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Abstract C059: Hub gene analysis identifies CAD as a potential mediator for drug resistance in pancreatic cancer

2024· article· en· W4390915367 on OpenAlexaff
Foram Vyas, Barbara T. Grünwald, Kazeera Aliar, Gun Ho Jang, Grainne M. O’Kane, Thomas Kislinger, Steven Gallinger, Benjamin Haibe‐Kains, Rama Khokha

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsPancreatic cancerBiologyCancer researchCancerGemcitabineBioinformaticsGenetics

Abstract

fetched live from OpenAlex

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.062
GPT teacher head0.460
Teacher spread0.398 · 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 designSimulation or modeling
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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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