Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers.
Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers.
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DOI：
10.1186/s13073-018-0591-9
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发表时间：
2018-11-15
期刊：
影响因子：
12.3
通讯作者：
Klauschen F
中科院分区：
文献类型：
--
作者：
Heim D;Montavon G;Hufnagl P;Müller KR;Klauschen F
现在所有主要癌症的全面突变图谱数据都导致了新的分子肿瘤分类的建议，这些分类修改或取代了现有的基于器官和组织的肿瘤分类。这种分子重新分类背后的基本原理是，癌症病理学背后的基因变化预测了治疗反应，因此可能提供比组织学更准确的癌症观点。使用个体可操作突变来选择不同组织类型的癌症进行治疗，已经在所谓的篮子试验中进行了测试，成功率各不相同。在这里，我们提出了一种计算方法，通过整合跨癌症的基因组和蛋白质组肿瘤图谱，促进对突变效应的组织学背景依赖性的系统分析。为了确定致癌突变对蛋白质谱的影响，我们使用了最大能量距离，即比较有致癌突变的肿瘤中蛋白质谱的欧几里德距离(内距离)和没有突变的肿瘤中的欧几里德距离(外距离)，并进行蒙特卡罗模拟进行显著性分析。最后，根据它们对图谱差异的贡献对蛋白质进行排序，以确定跨癌症致癌突变效应的特征蛋白质。我们将我们的方法应用于目前关于分子肿瘤分类和主要治疗相关可操作基因的四个建议。所有被评估的12个可操作基因在相应的肿瘤类型中都显示出对蛋白质水平的影响，并在21种肿瘤类型中显示了额外的突变相关蛋白图谱。此外，我们的分析发现，在14种肿瘤类型中，4个基因(FGFR1、ERRB2、IDH1、KRAS/NRAS)的跨癌效应是一致的。我们进一步使用细胞系药物反应数据来验证我们的发现。这种计算方法可用于识别具有蛋白质水平影响的突变特征，从而有助于对分子分类的有效性以及单个突变的可药性进行临床前测试。因此，它支持确定对癌症有效的新的靶向疗法，并指导有效的篮子试验设计。本文的在线版本(10.1186/s13073-0180591-9)包含补充材料，可供授权用户使用。
Comprehensive mutational profiling data now available on all major cancers have led to proposals of novel molecular tumor classifications that modify or replace the established organ- and tissue-based tumor typing. The rationale behind such molecular reclassifications is that genetic alterations underlying cancer pathology predict response to therapy and may therefore offer a more precise view on cancer than histology. The use of individual actionable mutations to select cancers for treatment across histotypes is already being tested in the so-called basket trials with variable success rates. Here, we present a computational approach that facilitates the systematic analysis of the histological context dependency of mutational effects by integrating genomic and proteomic tumor profiles across cancers. To determine effects of oncogenic mutations on protein profiles, we used the energy distance, which compares the Euclidean distances of protein profiles in tumors with an oncogenic mutation (inner distance) to that in tumors without the mutation (outer distance) and performed Monte Carlo simulations for the significance analysis. Finally, the proteins were ranked by their contribution to profile differences to identify proteins characteristic of oncogenic mutation effects across cancers. We apply our approach to four current proposals of molecular tumor classifications and major therapeutically relevant actionable genes. All 12 actionable genes evaluated show effects on the protein level in the corresponding tumor type and showed additional mutation-related protein profiles in 21 tumor types. Moreover, our analysis identifies consistent cross-cancer effects for 4 genes (FGFR1, ERRB2, IDH1, KRAS/NRAS) in 14 tumor types. We further use cell line drug response data to validate our findings. This computational approach can be used to identify mutational signatures that have protein-level effects and can therefore contribute to preclinical in silico tests of the efficacy of molecular classifications as well as the druggability of individual mutations. It thus supports the identification of novel targeted therapies effective across cancers and guides efficient basket trial designs. The online version of this article (10.1186/s13073-018-0591-9) contains supplementary material, which is available to authorized users.
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影响因子：
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作者：
Yokota, T.;Ura, T.;Shibata, N.;Takahari, D.;Shitara, K.;Nomura, M.;Kondo, C.;Mizota, A.;Utsunomiya, S.;Muro, K.;Yatabe, Y.
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Weinstein, John N.;Collisson, Eric A.;Mills, Gordon B.;Shaw, Kenna R. Mills;Ozenberger, Brad A.;Ellrott, Kyle;Shmulevich, Ilya;Sander, Chris;Stuart, Joshua M.
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