Evaluation of Activated Leukocyte Cell Adhesion Molecule As a Biomarker for Breast Cancer in Egyptian Patients

Evaluation of Activated Leukocyte Cell Adhesion Molecule as a Biomarker for Breast Cancer in Egyptian Patients

Presented by

Mostafa Saif El-Nasr Mahmoud El-Shepiny M. Sc. in Biochemistry Assistant Lecturer in National Center for Radiation Research and Technology Atomic Energy Authority

A Thesis Submitted to Faculty of Science

In Partial Fulfillment of the Requirements for the Degree of Ph. D. of Science (Biochemistry)

Under Supervision of

Prof. Dr. Amr Saad Mohammed Prof. Dr. Azza Abd-Alla Mohammed Professor of Biochemistry Professor of Clinical Pathology Faculty of Science National Center for Radiation Research Cairo University and Technology Atomic Energy Authority Prof. Dr. Amal Mohammed Nour El-Din Dr. Ahmed Mostafa Ahmed Professor of Pediatric Medicine Lecturer of Surgical Oncology Nuclear Research Center National Cancer Institute Atomic Energy Authority Cairo University

Chemistry Department Faculty of Science Cairo University

(2013) Approval Sheet for Submission

Title of (Ph. D.) thesis: Evaluation of Activated Leukocyte Cell Adhesion Molecule as a Biomarker for Breast Cancer in Egyptian Patients Name of the candidate: Mostafa Saif El-Nasr Mahmoud El-Shepiny This thesis has been approved for submission by the supervisors: 1. Prof. Dr. Amr Saad Mohammed Professor of Biochemistry Faculty of Science Cairo University 2. Prof. Dr. Azza Abd-Alla Mohammed Professor of Clinical Pathology National Center for Radiation Research and Technology Atomic Energy Authority 3. Prof. Dr. Amal Mohammed Nour El-Din Professor of Pediatric Medicine Nuclear Research Center Atomic Energy Authority 4. Dr. Ahmed Mostafa Ahmed Lecturer of Surgical Oncology National Cancer Institute Cairo University

Prof. Dr. Hamed Abd El-Latif Abd El-Rahman

Chairman of Chemistry Department Faculty of Science - Cairo University

Abstract

Name: Mostafa Saif El-Nasr Mahmoud El-Shepiny Title of thesis: Evaluation of Activated Leukocyte Cell Adhesion Molecule as a Biomarker for Breast Cancer in Egyptian Patients Degree: (Ph. D.) thesis, Faculty of Science, Cairo University, 2013 In this study, serum activated leukocyte cell adhesion molecule (ALCAM) levels were evaluated in 41 primary breast cancer patients and 20 healthy females, and its diagnostic value was quantified, and compared with those of carbohydrate antigen 15-3 (CA15-3) and carcinoembryonic antigen (CEA). Also, its prognostic value was examined. Serum ALCAM levels were also evaluated before and after surgical treatment. Serum levels of ALCAM and CA 15-3 were significantly higher in breast cancer patients than healthy controls (P=0.002, P=0.043 respectively), but the difference in serum CEA levels did not reach statistical significance. Serum ALCAM levels had significant area under the curve (AUC) (P=0.002), but serum levels of CA 15-3 and CEA had non- significant AUCs, and various combinations between them did not result in any improvement. A significant association was found between serum levels of ALCAM and CEA with age and menopausal status in breast cancer patients. Non-significant difference was shown in serum levels of ALCAM, CA 15-3 and CEA before and after surgical treatment. In conclusion, this study suggests that serum ALCAM may represent a novel diagnostic biomarker for breast cancer. Key words: ALCAM, Breast cancer, CA15-3, CEA, and biomarker. Supervisors: Prof. Dr. Amr Saad Mohammed Prof. Dr. Azza Abd-Alla Mohammed Prof. Dr. Amal Mohammed Nour El-Din Dr. Ahmed Mostafa Ahmed Prof. Dr. Hamed Abd El-Latif Abd El-Rahman

Chairman of Chemistry Department Faculty of Science - Cairo University Acknowledgement

Acknowledgement

Thanks to Allah first and foremost. I feel always indebted to Allah the kindest and the most beneficent and merciful. I would like to express my great thanks and deepest appreciation to Prof. Dr. Amr Saad Mohammed, professor of biochemistry, Faculty of Science, Cairo University, for his kind supervision, valuable guidance, support, invaluable advices and great help. My grateful acknowledgement, great thanks, sincere appreciation and deepest respect to Prof. Dr. Azza Abd-Alla Mohammed, professor of clinical pathology, National Center for Radiation Research and Technology, Atomic Energy Authority, for her tremendous effort, precious guidance, helpful instructions and powerful support under her continuous kind supervision to finish this study. I would like to express my great thanks to Prof. Dr. Amal Mohammed Nour El-Din, professor of pediatric medicine, Nuclear Research Center, Atomic Energy Authority, for her valuable advices, support and continuous encouragement. My great thanks and gratitude to Dr. Ahmed Mostafa Ahmed, lecturer of surgical oncology, National Cancer Institute, Cairo University, for his kind supervision, valuable guidance, advices and help in obtaining patients samples and their clinical data. I would like to thank all subjects who participated in this study. Finally, my deepest thanks to all my family members for their continuous help and encouragement especially my brother Ahmed.

To my beloved mother To my brothers and my sister

Contents

Page List of abbreviations…………………………………………………… I List of tables…………………………………………………………….. V List of figures…………………………………………………………… VI Introduction…………………………………………………………….. 1 Aim of the work………………………………………………………… 4 Review of literature…………………………………………………….. 5 Breast cancer………………………………………………………. 5 Breast cancer risk factors…………………………………… 5 Staging of breast cancer…………………………………….. 21 World Health Organization classification of breast tumor…. 24 Histopathological grades of breast cancer…………………... 26 Serum tumor markers in breast cancer……………………… 26 Cell adhesion molecules.………………………………………….. 36 Integrins.…………………………………………………….. 36 Cadherins……………………………………………………. 37 Selectins…………………………………………………….. 38 Immunoglobulin-like cell adhesion molecules……………… 39 Activated leukocyte cell adhesion molecule.……………………… 42 Transcriptional regulation and function.……………………. 42 Activated leukocyte cell adhesion molecule and various types of cancer.……………………………………………… 44 Subjects and methods………………………………………………….. 52 Subjects……………………………………………………………. 52 Specimen collection.………………………………………………. 54 Determination of activated leukocyte cell adhesion molecule.…… 55 Determination of carbohydrate antigen 15-3……………………… 59 Contents

Determination of carcinoembryonic antigen.……………………... 61 Determination of aspartate aminotransferase.…………………….. 63 Determination of alanine aminotransferase.………………………. 64 Determination of urea.…………………………………………….. 64 Determination of creatinine.………………………………………. 65 Statistical analysis.………………………………………………… 65 Results…………………………………………………………………... 67 Discussion……………………………………………………………….. 85 Summary………………………………………………………………... 90 References………………………………………………………………. 94 Arabic summary

List of Abbreviations

A Adenine ADAM(s) A disintegrin and metalloproteinase(s) ADH Alcohol dehydrogenase AHH Aryl hydrocarbon hydroxylase AICR American Institute for Cancer Research ALCAM Activated leukocyte cell adhesion molecule ALT Alanine aminotransferase AP-1 Activator protein-1 ASCO American Society of Clinical Oncology AST Aspartate aminotransferase AT Ataxia telangiectasia ATM Ataxia telangiectasia mutated AUC(s) Area under the curve(s) bp Base pair BRCA Breast cancer C Cytosine CA Carbohydrate antigen CAM(s) Cell adhesion molecule(s) CARE Contraceptive and Reproductive Experiences CD Cluster of differentiation CEA Carcinoembryonic antigen CI Confidence interval COMA Committee on Medical Aspects of Food and Nutrition Policy CPM Counts per minute CYP Cytochrome P450 DNA Deoxyribonucleic acid

List of Abbreviations

EC Extracellular cadherin ECM Extracellular matrix EGF Epidermal growth factor EGTM European Group on Tumor Markers ELISA Enzyme-linked immunosorbent assay ER Estrogen receptor ESMO European Society of Medical Oncology FA Fanconi anemia FAK Focal adhesion kinase Fc Fragment crystallizable FN Fibronectin Fra-2 Fos-related antigen-2 G Guanine GFP Green fluorescent protein GH Growth hormone GSH Glutathione GST Glutathione S-transferase HB2 High density lipoprotein-binding protein 2 HER-2 Human epidermal growth factor receptor-2 HEV(s) High endothelial venule(s) HRP Horseradish peroxidase HRT Hormone replacement therapy HSP70 Heat shock protein 70 ICAM(s) Intercellular adhesion molecule(s) Ig Immunoglobulin Ig-CAM(s) Immunoglobulin-like cell adhesion molecule(s) IGF Insulin-like growth factor IGFBP-3 Insulin-like growth factor binding protein-3

List of Abbreviations

IRMA Immunoradiometric assay IU International unit JAMs Junctional adhesion molecules kb Kilobase kBq Kilobecquerel kDa Kilodalton kU Kilounits M Molar MEMD Melanoma metastasis clone D MMP Matrix metalloproteinase mrad Millirad mRNA Messenger ribonucleic acid MT1-MMP Membrane type 1 matrix metalloproteinase MTHFR 5,10 -Methylenetetrahydrofolate reductase MUC-1 Mucin-1 n Number of subjects NACB National Academy of Clinical Biochemistry NAT N-acetyltransferase NBS Nijmegen breakage syndrome NCAM Neural cell adhesion molecule NPI Nottingham Prognostic Index OC(s) Oral contraceptive(s) P Probability p53 Protein 53 PECAM-1 Platelet endothelial cell adhesion molecule-1 PIN Prostatic intraepithelial neoplasia PR Progesterone receptor PROGINS Progesterone receptor gene polymorphism

III

List of Abbreviations

PSA Prostate-specific antigen PSGL-1 P-selectin glycoprotein ligand-1 PTEN Phosphatase and tensin homolog R Residual tumor ROC Receiver operating characteristic ROS Reactive oxygen species rpm Round per minute RTKs Receptor tyrosine kinases SCRs Short consensus repeats SD Standard deviation SHBG Sex-hormone-binding globulin sLeX Sialyl Lewis X SPSS Statistical package for the social sciences STK11 Serine/threonine kinase 11 T Thymine TACE Tumor necrosis factor-α-converting enzyme TIMP-2 Tissue inhibitor of metalloproteinase-2 TMB 3,3`,5,5`-Tetramethylbenzidine TNF-α Tumor necrosis factor-α TNM Tumor size, regional lymph nodes, and distant metastasis TPA Tissue polypeptide antigen UICC International Union Against Cancer VCAM-1 Vascular cell adhesion molecule-1 VEGF-R2 Vascular endothelial growth factor receptor 2 WHI Women's Health Initiative WHO World Health Organization XRCC1 X-ray repair cross-complementing group 1 μCi Microcurie

List of Tables

Page Table (1): TNM classification of breast cancer…………………………. 24 Table (2): The clinicopathological characteristics of breast cancer patients…………………………………………………………………... 53 Table (3): Serum levels of ALCAM, CA 15-3 and CEA in healthy controls and breast cancer patients………………………………………. 68 Table (4): Serum levels of liver functions (AST, ALT) and renal functions (urea, creatinine) in healthy controls and breast cancer patients 70 Table (5): Correlation between serum levels of ALCAM, CA 15-3 and CEA in healthy controls and breast cancer patients……………………... 70 Table (6): AUCs of serum levels of ALCAM, CA 15-3 and CEA, and various combinations between them…………………………………….. 79 Table (7): Sensitivities of serum levels of ALCAM, CA 15-3 and CEA, and various combinations between them at fixed values of 90%, 80% and 70% specificities…………………………………………………….. 79 Table (8): Association between serum levels of ALCAM, CA 15-3 and CEA with various clinicopathologic parameters in breast cancer patients 81 Table (9): Serum levels of ALCAM, CA 15-3 and CEA in breast cancer patients before and at one month after surgical treatment………………. 82

List of Figures

Page Fig. (1): Overview of the four main CAM classes. The general structure and interaction properties of the four superfamilies of CAMs are schematically depicted…………………………………………………... 41 Fig. (2): Preparation of ALCAM standard………………………………. 57 Fig. (3): Serum ALCAM levels (mean±SD) in healthy controls and breast cancer patients……………………………………………………. 68 Fig. (4): Serum CA 15-3 levels (mean±SD) in healthy controls and breast cancer patients……………………………………………………. 69 Fig. (5): Serum CEA levels (mean±SD) in healthy controls and breast cancer patients…………………………………………………………… 69 Fig. (6): ROC curve of serum ALCAM levels………………………….. 72 Fig. (7): ROC curve of serum CA 15-3 levels…………………………... 73 Fig. (8): ROC curve of serum CEA levels………………………………. 74 Fig. (9): ROC curve of combining serum levels of ALCAM and CA 15- 3………………………………………………………………………….. 75 Fig. (10): ROC curve of combining serum levels of ALCAM and CEA.. 76 Fig. (11): ROC curve of combining serum levels of ALCAM, CA 15-3 and CEA…………………………………………………………………. 77 Fig. (12): ROC curve of combining serum levels of CA 15-3 and CEA... 78 Fig. (13): Serum ALCAM levels (mean±SD) in breast cancer patients before and at one month after surgical treatment………………………... 83 Fig. (14): Serum CA 15-3 levels (mean±SD) in breast cancer patients before and at one month after surgical treatment………………………... 83 Fig. (15): Serum CEA levels (mean±SD) in breast cancer patients before and at one month after surgical treatment………………………... 84

INTRODUCTION

Introduction

About 12.7 million cancer cases and 7.6 million cancer deaths are estimated to have occurred in 2008 worldwide (Jemal et al., 2011). Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in females worldwide, accounting for 23% of the total new cancer cases and 14% of the total cancer deaths in 2008 (Jemal et al., 2011). Also, it is the most common female malignancy in women in almost all Arab countries (Omar et al., 2003; El-Attar, 2005; El-Saghir et al., 2007; Salim et al., 2009). In Egypt, breast cancer is the most common malignancy among Egyptian females, accounting to about 37.6% of all malignancies (Parkin et al., 2002; Parkin et al., 2005). Unfortunately, other than definitive diagnosis by biopsy and histopathology, no diagnostic or screening test is presently suitable for early detection of breast cancer (Harris et al., 2007). The ability to detect human malignancy via a simple blood test has long been a major objective in medical screening. Carbohydrate antigen (CA) 15-3 and carcinoembryonic antigen (CEA), discovered more than 2 and 4 decades ago, respectively, are the most commonly used tumor markers for breast cancer (Gold and Freedman, 1965; Hilkens et al., 1984; Kufe et al., 1984). CA15-3 and CEA levels in serum are recommended for monitoring therapy of advanced breast cancer (Harris et al., 2007). However, these cancer biomarkers have proven to be ineffective in detecting the early stages of the disease because of low diagnostic sensitivity and specificity (Fleisher et al., 2002; Lumachi and Basso, 2004;

Khatcheressian et al., 2006). Cell adhesion molecules (CAMs) are cell surface receptors that mediate cell-cell and cell-substrate interactions. These molecules can be grouped into four families: integrins, cadherins, selectins, and the immunoglobulin (Ig)-like 1

Introduction

CAMs (Ig-CAMs) superfamily (Johanning, 1996). Alterations in cellular adhesion and communication can contribute to uncontrolled cell growth (Ofori- Acquah and King, 2008). Activated leukocyte CAM (ALCAM) is a glycoprotein cell surface Ig superfamily member involved in cell-cell interactions through homophilic and heterophilic (ALCAM-Cluster of differentiation [CD] 6) binding (Bowen et al., 2000; Swart, 2002). ALCAM has been cloned in multiple species and has different names, which depend on the species and laboratory that cloned it: chicken neural adhesion molecule BEN/SC-1/DM-GRASP (Burns et al., 1991; Tanaka et al., 1991; Pourquié et al., 1992), rat KG-CAM (Peduzzi et al., 1994), fish neurolin (Paschke et al., 1992), SB-10 (Bruder et al., 1998), human melanoma metastasis clone D (MEMD) (Degen et al., 1998), mouse/human ALCAM (CD166) (Bowen et al., 1995; Bowen et al., 1997; Swart, 2002), and high density lipoprotein-binding protein 2 (HB2) (Matsumoto et al., 1997;

Kurata et al., 1998). ALCAM has 5 extracellular Ig domains (2 NH2-terminal, membrane-distal variable-(V)-type and 3 membrane-proximal constant-(C2)- type Ig folds) [D1–D5], a transmembrane region, and a short cytoplasmic tail (Bowen et al., 1995; Swart, 2002). The N-terminal domain (D1) regulates affinity, whereas membrane proximal domains D4 and D5 control affinity (van Kempen et al., 2001; Swart, 2002). The cytoplasmic tail contains 32 amino acid residues (Burns et al., 1991; Tanaka et al., 1991; Kanki et al., 1994; Laessing et al., 1994; Bowen et al., 1995; Matsumoto et al., 1997; Bruder et al., 1998). The molecular weight of the native protein is 65 kDa, and N-glycosylation at 8 putative sites results in a mature ALCAM species of 110 kDa (Denzinger et al., 1999). ALCAM is expressed in activated lymphocytes, neuronal cells, hepatocytes, pancreatic cells, and selected epithelia (i.e. in mammary ducts and acini), as well as in embryonic cells, i.e. bone marrow, endothelial, and yolk sac cells (Uchida et al., 1997; Ohneda et al., 2001). ALCAM may act as a cell surface sensor to register local growth saturation and to regulate cellular 2

Introduction signaling and dynamic responses (Swart et al., 2005). ALCAM-CD6 interaction is required for optimal activation of T-cells suggesting a possible ALCAM involvement in the immunologic response to tumor cells (Zimmerman et al., 2006). ALCAM may favor interactions between tumor and endothelial cells (Swart et al., 2005).

AIM OF THE WORK

Aim of the Work

In this study, evaluation of serum ALCAM levels was estimated in healthy controls and patients suffering from breast cancer, and its diagnostic value was quantified, aiming to investigate if ALCAM, either alone, or in combination with the classical breast cancer biomarkers (CA15-3 and CEA) represent a new strategy for breast cancer diagnosis with high sensitivity and specificity in serum, in an attempt to find a simple diagnostic blood test for early detection of breast cancer. The association between serum ALCAM levels with various clinicopathologic parameters was also examined. The study is also aiming to evaluate serum ALCAM levels in breast cancer patients before and after surgical treatment.

