基于肠道宏转录组学的从头组装揭示了预测非小细胞肺癌免疫治疗结果的微生物特征。

Gut metatranscriptomics based de novo assembly reveals microbial signatures predicting immunotherapy outcomes in non-small cell lung cancer.

机构信息

Department of Anatomy, Histology, and Embryology, Semmelweis University, Budapest, Hungary.

Pulmonology Hospital of Torokbalint, Torokbalint, Hungary.

出版信息

J Transl Med. 2024 Nov 19;22(1):1044. doi: 10.1186/s12967-024-05835-y.

DOI:10.1186/s12967-024-05835-y

PMID:39563352

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11577905/

Abstract

BACKGROUND

Advanced-stage non-small cell lung cancer (NSCLC) poses treatment challenges, with immune checkpoint inhibitors (ICIs) as the main therapy. Emerging evidence suggests the gut microbiome significantly influences ICI efficacy. This study explores the link between the gut microbiome and ICI outcomes in NSCLC patients, using metatranscriptomic (MTR) signatures.

METHODS

We utilized a de novo assembly-based MTR analysis on fecal samples from 29 NSCLC patients undergoing ICI therapy, segmented according to progression-free survival (PFS) into long (> 6 months) and short (≤ 6 months) PFS groups. Through RNA sequencing, we employed the Trinity pipeline for assembly, MMSeqs2 for taxonomic classification, DESeq2 for differential expression (DE) analysis. We constructed Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) machine learning (ML) algorithms and comprehensive microbial profiles.

RESULTS

We detected no significant differences concerning alpha-diversity, but we revealed a biologically relevant separation between the two patient groups in beta-diversity. Actinomycetota was significantly overrepresented in patients with short PFS (vs long PFS, 36.7% vs. 5.4%, p < 0.001), as was Euryarchaeota (1.3% vs. 0.002%, p = 0.009), while Bacillota showed higher prevalence in the long PFS group (66.2% vs. 42.3%, p = 0.007), when comparing the abundance of corresponding RNA reads. Among the 120 significant DEGs identified, cluster analysis clearly separated a large set of genes more active in patients with short PFS and a smaller set of genes more active in long PFS patients. Protein Domain Families (PFAMs) were analyzed to identify pathways enriched in patient groups. Pathways related to DNA synthesis and Translesion were more enriched in short PFS patients, while metabolism-related pathways were more enriched in long PFS patients. E. coli-derived PFAMs dominated in patients with long PFS. RF, SVM and XGBoost ML models all confirmed the predictive power of our selected RNA-based microbial signature, with ROC AUCs all greater than 0.84. Multivariate Cox regression tested with clinical confounders PD-L1 expression and chemotherapy history underscored the influence of n = 6 key RNA biomarkers on PFS.

CONCLUSION

According to ML models specific gut microbiome MTR signatures' associate with ICI treated NSCLC outcomes. Specific gene clusters and taxa MTR gene expression might differentiate long vs short PFS.

摘要

背景

晚期非小细胞肺癌（NSCLC）的治疗具有挑战性，免疫检查点抑制剂（ICI）是主要治疗方法。新出现的证据表明，肠道微生物组对 ICI 的疗效有重要影响。本研究使用基于从头组装的宏转录组学（MTR）分析，探索了 NSCLC 患者肠道微生物组与 ICI 结果之间的联系。

方法

我们对 29 名接受 ICI 治疗的 NSCLC 患者的粪便样本进行了基于从头组装的 MTR 分析，根据无进展生存期（PFS）将其分为长（>6 个月）和短（≤6 个月）PFS 组。通过 RNA 测序，我们使用 Trinity 管道进行组装，使用 MMSeqs2 进行分类，使用 DESeq2 进行差异表达（DE）分析。我们构建了随机森林（RF）、支持向量机（SVM）和极端梯度提升（XGBoost）机器学习（ML）算法和综合微生物特征。

结果

我们没有发现 alpha 多样性方面的显著差异，但在两组患者的 beta 多样性方面发现了具有生物学意义的分离。短 PFS 患者中 Actinomycetota 明显过表达（与长 PFS 相比，36.7% vs. 5.4%，p<0.001），Euryarchaeota 也过表达（1.3% vs. 0.002%，p=0.009），而 Bacillota 在长 PFS 组中更为普遍（66.2% vs. 42.3%，p=0.007），当比较相应 RNA 读数的丰度时。在鉴定的 120 个显著 DEG 中，聚类分析清楚地将一组在短 PFS 患者中活性更高的大量基因与一组在长 PFS 患者中活性更高的较小基因区分开来。对蛋白结构域家族（PFAMs）进行了分析，以鉴定在患者群体中富集的途径。与 DNA 合成和跨损伤相关的途径在短 PFS 患者中更为丰富，而代谢相关途径在长 PFS 患者中更为丰富。长 PFS 患者中以大肠杆菌衍生的 PFAMs 为主。RF、SVM 和 XGBoost ML 模型均证实了我们选择的基于 RNA 的微生物特征的预测能力，ROC AUC 均大于 0.84。使用临床混杂因素 PD-L1 表达和化疗史进行多元 Cox 回归检验强调了 n=6 个关键 RNA 生物标志物对 PFS 的影响。