Hosted by Cardiff University's School of Medicine
Cancer cells navigate various evolutionary challenges from the immune system which, if countered, can lead to transformative therapies. I will discuss how cancers are seen as “nonself” by both the innate and adaptive immune system. We will introduce mathematical frameworks for quantifying how tumors and the immune system co-evolve. Finally, we will discuss novel therapeutic approaches that emerge from this topic, particularly cancer vaccines and inhibitors of retrotransposition.
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Dr. Greenbaum is a Program Leader in Computational Immuno-Oncology at Memorial Sloan Kettering Cancer Center. After a PhD in theoretical physics from Columbia University, he focused on the effect of the innate immune system on viral evolution: his models predicted how the 1918 influenza virus evolved after the pandemic and continued to be cited during Covid-19. His next step took him from virally transcribed RNA to viral mimics: RNA produced from genomic repeat elements in cancer cells. He applied methods from statistical physics to quantify how the immune system recognizes these aberrantly transcribed self-RNAs. Dr. Greenbaum developed the first models to quantify this ‘viral mimicry’, molecular patterns reminiscent of pathogens that can activate the innate immune system. Within the last few years Dr. Greenbaum’s group and his collaborators also created models to predict the role of neoantigens in tumor evolution, and they established the concept of ‘neoantigen quality’ to assess the likelihood that a neoantigen will induce an immune response. His team discovered an immune tradeoff in cancer evolution: oncogenic mutations with lower fitness present poorly to the immune system, while mutations with high fitness generate potent neoantigens. Dr. Greenbaum’s work has contributed to several clinical trials, including a phase II trial to test an inhibitor of repeat elements in colorectal cancer and a phase I trial for an mRNA vaccine against neoantigens in pancreatic cancer.