Abstract
Breast cancer imposes a significant healthcare burden on women worldwide. Early detection is of paramount importance in reducing mortality, yet the diagnosis of breast cancer is hampered by the lack of an adequate detection method. In addition, better breast cancer prognostication may improve selection of patients eligible for adjuvant therapy. Hence, new markers for early diagnosis, accurate prognosis and prediction of response to treatment are warranted to improve breast cancer care. Since proteomics can bridge the gap between the genetic alterations underlying cancer and cellular physiology, much is expected from proteome analyses for the detection of better protein biomarkers. Recent technical advances in mass spectrometry, such as matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and its variant surface-enhanced laser desorption/ionisation (SELDI-) TOF MS, have enabled high-throughput proteome analysis. In the current review, we give a comprehensive overview of the results of expression proteomics (i.e. protein profiling) research performed in breast cancer using these two platforms. Many protein peaks have been reported to bear significant diagnostic, prognostic or predictive value, however, only few candidate markers have been structurally identified yet. In addition, although of pivotal importance in preventing overfitting of data and systematic bias by pre-analytical parameters, validation of biomarker candidates by other, quantitative, methods and/or in new populations is very limited. Moreover, none of the identified candidate biomarkers has been investigated for their utility as breast cancer markers in large, prospective, clinical settings. As such, the candidate biomarkers discussed in this overview have not been validated sufficiently to be used for clinical patient care. Nonetheless, regarding the promising results up to now, MALDI- and SELDI-TOF MS protein profiling studies could eventually fulfil the great promise that protein biomarkers have for improving cancer patient outcome, provided that these studies are performed with adequate statistical power and analytical rigour.