Pei Guotian, Sun Kunkun, Yang Yingshun, Wang Shuai, Li Mingwei, Ma Xiaoxue, Wang Huina, Chen Libin, Qin Jiayue, Cao Shanbo, Liu Jun, Huang Yuqing
Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China.
Department of Pathology, Peking University People's Hospital, Beijing, China.
Front Oncol. 2024 May 3;14:1388575. doi: 10.3389/fonc.2024.1388575. eCollection 2024.
Multiple primary lung cancer (MPLC) is an increasingly well-known clinical phenomenon. However, its molecular characterizations are poorly understood, and still lacks of effective method to distinguish it from intrapulmonary metastasis (IM). Herein, we propose an identification model based on molecular multidimensional analysis in order to accurately optimize treatment.
A total of 112 Chinese lung cancers harboring at least two tumors (n = 270) were enrolled. We retrospectively selected 74 patients with 121 tumor pairs and randomly divided the tumor pairs into a training cohort and a test cohort in a 7:3 ratio. A novel model was established in training cohort, optimized for MPLC identification using comprehensive genomic profiling analyzed by a broad panel with 808 cancer-related genes, and evaluated in the test cohort and a prospective validation cohort of 38 patients with 112 tumors.
We found differences in molecular characterizations between the two diseases and rigorously selected the characterizations to build an identification model. We evaluated the performance of the classifier using the test cohort data and observed an 89.5% percent agreement (PA) for MPLC and a 100.0% percent agreement for IM. The model showed an excellent area under the curve (AUC) of 0.947 and a 91.3% overall accuracy. Similarly, the assay achieved a considerable performance in the independent validation set with an AUC of 0.938 and an MPLC predictive value of 100%. More importantly, the MPLC predictive value of the classification achieved 100% in both the test set and validation cohort. Compared to our previous mutation-based method, the classifier showed better κ consistencies with clinical classification among all 112 patients (0.84 . 0.65, <.01).
These data provide novel evidence of MPLC-specific genomic characteristics and demonstrate that our one-step molecular classifier can accurately classify multifocal lung tumors as MPLC or IM, which suggested that broad panel NGS may be a useful tool for assisting with differential diagnoses.
多原发性肺癌（MPLC）是一种日益为人所知的临床现象。然而，其分子特征了解甚少，且仍缺乏将其与肺内转移（IM）相区分的有效方法。在此，我们提出一种基于分子多维分析的识别模型，以便准确优化治疗。
共纳入112例患有至少两个肿瘤（n = 270）的中国肺癌患者。我们回顾性选择了74例患者的121对肿瘤，并将肿瘤对按7:3的比例随机分为训练队列和测试队列。在训练队列中建立了一个新模型，使用由808个癌症相关基因的广泛面板分析的综合基因组谱对MPLC识别进行优化，并在测试队列和38例患者的112个肿瘤的前瞻性验证队列中进行评估。
我们发现两种疾病在分子特征上存在差异，并严格选择特征来构建识别模型。我们使用测试队列数据评估了分类器的性能，观察到MPLC的一致性百分比（PA）为89.5%，IM的一致性百分比为100.0%。该模型显示出0.947的优异曲线下面积（AUC）和91.3%的总体准确率。同样，该检测方法在独立验证集中表现出色，AUC为0.938，MPLC预测值为100%。更重要的是，分类的MPLC预测值在测试集和验证队列中均达到100%。与我们之前基于突变的方法相比，分类器在所有112例患者中与临床分类显示出更好的κ一致性（0.84. 0.65，<.01）。
这些数据提供了MPLC特异性基因组特征的新证据，并证明我们的一步分子分类器可以准确地将多灶性肺肿瘤分类为MPLC或IM，这表明广泛的二代测序（NGS）面板可能是辅助鉴别诊断的有用工具。
