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 $\le 0.005$).

使用图核方法和路径聚类过程来从患者基因突变的剖面中识别肿瘤亚型，并应用于卵巢癌数据中以评估患者的生存分组情况。