Abstract B024: Leveraging synthetic lethality across EP300-mutant solid cancers through selective CBP degradation
Bibliographic record
Abstract
Abstract CREB binding protein (CBP) and E1A binding protein P300 (EP300) are paralog histone acetyltransferases that can function as transcriptional coactivators to regulate diverse cellular processes. Mutations in CBP and EP300 have been implicated in the biology of various cancer types. Functional genomic screens have revealed a bidirectional synthetic lethal relationship between these paralogs in tumor cells, highlighting a therapeutic opportunity in targeting CBP selectively in EP300-mutant cancers while sparing other tissues in which EP300 is intact. This strategy would enable an improved therapeutic window beyond those observed with dual CBP/EP300 inhibitors, which exhibit hematological toxicities. However, due to the high degree of homology between the CBP and EP300 proteins, identifying chemical matter that selectively disrupts CBP activity has proven challenging. Here, we demonstrate selective and potent CBP degrader compounds that disrupt proliferation in EP300-mutant cancer cell line models. Notably, these degraders have little impact on cell lines with intact EP300. Moreover, in cell line-derived xenograft (CDX) tumor models, we achieve potent and sustained CBP degradation and associated tumor growth inhibition. Our CBP-selective protein degraders have the potential to be a first-in-class therapeutic option for patients with tumors harboring EP300 mutations. Citation Format: Darshan Sappal, Ammar Adam, Hafiz Ahmad, Benjamin Adams, Ketaki Adhikari, Wesley Austin, Breanna Bullock, Julie Di Bernardo, Thomas Dixon, Danette Daniels, Claudi Dominici, GiNell Elliott, Brian Ethell, Anais Gervais, Md Imran Hossain, David Huang, David Lahr, Laura La Bonte, Mei Yun Lin, David Mayhew, Karolina Mizeracka, Solymar Negretti, Tyler Nguyen, Olga Prifti, Shawn Schiller, Brenna Sherbanee, David Terry, Nihan Ucisik, Elizabeth Wittenborn, Molly M Wilson, Qianhe Zhou, Mark Zimmerman. Leveraging synthetic lethality across EP300-mutant solid cancers through selective CBP degradation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B024.
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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) | 0.000 | 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) | 0.004 | 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".