Optimizing Allogeneic Stem Cell Transplantation Following CAR-T Therapy in Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Optimizing Allogeneic Stem Cell Transplantation Following CAR-T Therapy in Acute Lymphoblastic LeukemiaBackground and Aim: Patients with B-cell acute lymphoblastic leukemia (B-ALL) who are refractory or relapsed (R/R) have few therapeutic options and a poor prognosis.Chimeric antigen receptor (CAR) T-cell therapy represents a significant advancement in contemporary cell and gene therapies, bridging the gap in the treatment of high-risk patients and linking immunotherapies with cancer treatments, particularly in hematologic malignancies.This review examines the clinical evidence supporting consolidative allogeneic hematopoietic stem cell transplantation (ALLO-HSCT) following CAR T-cell therapy, as well as the variables that may affect the effectiveness of ALLO-HSCT.Finally, we offer suggestions for evaluating and treating patients with R/R, B-ALL who are receiving CAR T-cell therapy. Materials and Methods:International databases, including PubMed, Google Scholar, Scopus, and ISI, as well as national databases, such as Magiran, SID, and IranMedex, were utilized to obtain the articles used in this study.The search terms included CAR-T cells, hematopoietic stem cell transplantation, acute lymphoblastic leukemia, and relapse.Results: According to numerous clinical investigations, complete remission (CR) rates of 70-90% can be achieved with CAR T-cell treatment.It is crucial to understand that remission induced by CAR T-cell therapy may not last forever.According to research, between 30% and 60% of individuals may relapse following treatment.Conclusion: Therefore, researchers are now questioning the necessity of consolidative ALLO-HSCT in light of the successful remissions achieved by CAR T-cell therapy.Due to a lack of reliable information, the role of CAR T-cell treatment as a temporary solution or a permanent remedy before ALLO-HSCT remains a topic of debate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".