Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.
Proc Natl Acad Sci U S A. 2013 Apr 16;110(16):6388-93. doi: 10.1073/pnas.1219651110. Epub 2013 Apr 1.
We introduce Pathifier, an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. We demonstrate the algorithm's performance on three colorectal cancer datasets and two glioblastoma multiforme datasets and show that our multipathway-based representation is reproducible, preserves much of the original information, and allows inference of complex biologically significant information. We discovered several pathways that were significantly associated with survival of glioblastoma patients and two whose scores are predictive of survival in colorectal cancer: CXCR3-mediated signaling and oxidative phosphorylation. We also identified a subclass of proneural and neural glioblastoma with significantly better survival, and an EGF receptor-deregulated subclass of colon cancers.
我们介绍了一种名为 Pathifier 的算法，它可以根据表达数据为每个肿瘤样本推断途径失调分数。该分数是针对每个特定数据集和正在研究的癌症类型以特定于上下文的方式确定的。该算法将基因水平的信息转换为途径水平的信息，为每个样本生成紧凑且与生物学相关的表示形式。我们在三个结直肠癌数据集和两个胶质母细胞瘤多形性数据集上展示了该算法的性能，并表明我们的多途径表示是可重复的，保留了大部分原始信息，并允许推断复杂的生物学意义信息。我们发现了几个与胶质母细胞瘤患者生存显著相关的途径，以及两个与结直肠癌生存相关的评分：CXCR3 介导的信号和氧化磷酸化。我们还鉴定了具有显著更好生存的神经原性和神经胶质母细胞瘤的亚类，以及 EGF 受体失调的结肠癌细胞亚类。
