Open access peer-reviewed chapter

Cancer Biomarkers

Written By

Hala Fawzy Mohamed Kamel, and Hiba Saeed Bagader Al-Amodi

Submitted: October 14th, 2015 Reviewed: February 8th, 2016 Published: August 17th, 2016

DOI: 10.5772/62421

Chapter metrics overview

4,055 Chapter Downloads

View Full Metrics


Cancer biomarkers (CB) are biomolecules produced either by the tumor cells or by other cells of the body in response to the tumor. Every cell type has its unique molecular signature and identifiable characteristics such as levels or activities of myriad of genes, proteins, or other molecular features; therefore, biomarkers can facilitate the molecular definition of cancer. Our aim was providing updated knowledge and performing detailed review about CB regarding their molecular and biochemical characterization and their clinical utility in screening, diagnosis, follow-up, or therapeutic stratification for cancer patients. Focusing on conventional, the FDA approved as well as promising future biomarkers in most common cancers. In addition, emphasizing on their prospective role may be of great value in improving the management of cancer patients. The challenge and future prospective of biomarkers, by facilitating the combination of therapeutics with diagnostics, promise to play an important role in the development of personalized medicine.


  • cancer
  • biomarkers
  • molecular markers
  • prognosis
  • diagnosis
  • proteomics

1. Introduction

Increasing cancer burden is a major health problem; GLOBOCAN estimated nearly 8.2 million deaths and 14.1 million new cancer cases all over the world in 2012 [1] and it is expected to be 16 million new cases every year by 2020 [2]. Widespread application of existing cancer control knowledge, early detection, appropriate therapy with proper follow-up, and prediction measures through cancer biomarkers could definitely be very effective tools for the amelioration of cancer burden. Biomarkers are “Any measurable diagnostic indicator that is used to assess the risk or presence of disease” as defined by the US Food and Drugs Administration (FDA), or they would be comprehensively defined as—“A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to therapeutic intervention” [3]. Cancer biomarkers (CB) are biomolecules produced either by the tumor cells or by other cells of the body in response to the tumor, and CB could be used as screening/early detection tool of cancer, diagnostic, prognostic, or predictor for the overall outcome of a patient. Moreover, cancer biomarkers may identify subpopulations of patients who are most likely to respond to a given therapy [4]. Biomarkers can be genes, gene products, specific cells, molecules, enzymes, or hormones which can be detected in blood, urine, tissues, or other body fluid [5].

1.1 Historical background of cancer biomarkers

Two thousand years ago, Ancient Egyptians were the first known who try to find markers for malignancy as described in an Egyptian papyrus, they had their first attempt in distinguishing breast cancer from mastitis [6]. Use of CB in medicine then started around 170 years ago, when Sir Bence Jones described a protein in urine of multiple myeloma patients that could be identified by its special heat coagulation properties. In 1847, Bence-Jones protein was the first cancer biomarker that was discovered as a tumor-produced light chain antibody of immunoglobulin G (IgG) in multiple myeloma patients, it was excreted in urine in excess and could be identified by heat denaturation [7]. Later, in 1986, Bence-Jones protein was reported to be present also in the serum of myeloma patients [8]. Two years later, in 1988, an immunodiagnostic test was approved by the FDA for the detection of Bence-Jones protein which may aid in the diagnosis of multiple myeloma, Waldenstrom’s macroglobulinemia, leukemia, and lymphoma. In 1867, amylase was introduced by Sir Michael Foster who reported the increase levels of serum amylase in patients with cancer pancreas. He suggested urinary amylase as a biomarker for cancer pancreas. Then, after years of studying pathology and physiology of pancreas, it was realized that cancer pancreas originate from ductal cells not acinar cell; the source of amylase enzyme. Therefore, elevation of amylase enzyme may occur in large tumors impinging on acinar cells [9]. During the next 100 years, numerous studies involved other CB including hormones as chorionic gonadotropin (hCG) in choriocarcinoma and catecholamines in pheochromocytoma and neuroblastoma, and enzymes as acid phosphatase in prostate cancer, and alkaline phosphatase in bone tumors [10]. Definitely, the development of the immunoassay concept in the 1950s by Yalow and Berson has very important impact on the field of CB testing using polyclonal antibodies. Later in 1970s, CEA immunoassay was commercially available. The field of cancer biomarkers showed uprising in 1975 with the development of monoclonal antibodies and in 1982 with the development of the immune-metric (sandwich) immunoassay. This leaded to feasible expansion in the introduction of several immunoassays and new tumor antigens to be used as available tests in routine clinical practice. Recombinant antibody techniques also provided better understanding of the hypothesized structure and functions of CB. Recent molecular biology techniques were the key for discovering and realizing the putative functions of CB as tumor suppresser genes, oncogenes, nuclear proteins, and telomerase [11, 12]. Unfortunately, along all these years since the discovery Bence-Jones protein, only very few CB have been approved by the FDA as diagnostic or prognostic cancer markers in spite of being extensively studied. However, emerging technology of omics, such as genomics and proteomics, may indeed encourage the generation and Validation of CB [10].

