title: Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs.
abstract: BACKGROUND
  Identification of synthetic lethal interactions in cancer cells could offer promising new therapeutic targets. Large-scale functional genomic screening presents an opportunity to test large numbers of cancer synthetic lethal hypotheses. Methods enriching for candidate synthetic lethal targets in molecularly defined cancer cell lines can steer effective design of screening efforts. Loss of one partner of a synthetic lethal gene pair creates a dependency on the other, thus synthetic lethal gene pairs should never show simultaneous loss-of-function. We have developed a computational approach to mine large multi-omic cancer data sets and identify gene pairs with mutually exclusive loss-of-function. Since loss-of-function may not always be genetic, we look for deleterious mutations, gene deletion and/or loss of mRNA expression by bimodality defined with a novel algorithm BiSEp.
  
  
  RESULTS
  Applying this toolkit to both tumour cell line and patient data, we achieve statistically significant enrichment for experimentally validated tumour suppressor genes and synthetic lethal gene pairings. Notably non-reliance on genetic loss reveals a number of known synthetic lethal relationships otherwise missed, resulting in marked improvement over genetic-only predictions. We go on to establish biological rationale surrounding a number of novel candidate synthetic lethal gene pairs with demonstrated dependencies in published cancer cell line shRNA screens.
  
  
  CONCLUSIONS
  This work introduces a multi-omic approach to define gene loss-of-function, and enrich for candidate synthetic lethal gene pairs in cell lines testable through functional screens. In doing so, we offer an additional resource to generate new cancer drug target and combination hypotheses. Algorithms discussed are freely available in the BiSEp CRAN package at http://cran.r-project.org/web/packages/BiSEp/index.html .
authors.affiliation: Oncology Innovative Medicines, AstraZeneca, Macclesfield, UK. mark.wappett@almacgroup.com.
authors.forename: Mark
authors.initials: M
authors.lastname: Wappett
authors.affiliation: Oncology Innovative Medicines, AstraZeneca, Waltham, USA. austin.dulak@astrazeneca.com.
authors.forename: Austin
authors.initials: A
authors.lastname: Dulak
authors.affiliation: School of Biosciences, University of Exeter, Exeter, UK. Z.R.Yang@exeter.ac.uk.
authors.forename: Zheng Rong
authors.initials: ZR
authors.lastname: Yang
authors.affiliation: School of Biosciences, University of Exeter, Exeter, UK. asa220@exeter.ac.uk.
authors.forename: Abdullatif
authors.initials: A
authors.lastname: Al-Watban
authors.affiliation: Oncology Innovative Medicines, AstraZeneca, Macclesfield, UK. j.r.bradford@sheffield.ac.uk.|Present address: Department of Oncology, University of Sheffield, Sheffield, UK. j.r.bradford@sheffield.ac.uk.
authors.forename: James R
authors.initials: JR
authors.lastname: Bradford
authors.affiliation: Oncology Innovative Medicines, AstraZeneca, Waltham, USA. Jonathan.Dry@astrazeneca.com.
authors.forename: Jonathan R
authors.initials: JR
authors.lastname: Dry
mesh_terms: D019295:Computational Biology / Q000379:methods; D005804:Genes, Lethal; D005813:Genes, Synthetic; D023281:Genomics; D006801:Humans; D009154:Mutation; D009369:Neoplasms / Q000235:genetics* / Q000628:therapy; D040901:Proteomics