title: An integrative genomic and proteomic approach to chemosensitivity prediction.
abstract: New computational approaches are needed to integrate both protein expression and gene expression profiles, extending beyond the correlation analyses of gene and protein expression profiles in the current practices. Here, we developed an algorithm to classify cell line chemosensitivity based on integrated transcriptional and proteomic profiles. We sought to determine whether a combination of gene and protein expression profiles of untreated cells was able to enhance the performance of chemosensitivity prediction. An integrative feature selection scheme was employed to identify chemosensitivity determinants from genome-wide transcriptional profiles and 52 protein expression levels in 60 human cancer cell lines (the NCI-60). A set of 118 anti-cancer drugs whose mechanisms of action were putatively understood was evaluated. Classifiers of the complete range of drug response (sensitive, intermediate, or resistant) were generated for the evaluated anti-cancer drugs, one for each agent. The classifiers were designed to be independent of the cells' tissue origins. The classification accuracy of all the evaluated 118 agents was remarkably better (P<0.001) than that would be achieved by chance. Furthermore, 76 out of the 118 classifiers identified from integrated genomic and protein profiles significantly (P<0.05) improved the accuracy of protein expression-based classifiers identified previously. These results demonstrate that our integrated genomic and proteomic approach enhances the performance of chemosensitivity prediction. This study presents a new analytical framework to identify integrated gene and protein expression signatures for predicting cellular behavior and clinical outcome in general.
authors.affiliation: Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300, USA.
authors.forename: Yan
authors.initials: Y
authors.lastname: Ma
authors.forename: Zhenyu
authors.initials: Z
authors.lastname: Ding
authors.forename: Yong
authors.initials: Y
authors.lastname: Qian
authors.forename: Ying-Wooi
authors.initials: YW
authors.lastname: Wan
authors.forename: Kursad
authors.initials: K
authors.lastname: Tosun
authors.forename: Xianglin
authors.initials: X
authors.lastname: Shi
authors.forename: Vincent
authors.initials: V
authors.lastname: Castranova
authors.forename: E James
authors.initials: EJ
authors.lastname: Harner
authors.forename: Nancy L
authors.initials: NL
authors.lastname: Guo
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology*; D019008:Drug Resistance, Neoplasm / Q000235:genetics*; D004354:Drug Screening Assays, Antitumor; D020869:Gene Expression Profiling; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D040901:Proteomics / Q000379:methods*; D014158:Transcription, Genetic; D014407:Tumor Cells, Cultured / Q000187:drug effects
chemical_list: D000970:Antineoplastic Agents; D009363:Neoplasm Proteins