Abstract B035: Deep learning-based spatially resolved immune clustering in the tumor microenvironment predicts distant metastasis risk in high-grade prostate cancer
Notice bibliographique
Résumé
Abstract Objectives: Contemporary computational pathology biomarker approaches in prostate cancer (PCa) generally either analyze the entire digitized image (without adequate human interpretability) or focus solely on tumor regions, in essence refining Gleason grading. We aimed to identify human-interpretable histologic features inclusive of the tumor microenvironment (TME) with prognostic value in localized PCa. Methods: We retrospectively identified two independent cohorts of patients with localized PCa who underwent radical prostatectomy (with longitudinal outcomes data) and had digitized H&E-stained slides from surgical specimens. Cohort A was the discovery cohort; Cohort B served as validation. A deep learning model (CellViT) with a vision transformer encoder pretrained on histopathology images was used to segment and classify immune cell nuclei, and immune cell clusters were identified using DBSCAN. Cox regression was used to examine association between clinicopathologic/histologic features and time to distant metastasis (DM). A third cohort from The Cancer Genome Atlas (TCGA) was used to evaluate relationships between genomic/transcriptomic features (from whole-exome and bulk RNA sequencing) and digital pathology-derived immune metrics in radical prostatectomy specimens. Results: Cohort A (n=272) had median age of 63; 87% was Gleason 6-7 and 93% pT2-T3a. Median immune cell proportion was 4.3% (interquartile range [IQR] 3.0-5.9%), with median of 8.7 immune clusters per 25 mm2 (IQR 0–26.5). Cohort B (n=218) had median age of 62; 83% was Gleason 6-7 and 86% pT2-T3a. Median immune cell proportion was 2.7% (IQR 1.9-3.5%), with median of 4.7 clusters per 25 mm2 (IQR 0-12.1). In Cohort A (median follow-up 12.7 years), log-transformed immune cluster (but not immune cell proportion) was independently associated with DM for Gleason 8-10 (adjusted hazard ratio [AHR] 0.42, 95% confidence interval [CI] 0.19-0.93) but not Gleason 6-7 (AHR 1.26, 95% CI 0.78-2.05), with a significant interaction (Pint=0.019). Similarly in Cohort B (median follow-up 8.1 years), immune cluster (but not immune cell proportion) was associated with DM for Gleason 8-10 (AHR 0.60, 95% CI 0.37-0.98) but not Gleason 6-7 (AHR 1.19, 95% CI 0.74-1.91; Pint=0.043). In TCGA (n=329), high immune cluster (top 10th percentile) was not associated with mutational differences. For Gleason 8-10 samples (but not Gleason 6-7), immune cell deconvolution with CIBERSORTx revealed enrichment of CD8+ T cells (P=0.023), activated memory CD4+ T cells (P=0.014), and Tregs (p=0.004). Immune repertoire profiling with TRUST4 demonstrated increased TRB clonality (P=0.027) indicative of clonally expanded T cell populations in high-cluster samples for Gleason 8-10 (but not Gleason 6-7). Conclusions: We identified and validated spatial immune clustering in the TME as a novel, human-interpretable computational pathology biomarker prognostic of distant metastasis in high-grade PCa. Our findings underscore the potential of biologically informed artificial intelligence approaches for biomarker discovery in PCa. Citation Format: David D. Yang, Alexander J. Haas, Aya Abdelnaser, Eddy Saad, Alfred A. Barney, Jett P. Crowdis, Cora A. Ricker, Jihye Park, Mary-Ellen Taplin, Paul L. Nguyen, Martin T. King, Keyan Salari, Chin-Lee Wu, Eliezer M. Van Allen. Deep learning-based spatially resolved immune clustering in the tumor microenvironment predicts distant metastasis risk in high-grade prostate cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B035.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».