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Natrajan R.,Institute of Cancer Research | Weigelt B.,Institute of Cancer Research | Weigelt B.,Cancer Research UK Research Institute | Mackay A.,Institute of Cancer Research | And 9 more authors.
Breast Cancer Research and Treatment

Breast cancer is a heterogeneous disease caused by the accumulation of genetic changes in neoplastic cells. We hypothesised that molecular subtypes of breast cancer may be driven by specific constellations of genes whose expression is regulated by gene copy number aberrations. To address this question, we analysed a series of 48 microdissected grade III ductal carcinomas using high resolution microarray comparative genomic hybridisation and mRNA expression arrays. There were 5, 931 genes whose expression significantly correlates with copy number identified; out of these, 1,897 genes were significantly differentially expressed between basal-like, HER2 and luminal tumours. Ingenuity Pathway Analysis (IPA) revealed that 'G1/S cell cycle regulation' and 'BRCA1 in DNA damage control' pathways were significantly enriched for genes whose expression correlates with copy number and are differentially expressed between the molecular subtypes of breast cancer. IPA of genes whose expression significantly correlates with copy number in each molecular subtype individually revealed that canonical pathways involved in oestrogen receptor (ER) signalling and DNA repair are enriched for these genes. We also identified 32, 157 and 265 genes significantly overexpressed when amplified in basal-like, HER2 and luminal cancers, respectively. These lists include known and novel potential therapeutic targets (e.g. HER2 and PPM1D in HER2 cancers). Our results provide strong circumstantial evidence that different patterns of genetic aberrations in distinct molecular subtypes of breast cancer contribute to their specific transcriptomic profiles and that biological phenomena characteristic of each subtype (e.g. proliferation, HER2 and ER signalling) may be driven by specific patterns of copy number aberrations. © Springer Science+Business Media, LLC. 2009. Source

Jiao Y.,University College London | Lawler K.,University College London | Lawler K.,Kings College London | Patel G.S.,Kings College London | And 9 more authors.
BMC Bioinformatics

Background: 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. 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.Results: We 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.Conclusions: Evaluating 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. © 2011 Jiao et al; licensee BioMed Central Ltd. Source

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