Mobilizing and evaluating anticancer T cells: pitfalls and solutions
Bibliographic record
Abstract
Immunotherapy is a promising means to fight cancer, prompting a steady increase in clinical trials and correlative laboratory studies in this field. As antitumor T cells play central roles in immunity against malignant diseases, most immunotherapeutic protocols aim to induce and/or strengthen their function. Various treatment strategies have elicited encouraging clinical responses; however, major challenges have been uncovered that should be addressed in order to fully exploit the potential of immunotherapy. Here, we outline pitfalls for the mobilization of antitumor T cells and offer solutions to improve their therapeutic efficacy. We provide a critical perspective on the main methodologies used to characterize T-cell responses to cancer therapies, with a focus on discrepancies between T-cell attributes measured in vitro and protective responses in vivo. This review altogether provides recommendations to optimize the design of future clinical trials and highlights important considerations for the proficient analysis of clinical specimens available for research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".