Abstract 79: Study of tumor microenvironment of ovarian clear cell carcinoma
Bibliographic record
Abstract
Abstract Background: Clear cell ovarian carcinoma (CCOC) is characterized by a distinct histologic and molecular profile, and associated with very poor responses to standard treatment consisting of surgery and carboplatin:taxol chemotherapy. CCOC is chemo-resistant at the time of diagnosis and response to chemotherapy in the recurrent setting is less than 10%. Literature suggests a potential role for immune checkpoint inhibitors (ICI). However, only a subgroup of patients (20%) responds to ICI and little is known about the mechanisms of response and resistance to therapy. We postulate that a better understanding of CCOC tumor microenvironment (TME) could help predict patients’ response to ICI. Objective: The aim of this study is to investigate the relationship between TME immune infiltrate and clinical/outcome in 22 CCOC cases and identify subsets of CCOC who may benefit from immunotherapy. Material & Methods: We characterized the immune landscape of 11 early and 11 advanced CCOC through a multiplex IHC Discovery Platform. Spatial single-cell proteomics analyses (cyclic-IF) and spatially-resolved RNAseq in 10 CCOC cases using an OC tissue microarray (TMA) were performed. Results were corelated with clinical and treatment outcome. Results: The percent of CD8, CD4, CD20 B cells Tregs, PD1, PDL1, monocytes and M2 monocytes and myeloid cells was significantly higher in advanced than early-stage cancers. Recurrent cancers were more immunosuppressive than cases with no recurrence. Tumor infiltrate was dense in 4 cases. Four patients with early-stage disease had a high number of CD8 naïve cells and experienced no recurrence. TME analysis of single case of advanced CCOC (before and after chemotherapy), revealed that hot tumor changed to cold tumor, suggesting the resistance to treatment. Cyclic IF analyses identified 3 tumor phenotype groups in a subset of 10 CCOC present on the OC TMA. The majority of CCOC had high expression of CCNE and high PI3K-AKT-mTor activity, low expression of hormone receptors (AR, ERa, PRg) and low cell cycle activity. Some tumors were also high for HER2. The stromal compartment was enriched in collagen VI, aSMA and PDGFR. The immune monitoring of several of those samples also revealed an immunosuppressive microenvironment, including the presence of M2 macrophages and expression of immune checkpoint proteins PD-L1 and B7-H4. Conclusion: Our study shows that various phenotypes of CCOC are defined by the cancer cell profiles and TME content. Importantly, a strong immunosuppressive microenvironment was detected in many samples, suggesting a potential response to ICI. Furthermore, we detected the expression of several therapeutic targets (MAPK, CCNE, PI3K-AKT-mTOR, HSP90, HER2), including oncogenic signaling pathways (RTK, MAPK). Different tumor phenotypes identified across our CCOC samples suggest that clear cell carcinoma could be subclassified into subtypes that should be treated differently. Citation Format: Tanja Pejovic, Sonali Joshi, Shawn Campbell, Dhanir Tailor, Joanna Pucilowska, Benjamin Tate, Pierre-Valérien Abate, Korina Mouzakitis, Marilyne Labrie, Elizabeth Munro, Jenna Emerson, Sanjay V. Malhotra. Study of tumor microenvironment of ovarian clear cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 79.
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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 | 0.001 | 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) | 0.001 | 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 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".