Mutations Affecting RNA Binding Proteins Are a Novel Feature of Mantle Cell Lymphoma
Bibliographic record
Abstract
Objectives Mantle cell lymphoma (MCL) is an uncommon B-cell non-Hodgkin lymphoma that is incurable with standard therapies. The genetic drivers of this cancer have not been firmly established and the features known to contribute to differences in clinical course remain limited. We sought to extend our understanding of the molecular etiology of this malignancy using an integrative genomic analysis of diagnostic biopsies. Methods We performed exome sequencing on 51 frozen MCL tumors and analyzed these alongside previously published exome cohorts. We sequenced tumour genomes and matched constitutional DNA from 34 frozen MCLs, along with matched constitutional DNA, to more broadly identify the pattern of non-coding mutations. Based on mutations identified in this discovery cohort, we re-sequenced 18 recurrently-mutated genes in 212 archival MCLs, each having clinical follow-up data. We also performed RNA-seq on 110 of these cases and analyzed these data for alternative splicing and differential expression, including the differential splicing of HNRNPH1 in the context of recurrent intronic mutations. We investigated the functional and phenotypic effect of mutations and deregulated HNRNPH1 protein through ectopic expression of full-length HNRNPH1 and a mini-gene containing the exons and introns affected by mutations. Using custom droplet digital PCR (ddPCR) assays, we validated alternative splicing patterns in HNRNPH1 itself and other targets identified through re-analysis of available CLIP-seq data. Results In addition to confirming the prognostic association of TP53 and NOTCH1 mutations in MCL, we identified two additional genes associated with outcome: EWSR1 with poor outcome (HR = 5.6) and MEF2B with good outcome (HR = 0.2). By comparing mutation patterns to diffuse large B-cell lymphoma (DLBCL), we identified an MCL-specific missense hot spot in MEF2B, non-specific truncating mutations in EWSR1, and truncating mutations affecting the DAZAP1 C-terminus in both MCL and DLBCL. The DAZAP1 mutations are predicted to alter protein sub-cellular localization and disrupt protein-protein interactions. We also identified the focal recurrence of non-coding mutations surrounding a single exon of the HNRNPH1 gene that were largely restricted to MCL. These mutations affected a region bound by HNRNPH1 protein and disrupted the preferred binding motif of this protein. Intronic mutations were significantly associated with alternative splicing of the HNRNPH1 mRNA and appear to disrupt a negative regulatory loop that normally limits the level of HNRNPH1. Using cell-based assays, we have evaluated the role of HNRNPH1 in cell survival and proliferation. Our interrogation of alternative splicing events in downstream targets implicate HNRNPH1 as a master splicing regulator which may broadly perturb the transcriptome and proteome to favor lymphomagenesis in MCL. Conclusions We discovered three novel MCL-related genes with roles in RNA trafficking or splicing, namely EWSR1, DAZAP1, and HNRNPH1. Mutations in these RNA-binding proteins were identified in 49 of 291 (17%) samples analyzed. Our results improve the current understanding of the MCL mutational landscape, highlight the similarities and differences between MCL and DLBCL, and strongly implicate a role for aberrant regulation of RNA metabolism in MCL pathobiology. We elucidated a functional role for recurrent non-coding HNRNPH1 mutations specific to MCL and identified multiple downstream targets. We continue to explore putative trans targets of HNRNPH1, a novel oncoprotein in MCL. Disclosures Steidl: Seattle Genetics: Consultancy; Roche: Consultancy; Bristol-Myers Squibb: Research Funding; Bayer: Consultancy; Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Juno Therapeutics: Consultancy; Tioma: Research Funding. Connors:Bristol-Myers Squibb: Consultancy; Seattle Genetics: Honoraria, Research Funding; Takeda Pharmaceuticals: Honoraria. Villa:Roche, Abbvie, Celgene, Seattle Genetics, Lundbeck, AstraZeneca, Nanostring, Janssen, Gilead: Consultancy, Honoraria. Johnson:Roche: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding; Abbvie: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Consultancy, Honoraria; BMS: Consultancy, Honoraria; BD Biosciences: Other: Provided a significant proportion of the antibodies used in this project free of cost.; Seattle Genetics: Honoraria; Lundbeck: Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding. Scott:Janssen: Consultancy, Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; Celgene: Consultancy; Roche/Genentech: Research Funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".