Eradication of Large Solid Tumors by Gene Therapy with a T-Cell Receptor Targeting a Single Cancer-Specific Point Mutation
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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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 | 0.000 |
| 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".