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Record W4414392198 · doi:10.18632/oncotarget.28768

Loss of <i>Trp53</i> results in a hypoactive T cell phenotype accompanied by reduced pro-inflammatory signaling in a syngeneic orthotopic mouse model of ovarian high-grade serous carcinoma

2025· article· en· W4414392198 on OpenAlexaffabout
Jacob Haagsma, Yudith Ramos Valdés, Xuejin Ou, Rasheduzzaman Rashu, S. M. Mansour Haeryfar, Jim Petrik, Trevor G. Shepherd

Bibliographic record

VenueOncotarget · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsUniversity of GuelphOvarian Cancer CanadaOccupational Cancer Research CentreWestern University
Fundersnot available
KeywordsSerous carcinomaTumor microenvironmentImmune systemOvarian carcinomaSerous fluidPhenotypeDiseaseT cellImmunotherapy

Abstract

fetched live from OpenAlex

// Jacob Haagsma 1 , 2 , Yudith Ramos Valdes 1 , Xuejin Ou 3 , 4 , Rasheduzzaman Rashu 3 , S.M. Mansour Haeryfar 3 , 5 , 6 , 7 , Jim Petrik 8 and Trevor G. Shepherd 1 , 2 , 5 , 9 1 The Mary and John Knight Translational Ovarian Cancer Research Unit, Verspeeten Family Cancer Centre, London, ON, Canada 2 Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 3 Department of Microbiology and Immunology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 4 Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Chengdu, China 5 Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 6 Department of Surgery, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 7 Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 8 Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada 9 Department of Obstetrics and Gynaecology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada Correspondence to: Trevor G. Shepherd, email: tshephe6@uwo.ca Keywords: high-grade serous ovarian carcinoma; orthotopic models; inflammation; microenvironment Received: May 27, 2025&emsp;&emsp;&emsp;&emsp; Accepted: September 03, 2025&emsp;&emsp;&emsp;&emsp; Published: September 22, 2025 Copyright: &copy; 2025 Haagsma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Ovarian high-grade serous carcinoma (HGSC) is an aggressive disease with an urgent need for improved therapies. Immunotherapies have proved useful for some cancers but have failed to provide benefits for HGSC. Improving our understanding of the mechanisms regulating the HGSC tumor microenvironment will facilitate the discovery of novel immunotherapies and help predict patient response. To this end, the development of syngeneic models is imperative to recapitulate immune responses observed in patients with HGSC. Yet, few syngeneic HGSC mouse models exist that accurately reflect the initiation and disease progression of human disease. In this study, we developed a syngeneic model reflecting both the site of origin and the genotype of early HGSC disease by deleting Trp53 in mouse oviductal epithelial (OVE) cells. Orthotopic injection of OVE cells demonstrated advanced disease progression due to loss of Trp53 , associated with a less active T cell phenotype. Molecular analyses uncovered altered inflammatory signaling in OVE4- Trp53 ko cells. Further analysis on an ascites-derived cell line identified selection for decreased pro-inflammatory signaling. These results highlight potential mechanisms by which loss of p53 function contributes to an immunosuppressive microenvironment in HGSC, and provide insight into the role of ovarian and peritoneal microenvironments in regulating HGSC cell-intrinsic inflammatory signaling.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2025
Admission routes2
Has abstractyes

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