The loss of B7-H4 expression in breast cancer cells escaping from T cell cytotoxicity contributes to epithelial-to-mesenchymal transition
Bibliographic record
Abstract
BACKGROUND: B7 homology 4 (B7-H4), a potential target for cancer therapy, has been demonstrated to inhibit T cell cytotoxicity in the early stages of breast cancer. However, B7-H4 manipulating breast tumor immune microenvironment (TIME) in the tumor progression remains unknown. METHODS: We engineered T cells with B7-H4-specific chimeric antigen receptors (CARs) and performed a T cell co-culture assay to characterize B7-H4 expression level in breast cancer cells escaping from T cell cytotoxicity. We generated B7-H4 knockout (KO) and overexpression (OE) breast cancer cells to determine the epithelial-to-mesenchymal transition (EMT) and stemness characteristics in vitro and in vivo, including tumor proliferation, migration, metastasis and chemoresistance. The Cancer Genome Atlas breast cancer database was accessed to investigate the correlation between B7-H4 expression levels and EMT characteristics in patients with breast cancer. RESULTS: Our result found that B7-H4 expression level was significantly reduced in a subset of breast cancer cells that escaped from the cytotoxicity of B7-H4 CAR-T cells. Compared with wild type cells, B7-H4 KO cells prompt EMT and stemness characteristics, including migration, invasion and metastasis, and OE cells vice versa. The increase in H3K27me3 in KO cells confirmed the epigenetic reprogramming of cancer stem cells. The IC50 of doxorubicin or oxaliplatin significantly increased in KO cells, which was in agreement with a decrease in OE cells. Moreover, a trend of downregulated B7-H4 from stage I to stage II breast cancer patients indicates that the low-expressing B7-H4 breast cancer cells escaping from TIME have spread to nearby breast lymph nodes in the cancer progression. CONCLUSIONS: Our study illuminates the novel role of renouncing B7-H4 in breast cancer cells through immune escape, which contributes to EMT processes and provides new insights for breast cancer treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".