The footprints of cancer development: Cancer biomarkers

Cancer Treatment Reviews 35 (2009) 193–200

Contents lists available at ScienceDirect

Cancer Treatment Reviews journal homepage: www.elsevierhealth.com/journals/ctrv

HOT TOPIC

The footprints of cancer development: Cancer biomarkers Mohd. Fahad Ullah *, Mohammad Aatif Department of Biochemistry, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, UP 202002, India

a r t i c l e

i n f o

Article history: Received 31 July 2008 Received in revised form 16 October 2008 Accepted 17 October 2008

Keywords: Biomarkers Diagnosis Proteomics Cancer Microarray profile

s u m m a r y Diagnostic detection and measurement of cancer disease progression are essential elements for successful cancer disease management. The early stages of cancer development carry the maximum potential for therapeutic interventions. However, these stages are often asymptomatic, leading to delayed diagnosis at the very advanced stages when effective treatments are unavailing. The application of biomarkers to cancer is leading the way because of the unique association of genomic changes in cancer cells with the disease process. They have the potential to not only help identify who will develop cancer but also to predict as to when the event is most likely to occur. In recent years, there has been an enormous effort to develop specific and sensitive biomarkers for precise and accurate screening, diagnosis, prognosis and monitoring of high risk cancer to assist with therapeutic decisions. The present article is a brief review of the emerging trends in the development of biomarkers for early detection and precise evaluation of cancer disease. Ó 2008 Elsevier Ltd. All rights reserved.

Introduction Cancer is a disease characterized by abnormal growth and development of normal cells beyond their natural boundaries. Despite of global efforts to limit the incident of this disease, cancer has become the leading cause of death in the last 50 years,1 with breast cancer the most common malignancy in women and the second most common cause of cancer-related mortality2 and prostate cancer being the most common solid organ malignancy diagnosed in men in Europe and USA and the second most frequent cause of cancer-related death in men.3 The management of these high risk cancers requires diagnosis at an early stage, which specifies the need for specific and sensitive biomarkers. A biomarker is a quantifiable laboratory measure of a disease specific biologically relevant molecule that can act as an indicator of a current or future disease state. Sometimes, certain molecules are differentially expressed in cancer cells relative to their normal counterparts and their altered levels could be measured to establish a correlation with the diseased state. Alternatively, certain molecules are specifically present in tumors and are different from corresponding normal tissue and can be identified as biomarkers of tumorigenesis. Depending on their site of evaluation they may be tissue or circulating biomarkers. Tissue markers include different categories such as membrane receptors, oncogenes, tumor suppressor genes, nuclear antigens, growth factors, components of degradome whereas circulating markers include the wide category of tumor-associated antigens (TAA). Besides these, emerging genomic and proteomic

* Corresponding author. Mobile: +91 9412397557. E-mail address: [email protected] (M.F. Ullah). 0305-7372/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.ctrv.2008.10.004

technologies have the potential to transform the way in which cancer is clinically managed. Tissue biomolecular markers, aside from being prognostic and predictor factors also play a central role in targeted therapies that are among the emerging directions of cancer therapeutics. Selective biomarkers may be able to define susceptibility risks and assist in tumor detection and diagnosis allowing timely therapeutic interventions for an effective treatment. In most cases, survival rates for patients with the cancers, especially those detected at an advanced stage, remain discouragingly low.1 Patients with early detection of cancer have better rate of recovery and survival than patients with more advanced cancer. In most cases, detection of stage 1 cancers is associated with a >90% five-year survival rate.4 When lesions are detected even earlier (at the premalignant stage), treatment is often curative. The anticipated benefit of sensitive biomarkers is based on the assumption that interventions exist that either prevent cancer in high risk individuals or more effectively eradicate cancer when individuals are diagnosed at a time of low tumor burden. The better clinical outcomes associated with early detection highlights the need for sensitive biomarkers of cancer. The present review seeks to describe biomolecules of interest having the potential to be developed as cancer biomarkers concomitant with the application of emerging techniques. Proteases as cancer biomarkers Cysteine proteases Cysteine cathepsins (CCs) are a family of lysosomal proteases of papain family that are often upregulated in various human cancers.5,6 This family has 11 members (cysteine cathepsin B, L, S, K,

194

M.F. Ullah, M. Aatif / Cancer Treatment Reviews 35 (2009) 193–200

H, C, O, F, V, W and X) which share a conserved active site that is formed by a cysteine residue.7 The classification of cysteine proteases can be found in the MEROPS database8 and has been reviewed in detail by Barrett and Rawlings9 Cysteine cathepsins are synthesized as 30–50 kDa preproenzymes which are directed into lysosomes where they serve their function of protein hydrolysis after cleavage to form active enzyme.10 The best known CCs, cathepsins B, L,H, F, O, C, X are distributed ubiquitously in most tissues whereas cathepsins S, K, W and V are comprised to specific tissues.11 Studies have shown a correlation between cancer development and differential expression and localization of cysteine cathepsins. In normal cells, cysteine cathepsins are usually located in lysosomes, whereas during cancer progression they move to the cell surface, from where they can be secreted into the extracellular milieu12 to promote tumor invasion through several possible mechanisms.13 Altered levels of cysteine cathepsins have been found to be associated with several pathological states including cancer.8 Several studies have reported tissue specific enhanced expression, activity and mis-localization of cysteine cathepsins in human cancers.11,14–18 The upregulation of cysteine cathepsins has been reported in cancers of both epithelial and mesenchymal origin, including breast, brain, lung, gastrointestinal, colorectal cancer and melanoma among others.17,10 Due to their enhanced levels and tumor specific localization cysteine cathepsins have been implicated as significant biomarkers for the prognosis of cancer.13 The usefulness of these enzymes in diagnosis and prognosis has been extensively analyzed by Lah19 and Kos et al.20 Elevated cathepsin B expression correlates with good prognostic value in lung, breast, ovarian, brain, head and neck cancer and melanoma17 and in premalignant lesions situated within colon, thyroid, liver, and prostate.21 Similarly, increased cathepsin L activity has been observed in multiple tumor types and can be used as a prognostic indicator of shorter survival rates in patients with breast, colorectal and head and neck cancers.10 It was also reported that both cathepsin B and L are also implicated in human pancreatic endocrine tumors.22 Previous studied have provided evidence that cysteine proteinases cathepsins B, H and L are linked to remodeling and degradation of tumor tissue and surrounding extracellular matrix proteins. The altered levels of these enzymes and their inhibitors in tumor as well as in some extracellular fluids have been linked to tumor progression.23 The enhanced levels concurrent with enhanced tumor growth, invasion and metastasis designates these molecules as potential prognostic indicator/marker in cancer.

