Abstract

Somatic mutations are the driving force of cancer genome evolution. The rate of somatic mutations appears to be greatly variable across the genome due to variations in chromatin organization, DNA accessibility and replication timing. In addition, other variables that influence the mutation rate in a local scale are starting to emerge. I will discuss recent findings from our lab on how genome conformation influences mutation rate. These findings have important implications for our understanding of mutational and DNA repair processes, genome evolution and in the identification of cancer driver mutations.

Given the evolutionary principles of cancer, one effective way to identify cancer genes is by tracing the signals left by the positive selection of driver mutations across tumours. Using this concept we analyze thousands of tumor genomes to generate a compendium of cancer genes across tumor types (http://www.intogen.org), and we build machine learning models inspired in evolutionary biology that effectively identify driver mutations in each gene and cancer type (http://www.intogen.org/boostdm). The results (integrated in CancerGenomeInterpreter.org) contribute to the interpretation of tumor mutations in precision cancer medicine.

Somatic mutations may also drive clonal expansions in normal tissues, such as clonal hematopoiesis. We have performed a “reverse calling” to reliably identify blood somatic mutations in 12000 cancer patients. Repurposing methods of cancer genomics we identify genes and mutations driving clonal hematopoiesis (http://www.intogen.org/ch).