hypermethylation in the gene of interest; and (ii) a LOLO Boolean implication, that an SL partner of a mutation of interest will be selectively hypomethylated in tumor samples with a mutation in the gene of interest.
  • Genes that are selected as candidates are then filtered to remove genes that are merely passengers in large chromosomal alterations. This is achieved by only including genes from (i) and (ii) that exhibit concordant changes in gene expression.
  • amplification of the gene must result in high expression of that gene.
  • deletion of the gene must result in low expression of that gene.
  • the resulting gene set is restricted to those genes that are differentially over-expressed in the presence of the mutation versus the wildtype. This analysis is restricted to the cancer under study, further eliminating genes that are unlikely to be essential in the context of the specific cancer.
  • the genes that satisfy all the above-mentioned filters form the set of candidate synthetic lethal (SL) partners for a given mutation in a given cancer.
  • the list of SL candidate genes is prioritized for candidate genes having a high likelihood of being a candidate for therapeutic intervention.
  • the list of candidate genes is prioritized by defining pathways, e.g. using KEGG or GO gene sets. Candidates that have a statistically significant overlap with known pathways may be selected for further downstream testing.
  • candidates that are targetable using existing drugs can be picked for downstream testing.
  • Druggability can be assessed in various ways.
  • existing drug databases e.g. DGidb, Guide to Pharmocology, Drugbank and PubChem
  • DGidb Guide to Pharmocology
  • PubChem PubChem
  • the methods comprise screening or validation of the SL partner.
  • Such methods may comprise screening cancer cells comprising the mutation to verify the association, e.g. by treating the cancer cells with an inhibitor of the SL partner, and determining the effect of the agent on growth or viability of cancer cells bearing the mutation.
  • Such methods may also comprise screening for candidate therapeutic agents, e.g. by treating cancer cells bearing the mutation with a candidate agent, and determining the effect of the agent on cell viability or growth. Screening methods may alternatively rely on an initial screening process to determine if a candidate agent inhibits the expression or activity of the SL partner. Screening methods may also utilize databases of known pharmacologic agents to select agents that inhibit SL partners; which agents may be tested to determine the effect of the agent on cell viability or growth of cancer cells bearing a mutation in the gene of interest.
  • SL partners for recurrent mutations are novel SL partners for recurrent mutations.
  • an SL partner for the recurrent mutation in IDH1 is identified, which IDH1 mutation may be present in hematologic or other malignancies, such as acute myeloid leukemia (AML) cells, glioblastoma, etc. It is shown herein that ACACA is an SL partner to IDH1 , thereby providing a new target for intervention of cancer in which this mutation is present.
  • AML acute myeloid leukemia
  • the methods of the invention may further comprise administering a therapeutic drug to a subject having a cancer with a recurrent mutation in a gene of interest, where an SL partner to the gene of interest has been identified by the methods of the invention.
  • the methods of the invention may comprise determining whether a patient cancer comprises the mutation of interest, and if the mutation is present, treating the cancer with an inhibitor of the SL partner, alone or in combination with additional therapeutic agents.
  • the method may comprise modifying a therapeutic regimen.
  • the method further comprises selecting a therapeutic regimen based on the analysis.
  • the method further comprises determining a treatment course for the subject based on the analysis.
  • the identification of an SL partner for a gene of interest, and identification of a mutation in the gene of interest in a cancer provides information to guide clinical decision making, both in terms of institution and escalation of therapy as well as in the selection of the therapeutic agent to which the patient is most likely to exhibit a robust response.
  • the information obtained by the methods of the invention for identification of SL partner genes can be used to (a) determine type and level of therapeutic intervention warranted (e.g. monotherapy versus combination therapy, type of combination therapy), and (b) to optimize the selection of therapeutic agents.
  • therapeutic regimens can be individualized and tailored, thereby providing a regimen that is individually appropriate.
  • Embodiments of the invention include computer software products, methods, and systems configured to perform the analysis described herein, which provide a user with the means to identify SL partners of a mutation of interest, which are optionally further defined by reference to a type of cancer of interest.
  • the software allows the user to interact with a database containing cancer cell genetic information, and to identify one or a set of potential SL partners by the methods set forth herein.
  • the methods comprise configuring a data processor to perform the analysis.
  • Embodiments of the user-provided application may include computer software which displays the SL partner data in various formats, styles, segments and filtering-depending modes.
  • a computer may be any type of computer platform such as a workstation, a personal computer, a server, or any other present or future compute, typically including known components such as a processor, an operating system, system memory, memory storage devices, and input-output controllers, input-output devices, and display devices.
  • Display devices may include display devices that provides visual information, this information typically may be logically and/or physically organized as an array of pixels.
  • GUI Graphical User Interface
  • processor may be a commercially available processor or it may be one or more different processors that are or will become available. Some embodiments of processor may also include what are referred to as Multi-core processors and/or be enabled to employ parallel processing technology in a single or multi-core configuration.
  • a multi-core architecture typically comprises two or more processor "execution cores.” In the present example each execution core may perform as an independent processor that enables parallel execution of multiple threads.
  • processor may be configured in what is generally referred to as 32 or 64 bit architectures, or other architectural configurations now known or that may be developed in the future.
  • the operating system interfaces with firmware and hardware in a well-known manner, and facilitates processor in coordinating and executing the functions of various computer programs that may be written in a variety of programming languages.
  • the operating system typically in cooperation with processor, coordinates and executes functions of the other components of computer, and may also provide scheduling, input-output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.
  • the system memory may be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic medium such as a resident hard disk or tape, an optical medium such as a read and write compact disc, or other memory storage device.
  • Memory storage devices may be any of a variety of known or future devices, including a compact disk drive, a tape drive, a removable hard disk drive, USB or flash drive, or a diskette drive.
  • Such types of memory storage devices typically read from, and/or write to, a program storage medium such as, respectively, a compact disk, magnetic tape, removable hard disk, USB or flash drive, or floppy diskette. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product.
  • these program storage media typically store a computer software program, such as the programs described in more detail below, and/or data.
  • Computer software programs, also called computer control logic typically are stored in system memory and/or the program storage device used in conjunction with memory storage device.
  • a computer program product comprising a computer usable medium having control logic (computer software program, including program code) stored therein.
  • the control logic when executed by processor, causes processor to perform functions described herein, and can be specifically configured to perform the Boolean analysis described herein to define SL partners of a gene of interest.
  • some functions are implemented primarily in hardware using, for example, a hardware state machine. Implementation of the hardware state machine so as to perform the functions described herein will be apparent to those skilled in the relevant arts.
