Abstract A25: TEM8 specific CAR T cells induce regression of patient-derived xenograft and metastatic models of triple-negative breast cancer
Bibliographic record
Abstract
Abstract Lacking marked expression of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR), triple-negative breast cancer (TNBC) is a breast cancer subtype in desperate need of targeted therapy options. Tumor endothelial marker 8 (TEM8), initially identified as a tumor endothelium marker in colon cancer, has been shown to be upregulated in TNBC. To confirm this, we stained primary TNBC tissues for TEM8; in all cases TEM8 was expressed with no expression in normal breast tissue. TEM8 is expressed by TNBC cell lines as indicated by flow cytometry and Western blot. We thus engineered chimeric antigen receptor (CAR) T cells to specifically target TEM8 in TNBC. TEM8 CAR T cells distinctly recognized TEM8, secreted immunostimulatory cytokines, and killed TEM8-positive TNBC cells in vitro. In vivo, the adoptive transfer of TEM8 CAR T cells induced regression against orthotopic patient-derived xenograft (PDX) models, including the aggressive claudin-low TNBC PDX, WHIM12. Systemic administration of TEM8 CAR T cells also induced regression against a lung metastasis TNBC model. In all models, treatment with TEM8 CAR T cells resulted in a survival advantage in mice compared to controls. Hence, TEM8 may serve as an attractive targeted immunotherapy of TNBC. Citation Format: Tiara Byrd, Kristen Fousek, Antonella Pignata, Christopher Szot, Heba Samaha, Lacey Dobrolecki, Htoo Zarni Oo, Poul Sorensen, Matthew Ellis, Michael Lewis, Meenakshi Hegde, Bradley Fletcher, Brad St. Croix, Nabil Ahmed. TEM8 specific CAR T cells induce regression of patient-derived xenograft and metastatic models of triple-negative breast cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Breast Cancer Research; 2017 Oct 7-10; Hollywood, CA. Philadelphia (PA): AACR; Mol Cancer Res 2018;16(8_Suppl):Abstract nr A25.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 0.001 |
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".