Abstract A025: Direct <i>in vivo</i> CRISPR screen identifies <i>BAP1</i> and <i>FAT1</i> as potent tumor suppressors in sarcomagenesis
Bibliographic record
Abstract
Abstract Undifferentiated pleomorphic sarcoma (UPS) is among the most common soft tissue sarcomas (STS) in adults. For decades, little therapeutic progress has been made for STSs, including UPSs. Targeted therapies for tumors driven by specific genetic mutations have proven to be more effective than standard chemotherapies. However, the molecular pathogenesis of UPSs remains unknown, hindering the development of targeted therapies for UPSs. Approximately 65% of UPSs harbor TP53 mutations, but somatic mutation of Trp53 alone is insufficient to induce sarcomas in vivo. The addition of Rb1 mutation alongside a Trp53 mutation induces sarcomas in vivo, though with low frequency of tumor onset and slow growth. The role of other genes in facilitating Trp53-driven sarcomas is largely unknown. Through a customized in vivo CRISPR/Cas9 screen of 35 genes commonly mutated in UPSs, followed by individual gene validation in vivo, we discovered that Bap1 knockout cooperates with Trp53 knockout to induce sarcomas in vivo. Furthermore, we demonstrated that Fat1 deletion increased the onset frequency and growth of Trp53 and Rb1-driven sarcomas in a genetically engineered mouse model. Through multiplex immunohistochemistry and flow cytometry, we found that mouse sarcomas induced by Trp53 and Rb1 mutations are significantly enriched with immune cells compared to other mouse sarcomas we generated in vivo. Finally, we show that PARP inhibition may be a potential targeted therapy for RB1-loss STSs and BRD4 inhibition may be a potential targeted therapy for FAT1-loss STSs. Citation Format: Jianguo Huang, Xingliang Liu, Warren Floyd, William Haugh, Andrea R Daniel, Zhaoyu Sun, Nerissa T Williams, Melissa J Kasiewicz, Yaping Wu, Diana M Cardona, Brian Piening, John T. Welle, Wesley K Rosales, Venkatesh Rajamanickam, So Young Kim, Eric Xu, Lixia Luo, Yan Ma, Kristianne M Oristian, Omar Lopez, Nicholas E. S. Sibinga, Rutulkumar Patel, Ziqiang Zhang, Alexander J Lazar, Corinne M Linardic, Brady Bernard, William L Redmond, Walter J Urba, David G Kirsch. Direct in vivo CRISPR screen identifies BAP1 and FAT1 as potent tumor suppressors in sarcomagenesis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A025.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".