Cancer Biomarkers – Discovery to Applications Abstract
Review Article THE SCITECH JOURNAL ISSN 2347-7318 ISSN 2348-2311 Online SAMANTHI Cancer Biomarkers – Discovery to Applications *M. Ravi, Madhumitha Haridoss, W. Sai Keerthana Department of Human Genetics, Faculty of Biomedical Sciences, Technology and Research,Sri Ramachandra University, Porur, Chennai 600 116, Tamil Nadu, India. Abstract Cancers continue to be one of the most important contributors for disease and mortality rates across the globe. Therefore, apart from understanding the molecular mechanisms of carcinogenesis, the cancer biomarkers have found an important place in cancer research. Proteins or metabolites expressed either uniquely or over-expressed by cancerous cells are useful pathological biomarkers for cancer detection, monitoring and prognostic applications. Cancer biomarker discovery has undergone significant evolution. These encompass genomic, proteomic and metabolomic markers and are present in a wide range of biological tissues and fluids. Although, biopsies and tissue samples/fluids from individuals with cancers have contributed well to biomarker research, cell lines, especially the cancer cell lines have become invaluable for several applications for research in general and biomarker discovery in particular. From traditional 2 Dimensional cultures, the cells are now being cultured in a 3 dimensional environment which is believed to mimic the in vivo systems much better. This approach while minimizing the needs for in vivo studies, still provides us with a good model for experimental approaches. With useful biological material in hand, both proteomic and genomic approaches can be utilized for cancer biomarker discovery. Such biomarkers have important applications in cancer diagnostics, therapeutics, monitoring and prognosis. Key words: Cancer biomarkers, cell lines, 2 D cultures, 3 D cultures, protein markers, genomic markers. Introduction The leading cause of death in economically developed countries, one of the most frequent malignant diseases, Cancer, comprises of more than 200 different diseases. Cancer is a recreant system of growth, more precisely a genetic disease which directs the cell towards limitless expansion on exposure to carcinogens or mutagens, resulted in mutations promoting clonal selection of cells with increasingly aggressive behavior. (Fearon et al., 1997). Cancer is the leading cause of death in economically developed countries and second leading cause of death in developing countries (WHO, 2004). The burden of cancer is being continuously increased largely all over the world due to aging and growth of world population, the main cause being changes in life style, increasing adoption to cancer-causing behaviors, particularly smoking, in economically developed countries. (Jemal et al.,2011). The GLOBOCAN 2008 report estimates that nearly 12.7 million new cancer cases and 7.6 million cancer deaths occurred in 2008 worldwide. Hence, there is a need to find the underlying cause leading to cancer development. increasing trends in developing nations. Cancers are physiologically complex conditions with multifactorial etiology. Management of cancers requires a multi directional approach from understanding the underlying mechanisms, cancer cell behaviour, conditions that promote cancer cell growth and migration, diagnostic markers and the interventional modalities. Cancer cells are deviations from their normal functioning and manifest such differences as altered gene expressions, usually translating into detectable protein or metabolomic products. Such products can be localized within the cells, expressed on the membranes or even secreted by the cells. These products are either uniquely expressed or over expressed and are denoted as cancer antigens, either cancer specific or associated. Such products are useful as cancer biomarkers with several applications ranging from diagnostics, therapy, monitoring to prognosis. In this review, we present comprehensively, the cancer biomarkers, the contributions of cancer cell lines and the evolution in the cell culture techniques. Detailed account of various types of biomarkers, the proteomic and genomic approaches, the indicated cancer types form an important part of this review. In this review, we also present data from our own studies conducted in two lung cancer cell lines, the A 549 and NCI H23 for stabilizing their 3 D cultures and using the same for biomarker discovery. Last several years, significant progress have been achieved in understanding the molecular basis of cancer and translating the understanding towards research on cancer diagnosis, prognosis, therapeutics thereby accomplishing reduced cancer mortality rates. The accumulation of this basic knowledge has established that Cancer biomarkers cancer is a broad term used for identifying a large number of diseases and that these diseases are caused by defective genes which Cancer biomarkers are endogenous proteins or metabolites