Data from Circulating Levels of Soluble KIT Serve as a Biomarker for Clinical Outcome in Gastrointestinal Stromal Tumor Patients Receiving Sunitinib following Imatinib Failure
Bibliographic record
Abstract
Abstract Purpose: To evaluate changes in circulating levels of soluble KIT (sKIT) extracellular domain as a potential biomarker for clinical outcome in gastrointestinal stromal tumor patients treated with the multitargeted tyrosine kinase inhibitor sunitinib following imatinib failure in a previously reported phase III study. Experimental Design: Patients received sunitinib 50 mg/d (n = 243) or placebo (n = 118) daily in 6-week cycles (4 weeks on, 2 weeks off treatment). Plasma sKIT levels were sampled every 2 weeks in cycle 1 and on days 1 and 28 of subsequent cycles; analyzed by ELISA; and evaluated using Prentice criteria, Cox proportional hazards models, and proportion of treatment effect (PTE) analysis. Results: From 4 weeks on treatment and onward, significant differences were shown between treatment groups (P < 0.0001) in sKIT level changes from baseline (median levels decreased with sunitinib and increased with placebo). Decreases in sKIT levels were a significant predictor of longer time to tumor progression (TTP). Patients with reduced levels at the end of cycle 2 had a median TTP of 34.3 weeks versus 16.0 weeks for patients with increased levels [hazard ratio, 0.71; 95% confidence interval (95% CI), 0.61-0.83; P < 0.0001], and changes in sKIT levels replaced treatment as a stronger predictor of TTP (PTE, 0.80; 95% CI, 0.34-3.70), showing even greater surrogacy on cycle 3 day 1 (PTE, 0.98; 95% CI, 0.39-3.40). Conclusions: The results suggest that circulating plasma sKIT levels seem to function as a surrogate marker for TTP in gastrointestinal stromal tumor patients. Additional studies are warranted to confirm and expand these findings. (Clin Cancer Res 2009;15(18):5869–77)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".