Cancer-driven cytokine immunomodulation ameliorates cardiac function and suppresses fibrosis
Bibliographic record
Abstract
Heart failure remains a leading cause of morbidity and mortality worldwide, with limited progress in the development of novel therapies. It has been demonstrated that tumor growth improves cardiac function and reduces myocardial fibrosis in mouse models of heart failure. It is clear that cancer cell implantation is not a possible therapeutic strategy for heart failure. Therefore, we further studied the underlying mechanism involved, with the objective of demonstrating its broad therapeutic applicability. We show that a single intravenous injection of serum from tumor-bearing mice rapidly augments left-ventricular fractional shortening and suppresses fibrosis in the heart, diaphragm, and skeletal muscles. Cytokine profiling identified IFNγ and TNFα as essential mediators secreted downstream of natural killer (NK) cell activation. Purified recombinant IFNγ and TNFα mimic the serum effect, polarizing cardiac and skeletal macrophages toward an anti-inflammatory, reparative state. We further show that macrophage depletion abrogates the observed beneficial effect, confirming their critical role. Our findings define a novel NK cell-macrophage cytokine axis that reverses cardiac dysfunction and fibrosis in pressure-overload (transverse aortic constriction) and ATF3-transgenic heart failure models. Together, these findings define a novel host-tumor microenvironment response through cytokine secretion, which leads to cardiac repair and dissolution of fibrosis. This work presents a novel therapeutic strategy for harnessing innate immune cells in the treatment of heart failure and fibrotic disease.
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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".