The impact of a high fat diet and platelet activation on pre-metastatic niche formation
Bibliographic record
Abstract
There is active crosstalk between tumor cells and the tumor microenvironment during metastatic progression, a process that is significantly affected by obesity, particularly in breast cancer. Here we analyze the impact of a high fat diet (HFD) on metastasis, focusing on the role of platelets in the formation of premetastatic niches (PMNs). We find that a HFD provokes pre-activation of platelets and endothelial cells, promoting the formation of PMNs in the lung. These niches are characterized by increased vascular leakiness, platelet activation and overexpression of fibronectin in both platelets and endothelial cells. A HFD promotes interactions between platelets, tumor cells and endothelial cells within PMNs, enhancing tumor cell homing and metastasis. Importantly, therapeutic interventions like anti-platelet antibody administration or a dietary switch reduce metastatic cell homing and outgrowth. Moreover, blocking fibronectin reduces the interaction of tumor cells with endothelial cells. Importantly, when coagulation parameters prior to neoadjuvant treatment are considered, triple negative breast cancer (TNBC) female patients with reduced Partial Thromboplastin time (aPTT) had a significantly shorter time to relapse. These findings highlight how diet and platelet activation in pre-metastatic niches affect tumor cell homing and metastasis, suggesting potential therapeutic interventions and prognostic markers for TNBC patients. Previous work has identified a link between obesity and breast cancer metastasis. Here, using preclinical mouse models, the authors show that high-fat diet promotes platelet and endothelial cell activation in the lungs resulting in the development premetastatic niches, enhancing tumor cell homing and metastasis.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".