Techniques

These techniques identify and characterise biomarkers relating to clinical outcome in human diseases (Matharoo-Ball et al 2007, Lancashire et al 2005, Mian et al 2005, Michael et al 2005, Ball et al 2002). These have been used to understand the role of key biomarkers in the progression of disease, particularly cancers, but also in the human immune system and microbial systems. We specialise in the following:

Diagnostic Decision Support Models
Development of highly accurate predictive diagnostic decision support models through the integration of a range of data types.

Example application - the prediction of an individual’s response to cancer vaccines based on their immunological profile – with Onyvax Ltd.

Example application – diagnosis of multiple stages of melanoma based on serum profiles derived from Maldi-TOF mass spectrometry - with DKFZ and Nottingham Trent University.

Identification of Biomarkers
Identification of key biomarkers associated with biomedical features of interest within the population. Characterisation of the biological influence of these markers, to elucidate the nature of their influence, both singly and through their interaction within biological pathways. To examine the form of these pathways and identify their representation across individuals and groups within the population.

Example application – elucidation of biological patterns and molecular interactions in the immune profile of responders and non responders to prostate cancer vaccination - with Onyvax Ltd.

Structure and Features within a biomedical population
Determination of structures and features within biomedical populations and derivation of biomarkers associated with these features.

Example application – determination of the population profile, based on mass spectrometry, of methecillin resistant and sensitive staphylococcus aureus (MRSA) - with the UK Health Protection Agency.

Measuring and profiling Risks
To identify a measure of risk associated with a patient’s condition, for example degeneration, spread or metastasise, based on the proteomic/genomic profile of the individual or their condition.

Example application – prediction of distant metastasis in breast cancer patients based on an optimised subset of affymetrix gene array markers, encoded within a decision support system - with the Nottingham Breast Institute and Hutchinson MRC Research Centre in Cambridge.

Confidence in Biomarkers
By the application of advanced sample cross validation techniques, we can assign a measure of confidence to predictions and to identify biomarkers.

Example application – characterisation of clinical groups within breast cancer and identification of prognostic outcome based on immuno-histochemical and clinically related biomarkers - with the Nottingham Breast Institute.