REVIEW OF LITERATURE

Review of Literature

Breast cancer Breast cancer risk factors Age: Age is the main risk factor for breast carcinoma. Breast cancer incidence is very low before age 25 and increases up to 100-fold by age 45 (Hulka and Moorman, 2001). This pattern suggests the involvement of reproductive hormones in breast cancer etiology (Pike et al., 1993), as hormone-independent cancers would not exhibit a dramatic change in the incidence of the disease during the active reproductive period. After menopause, there is a great divergence in the breast cancer risk among four different continents. The risk continues to rise up to 75 years of age in the United States and Sweden, while in Colombia, the age-specific increase is considerably smaller after age of 45. In contrast, in Japan breast cancer incidence after age of 45 exhibits a plateau followed by a slow decrease (Hulka and Moorman, 2001). Genetic and familial factors: Family history of breast cancer is another important risk factor. Having a mother or a sister with breast cancer increases the risk of developing the disease by 2 to 3 times. If there is more than one affected relative, if the disease appears at a young age and if it is bilateral or associated with ovarian cancer, the risk increases further (Thompson, 1994). Germline mutations in high-penetrance breast cancer susceptibility genes such as breast cancer (BRCA) 1, BRCA2, protein 53 (p53), ataxia telangiectasia (AT) mutated (ATM) and phosphatase and tensin homolog (PTEN) confer a high individual risk for developing hereditary breast cancer. However, these mutations have been shown to account for only up to 5-10% of all breast cancers, probably because of their low allele frequencies in general population

Review of Literature

(Easton et al., 1993; Oesterreich and Fuqua, 1999). Relatively common low- penetrance cancer susceptibility genes, acting together with endogenous and lifestyle risk factors are likely to account for most of the sporadic breast cancers, which comprise the majority of all breast cancers (Johnson-Thompson and Guthrie, 2000). Hereditary breast cancers usually arise at an earlier age and are often multifocal or bilateral, whereas sporadic cancers are in general unilateral and have later onset (Rebbeck, 1999). High-penetrance breast cancer susceptibility genes: Mutations in BRCA1 and BRCA2, two of the most commonly implicated genes in hereditary breast cancer, are responsible for approximately 80-90% of all hereditary breast cancers, whereas they are not very frequent in sporadic breast cancers (de Jong et al., 2002). However, BRCA1 expression is reduced in most sporadic breast cancer, suggesting other mechanisms that control BRCA1 expression and inactivation such as promoter methylation or protein ubiquitination (Deng and Brodie, 2000). Women who carry deleterious mutations in BRCA1 or BRCA2 have a considerably increased life-time risk of breast cancer (approximately 80%), that is roughly ten times greater than that of the general population. However, these figures might be lower as methods that estimate the risk based on family studies (with more than one case) generate higher risk values, as opposed to methods based on single cases, unselected for family history (Narod, 2002). In Ashkenazi Jewish population, there are three hot spot mutations (BRCA1- 185delAG, BRCA1-5382insC, and BRCA2-6174delT), which occur at much higher rates than in general population and are associated with an increased risk of breast cancer development (King et al., 2003). BRCA1 is a tumor suppressor gene whose primary function is maintaining genomic integrity (caretaker gene) (Deng and Brodie, 2000). Germline mutations in BRCA1 are associated with approximately 42% of breast cancer families and 81% of families with both ovarian and breast cancer (Ford 6

Review of Literature et al., 1998). Loss of heterozygosity in BRCA1 gene is frequently observed in hereditary breast cancers and it is one of the most common mechanisms by which the normal allele is inactivated (Osorio et al., 2002). Germline mutations in BRCA2 (another caretaker gene involved in deoxyribonucleic acid [DNA] repair processes) are linked with approximately 76% of breast cancer families in which both females and males are affected. This percentage decreases to 32% in families where only women are affected by breast cancer, and furthermore to 14% in breast-ovarian cancer families (Ford et al., 1998). Similar to BRCA1, loss of heterozygosity has been demonstrated to play an important role in the development of BRCA2-induced breast cancers (Eiriksdottir et al., 1998). Interestingly, biallelic germline mutations in BRCA2 are associated with the very rare D1 complementation group of Fanconi anemia (FA), a particular subgroup of FA. In addition to breast cancer, these patients have a high susceptibility to develop acute myeloid leukemia, Wilms' tumor and medulloblastoma (Venkitaraman, 2004). Among high-penetrance genes, p53 was the first tumor suppressor gene linked to hereditary breast cancer. Localized on chromosome 17p13, p53 is one of the most commonly mutated genes in all human cancers (approximately 50% cancers) (Malkin, 1994). Germline p53 mutations have been identified in patients with Li-Fraumeni cancer susceptibility syndrome, an autosomal dominant disorder characterized by a markedly increased risk of breast cancer with early-onset, among other types of cancers (sarcomas, leukemias, brain tumors, adrenocortical carcinomas, etc) (Malkin, 1994). Affected women have an 18-fold higher risk for developing breast cancer before age of 45 as compared to the general population, and the risk declines with age (maximum is before age of 20) (Garber et al., 1991). PTEN germ-line mutations are present in 80% of patients with Cowden syndrome, a rare hereditary breast and thyroid cancer predisposition syndrome associated with a 25-50% lifetime breast cancer risk (de Jong et al., 2002) 7

Review of Literature

(general population has a 8-10% lifetime risk). AT is an autosomal recessive genetic disease caused by mutations in ATM gene. AT carriers, who are heterozygous for ATM mutations, appear to be at an increased risk of developing breast cancer (de Jong et al., 2002), estimated at 11% by the age of 50 and 30% by the age of 70 (Easton, 1994). Germline missense mutations (resulting in a stable but functionally abnormal protein that acts in a dominant- negative fashion and inhibits the normal ATM protein), rather than truncating mutations (resulting in an unstable, abnormal ATM protein) confer the high breast cancer risk found in AT carriers (de Jong et al., 2002). In Nijmegen breakage syndrome (NBS), an AT-like condition, the germline Polish founder mutation in NBS1 gene (657del15) has been shown to occur with increased frequency in breast cancer cases, particularly in familial cases (Gorski et al., 2003). Patients with Peutz-Jeghers syndrome (an autosomal dominant disorder caused by truncating germline mutations in the Serine/threonine kinase 11 [STK11] gene) have also an increased breast cancer risk (de Jong et al., 2002). Low-penetrance breast cancer susceptibility genes: Polymorphisms in breast cancer susceptibility genes with low-penetrance (but present in a high percentage of individuals) have a greater contribution to breast tumorigenesis in combination with exogenous (such as diet, pollution) and endogenous (such as hormones) exposures (Rothman et al., 2001). Low- penetrance susceptibly genes can be identified by studying the biochemical or physiological pathways that are postulated to be involved in breast carcinogenesis. Candidate polymorphic genes include those encoding for enzymes implicated in the metabolism of estrogen or various carcinogens, detoxification of reactive oxygen species (ROS) emerging from these reactions, alcohol and one-carbon metabolism pathways or proteins that play a role in DNA repair or cell signaling processes (Dumitrescu and Cotarla, 2005). Enzymes from different metabolic pathways can be divided into phase I enzymes that metabolically activate carcinogens (such as cytochrome P450 8

Review of Literature

[CYP] 1A1 protein) and phase II enzymes that metabolically inactivate carcinogens (such as N-acetyltransferase [NAT] and glutathione S-transferase [GST] family proteins). Polymorphisms in both phase I and II enzyme genes involved in xenobiotic and endobiotic metabolism therefore may modulate the relative risk of breast cancer for an individual (Okobia and Bunker, 2003). CYP1A1 encodes aryl hydrocarbon hydroxylase (AHH), an enzyme that activates cigarette smoke constituents and polycyclic aromatic hydrocarbons leading to electrophilic, carcinogenic molecules (Bartsch et al., 2000), in addition, it also catalyses the 2-hydroxylation of estradiol in several extrahepatic tissues, including the breast (Hellmold et al., 1998). Among the identified polymorphisms in CYP1A1, m1 polymorphism is associated with a modest increase of breast cancer risk in the white population, and m2 (codon 462, isoleucine/valine) polymorphism is associated with a moderately increased breast cancer risk only in postmenopausal women (de Jong et al., 2002). A specific polymorphism (17.5 kb region deletion) in CYP2D6, another member of the CYP family that codes for debrisoquine hydroxylase, has been associated with an increased breast cancer susceptibility both in phenotypic (poor metabolizers' group) and genotypic (homozygous and heterozygous for the variant allele, combined group) studies (de Jong et al., 2002). Members of the GST superfamily catalyze the conjugation of glutathione (GSH) to a variety of electrophiles, increasing their water solubility and excretability (Strange and Fryer, 1999). Polymorphisms leading to the absence of different GST isoenzymes affect the tolerance of the organism to chemical challenges and may influence cancer susceptibility. A pooled analysis of studies on GSTM1 null genotype (homozygous deletion) had found small and only marginally significant association with increased breast cancer risk (de Jong et al., 2002). GSTP1 is expressed consistently in both normal and tumor breast tissues (Albin et al., 1993). A meta-analysis study found that an isoleucine to valine substitution at codon 105, which may reduce the conjugating activity of 9

Review of Literature the enzyme (Gudmundsdottir et al., 2001), has been associated with a moderately increased breast cancer risk in homozygous carriers (de Jong et al., 2002). Polymorphisms in the rate-limiting enzyme involved in alcohol oxidation, alcohol dehydrogenase (ADH), may modulate breast cancer risk, as alcohol is a well-documented risk factor. Indeed, premenopausal women with the ADH1C*1,1 genotype (homozygous for ADH1C*1 allele) have been found to be at an 1.8 times higher risk for breast cancer than women with the other two genotypes (Freudenheim et al., 1999; Coutelle et al., 2004). Genes involved in the metabolism of methyl group (one-carbon metabolism) illustrate very well the interaction between environmental and genetic factors. 5,10-methylenetetrahydrofolate reductase (MTHFR) gene encodes an enzyme crucial for DNA synthesis and maintenance of DNA methylation patterns, dependent on folate intake. Two functional polymorphisms in MTHFR gene, (C677T and A1298C), that result in a decreased enzyme activity in the variant carriers, are associated with an increased risk of developing breast cancer (Campbell et al., 2002; Ergul et al., 2003). DNA repair genes constitute another low-penetrance cancer susceptibility gene group. Polymorphisms in these genes leading to attenuated DNA repair capacities, especially after the exposure to endogenous and exogenous genotoxic agents, may contribute to breast cancer risk. One X-ray repair cross-complementing group 1 (XRCC1) 399 variant allele (a gene involved in base excision repair) has been shown to be sufficient to confer an increased risk of breast cancer in African-American carriers (Duell et al., 2001). Homozygotes for the variant allele of BRCA2 asparagine 372 histidine polymorphism have been associated with an increased breast cancer risk in different European populations, as well as a large Australian population (Goode et al., 2002). 10

Review of Literature

Polymorphisms in genes that are part of the steroid hormone pathways may alter the levels and/or actions of endogenous hormones and therefore influence breast cancer risk. One particular polymorphism found in CYP19, a CYP family member implicated in the estrogen biosynthesis pathway, is the multiallelic, tetranucleotide repeat (TTTA)n polymorphism (microsatellite). A pool analysis of studies examining the (TTTA)10 allele polymorphism has shown a correlation with an increased breast cancer risk (de Jong et al., 2002). On the other hand, four separate studies either alone or pooled showed a decreased breast cancer risk in homozygous carriers of the variant allele for the progesterone receptor (PR) gene polymorphism (PROGINS) (a 306 bp insertion of the Alu subfamily) (de Jong et al., 2002). Other polymorphisms in genes such as estrogen receptor (ER), Heat shock protein 70 (HSP70) or tumor necrosis factor-α (TNF-α) may also influence the risk of developing breast carcinoma (de Jong et al., 2002). Furthermore, p53 somatic mutations or loss of heterozygosity are fairly common (19-57% and 30-42%, respectively) in a high proportion of sporadic breast cancers (de Jong et al., 2002). Several studies reviewed in (de Jong et al., 2002) have identified three p53 polymorphisms (in intron 3, exon 4, intron 6) and shown that the haplotype composed of the three variant alleles is associated with an increased breast cancer risk, especially in the white population. Reproductive factors: Lifetime exposure to endogenous sex hormones is determined by several variables including age at menarche, age at first full-term pregnancy, number of pregnancies and age at menopause, which have been studied all in relation to breast cancer risk (Feigelson and Henderson, 1996). Age at menarche: Early age at menarche (less than 12 years of age versus more than 14 years of age) has been associated with an increase in breast cancer risk on the order of 10-20% magnitude (Brinton et al., 1988; Kelsey et al, 1993; Titus- 11

Review of Literature

Ernstoff et al., 1998; Berkey et al., 1999), probably because of a prolonged exposure of breast epithelium to estrogens and progesterone due to earlier regular ovulatory menstrual cycles (Bernstein, 2002). Other findings that may explain this increase in breast cancer risk include significantly higher levels of estradiol in women with early menarche during their adolescence, as well as higher follicular, but not luteal, phase estradiol levels and lower sex-hormone- binding globulin (SHBG) (meaning that more estradiol is bioavailable to enter the breast tissue) in those women after adolescence (Bernstein, 2002). Age at menopause: Delayed menopause maximizes the number of ovulatory cycles and therefore may lead to an increased breast cancer risk. Indeed, it has been shown that the risk of breast cancer increases by approximately 3% for every 1-year increase in the age at menopause (Collaborative Group on Hormonal Factors in Breast Cancer, 1996a). In contrast, surgically induced menopause (ovariectomy or hysterectomy) before the age of 35 results in a decrease of breast cancer risk. These women have only 40% of the risk of women experiencing natural menopause (McPherson et al., 2000). Even unilateral ovariectomy performed before the age of 45 has been demonstrated to be protective (Kreiger et al., 1999). Mechanistically, it has been demonstrated that mammary epithelial cells proliferation, which is linked to breast cancer development, can be correlated with serum ovarian hormonal levels. Proliferation rates are low in the follicular phase of the menstrual cycle, when estradiol and progesterone levels are low as well, whereas during the luteal phase proliferation rates are 2-fold higher and correlate with the significantly increased ovarian hormone levels (Pike et al., 1993). The higher cellular proliferative activity confers mammary gland a higher susceptibility to be transformed by chemical carcinogens (Russo et al., 2000). After menopause, the ovarian hormone levels drop and this correlates with a substantial decrease in mammary epithelial cell proliferation (Bernstein, 2002). Numerous prospective 12

Review of Literature epidemiological studies provide strong evidence for this mechanism. Accordingly, postmenopausal women who develop breast cancer have on average 15% higher levels of circulating estradiol than other postmenopausal women (Bernstein, 2002). :term pregnancyـParity and age at first full Early pregnancy has a protective effect against breast cancer (Pathak et al., 2000). Both early age (less than 20 years versus more than 30 years) at first full-term pregnancy and higher parity decrease breast cancer risk to half of the risk of nulliparous women. Early age at second pregnancy further reduces the risk of breast cancer (McPherson et al., 2000). In contrast, nulliparity and late age at first birth contribute towards an increased risk of developing breast cancer. Interestingly, women with their first birth after age of 35 are even at higher risk than nulliparous women (Kelsey et al., 1993; McPherson et al., 2000; Bernstein, 2002). Furthermore, during the first 5-7 years after pregnancy, women (especially older ones and the ones who experienced serious nausea and vomiting during pregnancy - indicative of higher estradiol levels [Bernstein, 2002]) are at higher risk for developing breast cancer, probably due to the exposure to high levels of gestational hormones (Lambe et al., 1994; Helewa et al., 2002). Prolonged lactation has been demonstrated to be protective, as well (Lipworth et al., 2000). There is a 4.3% decrease in the relative risk of breast cancer for every 12 months of breastfeeding, in addition to a decrease of 7.0% for each birth (Collaborative Group on Hormonal Factors in Breast Cancer, 2002). Some of the mechanisms explaining the protective effect of pregnancy have been explored in animal models of breast cancer. One mechanism may involve a markedly reduced susceptibility of the fully differentiated mammary gland to carcinogens due to, at least in part, a decrease in proliferative activity of parous epithelium. Another possibility is that the decrease of the risk is due to the altered hormonal environment during pregnancy such as specific molecular changes induced by estrogen and progesterone, and decrease in circulating 13

Review of Literature growth hormone (GH) (Sivaraman and Medina, 2002). The decrease of breast cancer risk due to prolonged lactation may be explained in part by the reduction of total number of ovulatory menstrual cycles and consequently cumulative ovarian hormone exposure (Bernstein, 2002). Exogenous hormones: Hormone replacement therapy: Hormonal usage after menopause increases breast cancer risk depending on the duration of exposure and whether the estrogen is used alone or in combination with progestins (Ross et al., 2000). Conversely, administration of antiestrogens such as tamoxifen reduces breast cancer incidence (Fisher et al., 1998). A large meta-analysis has demonstrated that long-term hormone replacement therapy (HRT) is responsible for the cumulative excess of breast tumors over those expected in women between age 50 and 70 never-users of HRT (2, 6 and 12 more cases for every 1,000 women taking HRT for 5, 10, and 15 years, respectively) (Collaborative Group on Hormonal Factors in Breast Cancer, 1997). If only conjugated equine estrogens are used, breast cancer risk increases about 2.2% per year of use (Bernstein, 2002). Data from Women's Health Initiative (WHI) Randomized Controlled Trial (Rossouw et al., 2002), as well as three other previous large studies (Magnusson et al., 1999; Ross et al., 2000; Schairer et al., 2000), have indicated that addition of a progestin to estrogen regimens increases breast cancer risk after 5 years of use from 10% (estrogen alone) to 30% (combined HRT). This observation can be translated into an excess of 8 breast carcinoma in 10,000 women per year of use (Rossouw et al., 2002). Oral contraceptives: The link between the use of oral contraceptives (OCs) and breast cancer risk has been investigated by several groups. In 1996, the Collaborative Group on Hormonal Factors published a metaanalysis on 54 epidemiological studies 14

Review of Literature showing a statistically significant increase of breast cancer risk in women taking combined OCs, independent of dose, age of first use, length of use, age of diagnosis or family history of breast cancer (Collaborative Group on Hormonal Factors in Breast Cancer, 1996a,b). The strongest effect was observed in current users of OC (24% increase in breast cancer risk) and the risk decreases after stopping use, up to 10 years. Afterwards, there is no significant excess risk. Furthermore, using OCs at a younger age, especially before age of 20, results in a higher increase of breast cancer risk than using OCs at an older age (Collaborative Group on Hormonal Factors in Breast Cancer, 1996a). Also in women diagnosed with breast cancer after 1992, Rosenberg et al., (2009) found a positive association of OC use with increased breast cancer risk. The association was found to be greater for black women than for white women (Hall et al., 2005). However, contrary to these evidences, no increases in risk were observed among black or white OC users in the Women’s Contraceptive and Reproductive Experiences (CARE) Study (Marchbanks et al., 2002). With regard to the hormone status of the tumer, stronger association of OC use with ER-negative cancer than with ER-positive cancer was found (Ma et al., 2006), but Rosenberg et al., (2009) found no difference. A smaller study showed that women with a first-degree family history of breast cancer who used OCs before 1975, when formulations most likely contained higher dosages of estrogen and progestins, have an approximately 3-fold increased risk of developing breast cancer (Grabrick et al., 2000). Moreover, a similar trend was found in women carrying BRCA1 mutations (Heimdal et al., 2002). However, Marchbanks et al., (2002) found no difference in the association between OC use and breast cancer by family history, and Brohet et al., (2007) showed that the association between OC use and breast cancer risk does not appear to be modified for women who have BRCA1 and BRCA2 mutations.

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Lifestyle factors: Alcohol and folate intake: Numerous epidemiological studies have found a positive association between alcohol intake and the risk of developing breast cancer in both pre- and postmenopausal women with an overall risk of 1.6 (Singletary and Gapstur, 2001). The risk increases linearly in a dose-dependent manner up to an intake of 60 g (approximatly 2-5 drinks)/day. For every 10 g-increment (approximatly 0.75-1 drink) increase in daily consumption of alcohol the risk increases with 9% (Smith-Warner et al., 1998). Although the exact mechanisms by which alcohol can cause breast cancer have not been elucidated completely, breakthrough has been made. Alcohol can act indirectly through its first metabolite, acetaldehyde, a well- characterized carcinogen and mutagen, and/or can be a tumor promoter, leading to enhanced procarcinogen activation (Pöschl and Seitz, 2004). Another mechanism of particular interest for breast cancer is the significant increase of estrogen levels in both premenopausal (especially in the peri-ovulatory phase of menstrual cycle) (Reichman et al., 1993; Coutelle et al., 2004) and postmenopausal (Ginsburg et al., 1995; Onland-Moret et al., 2005) women associated with alcohol consumption. Also, alcohol causes an increased exposure to endogenous androgens (Singletary and Gapstur, 2001). Furthermore, alcohol causes alterations of the immune system and nutritional deficiencies, including but not limited to folate, pyridoxal phosphate (vitamin B6 - linked to methyl group synthesis and transfer), vitamin B12, vitamin D, vitamin A and retinoids, vitamin E, zinc and selenium, all of which impair the ability of the human body to fight carcinogenesis (Pöschl and Seitz, 2004). At the molecular level, alterations of the cell cycle leading to hyperproliferation, modulation of cellular regeneration or induction of CYP2E1 leading to generation of ROS are just a few mechanisms that may explain the correlation

Review of Literature between alcohol intake and increased breast cancer risk (Pöschl and Seitz, 2004). It has been demonstrated that postmenopausal women who have a higher-alcohol and low-folate intake have an increased risk for developing ER- negative tumors (Sellers et al., 2002). Folate deficiency may increase the risk of malignancy by causing DNA hypomethylation and/or inducing uracil misincorporation during DNA synthesis, therefore leading to deficiencies in the DNA repair process (DNA strand breaks) (Duthie, 1999). In contrast, increased folate intake may play a role in the prevention of breast cancer in women who consume alcohol (Zhang, 2004). Diet: The human diet contains a great variety of natural and chemical carcinogens and anti-carcinogens (Sugimura, 2000). Some of these compounds may act through the generation of free oxygen radicals, which can lead to DNA damage, or other deleterious components. Accordingly, well-done meat consumption has been associated with increased breast cancer risk (Zheng et al., 1998), probably due to production of heterocyclic aromatic amines and other harmful compounds in the process of preparation of meat. A high intake of fat, especially unsaturated fatty acids, has been reported to be weakly associated with an increased breast cancer risk (Velie et al., 2000), while a particular type of polyunsaturated fatty acids, omega-3 polyunsaturated fatty acids, seem to be protective (Bartsch et al., 1999; Saadatian-Elahi et al., 2004). Intake of fruits and vegetables, rich sources of natural antioxidants, has been shown to decrease cancer risk in general, and breast cancer in particular, in numerous studies reviewed in reports from the American Institute for Cancer Research (AICR), Chief Medical Officer's Committee on Medical Aspects of Food and Nutrition Policy (COMA) or British Department of Health (Lee, 1999; van Duyn and Pivonka, 2000). The protective effects were reported to be more pronounced in postmenopausal women (Gaudet et al., 2004). Surprisingly, soy and genistein, a 17

