Integrating current analyses of the breast cancer microbiome

Many cancer types have significant associations with their resident microbial communities—emerging evidence suggests that breast cancers also interact with the local tissue-associated microbiota. Microbiome research advances rapidly and analysis pipelines and databases are updated frequently. This dynamic environment makes comparative evaluations challenging. Here, we have integrated all publicly available studies related to breast cancer and the mammary microbiome in light of advances in this rapidly progressing field. Based on alpha diversity, beta diversity, proportional abundance, and statistical analyses, we observed differences between our modern analytical approaches and the original findings. We were able to classify and identify additional taxa across samples through abundance analyses and identify previously unidentified statistically significant taxa. In our updated analyses there were more taxa identified as statistically significant in comparison to the original studies’ results. In the re-analysis for The Microbiome of Aseptically Collected Human Breast Tissue in Benign and Malignant Disease by Hieken et al., there were twelve statistically significant differentially abundant taxa identified in breast tissue microbiota in benign and invasive cancer disease states. In the re-analysis for The Microbiota of Breast Tissue and Its Association with Breast Cancer by Urbaniak et al., there were 18 taxa identified as statistically significant. In the re-analysis for Characterization of the microbiome of nipple aspirate fluid of breast cancer survivors by Chan et al., there were three genera identified as statistically significant in the skin and fluid samples. Our work has discovered that reanalyses are necessary for microbiome studies, especially older 16S studies. Through our re-analysis, we classified and identified more phyla and genera across studies, which supports the notion that reanalyses provide new insights to the microbiome field and help to assess robusticity of previously published findings by using new and updated tools and databases.