American Association for Cancer Research
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Supplementary Data from RNA Splicing Alterations Induce a Cellular Stress Response Associated with Poor Prognosis in Acute Myeloid Leukemia

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posted on 2023-03-31, 21:48 authored by Govardhan Anande, Nandan P. Deshpande, Sylvain Mareschal, Aarif M.N. Batcha, Henry R. Hampton, Tobias Herold, Soren Lehmann, Marc R. Wilkins, Jason W.H. Wong, Ashwin Unnikrishnan, John E. Pimanda

Supplementary Methods - revised, plus Supplementary Tables 1-6


National Health and Medical Research Council

Wilhelm Sander Foundation

Physician Scientists

Deutsche Forschungsgemeinschaft




RNA splicing is a fundamental biological process that generates protein diversity from a finite set of genes. Recurrent somatic mutations of splicing factor genes are common in some hematologic cancers but are relatively uncommon in acute myeloid leukemia (AML, < 20% of patients). We examined whether RNA splicing differences exist in AML, even in the absence of splicing factor mutations. We developed a bioinformatics pipeline to study alternative RNA splicing in RNA-sequencing data from large cohorts of patients with AML. We have identified recurrent differential alternative splicing between patients with poor and good prognosis. These splicing events occurred even in patients without any discernible splicing factor mutations. Alternative splicing recurrently occurred in genes with specific molecular functions, primarily related to protein translation. Developing tools to predict the functional impact of alternative splicing on the translated protein, we discovered that approximately 45% of the splicing events directly affected highly conserved protein domains. Several splicing factors were themselves misspliced and the splicing of their target transcripts were altered. Studying differential gene expression in the same patients, we identified that alternative splicing of protein translation genes in ELNAdv patients resulted in the induction of an integrated stress response and upregulation of inflammation-related genes. Finally, using machine learning techniques, we identified a splicing signature of four genes which refine the accuracy of existing risk prognosis schemes and validated it in a completely independent cohort. Our discoveries therefore identify aberrant alternative splicing as a molecular feature of adverse AML with clinical relevance.See related commentary by Bowman, p. 3503