that are are diverse in nature and can involve either loss or gain of gene produced or expressed in increased amounts under pathological function. (Martinez et al., 2003) conditions which help to distinguish abnormal from normal status. Lung cancers continue to dominate health care as well as a socioeconomic concern. Although cancer mortality rates have been reported to be declining in developed nations, the incidence rates and the mortalities rates from various types of cancers are seeing Received: September 2014 Accepted: September 2014 *Corresponding Author Email::maddalyravi@hotmail.com 18 THE SCITECH JOURNAL VOLUME 01 ISSUE 10 OCTOBER 2014 Review Article Authors to provide, P.A. Mary Helen et al., THE SCITECH JOURNAL ISSN 2347-7318 ISSN 2348-2311 Online Significant progress in the development of novel technologies for biomarker discovery can bring in deep understanding of carcinogenesis and guide cancer therapy by earlier diagnosis, verifying cancer stages, molecular characteristics and prognosis. The cancer biomarkers include genomic, epigenetic, proteomic and metabolomic markers among which genomic and proteomic have highly contributed to the biomarker discovery. The level of these biomarkers could be elevated in a wide range of samples including blood, serum, saliva, tissue, urine, other body fluids, cell lysates and cells secretome (Maurya et al., 2007). The proteins involved in oncogenesis, angiogenesis, development, differentiation, proliferation, apoptosis, hematopoiesis, immune and hormonal responses, cell signaling, nucleotide function, hydrolysis, cellular homing, cell cycle and structure and hormonal control can serve as SAMANTHI better candidates for biomarker discovery (Polanski et al., 2006). Mutations in DNA leading to activation of oncogenes/inactivation of tumor suppressor genes can be potential DNA biomarkers. DNA methylations contributing to carcinogenesis can be good epigenetic candidates for biomarker discovery. Thus, development of new genomic and proteomic approaches towards cancer biomarker discovery can lead to the identification of novel DNA and Protein biomarkers which has potential to facilitate earlier diagnosis of cancer and thereby decreasing its high mortality rate. The details of various cancer biomarkers, their biochemical nature, and their indication in specific cancer types along with their applications are presented in Table. 1 Table 1. The biomarkers indicated in different cancer types, their biochemical nature and their applications. Biomarkers Cancer Type Localisation/ Type Application Cadherin 1 Lung Cancer Transmembrane Diagnosis CD30 ligand Lung Cancer Glycoprotein Diagnosis Endostatin Lung Cancer Globular Protein Diagnosis HSP90á Lung Cancer Cytosolic protein Diagnosis LRIG3 Lung Cancer Lung Cancer Lung Cancer Transmem glyco Diagnosis Cytokines Diagnosis Growth factor Diagnosis Lung Cancer - Diagnosis Lung Cancer Glycoprotein Diagnosis Lung Cancer Growth factor Diagnosis Diagnosis Diagnosis Retinol binding protein Lung Cancer Lung Cancer Lung Cancer Soluble adhesion molecule Glycoprotein Transport protein Diagnosis Álpha 1 - antitrypsin Lung Cancer Lung Cancer Lung Cancer Serine Protease Diagnosis Squamous cell carcinoma antigen Neuron specific enolase (NSE) Cytoplasmic Enzyme Diagnosis Cytokeratin 19 fragment (CYFRA) Lung Cancer Lung Cancer Lung Cancer MIP-4 Pleiotrophin PRKCI RGM- C SCF-sR sL- Selectin Carcinoembriyonic antigen Progastrin releasing peptide (Pro GRP) Macrophage inflammatory protein 1 á Stem cell factor Patz et al., 2007 Strauss et al., 1994 Diagnosis Neuropeptide Diagnosis Chemotacti Cytokine Diagnosis Growth factor Diagnosis Interferon ã Cytokine Diagnosis Tumor necrosis factor á Lung Cancer Growth factor Diagnosis Her-2 & p53 Oncogene & tumor suppressor gene Prognosis - Prognosis CGA Lung Cancer Lung Cancer Lung Cancer Pro-GRP Lung Cancer Gastrin-releasing peptide Prognosis Interleukin-8 Lung Cancer Cytokine Prognosis Fibroblast Growth Factor Receptor 1 Lung Cancer Cell Bound Human glandular Kallikrein (hK2) Prostate Cancer Serine Protease Prognosis Diagnosis Early prostate cancer antigen ( EPCA) Prostate Cancer Nuclear Matrix protein Diagnosis Alpha crystalline antibodies Ostroff et al., 2010 Diagnosis Lung Cancer Lung Cancer Lung Cancer Tumor necrosis factor receptor 1 Reference Borgia et al., 2009 Diagnosis Prognosis 19 THE SCITECH JOURNAL VOLUME 01 ISSUE 10 OCTOBER 2014 Mano et al., 2007 Bensalah et al., 2008 Review Article Authors to provide, P.A. Mary Helen et al., THE SCITECH JOURNAL ISSN 2347-7318 ISSN 2348-2311 Online SAMANTHI Transforming growth factor â1 Prostate Cancer Growth factor Diagnosis Interleukin 6 Prostate Cancer Cytokine Diagnosis Prostate secretory protein (PSP) Prostate Cancer Microseminoprotein Diagnosis Prostate-specific membrane antigen Prostate Cancer Glycoprotein Diagnosis Prostate-specific auto antibodies Prostate Cancer - Diagnosis Álpha-methylacyl-CoA racemase (AMACR) Prostate Cancer - Diagnosis PCA3 Prostate Cancer - Diagnosis Ploussard et al., 2010 ZAG Prostate Cancer Glycoprotein Diagnosis Madu et al., 2010 Huntingtin interacting protein1 Prostate Cancer Cytoplasmic clthrin binding protein Diagnosis TSP-1 Prostate Cancer ECM glycoprotein Diagnosis Leptin Prostate Cancer Adipocyte derived peptide Diagnosis ILGF-1-2 Prostate Cancer Growth hormone dependent polypeptide Diagnosis Apolipoprotein A-II Prostate Cancer Lipoprotein Diagnosis MIF Prostate Cancer Cytokine Diagnosis Hepatocyte growth factor Prostate Cancer Heparin binding glycoprotein Diagnosis Methylated GSTP1 Prostate Cancer - Diagnosis Duffy et al., 2009 Neuron-specific enolase Prostate Cancer Cytoplasmic Enzyme Prognosis Madu et al., 2010 Testosterone Prostate Cancer Hormone Prognosis Estrogen Prostate Cancer Hormone Prognosis Sex-hormone binding globulin Prostate Cancer Glycoprotein Prognosis Caveolin-1 Prostate Cancer Cell bound membrane protein Prognosis E-Cadherin Prostate Cancer Transmembrane Glycoprotein Prognosis Âeta catenin Prostate Cancer Plasma membrane protein Prognosis Matrixmetalloproteinase-9 Prostate Cancer Soluble protein Prognosis TIMP-1,2 Prostate Cancer Glycoprotein Prognosis MIC-1 Prostate Cancer VEGF Prostate Cancer Cell Bound protein Prognosis CGRP Prostate Cancer Calcitonin family of peptides Prognosis uPAR Prostate Cancer Urokinase plsminogen activation system Prognosis Chromogranin A Prostate Cancer Soluble Glycoprotein Prognosis Macrophage inhibitory cytokine 1 Pancreatic Cancer Cyotokine Diagnosis Phosphoglycerate kinase 1 Pancreatic Cancer Transferase enzyme Diagnosis Misek et al.,2007 MUC1 Pancreatic Cancer Cell Surface associated Protein Diagnosis Liang et al.,2009 MUC4 Pancreatic Cancer Cell Surface associated Protein Diagnosis Tezel et al., 2000 PGP9.5 Pancreatic Cancer Antibody Prognosis CA 19-9 Pancreatic Cancer Cell Surface protein Monitoring Liang et al.,2009 Osteopontin Pancreatic Cancer Integrin binding glycoprotein Diagnosis Misek et al.,2007 BRCAI Breast Cancer Tumor suppressor gene Diagnosis Zhong et al.,2008 BRCA2 Breast Cancer Tumor suppressor gene Diagnosis Ross et al.,2003 B72.3 Breast Cancer Antibody Diagnosis Alpha-lactalbumin Breast Cancer Calcium metalloprotein Diagnosis Milk fat globule Breast Cancer Membrane protein Diagnosis Mammoglobin Breast Cancer Glycoprotein Diagnosis Maspin Breast Cancer Serine protease inhibitor (serpin) Diagnosis GATA3 Breast Cancer Transcription factor Prognosis Prognosis 20 THE SCITECH JOURNAL VOLUME 01 ISSUE 10 OCTOBER 2014 Bensalah et al., 2008 Ross et al.,2003 Mehra et al., 2005 Review Article Authors to provide, P.A. Mary Helen et al., THE SCITECH JOURNAL ISSN 2347-7318 ISSN 2348-2311 Online SAMANTHI Somasundaram et al.,2009 YKL-40 Glioma Human Cartilage glycoprotein Diagnosis PBEF1 Glioma Antibody Diagnosis NamPRTase Glioma Cytokine Diagnosis Visafatin Glioma Cytokine Diagnosis O-6-methylguanine-DNA methyl transferase Glioma Enzyme Diagnosis Glial fibrillary acidic protein Glioma Intermediate filament protein Diagnosis El-Jawahri et al.,2008 Galectins Glioma Family of Lectins Diagnosis El-Jawahri et al.,2008 Kir potassium channel proteins Glioma - Diagnosis CEA Colon Glycoprotein Monitoring Thyroglobin Thyroid Fibrin Bladder Insoluble Fobrous protein Monitoring VTA Bladder - Monitoring Mucin Bladder Glycosylated Protein Monitoring CA 125 Ovarian Membrane associated mucin Monitoring Monitoring Cell lines Cell culture is practiced extensively throughout the world. It has become an indispensible technology in the field of research. It has developed from a simple, observational science to a universally accepted technology, providing basis for studying the regulation of cell proliferation, differentiation and product formation in carefully controlled conditions. (Masters et al., 2000). Cancer cell lines Cell lines, invaluable tools for investigation in cancer research, are cells derived from single parental transformed cells which grow under conditions that mimic the in vivo environment. The techniques that are required for growth and maintenance of cells outside the body have been developed throughout the 20th century. Cancer cell lines have infinite life span and high growth potential. They are independent on growth factors and often lose normal functioning. Ability to provide a renewable source of cell material for several studies is the important characteristic of cancer cell lines. Cancer cell lines cultured in vitro reflect the properties of the original tumors such as maintenance of histopathology when transplanted into animal models, genotypic characters, phenotypic characters, gene expression and drug sensitivity. As there are thousands of cell lines in use, it is essential to characterize and authenticate cell lines to confirm the origin of the cell line, to assess uniqueness, and to study the properties of the cell line, by definitive methods such as DNA fingerprinting, Cytogenetic analysis, PCR and other studies. (Langdon et al., 2005) Three dimensional cell cultures have been