Abstract B11: Comparisons of microbial composition identified via 16s rRNA sequencing and whole-genome sequence-based analysis using gut samples for patients with cervical cancer
Authors: Greyson Biegert, Xiaogang Wu, Tatiana V. Karpinets, Melissa Mezzari, Jianhua Zhang, Lauren Colbert, Ann Klopp
Journal: Cancer Research (2020)

Abstract Introduction: 16S RNA sequencing and whole-genome sequencing (WGS) techniques have been previously shown to have a significant degree of correlation, particularly for higher-order-level taxa. However, the large majority of those studies utilize data derived from samples collected in similar but not identical contexts. This project provides a unique opportunity in that both 16S RNA and WGS sequencing datasets were derived from a single fecal sample originating from each patient while undergoing chemoradiation therapy. Using this high-quality information, we investigated the correlation of these two datasets in terms of microbial composition and taxa abundances. Methods: Forty-one rectal swab samples were collected at the time of pelvic examination before the initiation of chemoradiation treatment and sequenced via Illumina platforms. Alpha and beta diversity was evaluated using PCoA ordination derived from several distance metrics (Bray-Curtis, Jaccard, Weighted and Unweighted UniFrac, and Euclidean). Diversity measures from different data analysis tools were also compared to highlight differences in data processing. Taxa with the highest relative abundances within each set of sequencing data were used to evaluate consensus between the datasets. Results: Alpha diversity assessed by different measures from WGS analysis correlated (Spearman rho value) with the same measures derived from 16S rRNA sequencing. These measures include OTU abundances (rho = 0.763, p = 6.61e−09), Shannon diversity (rho = 0.809, p = 4.40e−09), and InvSimpson (rho = 0.797, p = 2.11e−08). Beta diversity analysis highlighted a greater level of similarity within WGS samples than 16sRNA samples and a high degree of dissimilarity between the two datasets. Among the top fourteen most abundant taxa identified in 16S rRNA sequencing, the four best correlated with WGS results were Faecalibacterium, rho = 0.475, p = 1.68e−03; Mollicutes, rho = 0.302, p = 5.48e−02; Clostridiales, rho = 0.021, p = 8.98e−01; and Bacteria, rho = 0.016, p = 9.23e−01. The remaining ten taxa were negatively correlated. Conclusions: The use of whole-genome sequencing approaches in microbiome analysis is expanding in order to provide greater depth of information about microbial function. However, comparisons of results from 16S rRNA sequencing and WGS will need to be evaluated with caution due to differences in the interpretation of relative abundances of specific taxa. Future efforts may help to better harmonize these analyses to facilitate comparisons across platforms. Citation Format: Greyson Biegert, Xiaogang Wu, Tatiana V. Karpinets, Melissa Mezzari, Jianhua Zhang, Lauren Colbert, Ann Klopp. Comparisons of microbial composition identified via 16s rRNA sequencing and whole-genome sequence-based analysis using gut samples for patients with cervical cancer [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr B11.