Signaling network assessment of mutations and copy number variations predict breast cancer subtype-specific drug targets

Download
  1. Get@NRC: Signaling network assessment of mutations and copy number variations predict breast cancer subtype-specific drug targets (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1016/j.celrep.2013.08.028
AuthorSearch for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for:
TypeArticle
Journal titleCell Reports
ISSN2211-1247
Volume5
Issue1
Pages216223; # of pages: 8
Subjectantineoplastic agent; bms 536924; bosutinib; docetaxel; erlotinib; nutlin 3; protein kinase B inhibitor; rapamycin; rdea 119; tamoxifen; tanespimycin; tcs 2312; tozasertib; unclassified drug; article; breast cancer; cancer cell; cancer classification; cell proliferation; cell survival; copy number variation; drug sensitivity; drug targeting; exome; gene identification; gene mutation; gene sequence; genetic screening; human; human cell; mutation; priority journal; signal transduction; tumor gene
AbstractIndividual cancer cells carry a bewildering number ofdistinct genomic alterations (e.g., copy number variations and mutations), making it a challenge touncover genomic-driven mechanisms governing tumorigenesis. Here, we performed exome sequencing on several breast cancer cell lines that represent two subtypes, luminal and basal. We integrated these sequencing data and functional RNAi screening data (for the identification of genes that are essential for cell proliferation and survival) onto a human signaling network. Two subtype-specific networks that potentially represent core-signaling mechanisms underlying tumorigenesis were identified. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening, whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes on the basis of genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated. © 2013 The Authors.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC)
Peer reviewedYes
NPARC number21270576
Export citationExport as RIS
Report a correctionReport a correction
Record identifier6f7d4adc-37d5-4bf8-9e45-130377e20eb8
Record created2014-02-17
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)