  • Input-output controllers could include any of a variety of known devices for accepting and processing information from a user, whether a human or a machine, whether local or remote. Such devices include, for example, modem cards, wireless cards, network interface cards, sound cards, or other types of controllers for any of a variety of known input devices. Output controllers of input-output controllers could include controllers for any of a variety of known display devices for presenting information to a user, whether a human or a machine, whether local or remote.
  • a network may include one or more of the many various types of networks well known to those of ordinary skill in the art.
  • a network may include a local or wide area network that employs what is commonly referred to as a TCP/IP protocol suite to communicate, that may include a network comprising a worldwide system of interconnected computer networks that is commonly referred to as the internet, or could also include various intranet architectures.
  • a network may include a local or wide area network that employs what is commonly referred to as a TCP/IP protocol suite to communicate, that may include a network comprising a worldwide system of interconnected computer networks that is commonly referred to as the internet, or could also include various intranet architectures.
  • Firewalls also sometimes referred to as Packet Filters, or Border Protection Devices
  • firewalls may comprise hardware or software elements or some combination thereof and are typically designed to enforce security policies put in place by users, such as for instance network administrators.
  • the invention disclosed herein also pertains to software applications which aid in the identification of SL partners from high throughput cancer cell data, such as expression levels and copy number variations.
  • Embodiments of the user-provided application described above include computer software which displays to the user the data obtained from the analysis, and may be tied to genetic sequence information for the relevant SL partner genes, information about biological pathways in which one or more candidate SL partner genes are involved, information about known pharmacological agents that on the candidate SL partner genes, and the like.
  • the data is displayed by the software in various manners on a computer screen or other visual media such as a visual projector, screen, and/or board.
  • the methods described herein may be performed by a computer program product that comprises a computer executable logic that is recorded on a computer readable medium.
  • the computer program can execute some or all of the following functions: (i) controlling input of cancer cell data, (ii) converting data to Boolean values (iii) performing Boolean analysis, (iv) identifying a list of candidate SL partners, (v) filtering the candidate SL partners for concordant gene expression, (vi) filtering candidate SL partners for high expression in cells comprising a mutation in the gene of interest, and (vii) providing the set of SL partners.
  • the computer program may calculate a recurrence index.
  • the computer executable logic can work in any computer that may be any of a variety of types of general-purpose computers such as a personal computer, network server, workstation, or other computer platform now or later developed.
  • a computer program product is described comprising a computer usable medium having the computer executable logic (computer software program, including program code) stored therein.
  • the computer executable logic can be executed by a processor, causing the processor to perform functions described herein.
  • some functions are implemented primarily in hardware using, for example, a hardware state machine. Implementation of the hardware state machine so as to perform the functions described herein will be apparent to those skilled in the relevant arts.
  • the invention provides a computer readable medium comprising a set of instructions recorded thereon to cause a computer to perform the steps of (i) to (vi). Screening Assays
  • Cancer cells comprising a recurrent mutation of interest, and SL partners identified therefrom are useful for in vitro assays and screening to detect chemotherapeutic agents that are active on cancer cells, particularly cancer cells bearing a mutation in the gene of interest.
  • screening assays for agents that are active on human cells.
  • a wide variety of assays may be used for this purpose, including immunoassays for protein binding; determination of cell growth, differentiation and functional activity; production of factors; and the like. Screening assays may utilize cells, proteins, polynucleotides, etc.
  • isolated polypeptides corresponding to a candidate or combination of candidates of the present invention are useful in drug screening assays.
  • the SL partner gene or encoded protein is contacted with the agent of interest, and the effect of the agent assessed by monitoring output parameters on cells, such as expression of markers, cell viability, and the like; or binding efficacy or effect on enzymatic or receptor activity for polypeptides.
  • a cell comprises a mutation or knockout of the gene of interest, although a control cell may also be included in which the gene of interest is not mutated.
  • the cells may be freshly isolated, cultured, genetically altered, and the like.
  • the cells may be environmentally induced variants of clonal cultures: e.g.
  • Parameters are quantifiable components of cells, particularly components that can be accurately measured, desirably in a high throughput system.
  • a parameter can be any cell component or cell product including cell surface determinant, receptor, protein or conformational or posttranslational modification thereof, lipid, carbohydrate, organic or inorganic molecule, nucleic acid, e.g. mRNA, DNA, etc. or a portion derived from such a cell component or combinations thereof. While most parameters will provide a quantitative readout, in some instances a semi-quantitative or qualitative result will be acceptable. Readouts may include a single determined value, or may include mean, median value or the variance, etc.
  • Agents of interest for screening include known and unknown compounds that encompass numerous chemical classes, primarily organic molecules, which may include organometallic molecules, inorganic molecules, genetic sequences, etc.
  • An important aspect of the invention is to evaluate candidate drugs, including toxicity testing; and the like.
  • candidate agents include organic molecules comprising functional groups necessary for structural interactions, particularly hydrogen bonding, and typically include at least an amine, carbonyl, hydroxyl or carboxyl group, frequently at least two of the functional chemical groups.
  • the candidate agents often comprise cyclical carbon or heterocyclic structures and/or aromatic or polyaromatic structures substituted with one or more of the above functional groups.
  • Candidate agents are also found among biomolecules, including peptides, polynucleotides, saccharides, fatty acids, steroids, purines, pyrimidines, derivatives, structural analogs or combinations thereof.
  • Compounds of interest include chemotherapeutic agents, hormones or hormone antagonists, etc.
  • chemotherapeutic agents include those described in, "The Pharmacological Basis of Therapeutics," Goodman and Gilman, McGraw-Hill, New York, New York, (1996), Ninth edition, under the sections: Water, Salts and Ions; Drugs Affecting Renal Function and Electrolyte Metabolism; Drugs Affecting Gastrointestinal Function; Chemotherapy of Microbial Diseases; Chemotherapy of Neoplastic Diseases; Drugs Acting on Blood-Forming organs; Hormones and Hormone Antagonists; Vitamins, Dermatology; and Toxicology, all incorporated herein by reference. Also included are toxins, and biological and chemical warfare agents, for example see Somani, S.M. (Ed.), "Chemical Warfare Agents,” Academic Press, New York, 1992).
  • Test compounds include all of the classes of molecules described above, and may further comprise samples of unknown content. Of interest are complex mixtures of naturally occurring compounds derived from natural sources such as plants. While many samples will comprise compounds in solution, solid samples that can be dissolved in a suitable solvent may also be assayed. Samples of interest include environmental samples, e.g. ground water, sea water, mining waste, etc.; biological samples, e.g. lysates prepared from crops, tissue samples, etc.; manufacturing samples, e.g. time course during preparation of pharmaceuticals; as well as libraries of compounds prepared for analysis; and the like. Samples of interest include compounds being assessed for potential therapeutic value, i.e. drug candidates.
