A Review of Computational Approaches and Multi-Omics Integration in Gut Microbiome: From Data to Precision Medicine with Machine Learning

The gut microbiome, as a complex ecosystem comprising trillions of microorganisms, plays a fundamental role in regulating immune responses and influencing the efficacy of immunotherapies. Despite the revolutionary impact of immune checkpoint inhibitors in non-small cell lung cancer treatment, clinical responses remain highly variable and current biomarkers cannot accurately predict them. This review aims to investigate and elucidate the role of the gut microbiome in predicting and modulating immune checkpoint inhibitor response in non-small cell lung cancer patients. In this review, findings from clinical and preclinical studies related to microbial diversity, taxonomic composition, and functional pathways of the microbiome and their association with treatment outcomes including progression-free survival and treatment response have been examined. Specific bacterial taxa including Akkermansia muciniphila, Bifidobacterium species, and Ruminococcaceae are associated with better outcomes, while certain species correlate with treatment resistance. Dietary interventions including high-fiber diets, as well as concomitant medications such as antibiotics, corticosteroids, and proton pump inhibitors, significantly impact microbiome composition and treatment outcomes. The results of this review demonstrate that microbiome-based biomarkers can be utilized for patient stratification and the design of personalized therapeutic approaches in lung cancer.