Cancer Patient´s Genome Analysis
Identifying the most appropriate therapies from cancer genome data is a major challenge in personalized cancer medicine. Physicians and researchers are faced with long lists of tumor-specific genomic variants where most variants are either clinically “unactionable”, their biological role unknown or they are irrelevant for tumor biology. Our group is interested in the development of novel bioinformatics methods to interpret genomic alterations, evaluate their clinical relevance and drug feasibility and provide a prioritized evidence-based list of tailored anticancer therapies to facilitate clinical decision making. To this aim we have developed PanDrugs, a bioinformatics platform to prioritize anticancer drug treatments according to individual genomic data.
GENCODE
GENCODE is a scientific project in genome research and part of the ENCODE (ENCyclopedia Of DNA Elements) scale-up project. The GENCODE consortium was initially formed as part of the pilot phase of the ENCODE project to identify and map all protein-coding genes within the ENCODE regions (approx. 1% of Human genome). GENCODE now aims to build an “Encyclopedia of genes and genes variants” by identifying all gene features in the human and mouse genome using a combination of computational analysis, manual annotation, and experimental validation, and annotating all evidence-based gene features in the entire human genome at a high accuracy. CNIO is one of the members of the GENCODE consortium and carries out the computational analysis of coding genes and gene models. The APPRIS database and web server, developed by our group, selects a single representative protein isoform for each coding gene based on cross-species conservation and the preservation of protein structural and functional features.
Drug Repositioning
Computational drug repositioning consists in finding new applications to already approved drugs by using computational methods. Our lab is interested in drug-associated gene expression signatures coming from large pharmacogenomics projects, drug structural info and artificial intelligence and machine learning algorithms to infer new potential therapeutical applications, to predict cancer drug response and to infer synergistic combinations of antitumor drugs.
Cancer Vulnerability
Cancer precision medicine has achieved promising results in the clinical practice, although only a small fraction of patients present the particular biomarkers of drug sensitivity to take benefit of this approach. Our group is interested in identifying novel druggable cancer dependencies in order to expand the current catalogue of targeted therapies for cancer treatment. To achieve this, we have developed VulcanSpot which integrates molecular profiling with gene essentiality screenings in cancer cell lines in order to identify gene dependencies.
Immunotherapy
Our lab is interested in to optimize the immune response to fight cancer while avoiding runaway immune responses that would damage normal tissues. To do so, we have developed DREIMT, a tool (and a database) for hypothesis generation and prioritization of drugs capable of modulating immune cell activity from transcriptomics data.
Lymphoid Neoplasm Precision Medicine
This is a multi-center project carried out in collaboration with Fundacion Jimenez Díaz, Fundación para la Investigación Hospital Puerta del Hierro, Centro Biología Molecular Severo Ochoa, Fundación MD Anderson and the Monoclonal Antibodies Unit from CNIO. The goal of the project is to effectively apply precision medicine for the treatment of aggressive lymphoma. To fulfil this task, we are integrating clinical and genomic data along with experimental models to identify new biomarkers for this disease. We are also studying the intrinsic tumoral heterogeneity, the interaction between the intratumoral molecular alterations and the modifications of the tumour stroma using bioinformatic approaches.
