Abstract 671: Development of an exosomal biomarker signature to detect minimal residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning

Abstract

Abstract Osteosarcoma, the most common primary bone tumor in humans and dogs, 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, the wide clinical use of exosomes is precluded by the challenges in identifying disease-associated cargo from the vastly larger background of normal exosome cargo. We developed a method using canine osteosarcoma xenografts to distinguish tumor-derived exosome mRNAs and host-response mRNAs, allowing for identification of osteosarcoma-specific gene signatures, which were then validated in samples from dogs with osteosarcoma. A canine osteosarcoma-associated gene signature was developed using exosomes from mouse xenograft experiments and a species-aware bioinformatics pipeline. Validation of the gene signature in canine serum exosomes was done by qRT-PCR analysis. Machine learning algorithms assigned dogs into healthy or disease groups based on the qRT-PCR data. Dogs in a validation set of clinical osteosarcoma cases with post-treatment samples were classified as “osteosarcoma -detected” or “osteosarcoma - NOT detected”, and clinical outcome measures were compared. An osteosarcoma-associated signature consisting of five mRNAs (SKA2, NEU1, PAF1, PSMG2, and NOB1) was identified using our canine osteosarcoma xenograft model. Serum exosomes were isolated from 53 dogs in distinct clinical groups, including “healthy”, “osteosarcoma”, “other bone tumor”, or “non-neoplastic disease”. Dogs in a validation set whose post-treatment samples were classified as “osteosarcoma - NOT detected” had longer remissions than dogs classified as “osteosarcoma - detected” for up to 15 months after treatment. In conclusion, we identified a gene signature associated with canine osteosarcoma for the detection of minimal residual disease. This gene signature was validated by qRT-PCR with serum exosomes from canine patients with osteosarcoma, and used to train artificial intelligence. The test results were predictive of molecular remissions in dogs up to 15 months after initiating therapy, suggesting it will have applications in the early detection and minimal residual disease settings. This study combines a bioinformatics approach to biomarker discovery with machine learning to correctly identify osteosarcoma in canine patients. These results set the stage for future discoveries to inform cancer risk, diagnosis, prognosis, and response to therapy. Citation Format: Kelly M. Makielski, Alicia J. Donnelly, Ali Khammanivong, Milcah C. Scott, Hirotaka Tomiyasu, John Garbe, Lauren J. Mills, Gary R. Cutter, Andrea Ortiz, Dana C. Galvan, Kristi Ward, Alexa N. Montoya, Brad A. Bryan, Joelle M. Fenger, William C. Kisseberth, Subbaya Subramanian, Jaime F. Modiano. Development of an exosomal biomarker signature to detect minimal residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 671.