New Horizons with Growth Differentiation Factor 15 in Oncology: From Cancer Cachexia and Tumour Immunity to Novel Therapeutic Strategies
Bibliographic record
Abstract
Growth differentiation factor 15 (GDF-15) is a stress-induced cytokine produced by tumour cells and peripheral cells. It is implicated in the development of cancer cachexia, a debilitating condition for which no effective pharmacological therapy currently exists. GDF-15 regulates appetite and metabolic processes through complex neural and hormonal networks. Furthermore, it has been implicated in chemotherapy-induced nausea and vomiting, representing a potential therapeutic target. GDF-15 negatively affects tumour immunity, suggesting that anti-GDF-15 therapy could potentially enhance immune responses and help overcome resistance to immunotherapy. Recently, early clinical trials have reported preliminary results of GDF-15-targeted therapies in alleviating cancer cachexia and potentially enhancing the efficacy of immunotherapy. This review aims to provide an overview of the role of GDF-15 in cancer cachexia, including the underlying neural mechanisms and their involvement in tumour immunity. This review also summarises recent clinical trial findings and discusses future perspectives on GDF-15-targeted therapy in oncology, offering important insights for future research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".