43: Precision Detection of Cell-Type-Specific Cancer-Specific RNA Editing Sites via a Novel Computational Pipeline from Single-Cell RNA Sequencing Data

Music City Center 

Cancer remains a leading cause of death worldwide, largely due to its high heterogeneity, which complicates effective treatments. While targeted therapies based on DNA and RNA mutations have gained traction, the role of RNA editing— a crucial post-transcriptional modification that introduces nucleotide changes into the transcriptome—remains underexplored. Dysregulated RNA editing pathways have been implicated in cancer pathogenesis. However, identifying and quantifying RNA editing from single-cell RNA sequencing (scRNA-seq) data is challenging due to sparsity of such datasets. Without an optimized analytical approach, error rates can exceed 90%.
Here, we present a comprehensive pipeline tailored to address these challenges and enable reliable RNA editing site detection from scRNA-seq data. The pipeline includes a discovery phase at the sample level and a quantification phase at the single-cell level. Key features include reference-based cell barcode correction, enhanced alignment, per-cell duplicate read removal, and a statistical framework to mitigate background noise and false positives.
We applied this pipeline to scRNA-seq data from 24 patients with chronic myelomonocytic leukemia (CMML), a clonal hematologic malignancy in urgent need of new therapeutic strategies. While genetic mutations in CMML have been studied extensively, RNA editing remains unexplored. Our analysis identified 3,326 high-confidence RNA editing sites with ~92% accuracy, predominantly in intronic and 3′UTR regions-consistent with previous reports. Clustering based on RNA-editing patterns revealed biologically and clinically distinct subpopulations that diverged from conventional gene-expression clusters. Importantly, genes frequently mutated in CMML—such as FLT3, RUNX1, HAVCR2, and ITGAX—also exhibited extensive editing. A copy-number-variation (CNV)–driven approach distinguished healthy-like from malignant cells. Comparative analysis of CMML- and cluster-specific editing sites against healthy-like cells uncovered candidate diagnostic biomarkers, while complementary survival analysis identified cluster-specific prognostic markers. Together, our study presents a robust computational framework for interrogating RNA editing in single-cell data and offers novel insights into CMML pathogenesis.