Clinics are increasingly sequencing cancer genomes and often find mutations whose functions are not clear. Commonly, medical practitioners see a mutation they suspect might engender sensitivity to a treatment but is sufficiently rare in the population that a clinical trial will never be performed to answer this question. So how should they proceed?
Benjamin Glicksberg explains how coupling cancer genomic data with treatment data through the blockchain will empower patients and citizen scientists to rapidly advance cancer research. Sharing these mutations—and the responses to treatment seen “in the wild”—will allow medical practitioners to classify those mutations sensitive to a given treatment away from those mutations that are resistant. The power of this approach is that it is populated by the patients themselves, by uploading the data to a blockchain that protects identity but reveals clinically relevant observations such as mutations, clinical imaging, and drugs prescribed for cancer.
Benjamin Glicksberg is a postdoctoral scholar in the lab of Atul Butte in the Bakar Computational Health Sciences Institute at the University of California, San Francisco. His work involves utilizing state-of-the-art computational methods, including artificial intelligence algorithms, on bio- and clinical informatics frameworks to make discoveries to push forward precision medicine. His work often ties together multiomic data types ranging from genomics to clinical data in the form of electronic health records (EHR). He’s also built software, tools, and applications for interacting with and visualizing EHR data across patients in the UC Health system, with a particular emphasis on interoperable common data model formats. He holds a PhD from the Icahn School of Medicine at Mount Sinai.
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