  • samples also includes the fluids described above to which additional components have been added, for example components that affect the ionic strength, pH, total protein concentration, etc.
  • the samples may be treated to achieve at least partial fractionation or concentration.
  • Biological samples may be stored if care is taken to reduce degradation of the compound, e.g. under nitrogen, frozen, or a combination thereof.
  • the volume of sample used is sufficient to allow for measurable detection, usually from about 0.1 to 1 ml of a biological sample is sufficient.
  • Compounds, including candidate agents are obtained from a wide variety of sources including libraries of synthetic or natural compounds. For example, numerous means are available for random and directed synthesis of a wide variety of organic compounds, including biomolecules, including expression of randomized oligonucleotides and oligopeptides. Alternatively, libraries of natural compounds in the form of bacterial, fungal, plant and animal extracts are available or readily produced. Additionally, natural or synthetically produced libraries and compounds are readily modified through conventional chemical, physical and biochemical means, and may be used to produce combinatorial libraries. Known pharmacological agents may be subjected to directed or random chemical modifications, such as acylation, alkylation, esterification, amidification, etc. to produce structural analogs.
  • Agents are screened for biological activity by adding the agent to at least one and usually a plurality of cell samples, usually in conjunction with cells lacking the agent.
  • the change in parameters in response to the agent is measured, and the result evaluated by comparison to reference cultures, e.g. in the presence and absence of the agent, obtained with other agents, etc.
  • the agents are conveniently added in solution, or readily soluble form, to the medium of cells in culture.
  • the agents may be added in a flow-through system, as a stream, intermittent or continuous, or alternatively, adding a bolus of the compound, singly or incrementally, to an otherwise static solution.
  • a flow-through system two fluids are used, where one is a physiologically neutral solution, and the other is the same solution with the test compound added. The first fluid is passed over the cells, followed by the second.
  • a bolus of the test compound is added to the volume of medium surrounding the cells. The overall concentrations of the components of the culture medium should not change significantly with the addition of the bolus, or between the two solutions in a flow through method.
  • Preferred agent formulations do not include additional components, such as preservatives, that may have a significant effect on the overall formulation.
  • preferred formulations consist essentially of a biologically active compound and a physiologically acceptable carrier, e.g. water, ethanol, DMSO, etc.
  • a physiologically acceptable carrier e.g. water, ethanol, DMSO, etc.
  • the formulation may consist essentially of the compound itself.
  • a plurality of assays may be run in parallel with different agent concentrations to obtain a differential response to the various concentrations.
  • determining the effective concentration of an agent typically uses a range of concentrations resulting from 1 : 10, or other log scale, dilutions.
  • the concentrations may be further refined with a second series of dilutions, if necessary.
  • one of these concentrations serves as a negative control, i.e. at zero concentration or below the level of detection of the agent or at or below the concentration of agent that does not give a detectable change in the phenotype.
  • a convenient method is to label a molecule with a detectable moiety, which may be fluorescent, luminescent, radioactive, enzymatically active, etc., particularly a molecule specific for binding to the parameter with high affinity.
  • Fluorescent moieties are readily available for labeling virtually any biomolecule, structure, or cell type. Immunofluorescent moieties can be directed to bind not only to specific proteins but also specific conformations, cleavage products, or site modifications like phosphorylation. Individual peptides and proteins can be engineered to autofluoresce, e.g. by expressing them as green fluorescent protein chimeras inside cells (for a review see Jones et al. (1999) Trends Biotechnol.
  • antibodies can be genetically modified to provide a fluorescent dye as part of their structure.
  • parameters may be measured using other than fluorescent labels, using such immunoassay techniques as radioimmunoassay (RIA) or enzyme linked immunosorbance assay (ELISA), homogeneous enzyme immunoassays, and related non-enzymatic techniques.
  • RIA radioimmunoassay
  • ELISA enzyme linked immunosorbance assay
  • the quantitation of nucleic acids, especially messenger RNAs is also of interest as a parameter. These can be measured by hybridization techniques that depend on the sequence of nucleic acid nucleotides. Techniques include polymerase chain reaction methods as well as gene array techniques.
  • Cancer cell data is readily available from public databases, but in the event that de novo data is desired, methods known in the art for determining copy number, gene expression and the presence of mutations can be used. [00132] In other embodiments, the presence of a mutation in the gene of interest is determined prior to treating an individual, or in conjunction with drug screening for activity against the SL partner in cells where the gene of interest is mutated.
  • Genotyping cancer cells and/or detection, identification and/or quantitation of the ctDNA can utilize sequencing. Sequencing can be accomplished using high-throughput systems. Sequencing can be performed using nucleic acids described herein such as genomic DNA, cDNA derived from RNA transcripts or RNA as a template. Sequencing may comprise massively parallel sequencing.
  • high-throughput sequencing involves the use of technology available by Helicos Biosciences Corporation (Cambridge, Massachusetts) such as the Single Molecule Sequencing by Synthesis (SMSS) method.
  • high-throughput sequencing involves the use of technology available by 454 Lifesciences, Inc. (Branford, Connecticut) such as the Pico Titer Plate device which includes a fiber optic plate that transmits chemiluminescent signal generated by the sequencing reaction to be recorded by a CCD camera in the instrument. This use of fiber optics allows for the detection of a minimum of 20 million base pairs in 4.5 hours.
  • high-throughput sequencing is performed using Clonal Single Molecule Array (Solexa, Inc.) or sequencing-by-synthesis (SBS) utilizing reversible terminator chemistry.
  • Solexa, Inc. Clonal Single Molecule Array
  • SBS sequencing-by-synthesis
  • high-throughput sequencing of RNA or DNA can take place using AnyDot.chips (Genovoxx, Germany), which allows for the monitoring of biological processes (e.g., miRNA expression or allele variability (SNP detection).
  • the AnyDot-chips allow for 10x - 50x enhancement of nucleotide fluorescence signal detection.
  • Other high- throughput sequencing systems include those disclosed in Venter, J., et al. Science 16 February 2001 ; Adams, M. et al, Science 24 March 2000; and M. J, Levene, et al. Science 299:682-686, January 2003; as well as US Publication Application No. 20030044781 and 2006/0078937.
  • the growing of the nucleic acid strand and identifying the added nucleotide analog may be repeated so that the nucleic acid strand is further extended and the sequence of the target nucleic acid is determined.
  • MiSL a method for mining synthetic lethal partners of recurrent cancer mutations uncovers novel mutation-specific therapeutic targets
  • SL synthetically lethal
  • MiSL Mining Synthetic Lethals
  • MiSL Mining Synthetic Lethals
  • MiSL solves two problems that are directly translatable to clinical applications: identifying novel mutation-specific SL interactions in multiple different cancers, and pinpointing predictive genetic biomarkers that can guide more precise targeting of existing therapies.
