Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Mining the Breast Cancer Proteome for Predictors of Drug Sensitivity

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. 

 

For more info : 

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
  • Share this page
  • Facebook
  • Twitter
  • LinkedIn
  • Google+
  • Pinterest
  • Blogger
Top