Abstract 2312: TEM8/ANTXR1 specific T cells co-target tumor stem cells and tumor vasculature in triple-negative breast cancer
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with no approved targeted therapies. Tumor endothelial marker 8 (TEM8), initially identified as a marker of tumor endothelial cells in colorectal cancer and other solid tumors has recently been shown to be upregulated in TNBC and breast cancer stem cells (BCSCs). We investigated whether TEM8 specific chimeric antigen receptor (CAR) T cells recognize and kill both tumor endothelial cells as well as TNBC tumor cells. TEM8 specific CAR molecules were generated using single chain variable fragment derived from the monoclonal antibody, L2. L2 CAR T cells selectively recognized TEM8, secreted immunostimulatory cytokines and effectively killed both TEM8 positive TNBC and tumor endothelial cell lines. Moreover, L2 CAR T cells targeted breast cancer stem cells significantly reducing the number of mammospheres relative to non-transduced T cells. In vivo, adoptive transfer of L2 CAR T cells induced regression of established vascularized TNBC xenografts. Hence, TEM8 may serve as an attractive target for immunotherapy of TNBC. Citation Format: Tiara Byrd, Kristen Fousek, Antonella Pignata, Christopher Szot, Kevin Bielamowicz, Steven Seaman, Daniel Landi, Nino Rainusso, Poul Sorensen, Joachim Koch, Winfried Wels, Bradley Fletcher, Meenakshi Hegde, Brad St Croix, Nabil Ahmed. TEM8/ANTXR1 specific T cells co-target tumor stem cells and tumor vasculature in triple-negative breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2312.
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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) | 0.008 | 0.002 |
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".