Multiomics Approach to Understanding Olaparib Resistance and Predicting Drug Response

We aimed to uncover genetic factors affecting resistance to the cancer drug olaparib. To do this, we utilized multiomics matrix factorization (MOFA), a multiomics approach, to explore omic-based features that might become biomarker candidates. Our results showed that 17 damaging mutations, 6 gene expression signatures, 17 DNA methylations, and 26 transcription-factor activities can impact the refractory response to olaparib. To verify the potential utility of the identified biomarker candidates, we generated a predictive model to differentiate between olaparib responding and nonresponding cell lines using machine learning techniques, including support vector machine algorithms, random forest algorithms, and Siamese neural networks. The model was centered around six gene-expression biomarker candidates and validated using the Genomics of Drug Sensitivity in Cancer database. Our findings suggest that using a multiomics approach with machine learning methods can lead to a better understanding of the mechanism of drug resistance and identify biomarkers, which will ultimately facilitate the appropriate administration of drugs to patients. The source codes can be found at https://github.com/wjlim/DrugResistance.