Cancer remains a leading cause of morbidity and mortality around the world. Despite significant advances in our understanding of the pathology of the disease, and the substantial public and private investment into treatment development, late-stage patients often exhaust therapeutic options. Indeed, in the US alone, there were >1.7 million new cancer diagnoses and >600,000 cancer-associated deaths in 2019. As biology in general and cancer research in particular become ever richer in data, we explore the role of machine learning (ML) in changing the cancer drug development landscape. In the first part of this analysis, we focussed on ML for target identification and drug design. We discussed the growing need for ML-based analysis as we enter an age of clinical -omic data and provided a primer to ML-based techniques for the non-statistician/mathematician. In this chapter, we will explore the problem of tumour heterogeneity together with the role of ML in the discovery and development of cancer biomarkers and for clinical trial design. We end with a brief consideration of the economics of personalised cancer treatment.
Part of the book: Artificial Intelligence in Oncology Drug Discovery and Development