sample size of 150 and 142 patients respectively. In contrast to the upregulation of these kallikreins in ovarian cancer, kallikreins 3, 10, 12, 13, and 14, are downregulated in breast cancer tissues.28–30 Microarray analysis profiling of the gene expression patterns in human lung adenocarcinomas indicated that KLK11 is uniquely overexpressed in a subgroup of neuroendocrine carcinomas.31 Another microarray study has characterized differential transcription profiles in pancreatic ductal adenocarcinomas and showed that KLK10 is one of the most highly and specifically overexpressed genes in pancreatic cancer compared with normal and benign pancreas tissues.32 Furthermore, the KLK10 gene is downregulated in acute lymphblastic leukemia.33 It is also noted that hK11and hK14 have emerged as complimentary biomarkers for prostate cancer along with hK3 (PSA).34,35 These have been shown to be overexpressed in prostate malignancy and shows an incremental expression pattern as the disease progresses from earlier stage (stage I) to late stage (stage II).36 Furthermore, the differential expression of KLK10 and KLK14, along with KLK13 splice variants in testicular cancer tissues, have been reported to be favorable markers, showing reduced expression in malignant forms of the disease than in healthy individuals.37–39 The urokinase plasminogen activator (uPA) system represents a family of serine proteases that are involved in the degradation of basement membrane and extracellular matrix, leading to tumor cell invasion and metastasis. Unlike healthy tissues, high endogenous levels of uPA and uPAR are associated with advanced metastatic cancers and carry potential for diagnostic significance.40 uPA system is primarily associated with the degradation and regeneration of the basement membrane and extracellular matrix that leads to metastasis. uPA catalyzes the activation of plasminogen into plasmin by cleaving the arginine–valine bond and in turn plasmin facilitates the release of several proteolytic enzymes, including gelatinase, fibronectin, fibrin, laminin, and latent forms of collagenases and stromelysins.41,42 High expression of uPA and uPAR has been reported for several cancers such as esophageal,43 gastric,44 pancreatic,45 endometrial46 and ovarian cancer.47 Table 1 summarizes the status of various proteases of diagnostic significance in cancer.10,25

Serine proteases

Serum-based prostate specific antigen (PSA) measurements have significant influence on current treatment strategies for men with prostate cancer (PCa). PSA is a serum protease that is secreted from prostate epithelial cells. There are evidences to show that PSA levels have prognostic value for men with prostate cancer.48 The median levels are approximately 0.7 ng/ml in the men aged 60 years49 and a modest elevation of the blood level of PSA 4-10 ng/ml is strongly associated with increased cancer risk.49–53 During the past few years, PSA has become an indispensable marker for diagnosis and follow up of prostate cancer patients. However, the specificity of total serum PSA is limited54 particularly in rising incidence of clinically relevant prostate cancers in patients with low PSA serum levels (less than 4.0 ng/ml).55–58 These findings suggest the need for new tools to improve the specificity of PSA determination in prostate cancer detection in men with low PSA levels.59 The ratio of free to total PSA (f/t PSA) has been found to be lower in men with prostate cancer compared with that in men with benign prostatic disease. Therefore, the determination of percentage free/total PSA (% fPSA) has been suggested as an additional screening tool.60 Using a f/t PSA ratio cutoff value of 0.1–0.2 ng/ml, 33–56% of prostate cancers have been detected in men with PSA serum levels between 2.5 and 4.0 ng/ml.61 Com-

Human kallikreins (hKs) are serine proteases widely expressed in diverse tissues and implicated in a range of normal physiological functions. Dysregulated enzyme expression is associated with multiple diseases including cancer. Their role has been suggested in cancer metastasis and invasion. This has made them promising diagnostic biomarkers for several cancer types, including ovarian, breast, and prostate.24,25 The most important kallikrein is prostate specific antigen (PSA) or hK3 having high diagnostic significance in prostate cancer (to be discussed in detail in the succeeding section). However, several other kallikreins are emerging as novel biomarkers for various cancers such as ovarian. These include hK1-2 and hK4-15 expressed in a myriad of tissues. All 15 kallikrein genes are differentially expressed in cancer with overexpression of kallikreins 4, 5, 6, 7, 8, 10, 11, 13, 14, and 15 observed in ovarian carcinoma tissues and serum.24,25 In particular, KLK4 and KLK5 mRNAs have been shown to be overexpressed and are indicators of poor prognostic outcome in grade 1 and grade 2 tumors, suggesting that these genes are associated with more aggressive forms of ovarian cancer.26,27 These studies investigating the status of KLK4 and KLK5 in tumor tissue tend to be significant owing to considerable

Tumor specific antigens and autoantibodies Prostate specific antigens

M.F. Ullah, M. Aatif / Cancer Treatment Reviews 35 (2009) 193–200 Table 1 Altered levels of proteases as biomarkers of cancer diagnosis and prognosis. Cancer

cysteine protease (cathepsins)

Expression

Localization in tissues/ fluids

Breast cancer

CatB, L

"

CatH

"

CatB CatB CatL CatB

" " " "

CatB, H, S

"

CatB, L CatB, L, H CatB CatL CatB CatH, L CatB, L CatB

; " " " " " " "

Cancer tissues and cell lines Cancer tissues and serum Cancer tissues Serum Serum Serum and cancer tissues Cancer tissues and cell lines Cancer tissues Cancer cell cultures Plasma and urine urine Cancer tissues Cancer tissues Cancer tissues Cancer tissues

CatH

"

CatB, L CatH CatH CatS CatB, L, H CatB, L

" ; " " " "

CatH CatB, H CatB CatL CatB, L, H CatH CatB, C, H, L, S CatL CatB

; " " " " ; ; "

Cancer Serum Cancer Cancer Cancer Cancer Cancer Cancer Cancer

KLK10 KLK6 KLK1, 14 KLK5, 6, 7, 8,10,12,13 KLK7, 8 KLK1 KLK6, 8, 10 KLK6 KLK6 KLK6, 10 KLK5, 10, 11 KLK2, 4,7,9,11,15 KLK5, 13 KLK6, 8, 10 KLK14 KLK6, 10 KLK2, 5, 6, 10, 13 KLK4, 11, 14, 15 KLK5, 6, 10, 11

; ; ; ; " ; " " " " " " " " ; " ; " ;

Cancer cells Cancer cells Cancer cells Serum Cancer cells Cancer tissues Cancer tissues Cancer cells Cancer cells Cancer cells Cancer cells Cancer cells Serum Atscites Cancer cells and serum Cancer tissue Cancer cells Cancer cells Cancer cells Cancer cells

Ovarian cancer

Uterine cervix cancer Prostate cancer

Bladder cancer Colorectal cancer Gastric cancer Pancreatic cancer

Lung cancer

Brain tumor Head and neck cancer Melanoma

Kidney cancer Uterus cancer Thyroid cancer Serine protease (kallikreins) Acute lymphoblastic leukemia (all) Brain Breast Cervical Colon Colorectal Esophageal Gastric Lung Ovarian

Pancreatic Prostate Renal cell carcinoma

SK-PC-1 pancreas cancer cells Cancer tissues Cancer tissues Serum Cancer tissues Cancer tissues Cancer tissues tissues tissues tissues tissues tissues tissues tissues tissues

(") Enhanced expression of protease in diseased as compared to normal material. (;) Lowered expression of protease in diseased as compared to normal material.

plexed PSA (cPSA) determined by specific immunoassays has been suggested to improve specificity of PSA.62 It has been recognized that the majority of PSA found in men with prostate cancer is complexed to a1-antichymotrypsin (ACT) compared with the PSA found in healthy men. After getting access to the systemic circulation, the majority becomes complexed to protease inhibitors such

