Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer: a case-control study.

Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85% for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.