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

From National Research Council Canada

DOI	Resolve DOI: https://doi.org/10.1016/j.celrep.2013.08.028
Author	Search for: Zaman, N.¹; Search for: Li, L.; Search for: Jaramillo, M.¹; Search for: Sun, Z.; Search for: Tibiche, C.¹; Search for: Banville, M.¹; Search for: Collins, C.¹; Search for: Trifiro, M.; Search for: Paliouras, M.; Search for: Nantel, A.¹; Search for: O'Connor-McCourt, M.¹; Search for: Wang, E.¹
Affiliation	National Research Council of Canada
Format	Text, Article
Subject	antineoplastic agent; bms 536924; bosutinib; docetaxel; erlotinib; nutlin 3; protein kinase B inhibitor; rapamycin; rdea 119; tamoxifen; tanespimycin; tcs 2312; tozasertib; unclassified drug; 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 cell; mutation; signal transduction; tumor gene
Abstract	Individual 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	2013
In	Cell Reports 5, no. 1 (2013): 216–223.
Language	English
Peer reviewed	Yes
NPARC number	21270576
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Record identifier	6f7d4adc-37d5-4bf8-9e45-130377e20eb8
Record created	2014-02-17
Record modified	2020-04-22

Date modified:: 2024-07-17