Revealing gut microbiota biomarkers associated with melanoma immunotherapy response and key bacteria-fungi interaction relationships: evidence from metagenomics, machine learning, and SHAP methodology.

INTRODUCTION

The gut microbiota is associated with the response to immunotherapy in cutaneous melanoma (CM). However, gut fungal biomarkers and bacterial-fungal interactions have yet to be determined.

METHODS

Metagenomic sequencing data of stool samples collected before immunotherapy from three independent groups of European ancestry CM patients were collected. After characterizing the relative abundances of bacteria and fungi, Linear Discriminant Analysis Effect Size (LEfSe) analysis, Random Forest (RF) model construction, and SHapley Additive exPlanations (SHAP) methodology were applied to identify biomarkers and key bacterial-fungal interactions associated with immunotherapy responders in CM.

RESULTS

Diversity analysis revealed significant differences in the bacterial and fungal composition between CM immunotherapy responders and non-responders. LEfSe analysis identified 45 bacterial and 4 fungal taxa as potential biomarkers. After constructing the RF model, the AUC of models built using bacterial and fungal data separately were 0.64 and 0.65, respectively. However, when bacterial and fungal data were combined, the AUC of the merged model increased to 0.71. In the merged model, the following taxa were identified as important biomarkers: , , , , , , , and , which were associated with responders, whereas was associated with non-responders. Moreover, the positive correlation interaction between and is considered a key bacterial-fungal interaction associated with CM immunotherapy response.

CONCLUSION

Our results provide valuable insights for the enrichment of responders to immunotherapy in CM patients. Moreover, this study highlights the critical role of bacterial-fungal interactions in CM immunotherapy.