Exploiting TCR Repertoire Analysis to Select Therapeutic TCRs for Cancer Immunotherapy
Bibliographic record
Abstract
Over the past decade, numerous innovative immunotherapy strategies have transformed the treatment of cancer and improved the survival of patients unresponsive to conventional chemotherapy and radiation therapy. Immune checkpoint inhibition approaches aim to block negative regulatory pathways that limit the function of endogenous T cells, while adoptive cell therapy produces therapeutic T cells with high functionality and defined cancer specificity. While CAR engineering successfully targets cancer surface antigens, TCR engineering enables targeting of the entire cancer proteome, including mutated neo-antigens. To date, TCR engineering strategies have focused on the identification of target cancer antigens recognised by well-characterised therapeutic TCRs. In this review, we explore whether antigen-focused approaches could be complemented by TCR-focused approaches, whereby information of the TCR repertoire of individual patients provides the basis for selecting TCRs to engineer autologous T cells for adoptive cell therapy. We discuss how TCR clonality profiles, distribution in T cell subsets, and bioinformatic screening against continuously improving TCR databases can guide the selection of TCRs for therapeutic application. We further outline in vitro approaches to prioritise TCR candidates to confirm cancer reactivity and exclude recognition of healthy autologous cells, which could provide validation for their therapeutic use even when the target antigen remains unknown.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".