Microbiome Differential Abundance Methodologies to Detect Relevant Taxa Associated with Chemotherapy Toxicity Rate in Colorectal Cancer

Abstract

[Abstract]: Motivation. The interplay between microbial communities and treatment outcomes represents a promising area in pharmacomicrobiomics. Identifying microbial biomarkers that differentiate toxicity levels could inform personalized cancer strategies. However, biomarker identification is strongly influenced by methodological choices in differential abundance analysis (DAA), and most studies focus on individual outcomes despite toxicity being inherently multifactorial. In this study, we defined a multi-dimensional toxicity variable integrating clinical symptoms and treatment modifications to stratify colorectal cancer patients. We then evaluated six widely used DAA methods (ALDEx2, ANCOM-BC, DESeq2, LEfSe, LinDA, and ZicoSeq) to assess how analytical variability affects the detection of microbiome signatures associated with chemotherapy-related toxicity. Analyses were performed under different preprocessing and multiple-testing correction strategies, and consistency was further examined using an independent validation dataset.

Results. Substantial variability was observed across methods, with limited overlap in detected taxa but moderate concordance in effect-size rankings. ANCOM-BC showed the most consistent overall performance across analytical scenarios, although trade-offs remained between taxa detection, ranking, and direction of association. Despite this variability, a subset of taxa was consistently identified across methods, including Parvimonas, Eubacterium ventriosum group, and Ruminococcus in the low-toxicity group, and members of the Lachnospiraceae family, such as Fusicatenibacter, Lachnospira, and the Lachnospiraceae NK4A136 group, in the severe-toxicity group. Analyses in the external validation dataset supported the reproducibility of methodological patterns, despite differences in cohort composition and sequencing strategy. These findings highlight the methodological dependence of microbiome biomarker discovery and the potential of pre-treatment microbial signatures to stratify toxicity risk. View collectively, our results support a context-dependent approach to DAA method selection in clinical microbiome studies.