The tumor ecosystem plays a critical role in tumor development, progression and therapeutic response. Recent studies have utilized dissociative and single-cell omics technologies to profile the tumor microenvironment, specifically to understand therapeutic resistance and identify predictive biomarkers for precision cancer medicine. Yet very few of these biomarkers have adequate performance characteristics for adoption in clinical practice. My hypothesize is that a fundamental facet of the tumor ecosystem, i.e., the spatial organization of cells, which encodes key information involving paracrine and juxtracrine interactions that drive “neighborhood- level” biology, can further inform predictive models. Quantitative spatial features can provide independent valuable information, which is unlikely to be captured by clinical, genetic and bulk-transcriptional predictors. Hence, I work on integrating highly multiplexed imaging data with omic approaches to delineate mechanisms of resistance and build predictive models of response for patients with T-cell lymphoma, who have a desperate unmet clinical need.
I recieved my PhD in cancer genetics and genomics from the University of Edinburgh, where I used large cancer datasets to identify, characterize and model immune system across different tumour types. I developed ImSig, a network-based computational framework that facilitates the characterization of immune cells within the tumor microenvironment.
- Department of Medical Oncology, Dana-Farber Cancer Institute
- Laboratory of Systems Pharmacology, Harvard Medical School
- The Broad Institute