Modelling the interface of metabolism, methylation and mitochondria in prostate cancer

The interface of metabolism, methylation and mitochondria is of key importance for understanding the aetiology, biology and prognosis of prostate cancer[1]. Each of these components individually is important and has potential for non-invasive monitoring, but their complex interactions may lead to a misleading signal, particularly when considering a bulk sample, as cell-to-cell heterogeneity may be great.
The prostate gland is a metabolically specialised organ with a primary function to secrete metabolites and proteins required to sustain sperm viability and reproductive efficacy.
Since hydrocarbon pools drawn from metabolic pathways are also used for histone and DNA modifications the implication is that epigenetic reprogramming is initimately connected to metabolic reprogramming and mitochondrial function.
Methylation changes are the most recurrent somatic events seen in prostate cancer (transcending both inter and intra heterogeneity), but whether these are a reflection of broader metabolic changes, or are driver events in and of themselves, has yet to be resolved. This work will allow for investigation of the ordering of events via the proposed model of the network.
The deconvolution of bulk tissue signals to determine the patterns of contribution from distinct tissue types has become commonplace in recent times, with applications to gene expression[e.g. 2-5], methylation[6] and ChIP[7] data. Existing deconvolution tools are generally characterised by a) having the entire genome in which to find signal, b) looking for large, discrete signals c) making little use of biological knowledge and d) generally operating on one dimension of data (transcription or methylation), meaning that we need to develop a new approach for our purposes.
We propose to develop methods that allow deconvolution of multiple combined data types for a well-defined network at the interface of methylation, mitochondria and metabolism. This will be predicated upon prior beliefs regarding a mathematical model of the network, combined with public data sets.