An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets.

• Type of Material: Article
• Authors: Katherine E. Hill, Harvard Medical School; Andrew D. Kelly, Temple University; Marieke L. Kuijjer, Dana-Farber Cancer Institute; William Barry, Dana-Farber Cancer Institute; Ahmed Rattani, Mount Auburn Hospital; Cassandra C. Garbutt, Harvard Medical School; Haydn Kissick, Emory University; Katherine Janeway, Harvard Medical School; Antonio Perez-Atayde, Harvard Medical School; Jeffrey Goldsmith, Harvard Medical School; Mark C. Gebhardt, Harvard Medical School; Mohamed S. Arredouani, Harvard Medical School; Greg Cote, Harvard Medical School; Francis Hornicek, Harvard Medical School; Edwin Choy, Harvard Medical School; Zhenfeng Duan, Harvard Medical School; John Quackenbush, Dana-Farber Cancer Institute; Benjamin Haibe-Kains, University of Toronto; Dimitrios Spentzos, Harvard Medical School
• Language: English
• Date: 2017-05-15
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s). 2017
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s13045-017-0465-4
• Title of Journal or Parent Work: Journal of Hematology and Oncology
• ISSN: 1756-8722
• Volume: 10
• Issue: 1
• Start Page: 107
• End Page: 107
• Grant/Funding Information: DS and EC wish to acknowledge the generous support of Wendell Colson and Joanne Casper to the MGH Sarcoma Research Program.; BHK was supported by the Gattuso-Slaight Personalized Cancer Medicine Fund at Princess Margaret Cancer Centre, the Cancer Research Society (Canada), Ministry of Economic Development, Employment and Infrastructure and the Ministry of Innovation of the Government of Ontario, and the Canadian Institutes of Health Research.; This study was supported by National Cancer Institute grant 1R01CA178908 to DS and grant 1R35CA197449 to JQ.
• Keywords: Molecular subtypes; Osteosarcoma prognosis; Methylation; MicroRNA expression; Loss of imprinting
• Research Categories: Biophysics, Medical; Health Sciences, Medicine and Surgery; Biology, Biostatistics

Abstract

BACKGROUND: A microRNA (miRNA) collection on the imprinted 14q32 MEG3 region has been associated with outcome in osteosarcoma. We assessed the clinical utility of this miRNA set and their association with methylation status. METHODS: We integrated coding and non-coding RNA data from three independent annotated clinical osteosarcoma cohorts (n = 65, n = 27, and n = 25) and miRNA and methylation data from one in vitro (19 cell lines) and one clinical (NCI Therapeutically Applicable Research to Generate Effective Treatments (TARGET) osteosarcoma dataset, n = 80) dataset. We used time-dependent receiver operating characteristic (tdROC) analysis to evaluate the clinical value of candidate miRNA profiles and machine learning approaches to compare the coding and non-coding transcriptional programs of high- and low-risk osteosarcoma tumors and high- versus low-aggressiveness cell lines. In the cell line and TARGET datasets, we also studied the methylation patterns of the MEG3 imprinting control region on 14q32 and their association with miRNA expression and tumor aggressiveness. RESULTS: In the tdROC analysis, miRNA sets on 14q32 showed strong discriminatory power for recurrence and survival in the three clinical datasets. High- or low-risk tumor classification was robust to using different microRNA sets or classification methods. Machine learning approaches showed that genome-wide miRNA profiles and miRNA regulatory networks were quite different between the two outcome groups and mRNA profiles categorized the samples in a manner concordant with the miRNAs, suggesting potential molecular subtypes. Further, miRNA expression patterns were reproducible in comparing high-aggressiveness versus low-aggressiveness cell lines. Methylation patterns in the MEG3 differentially methylated region (DMR) also distinguished high-aggressiveness from low-aggressiveness cell lines and were associated with expression of several 14q32 miRNAs in both the cell lines and the large TARGET clinical dataset. Within the limits of available CpG array coverage, we observed a potential methylation-sensitive regulation of the non-coding RNA cluster by CTCF, a known enhancer-blocking factor. CONCLUSIONS: Loss of imprinting/methylation changes in the 14q32 non-coding region defines reproducible previously unrecognized osteosarcoma subtypes with distinct transcriptional programs and biologic and clinical behavior. Future studies will define the precise relationship between 14q32 imprinting, non-coding RNA expression, genomic enhancer binding, and tumor aggressiveness, with possible therapeutic implications for both early- and advanced-stage patients.