Dora David, Weiss Glen J, Megyesfalvi Zsolt, Gállfy Gabriella, Dulka Edit, Kerpel-Fronius Anna, Berta Judit, Moldvay Judit, Dome Balazs, Lohinai Zoltan
Department of Anatomy, Histology and Embryology, Semmelweis University, 1094 Budapest, Hungary.
Department of Medicine, UMass Chan Medical School, Worcester, MA 01655, USA.
Cancers (Basel). 2023 Oct 21;15(20):5091. doi: 10.3390/cancers15205091.
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 ( = 129) and fecal microbiome ( = 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129例免疫检查点抑制剂（ICI）治疗的非小细胞肺癌（NSCLC）患者的CT扫描图像和58例患者的粪便微生物群，将基于计算机断层扫描（CT）的纹理分析（QTA）与基于微生物群的生物标志物特征相结合，以预测患者的总生存期（OS）。获得了105个连续的CT参数，其中主成分分析（PCA）确定了7个主要成分，这些成分解释了80%的数据变异。采用鸟枪法宏基因组学（MG）和ITS分析来揭示细菌和真菌物种的丰度。多雷拟杆菌和狄氏副拟杆菌的相对丰度与长生存期（>6个月）相关，而产气荚膜梭菌、粪肠球菌以及真菌类群戴氏丝膜菌、柔膜菌目、球壳孢目和银耳纲与短生存期（≤6个月）相关。在程序性死亡受体配体1（PD-L1）表达高（≥50%）的肿瘤患者中，不变肉杯菌和梭形棒瑚菌更为丰富，而在患有ICI相关毒性的患者中，革菌科和毛螺菌科细菌更为富集。一种基于极端梯度提升的人工智能（AI）方法评估了预后与各种临床病理参数之间的关联。AI分别以84%和79%的准确率识别出对ICI反应良好和PD-L1高表达患者的MG特征。QTA参数和MG的组合对治疗反应和OS的阳性预测值均为90%。根据我们的假设，QTA参数和肠道微生物群特征可以预测接受ICI治疗的NSCLC患者的OS、治疗反应、PD-L1表达和毒性，并且机器学习方法可以将这些变量结合起来创建一个可靠的预测模型，正如我们在本研究中所建议的那样。
