Abstract C059: Hub gene analysis identifies CAD as a potential mediator for drug resistance in pancreatic cancer
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) has a complex disease pathobiology and poor treatment options, emphasizing the need for novel therapeutic targets. Hub genes have high connectivity (i.e., exceptionally many interaction partners), for example, certain proteases with numerous interaction partners. Thereby, hub genes are often central biological regulators and dysregulated proteolysis is indeed a key trait of malignant tissues. Here, we aimed to identify novel protease-related hub genes with potential involvement in pancreatic cancer. We applied an integrative computational approach to compartment-specific multiOMIC profiles of laser-capture micro-dissected human PDAC tissues (n = 32) and queried associations of identified candidate genes with clinical outcomes in early (n = 186) and advanced PDAC patients (n = 253). This identified 20 protease-related genes with ≥ 10 physically verified interaction partners in pancreatic cancer. Among these, the pyrimidine biosynthesis regulator gene CAD emerged as a potentially druggable target in pancreatic cancer. CAD expression was nearly restricted to the malignant PDAC epithelium. Furthermore, higher CAD expression was associated with significantly worse overall survival in patients with advanced but not resectable disease, suggesting a role in treatment response rather than disease progression. Moreover, patients with higher CAD expression specifically demonstrated poor response to mFFX but not Gemcitabine-nab-Paclitaxel treatment, which is in line with previous reports on CAD expression being protective against 5-Fluouracil, a component of FFX. Moving forward, we will interrogate the role of CAD in PDAC tissue biology and scrutinize its involvement in FFX resistance, using patient-derived organoids. Citation Format: Foram Vyas, Barbara Grünwald, Kazeera Aliar, Gun Ho Jang, Grainne O'Kane, Thomas Kislinger, Steven Gallinger, Benjamin Haibe-Kains, Rama Khokha. Hub gene analysis identifies CAD as a potential mediator for drug resistance in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C059.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".