Abstract A26: Modeling the ovarian cancer immune response and tumor microenvironment
Bibliographic record
Abstract
Abstract High-grade serous ovarian cancer (HGSOC) is the leading cause of death from gynecologic malignancies. Many patients initially respond well to surgery followed by chemotherapy, yet ~80% of patients recur with disease that is frequently recalcitrant to chemotherapy. Across a number of chemoresistant cancers, immunotherapies have shown great promise. Correlative studies in patients with HGSOC support a role for immune system in patient outcome. Yet the HGSOC tumor microenvironment (TME) is highly immunosuppressive, which constitutes a major barrier to immunotherapy success. Understanding the molecular signals in HGSOC that promote resistance to chemo- or immunotherapies is required for identification of actionable targets within HGSOC; yet, there are limited models that recapitulate the TME in ovarian cancer. We have generated a new syngeneic C57Bl6 mouse model of ovarian cancer that exhibits genomic copy number gains in KRAS, MYC, and (PTK2) FAK genes (termed KMF) and aggressive malignant phenotypes commonly observed in HGSOC. The KMF TME, like HGSOC, is populated by immunosuppressive myeloid-derived suppressor cells (MDSC) and T-regulatory (Treg) cells but lacks CD8+ T cell infiltration. We find that tumor-associated FAK expression and kinase activity are essential for KMF tumor growth. Mechanistically, by knockout and cell reconstitution approaches, FAK promotes expression of a select group of proteins mediating chemo- and immune-resistance. Inhibiting FAK modulates checkpoint inhibitor protein expression, limits MDSC and Treg recruitment, and enhances CD4 and CD8 T cell infiltration. Pharmacologic FAK inhibition reduced CD112/CD155 expression on KMF cells and, together with anti-TIGIT immunotherapy, significantly increased mouse survival from 28 to 60+ days. The chemoresistant KMF model recapitulates key aspects of the aggressive HGSOC TME including the generation of an immunosuppressive milieu, and may be used as a preclinical model to identify new therapeutic targets for HGSOC. Citation Format: Duygu Ozmadenci, Jayanth S. Shankara Narayanan, Jacob R. Andrew, Allison M. Barrie, Shulin Jiang, Esra Bilir, Thomas Bertotto, Rebekah R. White, Vijay K. Kuchroo, Jonathan A. Pachter, Dwayne G. Stupack, David D. Schlaepfer. Modeling the ovarian cancer immune response and tumor microenvironment [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr A26.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".