Abstract 6171: A single-cell solution for solid tumors to detect mutations and quantify copy number variations

Cancer is a highly heterogeneous disease characterized by the presence of chromosomal structural copy number variation (CNV) and driver gene mutation events, which appear somatically at the early stages of oncogenesis and can drive tumor progression. Previously, we have developed a high throughput single cell DNA analysis platform that leverages droplet microfluidics and a multiplex-PCR based targeted DNA sequencing approach. It demonstrates high sensitivity detection of SNVs and indels in the same cells and generation of high resolution maps of clonal architecture based on mutational profiling. Here we expand upon the same approach and create an algorithm to accurately quantify CNVs along with genetic mutations from the same single cells. To estimate the CNV the amplicon read counts were corrected for artefacts related to cell to cell variability due sequencing efficiencies along with amplicon to amplicon variability that occur due to amplification efficiency differences. We used a spiked in diploid cell line as a reference to normalize all the cells. The spiked cell line was identified using its mutational profile. We validated the system using cell lines with known CNVs and found that we could at least accurately estimate copy numbers ranging from 0 through 5. Some of the regions were also validated orthogonally using droplet digital PCR. Another set of validation was performed using well characterized cell lines from NIST. We enable multiple visualizations of the copy number estimates in karyotype plots and line plots projected on SNV clones. With improved biochemistry, panel design and novel data analysis algorithms we develop a complete solution to detect amplification or loss of function in oncogenes and tumor suppressors reliably. Integration of CNVs and SNVs facilitates more accurate reconstruction of tumor evolution to better understand cancer progression mechanisms as well for quality control of gene edited cells, to further advance cancer research and therapy. Citation Format: Saurabh Parikh, Khushali Patel, Alex Li, Anup Parikh. A single-cell solution for solid tumors to detect mutations and quantify copy number variations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6171.