195

as a1-ACT. Using a cPSA cutoff value of 2.1 ng/ml, a sensitivity of 86% and a specificity of 34.2% for cancer detection in men with tPSA levels of 2.0–4.0 ng/ml was recorded.63 cPSA appears to be a useful tool in the early detection of prostate cancer due to the marked improvement in specificity in patients with low tPSA levels in the range from 4.0 ng/ml. Pro or/and precursor forms of PSA have also emerged as potentially important diagnostic serum markers for prostate cancer detection. Immunoassays have been developed to measure specific pPSA forms containing propeptides of 2, 4, and 7 amino acids {(2)proPSA, (4)proPSA, and (7)proPSA, respectively}. ProPSA is enriched in tumors compared with benign prostate tissue.64–67 In a study aimed to compare the proPSA levels (as percentage of total free PSA) in serum of men with biopsy-positive and biopsy-negative phenotype, Mikolajczyk et al have reported the highest percentage of (2) proPSA in 5 biopsypositive men (25–95%) compared to 3 biopsy-negative men with markedly reduced levels of truncated isoform (6–19%).66 With the development of highly specific immunoassays for pPSA, multiple studies have been conducted to establish its clinical usefulness in cancer detection.68,69 The expression analysis of percent pPSA significantly improved specificity for cancer detection.70 Furthermore, prostate cancer antigen 3 (PCA3) has been shown to be overexpressed in more than 95% of primary prostate cancers and such an expression is distinctly observed in the malignant tissue compared to adjacent non malignant prostatic tissue.71 The study has revealed that in 53 of 56 human radical prostectomy specimens, a 10–100-fold overexpression of PCA3 was seen in the tumor areas in comparison to the adjacent non neoplastic prostate tissue. Given the marked difference in the expression pattern of PCA3 in normal and malignant tissue, the antigen could present a reliable diagnostic tool for detection of early neoplastic events. Tumor specific autoantibodies Tumors are known to induce release of many proteins into the blood and since due to various modifications they appear as foreign molecules, they lead to the activation of immune system.72 The presence of autoantibodies in the sera of high risk individuals foretells the onset of cancer development and marks their significance as molecular signatures for useful clinical diagnostic and prognostic information. Such autoantibodies can be detected in the sera of patients with breast and other cancers prior to the clinical diagnosis of cancer.73–77 Autoantibodies in breast and other cancer sera target important molecules involved in signal transduction, cell cycle regulation, cell proliferation and apoptosis, all of which are key processes in carcinogenesis. Autoantigens detected by anti-p53-based autoantibodies were found in the sera of 9–26% of women with breast cancer.73 Anti-p53 antibodies have also been found in 11.1% of women with a positive family history of breast cancer, indicating that certain autoantibodies can have predictive value of risk.78 Autoantibody against 5- hydroxymethyl-2’-deoxyuridine, an oxidized DNA base can potentially serve as a marker for increased risk of breast cancer, because of their relatively high serum levels in patients with breast cancer and also in otherwise healthy women with a first degree family history of breast cancer and in women with the diagnosis of benign conditions.79 Autoantibodies of IgG isotype, with specificity for the N-terminal of replication protein A (RPA32) were found in approximately 11% of patients with breast cancer including ductal carcinoma in situ (DCIS) of the breast.80 Zhong et al.81 have reported an impressive profiling of tumor-associated antibodies for early detection of non-small cell lung cancer and the signature can be used to detect cancer risk 5 years prior to autoradiograph detection. Multiple prostate cancer specific antigens were identified via the detection of autoantibodies in the serum of patients with prostate cancer via high throughput phage peptide microarray

196

M.F. Ullah, M. Aatif / Cancer Treatment Reviews 35 (2009) 193–200

analysis. The measurement of serum autoantibodies against a panel of 22 tumor associated peptides detected prostate cancer with 88.2% specificity and 81.6% sensitivity in a case-control study.82 The gene for a-methylacyl-CoA racemase (AMACR) has been overexpressed in prostate cancer tissues.83,84 Autoantibodies to AMACR have been detected in the sera of patients with prostate cancer.85 Compared to PSA, this autoantibody signature had significantly better performance suggesting the use of autoantibodies against peptides derived from prostate cancer tissue as better tools for screening of prostate cancer. In cancer cells, cyclic AMP–dependent protein kinase (PKA) is secreted into the conditioned medium whereas in normal mammalian cells, it is present strictly intracellularly.86 It has been observed that cancer cells of various types excrete PKA into the conditioned medium. This PKA, designated as extracellular PKA (ECPKA) was found to be markedly upregulated in the serum of patients with cancer.87,88 A novel enzyme immunoassay reported by Nesterova et al.89 measures the anti-IgG autoantibody for ECPKA exhibiting 90% sensitivity and 88% specificity. In contrast to the detection of serum antigens, the detection of serum antibodies to tumor antigens may provide reliable serum markers as antibodies may be more abundant than antigens, especially at low tumor burden.

Nucleic acid based biomarkers Methylated DNA as biomarker The regulation of gene expression by aberrant methylation has been well established in tumor biology.90 The epigenetic phenomenon of hypermethylation in tumor-related genes has been implicated in cancer development and progression.91,92 Some CpG islands may be differentially susceptible to hypermethylation under certain unknown growth selection pressures, which may drive characteristic pathways leading to the development of certain tumor types. The assessment of epigenetic events is therefore one of the most promising means of identifying marker candidates for the early detection of cancer. Since hematogenous dissemination of tumor cells is the main mechanism for distant metastasis, assessment of blood may be a feasible approach for detecting systemic tumor cell spreading.93 Tumor-related free methylated DNA in blood of cancer patients have been assessed for their clinical utility. Circulating tumor cells are considered to be the source of floating DNA which is released into the circulation upon the death of the tumor cells.94,95 Studies have reported the presence of tumor specific hypermethylated DNA at tumor-related gene promoter regions in patients with metastatic tumors.96,97 Methylation-specific PCR (MSP) is a sensitive and specific assay for tumor-related DNA methylation in serum/plasma, urine and other fluids.98 Various studies have reported the diagnostic potential of circulating tumor-related methylated DNA in serum for detection of cancer.99 Lofton-Day et al.100 have identified three markers, TMEFF2 (transmembrane protein with EGF-like and two follistatin-like domains 2), NGFR [nerve growth factor receptor), and SEPT9 (septin 9), all having a colorectal cancer (CRC)-specific methylated pattern in plasma. However, SEPT9 predicted the presence of CRC significantly more accurately than TMEFF2 (P < 0.001) or NGFR (P < 0.01). A cutoff of 0.011 lg/L of methylated SEPT9 DNA was shown to produce a specificity of 95% and a sensitivity of 52%. Similar panels of cancer specific methylated markers for prostate and bladder cancer have also been evaluated in urine samples.101,102 Methylation pattern found in DNA sediments of urine samples matched the methylation status in the primary tumor. It was found that 87% of the samples from patients with prostate cancer demonstrated methylation in at least one of the four genes (p16, ARF, MGMT and GSTP1) with 100% specificity (i.e, all control samples

