Kisseberth, W. C. | Search bioRxiv

bioRxiv2021

Urine and Fecal Microbiota in a Canine Model of Bladder Cancer

IntroductionUrothelial carcinoma (UC) is the tenth most diagnosed cancer in humans worldwide. Dogs are a robust model for invasive UC as tumor development and progression is similar in humans and dogs. Recent studies on urine microbiota in humans revealed alterations in microbial diversity and composition in individuals with UC; however, the potential role of microbiota in UC has yet to be elucidated. Dogs could be valuable models for this research, but microbial alterations in dogs with UC have not been evaluated. ObjectiveThe objective of this this pilot study was to compare the urine and fecal microbiota of dogs with UC (n = 7) and age-, sex-, and breed-matched healthy controls (n = 7). MethodsDNA was extracted from mid-stream free-catch urine and fecal samples using Qiagen Bacteremia and PowerFecal kits, respectively. 16S rRNA gene sequencing was performed followed by sequence processing and analyses (QIIME 2 and R). ResultsCanine urine and fecal samples were dominated by taxa similar to those found in humans. Significantly decreased microbial diversity (Kruskal-Wallis: Shannon, p = 0.048) and altered bacterial composition were observed in the urine but not feces of dogs with UC (PERMANOVA: Unweighted UniFrac, p = 0.011). The relative abundances of Fusobacterium was also increased, although not significantly, in the urine and feces of dogs with UC. ConclusionThis study characterizes urine and fecal microbiota in dogs with UC, and it provides a foundation for future work exploring host-microbe dynamics in UC carcinogenesis, prognosis, and treatment.

cancer biology↗

bioRxiv2021

Development of an exosomal biomarker signature to detect minimal residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning

Osteosarcoma has a guarded prognosis. A major hurdle in developing more effective osteosarcoma therapies is the lack of disease-specific biomarkers to predict risk, prognosis, or therapeutic response. Exosomes are secreted extracellular microvesicles emerging as powerful diagnostic tools. However, their clinical application is precluded by challenges in identifying disease-associated cargo from the vastly larger background of normal exosome cargo. We developed a method using canine osteosarcoma in mouse xenografts to distinguish tumor-derived from host-response exosomal mRNAs. The model allows for the identification of canine osteosarcoma-specific gene signatures by RNA sequencing and a species-differentiating bioinformatics pipeline. An osteosarcoma-associated signature consisting of five gene transcripts (SKA2, NEU1, PAF1, PSMG2, and NOB1) was validated in dogs with spontaneous osteosarcoma by qRT-PCR, while a machine learning model assigned dogs into healthy or disease groups. Serum/plasma exosomes were isolated from 53 dogs in distinct clinical groups ("healthy", "osteosarcoma", "other bone tumor", or "non-neoplastic disease"). Pre-treatment samples from osteosarcoma cases were used as the training set and a validation set from post-treatment samples was used for testing, classifying as "osteosarcoma-detected" or "osteosarcoma-NOT detected". Dogs in a validation set whose post-treatment samples were classified as "osteosarcoma-NOT detected" had longer remissions, up to 15 months after treatment. In conclusion, we identified a gene signature predictive of molecular remissions with potential applications in the early detection and minimal residual disease settings. These results provide proof-of-concept for our discovery platform and its utilization in future studies to inform cancer risk, diagnosis, prognosis, and therapeutic response. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/429432v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1b612c4org.highwire.dtl.DTLVardef@8a1847org.highwire.dtl.DTLVardef@3ffd64org.highwire.dtl.DTLVardef@114df8c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