Tumors contain thousands of somatic mutations in their genomes. Most of these mutations are not involved in the disease, but few of them, which we call cancer driver mutations, are directly involved in the development of the tumor. Identifying these cancer driver mutations is key to understanding cancer biology and to progress towards personalized cancer medicine.
Most of the work on cancer research has been focused on the study of the coding genome, which comprises less than 2% of the genome sequence. This has allowed us to identify several hundreds of genes involved in tumorigenesis through mutations that affect their coding sequence. On the other hand, only a handful of non-coding driver elements have been identified to date. Our project aims to study what is the role of somatic mutations in the non-coding genome in cancer development.
To identify cancer driver genes and non-coding elements we apply methodologies to measure positive selection in the pattern of mutations. In order for this to be successful, it is key to be able to understand how mutations occur in our cell in order to estimate neutral mutagenesis. On the basis of a correct estimation of neutral mutagenesis, positive selection can be measured as a statistically significant deviation of the pattern of observed mutations across tumors to that estimated under neutral mutagenesis.
The estimation of the neutral mutagenesis is, however, not a trivial problem. Mutations occur randomly in the genomes of our cells, with every genome site having a different probability to mutate in each tissue and individual. This depends on the mutational processes that are active in those cells (e.g. UV light, tobacco mutagenesis..) the DNA repair machinery, and the interaction between mutagenic processes, the DNA sequence, features of the structure of the chromatin.
During this project we have gained a better understanding of the mutational processes that take place in tumors and normal cells. We have improved our methods to identify positive selection and we have systematically applied them to ever growing datasets of cancer genomes to generate a catalog of cancer driver genes and mutations along the genome.
The project also aimed to identify possible therapeutic interventions informed by the driver mutations detected. In this direction we have developed Cancer Genome Interpreter (CGI), which includes a database and algorithm to identify therapeutic opportunities for patients based on their tumor mutations. (
https://www.cancergenomeinterpreter.org(opens in new window))