from healthy individuals were negative for methylation in these four genes). Dulaimi et al.103 have identified a panel of tumor suppressor genes (APC, RASSF1A and p14) showing hypermethylation in bladder cancer with 87% sensitivity and 100% specificity. Promoter hypermethylation has also been used successfully to detect neoplastic DNA in sputum,103 bronchial lavage fluid104 and serum93 from lung cancer patients; and serum from liver,105 head and neck106 and breast cancer patients.107 Thus, diagnostic tools based on DNA alterations are expected to provide relatively high specificity and sensitivity and could therefore be useful for early cancer detection. MicroRNAs as cancer biomarker MicroRNAs (miRNA) are naturally occurring and highly conserved small non coding RNAs, 18–25 nucleotides in length, that regulate mRNA expression by binding to the 30 untranslated region (30 UTR) of mRNA leading to the impairment in the synthesis of the corresponding protein.108 These small molecules have been implicated in various biological processes as diverse as differentiation, proliferation, metabolism and cell death. Recently, miRNA expression has been linked to cancer. This link has been established from the observation that microRNAs miR-15a and miR16-1 are downregulated or deleted in >65% patients with chronic lymphocytic leukemia (CLL).109 A general downregulation of miRNAs has been observed in tumors compared with normal tissues.110 This is consistent with the earlier findings related to the functional aspects of first identified miRNAs, the products of the C. elegans genes lin-4 and let-7.111 When lin-4 or let-7 is inactivated, specific epithelial cells undergo additional cell divisions instead of their normal differentiation. Since, abnormal cell proliferation is a hallmark of human cancers; it seems possible that miRNA expression patterns might denote the malignant state. Indeed, altered level of various specific microRNAs have been observed in various malignancies including primary brain tumors (miR-21, miR-221 and miR-25),112 papillary thyroid carcinoma (miR-221, miR-222 and miR-146), breast cancer (miR-125b, miR145, miR-21 and miR-155)113 and hepatocellullar carcinoma.114 Limited expression of microRNAs miR-126 and miR-335 has been shown to lend metastatic potential to breast cancer. Restoring the expression of these microRNAs in malignant cells suppresses lung and bone metastasis by human cancer cells in vivo.115 Ma et al.116 have reported the involvement of microRNA-10b (miR10b) in tumor invasion and metastasis initiation in breast cancer. It was shown that overexpression of miR-10b in otherwise nonmetastatic breast tumours initiates robust invasion and metastasis. Thus, miR-335 and miR-126 are identified as metastasis suppressor and miR-10b as metastasis promoter microRNAs in human breast cancer. Thus, unlike downregulation of most miRNAs as observed in various malignancies, an overexpression of few may drive the cancer cells to metastasize. Such variation may well explain the differential expression pattern of specific miRNAs during developmental stages in oncogenesis. Of particular interest is their tissue specific asymmetric expression in tumorogenesis,117 making them potential cancer biomarkers especially in case of metastasized cancers of unknown primary origin. Metastatic cancer of unknown primary origin accounts for 3–5% of all new cancer cases and is usually a very aggressive disease with poor prognosis,118 due to the limitation of present methods to identify cancer origin. MiRNAs have thus emerged as strong markers due to high tissue-specificificity.119 The use of miRNA microarrays made possible large profiling studies in cancer patients, confirming that miRNAs are differentially expressed in normal and tumor samples.120 Gene expression signatures of mRNA expression levels have been used for development of molecular classification algorithms to trace tumor origin.

M.F. Ullah, M. Aatif / Cancer Treatment Reviews 35 (2009) 193–200

197

Using microRNA array with 217 miRNAs, Lu et al.121 reported the greater sensitivity compared to mRNA classifiers towards detecting the tumors of unknown origin from poorly differentiated tumor samples with non-diagnostic histological appearance, clearly demonstrating the advantage of miRNA profiles over mRNA profiles for diagnostic classification. Rosenfeld and colleagues have recently demonstrated an miRNA-based tissue classifier to identify the tissue origin of metastatic tumors118 using classification algorithm as a branched binary tree that based on roles of individual miRNAs, group together tissues with underlying similarities. The classifier used only 48 miRNA markers to reach an overall accuracy of  90% among 22 tissue origins, thereby evolving as first rate microRNA based diagnostic technology. MicroRNAs are emerging as preferred class of biomarkers due to their less numbers (approximately 1000) compared to the manifold number of mRNAs and proteins (>25 000), thus providing effective screening of entire genome for expression profiling.

Microarrays containing the repertoire of natural proteins expressed in tumour cells have the potential to substantially advance the pace of identifying tumour associated antigens and could provide a molecular signature for different types of cancer.131 There are two general types of Protein microarrays: analytical and functional protein Microarrays. Analytical microarrays involve a high density array of affinity reagents (e.g. antigens or antibodies) that are used for detecting proteins in a complex mixture. Functional protein chips are constructed by immobilization of large numbers of purified proteins on a solid surface. Unlike the antibody–antigen chips, protein chips have enormous potential in assaying for a wide range of biochemical activities (e.g. protein–protein, protein–lipid, protein–nucleic acid and enzyme–substrate interactions) as well as drug and target identification.132 The current wisdom suggests that a panel of multiple biomarkers133 will be needed for complex, multi-gene disease like cancer. Proteomics, being a high throughput technology that analyzes complete proteome profiles provides a suitable tool for the task.

Proteomics in biomarker profiling

Nanotechnology in biomarker profiling

Considerable progress in proteomic technologies has lead to major applications of clinical proteomics in the identification of new targets for treatment and therapeutic intervention, as well as discovery of novel biomarkers for diagnosis, prognosis, and therapeutic efficacy through comparison of proteome profiles between healthy and disease states.122,123 Proteomics refer to the analysis of entire protein compliment expressed by a genome (PROTEOME). In contrast to the genome, which is rather constant entity, the proteome is defined as a dynamic collection of proteins that varies between individuals, cell types, and also under different pathophysiological conditions thus providing personalized proteome profiles under normal and diseased conditions.124 The proteomic studies to investigate proteins and their interactions in a cancer cell have emerged as an important field of oncoproteomics.125 Proteomic studies have generated high throughput protein profiles of potential diagnostic significance in various human cancers such as the brain, breast, colorectal, prostate and leukemia. At the protein level, distinct changes occur during the transformation of a healthy cell into a neoplastic cell, including differential expression, post translational protein modifications, changes in specific activity and cellular localization, all of which may participate in carcinogenesis. Identifying and understanding these changes as natural signatures of cancer progression may unfold new diagnostic approaches for early stage cancer detection. Two key technologies underpinning these studies in cancer tissue are two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry (MS). Although surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF)-MS is the mainstay for serum or plasma analysis, other methods such as isotope-coded affinity tag technology (ICAT) and proteinchips (reverse-phase protein arrays and antibody microarrays) are emerging as alternative proteomic technologies. Technologies such as differential in-gel electrophoresis (DIGE),126 two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and multidimensional protein-identification technology (MudPIT) can be used for higher-throughput profiling with microgram quantities of protein. Other high throughput technologies, such as the reverse-phase microarray127 and surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) mass spectrometry128 are more sensitive quantifying in the femtomolar range. Protein chips have the advantage of testing multiple biomarkers simultaneously, which has the potential to accelerate the validation process. Multiplex measurement of biomarkers coupled with advanced bioinformatic applications will enhance the clinical utility and lead to new targeted diagnostics for personalized medicine.129,130

Cancer nanotechnology is emerging as a new field which is expected to lead to major advances in cancer detection, diagnosis, and therapy.134,135 Concomitant to the development of many new nanoscale platforms such as quantum dots, nanoshells, gold nanoparticles, paramagnetic nanoparticles and carbon nanotubes, cancer nanotechnology seeks to characterize the interaction of nanoscale devices with cellular and molecular components specifically related to cancer diagnosis and therapy. In particular, nanoparticle probes can be used to quantify a panel of biomarkers on intact cancer cells and tissue specimens. The development of cancer nanotechnology requires the use of nanoparticles to harvest and analyze circulating tumor cells and biomarkers in blood/serum samples.136 A valuable approach of biomarker harvesting is to tailor nanoparticle surfaces with specific molecular interaction characteristics to selectively bind a subset of biomarkers, sequestering them for later study using highly sensitive proteomic techniques.137 Bioconjugated particles with affinity proteins on their surface can be used to specifically target certain biomarkers for isolation from serum. A single nanoparticle is compatible enough for conjugation to multiple ligands, leading to enhanced binding affinity and exquisite specificity through a multivalency effect. This is especially important in the analysis and detection of cancer biomarkers that are present at low concentrations or in small numbers of cells at initial stages of low tumor burden.137 Nano devices are also under development for early cancer detection in body fluids. These nanoscale devices operate on the principles of selectively capturing cancer cells or target proteins. The sensors are often coated with a cancer specific antibody or other biorecognition ligands so that the capture of a cancer cell or target protein yields an electrical, mechanical, or optical signal for detection.137 The use of bioconjugated nanoparticles that carry an internal signal like inorganic fluorophores (Quantum dots) offer significant advantages over conventionally used fluorescent markers. They have high sensitivity, broad excitation spectra, stable fluorescence with simple excitation138 and emit light or color at different wavelengths, allowing for independent labeling and identification of multiplex biomarkers.139,140 Antibody-conjugated multicolor quantum dots have been used for multiplexed molecular profiling of cancer cells and clinical tissue specimens, and for correlation of a panel of 4–5 biomarkers with cancer behavior and patient outcome.137 Other nanodevices under consideration for biomarker profiling include carbon nanotubes (CNTs),141 nanowire arrays for multiplexed cancer biomarker detection,142 superparamagnetic iron oxide,143 magnetic nanoparticles144 and nanorobotics145 which may be applied in future for biomarker profiling in cancer.

