Genomics-based Precision Medicine for Patients with Cancer

Abstract

Cancer is a genetic disease and develops in a process called “genomic evolution”, in which healthy cells accumulate DNA mutations that drive uncontrolled cell growth. This makes DNA analyses not only a natural way to study cancer biology, but also to understand which therapeutic strategies may be successful to block the growth of a patient’s tumor. During his PhD in the groups of Emile Voest and Lodewyk Wessels, Joris van de Haar analyzed large genomics datasets to address fundamental questions about how the many mutations in a tumor’s DNA collaborate to drive cancerous growth, as well as clinical questions about how to use genomics to help doctors choose the best therapies for individual patients. Regarding the latter, Joris and colleagues were the first to show how to use the DNA of a colorectal tumor to predict its resistance to a particular chemotherapy called “trifluridine/tipiracil”, they showed how to effectively combine multiple genomic biomarkers to predict resistance to immunotherapy in lung cancer, and they demonstrated that a single, large-scale genomic analysis of a metastasis is generally sufficient to choose the optimal treatment for a patient with metastatic cancer. Furthermore, Joris and colleagues studied a mystery in cancer immunotherapy, a therapeutic strategy in which a patient’s own CD8 T cells are stimulated to kill tumor cells. Tumors with mutations in a gene called “B2M” are invisible to CD8 T cells, and are hence expected to be resistant to immunotherapies. But surprisingly, this is not always the case. Joris and colleagues unraveled that immunotherapies also activate another class of immune cells, named “γδ T cells”, which kill B2M-mutated tumors, extending our knowledge on the mechanism of action of this type of treatment. Together, Joris and colleagues employed genomic analysis as a tool to learn critical lessons about both the early and the late stages of tumor evolution, the mechanism of action of -and determinants of response and resistance to- anticancer treatments, and the ways in which genomics can be used to design more refined, genomics-guided treatment strategies to improve outcomes of patients with cancer.