The Computer Journal Advance Access originally published online on April 4, 2008
The Computer Journal 2009 52(3):326-333; doi:10.1093/comjnl/bxn021
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Serum Proteomic Abnormality Predating Screen Detection of Ovarian Cancer
1 Computer Learning Research Centre, Royal Holloway, University of London, UK
2 Ludwig Institute for Cancer Research, University College London, UK
3 BioCentre and Department of Chemistry, University of Reading, UK
4 Memorial Sloan-Kettering Cancer Center, New York, USA
5 Institute for Women's Health, University College London, UK
* Corresponding author: alex{at}cs.rhul.ac.uk
Received 19 November 2007; revised 20 January 2008
Ovarian cancer is characterized by vague, non-specific symptoms, advanced stage at diagnosis and poor overall survival. A nested case control study was undertaken on stored serial serum samples from women who developed ovarian cancer and healthy controls (matched for serum processing and storage conditions as well as attributes such as age) in a pilot randomized controlled trial of ovarian cancer screening. The unique feature of this study is that the women were screened for up to 7 years. The serum samples underwent prefractionation using a reversed-phase batch extraction protocol prior to MALDI-TOF MS data acquisition. Our exploratory analysis shows that combining a single MS peak with CA125 allows statistically significant discrimination at the 5% level between cases and controls up to 12 months in advance of the original diagnosis of ovarian cancer. Such combinations work much better than a single peak or CA125 alone. This paper demonstrates that mass spectra from the low molecular weight serum proteome carry information useful for early detection of ovarian cancer. The next step is to identify the specific biomarkers that make early detection possible.
Key Words: bioinformatics • biomarkers • CA125 • human serum proteome • mass spectra • ovarian cancer