  • the present invention provides new computational methods that analyze high- throughput primary tumor data to identify robust synthetic lethal interactions that are more likely to be directly relevant to clinical therapy than existing approaches.
  • MiSL is a computational pipeline to identify candidate synthetic lethal (SL) partners of mutations for subsequent focused experimental validation using high- throughput pan-cancer primary tumor datasets (Fig. 1 a).
  • the starting point is a mutation and a cancer type of interest.
  • TCGA Cancer Genome Atlas
  • the output of MiSL is a relatively short, high-quality list of candidate synthetic lethals that are then be validated experimentally to find the true synthetic lethals.
  • the reported results are based on data from approximately 3000 primary tumor samples that are used to identify candidate SL partners of each recurrent mutation in each of the 12 cancer types (Fig. 6a).
  • genes are identified that have more copies in the presence of a mutation as determined by: (1) preferred amplification in the presence of the mutation (amplification pipeline) (Fig. 1 b), or (2) deletion only in the absence of the mutation (deletion pipeline) (Fig. 1 b).
  • Boolean implications are used to efficiently extract the required relationships from genomic data.
  • Boolean implications are used to efficiently extract the required relationships from genomic data.
  • For the amplification pipeline we search for cases where B is amplified only in the presence of mutation X, and thereby capture cases where there is dependence on gene B in the presence of mutation X.
  • the logical statement "if gene B is amplified, then mutation X is present" holds for almost all samples. This relationship is called a HI-HI Boolean implication (Fig. 1 b, Fig. 6b).
  • the resulting gene set is filtered to include only those genes that are differentially over-expressed in the presence of the mutation versus the wild-type in the cancer of interest. This step eliminates genes that are unlikely to be essential in the context of the cancer of interest and the specific mutation, and also eliminates some false positives due to convergent evolution.
  • the genes that satisfy all the above-mentioned filters form the set of candidate SL partners for a given mutation in a given cancer, and will henceforth be referred to as MiSL candidates of the mutation. Furthermore, if a mutation has a non-zero number of MiSL candidates, we will refer to the mutation as MiSL-targetable.
  • MiSL identified candidate SL partners for a large fraction (0.3-0.8) of recurrent mutations in each of the 12 cancers (Fig. 1c). For the majority of recurrent mutations in each cancer type, MiSL identified fewer than 50 candidate SL partners (Fig. 6d), providing a small focused list of candidates for further experimental testing. Additionally, the candidates identified by MiSL were robust to changes in the p-value thresholds of the various filters, since the majority of candidates were retained even when the p-value thresholds were halved (Fig. 6e).
  • MiSL Predictions are Enriched for Mutation-Specific Synthetic Lethal Partners.
  • shRNA screens are highly accurate, rather that their results are better than random. Thus, we would expect to see some concordance between MiSL candidates and the results of such screens.
  • BCL2L2 (Bcl-w) as a candidate SL partner of the IDH1 mutation: IDH1 mutation and BCL2L2 deletion were mutually exclusive in the TCGA data, BCL2L2 deletion resulted in lowered expression, so BCL2L2 is unlikely to be a passenger deletion, and BCL2L2 was differentially over-expressed in /D/-/7-mutant AML compared to IDH1- wildtype AML (Fig. 2h).
  • the MiSL amplification pipeline uncovered a previously known synthetic lethal interaction: GLS as a SL partner of the VHL mutation in kidney cancer.
  • GLS was selectively amplified only in the presence of the VHL mutation, GLS amplification resulted in increased expression of GLS (hence GLS is unlikely to be a passenger amplification), and GLS was differentially over-expressed in -//--mutant kidney cancer compared to W-/ .-wildtype kidney cancer (Fig. 7g). This prediction is consistent with previous work that showed a selective in vivo dependence on the glutaminase pathway for VHL mutants in kidney cancer.
  • MiSL candidates were identified from mutation and cancer-specific patterns, but may be shared by multiple mutations and multiple cancers, suggesting that certain genes were prone to synthetic lethality both within a cancer and across cancers.
  • Pathway analysis using KEGG and GO gene sets revealed several pathways that were enriched among both cancer-specific and pan-cancer recurrent MiSL candidates including the TCA cycle and oxidative phosphorylation genes, DNA repair genes, ubiquitin mediated proteolysis genes, and Wnt pathway genes, suggesting that therapies targeting these pathways may be potentially beneficial for treating multiple patient- subgroups in different cancer types.
  • MiSL Identifies a Novel Druggable Target for the IDH1 Mutation: Acetyl Co A Carboxylase 1.
  • MiSL generated 89 SL candidates, with 17 being potentially druggable using available reagents in the clinic or under development (Fig. 3a, Fig. 9a).
  • the pharmacologic agents that inhibit the 17 druggable MiSL candidates were then tested for activity against IDH1 mutant AML by determining dose- response IC50 curves for each drug in the presence (+ dox) and absence (- dox) of IDH1-R132H in the inducible THP-1 cells (Fig. 3b).
  • MiSL Identifies Predictive Genetic Biomarkers for Existing Targeted Therapies. Identification of predictive biomarkers and their use as companion diagnostics for stratifying and assigning patients to targeted therapies is an area of active investigation in oncology. MiSL can be used to identify mutations and/or copy number alterations in specific cancers that are SL partners of the genes inhibited by a given drug (Fig. 4a), which would then function as predictive genetic biomarkers for the drug. To test this idea, we compared MiSL-based predictions of sensitive cell-lines with pharmacologic data available for the Cancer Cell Line Encyclopedia (CCLE). This dataset is comprised of pharmacologic data for 24 compounds (targeted and cytotoxic agents) across 479 cell-lines.
  • CCLE Cancer Cell Line Encyclopedia
  • DGidb To maximize the number of cell-lines with pharmacologic data, we grouped inhibitors of a target family, such as HDAC inhibitors. Next, we used DGidb to identify genes whose products were inhibited by the drug(s). In the case of the HDAC inhibitors, DGidb identified a list of 14 genes (Fig. 10a). MiSL was then used to identify mutations and/or copy number alterations in each cancer type that were SL partners of these inhibited genes. Specifically, the genes inhibited by the drug were MiSL candidates of the identified mutations and/or copy number alterations. Cell-lines that harbored these MiSL-identified biomarkers were predicted to be sensitive to the inhibitor according to our analysis.
  • MiSL identified several genetic biomarkers that were not represented in existing cell-lines. These included LAMA3 mutations in lung squamous cancer and NOS1 mutations in breast cancer, which were identified as MEK inhibitor-specific predictive biomarkers, and mutations in ST18 which were identified as a predictive biomarker for HDAC inhibitors in lung adenocarcinoma and ovarian cancer.
