| Imaging, Diagnosis, Prognosis Predicting Cancer Drug Response by Proteomic Profiling (2008) | |||||||||||||
Abstract | |||||||||||||
| Abstract Purpose: Accurate prediction of an individual patient’s drug response is an important prerequisite of personalized medicine. Recent pharmacogenomics research in chemosensitivity prediction has studied the gene-drug correlation based on transcriptional profiling. However, proteomic profiling will more directly solve the current functional and pharmacologic problems.We sought to determine whether proteomic signatures of untreated cells were sufficient for the prediction of drug response. Experimental Design: In this study, a machine learning model system was developed to classify cell line chemosensitivity exclusively based on proteomic profiling. Using reverse-phase protein lysate microarrays, protein expression levels were measured by 52 antibodies in a panel of 60 human cancer cell (NCI-60) lines. The model system combined several well-known algorithms, including randomforests, Relief, and the nearest neighbor methods, to construct the protein expression ^ based chemosensitivity classifiers.The classifiers were designed to be independent of the tissue origin of the cells. Results: A total of 118 classifiers of the complete range of drug responses (sensitive, intermediate, and resistant) were generated for the evaluated anticancer drugs, one for each agent. The | |||||||||||||
Publication details | |||||||||||||
| |||||||||||||