Abstract PR007: Delineating functional drivers of esophageal adenocarcinoma to identify synthetic lethal interactions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".