CAR‐T Cell Therapy in Diffuse Large B Cell Lymphoma: Hype and Hope
Bibliographic record
Abstract
Patients with non-Hodgkin lymphomas (NHLs) resistant to standard therapies have a dismal prognosis. The outcome is even poorer in patients relapsing after autologous stem cell transplantation. Most of these patients do not qualify for an allogeneic hematopoietic cell transplantation (HCT) due to refractory disease, lack of a suitable allogeneic donor, higher age, or cumulative toxicity of previous chemotherapy. Despite patients undergoing allogeneic HCT normally profit from a graft-versus-lymphoma effect, overall survival in patients with NHL after HCT remains short. Therefore, novel treatment modalities are urgently needed. Chimeric antigen receptor (CAR)-T cells, a new class of cellular immunotherapy involving ex vivo genetic modification of T cells to incorporate an engineered CAR have been used in clinical trials. In the majority of studies, B cell malignancies treated with CD19 targeting CAR-T cells have been analyzed. Recently, results from 2 CD19 directed CAR-T cell trials with an increased follow-up of patients led to Food and Drug Administration and European Medicines Agency approval of tisagenlecleucel and axicabtagene ciloleucel. Common adverse events (AEs) include cytokine release syndrome and neurological toxicity, which may require admission to an intensive care unit, B cell aplasia and hemophagocytic lymphohistiocytosis. These AEs are manageable when treated by an appropriately trained team following established algorithm. In this review, we summarize the results of 3 large phase II CD19 CAR-T cell trials and focus on AEs. We also provide a perspective of ongoing activity in this field with the intend to improve the potency of this emerging novel therapy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".