Anti–Platelet-Derived Growth Factor Receptor Alpha Chain Antibodies Predict for Response to Nilotinib in Steroid-Refractory or -Dependent Chronic Graft-Versus-Host Disease
Bibliographic record
Abstract
Imatinib has clinical activity in chronic graft-versus-host disease (cGVHD), a significant complication of allogeneic hematopoietic cell transplant. Nilotinib is a tyrosine kinase inhibitor that targets the same receptors as imatinib but with different affinities. We tested the hypothesis that nilotinib is safe and has clinical activity in cGVHD. Thirty-three participants were enrolled in a phase I/II dose escalation and dose extension clinical trial of nilotinib for the treatment of steroid-refractory or- dependent cGVHD (ClinicalTrials.gov, NCT01155817). We assessed safety, clinical response, and pretreatment anti-platelet-derived growth factor receptor alpha chain (anti-PDGFRA) antibody levels. The 200-mg dose was identified as the maximum tolerated dose and used for the phase II dose extension study. At 6 months the incidence of failure-free survival (FFS), cGVHD progression, and nilotinib intolerance resulting in its discontinuation was 50%, 23%, and 23%, respectively. cGVHD responses in skin, joints, and mouth were observed at 3 and 6 months based on improvement in respective National Institutes of Health organ severity scores. Pretreatment anti-PDGFRA antibody levels ≥ .150 optical density as measured by ELISA correlated with longer FFS time (P < .0005) and trended with time until cGVHD progression (P < .06) but not drug intolerance. Nilotinib may be effective for corticosteroid-resistant or -refractory cGVHD in some patients, but its use is limited by intolerable side effects. Selection of patients with high pretreatment anti-PDGFRA antibody levels might improve the risk-to-benefit ratio of nilotinib and better justify its side effects.
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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 | 0.002 |
| 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) | 0.001 | 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".