Gut Microbial Shifts Indicate Melanoma Presence and Bacterial Interactions in a Murine Model

• Published in: Diagnostics (Basel) 2022-04, Vol.12 (4), p.958
• Main Authors: Rossi, Marco, Aspromonte, Salvatore M, Kohlhapp, Frederick J, Newman, Jenna H, Lemenze, Alex, Pepe, Russell J, DeFina, Samuel M, Herzog, Nora L, Donnelly, Robert, Kuzel, Timothy M, Reiser, Jochen, Guevara-Patino, Jose A, Zloza, Andrew
• Format: Article
• Language: English
• Subjects: Algorithms; Bacteria; co-occurrence patterns; Colorectal cancer; Datasets; Disease; gut microbiota; Immune system; machine learning; Melanoma; Microbiota; Organisms; Principal components analysis; statistical algorithms; Tumors
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12040958

Through a multitude of studies, the gut microbiota has been recognized as a significant influencer of both homeostasis and pathophysiology. Certain microbial taxa can even affect treatments such as cancer immunotherapies, including the immune checkpoint blockade. These taxa can impact such processes both individually as well as collectively through mechanisms from quorum sensing to metabolite production. Due to this overarching presence of the gut microbiota in many physiological processes distal to the GI tract, we hypothesized that mice bearing tumors at extraintestinal sites would display a distinct intestinal microbial signature from non-tumor-bearing mice, and that such a signature would involve taxa that collectively shift with tumor presence. Microbial OTUs were determined from 16S rRNA genes isolated from the fecal samples of C57BL/6 mice challenged with either B16-F10 melanoma cells or PBS control and analyzed using QIIME. Relative proportions of bacteria were determined for each mouse and, using machine-learning approaches, significantly altered taxa and co-occurrence patterns between tumor- and non-tumor-bearing mice were found. Mice with a tumor had elevated proportions of , .g_rc4.4, and as well as significant information gains and ReliefF weights for , , , and . , , and were also implicated through shifting co-occurrences and PCA values. Using these seven taxa as a melanoma signature, a neural network reached an 80% tumor detection accuracy in a 10-fold stratified random sampling validation. These results indicated gut microbial proportions as a biosensor for tumor detection, and that shifting co-occurrences could be used to reveal relevant taxa.