Alvaro N. A. Monteiro

Laboratory of Cancer Genetics & Genomics The main theme of the Monteiro lab is the integration of epidemiological, genomic, and proteomics datasets to explore the role of genetic variation on cancer predisposition, development, and treatment. We apply a systems biology approach to three interconnected problems in cancer. First, we aim to understand how rare genetic (somatic or germline) variation impacts on signal transduction networks and its role in inherited cancer syndromes. We focus on the role of BRCA1 in cancer and integrate statistical, structural, and functional data to aid in clinical annotation of hundreds of variants found in the population. Our group pioneered the use of functional assays to analyze mutations in the BRCA1 BRCT domain to assess their pathogenicity. We have also developed interactive visualization tools to annotate functional data on BRCA variants (https://research.nhgri.nih.gov/bic/circos/) and participated in the development of BRCA Exchange (https://brcaexchange.org/). Second, we seek to understand how BRCT domains convey signals during the cellular response to DNA Damage. We combine detailed large scale yeast two-hybrid screening and tandem-affinity purification coupled to mass spectrometry to generate an annotated protein-protein interaction network mediated by all BRCT domains in the human proteome. Using this network we identified AZD1775 as synergistic to platinum in lung cancer cells and currently have an ongoing clinical trial with stage IV lung cancer patients. Finally, we are interested in the mechanism underlying cancer risk conferred by common genetic variants. We have played an active role in the NIH/NCI GAME-ON (Genetic Associations and Mechanisms in Oncology; http://epi.grants.cancer.gov/gameon/) consortium as part of the cross-GAME-ON Functional Working group. We have developed a conceptual framework as well as a bioinformatics and functional assay platform to systematically interrogate cancer predisposition loci revealed by GWAS of unprecedented sample sizes. This analytical framework includes chromosome conformation capture techniques, enhancer scanning techniques, ChIP-Seq, and liquid chromatography coupled to mass spectrometry, and organoids to functionally dissect cancer risk loci for breast and ovarian cancers and glioma. Twitter: @AccidentalGenet Google Scholar: http://scholar.google.com/citations?user=8D5gFOYAAAAJ&hl=en Publons: https://publons.com/author/1303743/alvaro-na-monteiro#profile NCBI My Bibliography: https://www.ncbi.nlm.nih.gov/myncbi/1bIN9gxOqLdkEN/bibliography/public/