Drug combination prioritization for cancer treatment using single-cell RNA-seq based transfer learning

Precision oncology seeks to match patients to the optimal pharmacological regimen; yet, due to tumor heterogeneity, this is challenging. Numerous studies have been conducted to produce clinically relevant pharmacological response forecasts by integrating modern machine learning algorithms and several data types. Insufficient patient numbers and lack of knowledge of the molecular targets for each drug under study limit their use. As a proof of concept, we use single-cell RNA-seq based transfer learning to contextualize patients tumor cells in terms of their more similar cell lines with known susceptibility to drug combinations. Our objective is to maximize the translational potential of in-vitro assays for identifying synergistic drug combinations and prioritizing them for clinical use. Consistent findings in a cohort of breast cancer patients corroborated our understanding of the diseases molecular subtypes. To aid in creating personalized treatments and data-driven clinical trials, we identified the most prevalent cell lines and prioritized synergistic combinations based on tumor compositions at various resolution levels.