Eradication of Large Solid Tumors by Gene Therapy with a T-Cell Receptor Targeting a Single Cancer-Specific Point Mutation
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Bibliographic record
Abstract
PURPOSE: Cancers usually contain multiple unique tumor-specific antigens produced by single amino acid substitutions (AAS) and encoded by somatic nonsynonymous single nucleotide substitutions. We determined whether adoptively transferred T cells can reject large, well-established solid tumors when engineered to express a single type of T-cell receptor (TCR) that is specific for a single AAS. EXPERIMENTAL DESIGN: By exome and RNA sequencing of an UV-induced tumor, we identified an AAS in p68 (mp68), a co-activator of p53. This AAS seemed to be an ideal tumor-specific neoepitope because it is encoded by a trunk mutation in the primary autochthonous cancer and binds with highest affinity to the MHC. A high-avidity mp68-specific TCR was used to genetically engineer T cells as well as to generate TCR-transgenic mice for adoptive therapy. RESULTS: When the neoepitope was expressed at high levels and by all cancer cells, their direct recognition sufficed to destroy intratumor vessels and eradicate large, long-established solid tumors. When the neoepitope was targeted as autochthonous antigen, T cells caused cancer regression followed by escape of antigen-negative variants. Escape could be thwarted by expressing the antigen at increased levels in all cancer cells or by combining T-cell therapy with local irradiation. Therapeutic efficacies of TCR-transduced and TCR-transgenic T cells were similar. CONCLUSIONS: Gene therapy with a single TCR targeting a single AAS can eradicate large established cancer, but a uniform expression and/or sufficient levels of the targeted neoepitope or additional therapy are required to overcome tumor escape. Clin Cancer Res; 22(11); 2734-43. ©2015 AACRSee related commentary by Liu, p. 2602.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it