Objectives: Loss-of-function (LOF) mutations in succinate dehydrogenase subunit (SDHx) genes result in SDH-deficiency and increase the lifetime risk for developing a number of cancers, including GI stromal tumors, paraganglioma, pheochromocytoma, and renal cell carcinoma. In patients with a known LOF/pathogenic germline SDHx mutation, genetic counseling (to screen other family members) and enhanced cancer screening procedures are indicated. When SDH-deficient tumors are detected at early stages, they are often curable. However, 3.2% of the population carry germline SDHx missense variants of unknown significance (VUS, not known to be pathogenic or benign). Therefore, we currently do not have the ability to identify many patients at risk and screen them appropriately because of our lack of knowledge of the functional consequences of germline SDHx VUS. Plan: This proposal will functionally test SDHA VUS utilizing a deep mutational scanning (DMS) approach in a novel SDHA-deficient cell line that we have generated. We will identify all LOF SDHA variants by pairing DMS with a negative metabolic selection method developed in our laboratory. To determine if this approach extends to test VUS of the other SDHx genes (SDHB, C, D), we will generate knockout models of these individual genes and asses negative metabolic selection in each cell line. Human models, which can provide strong evidence to be used for clinical variant classification will be generated and validated for testing individual variants. Methods: To address the large number of SDHA variants seen in the population, we will create libraries of all possible SDHA amino-acid variants by saturation mutagenesis and use this library to complement our SDHAdeficient cell line. We will select against SDHA LOF variants by growing the libraries under metabolite-depleted conditions requiring fully functional SDH (-pyruvate, -aspartic acid). Following deep sequencing, depletion analysis will be performed. Functional interpretations (functionally normal or LOF) will be made for each variant by comparing calculated effect scores with those of nonsense/ synonymous controls (Aim 1). To fill the current void of SDH-deficient cell lines, we will use CRISPR/Cas9 to generate SDHB-, SDHC-, and SDHD-knockout cell lines and validate them. Negative selection using metabolite-depleted medium will be applied to these novel models to determine their utility for future DMS experiments (Aim 2). Clinical Relevance: Our best chance of a cancer cure lies in early detection in patients with SDH-deficient tumors, thus the critical medical need lies in classifying SDHx genetic variants to enable identification and subsequent screening of at-risk patients.