Liu Zhenhua, Ma Ke, Jia Qingzhu, Yang Yunpeng, Fan Peng, Wang Ying, Wang Junhui, Sun Jiya, Sun Liansai, Shi Hongtai, Sun Liang, Zhu Bo, Xu Wei, Zhang Li, Jain Rakesh K, Qin Songbing, Huang Yuhui
Department of Radiotherapy, State Key Laboratory of Radiation Medicine and Prevention, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Cyrus Tang Medical Institute, State Key Laboratory of Radiation Medicine and Prevention, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, Jiangsu, China.
BMJ Oncol. 2024 Aug 21;3(1):e000473. doi: 10.1136/bmjonc-2024-000473. eCollection 2024.
Current biomarkers for predicting immunotherapy response in non-small-cell lung cancer (NSCLC) are derived from invasive procedures with limited predictive accuracy. Thus, identifying a non-invasive predictive biomarker would improve patient stratification and precision immunotherapy.
In this retrospective multicohort study, the discovery cohort included 205 NSCLC patients screened from ORIENT-11 and an external validation (EV) cohort included 99 real-world NSCLC patients. The 'onion-mode segmentation' method was developed to extract 'onion-mode perfusion' (OMP) from contrast-enhanced CT images. The predictive performance of OMP or its combination with the PD-L1 Tumour Proportion Score (TPS) was evaluated by the area under the curve (AUC).
High baseline OMP was associated with significantly longer survival and predicted patient response to combination anti-PD-(L)1 therapy in the discovery and EV cohorts. OMP complemented the PD-L1 TPS with superior predictive sensitivity (p=0.02). In the PD-L1 TPS<50% subgroup, OMP achieved an AUC of 0.77 for the estimation of treatment response (95% CI 0.66 to 0.86, p<0.0001). A simple bivariate model of OMP/PD-L1 robustly predicted therapeutic response in both the discovery (AUC 0.82, 95% CI 0.74 to 0.88, p<0.0001) and EV (AUC 0.80, 95% CI 0.67 to 0.89, p<0.0001) cohorts.
OMP, derived from routine CT examination, could serve as a non-invasive and cost-effective biomarker to predict NSCLC patient response to immune checkpoint inhibitor-based therapy. OMP could be used alone or in combination with other biomarkers to improve precision immunotherapy.
目前用于预测非小细胞肺癌（NSCLC）免疫治疗反应的生物标志物来自侵入性操作，预测准确性有限。因此，识别一种非侵入性预测生物标志物将改善患者分层和精准免疫治疗。
在这项回顾性多队列研究中，发现队列包括从ORIENT-11筛选出的205例NSCLC患者，外部验证（EV）队列包括99例真实世界的NSCLC患者。开发了“洋葱模式分割”方法，以从增强CT图像中提取“洋葱模式灌注”（OMP）。通过曲线下面积（AUC）评估OMP或其与PD-L1肿瘤比例评分（TPS）组合的预测性能。
高基线OMP与显著更长的生存期相关，并在发现队列和EV队列中预测患者对联合抗PD-（L）1治疗的反应。OMP以更高的预测敏感性补充了PD-L1 TPS（p=0.02）。在PD-L1 TPS<50%亚组中，OMP在估计治疗反应方面的AUC为0.77（95%CI 0.66至0.86，p<0.0001）。OMP/PD-L1的简单二元模型在发现队列（AUC 0.82，95%CI 0.74至0.88，p<0.0001）和EV队列（AUC 0.80，95%CI 0.67至0.89，p<0.0001）中均能有力地预测治疗反应。
源自常规CT检查的OMP可作为一种非侵入性且具有成本效益的生物标志物，以预测NSCLC患者对基于免疫检查点抑制剂的治疗的反应。OMP可单独使用或与其他生物标志物联合使用，以改善精准免疫治疗。
