Mining the Breast Cancer Proteome for Predictors of Drug Sensitivity
- *Corresponding Author:
- Leslie C Timpe
Department of Mathematics
San Francisco State University
San Francisco, California 94132, USA
E-mail: [email protected]
Received date: August 05, 2015; Accepted date: August 31, 2015; Published date: September 04, 2015
Citation: Timpe LC, Li D, Yen TY, Wong J, Yen R, et al. (2015) Mining the Breast Cancer Proteome for Predictors of Drug Sensitivity. J Proteomics Bioinform 8: 204-211. doi: 10.4172/jpb.1000370
Copyright: © 2015 Timpe LC, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Approximately 20 drugs have been approved by the FDA for breast cancer treatment, yet predictive biomarkers are known for only a few of these. The identification of additional biomarkers would be useful both for drugs currently approved for breast cancer treatment and for new drug development. Using glycoprotein expression data collected via mass spectrometry, in conjunction with statistical models constructed by elastic net or lasso regression, we modeled quantitatively the responses of breast cancer cell lines to ~90 drugs. Lasso and elastic net regression identified HER2 as a predictor protein for lapatinib, afatinib, gefitinib and erlotinib, which target HER2 or the EGF receptor, thus providing an internal control for the approach. Two additional protein datasets and two RNA datasets were also tested as sources of predictor proteins for modeling drug sensitivity. Protein expression measured by mass spectrometry gave models with higher coefficients of determination than did reverse phase protein array (RPPA) predictor data. Further, cross validation of the elastic net models shows that, for many drugs, the prediction error is lower when the predictor data is from proteins, rather than mRNA expression measured on microarrays. Drugs that could be modeled effectively include PI3K inhibitors, Akt inhibitors, paclitaxel and docetaxel, rapamycin, everolimus and temsirolimus, gemcitabine and vinorelbine. Strikingly, this modeling approach with protein predictors often succeeds for drugs that are targeted agents, even when the nominal target is not in the dataset.