TEM8/ANTXR1-Specific CAR T Cells as a Targeted Therapy for Triple-Negative Breast Cancer
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) is an aggressive disease lacking targeted therapy. In this study, we developed a CAR T cell–based immunotherapeutic strategy to target TEM8, a marker initially defined on endothelial cells in colon tumors that was discovered recently to be upregulated in TNBC. CAR T cells were developed that upon specific recognition of TEM8 secreted immunostimulatory cytokines and killed tumor endothelial cells as well as TEM8-positive TNBC cells. Notably, the TEM8 CAR T cells targeted breast cancer stem–like cells, offsetting the formation of mammospheres relative to nontransduced T cells. Adoptive transfer of TEM8 CAR T cells induced regression of established, localized patient-derived xenograft tumors, as well as lung metastatic TNBC cell line–derived xenograft tumors, by both killing TEM8+ TNBC tumor cells and targeting the tumor endothelium to block tumor neovascularization. Our findings offer a preclinical proof of concept for immunotherapeutic targeting of TEM8 as a strategy to treat TNBC. Significance: These findings offer a preclinical proof of concept for immunotherapeutic targeting of an endothelial antigen that is overexpressed in triple-negative breast cancer and the associated tumor vasculature. Cancer Res; 78(2); 489–500. ©2017 AACR.
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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".