Cancer Biomarkers – Discovery to Applications Abstract

Review Article
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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::[email protected]
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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
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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
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Bensalah et al., 2008
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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
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Bensalah et al., 2008
Ross et al.,2003
Mehra et al., 2005
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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
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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
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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.
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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.
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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,
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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
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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,
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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.
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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.
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