  • MiSL a selective inhibitor of AKT1, which is currently in phase 2 clinical trials in solid tumors.
  • MK-2206 a selective inhibitor of AKT1 which is currently in phase 2 clinical trials in solid tumors.
  • MiSL identified several predictive biomarkers for MK-2206 (Fig. 4c), including PIK3CA mutation in breast cancer, which was identified by MiSL because PIK3CA mutation and AKT1 deletion were mutually exclusive in pan-cancer data, AKT1 deletion resulted in lowered expression, and AKT1 was over-expressed in P/ 3C ⁇ -mutant breast cancer (Fig. 10d).
  • Fig. 10e with a consistent increase in apoptotic marker cleaved PARP and decrease in phospho-PRAS40, S6K, 4EBP1 and BAD after drug treatment (Fig. 10f).
  • the method also identified predictive biomarkers in other cancer types (Fig. 4c).
  • Several of the predictions involved mutations in genes that are functionally associated with PIK3CA and/or AKT1 signaling (including PTEN mutation in kidney cancer, LATS2 mutation in lung adenocarcinoma, and PIK3CG mutation in uterine cancer), suggesting that the method identifies biologically meaningful predictive biomarkers (Fig.
  • MiSL a simple and scalable Boolean- implication based computational method, that analyzes relationships between mutation, copy number and gene expression data of primary tumors to identify synthetic lethal partners of specific mutations in specific tumor types. Extensive validation for multiple mutation and cancer combinations using both existing data and our own large scale shRNA data confirmed that MiSL is an in silico screen that enriches for synthetic lethal interactions (Fig. 2).
  • MiSL provides a process in which a tractable list of candidate targets (say, 20 to 200) is first identified computationally and then these targets are validated in depth.
  • ACACA is one of very few purported synthetic lethal partners of recurrent tumor mutations to have been validated in vivo. This finding is especially interesting since several potent and selective inhibitors for Acetyl CoA carboxylase are currently in clinical development for the treatment of metabolic diseases including diabetes and steatohepatitis. Our results suggest they may have anti-proliferative activity in IDH1 mutant cancers including AML, low grade glioma, secondary glioblastoma, and osteosarcoma.
  • MiSL can be used successfully for the reverse task, which is to identify predictive genetic biomarkers (mutations and/or copy number alterations) for existing targeted therapies in specific tumor types (Fig. 4b-c), and experimentally validated a MiSL-identified predictive biomarker, PIK3CA mutation in breast cancer, for an existing targeted therapy, AKT1-inhibitor MK-2206 (Fig. 4d).
  • DAISY a computational method termed DAISY that can predict synthetic lethal interactions using tumor genomic data along with shRNA data from existing cell- lines.
  • MiSL has important differences with DAISY, even though both methods identify an initial set of candidates using mutations and copy number alterations from tumor genomic data and apply subsequent filtering to minimize false positives, leading to DAISY failing to identify many of the synthetic lethal interactions described here. The differences can be understood by asking why DAISY does not identify any of the synthetic lethal interactions we have validated: IDH1mut-BCL2/BCL2L2, VHLmut-GLS, IDHImut- ACACA and PIK3CAmut-AKT1.
  • DAISY'S first inference strategy "genomic survival of the fittest", which looks for mutual exclusion, considers a small number of inactivating mutations (nonsense and frame-shift mutations), while MiSL handles all types of mutations. Since IDH1 mutations are mainly missense mutations, this step would fail to identify interactions related to IDHImut
  • the second inference strategy uses cell line shRNA screens, which prevents DAISY from identifying synthetic lethals for recurrent mutations that are not well-represented in available cell-lines or for tumors for which very few cell-lines have been isolated. This DAISY step would miss all predictions related to the IDH1 mutations because IDH1 mutation is rarely present as an endogenous mutation in cell-lines.
  • the third inference strategy requires synthetic lethal pairs to have correlated expression (measured by Spearman correlation coefficient ⁇ 0.5): this step would miss all the interactions we validated as all these pairs fail this condition (the Spearman correlation coefficient for each of these is less than 0.25). This demonstrates that the set of filters DAISY uses removes many true SL interactions. We next sought to determine if DAISY'S predictions are enriched for mutation-specific SL partners for the IDH1 mutation in AML.
  • MiSL has several important features that increase its applicability to precision medicine.
  • MiSL is "mutation-centric" in conception, such that its focus on identifying synthetic lethal partners of recurrent somatic mutations lends itself directly to clinical application.
  • high-throughput sequencing data or somatic mutation data for common mutations are available to the clinician for a given patient's tumor.
  • outcome analysis of 570 targeted agents found that personalized therapy using a genomic biomarker had higher median response rate and prolonged median progression-free survival and overall survival compared with personalized arms using a protein biomarker, further justifying a "mutation-centric” approach.
  • MiSL does not require functional data from cell lines, which allowed it to identify SL partners for mutations, such as IDH1, that are not well-represented in cell lines.
  • MiSL is used to capture in vivo tumor evolutionary relationships that may not be present from cell-line data analysis.
  • Our analysis demonstrates that mutual exclusion and subset relationships between somatic mutation and copy number alterations in human cancer could indeed arise due to synthetic lethality effects, apart from other known reasons such as convergent evolution, cooperating pathways and genomic instability.
  • MiSL benefits from using Boolean implications, which represent stringent statistical mutual exclusion and subset relationships. This reduces the number of false positives and enriches for true SL partners (Fig. 2f, Fig. 7f).
  • MiSL also benefits from the use of pan-cancer data. When a gene is mutated in multiple tumor types, MiSL uses all of those tumor types to infer SL partners of the gene.
  • MiSL identified candidate SL partners for AML mutations because those mutations occurred in other tumor types.
  • the three experimentally validated examples presented here involve metabolic processes (PIK3CAmut-AKT1 , IDHImut- ACACA; VHLmut-GLS), and in particular, pathways that are known to be perturbed in cancer metabolism as described by the Warburg effect.
  • MiSL may be especially useful in finding mutation-specific metabolic dependencies, some of which might not be easily identifiable using shRNA cell-line screens.
  • MiSL uses data from 12 different TCGA cancers (Figure 6A). For each of these cancers, we used the mutation, copy number, and gene expression data. The only exception is AML (acute myeloid leukemia), where many samples did not have copy number data.
  • AML acute myeloid leukemia
  • the starting point of our analysis is the level 3 data downloaded from the TCGA website. The processing of the Level 3 TCGA data is:
  • the mutation data specify the mutated genes and the mutation type on a per sample basis.
  • a Boolean variable is introduced for each mutated gene.
