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首页> 外文会议>Pacific Symposium on Biocomputing >FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS
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FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS

机译：个性化信号通路网络频繁的子图挖掘常见的疾病途径患者患者并预测预后

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Motivation: 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. Results: 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. Conclusions: 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 information: Supplementary methods, figures, tables and code are available at https://github. com/be beklab/dysprog.
机译：动机：大规模基因组学研究产生了许多癌症类型的全面分子表征。已经建立了许多肿瘤类型的亚型;然而，这些分类基于小基因组的分子特性，其具有有限的功率来检测患者水平的缺陷。我们假设频繁的图形挖掘途径以收集与肿瘤功能相关的途径可以表征肿瘤类型，并为个性化疗法提供机会。结果：在本研究中，我们提出了一种基于其改变的途径特征的综合性常规方法来对患者进行群体，并显示乳腺癌（P <9.57e - 10）和胶质母细胞瘤的预后差异（P <0.05）患者。我们能够在副RNA-SEQ数据集中验证这种方法，分别具有P <0.05和P <0.01。我们还进行了途径富集分析，以进一步研究失调途径的生物学相关性。我们将我们的方法与基于网络的分类器算法进行了比较并显示了我们无监督的方法产生更强大和生物相关的聚类，而先前的方法未能向类似患者群体报告特定功能或将患者分类为预后组。结论：这些结果可以作为改善未来癌症患者预后的手段，并为改进的治疗方案和个性化干预提供机会。所提出的新建图形采矿方法能够将PPI网络与基因表达集成在生物声学的方法和临床上患者中的基因表达。我们利用来自微阵列和RNA-SEQ平台的乳腺癌和胶质母细胞瘤多形状数据集，并确定了分化样品的疾病机制。补充信息：HTTPS：// GitHub提供补充方法，数字，表格和代码。 com / be beklab / dysprog。

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