TY - JOUR
T1 - Systematic analysis of genotype-specific drug responses in cancer
AU - Kim, Nayoung
AU - He, Ningning
AU - Kim, Changsik
AU - Zhang, Fan
AU - Lu, Yiling
AU - Yu, Qinghua
AU - Stemke-Hale, Katherine
AU - Greshock, Joel
AU - Wooster, Richard
AU - Yoon, Sukjoon
AU - Mills, Gordon B.
PY - 2012/11/15
Y1 - 2012/11/15
N2 - A systematic understanding of genotype-specific sensitivity or resistance to anticancer agents is required to provide improved patient therapy. The availability of an expansive panel of annotated cancer cell lines enables comparative surveys of associations between genotypes and compounds of various target classes. Thus, one can better predict the optimal treatment for a specific tumor. Here, we present a statistical framework, cell line enrichment analysis (CLEA), to associate the response of anticancer agents with major cancer genotypes. Multilevel omics data, including transcriptome, proteome and phosphatome data, were integrated with drug data based on the genotypic classification of cancer cell lines. The results reproduced known patterns of compound sensitivity associated with particular genotypes. In addition, this approach reveals multiple unexpected associations between compounds and mutational genotypes. The mutational genotypes led to unique protein activation and gene expression signatures, which provided a mechanistic understanding of their functional effects. Furthermore, CLEA maps revealed interconnections between TP53 mutations and other mutations in the context of drug responses. The TP53 mutational status appears to play a dominant role in determining clustering patterns of gene and protein expression profiles for major cancer genotypes. This study provides a framework for the integrative analysis of mutations, drug responses and omics data in cancers.
AB - A systematic understanding of genotype-specific sensitivity or resistance to anticancer agents is required to provide improved patient therapy. The availability of an expansive panel of annotated cancer cell lines enables comparative surveys of associations between genotypes and compounds of various target classes. Thus, one can better predict the optimal treatment for a specific tumor. Here, we present a statistical framework, cell line enrichment analysis (CLEA), to associate the response of anticancer agents with major cancer genotypes. Multilevel omics data, including transcriptome, proteome and phosphatome data, were integrated with drug data based on the genotypic classification of cancer cell lines. The results reproduced known patterns of compound sensitivity associated with particular genotypes. In addition, this approach reveals multiple unexpected associations between compounds and mutational genotypes. The mutational genotypes led to unique protein activation and gene expression signatures, which provided a mechanistic understanding of their functional effects. Furthermore, CLEA maps revealed interconnections between TP53 mutations and other mutations in the context of drug responses. The TP53 mutational status appears to play a dominant role in determining clustering patterns of gene and protein expression profiles for major cancer genotypes. This study provides a framework for the integrative analysis of mutations, drug responses and omics data in cancers.
KW - cancer cell line modeling
KW - cancer genotype
KW - drug sensitivity and resistance
KW - network analysis
KW - reverse phase protein assay
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U2 - 10.1002/ijc.27529
DO - 10.1002/ijc.27529
M3 - Article
C2 - 22422301
AN - SCOPUS:84867070656
SN - 0020-7136
VL - 131
SP - 2456
EP - 2464
JO - International Journal of Cancer
JF - International Journal of Cancer
IS - 10
ER -