Abstract

e22133 Background: Breast cancer is one of the most frequent and deadly cancers worldwide. Although the survival of patients has increased over the last decades, many patients die from metastatic relapse. Progresses in screening or early diagnosis will improve survival of breast cancer. Breast cancer has never had any good serum tumor markers. Therefore, we developed and evaluated a proteomics approach to search for new biomarkers in serum of breast cancer patients. Methods: Blood samples of 50 women with breast cancer (CA) and 50 healthy women (CTRL), matched to the age, were drawn prior to surgery. We used SELDI-TOF-MS for protein profiling with three different active surfaces of the protein chips: cationic exchanger (CM-10), hydrophobic surface (H50) and a strong anion exchange surface (Q10) with different binding properties. Data were analyzed by multivariate statistical techniques and artificial neural networks. Results: SELDI-TOF- MS could discriminate between serum of breast cancer patients and healthy women. We could generate a statistic significant (p<0.001) panel with 15 biomarkers resulting of multiple peaks with different molecular weights. The diagnostic pattern could differentiate CA from CRTL with specificity of 77% and sensitivity of 85% in serum. Conclusions: In this study we could exemplify SELDI-TOF-MS as a potential screening method to detect breast cancer patients by serum analysis. The protein chip technology could greatly facilitate the discovery of new and better biomarkers in breast cancer patients. This promising approach provides a high sensitivity and specificity by a less invasive method similar to mammography that is used in screening programs. No significant financial relationships to disclose.

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