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对12种癌症类型的多平台分析揭示了原发组织内部和之间的分子分类。

Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

作者信息

Hoadley Katherine A, Yau Christina, Wolf Denise M, Cherniack Andrew D, Tamborero David, Ng Sam, Leiserson Max D M, Niu Beifang, McLellan Michael D, Uzunangelov Vladislav, Zhang Jiashan, Kandoth Cyriac, Akbani Rehan, Shen Hui, Omberg Larsson, Chu Andy, Margolin Adam A, Van't Veer Laura J, Lopez-Bigas Nuria, Laird Peter W, Raphael Benjamin J, Ding Li, Robertson A Gordon, Byers Lauren A, Mills Gordon B, Weinstein John N, Van Waes Carter, Chen Zhong, Collisson Eric A, Benz Christopher C, Perou Charles M, Stuart Joshua M

机构信息

Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA.

Buck Institute for Research on Aging, Novato, CA 94945, USA.

出版信息

Cell. 2014 Aug 14;158(4):929-944. doi: 10.1016/j.cell.2014.06.049. Epub 2014 Aug 7.

Abstract

Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.

摘要

最近对病理定义的肿瘤类型进行的基因组分析确定了“组织内”疾病亚型。然而,基因组特征在不同组织间共享的程度仍不清楚。我们使用五个全基因组平台和一个蛋白质组平台,对来自12种癌症类型的3527个标本进行了综合分析,揭示了一个统一的分类为11个主要亚型。其中五个亚型与其组织起源对应亚型几乎相同,但发现几种不同的癌症类型汇聚成共同的亚型。肺鳞状癌、头颈癌和一部分膀胱癌合并成一个以TP53改变、TP63扩增以及免疫和增殖途径基因高表达为特征的亚型。值得注意的是,膀胱癌分为三种泛癌亚型。这种多平台分类虽然与组织起源相关,但为预测临床结果提供了独立的信息。所有数据集都可从一个统一的资源中进行数据挖掘,以支持进一步的生物学发现和对新型治疗策略的深入了解。

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