of Cancer Specific Markers Amidst Massive Mass Spectral Data
We propose a comprehensive pattern recognition
procedure that will achieve best discrimination between two or more
sets of subjects with data in the same coordinate system. Applying the
procedure to mass spectrometry data of proteomic analysis of serum from
ovarian cancer patients and serum from cancer-free individuals in the
FDA/NCI Clinical Proteomics Database, we have achieved perfect discrimination
(100% sensitivity, 100% specificity) of patients with ovarian cancer,
including early stage disease, from normal controls for two independent
sets of data. Our procedure identifies the best subset of proteomic
biomarkers for optimal discrimination between the groups and appears
to have higher discriminatory power than other methods reported to date.
For large scale screening for diseases of relatively low prevalence
such as ovarian cancer, almost perfect specificity and sensitivity of
the detection system is critical to avoid unmanageably high numbers
of false positive cases.