Abstract A004: Introducing single-cell sequencing genomic DNA copy number analysis to study cancer heterogeneity in renal cell carcinoma and its potential benefits in cancer health disparities research

Abstract

Abstract Renal cell carcinoma (RCC) is one of the most lethal urological malignancies and is responsible for around 80 percent of all primary renal neoplasms. In the US, every year are reported approximately 74,000 new cases and almost 15,000 deaths. It has been reported significant racial disparities in survival for renal cell carcinoma (RCC) between African Americans patients (AA), Hispanics and Caucasians Americans (CA) but more efforts are needed in order to have a high resolution genomic profile of the tumor. Currently, RCC has been characterized by extensive cancer heterogeneity using bulk sequencing. In order to get new insight in the study of RCC cancer heterogeneity, we applied DNA single cell sequencing since it has the potential to improve our understanding of this genetic feature by providing sub-clonal and variant information in high resolution. To this end, we studied cancer heterogeneity applying single cell copy number analysis and clonotype detection in four RCC tumors. 10x Chromium™ Technology was used for processing single cells. This technology provides 100 Kb CNV events, calling clonotypes down to 10 of 1000 cell inputs. The most representative sample resulted with more than 50% of tumor content. Analysis of the tumor cells showed variable median ploidies. In addition, regional copy number was estimated by processing our data in 20 Kb cases of reads. Multiple sub-clones were identified in sample number one where four clusters of sub-clones characterized cancer heterogeneity. By using Fast maximum-likelihood and Bayesian Information Criterion, the clustering process were implemented on the most representative sample to select the optimal clustering solution. This allowed us to provide a better insight of sub-clonal evolution. We detected copy number changes on entire chromosome arms and mutations at variant detection level. For this last, we identified previously reported VHL gene mutations that have been reported in RCC samples as signatures of clinical prognosis. The use of single cell copy number analysis has the potential to uncover and characterize the evolution of hidden sub-clones, highlighting their important uses in cancer health disparities research to identify genomic racial differences in RCC risk and progression of AA, Hispanics and CA patients. Citation Format: Enrique I. Velazquez Villarreal, David W. Craig, John D. Carpten. Introducing single-cell sequencing genomic DNA copy number analysis to study cancer heterogeneity in renal cell carcinoma and its potential benefits in cancer health disparities research [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr A004.