基于平滑最短路径图核的癌症患者亚群识别
Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel
摘要：
通过下一代测序技术表征患者的体细胞突变为细化癌症亚型开辟了可能性。然而，突变目录显示，只有一小部分基因在患者中频繁改变。另一方面，不同的基因组改变可能干扰相同的途径。我们提出了一种新的聚类程序，通过一种新的图形内核量化的相似性的患者从他们的突变谱的途径。我们将每个KEGG通路表示为无向图。对于每个患者，顶点标签是根据她改变的基因分配的。平滑最短路径图内核（smSPK）通过比较其顶点标记的路径图来评估每对患者。我们的聚类过程包括两个步骤：smSPK核矩阵推导出的每个途径输入到核k-means算法和每个途径进行单独评估。在下一步中，只有那些成功的通路被组合到内核k-means的单个内核输入中，以对患者进行分层。对模拟数据的评估表明，smSPK聚类患者的准确率高达88%。最后，为了确定卵巢癌患者亚组，我们将我们的方法应用于涉及481名患者的癌症基因组图谱卵巢数据。通过生存分析评价确定的亚组。将患者分成四个聚类，结果是患者组的生存时间（$p$-值$\le 0.005$）显著不同。
英文摘要：
Characterizing patient somatic mutations through next-generation sequencing technologies opens up possibilities for refining cancer subtypes. However, catalogues of mutations reveal that only a small fraction of the genes are altered frequently in patients. On the other hand different genomic alterations may perturb the same pathways. We propose a novel clustering procedure that quantifies the similarities of patients from their mutational profile on pathways via a novel graph kernel. We represent each KEGG pathway as an undirected graph. For each patient the vertex labels are assigned based on her altered genes. Smoothed shortest path graph kernel (smSPK) evaluates each pair of patients by comparing their vertex labeled pathway graphs. Our clustering procedure involves two steps: the smSPK kernel matrix derived for each pathway are input to kernel k-means algorithm and each pathway is evaluated individually. In the next step, only those pathways that are successful are combined in to a single kernel input to kernel k-means to stratify patients. Evaluating the procedure on simulated data showed that smSPK clusters patients up to 88\% accuracy. Finally to identify ovarian cancer patient subgroups, we apply our methodology to the cancer genome atlas ovarian data that involves 481 patients. The identified subgroups are evaluated through survival analysis. Grouping patients into four clusters results with patients groups that are significantly different in their survival times ($p$-value $\le 0.005$).
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