Abstract：
Small blue round cell sarcomas (SBRCSs) are a heterogeneous group of tumors with overlapping morphologic features but markedly varying prognosis. They are characterized by distinct chromosomal alterations, particularly rearrangements leading to gene fusions, whose detection currently represents the most reliable diagnostic marker. Ewing sarcomas are the most common SBRCSs, defined by gene fusions involving EWSR1 and transcription factors of the ETS family, and the most frequent non-EWSR1-rearranged SBRCSs harbor a CIC rearrangement. Unfortunately, currently the identification of CIC::DUX4 translocation events, the most common CIC rearrangement, is challenging. Here, we present a machine-learning approach to support SBRCS diagnosis that relies on gene expression profiles measured via targeted sequencing. The analyses on a curated cohort of 69 soft-tissue tumors showed markedly distinct expression patterns for SBRCS subgroups. A random forest classifier trained on Ewing sarcoma and CIC-rearranged cases predicted probabilities of being CIC-rearranged >0.9 for CIC-rearranged-like sarcomas and <0.6 for other SBRCSs. Testing on a retrospective cohort of 1335 routine diagnostic cases identified 15 candidate CIC-rearranged tumors with a probability >0.75, all of which were supported by expert histopathologic reassessment. Furthermore, the multigene random forest classifier appeared advantageous over using high ETV4 expression alone, previously proposed as a surrogate to identify CIC rearrangement. Taken together, the expression-based classifier can offer valuable support for SBRCS pathologic diagnosis.
摘要：
小蓝圆细胞肉瘤（SBRCSs）是一组异质性肿瘤，具有重叠的形态学特征，但预后显着变化。它们的特征是不同的染色体改变，特别是导致基因融合的重排，其检测目前是最可靠的诊断标记。尤因肉瘤(ESs)是最常见的SBRCS，由涉及EWSR1和ETS基因家族转录因子的基因融合定义，而最常见的非EWSR1重排的SBRCSs有aCIC重排。不幸的是,CIC::DUX4易位事件的识别，最常见的CIC重排，目前的方法具有挑战性。这里,提出了一种支持SBRCS诊断的机器学习方法,该方法依赖于通过靶向测序测量的基因表达谱.对69个软组织肿瘤（STT）的精选队列的分析显示，SBRCS亚组的表达模式明显不同。在ES和CIC重排情况下训练的随机森林（RF）分类器预测CIC重排>0.9的概率，对于CIC重排样肉瘤，<0.6的概率。在1335例常规诊断病例的回顾性队列中进行的测试确定了15例candidateCIC重排的肿瘤，其概率>0.75，所有这些都得到了专家组织病理学重新评估的支持。此外，多基因RF分类器似乎优于单独使用高ETV4表达，先前被提议作为识别fyCIC重排的替代。一起来看,基于表达的分类器可以为SBRCS病理诊断提供有价值的支持。
