BCL-2 mutant B7H6-CAR-T cells synergized with venetoclax for treating small cell lung cancer
Bibliographic record
Abstract
BACKGROUND: Patients with small cell lung cancer (SCLC) generally have a poor prognosis, with an exceptionally high proliferative rate and a strong propensity for early metastasis, indicating the urgent need for novel therapies. The development of chimeric antigen receptor (CAR)s targeting solid tumors is limited owing to the lack of target antigens and low efficacy. In this study, we aimed to discover new targets for SCLC CAR-T therapy and develop CAR-T-based combinational treatment against SCLC in preclinical models. METHODS: The in vitro antitumor activity of B7H6-specific CAR-T cell was evaluated. Venetoclax-resistant B7H6 CAR-T cell were designed and the synergistic effect of venetoclax and B7-H6 CAR-T cells was tested in vitro and in vivo. RESULT: B7H6 is highly expressed in SCLC tumors. CAR-T cell against B7H6 displayed antigen-specific antitumor efficacy. BCL-2(D103E)-expressing CAR-T cells showed resistance to venetoclax-induced apoptosis. The combinational treatment of venetoclax and BCL-2(D103E)-expressing B7H6-targeting showed potent anti-SCLC effect in vitro and in vivo. CONCLUSIONS: Our findings suggest that the combination of BCL-2 mutant-expressing B7H6-targeting CAR-T cells and venetoclax could be a promising novel strategy against B7H6-expressing SCLCs and other solid tumors, providing the foundation for CAR-T cells and proapoptotic small molecules therapy in patients with SCLCs in a clinical trial.
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) | 0.001 | 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".