Title:SELDI-TOF-MS Profiling of Metastatic Phenotype in Histopathological Subtypes of Breast Cancer
VOLUME: 15 ISSUE: 3
Author(s):Turkan Yigitbasi*, Gizem Calibasi-Kocal, Nihal Buyukuslu, Murat Kemal Atahan, Hakan Kupeli, Seyran Yigit, Ercument Tarcan and Yasemin Baskin
Affiliation:Department of Biochemistry, School of Medicine, Istanbul Medipol University, Unkapani 34083, Istanbul, Department of Basic Oncology, Institute of Oncology, Dokuz Eylul University, Izmir, Department of Nutrition and Dietetics, School of Health Sciences, Istanbul Medipol University, Istanbul, Department of General Surgery, Ataturk Training and Research Hospital, Katip Celebi University, Izmir, Department of General Surgery, Ataturk Training and Research Hospital, Katip Celebi University, Izmir, Department of Pathology, Ataturk Training and Research Hospital, Katip Celebi University, Izmir, Department of General Surgery, Ataturk Training and Research Hospital, Katip Celebi University, Izmir, Department of Basic Oncology, Institute of Oncology, Dokuz Eylul University, Izmir
Keywords:Breast cancer, histopathological subtypes, SELDI-TOF-MS, profiling, serum proteome, metastatic phenotype.
Abstract:Background: Early detection of breast cancer is a key to the success of breast cancer management.
Serum proteome analysis using Surface-Enhanced Laser Desorption/Ionization Time-Of-
Flight Mass Spectrometry (SELDI-TOF-MS) generates useful information that can be utilized to describe
exclusive prognostic and diagnostic biomarkers.
Objective: This study aimed to use proteomics and bioinformatics to identify new biomarkers during
the metastatic process of breast cancers that were classified as invasive lobular cancer or invasive ductal
cancer.
Method: Blood samples from 64 breast cancer patients [36 with invasive ductal cancer (14 of whom
were lymph node positive); 28 with invasive lobular cancer (8 of whom were lymph node positive]
were analyzed using IMAC 30 protein chips. The data acquired from the spectra were processed with
univariate statistical analysis (Protein Chip Data Manager Software).
Results: One-hundred-eighteen clusters were generated from the individual serum samples. Thirty-six
proteins of the metastatic phenotype were found to be diagnostically accurate in cluster analysis. In the
breast cancer group, a single candidate peak (m/z 1090.8) that was able to discriminate the metastatic
progression was identified as a metastatic phenotype marker. Fifteen protein peaks were identified as
markers to separate the histopathological subtypes as either invasive ductal cancer or invasive lobular
cancer.
Conclusion: In recent years, proteomic methods have rapidly become widespread in breast cancer research.
This study revealed the pattern of a group of proteins that were not previously identified and
are recommended as candidate markers to diagnose metastatic progression.
