There is broad agreement that analysis of the human serum proteome has great potential for diagnosis and early detection of human disease. The challenges are immense given the complexity of the human proteome and the broad dynamic range in abundance of individual proteins. The key to unlocking this potential is the development of reproducible, sensitive and specific technology for proteomic analysis. Recent advances in technology suggest that this may now be feasible.

This study focuses on a unique serum bank collected from 200,000 postmenopausal women utilising existing and cutting edge proteomic technology and state-of-the-art bioinformatics. The results will provide a sound basis for assessing the screening and diagnostic potential of the serum proteome, establishing the identity of the discriminitive proteins and linking the findings to parallel genomic studies.

Project Main Goals

- Identify and analyse disease-specific biomarkers via proteomic technology and mass spectrometry (MS)

- Asses whether genomic and proteomic profiles correlate in predicting disease risk and clinical outcome in ovarian and breast cancer, and coronary artery disease

- Identify common low-moderate risk epithelial ovarian cancer (EOC) susceptibility genes

- Establish the significance of proteomic profiles and high-risk gene mutation status in ovarian and breast cancer

- Establish the association between proteomic bio-markers in EOC development and metastatic progression

Technical Approach

Subjects and Samples

The primary source fo samples for this research programme will be the unique serum bank collected via the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). This trial involves 200,000 apparently healthy postmenopausal women aged 50-74 years of age. All participants provide a serum sample at registration and 50,000 participants give additional samples annually for 6 years. Large numbers of samples will be available from women who have developed each of the common cancers (eg. colorectal, lung and ovary) and common non-malignant disorders (eg. cardiac disease, stroke).

Proteomic Technology

Several serum preparation methods will be investigated during Phase I of this study. Among them are surface-enhanced laser desorption/ionisation (SELDI, Ciphergen Biosystems), reproducible microfluidic sample clean-up by the Gyrolab worksation using chromatographic microstructures and electrospray ionisation (ESI). We will mainly use existing proteomic technologies and methods to define the analytical sensitivity and variability of these approaches for optimal diagnostic pattern analysis of human serum. However, emerging improvements will only be implemented if their implimentation is obvious, facile and fast.

Bioinformatics

The scale of data collection and dimensionality generated by proteomic technology requires non-convential data analysis. We plan to use the Support Vector Machine (SVM) and Confidence Machines along with the development of other learning algorithms for "proteomic pattern diagnostics."

Expected Achievments/Impact

A series of studies will be performed to asses and define:

- The 'normal' proteomic pattern in healthy postmenopausal women

- Variations in the pattern related to physiological factors such as age and years post menopause

- Variability over time in serial samples from the same healthy postmenopausal women

- The effect of differences in time to serum separation and ambient temperature

- The reproducibility in terms of intra/inter assay precision fo the proteomic technology

- The optimal data handling and processing of the proteomic data for subsequent pattern analysis

Reports

Slides