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Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.

ABSTRACT: Precise cancer classification is a central challenge in clinical cancer research such as diagnosis, prognosis and metastasis prediction. Most existing cancer classification methods based on gene or metabolite biomarkers were limited to single genomics or metabolomics, and lacked integration and utilization of multiple 'omics' data. The accuracy and robustness of these methods when applied to independent cohorts of patients must be improved. In this study, we propose a directed random walk-based method to evaluate the topological importance of each gene in a reconstructed gene-metabolite graph by integrating information from matched gene expression profiles and metabolomic profiles. The joint use of gene and metabolite information contributes to accurate evaluation of the topological importance of genes and reproducible pathway activities. We constructed classifiers using reproducible pathway activities for precise cancer classification and risk metabolic pathway identification. We applied the proposed method to the classification of prostate cancer. Within-dataset experiments and cross-dataset experiments on three independent datasets demonstrated that the proposed method achieved a more accurate and robust overall performance compared to several existing classification methods. The resulting risk pathways and topologically important differential genes and metabolites provide biologically informative models for prostate cancer prognosis and therapeutic strategies development.

SUBMITTER: Liu W

PROVIDER: S-EPMC4541321 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.

Liu Wei W Bai Xuefeng X Liu Yuejuan Y Wang Wei W Han Junwei J Wang Qiuyu Q Xu Yanjun Y Zhang Chunlong C Zhang Shihua S Li Xuecang X Ren Zhonggui Z Zhang Jian J Li Chunquan C

Scientific reports 20150819

Precise cancer classification is a central challenge in clinical cancer research such as diagnosis, prognosis and metastasis prediction. Most existing cancer classification methods based on gene or metabolite biomarkers were limited to single genomics or metabolomics, and lacked integration and utilization of multiple 'omics' data. The accuracy and robustness of these methods when applied to independent cohorts of patients must be improved. In this study, we propose a directed random walk-based ...[more]

PMID: 26286638

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Project description:BackgroundThe strong association between family history and prostate cancer (PCa) suggests a significant genetic contribution, yet specific highly penetrant PCa susceptibility genes have not been identified. Certain single-nucleotide-polymorphisms have been found to correlate with PCa risk; however uncertainty remains regarding their clinical utility and how to best incorporate this information into clinical decision-making. Genetic testing is available directly to consumers and both patients and healthcare providers are becoming more aware of this technology. Purchasing online allows patients to bypass their healthcare provider yet patients may have difficulty interpreting test results and providers may be called upon to interpret results. Determining optimal ways to educate both patients and providers, and strategies for appropriately incorporating this information into clinical decision-making are needed.MethodsA mixed-method study was conducted in Utah between October 2011 and December 2011. Eleven focus group discussions were held and surveys were administered to 23 first-degree relatives of PCa patients living in Utah and 24 primary-care physicians and urologists practicing in Utah to present specific information about these assessments and determine knowledge and attitudes regarding health implications of using these assessments.ResultsData was independently coded by two researchers (relative Kappa = .88; provider Kappa = .77) and analyzed using a grounded theory approach. Results indicated differences in attitudes and behavioral intentions between patient and provider. Despite the test's limitations relatives indicated interest in genetic testing (52%) while most providers indicated they would not recommend the test for their patients (79%). Relatives expected providers to interpret genetic test results and use results to provide personalized healthcare recommendations while the majority of providers did not think the information would be useful in patient care (92%) and indicated low-levels of genetic self-efficacy.ConclusionsAlthough similarities exist, discordance between provider and patient attitudes may influence the effective translation of novel genomic tests into clinical practice suggesting both patient and provider perceptions and expectations be considered in development of clinical decision-support tools.

Project description:The complete set of mouse genes, as with the set of human genes, is still largely uncharacterized, with many pieces of experimental evidence accumulating regarding the activities and expression of the genes, but the majority of genes as yet still of unknown function. Within the context of the MouseFunc competition, we developed and applied two distinct large-scale data mining approaches to infer the functions (Gene Ontology annotations) of mouse genes from experimental observations from available functional genomics, proteomics, comparative genomics, and phenotypic data. The two strategies - the first using classifiers to map features to annotations, the second propagating annotations from characterized genes to uncharacterized genes along edges in a network constructed from the features - offer alternative and possibly complementary approaches to providing functional annotations. Here, we re-implement and evaluate these approaches and their combination for their ability to predict the proper functional annotations of genes in the MouseFunc data set. We show that, when controlling for the same set of input features, the network approach generally outperformed a naive Bayesian classifier approach, while their combination offers some improvement over either independently. We make our observations of predictive performance on the MouseFunc competition hold-out set, as well as on a ten-fold cross-validation of the MouseFunc data. Across all 1,339 annotated genes in the MouseFunc test set, the median predictive power was quite strong (median area under a receiver operating characteristic plot of 0.865 and average precision of 0.195), indicating that a mining-based strategy with existing data is a promising path towards discovering mammalian gene functions. As one product of this work, a high-confidence subset of the functional mouse gene network was produced - spanning >70% of mouse genes with >1.6 million associations - that is predictive of mouse (and therefore often human) gene function and functional associations. The network should be generally useful for mammalian gene functional analyses, such as for predicting interactions, inferring functional connections between genes and pathways, and prioritizing candidate genes. The network and all predictions are available on the worldwide web.

Dataset Information

Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.

Publications

Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.

Similar Datasets

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