198

M.F. Ullah, M. Aatif / Cancer Treatment Reviews 35 (2009) 193–200

Conclusion Human cancer is a complex disease caused by genetic instability and accumulation of multiple molecular alterations.146 Last several decades have witnessed the emergence of various therapeutic regimens to contain this fatal disease. However, survival rates for patients with cancers, especially those detected at an advanced stage remains discouragingly low. This correlates with the observation that at the time of clinical presentation, more than 60% of patients with breast, lung, colon, prostate, and ovarian cancer have hidden metastatic colonies.128 At this stage, therapeutic modalities are limited in their effectiveness, thereby warranting early disease detection using sensitive biomarkers. By critically defining the interrelationships among these biomarkers, it could be possible to diagnose and prognosticate cancer based on a patient’s biomolecular profile. Since cancer is a disease in which several critical pathways are altered, multiple biomarker profiling appears to have an advantage over single marker. Further, on the basis of the marked heterogeneity of most human cancers, it is unlikely that a single gene, chromosome aberration or protein will provide sufficient accuracy for early detection. Multiplexing will enable diagnosis based on a more informative assessment of biomarker panels, providing better disease prognosis and more effective patient management. It is also suggested that such a multiplex profile could provide molecular signatures even at low tumor burden in initial stages of cancer development, thus providing footprints of neoplastic transformation. Conflict of interest statement There exists no conflict of interests. Acknowledgements The authors thankfully acknowledge the kind support from Prof. S.M. Hadi and Prof. Bilquees Bano, Department of Biochemistry, Faculty of Life Sciences, Aligarh Muslim University, India. The authors also acknowledge the financial assistance from University Grants Commission, New Delhi. References 1. American Cancer Society. Cancer statistics; 2005. www.cancer.org. 2. Jemal A, Murray T, Ward E, et al. Cancer statistics. CA Cancer J Clin 2005;55(1):10–30. 3. Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ. Cancer statistics. CA Cancer J Clin 2007;57(1):43–66. 4. Etzioni R, Urban N, Ramsey S, et al. The case for early detection. Nat Rev Cancer 2003;3(4):243–52. 5. Turk B. Targeting proteases: successes, failures and future prospects. Nat Rev Drug Discov 2006;5:785–99. 6. Mohamed MM, Sloane BF. Cysteine cathepsins: multifunctional enzymes in cancer. Nat Rev Cancer 2006;6(9):764–75. 7. Palermo C, Johanna AJ. Cysteine cathepsin proteases as pharmacological targets in cancer. Trends Pharmacol Sci 2008;29(1):22–8. 8. Rawlings ND, Barrett AJ. MEROPS – the peptidase database. Release 6.30. http://merops.sanger.ac.uk/2003. 9. Barrett AJ, Rawlings ND. Evolutionary lines of cysteine peptidases. Biol Chem 2001;382(5):727–33. 10. Berdowska I. Cysteine proteases as disease markers. Clin Chim Acta 2004;342(1–2):41–69. 11. Bromme D, Kaleta J. Thiol-dependent cathepsins: pathophysiological implications and recent advances in inhibitor design. Curr Pharm Des 2002;8(18):1639–58. 12. Turk D, Guncar G. Lysosomal cysteine proteases (cathepsins): promising drug targets. Acta Crystallogr D Biol Crystallogr 2003;59:203–13. 13. Gocheva V, Johanna AJ. Cysteine cathepsins and the cutting edge of cancer invasion. Cell Cycle 2007;6(1):60–4. 14. Joyce JA, Baruch A, Chehade K. Cathepsin cysteine proteases are effectors of invasive growth and angiogenesis during multistage tumorigenesis. Cancer Cell 2004;5(5):443–53. 15. Rao JS. Molecular mechanisms of glioma invasiveness: the role of proteases. Nat Rev Cancer 2003;3(7):489–501.

16. Grimm J, Kirsch DG, Antiago PM. Use of gene expression profiling to direct in vivo molecular imaging of lung cancer. Proc Natl Acad Sci USA 2005;102(40):14404–9. 17. Jedeszko C, Sloane BF. Cysteine cathepsins in human cancer. Biol Chem 2004;385(11):1017–27. 18. Joyce JA, Hanahan D. Multiple roles for cysteine cathepsins in cancer. Cell Cycle 2004;315(12):1516–9. 19. Kos J, Werle B, Lah T, Brunner N. Cysteine proteinases and their inhibitors in extracellular fluids: markers for diagnosis and prognosis in cancer. Int J Biol Markers 2000;15(1):84–9. 20. Lah TT, Kos J. Cysteine proteinases in cancer progression and their clinical relevance for prognosis. Biol Chem 1998;379(2):125–30. 21. Koblinski JE, Ahram M, Sloane BF. Unraveling the role of proteases in cancer. Clin Chim Acta 2000;291(2):113–35. 22. Pelosi G, Pasini F, Bresaola E, et al. High-affinity monomeric 67-kD laminin receptors and prognosis in pancreatic endocrine tumours. J Pathol 1997;183(1):62–9. 23. Levicar N, Kos J, Blejec A, et al. Comparison of Potential biological markers Cathepsin B, cathepsin L, stefin A and stefin B with urokinase and plasminogen activator inhibitor-1 and clinicopathalogical data of breast carcinoma patients. Cancer Detection Prev 2002;26(1):42–9. 24. Borgono CA, Michael IP, Diamandis EP. Human tissue kallikreins: physiologic roles and applications in cancer. Mol Cancer Res 2004;2(5):257–80. 25. Paliouras M, Borgono C, Diamandis EP. Human tissue kallikreins: the cancer biomarker family. Cancer Lett 2007;249(1):61–79. 26. Obiezu CV, Scorilas A, Katsaros D, et al. Higher human kallikrein gene 4 (KLK4) expression indicates poor prognosis of ovarian cancer patients. Clin Cancer Res 2001;7(8):2380–6. 27. Kim H, Scorilas A, Katsaros D, et al. Human kallikrein gene 5 (KLK5) expressions is an indicator of poor prognosis in ovarian cancer. Br J Cancer 2001;84(5):643–50. 28. Liu XL, Wazer DE, Watanabe K, Band V. Identification of a novel serine protease-like gene, the expression of which is down-regulated during breast cancer progression. Cancer Res 1996;56(14):3371–9. 29. Yousef GM, Chang A, Diamandis EP. Identification and characterization of KLKL4, a new kallikrein-like gene that appears to be down-regulated in breast cancer tissues. J Biol Chem 2000;275(16):11891–8. 30. Yousef GM, Magklara A, Diamandis EP. KLK12 is a novel serine protease and a new member of the human kallikrein gene family—differential expression in breast cancer. Genomics 2000;69(3):331–41. 31. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA 2001;98(24):13790–5. 32. Iacobuzio-Donahue CA, Ashfaq R, Maitra A, et al. Highly expressed genes in pancreatic ductal adenocarcinomas: a comprehensive characterization and comparison of the transcription profiles obtained from three major technologies. Cancer Res 2003;63(24):8614–22. 33. Roman-Gomez J, Jimenez-Velasco A, Agirre X, et al. The normal epithelial cellspecific 1 (NES1) gene, a candidate tumor suppressor gene on chromosome 19q13.3–4, is downregulated by hypermethylation in acute lymphoblastic leukemia. Leukemia 2004;18(2):362–5. 34. Scorilas A, Gregorakis AK. MRNA expression analysis of human kallikrein 11 (KLK11) may be useful in the discrimination of benign prostatic hyperplasia from prostate cancer after needle prostate biopsy. Biol Chem 2006;387(6):789–93. 35. Yousef GM, Stephan C, Scorilas A, et al. Differential expression of the human kallikrein gene 14 (KLK14) in normal and cancerous prostatic tissues. Prostate 2003;56(4):287–92. 36. Jema A, Tiwari RC, Murray T, Ghafoor A, et al. Cancer statistics. CA Cancer J Clin 2004;54(1):8–29. 37. Luo LY, Rajpert-De Meyts ER, Jung K, Diamandis EP. Expression of the normal epithelial cell-specific 1 (NES1; KLK10) candidate tumour suppressor gene in normal and malignant testicular tissue. Br J Cancer 2001;85(2):220–4. 38. Yousef GM, Magklara A, Chang A, Jung K, Katsaros D, Diamandis EP. Cloning of a new member of the human kallikrein gene family, KLK14, which is downregulated in different malignancies. Cancer Res 2001;61(8):3425–31. 39. Chang A, Yousef GM, Jung K, Rajpert-De Meyts E, Diamandis EP. Identification and molecular characterization of five novel kallikrein gene 13 (KLK13; KLKL4) splice variants: differential expression in the human testis and testicular cancer. Anticancer Res 2001;21(5):3147–52. 40. Dass K, Ahmad A, Azmi AS, Sarkar SH, Sarkar FH. Evolving role of uPA/uPAR system in human cancers. Cancer Treat Rev 2008;34(2):122–36. 41. Fisher JL, Mackie PS, Howard ML, Zhou H, Choong PF. The expression of the urokinase plasminogen activator system in metastatic murine osteosarcoma: an in vivo mouse model. Clin Cancer Res 2001;7(6):1654–60. 42. Swiercz R, Wolfe JD, Zaher A, Jankun J. Expression of the plasminogen activation system in kidney cancer correlates with its aggressive phenotype. Clin Cancer Res 1998;4(4):869–77. 43. Shiomi H, Eguchi Y, Tani T, Kodama M, Hattori T. Cellular distribution and clinical value of urokinase-type plasminogen activator, its receptor, and plasminogen activator inhibitor-2 in esophageal squamous cell carcinoma. Am J Pathol 2000;156(2):567–75. 44. Ji F, Chen YL, Jin EY, Wang WL, Yang ZL, Li YM. Relationship between matrix metalloproteinase-2 mRNA expression and clinicopathological and urokinasetype plasminogen activator system parameters and prognosis in human gastric cancer. World J Gastroenterol 2005;11(21):3222–6.

