Abstract 463: Integrated digital pathology and transcriptome analysis identifies molecular mediators of T cell exclusion in ovarian cancer
Notice bibliographique
Résumé
Background:Close proximity between cytotoxic T lymphocytes and tumor cells is required for effective immunotherapy. Three tumor-immune (TI) phenotypes, infiltrated, excluded and desert, have been previously described based on the infiltration patterns of CD8+ T cells. However, no quantitative methods exist to define these phenotypes robustly in human solid tumors. Importantly, the molecular features and mechanisms determining these phenotypes are not well understood. Here we report a novel integrated approach to classify and functionally dissect TI phenotypes in human ovarian cancer.Methods:CD8 IHC and RNAseq analysis were performed on 370 ovarian tumors from the ICON7 phase III clinical trial, a front-line trial testing the addition of bevacizumab to chemotherapies. A digital image analysis algorithm was developed to quantify the quantity and spatial distribution of CD8+ T cells. Coupling digital pathology with transcriptome analysis, a random forest machine learning algorithm was applied to identify genes associated with these two metrics using a training set (n=155). A gene expression-based classifier was developed for classifying TI phenotypes and validated using testing sets from ICON7 trial and a vendor collection. Functional characterization of key mediators promoting T cell exclusion were carried out by integrating in situ, in vitro and ex vivo analyses on ovarian tumor tissues, cancer associated fibroblasts (CAFs) and ovarian cancer cell lines. Anti-tumor activity of TGFβ blockade in combination with anti-PD-L1 was evaluated in the mouse BrKras ovarian cancer model in FVB background.Results:Integrating digital pathology and machine learning on large ovarian tumor cohorts, we developed and validated a 157-gene molecular classifier. We show the TI phenotypes are of biological and clinical importance in ovarian cancer. Two hallmarks of T cell exclusion were identified: 1) loss of MHC I on tumor cells and 2) upregulation of TGFβ/stromal activities. We show that MHC I in ovarian cancer cells is likely regulated by epigenetic mechanisms and TGFβ is a key mediator of T cell exclusion. TGFβ reduced MHC I expression in ovarian cancer cells and induced extracellular matrix and immunosuppressive molecules in human primary fibroblasts. Finally, we demonstrated that combining anti-TGFβ and anti-PD-L1 in the BrKras mouse model improved the anti-tumor efficacy and survival.Conclusion:This study provided the first systematic and in-depth characterization of the molecular features and mechanisms underlying the tumor-immune phenotypes in human ovarian cancer. We illuminated a multi-faceted role of TGFβ in mediating crosstalk between tumor cells and CAFs to shape the tumor-immune contexture. Our findings support that targeting the TGFβ pathway represents a promising therapeutic strategy to overcome T cell exclusion and optimize response to cancer immunotherapy.Citation Format: Melanie Desbois, Akshata Udyavar, Lisa Ryner, Cleopatra Kozlowski, Yinghui Guan, Milena Dürrbaum, Shan Lu, Jean-Philippe Fortin, Hartmut Koeppen, James Ziai, Ching-Wei Chang, Amy Lo, Shilpa Keerthivasan, Marie Plante, Richard Bourgon, Carlos Bais, Priti Hegde, Anneleen Daemen, Shannon Turley, Yulei Wang. Integrated digital pathology and transcriptome analysis identifies molecular mediators of T cell exclusion in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 463.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,051 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».