Abstract 4296: Investigating metabolic sensitivity and organization of renal cell carcinoma: To enhance effective therapies and patient survival

Abstract Background: Renal cell carcinoma (RCC) is the 6th most common cancer in men and the 9th most common in women in the United States. RCC tissues are metabolically dysfunctional, often resulting from loss of von-Hippel Lindau (VHL) and consequent upregulation of hypoxia-inducible factor (HIF1/2). HIF1a is a transcription factor that upregulates glycolytic gene expression. Current therapies, including immune checkpoint inhibitors and tyrosine kinase inhibitors, have shown improvements in the outcomes of RCC patients, however, there are no biomarkers clinically available to predict the efficacy of these drugs. While genomic data and transcriptomic signatures have been investigated, they are not used in clinical practice due to lack of prospective validation. Understanding additional pathways pertaining to metabolic features of RCC, is critical. Our bulk gene expression analysis of primary RCC samples from TCGA-KIRC show a predominance of genes expressing mitochondrial ribosomal proteins and electron transport chain in tumors that relapsed early after nephrectomy, warranting further validation. Aim: We aim to investigate changes in metabolic adaptation in RCC cells from kidney cancer cell lines (VHL mutant) and primary cells from human-matched primary and metastatic kidney cancer sites. Using a novel biosensor-based technique developed in the Albeck lab, we aim to identify the dependence of live RCC cells on specific metabolic pathways and test the effect of standard-of-care (SOC) pathway inhibitors such as tyrosine kinase, HIF pathway, metabolic pathway inhibitors impacting tumorigenicity. Methods: To examine functional metabolic behavior in single cells, we generated immortalized cell lines (MCF10A, 184A1, FL83B and HBE-1) stably transfected with biosensors for the activity of AKT, glycolysis, mTOR and AMPK pathway. Data from Live-cells treated with metabolic inhibitors were collected and analyzed using our MATLAB-based pipeline. We have extended the analysis to RCC cell lines (786-O cell lines) and primary cells collected from nephrectomy specimens and matched metastatic biopsy samples, using current SOC pathway inhibitors (including sorafenib, sunitinib, belzutifan) and investigational metabolic drugs (metformin, IACS-010759, niclosamide). Results Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4296.