Next-generation leukemia immunotherapy
Bibliographic record
Abstract
Allogeneic hematopoietic cell transplantation led to the discovery of the allogeneic GVL effect, which remains the most convincing evidence that immune cells can cure cancer in humans. However, despite its great paradigmatic and clinical relevance, induction of GVL by conventional allogeneic hematopoietic cell transplantation remains a quite rudimentary form of leukemia immunotherapy. It is toxic and its efficacy is far from optimal. It is therefore sobering that since the discovery of the GVL effect 3 decades ago, the way GVL is induced and manipulated has practically not changed. Preclinical and clinical studies suggest that injection of T cells primed against a single Ag present on neoplastic cells could enhance the GVL effect without causing any GVHD. We therefore contend that Ag-targeted adoptive T-cell immunotherapy represents the future of leukemia immunotherapy, and we discuss the specific strategies that ought to be evaluated to reach this goal. Differences between these strategies hinge on 2 key elements: the nature of the target Ag and the type of Ag receptor expressed on T cells.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".