extensively used with an aim to mimic the in vivo tumor behavior in conditions better amenable to experimental investigation. (Feder- Mengus et al., 2008). The major advantage of cells growing in three dimensional models is that they have a well defined geometry making it possible to relate to the structure and function of the tumor. They have a very strong impact on gene expression, cell adhesion, migration and Ludwig et al., 2005 assemble into multicellular structures due to culture dimensionality. (Ulrich et al., 2010) In vitro culture models- A tool for biomarker discovery The biological samples used commonly in the cancer biomarker discovery include blood, serum, tissue, sputum, urine and other body fluids such as CSF etc. Blood is a representative component of the whole body because of it circulation throughout all the organs. Therefore it is a good choice of sample for biomarker discovery. But the main disadvantage of using blood and serum sample is the high abundance of serum proteins which could mask the expression of low abundant cancer specific proteins. There are high possibilities for these low abundant proteins to be suitable candidates for biomarker discovery. Another limitation is the heterogeneity of samples between individuals. Genetic and environmental diversity among humans can contribute to this heterogeneity. Proteins are secreted in higher amounts in tissues specific to the cancer. These proteins become diluted when they reach the circulation. Therefore, the protein levels in blood may not be a representative of the actual level. At the same time tissue samples are invasive to obtain. Other samples such as sputum, urine and body fluids may not express all the secreted proteins in the body specific to cancer (Cho et al., 2009).Owing to these limitations, in vitro culture models can be a better choice for biomarker discovery. Protein profiling of cell lysates obtained from various cancer cell lines could possibly lead to the identification of new cancer biomarkers. Cells cultured in 2D matrix result in an artificial sheet of cells adhered to the culture ware. This may not reflect the actual in vivo characteristics. Therefore development of new in vitro culture models which can overcome these barriers is essential. 3D culture is one such novel culture model which is better amendable to experimental investigation. Advantages of 3 D culture models on comparison to 2 D culture models While traditional monolayer cultures are powerful tools to 21 SCITECH JOURNAL ISSUE 10 OCTOBER 2014 38 THE l Advanced Biotech. Vol. 10 VOLUME Issue 03 l 01 September 2010 Review Article THE SCITECH JOURNAL ISSN 2347-7318 ISSN 2348-2311 Online Flat, rigid surface, resulting to some degree, a misrepresentation of findings, including altered metabolism and declined functionality.Extensive studies have shown that growing cells within 3D scaffolds diminishes the gap between cell cultures and physiological tissues. Therefore, a 3D cell culture system may prove to be of tremendous advantage over conventional 2D cell culture system (Cukierman et al., 2001). Signaling pathways that function in parallel in cells cultured on plastic become reciprocally integrated when the cells are exposed to basement membrane–like gels (Lee et al., 2007). Multiple studies demonstrate that 3D organization can reveal novel and unexpected insights into the mechanisms of tumorigenesis and could represent an integral missing component in the in vitro cancer studies (Muthuswamy, 2011). Conventional studies based on two-dimensional cell monolayers have demonstrated their significant limitations (Lee et al., 2007). The extracellular matrix components,cell-to-cell and cell-to-matrix interactions that governs differentiation, proliferation and function of cells in vivo is lost under the simplified 2D conditions (Mazzoleni et al., 2009). Given the well-known problems with 2-dimensional (2 D) cell cultures as test beds, more realistic 3D tissue constructs are required. There are evidences that HepG2 colonies which are grown within the interior of the scaffold showed enhanced extracellular matrix deposition and also guarantees good SAMANTHI maintenance of liver-specific biomarker expression (Bazou, 2010). Previous studies on A549 spheroids have demonstrated persistently higher levels of interleukin (IL)-6 and IL-8 compared with monolayer cultures. 3D scaffolds have also proved to be a better in vitro model for inflammation studies. (Liu et al., 2010). Use of novel spheroid models, could promote improved understanding of molecular mechanisms involved in invasion and metastasis. 