Abstract 817: Unbiased discovery of exosome-associated biomarkers using xenograft models
Authors: Scott Milcah C. , Garbe John R. , Tomiyasu Hirotaka , Donnelly Alicia , Bryan Brad A. , Subramanian Subbaya , Modiano Jaime F. 
Published: July 2017

We developed a novel, genome-wide method for biomarker discovery that is able to separate intrinsic markers of disease from markers of host response. Specifically, we implanted cell lines derived from two primary canine osteosarcoma tumors with different biological behavior into the tibiae of athymic nude mice. Both cell lines formed orthotopic tumors, and each recapitulated the biological behavior of the original primary tumor in terms of growth rate and metastatic potential (1,2). We also developed a cross-species hybrid genome that allowed us to identify separate canine and mouse transcripts in tumor xenografts (2). In other words, we were able to determine the contribution of canine mRNA sequences (derived from the implanted tumor cells) and mouse mRNA sequences (derived from infiltrating stroma) to define tumor-intrinsic features and host-specific features that contribute to osteosarcoma progression. When we applied this analysis method to exosome-derived mRNAs, we found canine-derived transcripts that were only present in exosomes of tumor-bearing mice that were associated with functions such as protein kinase A (PKA) and actin cytoskeleton signaling, and with upstream transcriptional regulators such as FOXF1, ESR1, and TP53. We also found tumor-specific, canine-derived transcripts that were associated with functions such as caveolae-derived endocytosis, energy utilization and metabolism, immune interactions, and G-coupled receptor signaling, and with upstream transcriptional regulators such as HNF1A, ESR1, NIPR1, and HNF4A. This method has the potential to reduce the statistical uncertainty that arises from the use of pre-selected molecules among the background of thousands of genomic features in patient samples. Using genome-wide analyses, we are able to combine multiple, highly correlated genes to manage this problem, increase markers' robustness, and avoid random correlations. In conclusion, this technology is useful to identify nucleic acids that serve as cancer biomarkers with high precision, and inter-species sequence conservation can be used to further improve its predictions.

References

1. Scott MC, Sarver AL, Tomiyasu H, Cornax I, Van Etten J, Varshney J, et al. Aberrant Retinoblastoma (RB)-E2F Transcriptional Regulation Defines Molecular Phenotypes of Osteosarcoma. J Biol Chem 2015;290(47):28070-83.

2. Scott MC, Tomiyasu H, Garbe JR, Cornax I, Amaya C, O'Sullivan MG, et al. Heterotypic models of osteosarcoma recapitulate tumor heterogeneity and biological behavior. Disease Models & Mechanisms 2016, doi: 10.1242/dmm.026849.

Citation Format: Milcah C. Scott, John R. Garbe, Hirotaka Tomiyasu, Alicia Donnelly, Brad A. Bryan, Subbaya Subramanian, Jaime F. Modiano. Unbiased discovery of exosome-associated biomarkers using xenograft models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 817. doi:10.1158/1538-7445.AM2017-817