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Record W4402574381 · doi:10.1093/neuonc/noae194

“Splice of Life”: How RNA is Rewriting the Meningioma Story

2024· letter· en· W4402574381 on OpenAlexaff
Kira Tosefsky, Stephen Yip

Bibliographic record

VenueNeuro-Oncology · 2024
Typeletter
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRewritingMeningiomaspliceComputational biologyRNABiologyMedicineComputer scienceGeneticsProgramming languageGenePathology

Abstract

fetched live from OpenAlex

Meningiomas are the most common primary intracranial tumor.1 While the majority are benign, select tumors behave aggressively in manners not reliably predicted by their histopathologic features. Efforts to identify molecular biomarkers of clinical aggression led to the incorporation of TERT promoter mutations and CDKN2A/B homozygous deletion as independent criteria for WHO Grade 3 designation in the most recent 2021 WHO classification of central nervous systems tumors (CNS5).2 A series of subsequent molecular profiling studies have converged on at least 3 subgroups of meningiomas, distinguished principally by their DNA methylation signatures and differing in copy number alterations, mutational and transcriptional profiles, and clinical outcomes.3–6 Specifically, multi-omic studies cluster meningiomas into a benign, NF2-wildtype subgroup harboring non-NF2 driver mutations, a benign to intermediate-risk NF2-altered subgroup enriched for immunogenic signaling pathways and an aggressive, NF2-altered subgroup with a high burden of additional mutations and copy number alterations, and upregulation of genes involved in macromolecular metabolism or cell proliferation.3 However, the biological pathways linking DNA methylation signatures to characteristic changes in gene expression have henceforth remained elusive, as have any actionable therapeutic targets. In this context, Leclair and colleagues,1 explore alternative RNA splicing as a potential mediator of the relationship between methylation signatures and gene expression profiles in meningioma. Alternative RNA splicing represents a major mechanism of gene regulation known to be perturbed in many cancers, and previous studies have demonstrated dysregulated expression of genes regulating RNA processing and splicing between DNA methylation-based meningioma subgroups.4,5 However, the prognostic and therapeutic potential of alternative splicing (AS) events in meningioma has, until now, yet to be formally evaluated. The authors identify 184 unique differential AS events from RNA-sequencing in a discovery cohort of 486 meningiomas. These were matched into 3 classes (merlin-intact, immune-enriched, and hypermitotic) based on the UCSF classifier of DNA methylationmicroarray data.4 The benign subgroups within this classification scheme—“Merlin-intact” and “Immune-enriched” - overlap with “MenG A” and “MenG B” of the Integrated MenG classification,6 and “MG2: NF2-wildtype” and “MG1: Immunogenic” of the classification scheme proposed by Nassiri and colleagues.5 The malignant “Hypermitotic” subgroup corresponds to “MenG C” of the Integrated MenG classification,6 while Nassiri and colleagues further subdivide this high-risk cluster into “MG3 Hypermetabolic” and “MG4 Proliferative” on the basis of differences in gene expression profiles.3,5 Leclair and colleagues find that several AS events previously implicated in cancer, including those affecting the NASP, HNRNPM, and MFF transcripts, are among those significantly enriched in Hypermitotic meningiomas, and correlate with shorter recurrence-free and overall survival times in their overall cohort.1 The authors further demonstrate that these AS events can be used to predict UCSF methylation subgroups and are detectable by RT-PCR. Pending further validation of AS events as prognostic markers, RT-PCR for select splicing events may offer an alternative to more resource-intensive molecular testing strategies for meningioma classification. Changes in gene expression of RNA splicing machinery represents a key mechanism regulating AS events. Among 770 RNA-binding proteins examined, Leclair and colleagues,1 identify 2 oncogenic splicing factors—DDX39A and SRSF1—which were upregulated in hypermitotic meningiomas, whose expression levels in human samples correlated with shorter time to recurrence and OS, and whose knockdown in highly proliferative meningioma cell lines curtailed cell growth in vitro. Public cross-linking and immunoprecipitation data indicate binding capacity for SRSF1 around splice sites of interest in NASP, MFF and hnRNPM, suggesting a direct role for SRSF1 in promoting AS events associated with meningioma aggressiveness. How other differentially expressed RNA-binding proteins may contribute to these and other prognostically relevant events remains to be assessed. To date, no standardized systemic therapy regimens have been established for the treatment of meningiomas,7,8 leaving limited management options in the recurrent setting following surgery and radiotherapy. Dysregulated splicing represents a potentially attractive novel treatment target in meningiomas, as specific splicing events can be modulated using small molecule inhibitors of oncogenic splicing factors9 or splice-switching anti-sense oligonucleotides or ASO.10 As a proof-of-concept, the authors demonstrate the capacity of ASOs to modify the relative abundance of MFF and NASP isoforms in meningioma cell lines, albeit with mixed effects on cell viability and proliferation. Together, these results highlight the need for further validation of which, if any, AS events play a meaningful role in driving meningioma proliferation and/or treatment resistance. This study by Leclair and colleagues establishes a strong justification for further characterization of RNA splicing perturbations in meningioma pathogenesis and subgroup differentiation.1 Stephen Yip is a recipient of the 2022 Health Professional-Investigator Competition Award (HPI-2022-2834). Stephen Yip declares the following conflicts of interest - Member of advisory boards and has received honoraria from Amgen, AstraZeneca, Bayer, Janssen, Pfizer, Roche, and Servier.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.046
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0050.008
Scholarly communication0.0050.007
Open science0.0010.003
Research integrity0.0460.057
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.031
GPT teacher head0.293
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Published2024
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