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Abstract A004: Altered RNA splicing drives pancreatic cancer by mimicking chromosome deletions

2025· article· en· W4414582336 on OpenAlexaff
Natasha Pinto Medici, Diana Martínez‐Saucedo, Tianyi Chu, Saúl Rojas-Sánchez, Daniel Lee, Robert Tseng, Vincent L. Cannataro, Júlia C.F. Quintanilha, Ryon P. Graf, Gilbert Giri, Sheila Knupp Feitosa de Oliveira, Mariana DoCarmo, Lyanne A. Delgado-Coka, Lucia Roa‐Peña, Sumedha Chowdhury, Muaz Furaque, Nicolas Lecomte, Christine A. Iacobuzio–Donahue, John P. Morris, Katerina Politi, Pablo Pérez‐Piñera, Marie E. Robert, Kenneth R. Shroyer, Scott W. Lowe, Michael Cecchini, Omar Abdel‐Wahab, Steven D. Leach, Jeffrey P. Townsend, Daniel Domínguez, Mathieu Quesnel-Vallières, Luisa F. Escobar‐Hoyos

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsKRASRNA splicingCarcinogenesisExonMutationGeneMutantPancreatic cancer

Abstract

fetched live from OpenAlex

Abstract Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers, in part because we do not sufficiently understand its genetic basis to design effective therapies. Although KRAS mutations are present in 96% of PDACs, only 60% have concurrent TP53 mutations, leaving 40% with wild-type TP53 and undefined drivers. Identifying additional mutations is challenging due to the long tail of low-frequency events in PDAC (<10%), yet these rare mutations could reveal key drivers of tumorigenesis and therapeutic vulnerabilities. To identify novel driver mutations beyond TP53, we performed a genetic interaction analysis based on mutual exclusivity, which suggests that mutations exclusive to one another function within the same pathway. Using unbiased genetic interaction analyses, we queried >37,000 PDAC cases to prioritize rare mutations mutually exclusive with TP53 mutations. Mutations in the splicing factors SF3B1 and RBM10 (∼15% combined) emerged as the most significant and mutually exclusive with mutant TP53. Thus, we hypothesized that mutations K700E in SF3B1 and truncating mutations in RBM10 drives tumorigenesis and therapy resistance. We established the first autochthonous mouse models co-expressing mutant Kras with either Sf3b1K700E or Rbm10 loss, revealing these splicing alterations can cause PDAC. To uncover how splicing-factor mutations drive PDAC, we performed deep RNA sequencing and eCLIP in lineage-traced tumor cells from mouse models with wild-type or mutant Sf3b1/Rbm10. While tumors with these mutations lack chromosomal deletions, they exhibit widespread splicing defects, particularly in exon selection. Thousands of mRNAs transcribed from chromosomes 2, 7, 11, and 17 are mis-spliced and degraded by nonsense-mediated decay (NMD), leading to post-transcriptional gene loss. These patterns are conserved in human PDAC models. Notably, TP53-mutant PDACs require chromosomal deletions of chr11 and chr7 for tumor development, suggesting that SF3B1 and RBM10 mutations mimic this effect through splicing-dependent mechanisms. To define precision therapies for splicing-mutant tumors, we found that PDACs with these mutations are up to 5000X more sensitive to Gemcitabine versus 5FU. We combined Gemcitabine with the splicing modulator H3B-8800, which synergistically decreased tumor growth and extended survival of xenograft mice. Together, our findings reveal that diverse genetic events—TP53 mutations, SF3B1 mutations, or RBM10 loss—collaborate with mutant KRAS to by converging on a shared tumorigenic mechanism: loss of gene expression from specific chromosomes. In the case of inactivating mutations in TP53, this decrease in gene expression is caused by chromosome deletions, a genetic mechanism. In contrast, mutations in SF3B1 and RBM10 result in decreased gene expression through alternative splicing and mRNA decay, a post-transcriptional mechanism. This work uncovers a novel genetic convergence in PDAC and suggests that targeting splicing-mediated vulnerabilities may offer therapeutic opportunities across molecular subtypes. Citation Format: Natasha Pinto Medici, Diana Martinez-Saucedo, Tianyi Chu, Saúl Rojas-Sánchez, Daniel Lee, Robert Tseng, Vincent Cannataro, Julia C.F. Quintanilha, Ryon P. Graf, Gilbert Giri, Sheila Oliveira, Amelia Lower, Isabela Fuentes, Mariana do Carmo, Lyanne Delgado-Coka, Lucia Roa-Pena, Sumedha Chowdhury, Muaz Furaque, Emma Delannoy, Nicolas Lecomte, Christine Iacobuzio-Donahue, John P. Morris IV, Katerina Politi, Pablo Perez-Piñera, Marie E. Robert, Kenneth Shroyer, Scott Lowe, Michael Cecchini, Omar Abdel-Wahab, Steven D. Leach, Jeffrey P. Townsend, Daniel Dominguez, Mathieu Quesnel-Vallières, Luisa Escobar-Hoyos. Altered RNA splicing drives pancreatic cancer by mimicking chromosome deletions [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr A004.

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: 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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.397
Teacher spread0.355 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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