Gut Microbiome Composition in Lynch Syndrome With and Without History of Colorectal Neoplasia and Non-Lynch Controls

• Published in: Journal of gastrointestinal cancer 2024-03, Vol.55 (1), p.207-218
• Main Authors: Rifkin, S. B., Sze, M. A., Tuck, K., Koeppe, E., Stoffel, E. M., Schloss, P. D.
• Format: Article
• Language: English
• Subjects: Adult; Aged; Bacteria - classification; Bacteria - genetics; Bacteria - isolation & purification; Cancer Research; Case-Control Studies; Colorectal Neoplasms - microbiology; Colorectal Neoplasms, Hereditary Nonpolyposis - genetics; Colorectal Neoplasms, Hereditary Nonpolyposis - microbiology; Feces - microbiology; Female; Gastroenterology; Gastrointestinal Microbiome - genetics; Humans; Internal Medicine; Machine Learning; Male; Medicine; Medicine & Public Health; Middle Aged; Oncology; Radiotherapy; RNA, Ribosomal, 16S - genetics
• ISSN: 1941-6628; 1941-6636
• DOI: 10.1007/s12029-023-00925-4

Background While Lynch syndrome (LS) is a highly penetrant colorectal cancer (CRC) syndrome, there is considerable variation in penetrance; few studies have investigated the association between microbiome and CRC risk in LS. We analyzed the microbiome composition among individuals with LS with and without personal history of colorectal neoplasia (CRN) and non-LS controls. Methods We sequenced the V4 region of the 16S rRNA gene from the stool of 46 individuals with LS and 53 individuals without LS. We characterized within community and in between community microbiome variation, compared taxon abundance, and built machine learning models to investigate the differences in microbiome. Results There was no difference within or between community variations among LS groups, but there was a statistically significant difference in both within and between community variation comparing LS to non-LS. Streptococcus and Actinomyces were differentially enriched in LS-CRC compared to LS-without CRN. There were numerous differences in taxa abundance comparing LS to non-LS; notably, Veillonella was enriched and Faecalibacterium and Romboutsia were depleted in LS. Finally, machine learning models classifying LS from non-LS controls and LS-CRC from LS-without CRN performed moderately well. Conclusions Differences in microbiome composition between LS and non-LS may suggest a microbiome pattern unique to LS formed by underlying differences in epithelial biology and immunology. We found specific taxa differences among LS groups, which may be due to underlying anatomy. Larger prospective studies following for CRN diagnosis and microbiome composition changes are needed to determine if microbiome composition contributes to CRN development in patients with LS.