الفهرس 
TitleOnly 14 pages are availabe for public view
 |  | from | 110 |  |  |
| | | from | 110 | | |
Abstract
Breast cancer (BC) is the most common cancer both in developed and developing regions. Incidence and mortality due to BC has been increasing for the last 50 years. According to World Health Organization reports, BC is the leading cause of death in women, accounting for 23% of all cancer deaths. In Egypt BC is the most common cancer in females accounting for approximately 38.8% of the reported malignancies among Egyptian women. Biomarkers can be used for early detection of BC in asymptomatic individuals, and evaluation of treatment response. Thus, blood-based biomarkers might result in further improvement in early diagnosis of BC. Therefore, it is imperative to find potential blood biomarkers for early diagnosis of BC. Due to the complexity of breast cancer and the way the human body operates, no single gene, protein, or miRNA test has yet been developed that definitively proves whether a patient has breast cancer. Multiple tests are often required, and many biomarkers are examined before any conclusions can be drawn about the patient’s conditions and prognosis. It has become an attractive approach to search for novel biomarkers in biological fluids of cancer patients using protein and peptide profiling. Mass spectrometry (MS) has been used to compare proteomic patterns in cancer patients and healthy controls. Potential biomarkers could be found among the specific proteins or peptides that are up or down-regulated in serum proteomic profiling in cancer patients compared with controls. Serum or plasma proteome analysis has the potential to facilitate disease diagnosis and therapeutic monitoring, because they are easily accessible and widely collected samples, which contains >10,000 different proteins and peptides. Profiling studies using magnetic beads (MBs) in combination with matrix-assisted laser desorption/ ionization time of flight mass spectrometry (MALDI-TOF MS) is a suitable strategy for protein and peptide extraction. MBs with a different functionality allow protein and peptide enrichment based on different chemical–physical interactions, thereby broadening the range of components covered. Detecting changes in protein and peptide concentrations by MS are constrained by the complexity of the serum or plasma proteome. Potentially important biomarkers are expected to be present in extremely low concentrations. Furthermore, the fractionation of the biological fluids enriches low abundant proteins in fractions. The aim of the present work was to study plasma proteomic profile in BC females by MALDI-TOF MS and to correlate it with BC subtypes. One hundred and thirty females were included in the current study after the approval of the Ethical Committee of the Medical Research Institute. They were divided into two main groups; the BC patients group (group I) and the control group (group II). group I consisted of 80 females with recently diagnosed primary BC (stages III and IV) prior to surgical intervention or treatment of BC. group II consisted of 50 apparently healthy female volunteers free from any breast disease whether benign or malignant based on clinical examination. In the present study plasma samples of BC patients and control subjects were fractionated by hydrophobic interaction chromatography magnetic beads (MB-HIC8) and
Summary & Conclusion
84
analyzed by ultrafleXtreme MALDI-TOF MS. The data set was randomly split into model generation group (including 45 BC patients and 30 control subjects) and external validation group (including 35 BC patients and 20 control subjects). Classification model was generated by the Quick Classifier (QC) algorithm to discriminate BC patients from the control subjects of model generation group. The QC model provided three peptide ion signatures (m/z 1570.31, 1897.4 and 2568.17) as a proteomic profile which achieved 100% recognition and 96.4% cross validation accuracy. External validation was performed using ClinProTools to verify the accuracy of the QC classification model, resulting in 100% sensitivity 76.9% specificity. By searching previously published data using similar approaches, two potential biomarkers were previously identified. The peak with m/z 1897.4 was identified as a fragment of complement component 4a (C4a) and the peak at m/z 2568.17 was identified as a fragment of apolipoprotein E (ApoE).
In the present work ClinProTools was used to discriminate BC subtypes. The BC patients were divided into 3 subgroups based on ER, PR and HER2 expression status; group Ia included 42 BC patients (66.7%) with ER and/or PR +ve and HER2 –ve, group Ib included 13 BC patients (20.6%) with HER2 +ve (regardless of ER or PR status) and group Ic included 8 triple negative BC patients (12.7%). The QC model provided five peptide ion signatures (m/z 3956.41, 4282.77, 4710.43, 9132.65 and 9380.31) as a proteomic profile to discriminate the 3 groups of BC patients, which achieved 96.3% recognition and 87.8% cross validation accuracy. Three potential biomarkers were previously identified; peaks with m/z 3956.41 and 4282.77 were identified as a fragment of inter-α-trypsin inhibitor heavy chain 4 (ITIH4) and peak with m/z 9132.65 was identified as apolipoprotein C3 (ApoC3). In the current study the BC patients were grouped according to their tumor stage into 2 subgroups; stage III included 73 BC patients (91.3%) and stage IV included 7 BC patients (8.8%). The QC model provided three peptide ion signatures (m/z 7639.22, 7664.72 and 8765.55) as a proteomic profile for a cross validation set to discriminate between stage III and IV BC patients, which achieved 73.8% recognition and 69.4% cross validation accuracy. All three peaks were up-regulated in stage III BC patients. By searching previously published data using similar approaches, the peak with m/z 8765.55 was previously identified as ApoC3.