Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity.
cancer
loss‐of‐function
mutually exclusivity
synthetic lethality
Journal
FEBS letters
ISSN: 1873-3468
Titre abrégé: FEBS Lett
Pays: England
ID NLM: 0155157
Informations de publication
Date de publication:
08 Jul 2024
08 Jul 2024
Historique:
revised:
25
04
2024
received:
03
08
2023
accepted:
09
05
2024
medline:
9
7
2024
pubmed:
9
7
2024
entrez:
8
7
2024
Statut:
aheadofprint
Résumé
Mutually exclusive loss-of-function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.
Identifiants
pubmed: 38977941
doi: 10.1002/1873-3468.14950
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 The Author(s). FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.
Références
Lord CJ and Ashworth A (2017) PARP inhibitors: Synthetic lethality in the clinic. Science 355, 1152–1158.
O'Neil NJ, Bailey ML and Hieter P (2017) Synthetic lethality and cancer. Nat Rev Genet 18, 613–623.
Bryant HE, Schultz N, Thomas HD, Parker KM, Flower D, Lopez E, Kyle S, Meuth M, Curtin NJ and Helleday T (2005) Specific killing of BRCA2‐deficient tumours with inhibitors of poly(ADP‐ribose) polymerase. Nature 434, 913–917.
Oughtred R, Rust J, Chang C, Breitkreutz BJ, Stark C, Willems A, Boucher L, Leung G, Kolas N, Zhang F et al. (2021) The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci 30, 187–200.
Housden BE, Nicholson HE and Perrimon N (2017) Synthetic lethality screens using RNAi in combination with CRISPR‐based knockout in Drosophila cells. Bio Protoc 7.
Tong AH and Boone C (2006) Synthetic genetic array analysis in Saccharomyces cerevisiae. Methods Mol Biol 313, 171–192.
Behan FM, Iorio F, Picco G, Goncalves E, Beaver CM, Migliardi G, Santos R, Rao Y, Sassi F, Pinnelli M et al. (2019) Prioritization of cancer therapeutic targets using CRISPR‐Cas9 screens. Nature 568, 511–516.
Martins MM, Zhou AY, Corella A, Horiuchi D, Yau C, Rakhshandehroo T, Gordan JD, Levin RS, Johnson J, Jascur J et al. (2015) Linking tumor mutations to drug responses via a quantitative chemical‐genetic interaction map. Cancer Discov 5, 154–167.
Nijman SM (2011) Synthetic lethality: general principles, utility and detection using genetic screens in human cells. FEBS Lett 585, 1–6.
Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, Gill S, Harrington WF, Pantel S, Krill‐Burger JM et al. (2017) Defining a Cancer Dependency Map. Cell 170, 564–576.e16.
Wilding JL and Bodmer WF (2014) Cancer cell lines for drug discovery and development. Cancer Res 74, 2377–2384.
Wang J, Zhang Q, Han J, Zhao Y, Zhao C, Yan B, Dai C, Wu L, Wen Y, Zhang Y et al. (2022) Computational methods, databases and tools for synthetic lethality prediction. Brief Bioinform 23, bbac106.
Benstead‐Hume G, Chen X, Hopkins SR, Lane KA, Downs JA and Pearl FMG (2019) Predicting synthetic lethal interactions using conserved patterns in protein interaction networks. PLoS Comput Biol 15, e1006888.
Conde‐Pueyo N, Munteanu A, Sole RV and Rodriguez‐Caso C (2009) Human synthetic lethal inference as potential anti‐cancer target gene detection. BMC Syst Biol 3, 116.
Kranthi T, Rao SB and Manimaran P (2013) Identification of synthetic lethal pairs in biological systems through network information centrality. Mol BioSyst 9, 2163–2167.
Koch EN, Costanzo M, Bellay J, Deshpande R, Chatfield‐Reed K, Chua G, D'Urso G, Andrews BJ, Boone C and Myers CL (2012) Conserved rules govern genetic interaction degree across species. Genome Biol 13, R57.
Lu X, Kensche PR, Huynen MA and Notebaart RA (2013) Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets. Nat Commun 4, 2124.
VanderSluis B, Bellay J, Musso G, Costanzo M, Papp B, Vizeacoumar FJ, Baryshnikova A, Andrews B, Boone C and Myers CL (2010) Genetic interactions reveal the evolutionary trajectories of duplicate genes. Mol Syst Biol 6, 429.
Hoehndorf R, Hardy NW, Osumi‐Sutherland D, Tweedie S, Schofield PN and Gkoutos GV (2013) Systematic analysis of experimental phenotype data reveals gene functions. PLoS One 8, e60847.
Pesquita C, Faria D, Falcao AO, Lord P and Couto FM (2009) Semantic similarity in biomedical ontologies. PLoS Comput Biol 5, e1000443.
Srihari S, Singla J, Wong L and Ragan MA (2015) Inferring synthetic lethal interactions from mutual exclusivity of genetic events in cancer. Biol Direct 10, 57.
Canisius S, Martens JW and Wessels LF (2016) A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co‐occurrence. Genome Biol 17, 261.
Leiserson MD, Wu HT, Vandin F and Raphael BJ (2015) CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer. Genome Biol 16, 160.
Babur O, Gonen M, Aksoy BA, Schultz N, Ciriello G, Sander C and Demir E (2015) Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations. Genome Biol 16, 45.
