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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".