We employ statistical and machine-learning models to dissect the complexity of cancer genomes and to advance personalized oncology.
Mutational signatures are genomic footprints of biological processes inferred through pattern recognition algorithms. We design comprehensive statistical models to improve the accuracy and interpretability of signature analysis techniques, aiming to facilitate its integration into clinics.
We leverage signature analysis and big data of cancer genomes to catalog genomic instability, using which we develop patient stratification strategies. We design specific tools work for tissue, and also, liquid biopsies. Our goal is to find effective ways to identify more likely responders to targeted and immunotherapies.
We build tools to infer trajectories of the genomic evolution of cancer cells focusing on the very early-stage and late-stage settings, which still need to be mapped out. To do so, we analyze rich resources of datasets at MGH from the Rapid Autopsy Program and Cancer Early Detection Clinic).
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