A multilevel pan-cancer map links gene mutations to cancer hallmarks.
A multilevel pan-cancer map links gene mutations to cancer hallmarks.
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DOI：
10.1186/s40880-015-0050-6
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发表时间：
2015-09-14
影响因子：
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通讯作者：
Shmulevich I
中科院分区：
文献类型：
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作者：
Knijnenburg TA;Bismeijer T;Wessels LF;Shmulevich I
癌症研究的一个核心挑战是建立模型，弥合可以设计干预措施的分子水平与疾病表型表现的细胞和组织水平之间的差距。本研究的目的是从功能注释中构建这样一个模型，并探索其在与大规模癌症基因组学数据整合时的用途。我们创建了一个地图，通过信号通路将基因与癌症标志联系起来。我们将来自各种癌症类型的基因突变和焦点拷贝数数据投影到这个地图上。我们进行了统计学分析，以揭示这种拓扑结构中相互排斥和共同发生的致癌畸变。我们的分析表明，尽管肿瘤类型的遗传指纹可能非常不同，但在标志水平上的变异较少，这与不同的遗传改变具有相似的功能结果的想法一致。此外，我们还展示了多级图谱如何有助于阐明不常突变基因的作用，并且我们证明了互斥基因突变在途径中更普遍，而许多共同发生的基因突变与标志性特征相关。将来自各种癌症类型的基因突变和局灶性拷贝数数据覆盖在该图谱上，可以不仅在基因水平上，而且在途径和标志水平上系统地研究肿瘤样本之间的相似性和差异。本文的在线版本（doi：10.1186/s40880-015-0050-6）包含补充材料，可供授权用户使用。
A central challenge in cancer research is to create models that bridge the gap between the molecular level on which interventions can be designed and the cellular and tissue levels on which the disease phenotypes are manifested. This study was undertaken to construct such a model from functional annotations and explore its use when integrated with large-scale cancer genomics data. We created a map that connects genes to cancer hallmarks via signaling pathways. We projected gene mutation and focal copy number data from various cancer types onto this map. We performed statistical analyses to uncover mutually exclusive and co-occurring oncogenic aberrations within this topology. Our analysis showed that although the genetic fingerprint of tumor types could be very different, there were less variations at the level of hallmarks, consistent with the idea that different genetic alterations have similar functional outcomes. Additionally, we showed how the multilevel map could help to clarify the role of infrequently mutated genes, and we demonstrated that mutually exclusive gene mutations were more prevalent in pathways, whereas many co-occurring gene mutations were associated with hallmark characteristics. Overlaying this map with gene mutation and focal copy number data from various cancer types makes it possible to investigate the similarities and differences between tumor samples systematically at the levels of not only genes but also pathways and hallmarks. The online version of this article (doi:10.1186/s40880-015-0050-6) contains supplementary material, which is available to authorized users.