Abstract A011: PAX8-dependent signaling networks as drivers of progression and immune evasion in high grade serous ovarian cancer
Notice bibliographique
Résumé
Abstract High-grade serous cancer (HGSC) remains the most lethal subtype of ovarian cancer, due in part to its complex tumor microenvironment (TME) and resistance to current therapies. The complexity of this problem necessitates the identification of new therapeutic targets. One target of interest is PAX8, a lineage-specific transcription factor expressed in the majority of HGSCs as well as in normal fallopian tube tissue. Studies have shown that loss of PAX8 in non-tumorigenic cells does not significantly compromise viability, making it a compelling therapeutic target. In this study, we investigated how PAX8 drives tumor progression by orchestrating a signaling axis involving TGF-β, SOX17, and KRAS. We hypothesized that PAX8 interacts with distinct protein partners in tumor versus normal cells, reprogramming the TME to favor cancer growth. Using two murine cell models (MOE PTENshRNA KRASG12V and STOSE), we silenced PAX8 expression via shRNA and observed a reduction in TGF-β secretion, as measured by ELISA. Subsequent western blot analyses revealed that PAX8 loss destabilizes mutant KRAS, but not wild-type KRAS; such stabilization was reversed by recombinant TGF-β treatment, suggesting PAX8 – TGF-β collaborate to stabilize oncogenic KRAS. Further investigation identified SOX17, a transcription factor critical for angiogenesis regulation, as a downstream effector of PAX8 – TGF-β signaling. Antibody array profiling demonstrated that PAX8 knockdown downregulates SOX17, shifting the angiogenic balance toward inhibition (e.g., increased Serpin F1 and Pentraxin-3, decreased Coagulation factor III and Proliferin). Strikingly, the PAX8–TGF-β–SOX17 axis also regulates interferon gamma receptor 1 expression, leading to reduced MHC class I levels. Since MHC class I is essential for CD8+ T cell recognition, this pathway enables tumor immune evasion—a finding corroborated by enhanced immune infiltration in PAX8-deficient tumors. In immune-competent FVB/N mice, PAX8-deficient tumors exhibited significantly prolonged survival and slower growth compared to controls across both models. Further analysis of tumors from the MOE PTENshRNA KRASG12V group revealed increased infiltration of CD45+ immune cells and reduced stromal density relative to controls, indicating that PAX8 influences both cellular and structural aspects of the TME. Our findings establish PAX8 as a central driver of HGSC progression through three interconnected mechanisms: (1) TGF-β–dependent stabilization of oncogenic KRAS, (2) SOX17-mediated angiogenic reprogramming, and (3) MHC class I–dependent immune evasion. These results position PAX8 as a promising therapeutic target whose inhibition could simultaneously disrupt mutant KRAS signaling, normalize angiogenesis, and restore anti-tumor immunity, addressing key clinical challenges in HGSC treatment. Citation Format: Joohyun Im, Amrita Salvi, Yi-Chien Wu, Dan Lantvit, Steve Seung-Young Lee, Barbara Vanderhyden, Joanna Burdette. PAX8-dependent signaling networks as drivers of progression and immune evasion in high grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr A011.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».