Predicting Efficiency of Target Therapy by Means of Cell Lines Data

The clinical efficiency of anti-cancer drugs can be predicted for individual patients. One way to do it is to use intracellular signaling pathways activation. These features can be expressed in terms of transcriptomic data of patients. The majority of machine learning methods suffer from the deficiency of preceding cases, i.e. the gene expression data on patients combined with the confirmed outcome of known treatment methods. At the same time, there exist thousands of various cell lines that were treated with hundreds of anti-cancer drugs in order to check the ability of these drugs to stop the cell proliferation, and all these cell line cultures were profiled in terms of their gene expression. Here we present a new approach in machine learning, which can predict the response to treatment for individual patients. We use some ideas from the monitonic classification algorithms as well as transfer learning techniques in our method. The method was validated on three datasets for cancer-like diseases (lung adenocarcinoma, renal carcinoma and breast cancer) treated with targeted drugs–kinase inhibitors, such as sorafenib and paclitaxel.