TY - JOUR
T1 - An integrative genomic and proteomic approach to chemosensitivity prediction
AU - Ma, Yan
AU - Ding, Zhenyu
AU - Qian, Yong
AU - Wan, Ying Wooi
AU - Tosun, Kursad
AU - Shi, Xianglin
AU - Castranova, Vincent
AU - Harner, E. James
AU - Guo, Nancy L.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Bioinformatics
KW - Chemosensitivity
KW - Genomic profiling
KW - NCI-60 cell lines
KW - Proteomic profiling
UR - http://www.scopus.com/inward/record.url?scp=60749101615&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=60749101615&partnerID=8YFLogxK
U2 - 10.3892/ijo_00000134
DO - 10.3892/ijo_00000134
M3 - Article
C2 - 19082483
AN - SCOPUS:60749101615
SN - 1019-6439
VL - 34
SP - 107
EP - 115
JO - International Journal of Oncology
JF - International Journal of Oncology
IS - 1
ER -