Review of Literature soy component that has a structure similar to steroid estrogens, have been shown to have both anti-carcinogenic and breast cancer promoting effects (Bouker and Hilakivi-Clarke, 2000). Smoking: No relevant relationship is reported by many studies evaluating the effect of smoking on breast carcinoma risk. Women smokers tend to be thinner, are more often infertile, go into earlier menopause and are prone to osteoporosis. All these factors should reduce breast carcinoma risk after the menopause. Some studies have shown that smoking at younger ages (before 16–17 years) increases breast cancer risk by approximately 20%, independent of length of exposure (Baron et al., 1996; Marcus et al., 2000; Egan et al., 2002). The effect of passive smoke is still debated; some authors report a positive correlation (Lash and Aschengrau, 1999; Johnson et al., 2000), while others do not find any correlation (Wartenberg et al., 2000; Egan et al. 2002). Obesity and physical activity: Obesity has a complex relationship with breast cancer risk that seems to be modulated by menopausal status. Large studies conducted both in the United States and Europe had demonstrated that obesity and weight gain increase breast cancer risk among postmenopausal women. Risk is particularly evident among obese women who do not use HRT (Harris et al., 1992; Huang et al., 1997; Friedenreich, 2001; Lahmann et al., 2004). For each 5 kg of weight gain since the lowest adult weight, breast cancer risk increases by 8% (Trentham-Dietz et al., 2000). One plausible mechanism by which postmenopausal obesity increases the risk for developing breast cancer is through higher levels of endogenous estrogen in obese women, as adipose tissue is an important source of estrogens (McTiernan et al., 2003). In contrast, obesity in premenopausal women has been associated with a decrease of breast cancer risk before menopause, but the mechanism is still unclear (Huang et al., 1997; Friedenreich, 2001; Lahmann et al., 2004). 18

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A meta-analysis of 19 case-control and four cohort studies investigating the relationship between physical activity and breast cancer risk has shown a consistent 20% reduction associated with physical activity performed in adolescence and young adulthood (12-24 years old) (Lagerros et al., 2004). For each one-hour increase in recreational physical activity per week during adolescence, the breast cancer risk drops with 3% (Lagerros et al., 2004). Physical activity may reduce the risk by delaying the onset of menarche and modifying the bioavailable hormone levels (Hankinson et al., 2004). Other factors: Mammographic density: Mammographic density is another well-established risk factor for breast cancer in both pre- and postmenopausal women. Boyd et al., (1995) have shown that women with more than 75% increased breast density on the mammography have an approximately 5-fold increase in the risk of developing breast carcinoma over women with less than 5% increased breast density. Both pre- and postmenopausal nulliparous women, as well as thinner women have, in general, an increased breast density (Biglia et al., 2004) and therefore they may be at an increased risk for developing breast cancer. Nulliparity and high breast density seem to act synergistically since the breast cancer risk goes up to 7-fold when they are both present in a person (van Gils et al., 2000). It also has been shown that HRT users are more than twice as likely to have high-risk increased breast density patterns on mammography in comparison with nonusers (Sala et al., 2000). History of benign breast disease: History of benign breast disease is also known to increase the risk of developing breast cancer. Women with severe atypical epithelial hyperplasia have a 4-5 folds increased breast cancer risk when compared to women without proliferative changes in their breasts (McPherson et al., 2000). The risk

Review of Literature increases further more up to 9-fold if the woman also has a family history of breast cancer (first degree relative) (McPherson et al., 2000). Ionizing radiation: Exposure of the mammary gland to high-dose ionizing radiation such as in atomic bomb survivals, historically treated children for reduction of the thymus or repeated fluoroscopies for treatment of tuberculosis, and treatment of women for Hodgkin's disease has been demonstrated to increase the risk of breast carcinoma (Hulka and Moorman, 2001; Biglia et al., 2004 ). The risk is dose-dependent and decreases gradually over time, therefore the modern screening mammography, which delivers a very low dose of radiation (200-400 mrad), has a considerable benefit-risk ratio (Biglia et al., 2004). Bone density: Bone density is a risk factor for developing breast cancer related to estrogen. Studies in postmenopausal women have found a positive correlation between increased bone density and high breast cancer risk (Biglia et al., 2004). Since estrogen helps maintaining the bone mass, this correlation may be explained by an increased total amount of estrogen (endogenous and exogenous) available for target tissues, including mammary gland (Cauley et al., 1994). Height: Height is an independent factor that has been consistently shown to have a modest contribution to the development breast cancer in postmenopausal women (van den Brandt et al., 2000; Lahmann et al., 2004), whereas in premenopausal women, the relation is even weaker (van den Brandt et al., 2000). Supporting this finding, a meta-analysis of epidemiological studies on insulin-like growth factor (IGF)-1, the anabolic effector and linear growth promoting of pituitary GH, as well as its main plasma binding protein, IGF binding protein-3 (IGFBP-3), revealed a correlation between high circulating concentrations of IGF-1 and IGFBP-3 and increased breast cancer risk in premenopausal women (Renehan et al., 2004 ). Furthermore, a synergistic 20

Review of Literature effect of IGF-1 or IGFBP-3 with estrone or testosterone on breast cancer risk has been observed among both pre- and postmenopausal women (Yu et al., 2003). Prolactin level: Prolactin is another hormone that may contribute to breast cancer risk, but the association is not very strong (Clevenger et al., 2003), this correlation has been validated by the only large prospective study (Nurses' Health Study), which showed a 2-fold increase of breast cancer risk in premenopausal women with high plasma levels of prolactin (9.7-37.4 ng/ml) (Hankinson et al., 1999). Interestingly, Stat5a, an intracellular signaling molecule activated through tyrosine phosphorylation and then nuclear translocated by both GH and prolactin, was demonstrated to be expressed, nuclear localized and tyrosine phosphorylated in 76% of primary human breast adenocarcinomas (Cotarla et al., 2004). Staging of breast cancer There are two different staging systems (American Joint Committee on Cancer, 1992): 1. The Manchester system: In 1940, the four stage system for clinical evaluation was adopted at the Christi Hospital in Manchester. This classification was widely accepted, and still in use in many centers all over the world. The four stages are: Stage 1: The growth is confined to the breast. Stage 2: The growth is confined to the breast, but palpable, mobile lymph nodes are present in the axilla. Stage 3: The growth extends beyond the mammary parenchyma: (a) Skin invasion or fixation over an area large in relation to the size of the breast or skin ulceration. (b) Tumor fixation to the underlying muscle or fascia; axillary nodes, if present, are mobile. 21

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Stage 4: The growth extends beyond the breast area which shown by fixation or matting of the axillary nodes, complete fixation of the tumor to chest wall deposits in supraclavicular nodes or in the opposite breast, or distant metastases. 2. The TNM system: The modern system is based on the clinical features of tumor size (T), the status of regional lymph nodes (N), and the presence or absence of distant metastasis (M). TNM Classification According to (Iglehart, 1991) Table (1) illustrates TNM classification of breast cancer according to (Iglehart, 1991). Primary tumor (T): Tx: Primary tumor cannot be assessed. T0: No evidence of primary tumor. Tis: Carcinoma in situ, intraductal carcinoma, lobular carcinoma in situ, or Paget's disease of the nipple with no associated tumor mass. T1: Tumor 2.0 cm or less in greatest dimension. T1a: 0.5 cm or less in greatest dimension. T1b: More than 0.5 cm but not more than 1.0 cm in greatest dimension. T1c: More than 1.0 cm but not more than 2.0 cm in greatest dimension. T2: Tumor more than 2.0 cm but not more than 5.0 cm in greatest dimension. T3: Tumor more than 5.0 cm in greatest dimension. T4: Tumor of any size with direct extension to chest wall or skin. T4a: Extension to chest wall. T4b: Edema (including peau d'orange), ulceration of the skin of the breast, or satellite skin nodules confined to the same breast. T4c: Both of the above (T4a and T4b). T4d: Inflammatory carcinoma.

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Regional lymph nodes (N): Nx: Regional lymph node cannot be assessed. N0: No regional lymph node metastasis. N1: Metastasis in 4 or fewer ipsilateral axillary lymph nodes, not larger than 3.0 cm in greatest dimension. N1a: Only micrometastasis (not larger than 0.2 cm). ,axillary lymph nodes, any one larger than 0.2 cm 3ـN1b: Metastasis in 1 but not larger than 3.0 cm. N2: Metastasis in 4 or more ipsilateral axillary lymph node larger than 3.0 cm, or any ipsilateral internal mammary lymph node(s). N2a: Metastasis in 5 or more axillary lymph nodes or any ipsilateral axillary metastasis larger than 3.0 cm. N2b: Metastasis in any ipsilateral internal mammary lymph node(s). Distant metastasis (M): Mx: Distant metastasis cannot be assessed. M0: No distant metastasis. M1: Distant metastasis.

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Table (1): TNM classification of breast cancer. Stage Primary tumor Node Metastasis (T) (N) (M) 0 Tis N0 M0 I T1 N0 M0 II(a) T0 N1 M0 T1 N1 M0 T2 N0 M0 II(b) T2 N1 M0 T3 N0 M0 III(a) T3 N1 M0 T1 N2 M0 T2 N2 M0 T3 N2 M0 III(b) T4 Any N M0 IV Any T Any N M1

World Health Organization classification of breast tumor (Iglehart, 1991) Epithelial tumors: Benign Intraductal papilloma Adenoma of nipple Adenoma Tubular Lactating Malignant Noninvasive

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Intraductal carcinoma Lobular carcinoma Invasive Invasive ductal carcinoma Invasive ductal carcinoma with a predominant intraductal component Invasive lobular carcinoma Mucinous carcinoma Medullary carcinoma Papillary carcinoma Tubular carcinoma Adenoid cystic carcinoma Secretory (juvenile) carcinoma Apocrine carcinoma Carcinoma with metaplasia Squamous type Spindle cell type Cartilaginous and osseous type Mixed other type Others Paget's disease of the nipple Mixed connective tissue and epithelial tumors: Fibroadenoma Phyllodes tumor (cystosarcoma phyllodes) Carcinosarcoma Miscellaneous tumors: Soft tissue tumors Skin tumors Tumors of hematopoietic and lymphoid tissues 25

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Unclassified tumors Mammary dysplasia/fibrocystic disease Tumor like lesion: Ductectasia Inflammatory pseudo tumors Hamartoma Gynecomastia Others Histopathological grades of breast cancer Bloom and Richardson (1957) have adapted a grading system of breast carcinoma, which was recommended by World Health Organization (WHO) 1986. This grading system is based on the following criteria:  Tumor architecture (tubular formation or differentiation).  Irregularity of size, shape and nuclei staining (nuclear pleomorphism).  Mitotic activity and hyperchromatism. Thus the grading of breast cancer is divided into three histological grades: .Low grade malignancy ـ Grade I .Intermediate grade malignancy ـ Grade II .High grade malignancy ـ Grade III Serum tumor markers in breast cancer Aiding early diagnosis: Lack of sensitivity for early-stage disease combined with a lack of specificity precludes the use of all existing serum markers for the early diagnosis of breast cancer. For example, CA 15-3 concentrations are increased in ~10% of patients with stage I disease, 20% with stage II disease, 40% with stage III disease, and 75% with stage IV disease (American Society of Clinical Oncology, 1996). According to an American Society of Clinical Oncology

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(ASCO) Expert Panel, a CA 15-3 concentration 5- to 10-fold above the upper limit of the reference interval could alert a physician to the presence of metastatic disease (American Society of Clinical Oncology, 1996). However, a low concentration does not exclude metastasis (American Society of Clinical Oncology, 1996). As well as lacking sensitivity for early disease, CA 15-3 also lacks specificity for breast cancer. Increased concentrations of the marker can be found in a small proportion of apparently healthy individuals (~5%), in patients with certain benign diseases (especially liver disease), and in patients with other types of advanced adenocarcinomas (American Society of Clinical Oncology, 1996; Duffy, 1999; Cheung et al., 2000; Nicolini and Carpi, 2000). Determining prognosis: Available prognostic factors for breast cancer include pathology criteria such as tumor size, tumor grade, and lymph node status (Elston et al., 1999), as well as newer biological factors such as hormone receptors, human epidermal growth factor receptor-2 (HER-2)/neu, urokinase plasminogen activator, and plasminogen activator inhibitor 1 (Isaacs et al., 2001; Duffy, 2002). All of these factors require tumor tissue, thus necessitating either biopsy or surgery (Duffy, 2006). Shering et al., (1998); Kumpulainen et al., (2002); Duffy et al., (2004) concluded that high concentrations of CA 15-3 at initial presentation predicted adverse patient outcome and the prognostic impact was independent of tumor size and axillary nodal status. Significantly, CA 15-3 was found to be prognostic in lymph node-negative breast cancer patients (Kumpulainen et al., 2002; Duffy et al., 2004), the subgroup in which new prognostic factors are most urgently required. In another study, however, CA 15-3 was not prognostic in patients free of axillary nodal metastases (Molina et al., 2003). Although most studies relating CA 15-3 to prognosis have used preoperative values, concentrations during follow-up can also provide 27

Review of Literature prognostic information. Thus, Tampellini et al., (1997) reported that patients with CA 15-3 values <30 kU/L at the time of first recurrence survived significantly longer than those with higher concentrations. In another report, De La Lande et al., (2002) found that patients with a CA 15-3 lead time >30 days had a better prognosis than those with a shorter lead time. In that study (De La Lande et al., 2002), both the time interval between diagnosis and first abnormal CA 15-3 concentration (cutoff, 47 kU/L) were also of prognostic value. These findings suggest that determination of CA 15-3 can provide real- time prognostic information in patients with breast cancer. Indeed, preoperative concentrations could be combined with existing prognostic factors for selecting patients for adjuvant therapy. For example, in lymph node-negative patients, preoperative concentrations of CA 15-3 might be combined with tumor size, tumor grade, ER status, and HER-2/neu status for selecting who should or should not receive adjuvant chemotherapy (Duffy, 2006). Serum concentrations of the shed form of HER-2/neu have also been widely investigated for potential prognostic value in breast cancer. After performing a systematic review of the literature, Carney et al., (2003) identified 20 publications involving >4000 patients that related serum HER-2/neu concentration to outcome. These studies showed that high HER-2/neu concentrations in patients with either early or metastatic breast cancer predicted adverse outcome as demonstrated by decreased time to disease progression, decreased disease-free survival, and decreased overall survival. It was not clear, however, whether the prognostic information provided by HER-2/neu was independent of the traditional factors. Although less widely investigated as a prognostic factor than either CA 15-3 or HER-2/neu, high preoperative concentrations of CEA are also associated with poor prognosis in breast cancer (Cañizares et al., 2002; Ebeling et al., 2002; Molina et al., 2003). Furthermore, in one large study (n = 1046), patients with a decrease of >33% between pre- and postoperative concentrations were 28

Review of Literature found to have a worse outcome than those with a lesser decrease (Ebeling et al., 2002). In multivariate analysis, this decrease in CEA predicted outcome independent of tumor size, lymph node status, and PR status (Ebeling et al., 2002). Predicting response to therapy: As with prognostic factors, the available therapy-predictive markers in breast cancer, such as ER, PR, and HER-2/neu (Duffy, 2005), all require tumor tissue for analysis. Preliminary findings, however, suggest that high serum HER-2/neu concentrations are associated with both poor response to endocrine therapy and cyclophosphamide-methotrexate-5-fluorouracil–based chemotherapy but can predict an improved response to a combination of trastuzumab (Herceptin) and chemotherapy (Carney et al., 2003). CA 15-3 and other mucin-1 (MUC-1)-related markers may also have a role in predicting response to therapy. Ren et al., (2004) reported that overexpression of MUC-1 (the antigen detected in CA 15-3 and CA 27-29 assays) in a mouse model system conferred resistance to cis-platinum. This resistance appeared to result from the ability of MUC-1 to inhibit apoptosis. Clearly, studies should be carried out to determine whether either tumor tissue or serum concentrations of MUC-1–related markers predict response/resistance in patients undergoing treatment with platinum-based therapies (Duffy, 2006). Surveillance after primary treatment: Follow-up of patients after primary treatment for breast cancer with clinical examination, radiology, and biochemical testing is now standard practice in many centers. This practice is based on the assumption that the early detection of recurrent or metastatic disease enhances the chances of cure or survival. The evidence currently available, however, does not support this widely held assumption (Duffy, 2006). Two large multicenter randomized prospective trials (each with >1000 patients) compared outcome in patients followed up with clinical visits and 29

Review of Literature mammography versus those who were followed up with an intensive regime that included radiology and traditional laboratory testing (Rosselli del Turco et al., 1994; The GIVIO Investigators, 1994). Both studies concluded that use of an intensive follow-up program failed to improve outcome. Similarly, after pooling of the data from the above 2 studies, no significant difference in either disease- free interval or overall survival emerged between patients with intensive versus nonintensive surveillance (Rojas et al., 2004). In addition to these 2 large prospective trials, a systematic review of studies comparing control versus intensive follow-up regimes for newly diagnosed breast cancer patients has also been carried out (Collins et al., 2004). Of 4418 reports identified, 38 were considered eligible for analysis. Although the data were not sufficiently homogeneous to integrate statistically, the authors concluded that patient survival and quality of life were not affected by the intensity of follow-up or location of care. The authors also concluded that there was insufficient evidence to draw broad conclusions with respect to best practice for breast cancer follow-up care regarding morbidity reduction, cost- effectiveness, and patient involvement in care. Clearly, the available data do not support the use of an intensive follow- up program using standard biochemical testing and radiology after primary treatment for breast cancer. However, as pointed out by Emens and Davidson, (2003), the value of surveillance depends on both the sensitivity and specificity of the available diagnostic tests as well as the efficacy of therapy available for recurrent/metastatic disease. Several reports have shown that serial concentrations of tumor markers increase before radiologic or clinical evidence of disease relapse (American Society of Clinical Oncology, 1996; Duffy, 1999; Cheung et al., 2000; Nicolini and Carpi, 2000). In a review of the literature, an ASCO Expert Panel identified 12 studies that used serial CA 15-3 measurements to monitor patients for recurrence after breast cancer surgery. In 7 of these trials, data were available in 30

Review of Literature sufficient detail to allow pooling of results. Summation of the data showed that 67% of 352 patients had increased CA 15-3 either before or at the time of recurrence (American Society of Clinical Oncology, 1996). In 1320 patients without evidence of recurrence at the time of study, 92% had CA 15-3 concentrations within reference values. The mean lead time from marker increase to clinical diagnosis of recurrence varied from 2 to 9 months (American Society of Clinical Oncology, 1996). Although serial CA 15-3 concentrations can preclinically detect recurrent/metastatic disease, it is unclear whether the introduction of early treatment based on this lead time improves disease-free survival, overall survival, or quality of life for patients. In an attempt to address these issues, several small-scale studies have been carried out. In one of the first of these, Jager, (1995) randomized patients with increasing concentrations of tumor markers (CA 15-3 or CEA) but without evidence of metastatic disease to receive (n = 21) or not receive (n = 26) medroxyprogesterone acetate. For the untreated patients, the median time interval between increase in marker concentration and detectable metastasis was 4 months, but for the treated patients it was >36 months. In a second study, Nicolini et al., (1997); Nicolini et al., (2003) compared outcome in 36 asymptomatic patients who received salvage treatment based on tumor marker increases (CA 15-3, CEA, or tissue polypeptide antigen [TPA]) versus 32 patients who were given treatment only after radiologic confirmation of metastasis. Survival from both the time of mastectomy and salvage treatment was significantly improved in the group with tumor marker– guided treatment than in those treated conservatively. In a third study, Kovner et al., (1994) randomized asymptomatic patients with increasing mammary cancer antigen concentrations to receive (n = 23) or not receive tamoxifen (n = 26). After a median follow-up of 11 months, 7 of 29

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(24%) in the control group had relapsed, whereas none of the 23 patients randomized to receive treatment developed a recurrence. Although these 3 studies contained small numbers of patients, they all suggested that early treatment based exclusively on increasing marker concentrations improved prognosis. These findings, however, are not sufficiently strong to recommend a change in clinical practice, i.e., to recommend that asymptomatic patients with increasing marker concentrations should start new therapy. Many expert panels (including ASCO, European Society of Medical Oncology [ESMO], and European Society of Mastology) therefore recommend that tumor markers should not be used in the routine surveillance of patients after primary treatment for breast cancer (American Society of Clinical Oncology, 1996; Bast et al., 2001; Blamey, 2002; European Society of Medical Oncology, 2005). Other organizations, such as the European Group on Tumor Markers (EGTM) as well as the National Academy of Clinical Biochemistry (NACB), however, recommend the use of tumor markers during surveillance (Fleisher et al., 2002; Molina et al., 2005). Monitoring response to therapy in advanced disease: Traditionally, International Union Against Cancer (UICC) criteria have been used for assessing response to therapy in patients with advanced breast cancer (Bast et al., 2001). UICC criteria include physical examination, measurement of lesions, radiology, and isotope scanning (Hayward et al., 1977). Multiple studies (Williams et al., 1990; Robertson et al., 1991; Dixon et al., 1993) and 3 multicenter trials (van Dalen et al., 1996; Robertson et al., 1999; Kurebayashi et al., 2004), however, have shown that changes in serial concentrations of tumor markers, particularly CA 15-3, correlate with response. In 2 of these multicenter trials, the alterations in tumor marker concentrations were shown to correlate well with UICC criteria (van Dalen et al., 1996; Robertson et al., 1999). Indeed, the use of markers to monitor therapy has several advantages over conventional criteria, including increased sensitivity, 32

