Abstract A011: PAX8-dependent signaling networks as drivers of progression and immune evasion in high grade serous ovarian cancer
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".