Comparative transcriptome analysis quantifies immune cell transcript levels, metastatic progression, and survival in osteosarcoma

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37 Scopus citations

Abstract

Overall survival of patients with osteosarcoma (OS) has improved little in the past three decades, and better models for study are needed. OS is common in large dog breeds and is genetically inducible in mice, making the disease ideal for comparative genomic analyses across species. Understanding the level of conservation of intertumor transcriptional variation across species and how it is associated with progression to metastasis will enable us to more efficiently develop effective strategies to manage OS and to improve therapy. In this study, transcriptional profiles of OS tumors and cell lines derived from humans (n ¼ 49), mice (n ¼ 103), and dogs (n ¼ 34) were generated using RNA sequencing. Conserved intertumor transcriptional variation was present in tumor sets from all three species and comprised gene clusters associated with cell cycle and mitosis and with the presence or absence of immune cells. Further, we developed a novel gene cluster expression summary score (GCESS) to quantify intertumor transcriptional variation and demonstrated that these GCESS values associated with patient outcome. Human OS tumors with GCESS values suggesting decreased immune cell presence were associated with metastasis and poor survival. We validated these results in an independent human OS tumor cohort and in 15 different tumor data sets obtained from The Cancer Genome Atlas. Our results suggest that quantification of immune cell absence and tumor cell proliferation may better inform therapeutic decisions and improve overall survival for OS patients. Significance: This study offers new tools to quantify tumor heterogeneity in osteosarcoma, identifying potentially useful prognostic biomarkers for metastatic progression and survival in patients.

Original languageEnglish (US)
Pages (from-to)326-337
Number of pages12
JournalCancer Research
Volume78
Issue number2
DOIs
StatePublished - Jan 15 2018

Bibliographical note

Funding Information:
The authors would like to thank John Garbe for technical assistance in the RNA-seq data processing pipeline, Rebecca S. Larue for running the RNA-seq data processing pipeline, Colleen Forster and the Clinical and Translational Science Institute's Bionet laboratory for optimization and performance of immunohistochemistry, the Minnesota Supercomputing Institute for computing time and storage space, and the University of Minnesota Genomics Center for sequencing services. This work was generously supported by the Zach Sobiech Osteosarcoma Fund, Keren Wyckoff Rein in Sarcoma Foundation, grants to A.L. Sarver NCI (CA211249), J.F. Modiano NCI (CA208529) and Morris Animal Foundation (D13CA-032), D.A. Largaespada NCI (CA113636) and ACS (#123939), and A.E. Sarver NIH (CA099936), S. Subramanian ACS (RSG-13-381-01) and the Masonic Cancer Center Comprehensive Cancer Center Support Grant (CA077598)

Publisher Copyright:
© 2017 American Association for Cancer Research.

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