CAR T Cells Targeting B7-H3, a Pan-Cancer Antigen, Demonstrate Potent Preclinical Activity Against Pediatric Solid Tumors and Brain Tumors
Bibliographic record
Abstract
PURPOSE: Patients with relapsed pediatric solid tumors and CNS malignancies have few therapeutic options and frequently die of their disease. Chimeric antigen receptor (CAR) T cells have shown tremendous success in treating relapsed pediatric acute lymphoblastic leukemia, but this has not yet translated to treating solid tumors. This is partially due to a paucity of differentially expressed cell surface molecules on solid tumors that can be safely targeted. Here, we present B7-H3 (CD276) as a putative target for CAR T-cell therapy of pediatric solid tumors, including those arising in the central nervous system. EXPERIMENTAL DESIGN: We developed a novel B7-H3 CAR whose binder is derived from a mAb that has been shown to preferentially bind tumor tissues and has been safely used in humans in early-phase clinical trials. We tested B7-H3 CAR T cells in a variety of pediatric cancer models. RESULTS: , causing regression of established solid tumors in xenograft models including osteosarcoma, medulloblastoma, and Ewing sarcoma. We demonstrate that B7-H3 CAR T-cell efficacy is largely dependent upon high surface target antigen density on tumor tissues and that activity is greatly diminished against target cells that express low levels of antigen, thus providing a possible therapeutic window despite low-level normal tissue expression of B7-H3. CONCLUSIONS: B7-H3 CAR T cells could represent an exciting therapeutic option for patients with certain lethal relapsed or refractory pediatric malignancies, and should be tested in carefully designed clinical trials.
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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) | 0.002 | 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".