Abstract 1543: Obesity promotes triple negative breast cancer progression through regulating the plasticity of adipose tissue
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".