Abstract 5469: Targeting SWI/SNF ATPases in enhancer-addicted prostate cancer
Bibliographic record
Abstract
Abstract The switch/sucrose non-fermentable (SWI/SNF) complex plays a crucial role in chromatin remodeling and is recurrently altered in over 20% of human cancers. Here, we developed a proteolysis targeting chimera (PROTAC) degrader of ATPase subunits of the SWI/SNF complex, SMARCA2 and SMARCA4. Intriguingly, we found androgen receptor (AR)/forkhead box A1 (FOXA1)-positive prostate cancer to be exquisitely sensitive to dual SMARCA2 and SMARCA4 degradation relative to benign prostate as well as other cancer cell lines, including those with inactivating SMARCA4 mutations. Mechanistically, SWI/SNF inhibition rapidly compacts the cis-regulatory elements that are bound and activated by transcription factors that drive cancer proliferation, namely AR, FOXA1, ERG, and MYC. This ensues in chromatin untethering of these oncogenic drivers, chemical decommissioning of their core enhancer circuitry, and attenuation of downstream gene programs. Furthermore, we found SWI/SNF inhibition to disrupt super-enhancer and promoter DNA looping interactions that wire supra-physiologic expression of the AR, FOXA1, and MYC oncogenes, thereby tempering their expression in cancer cells. Monotherapy with the SMARCA2/4 degrader induced potent inhibition of tumor growth in cell line-derived xenograft models of prostate cancer and remarkably synergized with AR antagonists, inducing disease remission in models of castration-resistant prostate cancer. We also found the combinatorial treatment to significantly inhibit the growth of enzalutamide resistant disease using in vitro as well as patient-derived xenograft models. Notably, no major toxicities were seen in mice upon prolonged treatment with the SMARCA2/4 degrader, including no indications of thrombocytopenia, gastrointestinal goblet cell depletion, or germ cell degeneration. Taken together, these results suggest that impeding enhancer accessibility through SWI/SNF ATPase inactivation represents a novel therapeutic approach in enhancer addicted human cancers. Citation Format: Lanbo Xiao, Abhijit Parolia, Yuanyuan Qiao, Pushpinder Bawa Pushpinder, Sanjana Eyunni, Rahul Mannan, Sandra E. Carson, Yu Chang, Xiaoju Wang, Yuping Zhang, Josh Vo, Steven Kregel, Stephanie A. Simko, Andrew D. Delekta, Mustapha Jaber, Heng Zheng, Ingrid Apel, Lisa McMurry, Fengyun Su, Rui Wang, Sylvia Wang, Sanjita Sasmal, Leena K. Satyam, Subhendu Mukherjee, Chandrasekhar AbbinenI, Kiran Aithal, Mital S. Bhakta, Jay Ghurye, Xuhong Cao, Nora M. Navone, Alexey Nesvizhskii, Rohit Mehra, Ulka Vaishampayan, Marco Blanchette, Yuzhuo Wang, Susanta Samajdar, Murali Ramachandra, Arul M. Chinnaiyan. Targeting SWI/SNF ATPases in enhancer-addicted prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5469.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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".