ArticleeLife2025
Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning.
Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Medicinal Chemistry of Small-Molecule c-Met Inhibitors: From Approved Therapies to Emerging Multitarget Anticancer Agents.International journal of molecular sciences · 2026Review
- Dual functional genomics reveals a broad and convergent landscape of asciminib resistance in BCR::ABL1.Genome medicine · 2026Article
- Deep mutational scanning reveals pharmacologically relevant insights into TYK2 signaling and disease.eLife · 2026Article
- Rosace-AA: enhancing interpretation of deep mutational scanning data with amino acid substitution and position-specific insights.Bioinformatics advances · 2026Article
- Uncovering the molecular basis of kinase activity and substrate recognition with phospho-PCA.bioRxiv : the preprint server for biology · 2025Article
- Characterizing Variants of Uncertain Drug Resistance (VUDRs) Using Quantitative Measurements at Clinical Exposures.bioRxiv : the preprint server for biology · 2025Article
- Deep mutational scanning of the human insulin receptor ectodomain to inform precision therapy for insulin resistance.Nature communications · 2025Article
- Variant scoring tools for deep mutational scanning.Molecular systems biology · 2025Review
- Kinome analysis of Madurella mycetomatis identified kinases in the cell wall integrity pathway as novel potential therapeutic drug targets in eumycetoma caused by Madurella mycetomatis.PLoS neglected tropical diseases · 2025Article
- Cosmos: A Position-Resolution Causal Model for Direct and Indirect Effects in Protein Functions.bioRxiv : the preprint server for biology · 2025Article
- Tyrosine-Mediated Static and Dynamic Quenching of a Receptor Tyrosine Kinase Biosensor Reveals Inhibitor Binding Modes and Kinase Conformations.ACS chemical biology · 2025Article
Corrections and comments
- Update of
Authors and funding
12 authors.
Funding
Abstract
Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I ½ inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.