Fueling the Tumor Microenvironment with Cancer-Associated Adipocytes
Bibliographic record
Abstract
Despite their abundance throughout the body, adipocytes are often ignored for their contributions within the tumor microenvironment (TME). However, their role in fueling cancer is becoming increasingly apparent as interest in the TME has seen remarkable advances in recent years. A seminal study by Dirat and colleagues highlighted the essential impact of the peritumoral adipose tissue in breast cancer progression and was among the first to demonstrate that tumor cells can reprogram adipocytes within their immediate niche to adopt unique characteristics. These "cancer-associated adipocytes" (CAA) were found to exchange cytokines and lipids with tumor cells, leading to their metabolic rewiring and acquisition of proinflammatory and invasive phenotypes. These important discoveries have represented a breakthrough in understanding the bidirectional metabolic dialog between adipocytes and tumor cells, and have contributed renewed perspectives on the functional contributions of adipocytes within the TME. Moreover, the effects of CAA may be further amplified in the setting of obesity as lipids dramatically accumulate, providing insights into the link between breast cancer and its more advanced clinical state in obese conditions. Thus, the different molecular actors involved in the dialog between tumor cells and CAA represent promising therapeutic targets that may have particular relevance in improving prognosis in obese patients with cancer. See related article by Dirat and colleagues, Cancer Res 2011;71:2455-65.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".