Review of Literature more objective measurement, and more convenience for patients (Duffy, 1999; Cheung et al., 2000). On the basis of data from 11 low-level evidence studies (American Society of Clinical Oncology, 1996), an ASCO Panel concluded that 66% of patients with chemotherapy-induced disease regression exhibited decreases in marker concentrations, 73% of those with stable disease had no significant change in marker concentrations, and 80% with progressive disease displayed increasing concentrations. In most of these studies, a change in CA 15-3 concentration >25% was regarded as a significant alteration (American Society of Clinical Oncology, 1996). The same ASCO Panel also reviewed the literature on the use of CEA in monitoring response to treatment (American Society of Clinical Oncology, 1996). Eighteen low-level evidence studies were reviewed. Of these, 6 reported results only in patients with high concentrations of CEA. Overall, 82% of the patients were found to have decreasing concentrations with disease response, whereas 74% had increasing concentrations with progressive disease. Of the 12 studies reporting results for patients with advanced disease irrespective of whether CEA was increased, 61% of patients showed a decrease in CEA concentrations with tumor response and 65% showed an increase with tumor progression. Although the available data show relatively good correlations between alterations in serial tumor marker concentrations and response to therapy in advanced breast cancer, the ASCO Panel concluded that neither CA 15-3 nor CEA should be routinely used for this purpose (American Society of Clinical Oncology, 1996; Bast et al., 2001). However, the guidelines also stated “that in exceptional circumstances such as the presence of osseous metastasis, which are difficult to evaluate clinically, the marker level may be able to support the clinical estimate of disease status. However, the marker cannot in any situation stand alone to define response to treatment” (Bast et al., 2001). 33

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Although the ASCO Panel was unable to recommend routine use of tumor markers for monitoring treatment in advanced breast cancer, according to Cheung et al., (2000), measurement of tumor markers is the only validated method for determining response in patients with disease not assessable by UICC criteria. Overall, 10%–40% of patients with breast cancer have nonassessable disease, i.e., those with irradiated lesions, pleural effusion, ascites, lytic bone disease, and sclerotic bone disease (Cheung et al., 2000). In contrast to the ASCO Panel, both the NACB and EGTM Panels recommended use of CA 15-3 for monitoring therapy in patients with advanced breast cancer (Fleisher et al., 2002; Molina et al., 2005). According to the EGTM Panel, markers should be measured before every chemotherapy course and at 3-month intervals for patients receiving hormone therapy (Molina et al., 2005). This Panel defined a clinically significant increase in marker concentration as an increase of at least 25% over the previous value. This increased concentration should be confirmed with a second sample taken within 1 month. The Panel also stated that a confirmed decrease in marker concentration of >50% was consistent with tumor regression (Molina et al., 2005). Although CA 15-3 and CEA are the most widely used markers in monitoring chemotherapy in patients with advanced breast cancer, emerging data suggest that serum HER-2/neu may be of use in patients undergoing treatment with trastuzumab-based therapy. Trastuzumab is a humanized monoclonal antibody directed against the extracellular domain of HER-2/neu and is widely used in combination with chemotherapy for the treatment of patients with HER-2/neu–overexpressing advanced breast cancer (Vogel and Tan-Chiu, 2005). In a retrospective study, Esteva et al., (2005) compared serum HER- 2/neu and CA 15-3 for monitoring trastuzumab-based therapy in 99 patients with advanced breast cancer. Concordance between clinical status and HER- 34

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2/neu concentrations was 0.793 compared with 0.627 for CA 15-3. When both markers were combined, the concordance with clinical status increased to 0.83. Although progression-free survival did not differ significantly between patients with increased versus normal baseline HER-2/neu concentrations, it did differ according to whether the patient’s HER-2/neu concentration at 2 to 4 weeks after start of therapy was >77% or <77% of the baseline value. For patients with HER-2/neu concentrations >77% of baseline, the median progression-free survival was 217 days, whereas for those with concentrations <77% of baseline it was 587 days. In another preliminary report, Köstler et al., (2004) showed that in patients responding to trastuzumab-based therapy, serum HER-2/neu concentrations decreased significantly as early as from day 8 of treatment. In contrast, no significant changes were observed in patients with progressive disease. Using multiple logistic regression analysis, they found that change in HER-2/neu concentrations were the only factor that predicted the likelihood of response after 8 days of treatment. Furthermore, measurement of serial concentrations of HER-2/neu predicted risk of disease progression as early as day 15 of treatment.

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Cell adhesion molecules Integrins The integrin family is a widely expressed group of cell surface glycoprotein receptors for extracellular matrix (ECM) proteins and Ig superfamily molecules (Fig. 1). Integrins are formed by heterodimers of non- covalently associated α- and β-subunits. There are about 24 known combinations of heterodimers that are assembled from 18 α-subunits and 8 β- subunits. Each integrin subunit consists of an extracellular domain, a single transmembrane region and a short cytoplasmic tail (Hynes, 2002). The combination of α- and β-subunits determines the ligand binding specificity and signaling properties of a given integrin. Some integrins such as α5β1, primarily recognize a single ligand, whereas others, such as αvβ3, can bind several ligands (Takagi, 2007). The interaction with ECM proteins (such as fibronectin [FN] and vitronectin) or cell surface Ig proteins (such as intercellular adhesion molecule [ICAM]-1 and vascular CAM-1 [VCAM-1]) promotes integrin clustering and subsequent integrin-mediated intracellular signal transduction. Upon binding, integrins (which have no intrinsic enzymatic or kinase activities) activate complex signaling pathways by associating with kinases and adaptor proteins in focal adhesion complexes (Giancotti and Tarone, 2003). The latter, identified as specialized adhesive structures, are composed of integrins, protein kinases such as focal adhesion kinase (FAK) and Src-adaptor proteins such as Shc, signaling intermediates such as Rho family guanosine triphosphatases, actin-binding cytoskeletal proteins (such as talin, α-actinin, paxillin, tensin and vinculin), and other signaling proteins (Lo, 2006; Mitra and Schlaepfer, 2006). Moreover, integrin-mediated signaling regulates gene expression, cell growth, differentiation and survival (Giancotti and Ruoslahti, 1999; Jin and Varner, 2004). Impaired regulation of integrin signaling can contribute to diseases progression in a variety of pathologies, including autoimmune diseases,

Review of Literature thrombotic disorders and cancer (Guo and Giancotti, 2004; Avraamides et al., 2008). Cadherins The large cadherin family includes calcium-dependent CAMs responsible for cell-cell recognition and adhesion in solid tissues (Yagi and Takeichi, 2000; Gumbiner, 2005). With few exceptions, cadherins are single pass transmembrane proteins characterized by the presence of extracellular cadherin (EC) domains (Fig. 1). The so-called classic cadherins are calcium- dependent hemophilic adhesion molecules frequently associated with specific junctional structures referred to as adherens junctions (Niessen and Gottardi, 2008). Cadherins are expressed in several types of tissues with some specificity: E-cadherin is mostly present in epithelial cells, N-cadherin in the nervous system, smooth muscle cells, fibroblasts and endothelial cells, VE-cadherin is specific for the endothelium (Gumbiner, 2005; Cavallaro et al., 2006; Dejana et al., 2008). Classic cadherins interact through their cytoplasmic tail with β- catenin and plakoglobin/γ-catenin, which in turn bind to α-catenin. The latter is able to bind and polymerize actin microfilaments, either directly or indirectly through other actin binding proteins such as vinculin or α-actinin (Nelson, 2008). Cadherin anchorage to the actin cytoskeleton stabilizes the junctional structure and contributes to maintenance of cell morphology and control of cell motility. Cadherins can also dimerize through the extracellular domain and the dimers mediate trans homophilic adhesion (Patel et al., 2003). The cadherin family includes desmosomal cadherins, atypical cadherins such as T-cadherin, and the large group of protocadherins. Protocadherins are characterized by the presence of a variable number of EC domains, linked to a cytoplasmic tail which presents no homology with classic cadherins and does not bind β-catenin or plakoglobin (Morishita and Yagi, 2007). Although different cadherins interact with similar intracellular partners, they can exert specific activities. For instance, in epithelial tissues, E-cadherin 37

Review of Literature plays an important role in maintaining the epithelioid phenotype of the cells and in mediating density-dependent inhibition of cell growth. In contrast, N-cadherin exerts the opposite activity, as it has been associated with epithelial-to- mesenchymal transition, increased cell motility and invasion (Wheelock and Johnson, 2003). The molecular mechanisms underlying differential signaling are still poorly understood. Cadherin-specific intracellular partners such as kinases or phosphatases may associate to the cadherin/catenin complex and modulate its activity and intracellular signaling. It is also likely that cadherin function changes in different cell types depending on the cadherin repertoire of the cell. Furthermore, cadherins can associate to growth factor receptors and modulate their intracellular signaling (Orian-Rousseau and Ponta, 2008). For example, VE-cadherin associates to vascular endothelial growth factor receptor 2 (VEGF-R2) and modulates its signaling pathways. Indeed, in the absence of VE-cadherin, VEGF-R2 promotes endothelial cell growth in an uncontrolled way, leading to alterations in vascular development (Grazia Lampugnani et al., 2003). Selectins Selectins, a family of mammalian lectins engaged in adhesion reactions, are type-I membrane proteins that contain a N-terminal C-type lectin domain, followed by an epidermal growth factor (EGF)-like motif, a series of short consensus repeats (SCRs), a transmembrane domain, and a cytoplasmatic tail (Fig. 1). Selectins mediate heterotypic cell-cell interactions through calcium- dependent recognition of glycoproteins and glycolipids bearing sialyl Lewis X (sLeX) (Rosen and Bertozzi, 1994). The selectin family consists of three members: E-, P-, and L-selectin. E- selectin is expressed by cytokine-stimulated endothelial cells and participates in the rolling and adhesion of neutrophils and monocytes during inflammation (Lawrence et al., 1994). P-selectin, stored in normal conditions in granules of endothelial cells (Weibel-Palade bodies) and in α-granules of platelets, is 38

Review of Literature mobilized rapidly after cell activation, such as histamine stimulation of endothelial cell or thrombin stimulation of platelets (Ludwig et al., 2007). L- selectin is constitutively expressed on all leukocytes, but its surface levels are modulated by metalloprotease-dependent shedding of the extracellular domain (Smalley and Ley, 2005). The major ligands for L-selectin are P-selectin glycoprotein ligand-1 (PSGL-1), a sialomucin expressed on most leukocytes, and glycoproteins found on high endothelial venules (HEVs) of Peyer’s patches and of peripheral lymph nodes (Norman et al., 1995). L-selectin/PSGL-1 binding triggers leukocyte aggregation, and binding to HEV initiates the transmigration necessary for lymphocyte homing. Also P-selectin binds PSGL-1 and this event is critical for tethering and rolling of leukocytes on endothelial cells or surface-bound platelets (Yang et al., 1999). Immunoglobulin-like cell adhesion molecules The Ig-CAMs superfamily includes a diverse array of cell adhesion receptors. Proteins of this family are defined by the presence of one or more copies of the Ig-fold, a compact structure with two cysteine residues separated by 55–75 amino acids arranged as two antiparallel β-sheets. In many (but not all) cases, CAMs of the Ig superfamily also contain one or more copies of FN type III repeat (Vaughn and Bjorkman, 1996). Ig-CAMs typically have a large N-terminal extracellular domain, a single transmembrane helical segment, and a cytoplasmic tail (Fig. 1). Members of the Ig-CAM family function in a wide variety of cell types and are involved in many different biological processes. One of the most important contexts for Ig-CAM function is the developing nervous system, where many different members of this superfamily, such as the neural CAM (NCAM) and L1, are involved in axon guidance and in the establishment and maintenance of neural connections (Zhang et al., 2008). Ig-CAMs also exert important biological functions in the immune system. Several types of immune cells (such as lymphocytes, monocytes and dendritic cells) express Ig-CAMs, such as ICAM-1 and -2, platelet endothelial 39

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CAM-1 (PECAM-1), junctional adhesion molecules (JAMs) and L1. These molecules play important roles in antigen recognition and leukocytes trafficking (Greenwood et al., 2002; Bradfield et al., 2007; Garrido-Urbani et al., 2008; Zimmerman and Blanco, 2008; Maddaluno et al., 2009). In contrast to many of the neural Ig-CAMs, which often act as homotypic receptors, Ig family proteins involved in the immune system primarily engage in heterotypic interactions. For example, ICAMs on endothelial cells are recognized by β2 integrins on leukocytes (Hubbard and Rothlein, 2000). Moreover, Ig-CAMs such as PECAM-1, JAMs and VCAM-1 are found on endothelial cells and play an important role in leukocyte trafficking and in the formation and maintenance of endothelial cell junctions.

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Fig. (1): Overview of the four main CAM classes. The general structure and interaction properties of the four superfamilies of CAMs are schematically depicted (Francavilla et al., 2009).

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Activated leukocyte cell adhesion molecule Transcriptional regulation and function The gene encoding ALCAM is located on the long arm of human chromosome 3 (3q13.1-q13.2) (Bowen et al., 1995). It is organized into 16 exons that span nearly 150 kb of DNA (Ikeda and Quertermous, 2004). Tan et al., (2006) has started to provide the first insight into the transcriptional regulation of the ALCAM gene. The promoter is TATA-less and enriched with multiple GC-boxes in the proximal region. It contains multiple positive and negative regulatory regions, some with tissue-specific activity, which is consistent with the diametric regulation of the ALCAM gene in different cancers (Tan et al., 2006). The promoter contains multiple cis-active elements including a functional p65 nuclear factor kappa B motif, and it harbors an extensive array of CpG residues highly methylated exclusively in ALCAM-negative tumor cells (King et al., 2010). DNA-protein binding and reporter gene experiments indicate the nuclear factor kappa B element is functional, and it is likely involved in increasing expression of ALCAM in tumors because several members of the rel transcription factor family (c-rel, v-rel) induce ALCAM expression in avian lymphoma cell lines (Zhang et al., 1995). Studies of King et al., (2010) indicated this paradigm is true also in melanoma cell lines. ALCAM is recruited to intercellular junctions in cultured endothelial cells and at sites of cell-cell contact in the epithelium of several organs. To confirm the junctional localization of ALCAM in epithelium, Masedunskas et al., (2006) generated a green fluorescent-tagged ALCAM chimera and demonstrated recruitment of ALCAM-green fluorescent protein (GFP) to intercellular junctions of cultured lung endothelial (pulmonary microvascular endothelial cells) and epithelial (A549) cells. ALCAM-GFP was recruited to the cell surface in the K562 hematopoietic cell line, and massive clustering of these cells occurred in suspension culture. The use of an anti-ALCAM antibody

Review of Literature confirmed clustering was caused by ALCAM expression (Masedunskas et al., 2006). The biologic function of ALCAM has been studied using a variety of experimental systems. ALCAM null mice, which were created using homologous recombination techniques, are viable, fertile, and display no external morphologic defects or any pathophysiology at steady state (Weiner et al., 2004). Virtually all studies that examine ALCAM function have used antibodies and chimeric, fragment crystallizable (Fc)-tagged, soluble ALCAM variants to prevent cell-mediated homotypic and heterotypic ALCAM adhesions. The data from these studies implicate ALCAM in stabilization of the immunologic synapse, T-cell proliferation and activation, monocyte transendothelial migration, and axon fasciculation. The most widely studied is the role of ALCAM in T-cell biology (Bajorath et al., 1995; Bowen et al., 1995; Patel et al., 1995; Bowen et al., 1996; Singer et al., 1996; Starling et al., 1996; Aruffo et al., 1997; Singer et al., 1997; Gimferrer et al., 2004; Hassan et al., 2004; Kato et al., 2006; Zimmerman et al., 2006). Zimmerman et al., (2006) reported that long-term engagement of dendritic cell ALCAM and CD6 expressed on T-lymphocytes was essential for proliferation of T-cells long after the initial contact between the 2 immune cells had been established. This finding is consistent with image analysis of T-cell antigen-presenting cell conjugates, which demonstrates that CD6 and ALCAM colocalize with the T-cell receptor complex at the center of the immunological synapse (Gimferrer et al., 2004), and it extends findings by Hassan et al., (2004) that the ALCAM-CD6 interaction is required for optimal activation of T-cells. The costimulatory role of ALCAM in T-cell activation suggests an involvement for ALCAM in the immunologic response to tumor cells; indeed, one study has demonstrated an important role for ALCAM expressed on tumor cells in γδT-cell activation by tumor cells (Kato et al., 2006). Based on this extensive body of evidence, it is expected that ALCAM null dendritic cells will establish relatively unstable 43

Review of Literature contacts with T-cells, which results in weak T-cell activation and reduced proliferation. This idea high-lights an important role of the ALCAM null mice and other genetic models of ALCAM deficiency in establishing the major functions of this CAM, which remains poorly understood (Ofori-Acquah and King, 2008). Activated leukocyte cell adhesion molecule and various types of cancer Melanoma: Studies of melanoma cell lines (Degen et al., 1998) showed that expression of MEMD (ALCAM) correlated with cell-cell adhesion and therefore aggregation of tumor cells. These studies showed that homophylic ALCAM- ALCAM adhesion is essential in tumor (melanoma) cell clustering. Expression of ALCAM is correlated with metastatic capacity of human melanoma cell lines (Degen et al., 1998). Immunohistochemical studies of human melanocytic lesions showed as association between high ALCAM expression and melanoma progression (van Kempen et al., 2000). Most nevi (34/38), and all thin melanomas with Clark levels I and II, did not express ALCAM. Moreover, ALCAM was expressed in the vertical growth phase of 2/13 Clark level III lesions, 13/19 Clark level IV lesions, and 4/4 Clark level V lesions. Approximately half of the metastases had ALCAM positivity. When considering Breslow thickness, it was found that less than 10% of melanomas that are less than 1.5 mm-thick expressed ALCAM, whereas greater than 70% of melanomas that are greater than 1.5 mm-thick expressed ALCAM. ALCAM expression occurred in the vertical growth phase and not in the radial growth phase of malignant melanomas. The authors proposed that ALCAM plays an important role in melanoma cell invasion and neoplastic progression. This idea was tested by van Kempen et al., (2004) by stable transfection of a transmembrane, amino-terminally truncated ALCAM

Review of Literature variant into melanoma cells. The authors discovered that this truncated ALCAM variant diminished cell clustering mediated by wild-type ALCAM. Diminished cell clustering (with the truncated ALCAM) promoted motility in vitro and the transition from primary tumor growth to tissue invasion in reconstructed skin in culture. Using the same approach in a transplanted tumor model, reduced subcutaneous tumor growth, and accelerated spontaneous lung metastases with the truncated ALCAM occurred. The authors proposed that stepwise modulation of ALCAM-mediated cell adhesion is involved in melanoma metastasis. Klein et al., (2007) evaluated the presence or absence of stem cell markers (including CD166) in banal nevi, in situ and invasive melanomas, and metastatic melanomas. Using an immunohistochemical approach, these investigators found that 11/71 (15%) nevi, 37/70 (53%) primary melanomas, and 58/84 (69%) metastatic melanomas expressed ALCAM, and then they proposed that the progression to melanoma may involve genetic pathways involved in stem cell biology and normal tissue development because ALCAM is expressed on the surface of mesenchymal stem cells. Using 2-dimensional monolayers and 3-dimensional collagen-gel cultures, Lunter et al., (2005) studied gelatinase A/matrix metalloproteinase (MMP)-2 and an intermediate ternary complex of membrane type 1 MMP (MT1-MMP)/MMP-14, tissue inhibitor of metalloproteinase-2 (TIMP-2), and pro-MMP-2 in melanoma. Extensive cell-cell contacts, wild-type ALCAM, and cell-matrix interactions were needed for efficient conversion of pro-MMP-2 to its active form in melanoma. MMP-2 activation was decreased with truncated, dominant-negative ALCAM via reduced transcript levels and decreased processing of MT1-MMP. The authors proposed that ALCAM may be involved in a signaling role in regulation of proteolysis and in a sensor function for invasive growth. Swart et al., (2005) hypothesized that ALCAM may function as a cell-surface sensor to register local growth saturation and to regulate cellular signaling and dynamic responses. 45

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Prostate carcinoma: Several prostate cancer cell lines express ALCAM (Tomita et al., 2000). The protein is located at cell-cell contacts (by immunocytochemistry) in DU- 145 and LNCaP cells, in the cytoplasm in ALVA-31, PC-3, and PPC-1, and in multiple cellular compartments in JCA-1 and TSU-pr1 cell lines (Tomita et al., 2000). Ectopic expression of α-catenin recruited ALCAM and E-cadherin to sites of cell-cell contact in ALVA-31, PC-3, and PPC-1 cell lines, which suggests a regulatory role for the catenin family in subcellular ALCAM localization (Tomita et al., 2000). ALCAM gene expression was found to be upregulated (an average 3- fold change) in 9 Gleason grade 4/5 prostate cancers compared with 8 benign prostatic hyperplasia cases (Stamey et al., 2001). Using chip-based transcript analysis, Kristiansen et al., (2003) found upregulation of ALCAM transcripts in 22% of cases (2-fold to 3.8-fold change) of prostate cancer. By immunohistochemistry, ALCAM was found to be expressed in normal prostatic epithelia (Kristiansen et al., 2003; Kristiansen et al., 2005). Prostatic intraepithelial neoplasia (PIN) showed a broad variation in ALCAM expression (by immunohistochemistry), although in most cases staining for ALCAM was stronger in PIN than in normal epithelia (Kristiansen et al., 2003). By frozen section immunocytochemistry, 81% of prostate tumors had at least focal upregulation of ALCAM expression with 61% of tumors showing ubiquitous upregulation (Kristiansen et al., 2003). In the same study, 19% of prostate tumors had no upregulation or even loss of ALCAM expression. Downregulation occurred most frequently in higher Gleason grade tumors. In summary, study of Kristiansen et al., (2003) showed most low-grade tumors (Gleason grade 1–3) had upregulation of ALCAM, but high-grade tumors (Gleason grade 4 and 5) had downregulation. Two Gleason grade 5 tumors, however, had upregulation of ALCAM.

