A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities

Research output: Contribution to journalArticlepeer-review

Authors

  • Franco J Vizeacoumar
  • Frederick S Vizeacoumar
  • Megha Chandrashekhar
  • Alla Buzina
  • Jordan T F Young
  • Julian H M Kwan
  • Azin Sayad
  • Patricia Mero
  • Steffen Lawo
  • Hiromasa Tanaka
  • Kevin R Brown
  • Anastasia Baryshnikova
  • Anthony B Mak
  • Yaroslav Fedyshyn
  • Yadong Wang
  • Glauber C Brito
  • Dahlia Kasimer
  • Taras Makhnevych
  • Troy Ketela
  • Alessandro Datti
  • Mohan Babu
  • Andrew Emili
  • Laurence Pelletier
  • Jeff Wrana
  • Zev Wainberg
  • Philip M Kim
  • Robert Rottapel
  • Catherine A O'Brien
  • Brenda Andrews
  • Charles Boone
  • Jason Moffat

Colleges, School and Institutes

External organisations

  • 1] Donnelly Centre and Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada [2] Saskatchewan Cancer Agency, Department of Biochemistry, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

Abstract

Improved efforts are necessary to define the functional product of cancer mutations currently being revealed through large-scale sequencing efforts. Using genome-scale pooled shRNA screening technology, we mapped negative genetic interactions across a set of isogenic cancer cell lines and confirmed hundreds of these interactions in orthogonal co-culture competition assays to generate a high-confidence genetic interaction network of differentially essential or differential essentiality (DiE) genes. The network uncovered examples of conserved genetic interactions, densely connected functional modules derived from comparative genomics with model systems data, functions for uncharacterized genes in the human genome and targetable vulnerabilities. Finally, we demonstrate a general applicability of DiE gene signatures in determining genetic dependencies of other non-isogenic cancer cell lines. For example, the PTEN(-/-) DiE genes reveal a signature that can preferentially classify PTEN-dependent genotypes across a series of non-isogenic cell lines derived from the breast, pancreas and ovarian cancers. Our reference network suggests that many cancer vulnerabilities remain to be discovered through systematic derivation of a network of differentially essential genes in an isogenic cancer cell model.

Details

Original languageEnglish
Pages (from-to)696
JournalMolecular Systems Biology
Volume9
Publication statusPublished - 8 Oct 2013

Keywords

  • Breast Neoplasms, Cell Line, Tumor, Coculture Techniques, Epistasis, Genetic, Female, Gene Regulatory Networks, Genes, Essential, Genome, Human, Humans, Mutation, Neoplasm Proteins, Ovarian Neoplasms, PTEN Phosphohydrolase, Pancreatic Neoplasms, RNA, Small Interfering, Journal Article, Research Support, Non-U.S. Gov't