This study aims to combine computed tomography (CT)-based texture analysis (QTA) and a microbiome-based biomarker signature to predict the overall survival (OS) of immune checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients by analyzing their CT scans (n= 129) and fecal microbiome (n= 58). One hundred and five continuous CT parameters were obtained, where principal component analysis (PCA) identified seven major components that explained 80% of the data variation. Shotgun metagenomics (MG) and ITS analysis were performed to reveal the abundance of bacterial and fungal species. The relative abundance of Bacteroides dorei and Parabacteroides distasonis was associated with long OS (>6 mo), whereas the bacteria Clostridium perfringens and Enterococcus faecium and the fungal taxa Cortinarius davemallochii, Helotiales, Chaetosphaeriales, and Tremellomycetes were associated with short OS (≤6 mo). Hymenoscyphus immutabilis and Clavulinopsis fusiformis were more abundant in patients with high (≥50%) PD-L1-expressing tumors, whereas Thelephoraceae and Lachnospiraceae bacterium were enriched in patients with ICI-related toxicities. An artificial intelligence (AI) approach based on extreme gradient boosting evaluated the associations between the outcomes and various clinicopathological parameters. AI identified MG signatures for patients with a favorable ICI response and high PD-L1 expression, with 84% and 79% accuracy, respectively. The combination of QTA parameters and MG had a positive predictive value of 90% for both therapeutic response and OS. According to our hypothesis, the QTA parameters and gut microbiome signatures can predict OS, the response to therapy, the PD-L1 expression, and toxicity in NSCLC patients treated with ICI, and a machine learning approach can combine these variables to create a reliable predictive model, as we suggest in this research.
本研究旨在通过分析非小细胞肺癌（NSCLC）患者的CT影像（n=129）与粪便微生物组（n=58），结合基于计算机断层扫描（CT）的纹理分析（QTA）与微生物组生物标志物特征，预测接受免疫检查点抑制剂（ICI）治疗患者的总体生存期（OS）。研究获取了105个连续CT参数，通过主成分分析（PCA）提取出能解释80%数据变异的七个主要成分。采用鸟枪法宏基因组学（MG）与ITS分析揭示了细菌和真菌物种的丰度。其中多雷拟杆菌和狄氏副拟杆菌的相对丰度与较长OS（>6个月）相关，而产气荚膜梭菌、屎肠球菌以及真菌类群戴氏丝膜菌、柔膜菌目、毛球壳目和银耳纲则与较短OS（≤6个月）相关。在PD-L1高表达（≥50%）的肿瘤患者中，不变膜盘菌和梭形拟锁瑚菌更为富集；而革菌科和毛螺菌科细菌在发生ICI相关毒性的患者中丰度较高。基于极端梯度提升的人工智能（AI）方法评估了临床病理参数与结局之间的关联。AI识别出对ICI治疗反应良好及PD-L1高表达患者的MG特征，预测准确率分别达到84%和79%。QTA参数与MG特征联合对治疗反应和OS的阳性预测值均达90%。本研究证实，QTA参数与肠道微生物组特征能够预测ICI治疗的NSCLC患者的OS、治疗反应、PD-L1表达及毒性反应，而机器学习方法可整合这些变量构建可靠的预测模型。
肿瘤(癌症)患者之家