The landscape of T-cell engagers for the treatment of follicular lymphoma
Bibliographic record
Abstract
Follicular lymphoma (FL), the second most common subtype of non-Hodgkin lymphoma, relies on interactions with immune elements in the tumor microenvironment, including T-follicular helper cells and follicular dendritic cells, for its survival and progression. Despite its initial responsiveness to chemoimmunotherapy, FL is generally considered incurable. Strategies to improve immune-mediated control of FL could significantly benefit this population, particularly as it includes many elderly and comorbid patients. Immune cell engagers, especially bispecific antibodies (BsAbs), are crucial in targeting FL by bridging tumor and effector cells, thereby triggering T-cell activation and cytotoxic killing. CD3 × CD20 BsAbs have shown the most promise in clinical development for B-NHL patients, with structural variations affecting their target affinity and potency. This review summarizes the current clinical trials of BsAbs for relapsed/refractory FL, highlighting the approval of some agents, their role in first-line treatment or combination therapies, their toxicity profiles, and the future of this therapeutic approach compared to other immune cell therapies.
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 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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".