Cryptic leukemia antigens share homology with microbial epitopes and stimulate T-cell responses in healthy donors
Bibliographic record
Abstract
Abstract Leukemia cells express cryptic tumor-specific antigens (TSAs) derived from aberrantly transcribed non-exomic genome sequences. These antigens are generally absent from healthy tissues yet shared across patients, making them attractive immunotherapy targets by minimizing on-target/off-tumor toxicity while offering broad applicability. However, their immunogenic potential and the nature of the T-cell repertoire they stimulate remain unknown. Cryptic antigen-specific CD8 + T cells could be expanded from healthy donor T-cell repertoires for six out of nine candidate acute leukemia cryptic TSA. T-cell receptor (TCR) and epitope sequence analysis revealed oligoclonal or near-monoclonal responses, involving shared and donor-restricted clonotypes recognizing cryptic TSAs which shared sequence homology with microbial epitopes. Orthotopic TCR replacement with cryptic TSA-specific TCR chains using a one-step CRISPR-Cas9 approach further validated the antigenic specificity and therapeutic potential of two TCRs respectively targeting cryptic TSAs from acute myeloid and lymphoid leukemia. To our knowledge, this is the first report describing functional TCRs directed against cryptic leukemia TSAs and highlights their potential as a new class of antigens for T-cell-based immunotherapies. Key points A high proportion of cryptic leukemia TSAs shares homology with microbial epitopes and can stimulate expansion of low-frequency T cell repertoire in healthy individuals. Ex vivo expansion of cryptic TSA-specific T cells enables TCR identification that can be used to devise new T cell immunotherapies. Visual Abstract Conclusion Cryptic leukemia antigens elicit antigenic and specific T-cell responses and represents novel targets for TCR or BiTE immunotherapy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".