Abstract PR002: EP300 loss of function is a pan-cancer sensitizer to BET inhibition
Bibliographic record
Abstract
Abstract Genetically targeted therapies have proven to be a successful approach to treating cancer because they inhibit the causal (driver) biology of tumors that is absent in healthy cells. While targeted therapies exist for most oncogenes, only a small fraction of loss-of-function (LoF) tumor suppressor genes (TSGs) have been drugged to date, despite TSG mutations causing 2/3 of all cancer. Here we report a novel 1st-in-class genetically targeted opportunity using BET inhibitors to treat EP300 LoF cancers. Among the hundreds of pharmacogenetic screens that we have conducted, this interaction is similar to PARP-BRCA1/HRD in significance, and among the strongest we have discovered. It is cancer-type agnostic and holds up with all potent BET inhibitors in various pre-clinical models, including PDXs. Mechanistically, we demonstrate that apoptosis induced by DNA catastrophe arising from pre-mature entry into S phase plays a significant role. BET inhibition blocks timely assembly of DNA replication factors, which leads to G1 arrest in healthy cells; cancer cells that lack p300, a key regulator of CDK2, and CDK7, proceed through G1-S before replication deficiencies can be resolved. EP300 loss of function is a causal driver in more than 25,000 new cancer cases per year in the US and there currently are no genetically targeted options for these patients. This presentation will focus on the discovery, characterization, and clinical re-entry plan for our BET inhibitor LFB-190, to treat EP300 LoF solid tumors. Citation Format: Tomas Babak, Peter Truesdell, Greg Vontz, Peter Heinecke, Doris Coto Villa, Meiou Dai. EP300 loss of function is a pan-cancer sensitizer to BET inhibition. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr PR002
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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) | 0.009 | 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".