2024 World Conference on Lung Cancer (WCLC) - Posters-P2.11A.15 Gut Metatranscriptomics Predict Survival in Anti-PD Immunotherapy Treated Advanced-Stage Non-Small Cell Lung Cancer

P2.11A.15 Gut Metatranscriptomics Predict Survival in Anti-PD Immunotherapy Treated Advanced-Stage Non-Small Cell Lung Cancer

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The study investigates the relationship between gut microbiome metatranscriptomics (MTR) and treatment outcomes in advanced-stage non-small cell lung cancer (NSCLC) patients undergoing anti-PD immunotherapy. Although gut microbiome's metagenomic influence on immunotherapy efficacy is established in other cancers, its role in NSCLC is less understood. The research aims to assess how gut microbiome MTR signatures correlate with progression-free survival (PFS) among these patients.<br /><br />The methodology included 29 advanced NSCLC patients, with fecal samples analyzed using metatranscriptomic techniques. Key tools applied during data analysis were the Trinity pipeline for sequence assembly, MMSeqs2 for taxonomic classification, DESeq2 for differential gene expression (DEG), and machine learning models like Principal Component Analysis (PCA), Random Forest (RF), and Support Vector Machine (SVM). Progression-free survival was categorized into long (over 6 months) and short (6 months or less) durations.<br /><br />Results indicated 16 patients experienced long PFS, while 13 had short PFS. MTR signatures differentiated these groups, particularly by showing significant differential gene expressions of 689 genes, with 120 reaching higher significance levels. PCA on these gene transcripts revealed substantial variance between long and short PFS groups, confirmed by machine learning analyses. Multivariate Cox analyses were also employed.<br /><br />The study concludes that certain gut microbiome MTR signatures and gene expressions potentially predict survival durations in NSCLC treated with anti-PD ICI. This suggests that gut MTR profiles might be a biomarker for treatment outcome, emphasizing the need for further research to validate and explore these findings for clinical application.

Asset Subtitle

Zoltan Lohinai

Meta Tag

Speaker Zoltan Lohinai

Topic Metastatic NSCLC – Immunotherapy

Keywords

gut microbiome

metatranscriptomics

NSCLC

anti-PD immunotherapy

progression-free survival

differential gene expression

machine learning

biomarker

metagenomics

treatment outcomes