Estevam Gabriella O, Linossi Edmond, Rao Jingyou, Macdonald Christian B, Ravikumar Ashraya, Chrispens Karson M, Capra John A, Coyote-Maestas Willow, Pimentel Harold, Collisson Eric A, Jura Natalia, Fraser James S
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.
Tetrad Graduate Program, University of California, San Francisco, San Francisco, United States.
Elife. 2025 Feb 17;13:RP101882. doi: 10.7554/eLife.101882.
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.
MET受体酪氨酸激酶的激酶结构域和近膜结构域中的突变是多种癌症发生的原因，并且可导致对MET靶向治疗产生耐药性。在精准医学中，为每种突变谱确定最有效的抑制剂是MET驱动的癌症治疗面临的一项重大挑战。在此，我们对约5764个MET激酶结构域变体进行了深度突变扫描（DMS），以针对一组据报道靶向MET激酶结构域的11种抑制剂分析每种突变的生长情况。我们验证了先前确定的耐药突变，确定了I型、II型和I½型抑制剂中的常见耐药位点，揭示了每种抑制剂独特的耐药和敏感突变，并验证了I型和II型抑制剂对的非交叉耐药敏感性。我们用生物物理和化学特征增强蛋白质语言模型，以提高对抑制剂处理数据集的预测性能。总之，我们的研究展示了一种用于识别耐药突变的汇总实验流程，提供了对特定疗法敏感的突变的参考字典，并为未来的药物开发提供了见解。
