Durmaz Arda, Henderson Tim A D, Brubaker Douglas, Bebek Gurkan
Systems Biology and Bioinformatics Graduate Program, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, USA*Co-first Author,
Pac Symp Biocomput. 2017;22:402-413. doi: 10.1142/9789813207813_0038.
Large scale genomics studies have generated comprehensive molecular characterization of numerous cancer types. Subtypes for many tumor types have been established; however, these classifications are based on molecular characteristics of a small gene sets with limited power to detect dysregulation at the patient level. We hypothesize that frequent graph mining of pathways to gather pathways functionally relevant to tumors can characterize tumor types and provide opportunities for personalized therapies.
In this study we present an integrative omics approach to group patients based on their altered pathway characteristics and show prognostic differences within breast cancer (p < 9:57E - 10) and glioblastoma multiforme (p < 0:05) patients. We were able validate this approach in secondary RNA-Seq datasets with p < 0:05 and p < 0:01 respectively. We also performed pathway enrichment analysis to further investigate the biological relevance of dysregulated pathways. We compared our approach with network-based classifier algorithms and showed that our unsupervised approach generates more robust and biologically relevant clustering whereas previous approaches failed to report specific functions for similar patient groups or classify patients into prognostic groups.
These results could serve as a means to improve prognosis for future cancer patients, and to provide opportunities for improved treatment options and personalized interventions. The proposed novel graph mining approach is able to integrate PPI networks with gene expression in a biologically sound approach and cluster patients in to clinically distinct groups. We have utilized breast cancer and glioblastoma multiforme datasets from microarray and RNA-Seq platforms and identified disease mechanisms differentiating samples.
Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/dysprog.
大规模基因组学研究已经对众多癌症类型进行了全面的分子特征分析。许多肿瘤类型的亚型已经确定；然而，这些分类是基于一小部分基因集的分子特征，在检测患者水平的失调方面能力有限。我们假设频繁挖掘与肿瘤功能相关的通路图可以表征肿瘤类型，并为个性化治疗提供机会。
在本研究中，我们提出了一种整合组学方法，根据患者改变的通路特征对患者进行分组，并显示乳腺癌患者（p < 9.57E - 10）和多形性胶质母细胞瘤患者（p < 0.05）内的预后差异。我们能够分别在二级RNA测序数据集中以p < 0.05和p < 0.01验证该方法。我们还进行了通路富集分析，以进一步研究失调通路的生物学相关性。我们将我们的方法与基于网络的分类器算法进行了比较，结果表明我们的无监督方法产生了更稳健且生物学上相关的聚类，而以前的方法未能报告相似患者组的特定功能，也未能将患者分类到预后组中。
这些结果可以作为改善未来癌症患者预后的一种手段，并为改进治疗方案和个性化干预提供机会。所提出的新颖图挖掘方法能够以生物学合理的方式将蛋白质 - 蛋白质相互作用网络与基因表达整合起来，并将患者聚类到临床上不同的组中。我们利用了来自微阵列和RNA测序平台的乳腺癌和多形性胶质母细胞瘤数据集，并确定了区分样本的疾病机制。
补充方法、图、表和代码可在https://github.com/bebeklab/dysprog获取。
