Abstract A116: Single cell analyses of syngeneic ovarian cancer models implicates macrophage skewing as key to evoking anti-tumor immunity via coordinated TGF-β and PD-L1 blockade
Bibliographic record
Abstract
Abstract Introduction: High grade serous cancer (HGSC) is the most common and lethal subtype of epithelial ovarian cancer, with only ∼25% survival rates at 5 years post diagnosis. Transforming Growth Factor-beta (TGF-β) is an immunosuppressive cytokine found in high levels in ascites fluid that hinders the efficacy of PD-(L)1 immune checkpoint blockade. Specifically, TGF-β signaling promotes the recruitment and activation of immunosuppressive cells, including regulatory T cells (Tregs) and tumor-associated macrophages (TAMs). Here, we use single cell RNA sequencing (scRNAseq), macrophage depletion studies and ex vivo co-culture models to define molecular and cellular mechanisms of action of bintrafusp alfa (BA), a dual TGF-β/PD-L1 inhibitor, in ovarian cancer models. Methods: Murine HGSC syngeneic engraftment models were used to model metastatic disease using BR5-Luc (Trp53-/-:Brca1-/-:MycO/E) cells injected in female FVB mice, or ID8-Trp53-/-:Nf1-/--Luc cells injected in female C57BL/6 mice (i.p.). Mice were randomized between treatment groups: control IgG, anti-PD-L1, TGF-β Trap, or BA (treated in final week of endpoint studies). Some experiments included macrophage depletion using clodronate liposomes prior to treatments. Tumors and ascites were collected for cytokines and immunophenotyping. ScRNAseq was performed on dissociated ovarian tumors (WT Parse Evercode) and compared with publicly available human HGSC data. Interactions between HGSC, macrophages and other effector cells were visualized using ex vivo co-culture models using OT-1 T cells and ID8-Nf1 cells transduced with chicken ovalbumin antigen and a Granzyme B-cleavable forster resonance energy transfer (FRET) reporter. Results: In the BR5/FVB model, flow cytometry analysis of tumors from BA-treated mice showed significantly more CTLs expressing IFN-γ, CD8 Teff/memory cells, naive NK cells, and activated cytolytic NK cells expressing CD107a compared to controls. Also, distinct clusters of anti-tumor and proinflammatory immune populations were detected in the acites of BA-treated mice, especially upregulated M1 macrophages, activated CD69+ NK cells, and reduced Tregs. Differential immune transcriptome signatures and rare immune populations were explored using scRNAseq, and compared to publicly available human HGSC scRNAseq data. Flow cytometry on tumor & ascites showed a shift in immune balance and response in HGSC TIME when macrophages were depleted, compared to wildtype. Testing of BA in a HGSC/T cell co-culture model showed improved Granzyme B-mediated killing of HGSC cells compared to controls & delineated roles for macrophages, T cells and NK cells in BA immune response. Conclusion: These findings underscore the critical roles of T cells, NK cells, and macrophages in mediating the anti-tumor effects of BA in HGSC models. Translation of these findings should involve patient stratification for TGF-β-induced biomarkers to include in future trials of this new immunotherapy regime for ovarian cancer. Citation Format: Jacob LK Kment, Sai Agash Surendran, Stephanie Young, Ken Huang, Paul Kubes, Andrew W Craig. Single cell analyses of syngeneic ovarian cancer models implicates macrophage skewing as key to evoking anti-tumor immunity via coordinated TGF-β and PD-L1 blockade [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A116.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".