Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes
Details
Publication Year 2024-05-20,Volume 16,Issue #1,Page 70
Journal Title
Genome Medicine
Publication Type
Research article
Abstract
BACKGROUND: Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging. METHODS: To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes. RESULTS: We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities. CONCLUSIONS: Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.
Publisher
BioMed Central
Keywords
Humans; *Hematologic Neoplasms/genetics; *Transcriptome; RNA Splicing; Gene Expression Regulation, Neoplastic; Oncogenes; Gene Expression Profiling; Receptors, LDL/genetics; Lrp1b; Aberrant expression; Aberrant splicing; Driver gene prediction; Hairy cell leukemia variant (HCL-V)
Department(s)
Pathology
Open Access at Publisher's Site
https://doi.org/10.1186/s13073-024-01331-6
Terms of Use/Rights Notice
Refer to copyright notice on published article.


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