Ciriello G, Cerami E, Sander C and Schultz N (2012) Mutual exclusivity analysis identifies oncogenic network modules. Genome Res 22, 398–406.
McDonald ER 3rd, de Weck A, Schlabach MR, Billy E, Mavrakis KJ, Hoffman GR, Belur D, Castelletti D, Frias E, Gampa K et al. (2017) Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large‐scale, Deep RNAi screening. Cell 170, 577–592.e10.
Srivatsa S, Montazeri H, Bianco G, Coto‐Llerena M, Marinucci M, Ng CKY, Piscuoglio S and Beerenwinkel N (2022) Discovery of synthetic lethal interactions from large‐scale pan‐cancer perturbation screens. Nat Commun 13, 7748.
Shao C, Westermann F and Höfer T (2016) Synlet: an R package for systemically analyzing synthetic lethal RNA interference screen data. bioRxiv
Jerby‐Arnon L, Pfetzer N, Waldman YY, McGarry L, James D, Shanks E, Seashore‐Ludlow B, Weinstock A, Geiger T, Clemons PA et al. (2014) Predicting cancer‐specific vulnerability via data‐driven detection of synthetic lethality. Cell 158, 1199–1209.
Das S, Deng X, Camphausen K and Shankavaram U (2019) DiscoverSL: an R package for multi‐omic data driven prediction of synthetic lethality in cancers. Bioinformatics 35, 701–702.
Markowska M, Budzinska MA, Coenen‐Stass A, Kang S, Kizling E, Kolmus K, Koras K, Staub E and Szczurek E (2023) Synthetic lethality prediction in DNA damage repair, chromatin remodeling and the cell cycle using multi‐omics data from cell lines and patients. Sci Rep 13, 7049.
Lee JS, Nair NU, Dinstag G, Chapman L, Chung Y, Wang K, Sinha S, Cha H, Kim D, Schperberg AV et al. (2021) Synthetic lethality‐mediated precision oncology via the tumor transcriptome. Cell 184, 2487–2502.e13.
Wang J, Wu M, Huang X, Wang L, Zhang S, Liu H and Zheng J (2022) SynLethDB 2.0: a web‐based knowledge graph database on synthetic lethality for novel anticancer drug discovery. Database (Oxford) 2022, baac030.
Sondka Z, Bamford S, Cole CG, Ward SA, Dunham I and Forbes SA (2018) The COSMIC cancer gene census: describing genetic dysfunction across all human cancers. Nat Rev Cancer 18, 696–705.
Cotto KC, Wagner AH, Feng YY, Kiwala S, Coffman AC, Spies G, Wollam A, Spies NC, Griffith OL and Griffith M (2018) DGIdb 3.0: a redesign and expansion of the drug‐gene interaction database. Nucleic Acids Res 46, D1068–D1073.
Yeang CH, McCormick F and Levine A (2008) Combinatorial patterns of somatic gene mutations in cancer. FASEB J 22, 2605–2622.
Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, Cole CG, Ward S, Dawson E, Ponting L et al. (2017) COSMIC: somatic cancer genetics at high‐resolution. Nucleic Acids Res 45, D777–D783.
Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA and Staudt LM (2016) Toward a shared vision for cancer genomic data. N Engl J Med 375, 1109–1112.
Baylin SB and Herman JG (2000) DNA hypermethylation in tumorigenesis: epigenetics joins genetics. Trends Genet 16, 168–174.
Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA et al. (2013) Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339.
Hong Y (2013) On computing the distribution function for the Poisson binomial distribution. Comp Stats Data Analy 59, 41–51.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta‐Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P et al. (2019) STRING v11: protein‐protein association networks with increased coverage, supporting functional discovery in genome‐wide experimental datasets. Nucleic Acids Res 47, D607–D613.
Boshloo RD (1970) Raised conditional level of significance for the 2x2‐table when testing the equality of two probabilities. Statistica Neerlandica 24, 1–35.
Cohen H, Ben‐Hamo R, Gidoni M, Yitzhaki I, Kozol R, Zilberberg A and Efroni S (2014) Shift in GATA3 functions, and GATA3 mutations, control progression and clinical presentation in breast cancer. Breast Cancer Res 16, 464.
Nami B and Wang Z (2018) Genetics and expression profile of the tubulin gene superfamily in breast cancer subtypes and its relation to taxane resistance. Cancers (Basel) 10,
Xie Z, Zhang Y, Jin C and Fu D (2018) Gemcitabine‐based chemotherapy as a viable option for treatment of advanced breast cancer patients: a meta‐analysis and literature review. Oncotarget 9, 7148–7161.
de Sousa VML and Carvalho L (2018) Heterogeneity in lung cancer. Pathobiology 85, 96–107.
Dempster JM, Pacini C, Pantel S, Behan FM, Green T, Krill‐Burger J, Beaver CM, Younger ST, Zhivich V, Najgebauer H et al. (2019) Agreement between two large pan‐cancer CRISPR‐Cas9 gene dependency data sets. Nat Commun 10, 5817.
Deng X, Das S, Valdez K, Camphausen K and Shankavaram U (2019) SL‐BioDP: multi‐cancer interactive tool for prediction of synthetic lethality and response to cancer treatment. Cancers (Basel) 11, 1682.