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

ABSTRACT Metabolic alterations are a fundamental characteristic of tumorigenesis, suggesting that better understanding of cancer specific metabolic dependencies may yield novel targets for cancer therapy. Better understanding of how cancer cells adapt to metabolic challenges would provide opportunities to design novel therapeutic interventions to prevent these changes or target the novel liabilities they generate to improve outcomes for cancer patients. During research conducted as a part of our U54 Pilot Project, we established a novel experimental system to study metabolic alterations during tumorigenesis by identifying metabolic changes required to support cell proliferation in cancer cells with impairments in the mitochondrial enzyme succinate dehydrogenase (SDH, also known as complex II of the electron transport chain (ETC)). SDH is a tumor suppressor in several cancer types and loss of function mutations in SDH cause a complete block of oxidative tricarboxylic acid (TCA) cycle activity at the SDH step. Investigating the metabolic consequences of SDH loss, we found that acute SDH loss caused an aspartate-dependent defect in cancer cell proliferation but, over the course of months, chronically SDH deficient cells progressively adapted to mitigate the proliferation defects of SDH loss. Tandem-mass-tag proteomics of mitochondria from cells along the adaptation process revealed that adaptation was associated with a progressive decrease in protein expression of ETC complex I (CI) subunits. This effect was found to be causal for adaptation since acute treatment with CI inhibitors rescued the proliferation of early passage SDH deficient cells and restoration of CI activity was deleterious to cells with long term adaptation to SDH loss. These results identify a novel metabolic adaptation for cancer cell proliferation upon the loss of the tumor suppressor SDH and provide a defined system to better understand how cells enact adaptive metabolic expression changes. Here, we propose to use metabolic alterations and polyomic profiling combined with bioinformatic analyses and machine learning approaches to understand the biological mechanisms used by cancer cells to enact adaptive metabolic reprogramming in this cancer model and extend our findings into human tumor datasets. We hypothesize that these approaches will identify mechanisms by which cancer cells adapt to metabolic bottlenecks and allow for the identification of functionally important metabolic changes that support cancer progression.