A tailored <i>in vivo</i> CRISPR screen identifies <i>BAP1</i> as a potent tumor suppressor of soft tissue sarcoma
Bibliographic record
Abstract
Abstract Undifferentiated pleomorphic sarcoma (UPS) is one of the most common soft tissue sarcomas (STS) in adults. Despite decades of research, therapeutic advancements for STS, including UPS, have remained limited. The genetic complexity of UPS, characterized by the absence of recurrent driver oncogene mutations, has hindered the development of effective targeted therapies beyond conventional chemotherapy and immunotherapy. To address this challenge, we conducted a customized in vivo CRISPR/Cas9 screen in mice to systematically identify potential tumor suppressors involved in UPS development. Our screen revealed BRCA1-associated protein 1 (Bap1) as a potent tumor suppressor in STS. Using total RNA sequencing, multiplex immunohistochemistry, and flow cytometry, we found that Bap1 -deficient mouse sarcomas exhibit significant immune suppression. Further analysis indicated that polo-like kinase 1 (Plk1) is essential for the survival of Bap1 -deficient sarcomas. Treatment with volasertib, a Plk1 inhibitor, markedly inhibited tumor growth in both syngeneic and spontaneous mouse models of Bap1 -loss sarcoma. In conclusion, our findings suggest that PLK1 inhibition, or combined with immunotherapy, may represent a promising targeted therapeutic strategy for tumors lacking BAP1 .
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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) | 0.001 | 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".