DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference.

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.

Progression of lumbar disc degeneration over a decade: a heritability study.

OBJECTIVES: Lumbar disc degeneration (LDD) is prevalent, age-related and contributes to low back pain. Cross-sectional LDD as determined by MRI scan is known to be highly heritable. The authors postulated that the rate of progression might also be controlled by genetic factors. METHODS: A 10-year follow-up of MRI-determined LDD was performed in 234 pairs of twin volunteers in the UK and Australia, comprising 90 monozygotic pairs and 144 dizygotic same-sex twin pairs. Of the total sample, 95% were female. The mean age at baseline was 53.3 years (range 32.3-69.5). The rate of progression was calculated and, because the effect of age was non-linear, the sample was divided into age strata and heritability estimated for each trait's progression. RESULTS: All MRI-determined traits worsened significantly over the period of follow-up (p<0.0001 for each). Change in disc height was not heritable at any age while posterior disc bulge was heritable across all age categories (range 28-53%), with higher heritability in those over 60 years. Change in disc signal intensity and anterior osteophytes were found to be heritable only in those aged under 50 years at baseline (heritability estimates 76% (95% CI 44% to 100%) and 74% (42% to 100%), respectively). CONCLUSIONS: Longitudinal change in LDD traits is heritable for all traits except disc height, but there is a significant influence of age, which varies across traits. Future studies to define the genetic variants influencing LDD progression should examine MRI traits individually and in women should focus on those under 50 years of age.