Suboptimal T-cell Therapy Drives a Tumor Cell Mutator Phenotype That Promotes Escape from First-Line Treatment
Bibliographic record
Abstract
Abstract Antitumor T-cell responses raised by first-line therapies such as chemotherapy, radiation, tumor cell vaccines, and viroimmunotherapy tend to be weak, both quantitatively (low frequency) and qualitatively (low affinity). We show here that T cells that recognize tumor-associated antigens can directly kill tumor cells if used at high effector-to-target ratios. However, when these tumor-reactive T cells were present at suboptimal ratios, direct T-cell–mediated tumor cell killing was reduced and the ability of tumor cells to evolve away from a coapplied therapy (oncolytic or suicide gene therapy) was promoted. This T-cell–mediated increase in therapeutic resistance was associated with C to T transition mutations that are characteristic of APOBEC3 cytosine deaminase activity and was induced through a TNFα and protein kinase C–dependent pathway. Short hairpin RNA inhibition of endogenous APOBEC3 reduced rates of tumor escape from oncolytic virus or suicide gene therapy to those seen in the absence of antitumor T-cell coculture. Conversely, overexpression of human APOBEC3B in tumor cells enhanced escape from suicide gene therapy and oncolytic virus therapy both in vitro and in vivo. Our data suggest that weak affinity or low frequency T-cell responses against tumor antigens may contribute to the ability of tumor cells to evolve away from first-line therapies. We conclude that immunotherapies need to be optimized as early as possible so that, if they do not kill the tumor completely, they do not promote treatment resistance.
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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) | 0.001 | 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 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".