1.2 Cancer development and mechanisms for the production of cancer biomarkers

Cancer is a multifactorial cluster of diseases reflecting fundamental abnormality involving uncontrolled cell growth and proliferation alternating the normal cell behavior. Molecular mechanisms exhibit alterations in the expression of multiple genes mostly includes: (proto) oncogenes, tumor suppressor genes, and DNA repair genes that contribute to the development of cancer genotype and phenotype with a state of dysregulation of cell proliferation events. Cancer hallmarks hypothesis has been postulated in 2000 by Hanahan and Weinberg. They initially categorized biological mechanisms for the cancer development into six processes: proliferative signaling, avoiding growth suppression, cell death resistance (immortalization), enabling of replicative immortality, induction of angiogenesis, and finally activation of invasion and metastasis [13]. Increasing evidence suggest that cancer may be triggered also by epigenetic changes as histone modification and DNA alteration of methylation causing alterations in the condensation state of chromatin [14]. Genetic alterations of cancer cells, as point mutation, gene rearrangement or amplifications, and subsequent disturbances of cell division and proliferation will be manifested by release of biomarkers of such changes in majority of patients with a specific type of cancer. Therefore, they can be used as biomarkers for the cancer detection or predicting responses to various treatments [1517]. Comprehensive understanding of the altered molecular mechanisms and cellular processes underlying carcinogenesis or hallmarks of cancer may link cancer biomarkers and their clinical utility in cancer patient. Genetic, molecular, and metabolic biomarker may be identified through applying the sequential of events occurring in cancer cells from gene mutation following its effects on cellular proliferation and metabolism [18], as illustrated in Figure 1. One of the major challenges for oncology research is to establish the definite relationship between cancer biomarkers and cancer pathology, as well as, to detect cancer in early stage beside the development of targeted therapies targeting the exact altered gene or cellular process [16].

    Figure 1.

Identification of biomarkers in the process of carcinogenesis modified from Bhatt et al. [18].

1.3 Serum, biological fluid, and tissue Cancer Biomarkers

Understanding mechanisms of carcinogenesis could explain the production and release of CB in cancerous cells, blood or various body fluid and hence release of those molecules and elevation during cancer initiation, development, and progression or metastasizing. Mechanisms for elevation of CB levels in any of the biological fluid could be explained by three mechanisms. The first mechanism is overexpression or amplification of gene product, or enhancement of epigenetic changes (affect gene expression) as DNA methylation with release of such CB as protein human epididymal secretory protein 4 (HE4) in ovarian cancer. HE4 is overexpressed in ovarian carcinoma and could be also detected in serum [1921]. However, clinical evaluation of HE4 revealed that it is also overexpressed in endometrial, breast, and bronchial adenocarcinoma [22]. The second mechanism of elevation could be typically applied on serum biomarkers, which is the secretion of cellular proteins or shedding of membrane proteins. An example of such serum biomarker is alpha-fetoprotein (AFP); an oncofetal protein with altered single peptide that is elevated in circulation in patient with hepatocellular carcinoma [23] and HER2-neu, a cell membrane surface-bound tyrosine kinase, released and elevated in the serum of breast cancer patients after being cleaved by proteolysis. HER2-neu is also approved by the FDA for monitoring of metastatic cases of breast cancer [24]. The third mechanism is cell invasion and angiogenesis as occur with prostate-specific antigen (PSA). It is expressed normally by prostatic epithelium but elevation of PSA levels occurs due to distorted basement membrane of prostatic cell and lymph angiogenesis [25]. The clinical application of CB, especially circulating protein targets in cancer management, is emerging into a new era especially with the availability of promising sensitive techniques that implement the discovery of “omics” cancer biomarkers in body fluids that may represent a novel, highly sensitive diagnostic tools for the early detection of cancer. Of even much importance are hidden cancers that are not easily accessible, for example, nasopharyngeal, ovarian, and pancreatic cancers. However, there is mandatory need for validation of such biomarkers [26]. CB could be detected in cancerous cells or tissue of origin in solid tumors, bone marrow, and lymph node or as circulating cells. CB could be detected in biological body fluid such as serum, ascetic fluid, pleural fluid, or urine representing noninvasive specimens or samples. CSF fluid is a suitable candidate for brain and CNS cancer. Meanwhile, urine is one of the promising frontier for the detection of bladder cancer or for of patients’ surveillance [27]. In addition, it was postulated that prostate cancer antigen 3 (PCA3) is another promising new molecular marker for diagnosis and follow-up of cancer prostate [28]. Stool for colorectal cancer, nipple aspirate fluid, ductal lavage, and cyst fluid for breast cancer are other examples for biological fluid sources for discovery or clinical application tool for CB [29].


