Cancer Systems Biology

The aim of this research grouping is to work towards the development of a comprehensive and systems-level view of cancer.  Recent large scale, multi-dimensional molecular profiling studies of primary tumours have exposed the enormous heterogeneity of cancer. A formidable task is to translate these valuable data-sets into tangible improvements in cancer management, including improved diagnostic / prognostic tools and therapies. There are many challenges ahead, both theoretical and experimental, which need to be overcome. The theme will focus on four main areas:

Cancer signalling networks: Network biology studies of simpler eukaryotes such as yeast have shown that signalling pathways function in networks, exhibiting significant degrees of redundancy and cross-talk, implying that a networks perspective will be key to understanding the functional consequences of the DNA and epigenetic alterations which underlie any given tumour. Using ideas from network theory and statistical methodologies, one might begin to understand systems network-level properties of cancer phenotypes by addressing issues such as: what are the changes in gene expression noise as a function of environmental stress and carcinogenesis; what are the impacts of any increased noise on the information flow from genotype to phenotype how are cellular quality control systems (DNA/RNA/protein) modulated during stress and cancer?

Systems medicine: Cancer treatments often fail because they do not target the right genes or pathways. As tumours get profiled at an ever increasing level of detail we cannot only catalogue all the alterations in a given tumour, but we can also measure the combined functional effect of the aberration landscape with gene expression and protein phosphorylation arrays. Integrating the molecular fingerprint of a tumour with drug sensitivity signatures from model systems represents a key statistical challenge.

Cancer evolution: Cancer evolution poses a significant problem to treatment regimens, due to the increase in genetic heterogeneity during cancer progression and the development of drug resistance. Understanding cancer evolution could potentially lead to the possibility of individualised treatments. Combined experimental and modelling methods may allow us to trace the evolutionary path of a tumour, thus allowing the early and potentially causal alterations to be identified.

Imaging and protein interaction networks: Recent advances in optical proteomic imaging allow in-situ assessment of protein interactions and post-translational modifications in primary tumours, allowing direct integration of protein-interaction with gene or protein expression data. Integration of structural and functional data on specific cancer signalling pathways from the sae patients promises to deliver improved predictive tools.


The first meeting of the Cancer Systesm Biology group will be held on Tuesday 18th January 2011. Please see the events page for further details.

Page last modified on 23 nov 10 16:16

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