  • Boolean variables are also introduced for each type of mutated gene (such as frame shift deletions, missense mutations, nonsense mutations, and splice site mutations). For each sample, the Boolean variable associated with a given mutation is set to high if the mutation associated with the variable is present and to low otherwise.
  • the data for copy number alterations contain the segmented copy number data for the tumor and normal samples. For both types of samples, segments, where the absolute magnitude of the segment mean was greater than 0.3, were retained. Furthermore, only segments with 5 or more markers were retained to remove regions with low- confidence output. Next, for each tumor sample, the tumor-specific alterations were determined by removing segments that had more than 50% overlap with altered regions in the corresponding normal sample. The remaining segments were used to find, in each tumor sample, the genes affected by a copy number alteration. The hg19 assembly was used to identify the genes. Next, two Boolean variables are introduced - one for gene amplification and another for gene deletion. For each sample, the Boolean variables for amplification or deletion are high if the gene was found to be amplified or deleted (absolute magnitude of segment greater than 0.3).
  • RNAseq For microarray data, the data was normalized using the standard Robust Multi-chip Analysis algorithm. For RNAseq data, the RPKM values were log transformed. RNAseq data was primarily used for our analysis except for cases where there was limited RNAseq data.
  • MiSL algorithm Given a mutation and a cancer type of interest, the MiSL algorithm consists of the following steps: First, all the cancers in which the mutation is present in at least 2.5% of the samples were identified. Next, genes were identified that had more copies in the presence of a mutation as determined by using Boolean implications.
  • Boolean implications between pairs of variables were detected using a statistical test consisting of two parts: first, the Fisher's exact test was used to test dependence, then sparseness of a specific quadrant was tested by using a maximum-likelihood estimate of the error rate for the points in the sparse quadrant. An implication was considered significant if the p-value from the Fisher test was less than a cutoff threshold (always less than 0.05) and the error rate was less than 0.1. The cutoff was chosen to obtain an acceptable false discovery rate. In this work, the cutoff was set such that the FDR ⁇ 0.05 (as calculated by the procedure described in previous work).
  • the implication extraction procedure was augmented for genomic alterations by adding artificial normal samples for the HI-HI implication extraction. In both datasets, a few genomic alterations existed that were present in almost all tumor samples.
  • a deletion in gene A was considered to be a passenger in a tumor type if A was not differentially down-regulated (as per t-test with fold difference > 1.2, p-value ⁇ 0.05) in samples with deletions in A versus the remainder of the samples.
  • amplification in gene A was considered to be a passenger in a tumor type if A is not differentially up-regulated in samples with amplification of A.
  • the resulting gene set was filtered to only include genes that are differentially over-expressed in the presence of the mutation versus the wild-type, in the cancer of interest (as per t-test, p-value ⁇ 0.05).
  • shRNA data from Project Achilles was used.
  • the project used a library of 54,020 shRNAs targeting 1 1 , 194 genes individual shRNAs that were lentivirally delivered to the cells.
  • the abundance of the shRNAs was measured after the cells were propagated for 16 population doublings or 40 days in culture, whichever came first, and compared to the initial DNA plasmid pool.
  • the data was normalized along with some quality control steps based on replicate reproducibility and a measure of the overall distribution of shRNA normalized and logged read counts.
  • the final output was a shRNA summary score for each cell line for all the shRNA that passed the quality control steps.
  • the shRNA summary score was defined to be log2-normalized ratio of the raw read value for the shRNA divided by the total raw read value for the replicates. Thus, a lower shRNA summary score in a cell line implies greater dependence on the gene in that cell line.
  • the table of shRNA summary scores for the 216 cell-lines was the starting point of our analysis.
  • the first step in our analysis was to identify the evaluable mutations. Evaluability of a mutation was assessed as follows: (i) there were more than 5 cell-lines with the mutation in the cancer type of interest, and (ii) there were greater than 25 MiSL candidates for the mutation in the cancer type of interest for which shRNA data was available in Project Achilles. The former condition ensured we had enough mutated samples in a cancer type and the latter condition was a necessary requirement for our downstream analysis using GSEA which requires the gene sets to be larger than 25.
  • GSEA a necessary requirement for our downstream analysis using GSEA which requires the gene sets to be larger than 25.
  • the analysis was done as follows. The first step was to filter out genes which had data for less than 3 shRNAs per gene. There were 10,967 genes that remained after the filtering. The goal was to rank the genes in terms of essentiality in colorectal cancer cell-lines with a specific mutation versus wild-type samples
  • GSEA Gene set enrichment analysis
  • Each cell line used in the study was obtained from ATCC or DSMZ and identity confirmed using short tandem repeat analysis (Bio-synthesis, Louisville, TX). Periodically cells were tested for mycoplasma contamination using ELISA based method (Roche Life Science, Indianapolis, IN). Each library contains 27,5000 unique shRNA constructs targeting 5,043 human genes (approximately five or six redundant shRNAs per gene) in the pRSI9 shRNA expression vector.
  • the vector contains the following elements: (i) U6 RNA polymerase III promoter driving shRNA expression, (ii) 18-nucleotide DNA barcode sequence and (iii) UbiC promoter driving RFP expression to mark transduced cells.
  • U6 RNA polymerase III promoter driving shRNA expression For each inducible cell line, 12 million cells were transduced at an efficiency of 30-40% to ensure that -90% of the transduced cells were single integrants according to the Poisson distribution. The number of transduced cells was approximately 100- fold the complexity of the library. Three days after transduction, each cell population was divided into two flasks.
  • Doxycycline was added to one of the flasks at a concentration of 1 ⁇ g/ml to induce expression of either wild-type or mutant IDH1 R132H.
  • the cells were expanded and selected in culture for 12 additional days. During this period, the number of transduced cells in each flask was maintained at > 1000-fold the complexity of the library.
  • the cells were centrifuged, and genomic DNA was extracted using a QIAamp Blood DNA Maxi Kit (Qiagen, Valencia, CA) and submitted to Cellecta, Inc. for bar code amplification, high-throughput sequencing and deconvolution. Twenty million barcode reads were performed for each sample.
  • shRNA constructs with less than 100 barcode reads in the THP-1 R132H no-doxycycline sample were excluded for further analysis to minimize noise associated with inadequate baseline representation. Genes with less than three redundant shRNA were excluded.
  • 776 were considered synthetic lethal hits using this method.
  • Pathway enrichment For pathway analysis, KEGG and GO BP gene sets from the MSigDB website were used. The MiSL candidates of a mutation were said to be enriched for the same pathway if the following criterion were satisfied: (1) the mutated gene belongs to a pathway P, and (2) the MiSL candidates of X have a statistically significant overlap (p ⁇ 0.05) with the genes in P. To get as specific pathways as possible, all pathways that had greater than 500 genes were removed. Furthermore, to remove redundant results, certain pathways were filtered out according to the following criterion. If two pathways P1 and P2 found the same overlapping set, the pathway with the larger (worse) p-value was removed.
