Singh Sukrit, Gapsys Vytautas, Aldeghi Matteo, Schaller David, Rangwala Aziz M, White Jessica B, Bluck Joseph P, Scheen Jenke, Glass William G, Guo Jiaye, Hayat Sikander, de Groot Bert L, Volkamer Andrea, Christ Clara D, Seeliger Markus A, Chodera John D
Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse 2340, Belgium.
bioRxiv. 2025 Mar 1:2024.11.15.623861. doi: 10.1101/2024.11.15.623861.
Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.
小分子激酶抑制剂在现代癌症治疗中至关重要，超过80种获得美国食品药品监督管理局（FDA）批准的小分子激酶抑制剂的存在就证明了这一点。不幸的是，内在或获得性耐药常常导致治疗中断，而这通常是由激酶治疗靶点的突变引起的。临床肿瘤测序的出现为精准肿瘤学带来了更多机会，通过将最佳治疗方法与肿瘤突变谱相匹配来改善患者预后。然而，现代精准肿瘤学的努力受到阻碍，因为缺乏足够的生化或临床证据来将每个突变分类为对现有抑制剂耐药或敏感。基于结构的方法在回顾性基准测试中显示出有希望的准确性，能够预测激酶突变是否会干扰抑制剂结合，但这些比较是通过汇总不同条件下的不同实验测量结果进行的。我们使用纳米生物发光共振能量转移（NanoBRET）报告基因测定法，在一个关于细胞内激酶抑制剂与Abl激酶突变体亲和力的盲态数据集中，首次对基于结构的方法进行了前瞻性基准测试。我们将NanoBRET结果与基于结构的方法及其估计突变对抑制剂结合影响（以ΔΔG衡量）的能力进行比较。通过比较基于物理的模拟、Rosetta和以前的机器学习模型，我们发现基于结构的方法能够准确地将激酶突变分类为抑制剂耐药或抑制剂敏感，并且每种方法的准确性程度相似。我们表明，基于物理的模拟最适合估计激酶活性位点远端突变的ΔΔG。为了探究失败模式，我们回顾性研究了我们的方法预测效果不佳的两个具有临床意义的突变T315A和L298F，发现起始构型和质子化状态会显著改变我们预测的准确性。我们的实验和计算测量为估计突变对抑制剂结合亲和力的影响提供了一个基准，供未来的方法和基于结构的模型参考。这些基于结构的方法在为肿瘤特异性突变确定最佳治疗方法、在缺乏临床数据的情况下预测耐药突变以及确定对现有抑制剂的潜在敏感突变方面具有潜在用途。
