Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome‐wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients’ response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

[1]  E. Ruppin,et al.  Harnessing synthetic lethality to predict the response to cancer treatment , 2018, Nature Communications.

[2]  N. Schultz,et al.  Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies. , 2017, Cancer cell.

[3]  Steven J. M. Jones,et al.  Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma. , 2017, Cancer cell.

[4]  John G. Doench,et al.  In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target , 2017, Nature.

[5]  C. Fan,et al.  Immune-Related Gene Expression Profiling After PD-1 Blockade in Non-Small Cell Lung Carcinoma, Head and Neck Squamous Cell Carcinoma, and Melanoma. , 2017, Cancer research.

[6]  R. Iglesias-Bartolome,et al.  Abstract 351: mTOR-ERK co-targeting strategies for head and neck cancer therapy , 2017 .

[7]  Eric S. Lander,et al.  Gene Essentiality Profiling Reveals Gene Networks and Synthetic Lethal Interactions with Oncogenic Ras , 2017, Cell.

[8]  J. Sosman,et al.  Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma , 2017, Cell.

[9]  Thomas M. Keane,et al.  Genome-wide genetic screening with chemically-mutagenized haploid embryonic stem cells , 2016, Nature chemical biology.

[10]  Trey Ideker,et al.  Interaction Landscape of Inherited Polymorphisms with Somatic Events in Cancer. , 2017, Cancer discovery.

[11]  S. Gabriel,et al.  Genomic evolution and chemoresistance in germ-cell tumours , 2016, Nature.

[12]  Mojca Mattiazzi Usaj,et al.  Exploring genetic suppression interactions on a global scale , 2016, Science.

[13]  Trey Ideker,et al.  A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy. , 2016, Molecular cell.

[14]  Emanuel J. V. Gonçalves,et al.  A Landscape of Pharmacogenomic Interactions in Cancer , 2016, Cell.

[15]  Fan Liu,et al.  Beyond transcription factors: how oncogenic signalling reshapes the epigenetic landscape , 2016, Nature Reviews Cancer.

[16]  R Clarke,et al.  Acquisition of estrogen independence induces TOB1-related mechanisms supporting breast cancer cell proliferation , 2016, Oncogene.

[17]  S. Lippman,et al.  A synthetic-lethality RNAi screen reveals an ERK-mTOR co-targeting pro-apoptotic switch in PIK3CA+ oral cancers , 2016, Oncotarget.

[18]  Cathryn M. Gould,et al.  A genome scale RNAi screen identifies GLI1 as a novel gene regulating vorinostat sensitivity , 2016, Cell Death and Differentiation.

[19]  Matteo Benelli,et al.  Divergent clonal evolution of castration resistant neuroendocrine prostate cancer , 2016, Nature Medicine.

[20]  Gary D Bader,et al.  Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance , 2016, Cell.

[21]  A. Butte,et al.  Systematic pan-cancer analysis of tumour purity , 2015, Nature Communications.

[22]  D. Durocher,et al.  High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities , 2015, Cell.

[23]  David L. Gibbs,et al.  Combining Dependent P-values with an Empirical Adaptation of Brown’s Method , 2015, bioRxiv.

[24]  Joshua M. Korn,et al.  High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response , 2015, Nature Medicine.

[25]  Sridhar Ramaswamy,et al.  Landscape of Targeted Anti-Cancer Drug Synergies in Melanoma Identifies a Novel BRAF-VEGFR/PDGFR Combination Treatment , 2015, PloS one.

[26]  S. Gabriel,et al.  Genomic correlates of response to CTLA-4 blockade in metastatic melanoma , 2015, Science.

[27]  R. Scharpf,et al.  The Genomic Landscape of Response to EGFR Blockade in Colorectal Cancer , 2015, Nature.

[28]  Łukasz M. Boryń,et al.  Transcriptional plasticity promotes primary and acquired resistance to BET inhibition , 2015, Nature.

[29]  Mark A. Dawson,et al.  BET inhibitor resistance emerges from leukaemia stem cells , 2015, Nature.

[30]  Sridhar Ramaswamy,et al.  RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance , 2015, Science.

[31]  Antoni Ribas,et al.  Non-genomic and Immune Evolution of Melanoma Acquiring MAPKi Resistance , 2015, Cell.

[32]  Peter Bouwman,et al.  REV7 counteracts DNA double-strand break resection and affects PARP inhibition , 2015, Nature.

[33]  Joshy George,et al.  Whole–genome characterization of chemoresistant ovarian cancer , 2015, Nature.