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Follow-up studies by Kristiansen et al., (2005) showed that ALCAM was upregulated (compared with adjacent normal tissue) in 86% of prostate carcinomas by immunohistochemistry. Staining was membranous and cytoplasmic. Neither pattern of staining (membranous or cytoplasmic) showed a significant association with primary tumor stage, tumor grade, or residual tumor (R) status (R0/R1). Higher levels of cytoplasmic ALCAM staining were associated with shorter prostate-specific antigen (PSA) relapse times (mean, 46 versus 68 months) (Kristiansen et al., 2005). The combination of CD24 and cytoplasmic ALCAM was a stronger predictor for disease relapse than was the preoperative PSA serum level in a Cox regression model (Kristiansen et al., 2005). In summary, studies in prostate cancer suggest most low-grade tumors (Gleason grade 1–3) have upregulation of ALCAM expression, whereas high- grade tumors (Gleason grade 4 and 5) show ALCAM downregulation (Kristiansen et al., 2003). Breast cancer: ALCAM has been identified in the following breast cancer cell lines by Western blot analysis: MCF10A, MCF10AT, DCIS.com, MCF10CA Cl-A, MCF10CA Cl-D, and MDA-MB-231 (King et al., 2006; King et al., 2010). Cell lines MCF-7 and MDA-MB-435 have weak or no detectable ALCAM protein expression (King et al., 2010) in agreement with earlier findings by Degen et al., (1998) of weak messenger ribonucleic acid (mRNA) levels of ALCAM in MCF-7 cells. King et al., (2004) offered the first analysis of ALCAM mRNA expression in breast cancer. In a study of 120 primary breast carcinomas, levels of ALCAM transcripts measured by real-time polymerase chain reaction were analyzed in relation to clinical data from a 6-year follow-up period. Low-level ALCAM mRNA correlated with nodal involvement, higher grade, higher TNM stage, worse Nottingham Prognostic Index (NPI), and clinical outcome (local recurrence and death caused by breast cancer) (King et al., 2004). That same 47

Review of Literature cohort plus additional tumors were evaluated to observe whether levels of ALCAM correlate with skeletal metastasis. The authors found that low levels of ALCAM transcripts in the primary breast tumor correlate with skeletal metastases and poor prognosis (Davies et al., 2008). Burkhardt et al., (2006) performed immunohistochemical analysis of ALCAM in 162 primary breast carcinomas and correlated the staining pattern with the clinical findings. This study had a mean follow-up period of 53 months. Both intraductal and invasive breast carcinomas had higher ALCAM expression than did normal breast tissue. High cytoplasmic ALCAM expression was associated with shortened patient disease-free survival. Jezierska et al., (2006b) used laser scanning cytometry and confocal microscopy to evaluate 56 breast cancer specimens. The results were correlated with clinical and pathologic data from the cases. High levels of ALCAM correlated with small tumor diameter, low tumor grade, presence of PR, and presence of ER. Lower levels of ALCAM were associated with HER-2/neu gene amplification (but the levels were not statistically significant). Small tumors and those with low tumor grade had higher ALCAM/MMP-2 ratios. In a separate report, the same research group (Jezierska et al., 2006a) showed that ALCAM-ALCAM interactions between breast cancer cells are important for survival in the primary tumor. Loss of ALCAM was associated with programmed cell death, which involves both apoptotic and autophagic mechanisms. Unlike prior studies (King et al., 2004; Jezierska et al., 2006b), Ihnen et al., (2008) did not find significant correlations of ALCAM expression with age, menopausal status, histological tumor type, grading, PR status, clinical stage or nodal involvement neither based on ALCAM protein nor on ALCAM mRNA expression. Yet, similar to the results obtained by Jezierska et al., (2006b), Ihnen et al., (2008) found a correlation of ALCAM protein and ER expression. A stratified subgroup analysis showed positive correlation of high ALCAM mRNA expression with longer overall survival in patients treated with adjuvant 48

Review of Literature chemotherapy. In contrast, patients with high ALCAM mRNA expression who did not receive chemotherapy tended to have a worse prognosis. Similar but weaker correlations were found regarding ALCAM protein expression data. The predictive impact of ALCAM mRNA expression in chemotherapy treated patients was corroborated by multivariate Cox regression analysis also including histopathological markers (Ihnen et al., 2008). Tumor estrogen and progesterone positivity or negativity are important in the treatment of breast cancer. ER-negative/PR-negative breast cancer represents 25% to 30% of breast cancers, has a more aggressive clinical course, and has fewer therapeutic options. Doane et al., (2006) determined that a subset of ER-negative/PR-negative breast cancer has differentially expressed genes, which include ALCAM. Two subsets of ER-negative tumors were found: those designated as ER-negative class A breast cancer (that has expression of a transcriptional program that is associated with ER-positive breast cancer), and ER-negative class B breast cancer (that lacks expression of the transcriptional program that is associated with ER-positive breast cancer). ALCAM was overexpressed at the protein and transcript levels in ER-negative class A breast cancers. The authors proposed that ER-negative class A breast cancers are at least partially androgen regulated. Therefore, the potential for therapeutic strategies that targets the androgen signaling pathway exists in that subgroup of tumors. A study by Milde-Langosch et al., (2008) studied Fos-related antigen-2 (Fra-2), a member of the Fos family of activator protein-1 (AP-1) transcription factors, and found that Fra-2 overexpression is associated with a more aggressive breast cancer phenotype. Overexpression of Fra-2 is associated with decreased expression of ALCAM at the mRNA and protein level. They propose that upregulation of the Fra-2 protein promotes breast cancer metastasis in part by decreasing expression of cell-cell adhesion molecules such as ALCAM.

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Kulasingam et al., (2009); Witzel et al., (2012) indicated presence of ALCAM in the serum of patients with primary breast cancer, and reported that serum ALCAM levels were higher in breast cancer patients than the control group. They showed that serum ALCAM might has potential utility as a diagnostic tool. Kulasingam et al., (2009) found that the combination of ALCAM with CA15-3 improved the diagnostic sensitivity. Witzel et al., (2012) illustrated that serum ALCAM levels do not reflect ALCAM protein or mRNA expression in the corresponding tumor tissue. However, elevated serum levels might indicate more aggressive tumor behavior as they might be an independent factor for a worse prognosis in breast cancer patients (Witzel et al., 2012). Colorectal carcinoma: Weichert et al., (2004) evaluated the expression of ALCAM in colorectal cancer. Using immunohistochemical staining with a semiquantitative scoring system, cytoplasmic and membranous immunoreactivity were analyzed. Of the 111 colorectal carcinomas studied, 58.6% had strong cytoplasmic staining and 30.6% had strong membranous staining (compared with normal epithelium). No correlation was identified with patient age, tumor grade, stage, or nodal status. Membranous ALCAM expression correlated significantly with shortened patient survival. Also of note was that all adenomas (5) of the colon had cytoplasmic expression of ALCAM. The authors proposed that upregulation of ALCAM is an early event in the malignant transformation in colon cancers because it was identified in adenomas, which are considered to be precursor lesions. Bladder cancer: Degen et al., (1998) found strong expression of ALCAM in the T24 bladder carcinoma cell line by Northern blot analysis for mRNA. Tomita et al., (2003) studied the expression of ALCAM in the bladder using immunohistochemistry. Only the umbrella cells in normal bladder epithelium had positive membranous staining for ALCAM. Of 52 bladder carcinomas, 19 50

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(36.5%) were positive for ALCAM, and those areas also showed aberrant expression of α-catenin and/or E-cadherin. ALCAM was expressed in 51.4% of high-stage cancers and 51.1% of grade III tumors. Bladder cancer cells that infiltrate the muscle layer usually expressed ALCAM. A correlation between ALCAM expression in bladder cancer and stage and grade was discovered. ALCAM expression had predictive value in the group of grade III and invasive (>primary tumor stage 1) cancers with positive ALCAM expression being associated strongly with poor prognosis. Esophageal squamous cell carcinoma: Verma et al., (2005) reported expression of ALCAM in human esophageal squamous cell carcinoma. Using immunohistochemistry and semiquantitative reverse-transcription polymerase chain reaction, the investigators found that ALCAM was overexpressed at both protein and mRNA levels. By immunohistochemistry, increased ALCAM expression was observed in 65% of esophageal squamous cell carcinomas and in 68% of dysplasias (compared with normal esophagus). ALCAM mRNA levels were increased in esophageal squamous cell carcinomas and dysplasias. Overexpression of ALCAM in esophageal squamous cell carcinomas was associated with late clinical stage, enhanced tumor invasiveness, and nodal metastasis. The authors proposed that ALCAM may serve as a marker for early diagnosis (because it is increased in dysplasias), tumor invasion, and nodal metastasis.

SUBJECTS AND METHODS

Subjects and Methods

Subjects This study was carried out on forty one Egyptian females with histopathologically proven primary breast cancer, they were admitted to National Cancer Institute, Cairo University, from January 2011 to June 2011, and twenty healthy Egyptian females matched in age and socioeconomic status. They were divided into two groups: Group 1: 20 healthy females were considered as a normal control group (age, mean±standard deviation [SD], 49.950±11.095 years; 12 premenopausal, 8 postmenopausal). Group 2: 41 females breast cancer patients before taking any type of treatment (age, mean±SD, 50.150±10.468 years; 19 premenopausal, 22 postmenopausal). 15 from them were followed up after surgical treatment (9 modified radical mastectomy, 2 simple mastectomy, 4 breast conserving surgery). Exclusion criteria: 1. Subjects that had a history of any serious or chronic diseases. 2. Subjects that had a history of any type of cancer. An informed phrasal consent was obtained from each subject and the study was approved by the Local Committee of Ethics of the Scientific Research of National Cancer Institute. The clinicopathological data of breast cancer patients are shown in table (2).

All eligible control subjects and studied patients were subjected to baseline evaluation of the following: - Full medical history and thorough clinical examinations. - Histopathological examinations for patients. - Serum ALCAM levels (before and after surgical treatment). - Serum CA 15-3 levels (before and after surgical treatment).

Subjects and Methods

- Serum CEA levels (before and after surgical treatment). - Liver functions (aspartate aminotransferase [AST], alanine aminotransferase [ALT]). - Renal functions (urea, creatinine).

Table (2): The clinicopathological characteristics of breast cancer patients. Clinicopathological characteristics Breast cancer patients n (%) Age (years) ≤50 18 (44%) >50 23 (56%) Menopausal status Pre 19 (46%) Post 22 (54%) Histological type Invasive duct carcinoma 31 (75%) Invasive lobular carcinoma 4 (10%) Metaplastic carcinoma 2 (5%) Mixed invasive duct and lobular carcinoma 4 (10%) Tumor size T1 3 (7%) T2 20 (49%) T3 6 (15%) Unknown 12 (29%) Histological grade Grade II 27 (66%) Grade III 6 (15%) Unknown 8 (19%)

Subjects and Methods

Table (2) (continued) Clinicopathological characteristics Breast cancer patients n (%) ER status Positive 23 (56%) Negative 14 (34%) Unknown 4 (10%) PR status Positive 23 (56%) Negative 14 (34%) Unknown 4 (10%) HER-2/neu status Positive 6 (15%) Negative 27 (66%) Unknown 8 (19%) Lymph node status Positive 27 (66%) Negative 10 (24%) Unknown 4 (10%)

Specimen collection Venous blood samples were collected into vacutainer tubes containing clot activator after 12 hours overnight fasting, and were left to clot at room temperature, then were centrifuged at 3000 rpm for 10 minutes to remove serum, which was stored at -20 ˚C until further analysis.

Subjects and Methods

Determination of activated leukocyte cell adhesion molecule Serum ALCAM was determined using enzyme-linked immunosorbent assay (ELISA) technique by using RayBio® human ALCAM ELISA kit (RayBiotech Inc., USA). Principle of the assay: The RayBio® human ALCAM ELISA kit is an in vitro ELISA assay for the quantitative measurement of human ALCAM in serum, plasma, cell culture supernatants and urine. This assay employs an antibody specific for human ALCAM coated on a 96-well plate. Standards and samples are pipetted into the wells and ALCAM present in a sample is bound to the wells by the immobilized antibody. The wells are washed and biotinylated anti-human ALCAM antibody is added. After washing away unbound biotinylated antibody, Horseradish peroxidase (HRP)-conjugated streptavidin is pipetted to the wells. The wells are again washed, a 3,3`,5,5`-tetramethylbenzidine (TMB) substrate solution is added to the wells and color develops in proportion to the amount of ALCAM bound. The stop solution changes the color from blue to yellow, and the intensity of the color is measured at 450 nm. Reagents: 1. ALCAM microplate: 96 wells (12 strips x 8 wells) coated with anti- human ALCAM. 2. Wash buffer concentrate (20x): 25 ml of 20x concentrated solution. 3. Standards: 2 vials of recombinant human ALCAM. 4. Assay diluent A: 30 ml of diluent buffer, 0.09% sodium azide as preservative. For standard/sample dilution. 5. Assay diluent B: 15 ml of 5x concentrated buffer. 6. Detection antibody ALCAM: 2 vial of biotinylated anti-human ALCAM (each vial is enough to assay half microplate). 7. HRP-streptavidin concentrate: 8 μl of 25,000x concentrated HRP- conjugated streptavidin. 55

Subjects and Methods

8. TMB one-step substrate reagent: 12 ml of TMB in buffer solution. 9. Stop solution: 8 ml of 2 M sulfuric acid. Additional materials required: 1. Microplate reader capable of measuring absorbance at 450 nm. 2. Precision pipettes (2 μl to 1 ml volumes). 3. Adjustable 1-25 ml pipettes for reagent preparation. 4. 100 ml and 1 L graduated cylinders. 5. Absorbent paper. 6. Distilled or deionized water. 7. Log-log graph paper or computer and software for ELISA data analysis. 8. Tubes to prepare standard or sample dilutions. Reagents and samples preparation: 1. All reagents and samples were brought to room temperature (18-25°C) before use. 2. Sample dilution: assay diluent A was used for dilution of samples to be assayed to 1:10. 3. Assay diluent B was diluted 5-fold with deionized or distilled water before use. 4. Preparation of standard: briefly the vial was spinned and then 400 μl assay diluent A was added into the vial to prepare a 50 ng/ml standard. The powder was dissolved thoroughly by a gentle mix. 50 μl ALCAM standard (50 ng/ml) was added from the vial, into a tube with 450 μl assay diluent A to prepare a 5,000 pg/ml standard solution. 300 μl assay diluent A was pipetted into each tube. The 5,000 pg/ml standard solution was used to produce a dilution series as shown below in fig. (2). Each tube was mixed thoroughly before the next transfer. Vortexing gently was done to mix. Assay diluent A served as the zero standard (0 pg/ml).

Subjects and Methods

Fig. (2): Preparation of ALCAM standard. 5. If the wash concentrate (20x) contains visible crystals, warming to room temperature and mixing gently was done until dissolved. 20 ml of wash buffer concentrate was diluted into deionized or distilled water to yield 400 ml of 1x wash buffer. 6. Briefly the detection antibody vial was spinned before use. 100 μl of 1x assay diluent B was added into the vial to prepare a detection antibody concentrate. Pipetting up and down was done to mix gently. The detection antibody concentrate was diluted 80-fold with 1x assay diluent B. 7. Briefly the HRP-streptavidin concentrate vial was spinned and was pipetted up and down to mix gently before use. HRP-streptavidin concentrate was diluted 25,000-fold with 1x assay diluent B. For example: Briefly the vial was spinned and was pipetted up and down to mix gently. 2 μl of HRP-streptavidin concentrate was added into a tube with 198 μl 1x assay diluent B to prepare a 100-fold diluted HRP- streptavidin solution. Mixing through was done and then 40 μl of prepared 100-fold diluted solution was pipetted into a tube with 10 ml 1x assay diluent B to prepare a final 25,000 fold diluted HRP-streptavidin solution.

Subjects and Methods

Procedure: 1. 100 μl of each standard or sample was added into appropriate wells. Wells were covered and incubation for 2.5 hours at room temperature was done with gentle shaking. 2. The solution was discarded and washing 4 times with 1x wash solution was done. Washing was done by filling each well with wash buffer (300 μl) using a multi-channel pipette or autowasher. Complete removal of liquid at each step is essential to good performance. After the last wash, any remaining wash buffer was removed by aspirating or decanting. The plate was inverted and it was blotted against clean paper towels. 3. 100 μl of 1x prepared biotinylated antibody was added to each well. Incubation for 1 hour at room temperature was done with gentle shaking. 4. The solution was discarded. The wash was repeated as in step 3. 5. 100 μl of prepared streptavidin solution was added to each well. Incubation for 45 minutes at room temperature was done with gentle shaking. 6. The solution was discarded. The wash was repeated as in step 3. 7. 100 μl of TMB one-step substrate reagent was added to each well. Incubation for 30 minutes at room temperature was done in the dark with gentle shaking. 8. 50 μl of stop solution was added to each well. Reading at 450 nm was done immediately. Calculation of results: The average zero standard optical density was subtracted from absorbance of standards, controls and samples. The standard curve was plotted on log-log graph paper or using sigma plot software, with standard concentration on the X-axis and absorbance on the Y-axis. The best-fit straight line was drawn through the standard points.

Subjects and Methods

Determination of carbohydrate antigen 15-3 Serum CA 15-3 was determined using immunoradiometric assay (IRMA) technique by using MUC-1 gene associated antigen (CA 15-3) IRMA kit (Immunotech, France). Principle of the assay: The MUC-1 gene associated antigen (CA 15-3) assay is a two-step (sandwich type) assay in which two mouse monoclonal antibodies, directed against two different epitopes of the molecule, are employed. Samples or calibrators are incubated in tubes coated with the first monoclonal antibody, the contents of the tubes are then aspirated and the presence of CA 15-3 in the sample is revealed by incubation with a second, 125I-labeled monoclonal antibody. The contents of the tubes are aspirated after this second incubation and unbound labeled antibody is eliminated by washing. The amount of bound reactivity measured in a gamma counter is proportional to the CA 15-3 concentration. Reagents: 1. Anti-CA 15-3 antibody-coated tubes: 2 x 50 tubes. 2. 125I-labeled monoclonal antibody: one 22 ml vial. The vial contains 600 kBq at time of manufacture of 125I-labeled monoclonal antibody with bovine serum albumine, sodium azide (<0.1%) and a dye. 3. Calibrators: four 2 ml vials + one zero 4 ml vial. The calibrator vials contain between 0 and 250 U/ml of human CA 15-3 in buffer containing bovine serum albumin and sodium azide (<0.1%). The exact concentration was indicated on each vial label. 4. Control serum: 1 ml vial. The vial contains human CA 15-3 in human serum with sodium azide (<0.1%). The expected values are in the concentration range indicated on the vial label. 5. Diluent: one 50 ml vial. The vial contains bovine serum albumin in buffer. 59

Subjects and Methods

Additional materials required: In addition to standard laboratory equipment, the following items were required: 1. Gamma counter. 2. Precision micropipettes (10 μl, 200 μl, 500 μl). 3. Repeating micropipettes (200 μl, 2 ml). 4. Vortex mixer. 5. Horizontal or orbital shaker. 6. Aspiration system. Reagents and samples preparation: 1. All reagents and samples were brought to room temperature (18-25°C) before use. 2. Dilution of samples and controls: samples and controls were diluted to be assayed to 1:51, by adding 10 μl of sample or control into plastic or glass tubes and then to each tube 500 μl of diluent was added. Shaking gently was done before the assay. Procedure: Immunological step 1: 1. 200 μl of each calibrator, control or sample was added to coated tubes. 2. Incubation for 2 hours at 18-25°C was done with shaking (400 rpm). Immunological step 2: 3. The content of each tube was aspirated, then washing twice with 2 ml of distilled water was done. Carefull aspiration was done. 4. 200 μl of tracer was added in each tube (200 μl of tracer was added in 2 additionnal tubes to obtain total counts per minute [CPM]). 5. Incubation for 1 hour at 18-25°C was done with shaking (400 rpm).

