Abstract P229: Pre-clinical evaluation of next-generation inhibitor targeting a wide spectrum of oncogenic BRAF dimers
Bibliographic record
Abstract
Abstract The canonical BRAF V600E (Class I) mutation is a potent oncogene uniquely active as a RAS-independent monomer, successfully targeted by several FDA-approved inhibitors. However, these first-generation BRAF inhibitors are not active against non-canonical BRAF oncogenic mutations, including BRAF-fusions, that drive RAS-independent (Class II) or RAS-dependent (Class III) dimers. As such, developing inhibitors directed against dimeric BRAF oncogenic mutations that avoid paradoxical activation is a major unmet clinical need. We applied proprietary Mutation-Allostery-Pharmacology (MAP) platform technology developed by Black Diamond Therapeutics to identify and validate a group of previously uncharacterized non-canonical oncogenic Class II and Class III BRAF mutation clusters. We further demonstrate that this ensemble of novel and previously validated non-canonical oncogenic BRAF mutants can form the basis of a differentiated drug discovery program aimed at identifying small molecules that potently and selectively target this family of dimeric BRAF mutations. Herein, we describe a small molecule inhibitor, BDTX BRAF-A, with potent anti-proliferative activity directed against tumor cells expressing a wide spectrum of non-canonical Class II/III mutations. This broad activity (“MasterKey” profile) of BDTX BRAF-A is further demonstrated in cell lines that harbor endogenous oncogenic dimer-inducing BRAF mutations and in various solid tumor patient-derived xenograft (PDX) models ex vivo. Importantly, BDTX BRAF-A did not induce paradoxical RAF activation characteristic of Class I BRAF inhibitors. Finally, BDTX BRAF-A achieves robust anti-tumor efficacy and target engagement of dimeric BRAF oncogenes in mouse models. These data support the continued development of rationally designed molecules targeting a broad range of non-canonical BRAF dimer-promoting mutations to extend the prospect of precision medicine in patients. Citation Format: Yoon-Chi Han, Pui Yee Ng, Ryan Schulz, Shao Ning Yang, Alana Lelo, Luisa Shin, Matthew O'Connor, Ivan Jewett, Noboru Ishiyama, Darlene Romashko, Shalabh Thakur, Andrei Salomatov, Sherri Smith, Elizabeth Buck, Christopher Roberts, Matthew Lucas, Tai-An Lin. Pre-clinical evaluation of next-generation inhibitor targeting a wide spectrum of oncogenic BRAF dimers [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P229.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".