Potentials, challenges and future of chimeric antigen receptor T-cell therapy in non-Hodgkin lymphomas
Bibliographic record
Abstract
Background: Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma subtype. Disease progression or relapse following frontline chemoimmunotherapy, largely in the form of standard R-CHOP, occurs in 30–40% patients. Relapsed/refractory (R/R) DLBCL represents a major unmet medical need. In particular, patients with primary refractory disease or those whose lymphoma relapses after autologous stem cell transplantation have historically had poor outcomes.Material and methods: Chimeric antigen receptor T-cell (CART) therapy is a promising novel treatment with curative potential in this setting. CART is based on ex vivo genetic modification of autologous T-cells to express chimeric receptors targeting antigens highly expressed in tumors such as CD19 in DLBCL. After lymphocyte-depleting therapy, patients are infused with CARTs that expand in vivo and target CD19-positive lymphoma cells.Results: In initial phase I–II trials, investigators have demonstrated complete responses in 40–50% of patients with R/R DLBCL, resulting in durable remission approaching 3 years of follow-up in most of these patients without further treatment. The logistics of delivery are complex as cell products require timely long-distance transfer between hospitals and production facilities. The unique toxicity profile of CARTs, including the risk of fatal immunological and neurologic events, also requires specific hospital wide management approaches and education. The substantial direct and indirect costs of CART will limit access even in countries with well resourced health care systems.Conclusions: While only two products are commercially available at present, further approvals in coming years appear likely. Future directions include CARTs with reactivity to tumor antigens other than CD19 and products targeting multiple tumor antigens to overcome resistance. The availability of CART has altered the current treatment algorithm for R/R DLBCL, and indications will likely expand to earlier lines of therapy and other hematologic malignancies.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".