Subjects and Methods

Counting: 6. The content of each tube was aspirated, then washing twice with 2 ml of distilled water was done (except the 2 total CPM). Carefull aspiration was done. 7. Bound CPM (B) and total CPM (T) were counted for 1 minute. Calculation of results: Results were obtained from the standard curve by interpolation. The curve served for the determination of CA 15-3 concentrations in samples measured at the same time as the calibrator. Standard curve: The results were calculated using a semi-log curve fit (spline mode) with B/T% or B/Bmax% on the vertical axis and the CA 15-3 concentration of the calibrators (U/ml) on the horizontal axis. Samples: The B/T% or the B/Bmax% for each sample was located on the vertical axis and corresponding CA 15-3 concentration was read off on the horizontal axis. The concentrations obtained were in U/ml. The concentration indicated on each calibrator vial label already taked into account the 1:51 predilution. Determination of carcinoembryonic antigen

Serum CEA was determined using IRMA technique by using IRMA- coat® CEA kit (DiaSorin Inc., USA). Principle of the assay: Two-site IRMA assay (sandwich principle) using two highly specific monoclonal antibodies for coating of the solid phase (coated tubes) and the tracer. The tracer antibody and the coated antibody react simultaneously with the CEA present in patient samples or standards. Excess tracer is removed by a washing step and the radioactivity bound to the tube wall is measured in a gamma scintillation counter. 61

Subjects and Methods

Reagents: 1. Test tubes, coated with anti-CEA, monoclonal (mouse): 2 x 50. 2. 125I-anti-CEA, monoclonal (mouse): red, 11 ml, radioactive content < 705/19 kBq/μCi. 3. 6 standards A-F in buffer: 1.0 ml, the exact concentration was indicated on each vial label. 4. Diluent (0 ng/ml) in buffer: 11 ml. 5. Control serum, A and B, human, lyophilic: 2 x 1 ml. Additional materials required: 1. γ-scintillation counter. 2. Micropipettes (100 μl) with disposable plastic tips. 3. Vortex mixer. 4. Horizontal shaker. 5. Manual or automatic washer with aspiration device. 6. Alternatively an appropriate automated analyser system, if available. 7. 0.9% NaCl solution for washing steps. 8. Purified water. Reagents and samples preparation: 1. All reagents and samples were brought to room temperature (18-25°C) before use and were mixed thoroughly (foam formation was avoided). 2. Controls were opened carefully and were reconstituted with 1 ml of purified water. Heavy shaking was avoided when dissolving (foaming). Lyophilised material adherent to the screw cap was also dissolved. Procedure: 1. 100 μl of each standard, control or sample was pipetted onto the bottom of the corresponding coated tube. 2. 100 μl of 125I-anti-CEA was added, mixing gently was done. 3. Incubation for 4 hours (±5 minutes) at room temperature (18-25°C) was done on a horizontal shaker. 62

Subjects and Methods

4. The liquid was aspirated. 5. All tubes were washed 3 times with 2 ml of 0.9% NaCl solution. 6. Radioactivity (CPM) was measured in all tubes (at least 1 minute). Calculation of results: The standard curve was established manually as follows: 1. CPM of each standard (B) was divided by CPM of the highest standard (Bmax) and was multiplied by 100 in order to obtain the percentage of relative binding (B/Bmax%) for each standard. 2. On semi-log paper, the relative binding of each standard (B/Bmax%) was plotted on the Y-axis versus the corresponding concentrations (ng/ml) on the X-axis. 3. Sample concentrations (ng/ml) were read directly off the standard curve by their corresponding relative binding (B/Bmax%). Determination of aspartate aminotransferase Serum AST was determined using AST-colorimetric method according to Sherwin, 1984. Principle: The reaction involved in the assay system is as follows: The amino group is enzymatically transferred by AST present in the sample from L-aspartate to the carbon atom of 2-oxoglutarate yielding oxaloacetate and L-glutamate. L-Aspartate AST Oxaloacetat + + 2-Oxoglutarate L-Glutamate AST activity is measured by monitoring the concentration of oxaloacetate hydrazone formed with 2,4-dinitrophenylhydrazine. Procedure: The assay procedure was performed as described in diagnostic kit purchased from Egyptian Company for Biotechnology (S.A.E), Egypt.

Subjects and Methods

Determination of alanine aminotransferase Serum ALT was determined using ALT-colorimetric method according to Sherwin, 1984. Principle: The reaction involved in the assay system is as follows: The amino group is enzymatically transferred by ALT present in the sample from L-alanine to the carbon atom of 2-oxoglutarate yielding pyruvate and L- glutamate. L-Alanine ALT Pyruvate + + 2-Oxoglutarate L-Glutamate ALT activity is measured by monitoring the concentration of pyruvate hydrazone formed with 2,4-dinitrophenylhydrazine. Procedure: The assay procedure was performed as described in diagnostic kit purchased from Egyptian Company for Biotechnology (S.A.E), Egypt. Determination of urea Serum urea was determined using urease-colorimetric method (modified urease-berthlot method) according to Tietz, 1990. Principle: The reaction involved in the assay system is as follows:

Urea is hydrolyzed in the presence of H2O and urease to produce NH3 and CO2. Urease Urea + H2O 2NH3 + CO2

The free NH3 in an alkaline pH and in the presence of indicator forms coloured complex proportional to the urea concentration in the specimen. Procedure: The assay procedure was performed as described in diagnostic kit purchased from Egyptian Company for Biotechnology (S.A.E), Egypt.

Subjects and Methods

Determination of creatinine Serum creatinine was determined using buffered kinetic Jaffé reaction without deproteinization method according to Tietz, 1986. Principle: Creatinine reacts with picric acid under alkaline condition to form a yellow-red complex. The absorbance of the color produced, measured at a wavelength 492 nm, is directly proportional to creatinine concentration in the sample. Alkaline pH Creatinine + Picrate Yellow-red complex

Procedure: The assay procedure was performed as described in diagnostic kit purchased from Egyptian Company for Biotechnology (S.A.E), Egypt. Statistical analysis Data were presented as mean±SD. Independent-samples t-test was used to compare variables between breast cancer patients and healthy controls, and to examine the association between serum levels of ALCAM, CA 15-3 and CEA with various patients and tumor characteristics. Spearman’s rank correlation coefficient was used to assess the correlations among biomarkers. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic usefulness of the markers. For each ROC curve, the area under the curve (AUC) was calculated. The ROC curve analysis was ﬁrst conducted on individual markers and then in combination, to explore the potential that a marker panel can lead to improved performance. Paired-samples t-test was used to compare serum levels of biomarkers in breast cancer patients before and after surgical treatment. Probability (P)<0.05 was considered to be statistically significant. Statistical analysis was performed using statistical package for the

Subjects and Methods social sciences (SPSS) version 15 software, while the presentations were performed using microsoft excel 2007.

RESULTS

Results

In the present study serum levels of ALCAM, CA 15-3, CEA, liver functions (AST, ALT) and renal functions (urea, creatinine) were evaluated in breast cancer patients. The results of patients were compared with those of healthy controls. The diagnostic value of serum levels of ALCAM, CA 15-3 and CEA was evaluated. The association between serum levels of ALCAM, CA 15- 3 and CEA with various clinicopathologic parameters (age, menopausal status, tumor size, histological grade, ER status, PR status, HER-2/neu status, lymph node status) was examined. Serum levels of ALCAM, CA 15-3 and CEA were also evaluated and compared in patient group before and after surgical treatment. Table (3) illustrates serum levels of ALCAM, CA 15-3 and CEA in breast cancer patients as compared with those of healthy controls. Serum ALCAM levels were significantly higher in breast cancer patients (P=0.002) than healthy controls. There were a significantly higher serum CA 15-3 levels in breast cancer patients as compared with those of healthy controls (P= 0.043), but the difference in serum CEA levels did not reach statistical significance (Figs. 3- 5). Table (4) illustrates serum levels of liver functions (AST, ALT) and renal functions (urea, creatinine) in breast cancer patients as compared with those of healthy controls. There were no significant differences between breast cancer patients and healthy controls with respect to serum levels of AST, ALT, urea and creatinine. Table (5) illustrates the correlation between serum levels of ALCAM, CA 15-3 and CEA in healthy controls and breast cancer patients. No statistical correlation was shown between serum levels of ALCAM, CA 15-3 and CEA in the examined groups.

Results

Table (3): Serum levels of ALCAM, CA 15-3 and CEA in healthy controls and breast cancer patients. Healthy controls Breast cancer patients P Mean±SD Mean±SD n=20 n=41 ALCAM (µg/L) 86.412±9.813 97.000±10.646 0.002 CA 15-3 (U/ml) 23.274±12.675 33.835±19.212 0.043 CEA (µg/L) 1.369±0.797 1.755±1.438 0. 287 When P value < 0.05, it is statistically significant.

Fig. (3): Serum ALCAM levels (mean) in healthy controls and breast cancer patients.

Results

Fig. (4): Serum CA 15-3 levels (mean) in healthy controls and breast cancer patients.

Fig. (5): Serum CEA levels (mean) in healthy controls and breast cancer patients.

Results

Table (4): Serum levels of liver functions (AST, ALT) and renal functions (urea, creatinine) in healthy controls and breast cancer patients. Healthy controls Breast cancer patients P Mean±SD Mean±SD n=20 n=41 AST (IU/L) 32.995±13.123 26.867±14.938 0.224 ALT (IU/L) 15.246±4.691 19.911±11.962 0.082 Urea (mg/dl) 29.438±5.965 27.656±12.234 0.502 Creatinine (mg/dl) 0.678±0.186 0.650±0.183 0.607 When P value < 0.05, it is statistically significant.

Table (5): Correlation between serum levels of ALCAM, CA 15-3 and CEA in healthy controls and breast cancer patients. Healthy controls Breast cancer patients ALCAM CA 15-3 CEA ALCAM CA 15-3 CEA ALCAM 1 -0.198 0.243 1 0.260 0.212 P 0.447 0.347 0.232 0.333 CA 15-3 1 0.414 1 -0.183 P 0.098 0.404 CEA 1 1 When P value < 0.05, it is statistically significant.

Results

Figs. (6-12) illustrate the ROC curves of serum levels of ALCAM, CA 15-3 and CEA, and various combinations between them. Results of the AUCs of serum levels of ALCAM, CA 15-3 and CEA, and various combinations between them are given in table (6). Serum ALCAM levels had significant AUC (P=0.002), but serum levels of CA 15-3 and CEA had non-significant AUCs. Combining serum levels of ALCAM and CA 15-3, serum levels of ALCAM and CEA, and serum levels of ALCAM, CA 15-3 and CEA had significant AUCs (P=0.005, P=0.003, P=0.004 respectively), but combining serum levels of CA 15-3 and CEA had non-significant AUC. Table (7) illustrates sensitivities of serum levels of ALCAM, CA 15-3 and CEA, and various combinations between them at fixed values of 90%, 80% and 70% specificities. At specificity of 70%, serum ALCAM levels yielded a sensitivity of 76.5%, compared with 58.8% for serum CA15-3 levels, and 29.4% for serum CEA levels. At specificity of 80%, serum ALCAM levels yielded a sensitivity of 64.7%, compared with 47.1% for serum CA15-3 levels, and 17.6% for serum CEA levels. Likewise, at 90% specificity, serum ALCAM levels displayed higher sensitivity than serum levels of CA15-3 and CEA. Various combinations between them did not yield any improvement in the sensitivity compared with serum ALCAM levels.

Results

1.0

0.8

0.6 Sensitivity 0.4

0.2

0.0 0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity Fig. (6): ROC curve of serum ALCAM levels.

Results

1.0

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0.0 0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity Fig. (7): ROC curve of serum CA 15-3 levels.

Results

1.0

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0.0 0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity Fig. (8): ROC curve of serum CEA levels.

Results

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0.8

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0.0 0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity Fig. (9): ROC curve of combining serum levels of ALCAM and CA 15-3.

Results

1.0

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0.6 Sensitivity 0.4

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0.0 0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity Fig. (10): ROC curve of combining serum levels of ALCAM and CEA.

Results

1.0

0.8

0.6 Sensitivity 0.4

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0.0 0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity Fig. (11): ROC curve of combining serum levels of ALCAM, CA 15-3 and CEA.

Results

1.0

0.8

0.6 Sensitivity 0.4

0.2

0.0 0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity Fig. (12): ROC curve of combining serum levels of CA 15-3 and CEA.

Results

Table (6): AUCs of serum levels of ALCAM, CA 15-3 and CEA, and various combinations between them. AUC P 95% Cl ALCAM 0.785 0.002 0.642-0.929 CA 15-3 0.670 0.069 0.500-0.840 CEA 0.552 0.575 0.371-0.734 Combining ALCAM and CA 15-3 0.762 0.005 0.614-0.910 Combining ALCAM and CEA 0.775 0.003 0.629-0.920 Combining ALCAM, CA 15-3 and CEA 0.767 0.004 0.621-0.914 Combining CA 15-3 and CEA 0.673 0.065 0.504-0.841 When P value < 0.05, it is statistically significant.

Table (7): Sensitivities of serum levels of ALCAM, CA 15-3 and CEA, and various combinations between them at fixed values of 90%, 80% and 70% specificities. Sensitivity 90% 80% 70% Specificity Specificity Specificity ALCAM 0.412 0.647 0.765 CA 15-3 0.235 0.471 0.588 CEA 0.176 0.176 0.294 Combining ALCAM and CA 15-3 0.176 0.588 0.647 Combining ALCAM and CEA 0.412 0.588 0.765 Combining ALCAM, CA 15-3 and 0.176 0.588 0.706 CEA Combining CA 15-3 and CEA 0.235 0.471 0.588

Results

Table (8) illustrates the association between serum levels of ALCAM, CA 15-3 and CEA with various patients and tumor characteristics such as age, menopausal status, tumor size, histological grade, ER status, PR status, HER- 2/neu status and lymph node status in breast cancer patients. No statistical association was shown between serum levels of ALCAM, CA 15-3 and CEA with various clinicopathologic parameters in breast cancer patients except that, there was a significant association between serum levels of ALCAM and CEA with age and menopausal status. Breast cancer patients with age >50 years displayed significantly higher serum levels of ALCAM and CEA than breast cancer patients with age ≤50 years (P=0.001, P=0.016 respectively). Also, postmenopausal breast cancer patients displayed significantly higher serum levels of ALCAM and CEA than premenopausal breast cancer patients (P=0.002, P=0.015 respectively). Table (9) illustrates serum levels of ALCAM, CA 15-3 and CEA in breast cancer patients before and at one month after surgical treatment. Non- significant difference was shown in any of them before and after surgical treatment (Figs. 13-15).

Results

Table (8): Association between serum levels of ALCAM, CA 15-3 and CEA with various clinicopathologic parameters in breast cancer patients. n ALCAM (µg/L) CA 15-3 (U/ml) CEA (µg/L) Mean±SD Mean±SD Mean±SD Age (years) ≤50 18 88.444±18.966 31.628±16.815 1.143±0.667 >50 23 106.521±12.992 35.465±22.376 2.124±1.704 P 0.001 0.563 0.016 Menopausal status Pre 19 88.082±17.203 28.775±16.230 1.134±0.682 Post 22 105.244±16.270 36.347±20.940 2.131±1.681 P 0.002 0.225 0.015 Tumor size T1+T2 23 97.882±10.832 34.565±17.432 1.357±0.802 T3 6 98.750±11.405 33.815±24.676 2.312±2.351 P 0.896 0.951 0.111 Histological grade Grade II 27 98.028±11.102 32. 699±18.200 1.615±1.361 Grade III 6 95.500±10.424 31. 248±22.785 0.983±0.741 P 0.684 0.889 0.139 ER status Positive 23 98.000±11.176 28.896±15.220 1.416±0.903 Negative 14 96.313±8.540 34.550±21.793 1.434±1.668 P 0.686 0.414 0.971 PR status Positive 23 97.433±10.363 30.454±17.301 1.364±0.842 Negative 14 97.444±10.581 32.435±19.965 1.515±1.715 P 0.998 0.768 0.763

Results

Table (8) (continued) n ALCAM (µg/L) CA 15-3 (U/ml) CEA (µg/L) Mean±SD Mean±SD Mean±SD HER-2/neu status Positive 6 95.625±9.970 35.865±21.698 1.210±0.620 Negative 27 97.417±10.240 31.193±19.194 1.567±1.400 P 0.761 0.673 0.351 Lymph node status Positive 27 97.294±10.044 32.800±18.451 1.440±0.851 Negative 10 98.071±10.987 27.753±16.970 1.444±1.977 P 0.875 0.451 0.995 When P value < 0.05, it is statistically significant.

Table (9): Serum levels of ALCAM, CA 15-3 and CEA in breast cancer patients before and at one month after surgical treatment. Breast cancer patients Breast cancer patients P before surgical treatment after surgical treatment Mean±SD Mean±SD n=15 n=15 ALCAM (µg/L) 92.611±13.725 96.167±20.176 0.529 CA 15-3 (U/ml) 33.730±18.212 31.304±14.341 0.382 CEA (µg/L) 1.240±0.870 1.120±0.786 0.258 When P value < 0.05, it is statistically significant.

Results

Fig. (13): Serum ALCAM levels (mean) in breast cancer patients before and at one month after surgical treatment.

Fig. (14): Serum CA 15-3 levels (mean) in breast cancer patients before and at one month after surgical treatment.

Results

Fig. (15): Serum CEA levels (mean) in breast cancer patients before and at one month after surgical treatment.

DISCUSSION

Discussion

Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in females worldwide, accounting for 23% of the total new cancer cases and 14% of the total cancer deaths in 2008 (Jemal et al., 2011). It is a heterogeneous disease with a wide range of histological, clinical and molecular presentations. Unfortunately, other than definitive diagnosis by biopsy and histopathology, no diagnostic or screening test is presently suitable for early detection of breast cancer (Harris et al., 2007). The ability to detect human malignancy via a simple blood test has long been a major objective in medical screening. CA15-3 and CEA, discovered more than 2 and 4 decades ago, respectively, are the most commonly used tumor markers for breast cancer (Gold and Freedman, 1965; Hilkens et al., 1984; Kufe et al., 1984). CA15-3 and CEA levels in serum are recommended for monitoring therapy of advanced breast cancer (Harris et al., 2007). However, these cancer biomarkers have proven to be ineffective in detecting the early stages of the disease because of low diagnostic sensitivity and specificity (Fleisher et al., 2002; Lumachi and Basso, 2004; Khatcheressian et al., 2006). This study shows that serum ALCAM levels were significantly higher in breast cancer patients than healthy controls. This result is in agreement with previous studies which demonstrated that serum ALCAM levels were significantly elevated in breast cancer patients when compared with healthy controls (Kulasingam et al., 2009; Witzel et al., 2012). In this study, also there were a significantly higher serum CA 15-3 levels in breast cancer patients as compared with those of healthy controls, but the difference in serum CEA levels did not reach statistical significance. According to serum CA 15-3 levels this finding is in agreement with Kulasingam et al., (2009) who reported that serum CA 15-3 levels were significantly elevated in breast cancer patients when compared with healthy controls. However, 85

Discussion according to serum CEA levels the result is inconsistent with Kulasingam et al., (2009) who found that serum CEA levels were significantly elevated in breast cancer patients when compared with healthy controls. Elevated serum ALCAM levels may be due to shedding of the protein into the serum by a disintegrin and metalloproteinase (ADAM)-17, also known as TNF-α-converting enzyme (TACE). ADAM-17 is one of the most widely investigated ADAMs and one of the most important sheddases identified to date (Duffy et al., 2003; Blobel 2005). Based on a proteomic approach, Bech-Serra et al., (2006) showed that ALCAM is an ADAM-17 substrate. Rosso et al., (2007); Miccichè et al., (2011) indicated that surface ALCAM can be actively cleaved by ADAM-17-mediated proteolysis in epithelial ovarian cancer cells and thyroid cancer. Lendeckel et al., (2005) reported higher levels of ADAM-17 mRNA in 24 breast cancers compared with corresponding normal breast tissue. Also, McGowan et al., (2007); Narita et al., (2012) observed that, at both mRNA and protein levels, ADAM-17 expression was up-regulated in breast cancer compared with normal breast tissue. The proportion of active form to total ADAM-17 increased progressively from normal breast tissue to primary breast cancer to lymph node metastases (McGowan et al., 2007). In breast cancer (Jezierska et al., 2006a; Davies et al., 2008; King et al., 2010) and ovarian cancer (Mezzanzanica et al., 2008), ALCAM cytoplasmic overexpression and low membrane expression were associated with disease progression. The clinical relationship of membrane ALCAM loss with progression may relate to the process of ALCAM shedding by ADAM-17 (Miccichè et al., 2011). Witzel et al., (2012) illustrated that, his finding that elevated serum ALCAM levels were not significantly correlated with high ALCAM expression in tumor tissue was not contradictory, where he suggested that ALCAM serum

Discussion levels may be a sign of receptor activation and active shedding of the protein into the serum. This study found no significant differences between breast cancer patients and healthy controls with respect to serum levels of AST, ALT, urea and creatinine. Similar results have also been reported by Mohamad et al., (2010) who found no significant differences in breast cancer patients before taking any type of treatment as compared with those of healthy controls with respect to serum levels of AST, ALT, urea and creatinine. No statistical correlation was shown between serum levels of ALCAM, CA 15-3 and CEA in healthy controls and breast cancer patients. However, Kulasingam et al., (2009) observed that CEA appeared to be weakly correlated with ALCAM in both cases and controls, whereas CA15-3 was weakly correlated with ALCAM among cases only. By studing ROC curves of serum levels of ALCAM, CA 15-3 and CEA, and various combinations between them, this study shows that serum ALCAM levels had significant AUC, but serum levels of CA 15-3 and CEA had non- significant AUCs, and various combinations between them did not result in any improvement in the AUC compared with serum ALCAM levels. These results are in agreement with Kulasingam et al., (2009) who demonstrated that serum ALCAM levels had significant AUC. However, Kulasingam et al., (2009) also demonstrated that serum levels of CA 15-3 and CEA had significant AUCs, but ALCAM had the best performance. Also, Kulasingam et al., (2009) illustrated that combining CA15-3 and ALCAM yielded a ROC curve with higher AUC than ALCAM, and combining CA15-3, ALCAM and CEA did not result in any improvement in ROC curves compared with CA15-3 and ALCAM. At specificity of 90%, 80% and 70%, serum ALCAM levels displayed higher sensitivity than serum levels of CA15-3 and CEA. Various combinations between them did not yield any improvement in the sensitivity compared with serum ALCAM levels. These findings are consistent with Kulasingam et al., 87

Discussion

(2009) who showed that at 90% and 80% specificity, ALCAM displayed higher sensitivity than CA15-3 and CEA. However, Kulasingam et al., (2009) also showed that combining CA15-3 and ALCAM yielded a higher sensitivity than ALCAM. In this study, no statistical association was shown between serum levels of ALCAM, CA 15-3 and CEA with various clinicopathologic parameters in breast cancer patients except that, there was a significant association between serum levels of ALCAM and CEA with age and menopausal status. Breast cancer patients with age >50 years displayed significantly higher serum levels of ALCAM and CEA than breast cancer patients with age ≤50 years. Also, postmenopausal breast cancer patients displayed significantly higher serum levels of ALCAM and CEA than premenopausal breast cancer patients. These results are in concordance with previous studies which observed that no statistical association was shown between serum levels of ALCAM (Kulasingam et al., 2009; Witzel et al., 2012), CA 15-3 and CEA with various clinicopathologic parameters in breast cancer patients (Kulasingam et al., 2009), except that, a significant association was obtained for serum levels of ALCAM and CEA with age and menopausal status (Kulasingam et al., 2009). But Kulasingam et al., (2009) also, found that levels of ALCAM were not significantly associated with stage whereas CEA and CA15-3 were significant.

