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Prioritizing gut microbial SNPs linked to immunotherapy outcomes in NSCLC patients by integrative bioinformatics
Muhammad Faheem Raziq1,2, Nadeem Khan2, Haseeb Manzoor2
1Department of Infectious Disease, Children'S Hospital, Zhejiang University School of Medicine, 3333 Binsheng Road, Binjiang District, 310052, Hangzhou, China.
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.
Insights
Gut microbiome variations impact non-small cell lung cancer (NSCLC) patient response to immune checkpoint inhibitors (ICIs). Specific microbial genes and single nucleotide polymorphisms (SNPs) predict treatment outcomes, enabling personalized immunotherapy strategies.
Area of Science:
- Microbiome Research
- Cancer Immunology
- Bioinformatics
Background:
- The human gut microbiome influences treatment efficacy in various cancers, including non-small cell lung cancer (NSCLC).
- Immune checkpoint inhibitors (ICIs) are a key therapy for NSCLC, but patient response varies significantly.
- Understanding the role of gut microbial composition in ICI response is crucial for optimizing cancer treatment.
Purpose of the Study:
- To investigate the association between gut microbial variations and treatment response in NSCLC patients undergoing ICI therapy.
- To identify specific microbial strains, genes, and genetic variations (SNPs) linked to patient response or non-response.
- To develop predictive models for treatment response based on gut microbiome data.
Main Methods:
- Analysis of gut metagenomes from 87 NSCLC patients treated with nivolumab at multiple time points.
- Strain identification using StrainPhlAn3, followed by variant calling and annotation with Snippy.
- Association analysis using MaAsLin2, development of machine learning models for SNP prioritization, and structural bioinformatics for functional impact assessment.
Main Results:
- Distinct microbial strains were identified in responders versus non-responders (e.g., Lachnospira eligens, Parabacteroides distasonis).
- Variations in genes like ftsA, lpdA, and nadB were significantly associated with non-response.
- A Logistic Regression model achieved >90% accuracy in predicting response based on SNPs in lpdA and obgE, with structural analysis indicating impaired protein function.
Conclusions:
- The integration of metagenomics, machine learning, and structural bioinformatics provides a robust framework for understanding microbiome-treatment response associations.
- Identified microbial variations and SNPs serve as potential biomarkers for predicting ICI therapy outcomes in NSCLC patients.
- These findings support the development of personalized, microbiome-guided therapeutic strategies to improve immunotherapy efficacy.