3D culture systems have also been used to investigate tumor angiogenesis and the significance of vascular endothelial growth factors (Mueller-Klieser, 1997). We have studied the various 3 D culture phases of the non small cell lung cancer cell line NCI H 23 for the ideal expression of differently localized protein fractions. It was seen that lag, log and plateau phases of these 3 D cultures are most ideally suited for expression of cytosolic, nuclear and membrane proteins respectively (Figure 1). Such cells can be harvested at a specific phase of the cell culture and the cytoplasmic, membrane and nuclear protein fractions extracted for novel biomarker discovery and characterization. (Figure 2) Each of the cell types require unique optimal conditions to transform into non attached 3 D aggregates/masses progressively (Figure 3). Depending on the culture parameters provided, it is possible to grow a normally attached cell line as a monolayer into healthy 3 D Figure 1. Cells in culture follow sequentially distinct phases namely lag, log, plateau and decline phases.The time duration of each phase is unique for a particular cell type and is dependent primarily on the doubling time for a cell type. Each of these phase have specific properties, especially the numbers of cells at a particular cell cycle stage. For example, the exponential phase is characterized by maximumcells in the mitotic stage, thus contributing to the rapid increase in the cell numbers towards attaining culture confluency. From protein extraction, fractionation and quantitative comparison of cells harvested at each of these culture phases, we understand that each of these phases express particular cellular component protein biomarkers better. Thus, cells in a particular phase of culture are ideally suited for the identification and characterization of localized biomarkers; the lag, log and plateau phases expressing best the cytosolic, nuclear and membrane proteins respectively. 2238THE l Advanced SCITECH Biotech. JOURNAL Vol. 10VOLUME Issue 03 01 l September ISSUE 102010 OCTOBER 2014 Review Article Authors to provide, P.A. Mary Helen et al., THE SCITECH JOURNAL ISSN 2347-7318 ISSN 2348-2311 Online SAMANTHI Figure 2. Obtaining healthy 3 Dimensional cultures from a conventional monolayer cell line requires optimizations of parameters. These parameters include the matrix/scaffolds, the composition and volume of the same, the medium and media supplements. Each cell type has unique 3 D culture requirements and required standardization. Based on the conditions provided, a traditional monolayer can be either partially detached, grown as small aggregates or as healthy complete 3 D aggregates. The figure above shows the various possibilities of the NCI H 23, a human non-small cell lung cancer cell line to be cultured depending on the conditions provided. From complete monolayers through partial 3 D formations to complete 3 D aggregate formation, each of these culture morphologies can be ideally utilized for experiments in cancer biomarker discovery. Authors to provide, P.A. Mary Helen et al., Figure 3. The main advantage of 3 D cultures is the marked morphological changes when compared to their 2 D counter parts. This results in better gene expressions and mimics the in vivo systems better than the 2 D cultures, thus making them ideal choices for biomarker discovery. The figure above shows the morphological comparison of 2 human cancer cell lines grown as 2 D monolayers and 3 D aggregates. 1a and 1b represent the cell line NCI H 23 as 2 D monolayers and 3 D floating aggregates respectively and the 2a and 2b represent the cell line A 549 as 2 D monolayers and 3 D partially embedded aggregates respectively. Agarose hydrogels were used to obtained the 3 D aggregates as shown above. 23 THE SCITECH JOURNAL VOLUME 01 ISSUE 10 OCTOBER 2014 Review Article THE SCITECH JOURNAL ISSN 2347-7318 ISSN 2348-2311 Online SAMANTHI Figure 4. We used a 'three-fraction-model' approach to identify cancer biomarkers from cells grown in vitro. This model isolates the cellular proteins as three fractions, the cytoplasmic, membrane bound and nuclear proteins separately and not as a complete whole cell protein extract. This approach has multiple benefits including easier comparison of protein profiles towards identification of novel expressed proteins, the over expression or down regulation of the protein expression and finally to localize the identified biomarker. This approach also will give insights into the potential usefulness of the biomarkers in cancer research, diagnostics, therapy and prognosis. cultures. Again, the culture characteristics of each of the cell type can be different in the 3 D environment augmenting their usefulness for biomarker discovery. For example, the lung cancer cell line A 549 forms aggregates that are semi embedded in the hydrogel matrix whereas another cell type of the lung cancer, the NCI H 23 forms floating aggregates due the presence of extracellular matrix and acellular zones within the aggregates (Figure 4) Biomarker discovery Genomic approaches After the development of high throughput sequencing methods Genomics is now shifting towards the study of gene function and expression. High throughput genomic analyses can provide a basis for the discovery of clinically relevant biomarker signatures and give a new comprehensive view. Carcinogenesis