T-Cell Immunotherapies Targeting Histocompatibility and Tumor Antigens in Hematological Malignancies
Bibliographic record
Abstract
Over the last decades, T-cell immunotherapy has revealed itself as a powerful, and often curative, strategy to treat blood cancers. In hematopoietic cell transplantation, most of the so-called graft-vs.-leukemia (GVL) effect hinges on the recognition of histocompatibility antigens that reflect immunologically relevant genetic variants between donors and recipients. Whether other variants acquired during the neoplastic transformation, or the aberrant expression of gene products can yield antigenic targets of similar relevance as the minor histocompatibility antigens is actively being pursued. Modern genomics and proteomics have enabled the high throughput identification of candidate antigens for immunotherapy in both autologous and allogeneic settings. As such, these major histocompatibility complex-associated tumor-specific (TSA) and tumor-associated antigens (TAA) can allow for the targeting of multiple blood neoplasms, which is a limitation for other immunotherapeutic approaches, such as chimeric antigen receptor (CAR)-modified T cells. We review the current strategies taken to translate these discoveries into T-cell therapies and propose how these could be introduced in clinical practice. Specifically, we discuss the criteria that are used to select the antigens with the greatest therapeutic value and we review the various T-cell manufacturing approaches in place to either expand antigen-specific T cells from the native repertoire or genetically engineer T cells with minor histocompatibility antigen or TSA/TAA-specific recombinant T-cell receptors. Finally, we elaborate on the current and future incorporation of these therapeutic T-cell products into the treatment of hematological 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".