Abstract 2928: Solving the cancer mutation conundrum: A single cell, massively parallel approach for cancer mutation discovery, genome modelling and functional characterization

Abstract Large scale genomic studies are cataloguing tens of thousands of cancer mutations across a wide spectrum of tumor types. Conventional DNA sequencing methods have limitations in terms of identify mutations at high resolution among single cells. Moreover, determining cancer mutations’ phenotype and function remains an enormous challenge - most mutations are assigned to the category of variants of unknown significance. In-silico functional predictions of cancer mutations are frequently used as a stopgap solution. However, these computational methods lack the ability to make refined biological and functional predictions. We demonstrated a new single cell experimental strategy that accelerates the discovery of cancer mutations and their characterization. This genomic technology framework does the following: (1) identify disease-related mutations at the resolution of individual cells from tumors or cell lines; (2) genome engineer these candidate mutations into cellular experimental systems for further biological characterization. First, we developed a single cell approach to identify cancer somatic mutations and even gene fusion rearrangements found in coding regions of mRNAs. Primary tumors are processed into single cell RNA-seq libraries and analyzed with nanopore long read sequencing. Across thousands of cells from primary tumors, we identified single cell mutations from the nanopore long reads that covered cancer gene transcripts. This approach allows us to even identify cancer rearrangements at single cell resolution. Next, we used short-read single cell transcriptomes to characterize the cell type and determine the gene expression characteristics of those cells with mutations. For single cell phenotypic studies, we developed a highly multiplexed technology using CRISPR base editors to directly engineer hundreds of mutations into their original genes among single cells and determine their transcriptional phenotype. Among specific individual cells, nanopore-based long-read sequencing identifies these modelled mutations directly from a target’s transcript sequence. Then, we integrate this single cell mutation data with the short-read transcriptome profile from the same individual cell. Therefore, we determine both the genotype and phenotype of a given engineered mutation from an individual cell. In summary, our work demonstrates a new, direct and highly scalable method for identifying, modelling and functionally assessing cancer mutation phenotype at single cell resolution. This approach enables the broader assessment of cancer mutations impact on tumor biology and clinical ramifications. Citation Format: Susan M. Grimes, Heonseok Kim, Sharmili Roy, Anuja Sathe, Billy T. Lau, Hanlee P. Ji. Solving the cancer mutation conundrum: A single cell, massively parallel approach for cancer mutation discovery, genome modelling and functional characterization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2928.