is a multistep process which involves activation of oncogenes (K-ras, Raf,Cyclin D1 & E, β-Catenin) (Chial, 2008) and inactivation of tumor suppressor genes (p53, Bcl2, APC). Single gene mutations are the major causes which leads to the over expression of oncogenes and under expression of tumor supressors in cancer conditions. Molecular profiling of genome is now possible through the advent of new advanced technigues such as DNA microarray. Microarray offer analyses of expressions of 1000s of genes involved in carcinogenesis. This global gene profiling can help diagnose subtypes of disease and predict patient survival (Konstantinopoulos et al., 2008).This approach will lead to improvements in early detection, diagnosis and treatment monitoring by the identification of novel biomarkers. High throughput RNA profiling methods are also starting to available which can soon allow transcriptome analysis and translate the scientific research findings to be applicable in clinical practice (Gupta et al., 2007).Thus the biomarkers identified via these approaches should be unique molecular signatures which can validate novel drug targets and predict drug responses. Proteomic Approaches Proteomics is the study of large-scale protein expression patterns, 38 THE l Advanced Biotech. Vol. 10 VOLUME Issue 03 l 01 September 2010 24 SCITECH JOURNAL ISSUE 10 OCTOBER 2014 Review Article THE SCITECH JOURNAL ISSN 2347-7318 ISSN 2348-2311 Online which involves simple identification of a protein, study of its isoforms, modifications and interactions. In the postgenome era, proteomics has become the efficient tool to study the structure, interaction and function of a cell's protein. Many powerful proteomic technologies are now available including SDS PAGE, 2D-PAGE, 2D-DIGE, Laser capture microdissection (LCM), Tissue microarrays, SELDI-ToF-MS technology, MALDI-TOF-MS, protein arrays, Antibody arrays, Multiple Reaction Monitoring (MRM), ICAT, iTRAQ and MudPIT for the identification of cancer specific proteins (Maurya et al., 2007). Detection of such proteins opens a new window for biomarker discovery which facilitates early diagnosis and treatment monitoring. The expression of proteins and encoding genes is affected by various factors in the cell's microenvironment. Identification of these factors contributes massively for biomarker discovery. Though thousands of biomarkers have been identified, till date very few are approved by FDA. Even the approved ones are not highly specific for a particular cancer. Therefore, identification of more biomarkers for different cancer types with high specificity is very essential. Biomarkers for Cancer diagnostics SAMANTHI antibodies to AMACR, is routinely used for the diagnosis of PCa. It yields a high diagnostic accuracy with a sensitivity of 97% and specificity of 92%. TGF-ß1 and IL-6 are associated with cancer progression in prostate cancer patients (Bensalah et al., 2008). PCA3 is a urine biomarker in prostate cancer (Ploussard et al., 2010). It has been reported that PSP94, ZAG, Huntingtin-interacting protein 1, TSP-1, Leptin, ILGF-1,-2, Apolipoprotein A-II, Cytokine macrophage MIF, Hepatocyte growth factor are novel diagnostic markers of prostate cancer (Madu et al., 2010). There are also DNA methylation biomarkers including methylated GSTP1 which is promising and aids early diagnosis of prostate cancer (Duffy et al., 2009). Pancreatic Cancer Serum levels of osteopontin, macrophage inhibitory cytokine 1 (MIC-1), phosphoglycerate kinase1 are elevated in pancreatic ductal adenocarcinoma (PDAC) patients (Misek et al., 2007). MUC1 is found to be overexpressed in pancreatic cancer tissues. MUC4 is another protein which is highly expressed in pancreatic adenocarcinomas and other cancer cell lines (Liang et al., 2009). Breast Cancer Lung Cancer A panel of 12-proteins including cadherin-1, CD30 ligand, endostatin, HSP90α, LRIG3, MIP-4, pleiotrophin, PRKCI, RGM-C, SCF-sR, sL-selectin discriminates NSCLC from controls with 91% sensitivity and 84% specificity using a new aptamer-based proteomic technology. DNA aptamers are chemically modified nucleotides that are highly protein binding reagents.It transforms each targeted protein into a corresponding quantity of aptamer and is quantified by a hybridization array (Ostroff et al., 2010). Another panel of four serum proteins such as carcinoembryonic antigen, retinol binding protein, alpha1-antitrypsin, and squamous cell carcinoma antigen collectively classifies the majority of lung cancer and control patients with a sensitivity of 89.3% and specificity of 84.7%. CEA played a new role in predicting metastasis to mediastinal lymph nodes (Patz et al., 2007). Neuron-specific enolase (NSE), cytokeratin 19 fragment (CYFRA) and pro-gastrinreleasing peptide (proGRP) can also be used as potential tumor markers for lung cancer diagnosis (Strauss et al., 1994). Previous studies describe macrophage inflammatory protein-1alpha, carcinoembryonic antigen, stem cell factor, tumor