[34]  James S. Duncan,et al.  Inhibition of Lapatinib-Induced Kinome Reprogramming in ERBB2-Positive Breast Cancer by Targeting BET Family Bromodomains. , 2015, Cell reports.

[35]  L. Chin,et al.  Co-clinical assessment identifies patterns of BRAF inhibitor resistance in melanoma. , 2015, The Journal of clinical investigation.

[36]  S. Gabriel,et al.  A functional landscape of resistance to ALK inhibition in lung cancer. , 2015, Cancer cell.

[37]  Antonio Marchetti,et al.  The Hippo effector YAP promotes resistance to RAF- and MEK-targeted cancer therapies , 2015, Nature Genetics.

[38]  Davide Heller,et al.  STRING v10: protein–protein interaction networks, integrated over the tree of life , 2014, Nucleic Acids Res..

[39]  Sridhar Ramaswamy,et al.  Patient-derived models of acquired resistance can identify effective drug combinations for cancer , 2014, Science.

[40]  Jun S. Liu,et al.  MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens , 2014, Genome Biology.

[41]  Ellen T. Gelfand,et al.  Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies , 2014, Scientific Data.

[42]  J. Silvio Gutkind,et al.  The head and neck cancer cell oncogenome: a platform for the development of precision molecular therapies , 2014, Oncotarget.

[43]  Eytan Domany,et al.  Using high-throughput transcriptomic data for prognosis: a critical overview and perspectives. , 2014, Cancer research.

[44]  Eytan Ruppin,et al.  Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality , 2014, Cell.

[45]  Balázs Papp,et al.  The Genomic Landscape of Compensatory Evolution , 2014, PLoS biology.

[46]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[47]  D. G. MacArthur,et al.  Guidelines for investigating causality of sequence variants in human disease , 2014, Nature.

[48]  R. Bernards,et al.  Reversible and adaptive resistance to BRAF(V600E) inhibition in melanoma , 2014, Nature.

[49]  Mark D. Robinson,et al.  Robustly detecting differential expression in RNA sequencing data using observation weights , 2013, Nucleic acids research.

[50]  Wei Shi,et al.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..

[51]  Ji Luo,et al.  Using pooled miR30-shRNA library for cancer lethal and synthetic lethal screens. , 2014, Methods in molecular biology.

[52]  Martin Vingron,et al.  Synthetic sickness or lethality points at candidate combination therapy targets in glioblastoma , 2013, International journal of cancer.

[53]  G. Getz,et al.  Inferring tumour purity and stromal and immune cell admixture from expression data , 2013, Nature Communications.

[54]  Gary Ruvkun,et al.  Human disease locus discovery and mapping to molecular pathways through phylogenetic profiling , 2013, Molecular systems biology.

[55]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[56]  Erhan Bilal,et al.  Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling , 2013, PLoS Comput. Biol..

[57]  S. Lowe,et al.  RNAi screening uncovers Dhx9 as a modifier of ABT-737 resistance in an Eμ-myc/Bcl-2 mouse model. , 2013, Blood.

[58]  L. Gu,et al.  Methylation of RASSF1A gene promoter is regulated by p53 and DAXX , 2013, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[59]  Or Zuk,et al.  Identification of small RNA pathway genes using patterns of phylogenetic conservation and divergence , 2012, Nature.

[60]  A. McKenna,et al.  Evolution and Impact of Subclonal Mutations in Chronic Lymphocytic Leukemia , 2012, Cell.

[61]  James B. Mitchell,et al.  mTOR inhibition prevents epithelial stem cell senescence and protects from radiation-induced mucositis. , 2012, Cell stem cell.

[62]  Matthew J. Betts,et al.  Dissecting the genomic complexity underlying medulloblastoma , 2012, Nature.

[63]  A. McKenna,et al.  Absolute quantification of somatic DNA alterations in human cancer , 2012, Nature Biotechnology.

[64]  D. Sabatini,et al.  mTOR Signaling in Growth Control and Disease , 2012, Cell.

[65]  S. Ramaswamy,et al.  Systematic identification of genomic markers of drug sensitivity in cancer cells , 2012, Nature.

[66]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[67]  Martin H. Schaefer,et al.  HIPPIE: Integrating Protein Interaction Networks with Experiment Based Quality Scores , 2012, PloS one.

[68]  P. Soares,et al.  The mTOR Signalling Pathway in Human Cancer , 2012, International journal of molecular sciences.

[69]  Kevin R Brown,et al.  Essential gene profiles in breast, pancreatic, and ovarian cancer cells. , 2012, Cancer discovery.

[70]  A. Pandey,et al.  Human Protein Reference Database and Human Proteinpedia as resources for phosphoproteome analysis. , 2012, Molecular bioSystems.

