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Identification of discriminating biomarkers for human disease using integrative network biology.

ABSTRACT: There is a strong clinical imperative to identify discerning molecular biomarkers of disease to inform diagnosis, prognosis, and treatment. Ideally, such biomarkers would be drawn from peripheral sources non-invasively to reduce costs and lower potential for complication. Advances in high-throughput genomics and proteomics have vastly increased the space of prospective molecular biomarkers. Consequently, the elucidation of molecular biomarkers of clinical importance often entails a genome- or proteome-wide search for candidates. Here we present a novel framework for the identification of disease-specific protein biomarkers through the integration of biofluid proteomes and inter-disease genomic relationships using a network paradigm. We created a blood plasma biomarker network by linking expression-based genomic profiles from 136 diseases to 1,028 detectable blood plasma proteins. We also created a urine biomarker network by linking genomic profiles from 127 diseases to 577 proteins detectable in urine. Through analysis of these molecular biomarker networks, we find that the majority (> 80%) of putative protein biomarkers are linked to multiple disease conditions. Thus, prospective disease-specific protein biomarkers are found in only a small subset of the biofluids proteomes. These findings illustrate the importance of considering shared molecular pathology across diseases when evaluating biomarker specificity. The proposed framework is amenable to integration with complimentary network models of biology, which could further constrain the biomarker candidate space, and establish a role for the understanding of multi-scale, inter-disease genomic relationships in biomarker discovery.

SUBMITTER: Dudley JT

PROVIDER: S-EPMC2749008 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Identification of discriminating biomarkers for human disease using integrative network biology.

Dudley Joel T JT Butte Atul J AJ

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 20090101

There is a strong clinical imperative to identify discerning molecular biomarkers of disease to inform diagnosis, prognosis, and treatment. Ideally, such biomarkers would be drawn from peripheral sources non-invasively to reduce costs and lower potential for complication. Advances in high-throughput genomics and proteomics have vastly increased the space of prospective molecular biomarkers. Consequently, the elucidation of molecular biomarkers of clinical importance often entails a genome- or pr ...[more]

PMID: 19209693

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Project description:Histologically similar tumors even from the same anatomical position may still show high variability at molecular level hindering analysis of genome-wide data. Leveling the analysis to a gene regulatory network instead of focusing on single genes has been suggested to overcome the heterogeneity issue although the majority of the network methods require large datasets. Network methods that are able to function at a single sample level are needed to overcome the heterogeneity and sample size issues.We present a novel network method, Differentially Expressed Regulation Analysis (DERA) that integrates expression data to biological network information at a single sample level. The sample-specific networks are subsequently used to discover samples with similar molecular functions by identification of regulations that are shared between samples or are specific for a subgroup.We applied DERA to identify key regulations in triple negative breast cancer (TNBC), which is characterized by lack of estrogen receptor, progesterone receptor and HER2 expression and has poorer prognosis than the other breast cancer subtypes. DERA identified 110 core regulations consisting of 28 disconnected subnetworks for TNBC. These subnetworks are related to oncogenic activity, proliferation, cancer survival, invasiveness and metastasis. Our analysis further revealed 31 regulations specific for TNBC as compared to the other breast cancer subtypes and thus form a basis for understanding TNBC. We also applied DERA to high-grade serous ovarian cancer (HGS-OvCa) data and identified several common regulations between HGS-OvCa and TNBC. The performance of DERA was compared to two pathway analysis methods GSEA and SPIA and our results shows better reproducibility and higher sensitivity in a small sample set.We present a novel method called DERA to identify subnetworks that are similarly active for a group of samples. DERA was applied to breast cancer and ovarian cancer data showing our method is able to identify reliable and potentially important regulations with high reproducibility. R package is available at http://csbi.ltdk.helsinki.fi/pub/czliu/DERA/.

Dataset Information

Identification of discriminating biomarkers for human disease using integrative network biology.

Publications

Identification of discriminating biomarkers for human disease using integrative network biology.

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