Prediction of Chemosensitivity in Multiple Primary Cancer Patients Using Machine Learning

Many cancer patients face multiple primary cancers. It is challenging to find an anticancer therapy that covers both cancer types in such patients. In personalized medicine, drug response is predicted using genomic information, which makes it possible to choose the most effective therapy for these cancer patients. The aim of this study was to identify chemosensitive gene sets and compare the predictive accuracy of response of cancer cell lines to drug treatment, based on both the genomic features of cell lines and cancer types.

In this study, we identified a gene set that is sensitive to a specific therapeutic drug, and compared the performance of several predictive models using the identified genes and cancer types through machine learning (ML). To this end, publicly available gene expression datasets and drug sensitivity datasets of gastric and pancreatic cancers were used. Five ML algorithms, including linear discriminant analysis, classification and regression tree, k-nearest neighbors, support vector machine and random forest, were implemented.

The predictive accuracy of the cancer type models were 0.729 to 0.763 on the training dataset and 0.731 to 0.765 on the testing dataset. The predictive accuracy of the genomic prediction models was 0.818 to 1.0 on the training dataset and 0.759 to 0.896 on the testing dataset.

Performance of the specific gene models was much better than those of the cancer type models using the ML methods. Therofore, the most effective therapeutic drug can be chosen based on the expression of specific genes in patients with multiple primary cancers, regardless of cancer types.