Smith Margaret R, Wang Yuezhu, D'Agostino Ralph, Liu Yin, Ruiz Jimmy, Lycan Thomas, Oliver George, Miller Lance D, Topaloglu Umit, Pinkney Jireh, Abdulhaleem Mohammed N, Chan Michael D, Farris Michael, Su Jing, Mileham Kathryn F, Xing Fei
Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA.
Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
NPJ Precis Oncol. 2023 Mar 27;7(1):34. doi: 10.1038/s41698-023-00373-0.
Different types of therapy are currently being used to treat non-small cell lung cancer (NSCLC) depending on the stage of tumor and the presence of potentially druggable mutations. However, few biomarkers are available to guide clinicians in selecting the most effective therapy for all patients with various genetic backgrounds. To examine whether patients' mutation profiles are associated with the response to a specific treatment, we collected comprehensive clinical characteristics and sequencing data from 524 patients with stage III and IV NSCLC treated at Atrium Health Wake Forest Baptist. Overall survival based Cox-proportional hazard regression models were applied to identify mutations that were "beneficial" (HR < 1) or "detrimental" (HR > 1) for patients treated with chemotherapy (chemo), immune checkpoint inhibitor (ICI) and chemo+ICI combination therapy (Chemo+ICI) followed by the generation of mutation composite scores (MCS) for each treatment. We also found that MCS is highly treatment specific that MCS derived from one treatment group failed to predict the response in others. Receiver operating characteristics (ROC) analyses showed a superior predictive power of MCS compared to TMB and PD-L1 status for immune therapy-treated patients. Mutation interaction analysis also identified novel co-occurring and mutually exclusive mutations in each treatment group. Our work highlights how patients' sequencing data facilitates the clinical selection of optimized treatment strategies.
目前，根据肿瘤分期和潜在可靶向突变的情况，不同类型的疗法正被用于治疗非小细胞肺癌（NSCLC）。然而，几乎没有生物标志物可指导临床医生为所有具有不同基因背景的患者选择最有效的治疗方法。为了研究患者的突变谱是否与对特定治疗的反应相关，我们收集了Atrium Health Wake Forest Baptist治疗的524例III期和IV期NSCLC患者的综合临床特征和测序数据。应用基于Cox比例风险回归模型的总生存期来识别对于接受化疗（chemo）、免疫检查点抑制剂（ICI）和化疗+ICI联合治疗（Chemo+ICI）的患者“有益”（HR<1）或“有害”（HR>1）的突变，随后为每种治疗生成突变综合评分（MCS）。我们还发现MCS具有高度的治疗特异性，即来自一个治疗组的MCS无法预测其他组的反应。受试者工作特征（ROC）分析表明，对于接受免疫治疗的患者，MCS的预测能力优于肿瘤突变负荷（TMB）和程序性死亡受体配体1（PD-L1）状态。突变相互作用分析还在每个治疗组中鉴定出了新的共发生和相互排斥的突变。我们的工作突出了患者的测序数据如何有助于临床选择优化的治疗策略。
