Plasma Insulin-like Growth Factors, Insulin-like Binding Protein-3, and Outcome in Metastatic Colorectal Cancer: Results from Intergroup Trial N9741
Bibliographic record
Abstract
PURPOSE: Insulin-like growth factor (IGF)-I and IGF-II stimulate neoplastic cell growth and inhibit apoptosis, whereas IGF-binding protein-3 (IGFBP-3) inhibits the bioavailability of IGF-I and has independent proapoptotic activity. We examined the influence of baseline plasma levels of IGF-I, IGF-II, IGFBP-3, and C-peptide on outcome among patients receiving first-line chemotherapy for metastatic colorectal cancer. EXPERIMENTAL DESIGN: The plasma levels of IGF-I, IGF-II, IGFBP-3, and C-peptide as well as data on prognostic factors and body size were measured at baseline among 527 patients participating in a randomized trial of first-line chemotherapy for metastatic colorectal cancer. RESULTS: Higher baseline plasma IGFBP-3 levels were associated with a significantly greater chemotherapy response rate (P = 0.03) after adjusting for other prognostic factors, whereas neither IGF-I nor IGF-II levels significantly predicted tumor response. Higher levels of IGF-I, IGF-II, and IGFBP-3 were all univariately associated with improved overall survival (P = 0.0001 for all). In a model that mutually adjusted for IGF-I and IGFBP-3, as well as other prognostic factors, increasing baseline-circulating IGFBP-3 was associated with a significantly longer time to tumor progression (P = 0.03), whereas circulating IGF-I was not associated with disease progression (P = 0.95). Levels of C-peptide were not associated with any measure of patient outcome. CONCLUSION: Among colorectal cancer patients receiving first-line chemotherapy, increasing levels of IGFBP-3, an endogenous antagonist to IGF-I, are associated with an improved objective treatment response and a prolonged time to cancer progression. The IGF pathway may represent an important target for future treatment strategies.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".