Abstract PO-04: Noncoding mutations in mantle cell lymphoma disrupt regulation of HNRNPH1 by alternative splicing
Bibliographic record
Abstract
Abstract Non-Hodgkin lymphomas (NHL) are a collection of cancers with each malignancy having distinct clinical management and prognosis. Mantle cell lymphoma (MCL) is particularly genetically heterogeneous and is considered incurable. Through a combination of exome, genome, and targeted sequencing of MCL tumors, we identified recurrent mutations in HNRNPH1 (heterogeneous nuclear ribonucleoprotein H1). These mutations are largely intronic or silent and are associated with a putative cis regulatory region involving a single exon. In RNA-seq data from matched cases, we identified variable representation of two distinct HNRNPH1 isoforms. Based on the reading frame of the affected exons, canonical splicing is predicted to produce a functional protein, while alternative splicing introduces a premature termination codon leading to nonsense-mediated decay. We observed a significantly higher proportion of canonical transcripts in MCL tumors bearing HNRNPH1 mutations, leading us to conclude that mutations in HNRNPH1 significantly alter its splicing. Furthermore, increased canonical splicing results in higher HNRNPH1 protein abundance as determined by immunohistochemical analysis of an MCL tissue microarray. HNRNPH1 mutation status and splicing ratio are associated with shorter survival of MCL patients. We developed an in vitro reporter minigene and introduced three specific mutations corresponding to the patient-identified mutations in HNRNPH1. These mutations lead to an increase in canonical splicing and translation of the HNRNPH1 minigene-derived peptide. Additionally, when HNRNPH1 is overexpressed, we observe a decrease of this peptide, implicating HNRNPH1 in the regulation of its own splicing. This is supported by preliminary data indicating that HNRNPH1 binds its own RNA. Beyond its own splicing, HNRNPH1 is likely also involved in the splicing of additional splicing factors. Overexpression of HNRNPH1 affects the splicing of SRSF3 and HNRNPDL, and many other targets are likely to be identified. This work elucidates a functional role for recurrent noncoding HNRNPH1 mutations and implicates HNRNPH1 expression and splicing of its downstream targets in lymphomagenesis. We continue to explore trans regulatory targets of HNRNPH1 using in vitro and cell-based models. While splicing is a growing field of interest in lymphoma biology, the unique pattern and consequences of these largely silent mutations specifically implicate alternative splicing as an oncogenic mechanism in MCL. Citation Format: Krysta M. Coyle, Quratulain Qureshi, Prasath Pararajalingam, Nicole Thomas, Timothy E. Audas, Ryan D. Morin. Noncoding mutations in mantle cell lymphoma disrupt regulation of HNRNPH1 by alternative splicing [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr PO-04.
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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 | 0.000 | 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) | 0.002 | 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".