[71]  Davis J. McCarthy,et al.  Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation , 2012, Nucleic acids research.

[72]  J. Mesirov,et al.  Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer , 2011, Proceedings of the National Academy of Sciences.

[73]  U. Weidle,et al.  Synthetic lethality-based targets for discovery of new cancer therapeutics. , 2011, Cancer genomics & proteomics.

[74]  L. Esserman,et al.  A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. , 2011, JAMA.

[75]  S. Oliver,et al.  An integrated approach to characterize genetic interaction networks in yeast metabolism , 2011, Nature Genetics.

[76]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[77]  L. Cantley,et al.  Targeting a Common Collaborator in Cancer Development , 2010, Science Translational Medicine.

[78]  D. Adams,et al.  53BP1 loss rescues BRCA1 deficiency and is associated with triple-negative and BRCA-mutated breast cancers , 2010, Nature Structural &Molecular Biology.

[79]  Yibo Wu,et al.  GOSemSim: an R package for measuring semantic similarity among GO terms and gene products , 2010, Bioinform..

[80]  T. Chou.  Drug combination studies and their synergy quantification using the Chou-Talalay method. , 2010, Cancer research.

[81]  T. Alonzi,et al.  Convergence of Wnt signaling on the HNF4alpha-driven transcription in controlling liver zonation. , 2009, Gastroenterology.

[82]  Ran Kafri,et al.  Genetic Redundancy: New Tricks for Old Genes , 2009, Cell.

[83]  R. Abraham,et al.  A retroinhibition approach reveals a tumor cell-autonomous response to rapamycin in head and neck cancer. , 2008, Cancer research.

[84]  A. Barabasi,et al.  Predicting synthetic rescues in metabolic networks , 2008, Molecular systems biology.

[85]  Hyunsoo Kim,et al.  Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares , 2006 .

[86]  Raymond A. Molnar,et al.  Advances in Protein Chemistry, Volume 65: Proteome Characterization and Proteomics , 2004, Environmental Health Perspectives.

[87]  M. Carrington,et al.  The killer immunoglobulin-like receptor gene cluster: tuning the genome for defense. , 2006, Annual review of genomics and human genetics.

[88]  Ting-Chao Chou,et al.  Theoretical Basis, Experimental Design, and Computerized Simulation of Synergism and Antagonism in Drug Combination Studies , 2006, Pharmacological Reviews.

[89]  Weiwei Zhong,et al.  Genome-Wide Prediction of C. elegans Genetic Interactions , 2006, Science.

[90]  S. Hirohashi,et al.  E-Cadherin Regulates the Association between β-Catenin and Actinin-4 , 2005 .

[91]  T. Ideker,et al.  Systematic interpretation of genetic interactions using protein networks , 2005, Nature Biotechnology.

[92]  Y. Pilpel,et al.  Transcription control reprogramming in genetic backup circuits , 2005, Nature Genetics.

[93]  Eytan Domany,et al.  Outcome signature genes in breast cancer: is there a unique set? , 2004, Breast Cancer Research.

[94]  S. Hirohashi,et al.  E-cadherin regulates the association between beta-catenin and actinin-4. , 2005, Cancer research.

[95]  Alexander Kraskov,et al.  Published under the scientific responsability of the EUROPEAN PHYSICAL SOCIETY Incorporating , 2002 .

[96]  C. Pál,et al.  Dosage sensitivity and the evolution of gene families in yeast , 2003, Nature.

[97]  Shile Huang,et al.  Rapamycins: Mechanisms of Action and Cellular Resistance , 2003, Cancer biology & therapy.

[98]  M. Robert,et al.  DNMT1 is required to maintain CpG methylation and aberrant gene silencing in human cancer cells , 2003, Nature Genetics.

[99]  P. Grambsch,et al.  Modeling Survival Data: Extending the Cox Model , 2000 .

[100]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[101]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[102]  R. Young,et al.  Intragenic and extragenic suppressors of mutations in the heptapeptide repeat domain of Saccharomyces cerevisiae RNA polymerase II. , 1989, Genetics.

[103]  P. Lefebvre,et al.  Extragenic suppression and synthetic lethality among Chlamydomonas reinhardtii mutants resistant to anti-microtubule drugs. , 1989, Genetics.

[104]  R. Gill,et al.  Cox's regression model for counting processes: a large sample study : (preprint) , 1982 .

[105]  C. I. Bliss.  THE TOXICITY OF POISONS APPLIED JOINTLY1 , 1939 .

[106]  the original work is properly cited. , 2022 .