LXH254, a Potent and Selective ARAF-Sparing Inhibitor of BRAF and CRAF for the Treatment of MAPK-Driven Tumors
Bibliographic record
Abstract
Abstract Purpose: Targeting RAF for antitumor therapy in RAS-mutant tumors holds promise. Herein, we describe in detail novel properties of the type II RAF inhibitor, LXH254. Experimental Design: LXH254 was profiled in biochemical, in vitro, and in vivo assays, including examining the activities of the drug in a large panel of cancer-derived cell lines and a comprehensive set of in vivo models. In addition, activity of LXH254 was assessed in cells where different sets of RAF paralogs were ablated, or that expressed kinase-impaired and dimer-deficient variants of ARAF. Results: We describe an unexpected paralog selectivity of LXH254, which is able to potently inhibit BRAF and CRAF, but has less activity against ARAF. LXH254 was active in models harboring BRAF alterations, including atypical BRAF alterations coexpressed with mutant K/NRAS, and NRAS mutants, but had only modest activity in KRAS mutants. In RAS-mutant lines, loss of ARAF, but not BRAF or CRAF, sensitized cells to LXH254. ARAF-mediated resistance to LXH254 required both kinase function and dimerization. Higher concentrations of LXH254 were required to inhibit signaling in RAS-mutant cells expressing only ARAF relative to BRAF or CRAF. Moreover, specifically in cells expressing only ARAF, LXH254 caused paradoxical activation of MAPK signaling in a manner similar to dabrafenib. Finally, in vivo, LXH254 drove complete regressions of isogenic variants of RAS-mutant cells lacking ARAF expression, while parental lines were only modestly sensitive. Conclusions: LXH254 is a novel RAF inhibitor, which is able to inhibit dimerized BRAF and CRAF, as well as monomeric BRAF, while largely sparing ARAF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".