Our group focuses on developing, implementing, and integrating new technologies and methods that can help us provide direct answers to pressing questions in cancer research. We utilize extensively whole genome approaches, single cell assays, and spatial transcriptomics and proteomics to understand the mechanisms of tumor immune evasion or the prioritization of novel biomarkers and therapeutics. We employ artificial intelligence/machine learning technologies to transform these findings into optimized methods for patient stratification and management and to inform clinical decision making.
We are also intensely exploring the regulatory dark matter comprising the 98% of our genome. Non-coding mutations or RNAs such as long non-coding RNAs and microRNAs are an untapped resource for novel therapeutic targets and effective biomarkers. We are developing methods to concurrently interrogate coding and non-coding gene expression or mutations, epigenetic information, and immunomic data, in order to inform basic and translational research, as well as clinical decision making.
Our “Whole Genome and Beyond” approach aims to maximize the layers of information that are extracted and exploited from a single patient sample. To this end, we are extensively employing bulk, single cell, and spatial transcriptomic technologies to characterize cellular transcriptional programs and cell-cell communication, as well as how they are derailed in disease. This approach permits us to identify and prioritize novel biomarkers or therapeutic targets, regardless of their coding potential.
With the creation of the Spatial Technologies Unit (www.spatialtechnology.org) and the Precision RNA Medicine Core, Harvard Medical School Initiative for RNA Medicine, we provide access to technologies we develop in the lab to the research community.