Pre-treatment levels of circulating free IGF-1 identify NSCLC patients who derive clinical benefit from figitumumab
Post-publication record
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Bibliographic record
Abstract
BACKGROUND: Phase III trials of the anti-insulin-like growth factor type 1 receptor (IGF-IR) antibody figitumumab (F) in unselected non-small-cell lung cancer (NSCLC) patients were recently discontinued owing to futility. Here, we investigated a role of free IGF-1 (fIGF-1) as a potential predictive biomarker of clinical benefit from F treatment. MATERIALS AND METHOD: Pre-treatment circulating levels of fIGF-1 were tested in 110 advanced NSCLC patients enrolled in a phase II study of paclitaxel and carboplatin given alone (PC) or in combination with F at doses of 10 or 20 mg kg(-1) (PCF10, PCF20). RESULTS: Cox proportional hazards model interactions were between 2.5 and 3.5 for fIGF-1 criteria in the 0.5-0.9 ng ml(-1) range. Patients above each criterion had a substantial improvement in progression-free survival on PCF20 related to PC alone. Free IGF-1 correlated inversely with IGF binding protein 1 (IGFBP-1, ρ=-0.295, P=0.005), and the pre-treatment ratio of insulin to IGFBP-1 was also predictive of F clinical benefit. In addition, fIGF-1 levels correlated with tumour vimentin expression (ρ=0.594, P=0.021) and inversely with E-cadherin (ρ=-0.389, P=0.152), suggesting a role for fIGF-1 in tumour de-differentiation. CONCLUSION: Free IGF-1 may contribute to the identification of a subset of NSCLC patients who benefit from F therapy.
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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 | 0.001 |
| 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 | 0.001 |
| 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".