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Published on: February 28, 2018
Microbiome differential abundance methodologies to detect relevant taxa associated with chemotherapy toxicity rate in
Elsa Martín-De Arribas1, Kelly Conde-Pérez2, Pablo Aja-Macaya2
1Universidade da Coruña, CITIC, Database Laboratory, A Coruña, 15071, Spain.
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.
Availability And Implementation:
The data supporting this study are available at NCBI SRA database (PRJNA911189) and NCBI SRA database (PRJNA893853).
Insights
Microbiome analysis methods significantly impact the identification of chemotherapy toxicity biomarkers in colorectal cancer patients. ANCOM-BC demonstrated consistent performance, highlighting the need for careful method selection in pharmacomicrobiomics research.
Area of Science:
- Pharmacogenomics and Microbiome Research
- Computational Biology and Bioinformatics
- Oncology and Cancer Therapeutics
Background:
- The gut microbiome influences cancer treatment efficacy and toxicity.
- Identifying microbial biomarkers for chemotherapy toxicity is crucial for personalized medicine.
- Differential abundance analysis (DAA) methods vary, impacting biomarker discovery.
Purpose of the Study:
- To evaluate the impact of different DAA methods on identifying microbiome signatures associated with colorectal cancer chemotherapy toxicity.
- To define a multi-dimensional toxicity variable for patient stratification.
- To assess the consistency and reproducibility of DAA methods across different analytical scenarios and datasets.
Main Methods:
- Defined a multi-dimensional toxicity variable integrating clinical symptoms and treatment modifications.
- Evaluated six DAA methods (ALDEx2, ANCOM-BC, DESeq2, LEfSe, LinDA, ZicoSeq) under various preprocessing and multiple-testing correction strategies.
- Validated findings using an independent dataset to assess reproducibility.
Main Results:
- Substantial variability in detected microbial taxa was observed across DAA methods, with limited overlap.
- ANCOM-BC exhibited the most consistent performance across analytical scenarios.
- Specific microbial taxa (e.g., Parvimonas, Eubacterium ventriosum group, Lachnospiraceae family members) were consistently associated with low or severe toxicity.
- Methodological patterns were reproducible in the validation dataset, despite cohort and sequencing differences.
Conclusions:
- Microbiome biomarker discovery for chemotherapy toxicity is highly dependent on the chosen DAA method.
- Pre-treatment microbial signatures hold potential for stratifying colorectal cancer patients by toxicity risk.
- A context-dependent approach to DAA method selection is recommended for clinical microbiome studies.
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