Computational models for clinical drug response prediction

Abstract

Extensive efforts in cancer research over the past decades have markedly improved diagnosis and treatments, leading to better outcomes for cancer patients. Paradoxically, however, these discoveries have begun to shed light on a level of complexity that rules out the emergence of a universal cancer treatment. As any tumor is now known to be essentially a unique disease, clinicians and researchers are moving towards a new paradigm, termed “precision medicine”, which consists of designing bespoke lines of treatment for each patient.

This paradigm-shift has been fueled by international consortia that have characterized large collections of tumors, thereby providing a vast reference for cancer heterogeneity. Two main strategies have been employed: sequencing of tumor biopsies directly extracted from patients or studying pre-clinical models, i.e., tumor cells cultured in artificial environments. While the first strategy generates clinically faithful data, the second strategy is flexible and cost-effective, and allows for the study of effects of various drugs at different concentrations.

Based on the large amount of data generated from pre-clinical models, computer
scientists have developed various machine learning algorithms to model drug response based on these data. However, these models do not take into account the complexity of human tumors and the differences between model systems and human tumors, and are therefore not directly applicable in a clinical setting. In this thesis, we aim at bridging this gap. Specifically, we develop algorithms to integrate and align data generated from the two aforementioned strategies with a goal to predict drug response in patients from datasets generated using pre-clinical models.