Samir Hanash, The University of Texas MD Anderson Cancer Center | PMWC 2027 Silicon Valley, Track 3, Day 1, Session 2: The End of One Size Fits All
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Hanash argues that reducing cancer deaths starts with determining who is at risk and for which cancer. He points to blood-based risk assessment across nine common cancers as a way to personalize screening and potentially enable prevention.
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For broad clinical adoption, Hanash calls for test performance to be calibrated for each cancer type. Mortality reduction remains the ultimate goal, with cost effectiveness and potential health care savings also part of the evaluation.
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Hanash sees proteomics as essential for characterizing proteins and their locations. As vast proteomic datasets are combined with other omics and individual characteristics, AI becomes critical to translating that complexity into useful discoveries.