Abstract 1543: Obesity promotes triple negative breast cancer progression through regulating the plasticity of adipose tissue
Notice bibliographique
Résumé
Abstract The rising global prevalence of overweight and obesity is linked to cancer initiation and poorer overall survival in triple-negative breast cancer (TNBC), making it a modifiable risk factor for both cancer prevention and treatment. The tumor microenvironment (TME) plays a crucial role in tumor development, progression, and response to chemotherapy, and is influenced by both local and systemic factors, such as aging and metabolism. However, most previous studies in this area have relied on animal models or in vitro systems, which provide limited insights into the comprehensive nature of the TME. The mammary gland consists of lobules and ducts with a connective stroma composed of fibro-adipose pockets. Excessive fat deposition in obese patients alters the quantity, function, and plasticity of adipose tissue. It was reported that the adipose tissue undergoes metabolic reprogramming during mammary tumor infiltration into the stromal compartment, which leads to adipose tissue possessing fibroblast/myofibroblast and macrophage-like features, modifying the TME through ECM remodeling and immune response activation, potentially contributing to cancer progression. Adipocyte precursor cells (APCs) are key regulators of adipose tissue plasticity, with the ability to differentiate into either adipogenic or fibrotic cell types. Moreover, fibroblasts are the most abundant stromal cells in TME and strongly associated with breast cancer progression and chemoresistance. Based on the above, I hypothesize that obesity promotes the differentiation of APCs into fibrotic cells, thereby accelerating cancer progression. Single cell multiomics (ATAC + RNA sequencing) has emerged as a powerful tool for studying the TME, as it enables the simultaneous analysis of epigenetic and genetic profiles from the same tumor sample. In this study, we performed single cell multiomics sequencing on primary TNBC tissue samples from 7 lean and 7 obese patients, stratified by body mass index (BMI). Our results revealed that the fibroblasts population in obese patients was more abundant than in lean patients, with significantly increased interactions between fibroblasts and cancer cells. The dominant subtype of fibroblasts in the obese group were identified as myofibroblasts (mCAFs), which were highly enriched in the TGFβ signaling pathway. This enrichment was correlated with a higher epithelial-mesenchymal transition (EMT) signature in cancer cells, observed at both the epigenetic and genetic levels. Furthermore, we leveraged a published human white adipocyte tissue atlas and found that mCAFs shared a high degree of transcriptomic similarity with APCs. This suggests that obesity promotes the differentiation of APCs into mCAFs via the TGFβ signaling pathway, thereby contributing to cancer progression. Our findings provide a novel direction for targeted therapeutic development in the obese patient population. Citation Format: Xi Xu, Yang Lin, Shalini Bahl, Mathieu Lupien. Obesity promotes triple negative breast cancer progression through regulating the plasticity of adipose tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1543.
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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 | 0,000 |
| 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».