M.F. Ullah, M. Aatif / Cancer Treatment Reviews 35 (2009) 193–200 45. Shetty S, Kumar A, Johnson A, Pueblitz S, Idell S. Urokinase receptor in human malignant mesothelioma cells: role in tumor cell mitogenesis and proteolysis. Am J Physiol 1995;268(6 pt 1):L972–82. 46. Memarzadeh S, Kozak KR, Chang L, et al. Urokinase plasminogen activator receptor: prognostic biomarker for endometrial cancer. Proc Natl Acad Sci USA 2002;99(16):10647–52. 47. Chambers SK, Gertz Jr RE, Ivins CM, Kacinski BM. The significance of urokinase-type plasminogen activator, its inhibitors, and its receptor in ascites of patients with epithelial ovarian cancer. Cancer 1995;75(7):1627–33. 48. Figler BD, Reuther AM, Dhar N, et al. Preoperative PSA is still predictive of cancer volume and grade in late PSA era. Urology 2007;70(4):711–6. 49. Lilja H, Ulmert D, Björk T, et al. Long-term prediction of prostate cancer up to 25 years before diagnosis of prostate cancer using prostate kallikreins measured at age 44–50 years. J Clin Oncol 2007;25(4):431–6. 50. Fang J, Metter EJ, Landis P, Chan DW, Morrell CH, Carter HB. Low levels of prostate-specific antigen predict long-term risk of prostate cancer: results from the baltimore longitudinal study of aging. Urology 2001;58(3):411–6. 51. Loeb S, Roehl KA, Antenor JA, Catalona WJ, Suarez BK, Nadler RB. Baseline prostate-specific antigen compared with median prostate-specific antigen for age group as predictor of prostate cancer risk in men younger than 60 Years old. Urology 2006;67(2):316–20. 52. Thompson IM, Pauler DK, Goodman PJ, et al. Prevalence of prostate cancer among men with a prostate-specific antigen level < or = 4.0 ng per milliliter. N Engl J Med 2004;350(22):2239–46. 53. Vickers AJ, Ulmert D, Serio AM, et al. The predictive value of prostate cancer biomarkers depends on age and time to diagnosis: towards a biologicallybased screening strategy. Int J Cancer 2007;121(10):2212–7. 54. Brawer MK, Cheli CD, Neaman IE, et al. Complexed prostate specific antigen provides significant enhancement of specificity compared with total prostate specific antigen for detecting prostate cancer. J Urol 2000;163(5): 1476–80. 55. Catalona WJ, Smith DS, Ornstein DK. Prostate cancer detection in men with serum PSA concentrations of 2.6 to 4.0 ng/ml and benign prostate examination. Enhancement of specificity with free PSA measurements. JAMA 1997;277(18):1452–5. 56. Catalona WJ, Partin AW, Finlay JA, et al. Use of percentage of free prostate specific antigen to identify men at high risk of prostate cancer when PSA levels are 2.51 to 4 ng/ml and digital rectal examination is not suspicious for prostate cancer: an alternative model. Urology 1999;54(2):220–4. 57. Partin AW, Catalona WJ, Smith DS. Use of human glandular kallikrein 2 for detection of prostate cancer: preliminary analysis. Urology 1999;54(5): 839–45. 58. Babaian RJ, Fritsche H, Ayala A, et al. Performance of a neural network in detecting prostate cancer in the prostate-specific antigen reflex range of 2.5 to 4.0 ng/ml. Urology 2000;56(6):1000–6. 59. Stangelberger A, Margreiter M, Seitz C, Djavan B. Prostate cancer screening markers. JMHG 2007;4(3):233–44. 60. Catalona WJ, Partin AW, Slawin KM, et al. Use of percentage of free prostate specific antigen to enhance differentiation of prostate cancer from benign prostatic disease: a prospective multicenter clinical trial. JAMA 1998;279(19):1542–7. 61. Djavan B, Zlotta A, Kratzik C, et al. PSA, PSA density, PSA density of transitional zone, free/total PSA ratio, and PSA velocity for early detection of prostate cancer in men with serum PSA 25 to 40 ng/ml. Urology 1999;54(3):517–22. 62. Porter JR, Hayward R, Brawer MK. The significance of short term PSA change in men undergoing ultrasound guided prostate biopsy. J Urol 1994;264:239A. 63. Horninger W, Cheli CD, Babaian RJ, et al. Complexed prostate-specific antigen for early detection of prostate cancer in men with serum prostate- specific antigen levels of 2 to 4 nanograms per milliliter. Urology 2002;60(4):31–5. 64. Mikolajczyk SD, Millar LS, Wang TJ, et al. A precursor form of prostate specific antigen is highly elevated in prostate cancer compared with benign transitional zone prostate tissue. Cancer Res 2000;60(3):756–9. 65. Mikolajczyk SD, Grauer LS, Millar LS, et al. A precursor form of PSA (pPSA) is a component of the free PSA in prostate cancer serum. Urology 1997;50(5): 710–4. 66. Mikolajczyk SD, Marker KM, Millar LS, et al. A truncated precursor form of prostate specific antigen is a more specific serum marker of prostate cancer. Cancer Res 2001;61(18):6958–63. 67. Peter J, Unverzagt C, Krogh TN, Vorm O, Hoesel W. Identification of precursor forms of free prostate specific antigen in serum of prostate cancer patients by immunosorption and spectrometry. Cancer Res 2001;61(3):957–62. 68. Mikolaiczyk SD, Rittenhouse HG. Pro PSA: a more cancer specific form of prostate specific antigen for the early detection of prostate cancer. Keio J Med 2003;52(2):86–91. 69. Sokoll LJ, Chan DW, Mikolajczyk SD. Proenzyme PSA for the early detection of prostate cancer in the 2.5–4.0 ng/ml total PSA range: preliminary analysis. Urology 2002;59:261–5. 70. Catalona WJ, Bartsch G, Rittenhouse HG, et al. Serum pro prostate specific antigen improves cancer detection compared to free and complexed prostate specific antigen in men with prostate specific antigen 2.4–4 ng/ml. J Urol 2003;170(6):2181–5. 71. Bussemakers MJ, Van Bokhoven A, Verhaegh GW, et al. DD3: a new prostatespecific gene, highly overexpressed in prostate cancer. Cancer Res 1999;59(23):5975–9. 72. Mintz PJ, Kim J, Do KA, et al. Fingerprinting the circulating repertoire of antibodies from cancer patients. Nat Biotechnol 2003;21(1):57–63.