Although a statistically significant P-value was not obtained for an association between ALCAM values and tumor grade, a general trend was observed with elevated ALCAM levels corresponding to increased tumor grade (Kulasingam et al., 2009). Witzel et al., (2012) illustrated that high serum ALCAM levels were significantly associated with shorter disease-free survival. When comparing serum levels of ALCAM, CA 15-3 and CEA in breast cancer patients before and at one month after surgical treatment, non-significant difference was shown in any of them before and after surgical treatment.

Discussion

In conclusion, this study shows that breast cancer patients have higher serum ALCAM levels than healthy controls, and that ALCAM has better diagnostic value than the classical breast cancer biomarkers, CA 15-3 and CEA. The present data provides evidence that serum ALCAM may represent a novel biomarker for breast cancer patients, which may have potential utility as a diagnostic tool. Further studies with larger number of subjects as well as examining serum ALCAM levels in larger number of samples obtained from patients before and after surgical treatment are needed. Further validation studies that integrate serum ALCAM levels with mammography may reveal potential clinical utility of serum ALCAM for breast cancer. Also, further studies are needed to establish the other clinical usefulness of this biomarker such as predicting response to therapy, surveillance after primary treatment, and monitoring response to therapy for breast cancer.

SUMMARY

Sammary

Summary

Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in females worldwide, accounting for 23% of the total new cancer cases and 14% of the total cancer deaths in 2008. It is a heterogeneous disease with a wide range of histological, clinical and molecular presentations. Unfortunately, other than definitive diagnosis by biopsy and histopathology, no diagnostic or screening test is presently suitable for early detection of breast cancer. The ability to detect human malignancy via a simple blood test has long been a major objective in medical screening. CA15-3 and CEA, discovered more than 2 and 4 decades ago, respectively, are the most commonly used tumor markers for breast cancer. CA15-3 and CEA levels in serum are recommended for monitoring therapy of advanced breast cancer. However, these cancer biomarkers have proven to be ineffective in detecting the early stages of the disease because of low diagnostic sensitivity and specificity. This study was carried out on forty one Egyptian females with histopathologically proven primary breast cancer, they were admitted to National Cancer Institute, Cairo University, from January 2011 to June 2011, and twenty healthy Egyptian females matched in age and socioeconomic status. They were divided into two groups: Group 1: 20 healthy females were considered as a normal control group (age, mean±SD, 49.950±11.095 years; 12 premenopausal, 8 postmenopausal). Group 2: 41 females breast cancer patients before taking any type of treatment (age, mean±SD, 50.150±10.468 years; 19 premenopausal, 22 postmenopausal). 15 from them were followed up after surgical treatment (9 modified radical mastectomy, 2 simple mastectomy, 4 breast conserving surgery).

Sammary

Exclusion criteria: 1. Subjects that had a history of any serious or chronic diseases. 2. Subjects that had a history of any type of cancer. All eligible control subjects and studied patients were subjected to baseline evaluation of the following: - Full medical history and thorough clinical examinations. - histopathological examinations for patients. - Serum ALCAM levels (before and after surgical treatment). - Serum CA 15-3 levels (before and after surgical treatment). - Serum CEA levels (before and after surgical treatment). - Liver functions (AST, ALT). - Renal functions (urea, creatinine). Serum ALCAM levels were significantly higher in breast cancer patients (P=0.002) than healthy controls. There were a significantly higher serum CA 15- 3 levels in breast cancer patients as compared with those of healthy controls (P= 0.043), but the difference in serum CEA levels did not reach statistical significance. There were no significant differences between breast cancer patients and healthy controls with respect to serum levels of AST, ALT, urea and creatinine. No statistical correlation was shown between serum levels of ALCAM, CA 15-3 and CEA in healthy controls and breast cancer patients. Serum ALCAM levels had significant AUC (P=0.002), but serum levels of CA 15-3 and CEA had non-significant AUCs. Combining serum levels of ALCAM and CA 15-3, serum levels of ALCAM and CEA, and serum levels of ALCAM, CA 15-3 and CEA had significant AUCs (P=0.005, P=0.003, P=0.004 respectively), but combining serum levels of CA 15-3 and CEA had non- significant AUC. At specificity of 70%, serum ALCAM levels yielded a sensitivity of 76.5%, compared with 58.8% for serum CA15-3 levels, and 29.4% for serum 91

Sammary

CEA levels. At specificity of 80%, serum ALCAM levels yielded a sensitivity of 64.7%, compared with 47.1% for serum CA15-3 levels, and 17.6% for serum CEA levels. Likewise, at 90% specificity, serum ALCAM levels displayed higher sensitivity than serum levels of CA15-3 and CEA. Various combinations between them did not yield any improvement in the sensitivity compared with serum ALCAM levels. No statistical association was shown between serum levels of ALCAM, CA 15-3 and CEA with various clinicopathologic parameters in breast cancer patients except that, there was a significant association between serum levels of ALCAM and CEA with age and menopausal status. Breast cancer patients with age >50 years displayed significantly higher serum levels of ALCAM and CEA than breast cancer patients with age ≤50 years (P=0.001, P=0.016 respectively). Also, postmenopausal breast cancer patients displayed significantly higher serum levels of ALCAM and CEA than premenopausal breast cancer patients (P=0.002, P=0.015 respectively). Non-significant difference was shown in serum levels of ALCAM, CA 15-3 and CEA in breast cancer patients before and at one month after surgical treatment. In conclusion, this study shows that breast cancer patients have higher serum ALCAM levels than healthy controls, and that ALCAM has better diagnostic value than the classical breast cancer biomarkers, CA 15-3 and CEA. The present data provides evidence that serum ALCAM may represent a novel biomarker for breast cancer patients, which may have potential utility as a diagnostic tool. Further studies with larger number of subjects as well as examining serum ALCAM levels in larger number of samples obtained from patients before and after surgical treatment are needed. Further validation studies that integrate serum ALCAM levels with mammography may reveal potential clinical utility of serum ALCAM for breast cancer. Also, further studies are 92

Sammary needed to establish the other clinical usefulness of this biomarker such as predicting response to therapy, surveillance after primary treatment, and monitoring response to therapy for breast cancer.

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ARABIC SUMMARY

اﻟﻤﻠﺨﺺ اﻟﻌﺮﺑﻲ

ALCAM وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻋﻦ ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي اﻟﻼﺗﻲ ﺳﻨﮭﻦ ≤٥٠ ﺳﻨﺔ (P=0.001، P=0.016 ﻋﻠﻰ اﻟﺘﺮﺗﯿﺐ). أﯾﻀﺎ، ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي اﻟﻼﺗﻲ ﻓﻲ ﻣﺮﺣﻠﺔ ﻣﺎ ﺑﻌﺪ ﺳﻦ اﻟﯿﺄس أﻇﮭﺮن ﻣﺴﺘﻮﯾﺎت ﻣﺮﺗﻔﻌﺔ إرﺗﻔﺎﻋﺎ ذو دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ ﻣﻦ ALCAM وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻋﻦ ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي اﻟﻼﺗﻲ ﻓﻲ ﻣﺮﺣﻠﺔ ﻣﺎ ﻗﺒﻞ ﺳﻦ اﻟﯿﺄس (P=0.015 ،P=0.002 ﻋﻠﻰ اﻟﺘﺮﺗﯿﺐ). أﻇﮭﺮت اﻟﺪراﺳﺔ إﺧﺘﻼﻓﺎ ﻟﯿﺲ ﻟﮫ دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ ﻓﻲ ﻣﺴﺘﻮﯾﺎت CA 15-3 ،ALCAM وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻓﻲ ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي ﻗﺒﻞ وﻋﻨﺪ ﺷﮭﺮ ﺑﻌﺪ اﻟﻌﻼج اﻟﺠﺮاﺣﻲ. ﯾﺴﺘﻨﺘﺞ ﻣﻦ ھﺬه اﻟﺪراﺳﺔ أن ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي ﻟﮭﻦ ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم أﻋﻠﻰ ﻣﻦ اﻟﺴﯿﺪات اﻷﺻﺤﺎء، وأن ALCAM ﻟﮫ ﻗﯿﻤﺔ ﺗﺸﺨﯿﺼﯿﺔ أﻓﻀﻞ ﻣﻦ دﻻﻟﺘﺎ ﺳﺮﻃﺎن اﻟﺜﺪي اﻟﺤﯿﻮﯾﺘﺎن اﻟﻘﺪﯾﻤﺘﺎن، CA 15-3 وCEA. ﺗﻘﺪم اﻟﺒﯿﺎﻧﺎت اﻟﺤﺎﻟﯿﺔ إﺛﺒﺎﺗﺎ أن ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم رﺑﻤﺎ ﯾﻤﺜﻞ دﻻﻟﺔ ﺣﯿﻮﯾﺔ ﺟﺪﯾﺪة ﻟﻤﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي، اﻟﺘﻲ رﺑﻤﺎ ﯾﻜﻮن ﻟﮭﺎ إﺳﺘﺨﺪام ﻣﺤﺘﻤﻞ ﻛﺄداة ﺗﺸﺨﯿﺼﯿﺔ. ﻧﺤﺘﺎج دراﺳﺎت أﺧﺮى ﺑﻌﺪد أﻛﺒﺮ ﻣﻦ اﻟﺴﯿﺪات ﺑﺎﻹﺿﺎﻓﺔ إﻟﻰ إﺧﺘﺒﺎر ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻓﻲ ﻋﺪد أﻛﺒﺮ ﻣﻦ اﻟﻌﯿﻨﺎت اﻟﺘﻲ ﯾﺘﻢ اﻟﺤﺼﻮل ﻋﻠﯿﮭﺎ ﻣﻦ اﻟﻤﺮﯾﻀﺎت ﻗﺒﻞ وﺑﻌﺪ اﻟﻌﻼج اﻟﺠﺮاﺣﻲ. دراﺳﺎت ﺗﺄﯾﯿﺪ أﺧﺮى اﻟﺘﻲ ﺗﻜﺎﻣﻞ ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻣﻊ اﻟﻤﺎﻣﻮﺟﺮاﻓﻲ رﺑﻤﺎ ﺗﻜﺸﻒ إﺳﺘﺨﺪام إﻛﻠﯿﻨﯿﻜﻲ ﻣﺤﺘﻤﻞ ﻠ ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻟﺴﺮﻃﺎن اﻟﺜﺪي. أﯾﻀﺎ، ﻧﺤﺘﺎج دراﺳﺎت أﺧﺮى ﻟﺘﺄﺳﯿﺲ اﻟﻔﻮاﺋﺪ اﻹﻛﻠﯿﻨﯿﻜﯿﺔ اﻷﺧﺮى ﻟﮭﺬه اﻟﺪﻻﻟﺔ اﻟﺤﯿﻮﯾﺔ ﻣﺜﻞ ﺗﻮﻗﻊ اﻹﺳﺘﺠﺎﺑﺔ ﻟﻠﻌﻼج، اﻟﻤﺮاﻗﺒﺔ ﺑﻌﺪ اﻟﻤﻌﺎﻟﺠﺔ اﻷوﻟﯿﺔ، وﻣﺮاﻗﺒﺔ اﻹﺳﺘﺠﺎﺑﺔ ﻟﻠﻌﻼج ﻟﺴﺮﻃﺎن اﻟﺜﺪي.

اﻟﻤﻠﺨﺺ اﻟﻌﺮﺑﻲ

- ﻣﺴﺘﻮﯾﺎت CA 15-3 ﻓﻲ ﻣﺼﻞ اﻟﺪم (ﻗﺒﻞ وﺑﻌﺪ اﻟﻌﻼج اﻟﺠﺮاﺣﻲ). - ﻣﺴﺘﻮﯾﺎت CEA ﻓﻲ ﻣﺼﻞ اﻟﺪم (ﻗﺒﻞ وﺑﻌﺪ اﻟﻌﻼج اﻟﺠﺮاﺣﻲ). - وﻇﺎﺋﻒ اﻟﻜﺒﺪ (ALT ،AST). - وﻇﺎﺋﻒ اﻟﻜﻠﻰ (ﺑﻮﻟﯿﻨﺎ، ﻛﺮﯾﺎﺗﯿﻨﯿﻦ). ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم إرﺗﻔﻌﺖ إرﺗﻔﺎﻋﺎ ذو دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ ﻓﻲ ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي (P=0.002) ﻋﻦ اﻟﺴﯿﺪات اﻷﺻﺤﺎء. وﺟﺪ إرﺗﻔﺎﻋﺎ ذو دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ ﻓﻲ ﻣﺴﺘﻮﯾﺎت CA 15-3 ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻓﻲ ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي ﻣﻘﺎرﻧﺔ ﺑﺎﻟﺴﯿﺪات اﻷﺻﺤﺎء (P=0.043)، وﻟﻜﻦ اﻹﺧﺘﻼف ﻓﻲ ﻣﺴﺘﻮﯾﺎت CEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻟﻢ ﯾﺼﻞ إﻟﻰ دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ. ﻟﻢ ﺗﻮﺟﺪ إﺧﺘﻼﻓﺎت ذات دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ ﺑﯿﻦ ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي واﻟﺴﯿﺪات اﻷﺻﺤﺎء ﻓﯿﻤﺎ ﯾﺘﻌﻠﻖ ﺑﻤﺴﺘﻮﯾﺎت ALT ،AST، اﻟﺒﻮﻟﯿﻨﺎ واﻟﻜﺮﯾﺎﺗﯿﻨﯿﻦ ﻓﻲ ﻣﺼﻞ اﻟﺪم. أﻇﮭﺮت اﻟﺪراﺳﺔ ﻋﻼﻗﺔ ﻏﯿﺮ إﺣﺼﺎﺋﯿﺔ ﺑﯿﻦ ﻣﺴﺘﻮﯾﺎت CA 15-3 ،ALCAM وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻓﻲ اﻟﺴﯿﺪات اﻷﺻﺤﺎء وﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي. ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم وﺟﺪ أن ﻟﮭﺎ AUC ذات دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ (P=0.002)، وﻟﻜﻦ ﻣﺴﺘﻮﯾﺎت CA 15-3 وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم وﺟﺪ أن ﻟﮭﺎ AUCs ﻟﯿﺴﺖ ذوات دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ. ﺟﻤﻊ ﻣﺴﺘﻮﯾﺎت ALCAM وCA 15-3 ﻓﻲ ﻣﺼﻞ اﻟﺪم، ﻣﺴﺘﻮﯾﺎت ALCAM وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم، وﻣﺴﺘﻮﯾﺎت CA 15-3 ،ALCAM وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم وﺟﺪ أن ﻟﮭﺎ AUCs ذوات دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ (P=0.004 ،P=0.003 ،P=0.005 ﻋﻠﻰ اﻟﺘﺮﺗﯿﺐ)، وﻟﻜﻦ ﺟﻤﻊ ﻣﺴﺘﻮﯾﺎت CA 15-3 وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم وﺟﺪ أن ﻟﮭﺎ AUC ﻟﯿﺴﺖ ذات دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ. ﻋﻨﺪ ﺧﺼﻮﺻﯿﺔ ٧٠٪، ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم أﻋﻄﺖ ﺣﺴﺎﺳﯿﺔ ٧٦٫٥٪، ﻣﻘﺎرﻧﺔ ﺒ ٥٨٫٨٪ ﻟﻤﺴﺘﻮﯾﺎت CA 15-3 ﻓﻲ ﻣﺼﻞ اﻟﺪم، و٢٩٫٤٪ ﻟﻤﺴﺘﻮﯾﺎت CEA ﻓﻲ ﻣﺼﻞ اﻟﺪم. ﻋﻨﺪ ﺧﺼﻮﺻﯿﺔ ٨٠٪، ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم أﻋﻄﺖ ﺣﺴﺎﺳﯿﺔ ٦٤٫٧٪، ﻣﻘﺎرﻧﺔ ﺒ ٤٧٫١٪ ﻟﻤﺴﺘﻮﯾﺎت CA 3-15 ﻓﻲ ﻣﺼﻞ اﻟﺪم، و١٧٫٦٪ ﻟﻤﺴﺘﻮﯾﺎت CEA ﻓﻲ ﻣﺼﻞ اﻟﺪم. ﻋﻠﻰ ﻧﻔﺲ اﻟﻨﻤﻂ، ﻋﻨﺪ ﺧﺼﻮﺻﯿﺔ ٩٠٪، ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم أﻇﮭﺮت ﺣﺴﺎﺳﯿﺔ أﻋﻠﻰ ﻣﻦ ﻣﺴﺘﻮﯾﺎت CA 15-3 وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم. اﻟﺠﻤﻮع اﻟﻤﺨﺘﻠﻔﺔ ﺑﯿﻨﮭﻢ ﻟﻢ ﺗﻌﻄﻲ أي ﺗﺤﺴﻦ ﻓﻲ اﻟﺤﺴﺎﺳﯿﺔ ﻣﻘﺎرﻧﺔ ﺑﻤﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم. أﻇﮭﺮت اﻟﺪراﺳﺔ إرﺗﺒﺎﻃﺎ ﻏﯿﺮ إﺣﺼﺎﺋﯿﺎ ﺑﯿﻦ ﻣﺴﺘﻮﯾﺎت CA 15-3 ،ALCAM وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻣﻊ اﻟﻤﻘﺎﯾﯿﺲ اﻟﺒﺎﺛﻮﻟﻮﺟﯿﺔ اﻹﻛﻠﯿﻨﯿﻜﯿﺔ اﻟﻤﺨﺘﻠﻔﺔ ﻓﻲ ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي ﻣﺎﻋﺪا أﻧﮫ، وﺟﺪ إرﺗﺒﺎﻃﺎ ذو دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ ﺑﯿﻦ ﻣﺴﺘﻮﯾﺎت ALCAM وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻣﻊ اﻟﺴﻦ واﻟﺤﺎﻟﺔ ﺑﺎﻟﻨﺴﺒﺔ ﻟﺴﻦ اﻟﯿﺄس. ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي اﻟﻼﺗﻲ ﺳﻨﮭﻦ >٥٠ ﺳﻨﺔ أﻇﮭﺮن ﻣﺴﺘﻮﯾﺎت ﻣﺮﺗﻔﻌﺔ إرﺗﻔﺎﻋﺎ ذو دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ ﻣﻦ ٢

