Systems biology approaches to cervical pre-cancer and cancer "SYSTEMCERV"

Project status

In progress
 
 

Workstream

 

Last updated

Sun, 2015-04-19 20:48
Testing in the laboratory

The research we propose in SYSTEMCERV adopts a systems biology approach to address the need for specific biomarkers to aid objective CIN lesion grading, to identify true high grade cervical disease, and to increase the specificity and PPV of disease detection. We will build on work performed within a previously funded FP7 collaborative research grant called AUTOCAST, where we have identified and validated a novel panel of RNA based biomarkers for detection of cervical precancerous lesions. This panel of mRNA markers has been developed using systems biology and data mining tools and has demonstrated high specificity [93%] and sensitivity [88%] for detecting CIN 2-3 lesions. This compares to sensitivity and specificity metrics for cytology [specificity: 80-95%, sensitivity: 60-85%] and for HPV screening [specificity: 70-85%, sensitivity: 90-95%]. The combined biomarker panel has a specificity of 92% and sensitivity of 91%, for detecting CIN2+ disease in younger women under 30 years of age, where it outperforms HPV, whose specificity is unacceptably low. This enabling clinical validation work on the biomarker panel provides sufficient evidence that further exploration of existing data sets and biological pathways using a methodical systems biology approach, which combines de novo discovery and used computational, and simulation approaches to extrapolate from existing data sets.

To complement the systems biology discovery component, high throughput biomarker analysis and validation will be performed using the SYSTEMCERV approach. SYSTEMCERV proposes a novel strategy for this which will integrate several technologies to develop cervical pre-cancer and cancer specific protein arrays [IMTEK DNA-to-protein copying technology] for subsequent down-stream generation of antibodies using phage display [CysDisplay] and antibody array technologies [Human Combinatorial Antibody Library (HuCAL)] antibody technology (GenoID). We then intend to use a label free detection approach for the detection of proteins using imaging Reflectometric Interference Spectroscopy [iRIfS] technology (Biametrics). The ultimate goal of the project is to generate a panel of protein and specific detection antibodies [targeted against wild type and mutant protein] for use in cervical pre-cancer screening and for stratification of patients with CIN disease (TCD and GenoID). This novel approach is not limited to cervical cancer biomarker discovery and validation; it can be translated to other diseases and biomarkers, and even has personalised medicine applications.

Study co-ordinators