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

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 independen...

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Autores principales: Hill, Katherine E., Kelly, Andrew D., Kuijjer, Marieke L., Barry, William, Rattani, Ahmed, Garbutt, Cassandra C., Kissick, Haydn, Janeway, Katherine, Perez-Atayde, Antonio, Goldsmith, Jeffrey, Gebhardt, Mark C., Arredouani, Mohamed S., Cote, Greg, Hornicek, Francis, Choy, Edwin, Duan, Zhenfeng, Quackenbush, John, Haibe-Kains, Benjamin, Spentzos, Dimitrios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5433149/
https://www.ncbi.nlm.nih.gov/pubmed/28506242
http://dx.doi.org/10.1186/s13045-017-0465-4
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author Hill, Katherine E.
Kelly, Andrew D.
Kuijjer, Marieke L.
Barry, William
Rattani, Ahmed
Garbutt, Cassandra C.
Kissick, Haydn
Janeway, Katherine
Perez-Atayde, Antonio
Goldsmith, Jeffrey
Gebhardt, Mark C.
Arredouani, Mohamed S.
Cote, Greg
Hornicek, Francis
Choy, Edwin
Duan, Zhenfeng
Quackenbush, John
Haibe-Kains, Benjamin
Spentzos, Dimitrios
author_facet Hill, Katherine E.
Kelly, Andrew D.
Kuijjer, Marieke L.
Barry, William
Rattani, Ahmed
Garbutt, Cassandra C.
Kissick, Haydn
Janeway, Katherine
Perez-Atayde, Antonio
Goldsmith, Jeffrey
Gebhardt, Mark C.
Arredouani, Mohamed S.
Cote, Greg
Hornicek, Francis
Choy, Edwin
Duan, Zhenfeng
Quackenbush, John
Haibe-Kains, Benjamin
Spentzos, Dimitrios
author_sort Hill, Katherine E.
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13045-017-0465-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-54331492017-05-17 An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets Hill, Katherine E. Kelly, Andrew D. Kuijjer, Marieke L. Barry, William Rattani, Ahmed Garbutt, Cassandra C. Kissick, Haydn Janeway, Katherine Perez-Atayde, Antonio Goldsmith, Jeffrey Gebhardt, Mark C. Arredouani, Mohamed S. Cote, Greg Hornicek, Francis Choy, Edwin Duan, Zhenfeng Quackenbush, John Haibe-Kains, Benjamin Spentzos, Dimitrios J Hematol Oncol Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13045-017-0465-4) contains supplementary material, which is available to authorized users. BioMed Central 2017-05-15 /pmc/articles/PMC5433149/ /pubmed/28506242 http://dx.doi.org/10.1186/s13045-017-0465-4 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Hill, Katherine E.
Kelly, Andrew D.
Kuijjer, Marieke L.
Barry, William
Rattani, Ahmed
Garbutt, Cassandra C.
Kissick, Haydn
Janeway, Katherine
Perez-Atayde, Antonio
Goldsmith, Jeffrey
Gebhardt, Mark C.
Arredouani, Mohamed S.
Cote, Greg
Hornicek, Francis
Choy, Edwin
Duan, Zhenfeng
Quackenbush, John
Haibe-Kains, Benjamin
Spentzos, Dimitrios
An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets
title An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets
title_full An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets
title_fullStr An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets
title_full_unstemmed An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets
title_short An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets
title_sort imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5433149/
https://www.ncbi.nlm.nih.gov/pubmed/28506242
http://dx.doi.org/10.1186/s13045-017-0465-4
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