Abstract B033: Identifying triple-negative breast cancer patients at high risk of worse prognosis using molecular features derived from histology images
Notice bibliographique
Résumé
Abstract Triple-negative breast cancer (TNBC) is the most aggressive subtype of invasive breast cancer characterized by the lack of estrogen receptor (ER), progesterone receptor (PR), and HER2 expression. TNBC is heterogeneous in terms of the biological and clinical perspective and a subset of these tumors exhibits markedly poor prognosis. Identification of this TNBC subset is an unmet clinical need. To identify patients with primary TNBC at high risk of worse prognosis, we developed a graph (network)-based analysis approach combined with an unbalanced optimal transport technique, utilizing molecular features derived from histology images. A total of 143 H&E-stained histology images from The Cancer Genome Atlas (TCGA) primary TNBC cases were analyzed. Tumor tissues were segmented on whole-slide images using a pre-trained ResNet18 model with a patch size of 512×512 and a processing resolution of 0.5 microns per pixel. A pre-trained ResNet34 model was then used to estimate four molecular features—microsatellite instability, hypermutation density, chromosomal instability, and TP53 mutation—on the segmented tumor tissues. In addition, the spatial fraction of tumor-infiltrating lymphocytes (TILs), derived from histology images (Saltz et al., Cell Reports, 2018), and four morphology features (epithelial area, tubule formation, nuclear pleomorphism, and mitosis; Thennavan et al., Cell Genomics, 2021) graded by the breast cancer pathology expert committee were analyzed. Following exclusion of cases with incomplete data, 113 cases were used for network analysis. A feature network was constructed using nine histology-derived features, based on Spearman’s correlation. K-means clustering, employing unbalanced optimal transport to calculate Wasserstein distance, was used to identify subgroups in the resulting feature network. The Wasserstein distance computed between samples and cluster centroids served as the cost function during the K-means clustering process. The two identified subgroups, categorized as a high-risk group (N=81) and a low-risk group (N=32) based on disease-specific survival (DSS) rates, showed a statistically significant difference in DSS (log-rank p=0.047). Estimated TILs were significantly different between the high and low-risk groups (p<0.0001). CIBERSORT scores that quantify 22 immune cell types were assessed. The low-risk group showed significantly higher CD8 T cells (p=0.030), regulatory Tregs T cells (p=0.029), and M1 macrophages (p=0.006), whereas the high-risk group showed significantly higher M0 macrophages (p=0.026) and M2 macrophages (p=0.006). Restricting the analysis to TNBC cases with tumor stage ≥2 revealed a greater DSS difference between the high (N=64) and low-risk (N=28) groups (p=0.026). CIBERSORT analysis revealed that the low-risk group had significantly higher levels of CD8 T cells (p=0.009) and activated CD4 memory T cells (p=0.027). Our analyses show that a cold immune milieu characterized by low TILs and a dominance of non-activated and anti-inflammatory macrophages are associated with poor prognosis in TNBC. Citation Format: Jung Hun Oh, Fresia Pareja, Rena Elkin, Larry Norton, Joseph Deasy. Identifying triple-negative breast cancer patients at high risk of worse prognosis using molecular features derived from histology images [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 B033.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 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 | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».