Abstract
BackgroundInferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Increasingly, approaches towards pathway activity inference combine molecular profiles (e.g gene or protein expression) with independent and highly curated structural interaction data (e.g protein interaction networks) or more generally with prior knowledge pathway databases. However, it is unclear how best to use the pathway knowledge information in the context of molecular profiles of any given study.ResultsWe present an algorithm called DART (Denoising Algorithm based on Relevance network Topology) which filters out noise before estimating pathway activity. Using simulated and real multidimensional cancer genomic data and by comparing DART to other algorithms which do not assess the relevance of the prior pathway information, we here demonstrate that substantial improvement in pathway activity predictions can be made if prior pathway information is denoised before predictions are made. We also show that genes encoding hubs in expression correlation networks represent more reliable markers of pathway activity. Using the Netpath resource of signalling pathways in the context of breast cancer gene expression data we further demonstrate that DART leads to more robust inferences about pathway activity correlations. Finally, we show that DART identifies a hypothesized association between oestrogen signalling and mammographic density in ER+ breast cancer.ConclusionsEvaluating the consistency of prior information of pathway databases in molecular tumour profiles may substantially improve the subsequent inference of pathway activity in clinical tumour specimens. This de-noising strategy should be incorporated in approaches which attempt to infer pathway activity from prior pathway models.
Highlights
Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits
DART: Denoising Algorithm based on Relevance network Topology We assume a given pathway P with prior information consisting of genes which are upregulated in response to pathway activation PU and genes which are downregulated PD
We propose that the prior information ought to be tested first for its consistency in the data set under study and that pathway activity should be estimated a posteriori using only the prior information that is consistent with the actual data
Summary
Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Proper systems biology approaches that attempt to infer differential pathway activity by combining highly curated structural networks of molecular interactions (e.g KEGG pathway database) with transcriptional changes on these networks were subsequently developed [8,9,10,11,12,13,14] These systems biology approaches can be distinguished depending on whether the discriminatory genes or gene subnetworks are inferred de-novo in relation to a phenotype of interest [9,10,11,14], or whether the molecular pathway models are given as prior information [12,13]. It is important to stress again that most of these methods (e.g [9,10,11,12,14]) are geared towards measuring differential pathway activity and are supervised in the sense that the phenotypic information is used from the outset to infer discriminatory genes or gene subnetworks
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