Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations
Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations
复制标题
DOI：
10.1073/pnas.1819430116
复制
发表时间：
2019-05-14
影响因子：
11.1
通讯作者：
Soejima, Kenzo
中科院分区：
文献类型：
--
作者：
Ikemura, Shinnosuke;Yasuda, Hiroyuki;Soejima, Kenzo
基于下一代测序(NGS)的肿瘤分析发现了压倒性数量的未表征的体细胞突变，也称为未知意义的变体(VUS)。在非小细胞肺癌(NSCLC)中，由>；50类型组成的突变热点外的EGFR突变的治疗意义尚不清楚。事实上，我们对无热点EGFR突变的NSCLC(n=3,779)进行的泛国家筛查显示，大多数(>；90%)罕见的EGFR突变病例(占队列受试者的5.5%)没有接受EGFR酪氨酸激酶抑制剂(TKIs)作为一线治疗。为了解决这个问题，我们应用了一个基于分子动力学模拟的模型来预测罕见的EGFR突变体对EGFR-TKI的敏感性。该模型成功地预测了外显子20插入突变体对第三代EGFR-TKI-osimertinib的不同体内外敏感性(R-2=0.72，P=0.0037)。此外，我们的模型与实验获得的敏感性数据显示出比其他预测方法更高的一致性，表明它在分析复杂的癌症突变时具有稳健性。因此，电子预测模型将成为临床环境中携带罕见EGFR突变的非小细胞肺癌患者精准医学的有力工具。在这里，我们提出了一种克服肺癌突变多样性的见解。
Next generation sequencing (NGS)-based tumor profiling identified an overwhelming number of uncharacterized somatic mutations, also known as variants of unknown significance (VUS). The therapeutic significance of EGFR mutations outside mutational hotspots, consisting of >50 types, in nonsmall cell lung carcinoma (NSCLC) is largely unknown. In fact, our pan-nation screening of NSCLC without hotspot EGFR mutations (n = 3,779) revealed that the majority (>90%) of cases with rare EGFR mutations, accounting for 5.5% of the cohort subjects, did not receive EGFR-tyrosine kinase inhibitors (TKIs) as a first-line treatment. To tackle this problem, we applied a molecular dynamics simulation-based model to predict the sensitivity of rare EGFR mutants to EGFR-TKIs. The model successfully predicted the diverse in vitro and in vivo sensitivities of exon 20 insertion mutants, including a singleton, to osimertinib, a third-generation EGFR-TKI (R-2 = 0.72, P = 0.0037). Additionally, our model showed a higher consistency with experimentally obtained sensitivity data than other prediction approaches, indicating its robustness in analyzing complex cancer mutations. Thus, the in silico prediction model will be a powerful tool in precision medicine for NSCLC patients carrying rare EGFR mutations in the clinical setting. Here, we propose an insight to overcome mutation diversity in lung cancer.