Multi-omics Modeling for Predictive Cancer Immunotherapy
In this seminar, Dr. Elana J. Fertig will demonstrate how CoGAPS, an established method for identifying transcriptional signatures related to cell type and state, can be applied to single cell data to identify patterns underlying immunotherapy response and resistance.
Therapeutic response in cancer is critically dependent on the states of cells (both cancer and immune cells) in the tumor microenvironment, which evolve over time. New single-cell and spatial molecular technologies enable unprecedented characterization of these states across molecular and cellular scales, but they are challenging to interpret because of the multi-dimensional nature of these data. New computational methodologies offer an effective means for interpreting these complex data.
Combining CoGAPS with transfer learning approaches provides insight into the patterns found in preclinical models, which then can be applied to patient samples to find shared features across data sets from different species. This facilitates the discovery of unknown or unanticipated findings, such as the activation of natural killer cells in response to anti-CTLA4 (i.e., an immune checkpoint inhibitor). Spatial distribution of cells in the tumor microenvironment also mediate response and resistance to therapies. Emerging spatial molecular technologies offer a powerful tool for modeling these interactions.
Dr. Fertig is an associate professor of oncology and director of the division/research program in quantitative sciences, co-director of the Convergence Institute, and associate director of quantitative sciences at the Sidney Kimmel Comprehensive Cancer Center at the Johns Hopkins University. She leads an NCI-funded hybrid computational and experimental lab focused on exploring the systems biology of cancer and therapeutic response. That work includes looking at therapeutic resistance over time using single cell technology. Her lab also uses multiplatform genomics data, blended with mathematical modeling and artificial intelligence, to find biomarkers and molecular mechanisms associated with therapeutic resistance. These techniques have broad applications beyond therapeutic resistance, including the analysis of clinical biospecimens, and the study of developmental biology and neuroscience.
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