Project 2: Systemic Understanding of Cellular Mechanisms of Metabolic Adaptations in Cancer

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ABSTRACT Cancers are characterized by alterations to cellular metabolism that promote tumorigenesis, however our understanding of how these metabolic changes are enacted and how they mechanistically support cancer cell function remains lacking. While some metabolic changes reproducibly occur as a direct result of oncogenic mutations, other metabolic alterations occur over time through adaptive metabolic protein expression changes that allow cells to surmount metabolic deficiencies during cancer progression. Notably, this latter group is predicted to be enriched for functionally important metabolic changes, suggesting that their identification would have particularly relevance for cancer research. However, detailed understanding of which metabolic alterations are the result of adaptations is hampered by the lack of models for directly studying reproducible metabolic adaptations in cancer. Additionally, data on differences in cancer-related adaptations based on race or ethnicity that contribute to health disparities are lacking. Here, we propose to address this knowledge gap by investigating the mechanisms of metabolic adaptation system that we have recently established, where loss of function mutations in the tumor suppressor succinate dehydrogenase (SDH) selects for cells with concordant expression of components of mitochondrial electron transport chain complex I, which ultimately supports cell metabolism and cell proliferation. Using this system, we will investigate the metabolic drivers of complex I adaptations in SDH deficient cells (Aim 1) and use polyomics measurements of gene expression processes, such a epigenetics, transcription, and proteostasis, to identify the cellular processes that enact adaptive changes to complex I expression (Aim 2). We will also take a systematic approach using state-of-the- art statistical methods to leverage publicly available patient tumor datasets containing polyomic measurements and patient metadata in order to identify adaptive changes to metabolic protein expression during cancer progression (Aim 3). This multidisciplinary Full Project also addresses the Partnership’s Guiding Principles (see Overall section) for inclusive mentoring of diverse research teams and for research dissemination to underserved communities, helping to educate and provide opportunities for next generation of researchers.