Abstract 990: Role pf viral infection in APOBEC3 dysreguation in cancer: a single-cell gene expression profiling approach

Abstract The Cancer Genome Atlas (TCGA) provides comprehensive genomic and transcriptomic data from over 10,000 cancer patients, along with metadata including viral infection status and survival information. We investigated the role of viral infections in dysregulating the expression of A3 enzymes (A3A/B/C/D/F/G/H). These enzymes primarily function to restrict viral infections by introducing C-to-U mutations in viral genomes. However, certain members (notably A3A and A3B) can also mutate cellular DNA, contributing to tumor initiation and progression. Cancers such as breast, bladder, cervical, head & neck, and skin exhibit characteristic C-to-T mutations attributed to the aberrant expression/activity of A3 enzymes. Accurate quantification of A3 dysregulation in tumor cells using TCGA bulk RNAseq data is complicated by the ubiquitous expression of these enzymes in infiltrating immune cells. This highlights a broader challenge in analyzing heterogeneous tumor biopsies to uncover virus-specific dysregulated processes in cancer. To address this challenge, we employed Nonnegative Matrix Factorization (NMF) to deconvolute A3 expression profiles from TCGA bulk RNA-seq data, i.e. to separate tumor cell profiles from those of the tumor microenvironment. We validated our NMF-extracted profiles by analyzing publicly available single-cell RNA-seq datasets from approximately 3,000 cancer projects in the Gene Expression Omnibus (GEO). Additionally, to identify cell types represented by these profiles, we compared them with experimentally obtained A3 expressions from purified cell types. Our analyses identified up to four distinct A3 profiles: two associated with T cells and NK cells, one linked to dendritic cells and macrophages, and one corresponding to cancerous epithelial cells. Notably, the A3 profiles derived from our analysis of single-cell RNA-seq data closely matched those deconvoluted from TCGA bulk RNA-seq data, demonstrating that our bulk RNA-seq deconvolution effectively captures single-cell population-level profiles. We found that both tumor-specific and immune cell-specific A3 profiles correlate with viral infection; however, significant variations were observed across different viral types and tumor types. None of these profiles were correlated with the extent of A3 mutational signatures SBS2 and SBS13.In conclusion, our study demonstrates that deconvolution of bulk RNA-seq datasets can reveal single-cell population-level insights into the cellular composition of tumor biopsies. Moreover, this approach offers the resolution needed to pinpoint virus-specific dysregulated processes within the complex and heterogeneous tumor microenvironment. Citation Format: Mohadeseh Soleimanpour-Moghadam, diako Ebrahimi. Role pf viral infection in APOBEC3 dysreguation in cancer: a single-cell gene expression profiling approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 990.