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
Hematology-Oncology, Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
J Hematol Oncol. 2017 May 15;10(1):107. doi: 10.1186/s13045-017-0465-4.
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.
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.
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.
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.
位于印记14q32 MEG3区域的一组微小RNA(miRNA)与骨肉瘤的预后相关。我们评估了这组miRNA的临床实用性及其与甲基化状态的关联。
我们整合了来自三个独立注释的临床骨肉瘤队列(n = 65、n = 27和n = 25)的编码和非编码RNA数据,以及来自一个体外数据集(19个细胞系)和一个临床数据集(美国国立癌症研究所治疗应用研究以生成有效治疗方法(TARGET)骨肉瘤数据集,n = 80)的miRNA和甲基化数据。我们使用时间依赖性受试者工作特征(tdROC)分析来评估候选miRNA谱的临床价值,并使用机器学习方法比较高风险和低风险骨肉瘤肿瘤以及高侵袭性和低侵袭性细胞系的编码和非编码转录程序。在细胞系和TARGET数据集中,我们还研究了14q32上MEG3印记控制区域的甲基化模式及其与miRNA表达和肿瘤侵袭性的关联。
在tdROC分析中,14q32上的miRNA组对三个临床数据集中的复发和生存显示出强大的区分能力。使用不同的miRNA组或分类方法,高风险或低风险肿瘤分类都很稳健。机器学习方法表明,两个预后组之间全基因组miRNA谱和miRNA调控网络有很大差异,并且mRNA谱以与miRNA一致的方式对样本进行分类,提示可能存在分子亚型。此外,在比较高侵袭性和低侵袭性细胞系时,miRNA表达模式具有可重复性。MEG3差异甲基化区域(DMR)中的甲基化模式也区分了高侵袭性和低侵袭性细胞系,并且与细胞系和大型TARGET临床数据集中几种14q32 miRNA的表达相关。在可用的CpG阵列覆盖范围内,我们观察到一种已知的增强子阻断因子CTCF对非编码RNA簇的潜在甲基化敏感调控。
14q32非编码区域的印记缺失/甲基化变化定义了具有不同转录程序、生物学和临床行为的可重复的、先前未被认识的骨肉瘤亚型。未来的研究将确定14q32印记、非编码RNA表达、基因组增强子结合与肿瘤侵袭性之间的确切关系,这可能对早期和晚期患者都有治疗意义。