Cancer is one of the most complex human diseases. This has resulted in the production of large-scale ‘omics’ datasets from cancer patients in order to understand this complex disease. Classically, researchers have analysed genetic data to predict patient phenotypes. However, to what extent is this feasible? Despite rapid advances in the multi-omics methods, it is still not clear why some individuals are healthy despite carrying mutations associated with severe phenotypes, or why some patients respond positively to treatment while others display side effects. In our laboratory, we are interested in understanding how and why each mutation in a given gene might have different effects. In order to address this question, we develop systematic approaches based on big data analyses of genomics, transcriptomics and epigenomics data to study how mutation effects differ between cancer types and human populations, as well as other parameters such as age or gender.
- (2019). Integrated Analysis of Germline and Tumor DNA Identifies New Candidate Genes Involved in Familial Colorectal Cancer.. Cancers 11, E362. Publicación en otras instituciones.
- (2018). Systematic discovery of germline cancer predisposition genes through the identification of somatic second hits.. Nat Commun 9, 2601. Publicación en otras instituciones.
- (2015). Cancer type-dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types.. Mol Syst Biol 11, 824. Publicación en otras instituciones.