Adverse effects of systemic cancer therapy on skeletal muscle: myotoxicity comes out of the closet
Bibliographic record
Abstract
PURPOSE OF REVIEW: Systemic cancer therapy-associated skeletal muscle wasting is emerging as a powerful impetus to the overall loss of skeletal muscle experienced by patients with cancer. This review explores the clinical magnitude and biological mechanisms of muscle wasting during systemic cancer therapy to illuminate this adverse effect. Emerging strategies for mitigation are also discussed. RECENT FINDINGS: Clinical findings include precise, specific measures of muscle loss over the course of chemotherapy, targeted therapy and immunotherapy. All these therapeutic classes associate with quantitatively important muscle loss, independent of tumor response. Parallel experimental studies provide understanding of the specific molecular basis of wasting, which can include inhibition of protein synthesis, proliferation and differentiation, and activation of inflammation, reactive oxygen species, autophagy, mitophagy, apoptosis, protein catabolism, fibrosis and steatosis in muscle. Strategies to mitigate these muscle-specific adverse effects of cancer therapy remain in the earliest stages of development. SUMMARY: The adverse side effect of cancer therapy on skeletal muscle has been largely ignored in the development of cancer therapeutics. Given the extent to which loss of muscle mass and function can bear on patients' function and quality of life, protection/mitigation of these side effects is a research priority.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".