Sorafenib treatment following hematopoietic stem cell transplant in pediatric <i>FLT3</i>/ITD acute myeloid leukemia
Bibliographic record
Abstract
BACKGROUND: FLT3/ITD is associated with poor outcomes in adult and pediatric acute myeloid leukemia (AML). Allogeneic hematopoietic stem cell transplantation (HSCT) can improve cure rates, however relapse is still common. Recent studies demonstrate the activity of FLT3 inhibitors, including sorafenib, in targeting the underlying mutation. PROCEDURE: We conducted a retrospective study of 15 pediatric patients with FLT3/ITD+ AML treated with sorafenib within 18 months after receiving HSCT. Sorafenib was administered either as prophylaxis in patients considered at very high risk for relapse (n = 6) or at the time of disease recurrence (n = 9). RESULTS: Sorafenib was initiated at a median of 100 days post HSCT. Overall, 11/15 (73%) of patients experienced medically significant toxicities. Among patients who experienced toxicity, 6/11 (55%) received treatment at doses above what was later determined to be the maximum tolerated dose of sorafenib for pediatric leukemia. Importantly, sorafenib did not appear to exacerbate graft versus host disease. Our findings suggest that sorafenib may be of particular efficacy in patients with minimal residual disease (MRD); all patients who received sorafenib for MRD immediately prior to transplant or with emergence post-HSCT are alive and remain in complete remission at a median of 48 months post HSCT. CONCLUSIONS: Our case series suggests that sorafenib administration is feasible and tolerable in pediatric FLT3/ITD+ AML patients early post HSCT. Ongoing prospective controlled studies are needed to further define the dosing of sorafenib in the post-HSCT period and to determine the optimal context for this treatment approach.
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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 | 0.000 |
| 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 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".