  • the overlap set for a pathway was completely contained in the overlap set of another pathway, the first pathway was removed.
  • the MiSL candidates of a mutation were said to be enriched for a pathway if the MiSL candidates of X have a statistically significant overlap (p ⁇ 0.05 after Benjamini-Hochberg correction) with the genes in P.
  • redundant pathways were filtered out.
  • a pathway was identified to be druggable if the genes in the pathway had a statistically significant overlap with the genes inhibited by a drug as per DGidb.
  • CAL-120 and CAL-51 cell-lines were cultured in Dulbecco's Modified Eagle Medium (DMEM); T47D, MCF-7, HCC-1806, and HCC-38 cells in Roswell Park Memorial Institute (RPMI) 1640; CAL-148 were cultured in Minimum Essential Media (MEM) supplemented with 1 ⁇ g/100 ml EGF; SKBR-7 cells were cultured in DMEM/F12 medium. Medium was supplemented with 1 % glutamine, 1 % penicillin/streptomycin, and 8% fetal calf serum (FCS) or 16% FCS (CAL-148 and CAL-51 cells).
  • DMEM Dulbecco's Modified Eagle Medium
  • T47D, MCF-7, HCC-1806, and HCC-38 cells in Roswell Park Memorial Institute 1640
  • CAL-148 were cultured in Minimum Essential Media (MEM) supplemented with 1 ⁇ g/100 ml EGF
  • SKBR-7 cells were cultured in
  • CellTiter-Blue viability assay Breast cancer cells were seeded in a 384-well plate. After 24 hours, inhibitor was added to the medium in 2-fold serial dilutions using a HP Direct Digital Dispenser. After 72 hours of culture CellTiter-Blue (Promega) was added. The conversion of resazurin into resofurin was measured by using an EnVision Multilabel Reader. Treatment with 10 ⁇ phenyl arsenic oxide was used as a baseline for viability.
  • Antibodies against acetyl CoA carboxylase 1 (#4190), beta- actin (#4967), cl-PARP (#9542), p-AKT (#4060), p-PRAS40 (#2997), p-S6 (#221 1), p-4EBP1 (#9456), p-BAD (#5284) were from Cell Signaling. Secondary antibodies were obtained from Bio-Rad Laboratories and Thermo Scientific. [00187] Predicting genetic biomarkers with MiSL. For a given drug, the set of genes that are inhibited by the drug using DGidb were identified. Assume that drug D inhibits a set of genes S.
  • MiSL was used to determine which genomic alterations in a given cancer would be synthetically lethal with the inhibition of each gene in S, say gene Y.
  • AML druggability screen THP-1 cell-lines were cultured in RPMI supplemented with 10% fetal calf serum, 100u/ml of penicillin, and 100 ⁇ 9/ ⁇ of streptomycin and 1 ⁇ 9/ ⁇ puromycin.
  • ABT-199 was purchased from ChemieTek (Indianopolis, IN). Cantharidin, digoxigenin, proscardillin, wortmannin, SB203580, TOFA, IC 261 , vorinostat were all purchased from Sigma.
  • ABT-737, GSK-J4, GSK-126, G007-LK, MM-102, SKI-606, JNK-IN-8 were purchased from Sellekchem (Houston, TX).
  • LB100 (HY- 18597) was obtained from MedChemExpress (MonMouth Junction, NJ). After 4 days of doxycycline induction (or control without doxycycline), cells were plated in RPMI 20% fetal bovine serum at 2 x 10 5 /ml in 96 well plate with 2-fold dilutions of each drug performed in duplicate. At 72 hours cell viability was measured using a plate-reader after addition of 10% Presto Blue Cell Viability Reagent (ThermoFisher Scientific) at emission fluorescence 590 nM. IC50 curves were calculated for each drug in the presence and absence of doxycycline using GraphPad Prism 6.0 (Dose Response Inhibition) and the difference in IC50 was plotted as a percentage of control (no dox).
  • transduced THP-1 cells were induced with doxycycline for 3 days then washed into low serum RPMI (0.5%) and cultured at low cell density for 7 days +/- TOFA before cell growth was measured with Presto Blue on a plate reader.
  • Lentiviral expression vectors Lentivirally transduced pools of cells were selected in 1 ⁇ g/ml puromycin. IDH1 wild-type and R132H mutation were expressed in the pTRIPZ (Open Biosystems) tet inducible lentiviral vector with GFP encoded in the same open reading frame by T2A peptide. AML druggability screen.
  • THP-1 cell-lines were cultured in RPMI supplemented with 10% fetal calf serum, 100u/ml of penicillin, and 100 ⁇ g/ml of streptomycin and 1 ⁇ g/ml puromycin.
  • ABT- 199 was purchased from ChemieTek (Indianopolis, IN). Cantharidin, digoxigenin, proscardillin, wortmannin, SB203580, TOFA, IC 261 , vorinostat were all purchased from Sigma.
  • ABT-737, GSK-J4, GSK-126, G007-LK, MM-102, SKI-606, JNK-IN-8 were purchased from Sellekchem (Houston, TX).
  • LB100 (HY- 18597) was obtained from MedChemExpress (MonMouth Junction, NJ). After 4 days of doxycycline induction (or control without doxycycline), cells were plated in RPMI 20% fetal bovine serum at 2 x 10 5 /ml in 96 well plate with 2-fold dilutions of each drug performed in duplicate. At 72 hours cell viability was measured using a plate-reader after addition of 10% Presto Blue Cell Viability Reagent (ThermoFisher Scientific) at emission fluorescence 590 nM. IC50 curves were calculated for each drug in the presence and absence of doxycycline using GraphPad Prism 6.0 (Dose Response Inhibition) and the difference in IC50 was plotted as a percentage of control (no dox).
  • transduced THP-1 cells were induced with doxycycline for 3 days then washed into low serum RPMI (0.5%) and cultured at low cell density for 10 days +/- TOFA before cell growth was measured with Presto Blue on a plate reader.
  • AG-120 was obtained from ChemieTek. Lentiviral expression vectors. Lentivirally transduced pools of cells were selected in 1 ⁇ g/ml puromycin. IDH1 wild-type and R132H mutation were expressed in the pTRIPZ (Open Biosystems) tet inducible lentiviral vector with GFP encoded in the same open reading frame by T2A peptide.
