Abstract A004: Altered RNA splicing drives pancreatic cancer by mimicking chromosome deletions
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».