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

Makielski, K.; Donnelly, A. J.; Khammanivong, A.; Scott, M. C.; Ortiz, A. R.; Galvan, D. C.; Tomiyasu, H.; Amaya, C.; Ward, K.; Montoya, A.; Garbe, J. R.; Mills, L. J.; Cutter, G. R.; Fenger, J. M.; Kisseberth, W. C.; O'Brien, T. D.; Spector, L. G.; Bryan, B. A.; Subramanian, S.; Modiano, J. F.

doi:10.1101/2021.02.11.429432

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bioRxiv · 10.1101/2021.02.11.429432

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

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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 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

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Makielski, K., Donnelly, A. J., Khammanivong, A., Scott, M. C., Ortiz, A. R., Galvan, D. C., Tomiyasu, H., Amaya, C., Ward, K., Montoya, A., Garbe, J. R., Mills, L. J., Cutter, G. R., Fenger, J. M., Kisseberth, W. C., O'Brien, T. D., Spector, L. G., Bryan, B. A., Subramanian, S., Modiano, J. F.. 2021-02-12. Development of an exosomal biomarker signature to detect minimal residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning. https://doi.org/10.1101/2021.02.11.429432

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