Support vector machines predict advanced cancer patient response to therapies from bulk RNA sequencing data

Personalized medicine approaches for cancer therapy seek to determine optimal therapies for cancer patients based on the molecular profile of their tumour. The motivation is to target oncogenomic alterations in tumours with the appropriate therapies. However, it is currently infeasible to determine the optimal therapy simply given the genomic profile of a tumour. There has been significant recent work in attempting to use the computational approach of machine learning for predicting tumour drug response. Machine learning methods have been successfully used for drug response prediction in cancer cell lines and even have been extended to predicting individual cancer patient response to a small number of chemotherapies. This work uses support vector machines (SVM) to predict the response to chemotherapies of 570 advanced cancer patients from the BC Cancer Personalized OncoGenomics program using the transcriptomic profile of their tumours. This dataset of advanced cancers presents over 20 cancer types and 130 unique chemotherapies. F-measures for the SVM predictions were found to be as high as 1.0 for some cohorts. Further analysis on the set of important genes for the SVMs revealed biological explanations that may explain the SVM predictions. This work demonstrates the value of large-scale sequencing projects and the potential of data mining and machine learning in personalized cancer medicine.Science, Faculty ofGraduat