  • pTRIPZ Open Biosystems
  • In- del mutation frequency was measured using TIDE (Tracking of Indels by Decomposition: https://tide.nki.nl) after nucleofection of 10 6 K562 cells with 2 ⁇ g of plasmid DNA using the Amaxa Nucleofector II® , program T-016. Genomic DNA was isolated after 3 days in culture and exon 4 of ACACA flanking the cut site was amplified using forward (TAGGATGCTAGGGAGGCAGA) and reverse primers (TGATGGCATCTGCTGGTAAA) with annealing temperature of 61 °C.
  • the sgRNA sequence with the highest cutting efficiency (65%) was cloned into lentiviral LentiCRISPRv2_tagRFP vector (gift from Feng Zhang, Addgene plasmid #52961). Knockdown of ACACA was confirmed by Western blot with antiACCI rabbit polyclonal (Cell Signaling Technologies).
  • AML blasts were cultured in OPTIMEM with 10 "6 M hydrocortisone and 20ng/ml each of IL-3, GM-CSF, G-CSF, IL- 6, SCF, FLT3L and TPO (Peprotech).
  • mice Up to five mice were transplanted for each treatment group if a sufficient amount of primary patient material was available, giving enough power to see a statistical difference of > 30% Mann-Whitney U. A fewer number of mice were used if the sample was limiting. Mice were randomized per block prior to engraftment with either scrambled or ACACA shRNA. Investigators were not blinded to shRNAs transduced into AML after engraftment. Only IDHI mut AML samples were used in the study.
  • Bone marrow engraftment analysis Bone marrow cells were collected by aspiration of the femur using a 27-gauge needle and stained for 30 min at 4 C with the following fluorophore- conjugated monoloncal antibodies: mTER199-PE-Cy5 (dilution 1 : 100; clone TER-199, eBioscience), mCD45-PE-Cy7 (dilution 1 :50; clone A20, eBioscience), hCD45-V450 (dilution 1 :25; clone HI30, BD), hCD33-APC (dilution 1 :25, clone WM53, BD).
  • Viable cells were identified by propidium iodide exclusion.
  • the human leukemic population was identified as mTER199-, mCD45-, hCD45+ and hCD33+ .
  • Cells stably transduced with shRNA were identified as RFP+.
  • ACACA shRNA expression lentiviral vectors The human ACACA (GenBank accession code: NM_198834.2) shRNA target sequences were selected using the BLOCK-iT RNAi Designer tool (Life Technologies). Knockdown efficiency of ACACA shRNA constructs was determined by quantitative real-time PCR and Western blotting.
  • a pair of DNA oligo nucleotides containing the sense target sequence followed by a loop sequence (5'- TCAAGAG-3') and the reverse complement of the sense sequence were synthesized and annealed at 50 ⁇ in annealing buffer (10 mM Tris-HCI pH 8.0, 50 mM NaCI, 1 mM EDTA) at 95°for 10 min, followed by a slow cooling over 1 h to room temperature.
  • the double-stranded DNA template was then cloned into the pRSI9 DECIPHER shRNA expression vector (Cellecta, Mountain View, CA) digested with Bsbl.
  • the ACACA sequences targeted by shRNA vectors used in this study were 5'-UGGCAUUGCAGCAGUGAAA-3' (shRNA 1); 5'- UGGAAUGAUUGCUGGAGAA-3' (shRNA 2).
  • DAISY has three inference strategies: (1) Genomic survival of the fittest, (2) shRNA-based functional examination, and (3) pairwise gene co- expression. DAISY intersects the predictions from these three inference strategies to determine its list of candidates for a specific alteration. For steps 1 and 3, we used the same thresholds as outlined in the Experimental Procedures section in the paper. Since the IDH1 mutation is present in very few cell-lines (less than 2%), the shRNA-based functional examination cannot be done for this particular mutation. Hence, we excluded step 2 from the analysis. Thus, the list of DAISY candidates for the IDH1 mutation was obtained by intersecting the results from steps 1 and 3.

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Abstract

L'invention concerne des procédés analytiques et des systèmes pour déterminer les partenaires létaux synthétiques (LS) d'un gène d'intérêt. L'identification d'un ou un ensemble de candidats partenaires LS associés à une mutation dans un gène d'intérêt permet de développer des thérapies ciblées pour une oncologie de précision, fournit des cibles pour le criblage de médicaments, permet de nouvelles application pour des médicaments existants et pour des applications théranostiques. Un partenaire LS pour une mutation récurrente dans IDH1 est identifié, laquelle mutation de IDH1 peut être présente dans des tumeurs malignes hématologiques, telles que des cellules de leucémie myéloïde aiguë (LMA) et dans des tumeurs solides.
PCT/US2016/061623 2015-11-13 2016-11-11 Détermination de partenaires létaux synthétiques de modifications spécifiques du cancer et procédés pour les utiliser WO2017083716A2 (fr)

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CN110797080A (zh) * 2019-10-18 2020-02-14 湖南大学 基于跨物种迁移学习预测合成致死基因
CN113362894A (zh) * 2021-06-15 2021-09-07 上海基绪康生物科技有限公司 一种对协同致死的癌症驱动基因进行预测的方法
CN114566211A (zh) * 2022-03-14 2022-05-31 杭州师范大学 基于生物网络与机器学习的合成致死基因组合预测系统
WO2023022659A1 (fr) * 2021-08-19 2023-02-23 Engine Biosciences Pte. Ltd. Compositions et procédés de génération de létalité synthétique dans des tumeurs

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US20040126840A1 (en) * 2002-12-23 2004-07-01 Affymetrix, Inc. Method, system and computer software for providing genomic ontological data
WO2011144738A1 (fr) * 2010-05-21 2011-11-24 Emergentec Biodevelopment Gmbh Cibles géniques critiques pour une thérapie cytotoxique
WO2014194092A1 (fr) * 2013-05-30 2014-12-04 Memorial Sloan-Kettering Cancer Center Système et procédé pour la prévision automatisée de vulnérabilités dans des échantillons biologiques
US20160300036A1 (en) * 2013-10-28 2016-10-13 New York University Methods, computer-accessible medium and systems to model disease progression using biomedical data from multiple patients

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110797080A (zh) * 2019-10-18 2020-02-14 湖南大学 基于跨物种迁移学习预测合成致死基因
CN113362894A (zh) * 2021-06-15 2021-09-07 上海基绪康生物科技有限公司 一种对协同致死的癌症驱动基因进行预测的方法
WO2023022659A1 (fr) * 2021-08-19 2023-02-23 Engine Biosciences Pte. Ltd. Compositions et procédés de génération de létalité synthétique dans des tumeurs
CN114566211A (zh) * 2022-03-14 2022-05-31 杭州师范大学 基于生物网络与机器学习的合成致死基因组合预测系统
CN114566211B (zh) * 2022-03-14 2024-05-14 杭州师范大学 基于生物网络与机器学习的合成致死基因组合预测系统

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