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 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 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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 ».