necrosis factorreceptor I, interferon-gamma, and tumor necrosis factor-alpha can be an optimum combination of biomarkers for identifying a patient's pathologic nodal status (Borgia et al., 2009). Prostate Cancer There are many identified potential biomarkers for the diagnosis of prostate cancer. Human Glandular Kallikrein 2 (hK2), Early Prostate Cancer Antigen, Transforming Growth Factor-b1 and Interleukin-6, Prostate Secretory Protein (PSP), Prostate-specific Membrane Antigen, Prostate Cancer-specific Autoantibodies and Amethylacyl-CoA Racemase, Insulin-like Growth Factors Family are potential candidates for the diagnosis of prostate cancer. hK2 is similar to PSA. α-Methylacyl-CoA racemase (AMACR) is an enzyme involved in fat metabolism, which has a strong expression in PCa tissues. Immunostaining performed using monoclonal Mutations in BRCA1 and BRCA2 are commonly used for the screening of breast cancers. Using SELDI analysis, a 15,940 Da protein was detected with 80% sensitivity and 100% specificity which can be used for breast cancer diagnosis.A panel of breast cancer associated glycoproteins including B72.3, α-lactalbumin and milk fat globule have also been proposed for breast cancer diagnosis using immunohistochemical stains (Ross et al., 2003). In another study by serum autoantibody profiling, they have identified six phage proteins that showed significance in discriminating patients from normal individuals. Additionally it has also been reported that mammoglobin and maspin are new markers for primary breast cancer (Ross et al., 2003). Gliomas YKL-40 is a secreted glycoprotein, where its serum level correlates with the presence of astrocytoma which is the most common type of malignant glioma. Correlation of astrocytoma with the serum levels of PBEF1/NamPRTase/Visafatin has also been reported (Somasundaram et al., 2009). To date the most promising biomarker for glioblastoma diagnosis includes loss of chromosomes 1p/19q in oligodendrogliomas and expression of O-6-methylguanine-DNA methyltransferase. Other promising biomarkers in glioma research include glial fibrillary acidic protein, galectins, Kir potassium channel proteins, angiogenesis, and apoptosis pathway markers (ElJawahri et al., 2008). Biomarkers in Cancer Therapeutics Biomarker discovery and validation is an important prerequisite for a rational and efficient development of drugs towards cancer therapeutics. (Sarker et al., 2007). The biomarkers that have potential as therapeutic agents are targeted during clinical trials for treatment of various types of cancers by assessing drug targets and their mechanisms of action. The biomarkers for therapeutics can be classified into different approaches such as vaccines, antibodies, 25 SCITECH JOURNAL ISSUE 10 OCTOBER 2014 38 THE l Advanced Biotech. Vol. 10 VOLUME Issue 03 l 01 September 2010 Review Article THE SCITECH JOURNAL ISSN 2347-7318 ISSN 2348-2311 Online anti- angiogenic agents, agents for gene therapy, pro-apoptotic agents etc. (Detchokul et al., 2011). Biomarkers have a great impact on cancer therapeutics as their role is increasingly promising, suggesting an integrated approach for treatment selection and patient management. (Jain et al., 2010)The biomarkers used in therapeutics may be of different types. Enzymes – L- Asparaginase, a bacterial enzyme can be used in treatment of acute lymphoblastic leukemia, by starving the cells that cannot synthesize asparagine. Peptide – based agents – A high-affinity peptidomimetic ligand (LLP2A) can be used to image α4β1- expressing lymphomas when conjugated to a fluorescent dye in mouse xenograft models. (peng et al., 2006). The side effects of chemotherapy in patients has directed to research on biomarkers that can predict the patients response to therapy. The level of a nuclease, required for nucleotide excision repair of DNA damage could indicate whether a tumor will be sensitive or resistant to a chemotherapeutic agent. (Bhagwat et al., 2009). GRP78, 78-kDa glucose-regulated protein was found to confer chemoresistance to topoisomerase inhibitors used for the treatment of breast cancer. (Lee et al., 2006). Expression of HtrA1, a serine protease, which is frequently down regulated in ovarian cancer, influences tumor response to chemotherapy by modulating chemotherapy-induced cytotoxicity. (Chien et al., 2006). Other molecular targets for therapy include extracellular targets such as EGFR/HER (Cetuximab), VEGF (bevacizumab), HER2 (Trastuzumab) and intracellular targets such as EGFR (erlotinib), VEGFR (sorafenib), mTOR (temsirolimus), PDGFR (sorafenib) etc. Biomarker for cancer prognosis Biomarker discovery towards cancer prognosis is very essential. There are many tumor markers that correlate with the survival of patients. It has been proved that co-expression of Her-2/neu and p53 in non-small cell lung carcinoma patients is associated with poor prognosis and indicates the necessity for more aggressive therapy in those patients. Another study on NSCLC describes that