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@ARTICLE{Singh:305576,
      author       = {G. Singh and T. Schmenger and J. C. Gonzalez-Sanchez and A.
                      Kutkina and N. Bremec and G. D. Diwan and P. Mozas and C.
                      López and R. Siebert and R. Sotillo$^*$ and R. B. Russell},
      title        = {{D}iscriminating activating, deactivating and resistance
                      variants in protein kinases.},
      journal      = {Genome medicine},
      volume       = {17},
      number       = {1},
      issn         = {1756-994X},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2025-02238},
      pages        = {133},
      year         = {2025},
      abstract     = {Distinguishing whether genetic variants in protein kinases
                      cause gain or loss of function is critical in clinical
                      genetics. In particular, gain (and not loss)-of-function
                      variants are often immediately amenable to treatment by
                      inhibitors, making their identification a potential boon to
                      personalised medicine. Most existing computational methods
                      for variant pathogenicity prediction simply distinguish
                      damaging from benign variants and provide no further
                      functional insights. Here, we present a data-driven approach
                      that differentiates activating, deactivating, and resistance
                      variants.To train and evaluate our method, we curated a
                      dataset of 2505 variants (375 activating, 1028 deactivating,
                      98 resistance and 1004 neutral) across 441 kinases from the
                      literature and public databases. Each variant was
                      represented as a vector of sequence, evolutionary and
                      structural features, which we then used to train machine
                      learning models to distinguish among the four types of
                      variants. The resulting predictors achieved excellent
                      performance (mean AUC = 0.941). We tested a selection of
                      variants by over-expression in T-REx-293 cells followed by
                      gene expression or biochemical tests.Applying the predictors
                      to uncharacterised variants, we observed a strong enrichment
                      of activating mutations in cancer genomes, deactivating
                      variants in hereditary disease, and few of either in
                      variants from healthy individuals. We experimentally
                      validated several predicted activating variants from cancer
                      samples. For p.Ser97Asn in PIM1, phosphorylation events
                      suggested increased activity. For p.Ala84Thr in MAP2K3, gene
                      expression and mitochondrial staining showed a reduction in
                      mitochondrial function, the opposite effect of MAP2K3
                      deletions. We provide an online application that enables
                      users to analyse any kinase-domain variant, obtain
                      prediction scores and explore known nearby variants in other
                      kinases.Our predictors, together with the rapid experimental
                      validations, demonstrates a feasible strategy for
                      identifying activating variants in kinases in a time frame
                      that would enable clinical decisions.},
      keywords     = {Humans / Protein Kinases: genetics / Protein Kinases:
                      metabolism / Protein Kinases: chemistry / Genetic Variation
                      / Mutation / Computational Biology: methods / Machine
                      Learning / Cancer genomics (Other) / Gain-of-function
                      (Other) / Genetic variants (Other) / Loss-of-function
                      (Other) / Machine learning (Other) / Precision medicine
                      (Other) / Protein kinases (Other) / Resistance (Other) /
                      Variant pathogenicity prediction (Other) / Protein Kinases
                      (NLM Chemicals)},
      cin          = {B220},
      ddc          = {610},
      cid          = {I:(DE-He78)B220-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41152984},
      pmc          = {pmc:PMC12570665},
      doi          = {10.1186/s13073-025-01564-z},
      url          = {https://inrepo02.dkfz.de/record/305576},
}