Abstract LB047: Resolving tumor heterogeneity by uncovering novel genomic and transcriptomic events with a new scaled and automated workflow

Abstract Single-cell omics has been widely applied in oncology research for biomarker discovery, providing an in-depth understanding of cancer heterogeneity. While bulk sequencing methods lack the specificity afforded by single cell studies, single cell applications miss insights due to trade-offs for sensitivity at scale. Current high throughput single cell DNA-seq applications are limited to targeted sequencing approaches, while scaled single cell RNA-seq applications are limited to 3’ or 5’ end counting methods or only capture polyadenylated RNA transcripts. To address these challenges, we have developed a nanoliter dispensing instrument, library prep chemistries and a bioinformatics analyses suite that scales both single cell genomics and transcriptomics assays while maintaining whole genome and whole transcriptome coverage, respectively. To demonstrate the ability to scale a non-targeted single cell whole genome amplification (WGA) application, we applied our new WGA workflow to cancer cell lines and primary Clear Cell Renal Cell Carcinoma samples. The data revealed segmental aneuploidies and both germline and putative somatic variants in thousands of single cancer cells in a single day, at shallow sequencing depths of approximately 300,000 paired end reads per single cell. Addressing the limitation of scaled single-cell transcriptomic solutions, our new total RNA-seq workflow is capable of generating data on up to 100,000 single cells at a time in two days. We applied this high-throughput workflow on cancer cells treated and untreated with epigenetic therapy and selected 11,000 cells to reach a deeper sequencing depth. The results demonstrate the ability to identify new biomarkers through comprehensive profiling of both protein-coding and noncoding genes with full gene-body coverage, revealing significant expression differences across multiple RNA biotypes as well as identifying splice junction isoforms. Overall, our data highlights the advantages of complex and rich datasets generated from single-cell workflows, which, when paired with an unbiased, non-targeted approach, enable the discovery of novel genomic and transcriptomic events in oncology samples. Citation Format: Shuwen Chen, Peng Xu, Xuan Li, Joseph Liu, Yana Ryan, Kazuo Tori, Hima Anbunathan, Alan Du, Mike Covington, Raymond Mendoza, Samantha Leong, Tomoya Uchiyama, Mohammad Fallahi, Xuan Qu, Xiaoyun Xing, Bryan Bell, Patricio Espinoza, Ting Wang, Yue Yun, Andrew Farmer. Resolving tumor heterogeneity by uncovering novel genomic and transcriptomic events with a new scaled and automated workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB047.