Wu S Peter, Cooper Benjamin T, Bu Fang, Bowman Christopher J, Killian J Keith, Serrano Jonathan, Wang Shiyang, Jackson Twana M, Gorovets Daniel, Shukla Neerav, Meyers Paul A, Pisapia David J, Gorlick Richard, Ladanyi Marc, Thomas Kristen, Snuderl Matija, Karajannis Matthias A
Department of Radiation Oncology, NYU Langone Medical Center, New York, NY.
Department of Pathology, NYU Langone Medical Center, New York, NY.
JCO Precis Oncol. 2017;2017. doi: 10.1200/PO.17.00031. Epub 2017 Oct 6.
Pediatric sarcomas provide a unique diagnostic challenge. There is considerable morphologic overlap between entities, increasing the importance of molecular studies in the diagnosis, treatment, and identification of therapeutic targets. We developed and validated a genome-wide DNA methylation based classifier to differentiate between osteosarcoma, Ewing's sarcoma, and synovial sarcoma.
DNA methylation status of 482,421 CpG sites in 10 Ewing's sarcoma, 11 synovial sarcoma, and 15 osteosarcoma samples were determined using the Illumina Infinium HumanMethylation450 array. We developed a random forest classifier trained from the 400 most differentially methylated CpG sites within the training set of 36 sarcoma samples. This classifier was validated on data drawn from The Cancer Genome Atlas (TCGA) synovial sarcoma, TARGET Osteosarcoma, and a recently published series of Ewing's sarcoma.
Methylation profiling revealed three distinct patterns, each enriched with a single sarcoma subtype. Within the validation cohorts, all samples from TCGA were accurately classified as synovial sarcoma (10/10, sensitivity and specificity 100%), all but one sample from TARGET-OS were classified as osteosarcoma (85/86, sensitivity 98%, specificity 100%) and 14/15 Ewing's sarcoma samples classified correctly (sensitivity 93%, specificity 100%). The single misclassified osteosarcoma sample demonstrated high EWSR1 and ETV1 expression on RNA-seq although no fusion was found on manual curation of the transcript sequence. Two additional clinical samples, that were difficult to classify by morphology and molecular methods, were classified as osteosarcoma when previously suspected to be a synovial sarcoma and Ewing's sarcoma on initial diagnosis, respectively.
Osteosarcoma, synovial sarcoma, and Ewing's sarcoma have distinct epigenetic profiles. Our validated methylation-based classifier can be used to provide diagnostic assistance when histological and standard techniques are inconclusive.
小儿肉瘤的诊断颇具挑战性。不同实体之间存在相当大的形态学重叠,这增加了分子研究在诊断、治疗及确定治疗靶点方面的重要性。我们开发并验证了一种基于全基因组DNA甲基化的分类器,用于区分骨肉瘤、尤因肉瘤和滑膜肉瘤。
使用Illumina Infinium HumanMethylation450芯片测定10例尤因肉瘤、11例滑膜肉瘤和15例骨肉瘤样本中482,421个CpG位点的DNA甲基化状态。我们从36例肉瘤样本的训练集中400个甲基化差异最大的CpG位点开发了一个随机森林分类器。该分类器在来自癌症基因组图谱(TCGA)的滑膜肉瘤、TARGET骨肉瘤以及最近发表的一系列尤因肉瘤的数据上进行了验证。
甲基化谱揭示了三种不同模式,每种模式都富集了单一肉瘤亚型。在验证队列中,TCGA的所有样本均被准确分类为滑膜肉瘤(10/10,敏感性和特异性均为100%),TARGET-OS除一个样本外的所有样本均被分类为骨肉瘤(85/86,敏感性98%,特异性100%),15例尤因肉瘤样本中有14例分类正确(敏感性93%,特异性100%)。唯一分类错误的骨肉瘤样本在RNA测序中显示出高EWSR1和ETV1表达,尽管在手动整理转录本序列时未发现融合。另外两个临床样本,通过形态学和分子方法难以分类,在最初诊断时分别被怀疑为滑膜肉瘤和尤因肉瘤,现在被分类为骨肉瘤。
骨肉瘤、滑膜肉瘤和尤因肉瘤具有不同的表观遗传特征。当组织学和标准技术无法得出结论时,我们经过验证的基于甲基化的分类器可用于提供诊断帮助。