Protein profiling with mass spectrometry is a promising approach for classification and identification of biomarkers; however, there is debate about measurement quality and reliability. Here, we present a pipeline for preprocessing, statistical data analysis and presentation. Serum samples of 16 healthy individuals are used to generate protein profiles with high-resolution MALDI-TOF after isolation of peptides with C8 magnetic beads. Analysis of variance was performed after binning, baseline correction and normalization of the mean spectra. Relative variations in the spectra are expressed as coefficient of variation, which depending on the respective preanalytical variation parameter investigated, was found to range between 0.15 and 0.67 in this study. With this novel method, the reproducibility of our protein profiling procedure could be quantified. We showed that circadian rhythm and the number of freeze-thaw cycles had relatively limited influence on serum protein profiles, whereas the period between collection and serum centrifugation had a more pronounced effect.

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Proteomic expression profiling has been suggested as a potential tool for the early diagnosis of cancer and other diseases. The objective of our study was to assess the feasibility of this approach for the detection of breast cancer. Materials and Methods: In a randomized block design pre-operative serum samples obtained from 78 breast cancer patients and 29 controls were used to generate high-resolution MALDI-TOF protein profiles. The spectra generated using C8 magnetic beads assisted mass spectrometry were smoothed, binned and normalized after baseline correction. Linear discriminant analysis with double cross-validation, based on principal component analysis, was used to classify the protein profiles. Results: A total recognition rate of 99%, a sensitivity of 100%, and a specificity of 97.0% for the detection of breast cancer were shown. The area under the curve of the classifier was 98.3%, which demonstrates the separation power of the classifier. The first 2 principal components account for most of the between- group separation. Conclusions: Double cross-validation showed that classification could be attributed to actual information in the protein profiles rather than to chance. Although preliminary, the high sensitivity and specificity indicate the potential usefulness of serum protein profiles for the detection of breast cancer.

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