199

73. Montenarh M. Humoral immune response against the growth suppressor p53 in human malignancies. In: Shoenfeld Y, Gershwin ME, editors. Cancer and autoimmunity. Amsterdam, NY: Elsevier; 2000. p. 193–203. 74. Korneeva I, Bongiovanni AM, Girotra M, Caputo TA, Witkin SS. Serum antibodies to the 27-kd heat shock protein in women with gynecologic cancers. Am J Obstet Gynecol 2000;183(1):18–21. 75. Kirman I, Kalantarov GF, Lobel LI, et al. Isolation of native human monoclonal autoantibodies to breast cancer. Hybrid Hybridomics 2002;21(6): 405–14. 76. Dorn C, Knobloch C, Kupka M, Morakkabati-Spitz N, Schmolling J. Paraneoplastic neurological syndrome: patient with anti-Yo antibody and breast cancer: a case report. Arch Gynecol Obstet 2003;269(1):62–5. 77. Pittock SJ, Lucchinetti CF, Lennon VA. Anti-neuronal nuclear autoantibody type 2: paraneoplastic accompaniments. Ann Neurol 2003;53(5):580–7. 78. Trivers GE, De Benedetti VM, Cawley HL, et al. Anti-p53 antibodies in sera from patients with chronic obstructive pulmonary disease can predate a diagnosis of cancer. Clin Cancer Res 1996;2(10):1767–75. 79. Frenkel K, Karkoszka J, Glassman T, et al. Serum autoantibodies recognizing 5hydroxymethyl-20-deoxyuridine, an oxidized DNA base, as biomarkers of cancer risk in women. Cancer Epidemiol Biomarkers Prev 1998;7(1): 49–57. 80. Tomkiel JE, Alansari H, Tang N, et al. Autoimmunity to the M(r) 32,000 subunit of replication protein A in breast cancer. Clin Cancer Res 2002;8(3):752–8. 81. Zhong L, Coe SP, Stromberg AJ, Khattar NH, Jett JR, Hirschowitz EA. Profiling tumor-associated antibodies for early detection of non-small cell lung cancer. J Thorac Oncol 2006;1(6):513–9. 82. Wang X, Yu J, Sreekumar A, et al. Autoantibody signatures in prostate cancer. N Engl J Med 2005;353(12):1224–35. 83. Luo J, Zha S, Gage WR, et al. Alpha-methylacyl-CoA racemase: a new molecular marker for prostate cancer. Cancer Res 2002;62(8):2220–6. 84. Rubin MA, Zhou M, Dhanasekaran SM, et al. Alpha-methylacyl coenzyme A racemase as a tissue biomarker for prostate cancer. JAMA 2002;287(13): 1662–70. 85. Sreekumar A, Laxman B, Rhodes DR, et al. Humoral immune response to alpha-methylacyl-CoA racemase and prostate cancer. J Natl Cancer Inst 2004;96(11):834–43. 86. Krebs EG, Beavo JA. Phosphorylation–dephosphorylation of enzymes. Annu Rev Biochem 1979;48:923–39. 87. Cho YS, Park YG, Lee YN, et al. Extracellular protein kinase A as a cancer biomarker: its expression by tumor cells and reversal by a myristate-lacking Ca and RIIh subunit overexpression. Proc Natl Acad Sci USA 2000;97(2): 835–40. 88. Cvijic ME, Kita T, Shih W, DiPaola RS, Chin KV. Extracellular catalytic subunit activity of the cAMPdependent protein kinase in prostate cancer. Clin Cancer Res 2000;6(6):2309–17. 89. Nesterova MV, Johnson M, Cheadle C, et al. Autoantibody cancer biomarker: extracellular protein kinase A. Cancer Res 2006;66(18):8971–4. 90. Jones PA, Laird PW. Cancer epigenetics comes of age. Nat Genet 1999;21(2): 163–7. 91. Esteller M, Sanchez-Cespedes M, Rosell R, Sidransky D, Baylin SB, Herman JG. Detection of aberrant promoter hypermethylation of tumor suppressor genes in serum DNA from non-small cell lung cancer patients. Cancer Res 1999;59(1):67–70. 92. Fiegl H, Millinger S, Mueller-Holzner E, et al. Circulating tumor-specific DNA: a marker for monitoring efficacy of adjuvant therapy in cancer patients. Cancer Res 2005;65(4):1141–5. 93. Pantel K, Cote RJ, Fodstad O. Detection and clinical importance of micrometastatic disease. J Natl Cancer Inst 1999;91(13):1113–24. 94. Lee TH, Montalvo L, Chrebtow V, Busch MP. Quantitation of genomic DNA in plasma and serum samples: higher concentrations of genomic DNA found in serum than in plasma. Transfusion 2001;41(2):276–82. 95. O’Day SJ, Gammon G, Boasberg PD, et al. Advantages of concurrent biochemotherapy modified by decrescendo interleukin-2, granulocyte colony-stimulating factor, and tamoxifen for patients with metastatic melanoma. J Clin Oncol 1999;17(9):2752–61. 96. Hoon DS, Spugnardi M, Kuo C, Huang SK, Morton DL, Taback B. Profiling epigenetic inactivation of tumor suppressor genes in tumors and plasma from cutaneous melanoma patients. Oncogene 2004;23(22):4014–22. 97. Herrera LJ, Raja S, Gooding WE, et al. Quantitative analysis of circulating plasma DNA as a tumor marker in thoracic malignancies. Clin Chem 2005;51(1):113–8. 98. Herman JG, Graff JR, Myohanen S, Nelkin BD, Baylin SB. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci USA 1996;93(18):9821–6. 99. Herman JG. Circulating methylated DNA. Ann NY Acad Sci 2004;1022:33–9. 100. Lofton-Day C, Model F, Vos TD, et al. DNA-methylation biomarkers for bloodbased colorectal cancer screening. Clin Chem 2008;54(2):414–23. 101. Hoque MO, Topaloglu O, et al. Quantitative methylation-specific polymerase chain reaction gene patterns in urine sediment distinguish prostate cancer patients from control subjects. J Clin Oncol 2005;23(27):6569–75. 102. Dulaimi E, Uzzo RG, Greenberg RE, Al-Saleem T, Cairns P. Detection of bladder cancer in urine by a tumor suppressor gene hypermethylation panel. Clin Cancer Res 2004;10(6):1887–93. 103. Belinsky SA, Nikula KJ, Palmisano WA, et al. Aberrant methylation of p16(INK4a) is an early event in lung cancer and a potential biomarker for early diagnosis. Proc Natl Acad Sci USA 1998;95(20):11891–6.

