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Abstract IA022: Mechanisms of splicing dysregulation and dependency in cancer

2024· article· en· W4399505148 on OpenAlexaboutno aff
Kristen L. Karlin

Bibliographic record

VenueMolecular Cancer Therapeutics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsnot available
Fundersnot available
KeywordsRNA splicingBiologyAlternative splicingSplicing factorRNA-binding proteinExonic splicing enhancerGeneticsRNAGeneComputational biologyMessenger RNA

Abstract

fetched live from OpenAlex

Abstract Dysregulated RNA splicing is a hallmark feature of cancer. Much like mutational signatures in tumors, RNA mis-splicing patterns are widely heterogeneous and segregate tumors into clades, suggesting there are distinct mechanisms underlying these splicing aberrations. However, the mechanisms driving such distinct RNA splicing dysregulation remain largely unknown. Herein, we discovered that copy-number variation of essential splicing factors is a pervasive cause of splicing dysregulation, and in some tumor contexts, confers deep dependencies on RNA-binding proteins (RBPs) that converge on these mechanisms. Using a systematic chemical biology approach, we delineate how splicing RBPs participate in distinct types of RNA quality control in cells, and when disrupted, produce surprisingly unique patterns of RNA mis-splicing. These RBP-specific mis-splicing patters are common in distinct cancer types. Notably, unbiased genome-wide analysis revealed that RBP-specific mis-splicing patterns found in tumors were significantly associated with copy number loss of a network of functionally linked splicing factors. These splicing factors often reside on tumor suppressor loci that are frequently deleted in breast and other cancers, suggesting that copy number loss of essential splicing RBPs is a collateral event with selected loss of tumor suppressors. Importantly, RBP-specific mis-splicing signatures also segregate tumor models that are dependent on the concordant RBP, suggesting that these tumors evolve defects in RBP function that predispose them to further perturbation. Together, our work suggests that deletions of tumor suppressor loci may drive collateral dysregulation of RNA splicing through partial loss of splicing RBPs and provokes the hypothesis that these mis-splicing signatures may predict actionable dependencies in the cancers that harbor them. Citation Format: Kristen Karlin. Mechanisms of splicing dysregulation and dependency in cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA022.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.056
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.022
GPT teacher head0.322
Teacher spread0.301 · 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 teacher head, 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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