The Genomic Landscape of Antigenic Targets for T Cell-Based Leukemia Immunotherapy
Bibliographic record
Abstract
Intensive fundamental and clinical research in cancer immunotherapy have led to the emergence and evolution of two parallel universes with surprisingly little interactions: the realm of hematologic malignancies and that of solid tumors. Treatment of hematologic cancers using allogeneic hematopoietic cell transplantation (AHCT) serendipitously led to the discovery that T cells specific for minor histocompatibility antigens (MiHAs) could cure hematopoietic cancers. Besides, studies based on treatment of solid tumor with ex vivo-expanded tumor infiltrating lymphocytes or immune checkpoint therapy demonstrated that anti-tumor responses could be achieved by targeting tumor-specific antigens (TSAs). It is our contention that much insight can be gained by sharing the tremendous amount of data generated in the two-abovementioned universes. Our perspective article has two specific goals. First, to discuss the value of methods currently used for MiHA and TSA discovery and to explain the key role of mass spectrometry analyses in this process. Second, to demonstrate the importance of broadening the scope of TSA discovery efforts beyond classic annotated protein-coding genomic sequences.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".