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

Kelly M Makielski, Alicia J Donnelly, Ali Khammanivong, Milcah C Scott, Andrea R. Ortiz, Dana C. Galvan, Hirotaka Tomiyasu, Clarissa Amaya, Kristin A. Ward, Alexa Montoya, John R. Garbe, Lauren J. Mills, Gary R. Cutter, Joelle M. Fenger, William C. Kisseberth, Timothy D. O’Brien, Brenda J. Weigel, Logan G. Spector, Brad A. Bryan, Subbaya SubramanianJaime F. Modiano

Research output: Contribution to journalArticlepeer-review

Abstract

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 messenger RNAs (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 real-time quantitative reverse transcription PCR (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.

Original languageEnglish (US)
JournalLaboratory Investigation
Volume101
Issue number12
Early online dateSep 6 2021
DOIs
StatePublished - Sep 6 2021

Bibliographical note

Funding Information:
The authors would like to thank Mitzi Lewellen for assistance with the mouse work, Aaron Sarver and Don Bellgrau for manuscript review and discussion, Mike Farrar, Daniel Vallera, Shruthi Naik, and Steven J. Russell for assistance with funding, and M. Gerard O’Sullivan and Ingrid Cornax for their assistance with mouse pathology. In addition, we would like to thank Jong-Hyuk Kim, Ashley Schulte, Taylor DePauw, and Erin Dickerson for laboratory support and discussion, Jonah Cullen for assistance with figures, and Dayane Alcantara for technical assistance. Finally, we thank Dr. Holly Borghese of the OSU CVM Veterinary Biospecimen Repository and Blue Buffalo Veterinary Clinical Trials Office (BBVCTO) and Kathleen M. Stuebner, Sara Pracht, Kelly Bergsrud, Andrea Chehadeh, Amber Winter, and Donna Groschen of the Clinical Investigation Center (CIC) of the University of Minnesota for their expertise in sample procurement. This work was supported in part by grants D15CA-047 (to J.F.M., B.A.B., S.S., T.D.O.) and D13CA-032 (to J.F.M. and S.S.) from Morris Animal Foundation, CA170218 (to J.F.M. and S.S.) from the Congressionally designated Medical Research Program of the Department of Defense, MNP #15.25 (to J.F.M.) from the Minnesota Partnership for Biotechnology and Medical Genomics, RSG-13-381-01 (to S.S.) from the American Cancer Society, 2011-1 (to J.F.M. and S.S.) from the Karen Wykoff Rein in Sarcoma Foundation. Philanthropic support for this project included directed grants from the Nat Fund of the Children’s Cancer Research Fund (to J.F.M., L.G.S., and B.J. W.), GREYlong (to J.F.M.), the Skippy Frank Fund for Life Sciences and Translational Research (J.F.M.), as well as unrestricted gifts from public and anonymous donors supporting the Animal Cancer Care and Research Program of the University of Minnesota. K.M.M. was supported in part by a postdoctoral fellowship from the institutional training grant in Molecular, Genetic, and Cellular Targets of Cancer (T32 CA009138). A.J.D. was supported in part by a postdoctoral fellowship from Morris Animal Foundation (D16CA-405). J.F.M. was supported in part by the Alvin and June Perlman Endowed Chair in Animal Oncology. Portions of this work were conducted in the Minnesota Nano Center, which is supported by the National Science Foundation through the National Nano Coordinated Infrastructure Network (NNCI) under Award Number ECCS-2025124. Sample collection was supported in part by the Ohio State University CTSA grant UL1TR002733 from the National Center for Advancing Translational Sciences and by the Ohio State Cancer Center designated cancer center support grant P30 CA016058. Support from bioinformatics was provided by the Masonic Cancer Center designated cancer center support grant P30 CA077598. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies listed above.

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to United States and Canadian Academy of Pathology.

PubMed: MeSH publication types

  • Journal Article

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