Prioritizing gut microbial SNPs linked to immunotherapy outcomes in NSCLC patients by integrative bioinformatics analysis.

Background: The human gut microbiome has emerged as a potential modulator of treatment efficacy for different cancers, including non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor (ICI) therapy. In this study, we investigated the association of gut microbial variations with response against ICIs by analyzing the gut metagenomes of NSCLC patients.

Methods: Strain identification from the publicly available metagenomes of 87 NSCLC patients, treated with nivolumab and collected at three different timepoints (T0, T1, and T2), was performed using StrainPhlAn3. Variant calling and annotations were performed using Snippy and associations between microbial genes and genomic variations with treatment responses were evaluated using MaAsLin2. Supervised machine learning models were developed to prioritize single nucleotide polymorphisms (SNPs) predictive of treatment response. Structural bioinformatics approaches were employed using MUpro, I-Mutant 2.0, CASTp and PyMOL to access the functional impact of prioritized SNPs on protein stability and active site interactions.

Results: Our findings revealed the presence of strains for several microbial species (e.g., Lachnospira eligens) exclusively in Responders (R) or Non-responders (NR) (e.g., Parabacteroides distasonis). Variant calling and annotations for the identified strains from R and NR patients highlighted variations in genes (e.g., ftsA, lpdA, and nadB) that were significantly associated with the NR status of patients. Among the developed models, Logistic Regression performed best (accuracy > 90% and AUC ROC > 95%) in prioritizing SNPs in genes that could distinguish R and NR at T0. These SNPs included Ala168Val (lpdA) in Phocaeicola dorei and Tyr233His (lpdA), Leu330Ser (lpdA), and His233Arg (obgE) in Parabacteroides distasonis. Lastly, structural analyses of these prioritized variants in objE and lpdA revealed their involvement in the substrate binding site and an overall reduction in protein stability. This suggests that these variations might likely disrupt substrate interactions and compromise protein stability, thereby impairing normal protein functionality.

Conclusion: The integration of metagenomics, machine learning, and structural bioinformatics provides a robust framework for understanding the association between gut microbial variations and treatment response, paving the way for personalized therapies for NSCLC in the future. These findings emphasize the potential clinical implications of microbiome-based biomarkers in guiding patient-specific treatment strategies and improving immunotherapy outcomes.