David J.H. Shih, Marc Remke, Vijay Ramaswamy, Betty Luu, Yuan Yao, Xin Wang, Adrian M. Dubuc, Livia Garzia, John Peacock, Stephen C. Mack, Xiaochong Wu, Adi Rolider, A. Sorana Morrissy, Florence M.G. Cavalli, Claudia C. Faria, Stephen W. Scherer, Uri Tabori, Cynthia E. Hawkins, David Malkin, Eric Bouffet, James T. Rutka, and Michael D. Taylor, Hospital for Sick Children; David J.H. Shih, Marc Remke, Vijay Ramaswamy, Yuan Yao, Xin Wang, Adrian M. Dubuc, John Peacock, Stephen C. Mack, and Michael D. Taylor, University of Toronto, Toronto; Boleslaw Lach, McMaster University, Hamilton, Ontario; Jennifer A. Chan, University of Calgary, Calgary, Alberta; Steffen Albrecht, Adam Fontebasso, and Nada Jabado, McGill University, Montreal, Quebec, Canada; Paul A. Northcott, Andrey Korshunov, Marcel Kool, David T.W. Jones, and Stefan M. Pfister, German Cancer Research Center; Stefan M. Pfister, University Hospital Heidelberg, Heidelberg; Ulrich Schüller, Ludwig-Maximilians-University, Munich; Stefan Rutkowski, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Karel Zitterbart, Masaryk University School of Medicine; Karel Zitterbart and Leos Kren, University Hospital Brno, Brno, Czech Republic; Toshihiro Kumabe and Teiji Tominaga, Tohoku University Graduate School of Medicine, Sendai, Japan; Young Shin Ra, University of Ulsan, Asan Medical Center; Ji-Yeoun Lee, Byung-Kyu Cho, Seung-Ki Kim, and Kyu-Chang Wang, Seoul National University Children's Hospital, Seoul; Shin Jung, Chonnam National University Research Institute of Medical Sciences, Chonnam National University Hwasun Hospital and Medical School, Chonnam, South Korea; Peter Hauser and Miklós Garami, Semmelweis University, Budapest; László Bognár and Almos Klekner, University of Debrecen, Medical and Health Science Centre, Debrecen, Hungary; Shenandoah Robinson, Boston Children's Hospital; Scott L. Pomeroy, Harvard Medical School, Boston, MA; Ali G. Saad, University of Arkansas for Medical Sciences, Little
J Clin Oncol. 2014 Mar 20;32(9):886-96. doi: 10.1200/JCO.2013.50.9539. Epub 2014 Feb 3.
Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication.
Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models.
Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas.
Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials.
髓母细胞瘤由四个不同的分子亚组组成:WNT、SHH、第 3 组和第 4 组。目前的髓母细胞瘤方案根据临床特征对患者进行分层:患者年龄、转移阶段、切除范围和组织学变异。四个亚组之间存在明显的预后和遗传差异,这表明亚组特异性分子生物标志物可以改善患者的预后预测。
从全球 43 个城市的 673 例髓母细胞瘤中发现了分子生物标志物。基于多变量 Cox 比例风险分析确定的临床和细胞遗传学生物标志物,设计了联合风险分层模型。使用荧光原位杂交(FISH)对非重叠的髓母细胞瘤组织微阵列(n=453)进行了鉴定的生物标志物检测,随后对风险分层模型进行了验证。
与仅使用临床生物标志物相比,亚组信息可提高多变量生存模型的预测准确性。大多数先前发表的细胞遗传学生物标志物仅在单个髓母细胞瘤亚组中具有预后意义。对 6 种 FISH 生物标志物(GLI2、MYC、染色体 11 [chr11]、chr14、17p 和 17q)进行分析,我们可以在 SHH、第 3 组和第 4 组髓母细胞瘤中可靠且可重复地识别极低风险和极高风险的患者。
将亚组和细胞遗传学生物标志物与既定的临床生物标志物相结合,可以大大提高患者的预后预测能力,即使在异质性临床治疗的情况下也是如此。大多数分子生物标志物的预后意义仅限于特定的亚组。我们已经确定了一组可靠的细胞遗传学生物标志物,可以可靠地识别极高风险和极低风险的患者群体,这使其成为未来临床试验中选择强化治疗和降低治疗强度的患者的理想工具。