Abstract PR007: Delineating functional drivers of esophageal adenocarcinoma to identify synthetic lethal interactions
Notice bibliographique
Résumé
Abstract Application of molecular targeted therapies for esophageal adenocarcinoma (EAC) has been limited by a lack of druggable oncogenic drivers. We propose that synthetic lethal interactions may provide new opportunities for targeted therapies in EAC. We have taken an integrated multi-omics approach incorporating Perturb-Seq (CRISPR editing combined with single cell RNA sequencing) and in vivo tumorigenesis assays to perform high-throughput characterisation of >70 high-confidence EAC driver genes, and genome-wide CRISPR-Cas9 knockout screens in isogenic models of EAC tumorigenesis to identify disease relevant synthetic lethal genetic interactions. MS-based proteomics, reverse phase protein arrays, polysome profiling and bulk RNA-sequencing of isogenic models were utilised to interrogate the biology of bona fide EAC drivers and associated driver specific gene dependencies. The overall goals were to (i) enhance our understanding of EAC tumorigenesis, (ii) identify potential opportunities for therapeutic interventions targeting EAC drivers via synthetic lethal-like approaches, and (iii) reduce the complexity of genetic heterogeneity by categorising EAC drivers with similar phenotypic outcomes. Through our approach we have identified complex crosstalk between the tumor suppressor SMAD4 and regulation of mTOR signaling, with specific downstream effects on translational reprogramming in EAC. Mutation or loss of SMAD4 occurs in up to 20% of EAC, but not pre-malignant tissue (Barrett’s esophagus), and is sufficient to promote transformation of pre-malignant cells in our in vivo tumorigenesis model. In this model, xenotransplanted SMAD4-deficient (via CRISPR-Cas9 knockout or shRNA knockdown) Barrett’s metaplasia cells formed invasive, metastatic tumors after a period of latency. SMAD4 deficient cells had downregulated expression of 4E-BP1, which inhibits EIF4E, the cap-dependent translation initiation factor. This was accompanied by increased mTOR activity, including phosphorylation and inactivation of 4EBP1. Moreover, we found that SMAD4-deficient cells preferentially upregulate cap-dependent translation at the expense of IRES mediated translation. Furthermore, perturbation of additional negative regulators of mTOR signaling in combination with SMAD4 knockout exacerbated these effects and accelerated tumorigenesis in vivo. We have extended these findings to a model of Barrett’s esophagus patient-derived organoids (PDOs) and observed increased proliferative potential of our genetically modified PDOs. Finally, analysing gene ontologies for differentially expressed genes from Perturb-seq revealed that driver-dependent transcriptional changes can be categorized into a smaller number of functional pathways allowing us to potentially consider groups of drivers as functional units. This work advances our understanding of EAC tumorigenesis, provides new mechanistic insights into SMAD4-driven transformation as well as novel potential therapeutic avenues for SMAD4-deficient EAC. Citation Format: Julia V. Milne, Ebtihal Mustafa, Kenji Fujihara, Eric Kusnadi, Anna Trigos, Niko Thio, Maree Pechlivanis, Carlos Cabalag, Twishi Gulati, Kaylene Simpson, Cuong Duong, Luc Furic, Wayne Phillips, Nicholas Clemons. Delineating functional drivers of esophageal adenocarcinoma to identify synthetic lethal interactions [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 PR007.
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 | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».