Evidence map›Paper›PMID 28506242›Full record

ArticleJournal of hematology & oncology2017

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

Katherine E Hill, Andrew D Kelly, Marieke L Kuijjer, William Barry, Ahmed Rattani, Cassandra C Garbutt, Haydn Kissick, Katherine Janeway, Antonio Perez-Atayde, Jeffrey Goldsmith and 9 more

Open access · goldAbstract read
In one paragraph

Article in Journal of hematology & oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.

  1. Pooled it
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  6. YBX1-interacting small RNAs andJournal of bone oncology · 2023
    Article
  7. Navigating the genomic instability mine field of osteosarcoma to better understand implications of non-coding RNAs.Biocell : official journal of the Sociedades Latinoamericanas de Microscopia Electronica ... et. al · 2022
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  14. PUMA: PANDA Using MicroRNA Associations.Bioinformatics (Oxford, England) · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors at 9 institutions in 2 countries.

Katherine E HillHematology-Oncology, Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Andrew D KellyFels Institute for Cancer Research and Molecular Biology, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA.
Marieke L KuijjerDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
William BarryDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
Ahmed RattaniDepartment of Medicine, Mount Auburn Hospital, Cambridge, MA, USA.
Cassandra C GarbuttCenter for Sarcoma and Connective Tissue Oncology, Department of Orthopedics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Haydn KissickDepartment of Urology, Medical School, Emory University, Atlanta, GA, USA.
Katherine JanewayDepartment of Pediatric Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Antonio Perez-AtaydeDepartment of Pathology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Jeffrey GoldsmithDepartment of Pathology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Mark C GebhardtOrthopedics, Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Mohamed S ArredouaniSurgery, Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Greg CoteCancer Center, Division of Hematology and Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Francis HornicekCenter for Sarcoma and Connective Tissue Oncology, Department of Orthopedics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Edwin ChoyCancer Center, Division of Hematology and Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Zhenfeng DuanCenter for Sarcoma and Connective Tissue Oncology, Department of Orthopedics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
John QuackenbushDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
Benjamin Haibe-KainsPrincess Margaret Cancer Centre, University Health Network, Toronto, Canada.
Dimitrios SpentzosCenter for Sarcoma and Connective Tissue Oncology, Department of Orthopedics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. dspentzos@mgh.harvard.edu.
Harvard University · USDana-Farber Cancer Institute · USMassachusetts General Hospital · USBeth Israel Deaconess Medical Center · USBoston Children's Hospital · USEmory University · USMount Auburn Hospital · USTemple University · USUniversity Health Network · CA

Funding

PREDOCTORAL TRAINING PROGRAM IN GENETICST32GM007388 · NIGMS · PRINCETON UNIVERSITY · PI CRISTEA, ILEANA M. · 1985 to 2022
$27.4M
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Integrated models for metastatic phenotype and outcome prediction in osteosarcomaR01CA178908 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI SPENTZOS, DIMITRIOS · 2014 to 2018
$1.8M
NCI NIH HHS R01 CA178908NCI NIH HHS R35 CA197449NIGMS NIH HHS T32 GM007388
6 · The paper itself

Abstract

backgroundA 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.

methodsWe 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.

resultsIn 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.

conclusionsLoss 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.

Indexed as

Genomic ImprintingAntineoplastic Combined Chemotherapy ProtocolsBone NeoplasmsBostonCell Line, TumorChromosomes, Human, Pair 14Datasets as TopicDisease-Free SurvivalDNA MethylationDNA, NeoplasmGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMicroRNAsNeoplasm InvasivenessOsteosarcomaDNA, NeoplasmMicroRNAsRNA, NeoplasmLoss of imprintingMethylationMicroRNA expressionMolecular subtypesOsteosarcoma prognosis

Identifiers

PMID28506242
PMCPMC5433149
OpenAlexW2616354644

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.