MAP3K11/GDF15 axis is a critical driver of cancer cachexia
Bibliographic record
Abstract
BACKGROUND: Cancer associated cachexia affects the majority of cancer patients during the course of the disease and thought to be directly responsible for about a quarter of all cancer deaths. Current evidence suggests that a pro-inflammatory state may be associated with this syndrome although the molecular mechanisms responsible for the development of cachexia are poorly understood. The purpose of this work was the identification of key drivers of cancer cachexia that could provide a potential point of intervention for the treatment and/or prevention of this syndrome. METHODS: Genetically engineered and xenograft tumour models were used to dissect the molecular mechanisms driving cancer cachexia. Cytokine profiling from the plasma of cachectic and non-cachectic cancer patients and mouse models was utilized to correlate circulating cytokine levels with the cachexia phenotype. RESULTS: Utilizing engineered tumour models we identified MAP3K11/GDF15 pathway activation as a potent inducer of cancer cachexia. Increased expression and high circulating levels of GDF15 acted as a key mediator of this process. In animal models, tumour-produced GDF15 was sufficient to trigger the cachexia phenotype. Elevated GDF15 circulating levels correlated with the onset and progression of cachexia in animal models and in patients with cancer. Inhibition of GDF15 biological activity with a specific antibody reversed body weight loss and restored muscle and fat tissue mass in several cachectic animal models regardless of their complex secreted cytokine profile. CONCLUSIONS: The combination of correlative observations, gain of function, and loss of function experiments validated GDF15 as a key driver of cancer cachexia and as a potential therapeutic target for the treatment and/or prevention of this syndrome.
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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".