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Record W4393087474 · doi:10.1158/1538-7445.am2024-1256

Abstract 1256: RPL22 is a tumor suppressor in MSI-high cancers and a key splicing regulator of MDM4

2024· article· en· W4393087474 on OpenAlexaff
H Weinstein, Kevin Hu, Lisa Fish, Yih-An Chen, Paul Allegakoen, Keliana Hui, Julia H. Pham, Maria B. Baco, Hanbing Song, Andrew O. Giacomelli, Francisca Vázquez, Mahmoud Ghandi, Hani Goodarzi, Franklin W. Huang

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsRegulatorSuppressorRNA splicingKey (lock)Cancer researchBiologyCancerGeneticsGeneEcology

Abstract

fetched live from OpenAlex

Abstract Microsatellite instability high (MSI-H) tumors are marked by 30% or more mutations in microsatellite regions and are generated by somatic or germline alterations in DNA mismatch repair machinery. Despite a high tumor mutational burden, MSI-H tumors do not frequently harbor mutations in TP53. Notably, a frameshift mutation (p.K15fs) in the gene RPL22 is among the most recurrent mutations in MSI-H tumors. RPL22 and its paralog, RPL22L1, form part of the large subunit of the 60S ribosome and are known to affect protein synthesis as well as splicing of genes and transcription factors that affect development and tumorigenesis. Here we explore the result of RPL22 loss on the regulation of MDM4, an upstream target of p53. MDM4 protein expression is mediated through alternative mRNA splicing of its sixth exon, which leads to two isoforms, the exon 6-inclusive MDM4-FL and the exon 6-exclusive MDM4-S, which is prone to degradation. To date, no mutational events have been associated with this alternative splicing event. We show the prevalence of RPL22fs mutations is more than 70% in certain MSI-H cell lines (e.g. colon, endometrial) and tumors (e.g. stomach adenocarcinoma) in the CCLE and TCGA. RPL22 copy number loss and RPL22 frameshift mutations are strongly correlated with MDM4 exon 6 inclusion in TCGA samples, and RPL22 knockout increases MDM4 exon 6 inclusion and MDM4 protein in multiple cell lines. RPL22 loss increases cell proliferation and augments resistance to the MDM inhibitor Nutlin-3a. Downstream targets of p53 (p21, BBC3) are downregulated due to RPL22 loss and Nutlin-3a inhibition, suggesting mutation of RPL22 and subsequent overexpression of MDM4 promotes resistance to Nutlin-3a. CLIP-seq revealed that RPL22 binds the MDM4 3’-UTR. We find that RPL22 loss is associated with an alternative 3’-splicing event in RPL22L1 adjacent to exon 3 and results in the expression of the full-length transcript of RPL22L1. In summary, we identify RPL22 as a key modulator of MDM4 splicing through an alternative splicing switch in exon 6. Furthermore, RPL22 represses the expression of its paralog, RPL22L1, by mediating the splicing of a cryptic exon corresponding to a truncated functional transcript. Therefore, we propose that damaging mutations in RPL22 drive oncogenic MDM4 induction and reveal a common splicing circuit in MSI-H tumors that may inform therapeutic targeting of the MDM4-p53 axis and oncogenic RPL22L1 induction. Our results also suggest that the presence of RPL22 may act as a rheostat to maintain MDM4 exon 6 skipping under normal cellular contexts. Citation Format: Hannah N. Weinstein, Kevin Hu, Lisa Fish, Yih-An Chen, Paul Allegakoen, Keliana S. Hui, Julia H. Pham, Maria B. Baco, Hanbing Song, Andrew O. Giacomelli, Francisca Vazquez, Mahmoud Ghandi3, Hani Goodarzi, Franklin W. Huang. RPL22 is a tumor suppressor in MSI-high cancers and a key splicing regulator of MDM4 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1256.

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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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.386
Teacher spread0.340 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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