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Lynn, E. J.

Publications and source records attributed to Lynn, E. J..

2 recordsLinked to original sources

Diversity and composition of gut microbiome of cervical cancer patients by 16S rRNA and whole-metagenome sequencing

PurposeNext generation sequencing has progressed rapidly, characterizing microbial communities beyond culture-based or biochemical techniques. 16S ribosomal RNA gene sequencing (16S) produces reliable taxonomic classifications and relative abundances, while shotgun metagenome sequencing (WMS) allows higher taxonomic and functional resolution at greater cost. The purpose of this study was to determine if 16S and WMS provide congruent information for our patient population from paired fecal microbiome samples. MethodsPatients with locally advanced cervical cancers were enrolled on a prospective, observational clinical trial with a rectal swab sample collected prior to chemoradiation. Bacterial DNA was extracted from each sample and divided in two parts for 16S or WMS sequencing. We used measures of diversity richness and evenness as comparators of 16S and WMS sequencing. Relative abundances of the most common taxa were also compared between both datasets. Both techniques were tested against baseline patient demographics to assess associations identified with either or both methods. ResultsComparative indices were highly congruent between 16S and WMS. The most abundant genera for 16S and WMS data did not overlap. Overlap was observed at the Phylum level, as expected. However, relative abundances correlated poorly between the two methodologies (all p>0.05). Hierarchical clustering of both sequencing analyses identified overlapping enterotypes. Both approaches were in agreement with regard to demographic variables. ConclusionDiversity, evenness and richness are comparable when using 16S and WMS techniques, however relative abundances of individual genera are not. Clinical associations with diversity and evenness metrics were similarly identified with WMS or 16S. ImportanceThe gut microbiome plays an important role in regulating human health and disease. 16S rRNA gene sequencing (16S) and the whole-metagenome shotgun DNA sequencing (WMS) are two approaches to describe the microbial community. 16S sequencing via any amplicon sequencing-based method offers advantages over WMS in terms of precision (specific gene targeting). Additionally, 16S has historically been less costly due to the simplicity of library preparation and it does not require the same level read coverage as WMS. In this study, we performed both sequencing methods on a single rectal swab sample obtained from each cervical cancer patient prior to treatment. We showed that these two methods provide comparable information for diversity, evenness, and richness at higher taxonomic resolution, but are discrepant at a lower resolution. These methodological findings provide valuable information for the design and interpretation of future investigations of the role of the gut microbiome in cancer. Tweet(optional: 256 words, please submit a Tweet that conveys the essential message of your manuscript.) 16S may be sufficient for most initial studies of the gut microbiome in cancer patients, but WMS may be required for analysis of lower level taxonomy. Research supportThis research was supported in part by the Radiological Society of North America Resident/Fellow Award (to L.E.C.), the National Institutes of Health (NIH) through MD Andersons Cancer Center Support Grant P30CA016672, the Emerson Collective and the National Institutes of Health T32 grant #5T32 CA101642-14 (T.T.S). This study was partially funded by The University of Texas MD Anderson Cancer Center HPV-related Cancers Moonshot (L.E.C and A.K.).

microbiology↗

Process-specific somatic mutation distributions vary with three-dimensional genome structure

Somatic mutations arise during the life history of a cell. Mutations occurring in cancer driver genes may ultimately lead to the development of clinically detectable disease. Nascent cancer lineages continue to acquire somatic mutations throughout the neoplastic process and during cancer evolution (Martincorena and Campbell, 2015). Extrinsic and endogenous mutagenic factors contribute to the accumulation of these somatic mutations (Zhang and Pellman, 2015). Understanding the underlying factors generating somatic mutations is crucial for developing potential preventive, therapeutic and clinical decisions. Earlier studies have revealed that DNA replication timing (Stamatoyannopoulos et al., 2009) and chromatin modifications (Schuster-Bockler and Lehner, 2012) are associated with variations in mutational density. What is unclear from these early studies, however, is whether all extrinsic and exogenous factors that drive somatic mutational processes share a similar relationship with chromatin state and structure. In order to understand the interplay between spatial genome organization and specific individual mutational processes, we report here a study of 3000 tumor-normal pair whole genome datasets from more than 40 different human cancer types. Our analyses revealed that different mutational processes lead to distinct somatic mutation distributions between chromatin folding domains. APOBEC- or MSI-related mutations are enriched in transcriptionally-active domains while mutations occurring due to tobacco-smoke, ultraviolet (UV) light exposure or a signature of unknown aetiology (signature 17) enrich predominantly in transcriptionally-inactive domains. Active mutational processes dictate the mutation distributions in cancer genomes, and we show that mutational distributions shift during cancer evolution upon mutational processes switch. Moreover, a dramatic instance of extreme chromatin structure in humans, that of the unique folding pattern of the inactive X-chromosome leads to distinct somatic mutation distribution on X chromosome in females compared to males in various cancer types. Overall, the interplay between three-dimensional genome organization and active mutational processes has a substantial influence on the large-scale mutation rate variations observed in human cancer.

genomics↗