Does IGFR1 inhibition result in increased muscle mass loss in patients undergoing treatment for pancreatic cancer?
Bibliographic record
Abstract
BACKGROUND: IGF-1 plays a role in the growth of multiple tumor types, including pancreatic cancer. IGF-1 also serves as a growth factor for muscle. The impact of therapeutic targeting of IGF-1 on muscle mass is unknown. METHODS: We evaluated muscle mass at L3 in patients enrolled in a randomized phase II study of MK-0646 (M), a monoclonal antibody directed against the IGF-1 protein, in patients with metastatic pancreatic cancer (MPC). Two different doses of M were tested, 5 and 10 mg/kg. We used the Slice-o-matic (ver 4.3) software to segregate CT images into muscle and fat components and measured muscle area (cm(2)) at baseline and after 2 and 4 months of treatment. Patients received either gemcitabine with erlotinib (G + E), G + E + M, or G + M. Differences between the groups were compared using t tests. RESULTS: Fifty-three patients had both baseline and 2-month imaging available for analysis. Of these, 42 received M with their chemo, and 11 had G + E only. After 2 months of treatment, both groups demonstrated decrease in muscle mass. G + E patients lost 5.6 % of muscle mass; M patients lost 9.1 and 8.6 % after treatment with 5 and 10 mg/kg, respectively (p = 0.53). Patients demonstrating a response lost less muscle (median 4.6 %) than those with stable disease (9.6 %) and progressive disease (8.9 %, p = 0.14). Muscle retention from baseline to 2-month imaging, defined as loss of <6 cm(2) of muscle, correlated with better survival than those patients demonstrating a muscle loss (HR 0.51, p = 0.03). CONCLUSIONS: MPC patients can be expected to lose muscle mass even while having clinical benefit (PR or SD) from chemotherapy. Muscle loss correlated with a risk of study drop-out and death. There was a non-significant trend toward greater muscle mass loss in patients on anti-IGF-1R therapy. However, it is unclear if this loss translates into functional differences between patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".