A Pan-Cancer, Pan-Treatment (PCPT) Model For Predicting Drug Responses From Patient-Derived Xenografts With Application To Cancer Types With Sparse Training Data

Predicting personalized treatment-specific responses in cancer patients requires not only robust experimental models such as PDX, which generate accurate training data on different treatment responses for the same model, but also computational models that can translate this knowledge into decisions for real patients in a timely manner. The translatability of PDX data into patient-specific outcomes is limited by the challenges in the data size requirements for training machine learning models. Previously, ML models have been developed for the two most abundant cancer types, viz. BRCA and CRC, but the same could not be scaled up to other cancer types with a smaller number of PDX samples. Here, we provide an ML framework to train a single pan-cancer, pan-treatment model for predicting treatment outcomes. We show that such models give promising results for all cancer types considered and reproduce the accuracy levels of individually trained cancer types. In the proposed model, all PDX genomic profiles from all cancer types are used as the training data, and instead of partitioning them into cancer types for each model, the cancer type and treatment name are appended as the input features of the training model. Using genomic-only and treatment-only embeddings and combining them with PCA-based dimensionality reduction, our models show promising results and provide a framework for further improvements and real-time use for best treatment selections for cancer patients.