Chemosensitivity Prediction of Tumours Based on Expression, miRNA, and Proteomics Data

The chemosensitivity of tumours to specific drugs can be predicted based on molecular quantities, such as gene expressions, miRNA expressions, and protein concentrations. This finding is important for improving drug efficacy and personalizing drug use. In this paper, we present an analysis strategy that, compared to prior work, retains more information in the data for analysis and may lead to improved chemosensitivity prediction. We apply improved methods for estimating the GI50 value of a drug (an indicator of the response to the drug), regression methods for constructing predictive models of the GI50 value, advanced variable selection techniques, such as MMPC, and a multi-task variable selection technique for identifying a smallsize signature that is simultaneously predictive for several drugs and cell lines. The methods are applied on gene expression, miRNA expression, and proteomics data from 53 tumour cell lines after treatment with 120 drugs, obtained from the National Cancer Institute databases. A biological interpretation and discussion of the results is presented for the most clinically important subset of 14 drugs.