bioRxivCancer BiologyPreprint
Widespread remodeling of the RNA editome underlies transcriptional and clinical heterogeneity in pediatric acute lymphoblastic leukemia
Sara Brin RosenthaliD, Thomas C. WhisenantiD, Roman ŠášikiD, Maria RiveraiD, Geena Il Defonso, Charles-Alexandre Roy, Sabina Enlund, Rawan ShraimiD, Kathleen M FischiD, David T. TeacheyiD, Frida HolmiD, Dennis John KuoiD, Qingfei Jiang
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Abstract
Aberrant RNA editing by adenosine deaminases is increasingly recognized as a source of transcriptional diversity in adult cancer, yet its role in pediatric leukemia remains poorly understood. Here, we systematically profiled RNA editing events from bulk RNA-seq data of 1,025 pediatric T-cell acute lymphoblastic leukemia (T-ALL) samples, 37 matched B-ALL diagnosis/relapse pairs, and 6 age-matched non-leukemic controls. We uncovered a widespread global increase in A-to-I editing in T-ALL, affecting both non-coding Alu elements and coding sequences. By contrast, B-ALL shows relatively modest increases in RNA editing at both diagnosis and relapse with no differences in ADAR1 levels. ADAR1 expression is strongly associated with double-stranded RNA (dsRNA) sensors and interferon signaling in T-ALL. Genelevel analyses highlight recurrent editing of oncogenic drivers and regulators of immune signaling, chromatin remodeling, and RNA processing. Unexpectedly, increased editing levels in select genes (CD247, PRKCA and TRAF3IP2.ASI in T-ALL; SPPL3 in B-ALL) were significantly associated with better patient survival, suggesting a potential prognostic role for editing dysregulation at individual gene levels. Together, these results deepen our understanding of the pediatric ALL transcriptome landscape and provided novel candidate regulators and therapeutic targets for future mechanistic and translational investigation.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: protein structure prediction
- research focus: drug response / drug discovery
- approach: statistical model
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