Analysis of 3,760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

Abstract

Rare driver mutations are suspected to substantially contribute to the large heterogeneity of hematologic malignancies, but their identification remains challenging. To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from over 3,760 patients spanning 24 disease entities. To discover rare and large regulatory aberrations, we analyzed aberrant expression and splicing events using an extension of DROP (Detection of RNA Outliers Pipeline) and AbSplice, an algorithm that identifies genetic variants causing aberrant splicing. We found a median of seven aberrantly expressed genes, two aberrantly spliced genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for well-characterized driver genes, with odds ratios exceeding three among genes called in more than one sample. We next trained disease-specific driver gene prediction models integrating these data with recurrent mutation analyses. On held-out data, integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Moreover, we found a truncated form of the low density lipoprotein receptor LRP1B 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 be a novel biomarker and a yet unreported functional role of LRP1B within these rare entities. Altogether, this dataset and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.