200

M.F. Ullah, M. Aatif / Cancer Treatment Reviews 35 (2009) 193–200

104. Topaloglu O, Hoque MO, Tokumaru Y, et al. Detection of promoter hypermethylation of multiple genes in the tumor and bronchoalveolar lavage of patients with lung cancer. Clin Cancer Res 2004;10(7):2284–8. 105. Wong IH, Lo YM, Zhang J, et al. Detection of aberrant p16 methylation in the plasma and serum of liver cancer patients. Cancer Res 1999;59(1):71–3. 106. Sanchez-Cespedes M, Esteller M, Wu L, et al. Gene promoter hypermethylation in tumors and serum of head and neck cancer patients. Cancer Res 2000;60(4):892–5. 107. Silva JM, Dominguez G, Garcia JM, et al. Presence of tumor DNA in plasma of breast cancer patients: Clinicopathological correlations. Cancer Res 1999;59(13):3251–6. 108. Ruvkun G. Glimpses of a Tiny RNA World. Science 2001;294(5543):797–9. 109. Calin GA, Dumitru CD, Shimizu M, et al. Frequent deletions and downregulation of micro-RNA genes miR-15 and miR-16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci USA 2002;99(24):15524–9. 110. Volinia S, Calin GA, Liu CG, et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA 2006;103(7): 2257–61. 111. Ambros V, Horvitz HR. Heterochronic mutants of the nematode Caenorhabditis elegans. Science 1984;226(4673):409–16. 112. Ciafre SA, Galardi S, Mangiola A, et al. Extensive modulation of a set of microRNAs in primary glioblastoma. Biochem Biophys Res Commun 2005;334(4):1351–8. 113. Iorio MV, Ferracin M, Liu CG, et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res 2005;65(16):7065–70. 114. Murakami Y, Yasuda T, Saigo K. Comprehensive analysis of microRNA expression patterns in hepatocelullar carcinoma and non-tumorous tissues. Oncogene 2006;25(17):2537–45. 115. Tavazoie SF, Claudio Alarco´n, Thordur Oskarsson. Endogenous human microRNAs that suppress breast cancer metastasis. Nature 2008;451(7175): 147–52. 116. Li Ma, Feldstein JT, Weinberg RA. Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 2007;449(7163):682–8. 117. Garzon R, Fabbri M, Cimmino A, George AC, Carlo MC. MicroRNA expression and function in cancer. Trends in Mol Med 2006;12(12):580–7. 118. Rosenfeld N, Aharonov R, Meiri E, et al. MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol 2008;28(4):462–9. 119. Landgraf P, Rusus M, Sheridan A, et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 2007;129(7):1401–14. 120. Calin GA, Croce CM. MicroRNA–cancer connection: ‘the beginning of a new tale’. Cancer Res 2006;66(15):7390–4. 121. Jun Lu, Gad Getz, Eric A Miska, et al. MicroRNA expression profiles classify humanCancers. Nature 2005;435(7043):834–8. 122. Rai AJ, Chan DW. Clinical proteomics: new developments in clinical chemistry. Laboratoriums Medizin 2001;25:399–403. 123. Palmer-Toy DE, Kuzdzal S, Chan DW. Proteomic approaches to tumor marker discovery. In: Diamandis EP, Fritsche HA, Lilja H, Chan DW, Schwartz MK, editors. Tumor markers: physiology pathobiology technology and clinical applications. Washington, DC: AACC Press; 2002. p. 391–400. 124. Huber LA. Is proteomics heading in the wrong direction? Nat Rev Mol Cell Biol 2003;4(1):74–80. 125. Jain KK. Recent advances in clinical oncoproteomics. J BUON 2007;12(1): S31–8.

126. Zhou G, Li H, DeCamp D, Chen S, Shu H, Gong Y. 2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markers. Mol Cell Proteomics 2002;1(2):117–24. 127. Nishizuka S, Charboneau L, Young L, et al. Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays. Proc Natl Acad Sci USA 2003;100(24):14229–34. 128. Wulfkuhle JD, Liotta LA, Petricoin EF. Proteomic applications for the early detection of cancer. Nature Rev Cancer 2003;3(4):267–75. 129. Reymond MA, Schlegel W. Proteomics in cancer. Adv Clin Chem 2007;44: 103–42. 130. Cho WC. Contribution of oncoproteomics to cancer biomarker discovery. Mol Cancer 2007;6:25. 131. Madoz-Gurpide J, Kuick R, Wang H, Misek DE, Hanash SM. Integral protein microarrays for the identification of lung cancer antigens in sera that induce a humoral immune response. Mol Cell Proteomics 2008;7(2):268–81. 132. Zhu H, Snyder M. Protein Chip technology. Curr Opin Chem Biol 2003;7(1): 55–63. 133. Kingsmore SF. Multiplexed protein measurement: technologies and applications of protein and antibody arrays. Nat Rev Drug Discov 2006;5(4): 310–20. 134. Ferrari M. Cancer nanotechnology: opportunities and challenges. Nat Rev Cancer 2005;5(3):161–71. 135. Srinivas PR, Barker P, Srivastava S. Nanotechnology in early detection of cancer. Lab Invest 2002;82(5):657–62. 136. Cristofanilli M, Budd GT, Ellis MJ, et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 2004;351(8):781–91. 137. Nie S, Xing Y, Kim GJ, Simons JW. Nanotechnology applications in cancer. Annu Rev Biomed Eng 2007;9:257–88. 138. Jain KK. Applications of Nanobiotechnology in Clinical Diagnostics. Clin Chem 2007;53(11):2002–9. 139. Gao X, Cui Y, Levenson RM, Chung LW, Nie S. In vivo cancer targeting and imaging with semiconductor quantum dots. Nat Biotechnol 2004;2(8): 969–76. 140. Voura EB, Jaiswal JK, Mattoussi H, Simon SM. Tracking metastatic tumor cell extravasation with quantum dot nanocrystals and fluorescence emissionscanning microscopy. Nat Med 2004;10(9):993–8. 141. Wong SS, Joselevich E, Woolley AT, Cheung CL, Lieber CM. Covalently functionalized nanotubes as nanometre-sized probes in chemistry and biology. Nature 1998;394(6688):52–5. 142. Zheng GF, Patolsky F, Cui Y, Wang WU, Lieber CM. Multiplexed electrical detection of cancer markers with nanowire sensor arrays. Nat Biotechnol 2005;23(10):1294–301. 143. Cuenca AG, Jiang H, Hochwald SN, Delano M, Cance WG, Grobmyer SR. Emerging implications of nanotechnology on cancer diagnostics and therapeutics. Cancer 2006;107(3):459–65. 144. Maeda M, Kuroda CS, Shimura T. Magnetic carriers of iron nanoparticles coated with a functional polymer for high throughput bioscreening. J Appl Phys 2006;99:08H103. 145. Jain KK. Role of nanobiotechnology in developing personalized medicine for cancer. Technol Cancer Res Treat 2005;4:407–16. 146. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000;100(1): 57–70.