اﻟﻤﻠﺨﺺ اﻟﻌﺮﺑﻲ

ﯾﻌﺪ ﺳﺮﻃﺎن اﻟﺜﺪي أﻛﺜﺮ ﺳﺮﻃﺎن ﺗﺸﺨﯿﺼﺎ واﻟﺴﺒﺐ اﻟﺮﺋﯿﺴﻲ ﻟﻠﻮﻓﺎه ﺑﺴﺒﺐ اﻟﺴﺮﻃﺎن ﻓﻲ اﻟﺴﯿﺪات ﺣﻮل اﻟﻌﺎﻟﻢ، ﻓﮭﻮ ﯾﻤﺜﻞ ٢٣٪ ﻣﻦ ﺣﺎﻻت اﻟﺴﺮﻃﺎن اﻟﺠﺪﯾﺪة اﻟﻜﻠﯿﺔ و١٤٪ ﻣﻦ اﻟﻮﻓﯿﺎت اﻟﻜﻠﯿﺔ ﺑﺴﺒﺐ اﻟﺴﺮﻃﺎن ﻓﻲ ﻋﺎم ٢٠٠٨. ﻓﮭﻮ ﻣﺮض ﻏﯿﺮ ﻣﺘﺠﺎﻧﺲ ﻟﮫ ھﯿﺌﺎت ﻧﺴﯿﺠﯿﺔ وإﻛﻠﯿﻨﯿﻜﯿﺔ وﺟﺰﯾﺌﯿﺔ واﺳﻌﺔ اﻟﻤﺪى. وﻟﺴﻮء اﻟﺤﻆ، ﻻ ﯾﻮﺟﺪ إﺧﺘﺒﺎر ﺗﺸﺨﯿﺼﻲ أو ﻣﺴﺤﻲ ﻣﻨﺎﺳﺐ ﻟﻺﻛﺘﺸﺎف اﻟﻤﺒﻜﺮ ﻟﺴﺮﻃﺎن اﻟﺜﺪي ﺳﻮى اﻟﺘﺸﺨﯿﺺ ﺑﺄﺧﺬ ﻋﯿﻨﺔ ﻣﻦ ﻧﺴﯿﺞ اﻟﻮرم ﻟﻠﻔﺤﺺ واﻟﮭﺴﺘﻮﺑﺎﺛﻮﻟﻮﺟﯿﺎ. إن اﻟﻘﺪرة ﻋﻠﻰ إﻛﺘﺸﺎف اﻟﺴﺮﻃﺎن ﻓﻲ اﻹﻧﺴﺎن ﺑﻮاﺳﻄﺔ إﺧﺘﺒﺎر ﺑﺴﯿﻂ ﻓﻲ اﻟﺪم ھﻮ ﻣﻨﺬ ﻓﺘﺮة ﻃﻮﯾﻠﺔ ھﺪف رﺋﯿﺴﻲ ﻓﻲ اﻟﻔﺤﺺ اﻟﻄﺒﻲ. CA 15-3 وCEA، اﻟﺬﯾﻦ ﺗﻢ إﻛﺘﺸﺎﻓﮭﻤﺎ ﻣﻨﺬ أﻛﺜﺮ ﻣﻦ ﻋﻘﺪﯾﻦ وأرﺑﻌﺔ ﻋﻘﻮد، ﻋﻠﻰ اﻟﺘﺮﺗﯿﺐ، ھﻢ أﻛﺜﺮ دﻻﻻت اﻷورام إﺳﺘﺨﺪاﻣﺎ ﻟﺴﺮﻃﺎن اﻟﺜﺪي. ﻣﺴﺘﻮﯾﺎت CA 15-3 وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﯾﻮﺻﻰ ﺑﮭﺎ ﻟﻤﺘﺎﺑﻌﺔ ﻋﻼج ﺳﺮﻃﺎن اﻟﺜﺪي اﻟﻤﺘﻘﺪم. ﻋﻠﻰ أﯾﺔ ﺣﺎل، دﻻﻟﺘﺎ اﻟﺴﺮﻃﺎن اﻟﺤﯿﻮﯾﺘﺎن ھﺎﺗﺎن أﺛﺒﺘﺎ أﻧﮭﻤﺎ ﻏﯿﺮ ﻓﻌﺎﻟﺘﯿﻦ ﻓﻲ إﻛﺘﺸﺎف اﻟﻤﺮاﺣﻞ اﻟﻤﺒﻜﺮة ﻟﻠﻤﺮض ﺑﺴﺒﺐ اﻟﺤﺴﺎﺳﯿﺔ واﻟﺨﺼﻮﺻﯿﺔ اﻟﺘﺸﺨﯿﺼﯿﺔ اﻟﻤﻨﺨﻔﻀﺔ. أﺟﺮﯾﺖ ھﺬه اﻟﺪراﺳﺔ ﻋﻠﻰ واﺣﺪ وأرﺑﻌﯿﻦ ﺳﯿﺪة ﻣﺼﺮﯾﺔ ﻣﺮﯾﻀﺔ ﺑﺴﺮﻃﺎن اﻟﺜﺪي اﻷوﻟﻲ اﻟﻤﺜﺒﺖ ھﺴﺘﻮﺑﺎﺛﻮﻟﻮﺟﯿﺎ ﻣﻦ ﻣﻌﮭﺪ اﻷورام اﻟﻘﻮﻣﻲ، ﺟﺎﻣﻌﺔ اﻟﻘﺎھﺮة، ﻓﻲ اﻟﻔﺘﺮة ﻣﻦ ﯾﻨﺎﯾﺮ ٢٠١١ إﻟﻰ ﯾﻮﻧﯿﺔ ٢٠١١، وﻋﺸﺮﯾﻦ ﺳﯿﺪة ﻣﺼﺮﯾﺔ ﺻﺤﯿﺤﺔ ﻣﺘﻮاﻓﻘﺎت ﻓﻲ اﻟﺴﻦ واﻟﺤﺎﻟﺔ اﻹﺟﺘﻤﺎﻋﯿﺔ. ﺗﻢ ﺗﻘﺴﯿﻤﮭﻦ إﻟﻰ ﻣﺠﻤﻮﻋﺘﯿﻦ: ﻣﺠﻤﻮﻋﺔ ١: ٢٠ ﺳﯿﺪة ﺻﺤﯿﺤﺔ ﺗﻢ إﻋﺘﺒﺎرھﻦ ﻛﻤﺠﻤﻮﻋﺔ ﺿﺎﺑﻄﺔ ﻃﺒﯿﻌﯿﺔ (ﺳﻦ، ﻣﺘﻮﺳﻂ±SD، ٤٩٫٩٥٠±١١٫٠٩٥ ﺳﻨﺔ؛ ١٢ ﻓﻲ ﻣﺮﺣﻠﺔ ﻣﺎ ﻗﺒﻞ ﺳﻦ اﻟﯿﺄس، ٨ ﻓﻲ ﻣﺮﺣﻠﺔ ﻣﺎ ﺑﻌﺪ ﺳﻦ اﻟﯿﺄس). ﻣﺠﻤﻮﻋﺔ ٢: ٤١ ﺳﯿﺪة ﻣﺮﯾﻀﺔ ﺑﺴﺮﻃﺎن اﻟﺜﺪي ﻗﺒﻞ أﺧﺬ أي ﻧﻮع ﻣﻦ اﻟﻌﻼج (ﺳﻦ، ﻣﺘﻮﺳﻂ±SD، ٥٠٫١٥٠±١٠٫٤٦٨ ﺳﻨﺔ؛ ١٩ ﻓﻲ ﻣﺮﺣﻠﺔ ﻣﺎ ﻗﺒﻞ ﺳﻦ اﻟﯿﺄس، ٢٢ ﻓﻲ ﻣﺮﺣﻠﺔ ﻣﺎ ﺑﻌﺪ ﺳﻦ اﻟﯿﺄس). ١٥ ﻣﻨﮭﻦ ﺗﻢ ﻣﺘﺎﺑﻌﺘﮭﻦ ﺑﻌﺪ اﻟﻌﻼج اﻟﺠﺮاﺣﻲ (٩ إﺳﺘﺌﺼﺎل ﺛﺪي ﺟﺬري ﻣﻌﺪل، ٢ إﺳﺘﺌﺼﺎل ﺛﺪي ﺑﺴﯿﻂ، ٤ ﺟﺮاﺣﺔ ﻣﺤﺎﻓﻈﺔ ﻟﻠﺜﺪي) ﻣﻌﺎﯾﯿﺮ اﻹﺳﺘﺜﻨﺎء: ١. اﻟﺴﯿﺪات اﻟﻼﺗﻲ ﻟﮭﻦ ﺗﺎرﯾﺦ ﻷي ﻣﺮض ﺧﻄﯿﺮ أو ﻣﺰﻣﻦ. ٢. اﻟﺴﯿﺪات اﻟﻼﺗﻲ ﻟﮭﻦ ﺗﺎرﯾﺦ ﻷي ﻧﻮع ﻣﻦ اﻟﺴﺮﻃﺎن. ﻛﻞ اﻷﺻﺤﺎء واﻟﻤﺮﯾﻀﺎت اﻟﺼﺎﻟﺤﺎت ﻟﻠﺪراﺳﺔ ﺗﻢ ﺗﻘﯿﯿﻢ ﻟﮭﻦ اﻵﺗﻲ: - اﻟﺘﺎرﯾﺦ اﻟﻄﺒﻲ اﻟﻜﺎﻣﻞ واﻟﻔﺤﻮﺻﺎت اﻹﻛﻠﯿﻨﯿﻜﯿﺔ اﻟﺸﺎﻣﻠﺔ. - اﻟﻔﺤﻮﺻﺎت اﻟﮭﺴﺘﻮﺑﺎﺛﻮﻟﻮﺟﯿﺔ ﻟﻠﻤﺮﯾﻀﺎت. - ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم (ﻗﺒﻞ وﺑﻌﺪ اﻟﻌﻼج اﻟﺠﺮاﺣﻲ). ١

ﻣﺴﺘﺨﻠﺺ

اﻻﺳﻢ: ﻣﺼﻄﻔﻰ ﺳﯿﻒ اﻟﻨﺼﺮ ﻣﺤﻤﻮد اﻟﺸﺒﯿﻨﻲ ﻋﻨﻮان اﻟﺮﺳﺎﻟﺔ: ﺗﻘﯿﯿﻢ ﺟﺰئ إﻟﺘﺼﺎق ﺧﻠﯿﺔ اﻟﺪم اﻟﺒﯿﻀﺎء اﻟﻤﺤﻔﺰة ﻛﺪﻻﻟﺔ ﺣﯿﻮﯾﺔ ﻟﺴﺮﻃﺎن اﻟﺜﺪي ﻓﻲ اﻟﻤﺮﯾﻀﺎت اﻟﻤﺼﺮﯾﺎت اﻟﺪرﺟﺔ: دﻛﺘﻮراه ﻓﻲ اﻟﻌﻠﻮم (ﻛﯿﻤﯿﺎء ﺣﯿﻮﯾﺔ) ﻣﻠﺨﺺ اﻟﺒﺤﺚ: ﻓﻲ ھﺬه اﻟﺪراﺳﺔ، ﺗﻢ ﺗﻘﯿﯿﻢ ﻣﺴﺘﻮﯾﺎت ﺟﺰئ إﻟﺘﺼﺎق ﺧﻠﯿﺔ اﻟﺪم اﻟﺒﯿﻀﺎء اﻟﻤﺤﻔﺰة (ALCAM) ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻓﻲ ٤١ ﺳﯿﺪة ﻣﺮﯾﻀﺔ ﺑﺴﺮﻃﺎن اﻟﺜﺪي اﻷوﻟﻲ و٢٠ ﺳﯿﺪة ﺻﺤﯿﺤﺔ، وﺗﻢ ﺗﺤﺪﯾﺪ ﻗﯿﻤﺘﮫ اﻟﺘﺸﺨﯿﺼﯿﺔ، وﻣﻘﺎرﻧﺘﮭﺎ ﺑﺄﻧﺘﯿﺠﯿﻦ اﻟﻜﺮﺑﻮھﯿﺪرات ١٥- ٣ (CA 15-3) وأﻧﺘﯿﺠﯿﻦ اﻟﺴﺮﻃﺎن اﻟﺠﻨﯿﻨﻲ (CEA). أﯾﻀﺎ، ﺗﻢ إﺧﺘﺒﺎر ﻗﯿﻤﺘﮫ اﻟﺘﻨﺒﺆﯾﺔ ﻟﻤﺴﺎر اﻟﺤﺎﻟﺔ اﻟﻤﺮﺿﯿﺔ. ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم ﺗﻢ أﯾﻀﺎ ﺗﻘﯿﯿﻤﮭﺎ ﻗﺒﻞ وﺑﻌﺪ اﻟﻌﻼج اﻟﺠﺮاﺣﻲ. ﻣﺴﺘﻮﯾﺎت ALCAM وCA 15-3 ﻓﻲ ﻣﺼﻞ اﻟﺪم إرﺗﻔﻌﺖ إرﺗﻔﺎﻋﺎ ذو دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ ﻓﻲ ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي ﻋﻦ اﻟﺴﯿﺪات اﻷﺻﺤﺎء (P=0.043 ،P=0.002 ﻋﻠﻰ اﻟﺘﺮﺗﯿﺐ)، وﻟﻜﻦ اﻹﺧﺘﻼف ﻓﻲ ﻣﺴﺘﻮﯾﺎت CEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻟﻢ ﯾﺼﻞ إﻟﻰ دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ. ﻣﺴﺘﻮﯾﺎت ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم وﺟﺪ أن ﻟﮭﺎ ﻣﺴﺎﺣﺔ ﺗﺤﺖ اﻟﻤﻨﺤﻨﻰ (AUC) ذات دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ (P=0.002)، وﻟﻜﻦ ﻣﺴﺘﻮﯾﺎت CA 15-3 وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم وﺟﺪ أن ﻟﮭﺎ AUCs ﻟﯿﺴﺖ ذوات دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ، واﻟﺠﻤﻮع اﻟﻤﺨﺘﻠﻔﺔ ﺑﯿﻨﮭﻢ ﻟﻢ ﺗﻨﺘﺞ أي ﺗﺤﺴﻦ. وﺟﺪ إرﺗﺒﺎﻃﺎ ذو دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ ﺑﯿﻦ ﻣﺴﺘﻮﯾﺎت ALCAM وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻣﻊ اﻟﺴﻦ واﻟﺤﺎﻟﺔ ﺑﺎﻟﻨﺴﺒﺔ ﻟﺴﻦ اﻟﯿﺄس ﻓﻲ ﻣﺮﯾﻀﺎت ﺳﺮﻃﺎن اﻟﺜﺪي. أﻇﮭﺮت اﻟﺪراﺳﺔ إﺧﺘﻼﻓﺎ ﻟﯿﺲ ﻟﮫ دﻻﻟﺔ إﺣﺼﺎﺋﯿﺔ ﻓﻲ ﻣﺴﺘﻮﯾﺎت CA 15-3 ،ALCAM وCEA ﻓﻲ ﻣﺼﻞ اﻟﺪم ﻗﺒﻞ وﺑﻌﺪ اﻟﻌﻼج اﻟﺠﺮاﺣﻲ. ﯾﺴﺘﻨﺘﺞ ﻣﻦ ھﺬه اﻟﺪراﺳﺔ أن ALCAM ﻓﻲ ﻣﺼﻞ اﻟﺪم رﺑﻤﺎ ﯾﻤﺜﻞ دﻻﻟﺔ ﺣﯿﻮﯾﺔ ﺗﺸﺨﯿﺼﯿﺔ ﺟﺪﯾﺪة ﻟﺴﺮﻃﺎن اﻟﺜﺪي. اﻟﻜﻠﻤﺎت اﻟﺪاﻟﺔ: ﺟﺰئ إﻟﺘﺼﺎق ﺧﻠﯿﺔ اﻟﺪم اﻟﺒﯿﻀﺎء اﻟﻤﺤﻔﺰة، ﺳﺮﻃﺎن اﻟﺜﺪي، أﻧﺘﯿﺠﯿﻦ اﻟﻜﺮﺑﻮھﯿﺪرات ١٥- ٣، أﻧﺘﯿﺠﯿﻦ اﻟﺴﺮﻃﺎن اﻟﺠﻨﯿﻨﻲ، ودﻻﻟﺔ ﺣﯿﻮﯾﺔ. "ﺗﻮﻗﯿﻊ اﻟﺴﺎدة اﻟﻤﺸﺮﻓﻮن" ١. أ.د/ ﻋﻤﺮو ﺳﻌﺪ ﻣﺤﻤﺪ ٢. أ.د/ ﻋﺰه ﻋﺒﺪ اﷲ ﻣﺤﻤﺪ ٣. أ.د/ أﻣﻞ ﻣﺤﻤﺪ ﻧﻮر اﻟﺪﯾﻦ ٤. د/ أﺣﻤﺪ ﻣﺼﻄﻔﻰ أﺣﻤﺪ ﯾﻌﺘﻤﺪ ،،،، أ.د/ ﺣﺎﻣﺪ ﻋﺒﺪ اﻟﻠﻄﯿﻒ ﻋﺒﺪ اﻟﺮﺣﻤﻦ

رﺋﯿﺲ ﻣﺠﻠﺲ ﻗﺴﻢ اﻟﻜﯿﻤﯿﺎء ﻛﻠﯿﺔ اﻟﻌﻠﻮم ـ ﺟﺎﻣﻌﺔ اﻟﻘﺎھﺮة

ﺗﻘﯿﯿﻢ ﺟﺰئ إﻟﺘﺼﺎق ﺧﻠﯿﺔ اﻟﺪم اﻟﺒﯿﻀﺎء اﻟﻤﺤﻔﺰة ﻛﺪﻻﻟﺔ ﺣﯿﻮﯾﺔ ﻟﺴﺮﻃﺎن اﻟﺜﺪي ﻓﻲ اﻟﻤﺮﯾﻀﺎت اﻟﻤﺼﺮﯾﺎت

إﻋﺪاد

ﻣﺼﻄﻔﻰ ﺳﯿﻒ اﻟﻨﺼﺮ ﻣﺤﻤﻮد اﻟﺸﺒﯿﻨﻲ ﻣﺎﺟﺴﺘﯿﺮ ﻓﻲ اﻟﻜﯿﻤﯿﺎء اﻟﺤﯿﻮﯾﺔ ﻣﺪرس ﻣﺴﺎﻋﺪ ﺑﺎﻟﻤﺮﻛﺰ اﻟﻘﻮﻣﻲ ﻟﺒﺤﻮث وﺗﻜﻨﻮﻟﻮﺟﯿﺎ اﻹﺷﻌﺎع ھﯿﺌﺔ اﻟﻄﺎﻗﺔ اﻟﺬرﯾﺔ

رﺳﺎﻟﺔ ﻣﻘﺪﻣﺔ إﻟﻲ ﻛﻠﯿﺔ اﻟﻌﻠﻮم

ﻛﺠﺰء ﻣﻦ ﻣﺘﻄﻠﺒﺎت اﻟﺤﺼﻮل ﻋﻠﻰ درﺟﺔ اﻟﺪﻛﺘﻮراه (ﻛﯿﻤﯿﺎء ﺣﯿﻮﯾﺔ)

ﺗﺤﺖ إﺷﺮاف

أ.د/ ﻋﻤﺮو ﺳﻌﺪ ﻣﺤﻤﺪ أ.د/ ﻋﺰه ﻋﺒﺪ اﷲ ﻣﺤﻤﺪ أﺳﺘﺎذ اﻟﻜﯿﻤﯿﺎء اﻟﺤﯿﻮﯾﺔ أﺳﺘﺎذ اﻟﺒﺎﺛﻮﻟﻮﺟﯿﺎ اﻹﻛﻠﯿﻨﯿﻜﯿﺔ ﻛﻠﯿﺔ اﻟﻌﻠﻮم اﻟﻤﺮﻛﺰ اﻟﻘﻮﻣﻲ ﻟﺒﺤﻮث وﺗﻜﻨﻮﻟﻮﺟﯿﺎ اﻹﺷﻌﺎع ﺟﺎﻣﻌﺔ اﻟﻘﺎھﺮة ھﯿﺌﺔ اﻟﻄﺎﻗﺔ اﻟﺬرﯾﺔ أ.د/ أﻣﻞ ﻣﺤﻤﺪ ﻧﻮر اﻟﺪﯾﻦ د/ أﺣﻤﺪ ﻣﺼﻄﻔﻰ أﺣﻤﺪ أﺳﺘﺎذ ﻃﺐ اﻷﻃﻔﺎل ﻣﺪرس ﺟﺮاﺣﺔ اﻷورام ﻣﺮﻛﺰ اﻟﺒﺤﻮث اﻟﻨﻮوﯾﺔ ﻣﻌﮭﺪ اﻷورام اﻟﻘﻮﻣﻲ ھﯿﺌﺔ اﻟﻄﺎﻗﺔ اﻟﺬرﯾﺔ ﺟﺎﻣﻌﺔ اﻟﻘﺎھﺮة

ﻗﺴﻢ اﻟﻜﯿﻤﯿﺎء ﻛﻠﯿﺔ اﻟﻌﻠﻮم ﺟﺎﻣﻌﺔ اﻟﻘﺎھﺮة

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