Venkatasubramanian, M. | bioRxiv Science

bioRxiv2024

Broad de-regulated U2AF1 splicing is prognostic and augments leukemic transformation via protein arginine methyltransferase activation

The role of splicing dysregulation in cancer is underscored by splicing factor mutations; however, its impact in the absence of such rare mutations is poorly understood. To reveal complex patient subtypes and putative regulators of pathogenic splicing in Acute Myeloid Leukemia (AML), we developed a new approach called OncoSplice. Among diverse new subtypes, OncoSplice identified a biphasic poor prognosis signature that partially phenocopies U2AF1-mutant splicing, impacting thousands of genes in over 40% of adult and pediatric AML cases. U2AF1-like splicing co-opted a healthy circadian splicing program, was stable over time and induced a leukemia stem cell (LSC) program. Pharmacological inhibition of the implicated U2AF1-like splicing regulator, PRMT5, rescued leukemia mis-splicing and inhibited leukemic cell growth. Genetic deletion of IRAK4, a common target of U2AF1-like and PRMT5 treated cells, blocked leukemia development in xenograft models and induced differentiation. These analyses reveal a new prognostic alternative-splicing mechanism in malignancy, independent of splicing-factor mutations. Statement of significanceUsing a new in silico strategy we reveal counteracting determinants of patient survival in Acute Myeloid Leukemia that co-opt well-defined mutation-dependent splicing programs. Broad poor-prognosis splicing and leukemia stem cell survival could be rescued through pharmacological inhibition (PRMT5) or target deletion (IRAK4), opening the door for new precision therapies. Competing InterestsConflict-of-interest disclosure: DTS. serves on the scientific advisory board at Kurome Therapeutics; is a consultant for and/or received funding from Kurome Therapeutics, Captor Therapeutics, Treeline Biosciences, and Tolero Therapeutics; and has equity in Kurome Therapeutics. AV has received research funding from GlaxoSmithKline, BMS, Jannsen, Incyte, MedPacto, Celgene, Novartis, Curis, Prelude and Eli Lilly and Company, has received compensation as a scientific advisor to Novartis, Stelexis Therapeutics, Acceleron Pharma, and Celgene, and has equity ownership in Throws Exception and Stelexis Therapeutics.

cancer biology↗

bioRxiv2019

Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF

The rapid proliferation of single-cell RNA-Sequencing (scRNA-Seq) technologies has spurred the development of diverse computational approaches to detect transcriptionally coherent populations. While the complexity of the algorithms for detecting heterogeneity have increased, most existing algorithms require significant user-tuning, are heavily reliant on dimensionality reduction techniques and are not scalable to ultra-large datasets. We previously described a multi-step algorithm, Iterative Clustering and Guide-gene selection (ICGS), which applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally coherent cell populations from an intuitive graphical user interface. Here, we describe a new iteration of ICGS that outperforms state-of-the-art scRNA-Seq detection workflows when applied to well-established benchmarks. This approach combines multiple complementary subtype detection methods (HOPACH, sparse-NMF, cluster \"fitness\", SVM) to resolve rare and common cell-states, while minimizing differences due to donor or batch effects. Using data from the Human Cell Atlas, we show that the PageRank algorithm effectively down samples ultra-large scRNA-Seq datasets, without losing extremely rare or transcriptionally similar distinct cell-types and while recovering novel transcriptionally unique cell populations. We believe this new approach holds tremendous promise in reproducibly resolving hidden cell populations in complex datasets.\n\nHighlightsO_LIICGS2 outperforms alternative approaches in small and ultra-large benchmark datasets\nC_LIO_LIIntegrates multiple solutions for cell-type detection with supervised refinement\nC_LIO_LIScales effectively to resolve rare cell-states from ultra-large datasets using PageRank sampling with a low memory footprint\nC_LIO_LIIntegrated into AltAnalyze to enable sophisticated and automated downstream analysis\nC_LI

bioinformatics↗