Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.
Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Marchstr. 23, 10587, Berlin, Germany.
Genome Med. 2018 Nov 15;10(1):83. doi: 10.1186/s13073-018-0591-9.
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.
目前所有主要癌症都有全面的突变谱数据，这导致了提出新的分子肿瘤分类，这些分类修改或取代了现有的基于器官和组织的肿瘤分型。这种分子重新分类的基本原理是，癌症病理的遗传改变预测治疗反应，因此可能比组织学更准确地反映癌症。通过所谓的篮子试验，已经在各种成功率下测试了使用单个可操作突变来选择跨越组织类型的癌症进行治疗的方法。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谱，系统地分析突变效应的组织学上下文依赖性。
为了确定致癌突变对蛋白质谱的影响，我们使用了能量距离，该距离比较了带有致癌突变的肿瘤（内距离）和没有突变的肿瘤（外距离）的蛋白质谱的欧几里得距离，并对显著性分析进行了蒙特卡罗模拟。最后，根据对谱差异的贡献对蛋白质进行排序，以确定跨癌症的致癌突变效应特征的蛋白质。
我们将我们的方法应用于当前提出的四种分子肿瘤分类和主要治疗相关的可操作基因。评估的 12 个可操作基因在相应的肿瘤类型中均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与突变相关的蛋白质谱。此外，我们的分析确定了 4 个基因（FGFR1、ERRB2、IDH1、KRAS/NRAS）在 14 种肿瘤类型中的一致跨癌症效应。我们还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效应的突变特征，因此有助于分子分类疗效的临床前计算机测试以及单个突变的可用药性。它支持识别有效的跨癌症的新靶向治疗方法，并指导有效的篮子试验设计。
