Abstract 79: Study of tumor microenvironment of ovarian clear cell carcinoma
Notice bibliographique
Résumé
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.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».