plasma level of alpha-crystallin antibodies could serve as potential biomarkers for disease progression. It has also been found that CGA and Pro-GRP appear to be important prognostic makers for prognosis of NSCLC. IL-8 serum levels are also elevated in advanced NSCLC patients and are correlated with the overall survival. It has been reported that Nestin expression is also significantly associated with poor prognosis of NSCLC. Fibroblast growth factor receptor 1 oncogene partner was found to be a novel prognostic marker and therapeutic target for lung cancer (Mano et al., 2007). Neuron-specific enolase, Interleukin-6, Transforming growth factor-β, Prostate-specific cell antigen, Testosterone, Estrogen, Sex hormone-binding globulin, Caveolin-1, E-cadherin, β-Catenin, MMP-9, TIMP 1, 2, MIC-1, Progastrin-releasing peptide, VEGF, CGRP are the tumor markers that are associated with poor prognosis of prostate cancer patients (Madu et al., 2010). uPAR is a cell surface receptor in urokinase Plasminogen Activation system and is found to be associated with prognosis of prostate cancer. Both in vivo and in vitro studies have shown that IL-6 and TGF-β1 expressions in prostate cancer are used to predict cancer progression and patient survival. Increased chromgranin A levels are also found to be associated with survival of SAMANTHI prostate cancer patients (Bensalah et al., 2008). By Global gene expression meta-analysis it has been identified that GATA3 which is a transcriptional activator is highly expressed in breast cancer and could be used as a prognostic marker (Mehra et al., 2005). PGP9.5 is used as a marker to predict the outcome of resection-treated pancreatic cancer patients. Its expression has been evaluated using immunohistochemistry (Tezel et al., 2000). CA 19-9 is used in the monitoring of pancreatic cancer. CEA and Thyroglobin are used in the monitoring of colon and thyroid carcinoma respectively. Fibrin/FDP, BTA, CEA and mucin are found to be the markers that are used for the monitoring of bladder cancer. CA 125 has been used in the ovarian cancer monitoring (Ludwig et al., 2005). Conclusion The overall cancer incidence rates continue to be on the increasing side globally. However, a positive change is seen of late in terms of management, therapy towards increased survival of affected individuals. The decreasing mortality rate is primarily due to progressive inputs in cancer research combined with technological advancements which synergistically translate the benefits to the affected individuals. Cancer biomarkers have contributed tremendously from various angles, from diagnostics to therapy. Several unique diagnostic markers for cancer types and targets for therapy are based on biomarker discovery. Cell lines, especially cancer cell lines are contributing important insights into the cancer mechanisms, drug discovery and biomarker identification. Both genomic and proteomic approaches using human cancer cell lines in advanced culture systems are made possible today and will sure continue to contribute to cancer research and biomarker discovery. References Bensalah, K., Lotan, Y., Karam, J.A., Shariat, S.F., 2008. 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Vol. 10 VOLUME Issue 03 l 01 September 2010 27 SCITECH JOURNAL ISSUE 10 OCTOBER 2014 Review Article Authors to provide, P.A. Mary Helen et al., THE SCITECH JOURNAL ISSN 2347-7318 ISSN 2348-2311 Online Ulrich TA, Jain A, Tanner K, MacKay JL, Kumar S. 2010. Probing CZhellular Mechanobiology in Three-dimensional Culture with Collagen-Agarose Matrices. Biomaterials. 31(7):1875-84. SAMANTHI World Health Organization. 2008. The Global Burden of Disease: 2004 Update. Geneva: World Health Organization.ong L, Ge K, Zu J, Zhoa L, Shen W, et al., 2008. Autoantibodies as Potential Biomarkers for Breast Cancer. Breast Cancer Res. 10(3). New citation: M. Ravi, Madhumitha Haridoss, W. Sai Keerthana (2014). Cancer Biomarkers – Discovery to Applications. The Scitech Journal, Vol.1(10): 18-28 Note: This review has previously been published in Advanced Biotech, Vol 10, Issue 11, May 2011. The journal Advanced Biotech has been merged into The Scitech with effect from January 2014. As the archives of Advanced Biotech might not be available online or through certain indexing portals, we intend to re-publish in The Scitech, certain articles that were published in the journal Advanced Biotech. Advanced Biotech and The Scitech are publications of the same publisher and the copyrights of manuscripts published in Advanced Biotech remain with the publisher. Hence, to prevent loss of important papers that were published in the journal Advanced Biotech, these will be re-published in The Scitech. Such articles will be presented with the former citation and the new citation as well for clarity to the authors and the readers. 38 THE l Advanced Biotech. Vol. 10 VOLUME Issue 03 l 01 September 2010 28 SCITECH JOURNAL ISSUE 10 OCTOBER 2014
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