The joint WCM-NYGC Center for Functional and Clinical Interpretation of Tumor Profiles

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The Weill Cornell Medicine-New York Genome Center (WCM-NYGC) Center for Functional and Clinical Interpretation of Tumor Profiles is submitted in response to RFA-CA-20-053. Continuing our involvement in the Genome Data Analysis Network (GDAN) over the past five years and leveraging novel algorithms and methods developed by our group, the Center will perform integrative analyses of coding and non-coding variants to unravel the function of specific classes of mutations and assess their clinical potential. As specified in the RFA, we have chosen to focus on two Core Competencies: (1) DNA Mutations (in coding and non-coding regions, somatic and/or germline) and (2) Copy Number / Purity Analysis ,with a focus on complex structural variants.Our team has developed novel algorithms and pipelines for the analysis of DNA mutations in coding and non-coding regions, characterization of complex structural variants, tumor evolution and linked-read sequencing. We have developed three Specific Aims. In Aim 1, we will perform systematic clinical and functional annotation of coding and non- coding mutations. This includes (1) clinical annotation of coding variants, (2) prioritization and functional annotation of non-coding variants (3) integration of transcriptomic analyses, such as cell type deconvolution of impure tumor samples to provide stromal context to somatic variants (4) correlation of variants with clinical phenotypes, including response to therapy. In Aim 2, we will analyze clinically relevant signatures of genome-wide somatic alteration patterns. We will utilize our state-of-the-art analytic tools for complex structural variant characterization and mutational topography to link (1) mutational processes and (2) cell-of-origin footprints to cancer outcome and drug response. We will also (3) adapt our cutting-edge genome graph visualization tools to build interactive data portals for browsing complex structural variation patterns in impure samples. In Aim 3, we will dentify and characterize variants that drive tumor evolution using multi-samples analysis. We will apply our state- of-the-art computational tools to study structural variant evolution across multiple tumor samples to (1) identify drivers of drug resistance and relapse in matched primary and recurrence/metastasis samples and (2) assess genomic divergence between primary tumors and matched tumor organoids.