Epithelial ovarian cancer is the leading cause of death associated with gynecologic cancer in adult women in developed countries, with overall five-year survival rates  of 45% that have remained practically unchanged in recent decades. It is therefore a priority to define prognostic and predictive biomarkers that contribute to raising this figure.  

   Although some risk genes associated with ovarian cancer are currently known (BRCA1, BRCA2, RAD51C, RAD51D or BRIP1), more than 50% of the susceptibility to ovarian cancer remains unexplained. Determining the susceptibility gene within a family is key, not only for prevention and early detection, but can also have implications for treatment, as is the case with BRCA1 or BRCA2 patients, who are particularly sensitive to PARP inhibitors. 

 Taking into account these challenges, our main lines of research are:

• The definition of new prognostic and predictive factors based on the genome and immunogenicity of tumours.

   In our group the genetic information of tumors plays a central role in the search for new biomarkers. Beyond individual changes in DNA sequence, we seek to identify global patterns of alterations in the tumor genome with potential clinical impact (e.g. patterns related to mutational signatures, profiles of genetic material gain and loss or the degree of instability of microsatellite regions). With these markers we aim to optimize the stratification of patients according to the aggressiveness of their disease and to guide individualized treatments by anticipating therapy response. Within this line we are interested in exploring the interaction between genomic biomarkers and tumour immunogenicity. We seek to understand how the combination of these two aspects can inform tumour evolution and offer innovative perspectives for the design of more effective therapeutic strategies.

• Identification of genes and genetic variants associated with susceptibility to ovarian cancer.

   In this area of work we use different strategies that include the study of highly selected risk families and case-control studies in the context of collaborations with international consortia that allow us to evaluate thousands of patients. This line includes in-depth characterization of candidate variants to elucidate their pathogenicity including in silico tools and in vitro functional studies.

• Elucidation of the clinical relevance of defects in Mismatch Repair (MMR) in ovarian cancer.

   The correct identification of tumors with alterations in MMR has become particularly relevant in recent years since it has been described that they respond favorably to immunotherapy treatments. We are therefore analyzing the molecular and clinical characteristics of ovarian tumors with alterations in this pathway and generating, through the use of machine learning tools, a predictor of defects in the MMR based on characteristics of the tumor genome. With this predictor we seek to overcome the existing limitations of standard diagnostic techniques and to favor a correct stratification of patients to adequately guide clinical decisions.

   Our group uses a multidisciplinary approach that integrates omics, molecular and cellular biology approaches, bioinformatics analysis and close collaborations with clinical and computational experts.