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Record W4362539767 · doi:10.1158/1538-7445.am2023-3814

Abstract 3814: In CLL the U1 snRNA driver mutation alters splicing in multiple genes and pathways

2023· article· en· W4362539767 on OpenAlexaff
Andrea Senff‐Ribeiro, Fatemeh Almodaresi, Quang M. Trinh, Shimin Shuai, David Spaner, Xosé S. Puente, Elı́as Campo, Lincoln Stein

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreOntario Institute for Cancer Research
Fundersnot available
KeywordsSpliceosomeBiologyRNA splicingIntronGeneticsSmall nuclear RNAExonGeneMutationSplicing factorRNANon-coding RNA

Abstract

fetched live from OpenAlex

Abstract 5-10% of patients with the IGHV wild type form of chronic lymphocytic leukemia (CLL) carry a g.3A>C driver mutation in the U1 small nuclear RNA (snRNA). We investigatedthe patterns of mis-splicing and their pathway consequences in three U1-mutant CLL cellline models using long read sequencing in order to better understand the mechanisms ofoncogenicity in tumors carrying this mutation. CLL cell lines (HG3, JVM3 and MEC1) expressing the U1 mutation and their wild-type counterparts were submitted to Oxford Nanopore sequencing to generate full-length RNA transcripts. This transcriptomic datawas evaluated, together with previously obtained short-read data. The g.3A>C mutation occurs at a specific location of the U1 snRNA, a core component of the eukaryotic spliceosome, and acts by altering the 5' splice site (5'SS) recognition sequence to cause consistent patterns of mis-splicing. Long read analysis identified multiple instances of intron retention and suppression of exon skipping, and in silico translation of these mis-splicing events predicted stop-gain and other loss of function mutations in several expressed genes, as well as widespread changes in gene expression levels. We performed a pathway overrepresentation analysis of the altered gene expression patterns in the mutant cells using the Reactome knowledgebase and identified an enrichment in the processes of translation, non-sense mediated decay (NMD) and immune signaling, including interferon signaling. In particular, there was a marked down-regulation of genes related to ribosomal assembly and the translational machinery. A more comprehensiveanalysis of the U1 mutation phenotype in CLL may accelerate the development ofbetter therapeutic and diagnostic approaches in patients. Citation Format: Andrea Senff-Ribeiro, Fatemeh Almodaresi, Quang Trinh, Shimin Shuai, David Spaner, Xose S. Puente, Elias Campo, Lincoln D. Stein. In CLL the U1 snRNA driver mutation alters splicing in multiple genes and pathways. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3814.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.102
GPT teacher head0.383
Teacher spread0.281 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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