2. Clinical applications and performance indications of Cancer Biomarkers

More than 25 years ago, the clinical usefulness of CB was limited to be an effective tool for patient’s prognosis, surveillance, and therapy monitoring. Definition of tumor markers that have been adopted by the fifth International Conference on Human Tumor Markers held in Stockholm, Sweden, in 1988 stated that “Biochemical tumor markers are substances developed in tumor cells and secreted into body fluids in which they can be quantitated by non-invasive analyses. Because of a correlation between marker concentration and active tumor mass, tumor markers are useful in the management of cancer patients. Markers, which are available for most cancer cases, are additional, valuable tools in patient prognosis, surveillance, and therapy monitoring, whereas they are presently not applicable for screening. Sero-diagnostic measurements of markers should emphasize relative trends instead of absolute values and cut-off levels.” However, CB have been reported to be used also for screening of general population or risk groups, for differential diagnosis, and for clinical staging or stratification of cancer patients. Additionally, CB are used to estimate tumor burden and to substitute for a clinical endpoint and/or to measure clinical benefit, harm or lack of benefit, or harm [4, 18, 30]. Among commonly utilized biomarkers in clinical practice are PSA, AFP, CA125, and CEA. PSA is one of the serum biomarker currently used consistently in primary care to assess the risk of underlying prostate cancer. Cancer antigen 125 (CA-125) can be a biomarker of ovarian cancer risk or an indicator of malignancy, but it has low sensitivity and specificity. CEA is another biomarker that is elevated in patients with colorectal, breast, lung, or pancreatic cancer [31]. A major challenge is to develop promising CB for the stratification of cancer patients not only to predict outcome or response for therapy, providing customized treatment, but also for personalized therapeutic strategies of cancer patients. Among promising biomarkers in that field is survivin and HER2-neu [32, 33].

2.1. Sensitivity and specificity for evaluation of accuracy of CB

As being released from tumor cells, or body cells in response to the tumor, CB can be detected in any of the body fluids, secretions, or tumor tissue and cells. CB can be detected in serum, plasma, or whole blood, also in whole excretions as urine, sputum, or CSF. Therefore, CB could be assessed in noninvasive and in serial manner. Evaluation of cancer biomarker in tissue or cells requires tissue biopsy or more invasive technique than serum biomarkers. CB can be detected in tissues by special techniques but in an invasive manner than serum or urine biomarkers. Genetic biomarkers could be detected in DNA derived from tumor tissue, whole blood, or buccal mucosa cells [34]. Evaluation of diagnostic value of any test or marker is usually performed with referral to the terms of sensitivity and specificity of that marker. Specificity means that ability of the marker to detect non-diseased subjects whereas sensitivity refers to the ability of that test to identify diseased subjects (patients) [35]. At definitive cutoff value, a test or biomarker may be found above that value (positive), but actually not all positives are diseased subjects. Therefore, sensitivity is calculated, as the ratio of the all positives who are found by that test, above the cutoff value to the total number of abnormals known to have the disease (true positive); simply sensitivity is the true positive rate (TPR). Similarly, by applying the same cutoff value for the same test, some people with normal results below cutoff value are actually normal (true negative) but not all of them are not having the disease (false negative). Therefore, the true negative rate or specificity could be calculated as the ratio of the all negatives who found by the test below cutoff value to the total number of normals known not to have the disease (true negative) [36]. Therefore, a CB with 100% specificity could be used to correctly identifies all non-cancerous subjects, CB with 70% specificity could identify only 70% of the non-cancerous as being negative (true negatives), and however, 30% of non-cancerous are falsely identified positive (false positives) [37]. Supposing sensitivity of a CB is 100%, this means that it could identify all cancer patients and if another CB supposed to be with 90% sensitivity, it could detects 90% of patients with cancer (true positives) but fail to detect it only in 10% of cancer patients (false negatives). Consequently, sensitivity and specificity could be computed across all possible cutoff or threshold values and both are inversely related to each other [38].

Figure 2.

Cancer biomarker range of results among cancer and non-cancerous patients.

2.2. Receiver operating characteristics (ROC) curve analysis

Comparative analysis of different sensitivities and specificities at different thresholds would be very effective to judge the accuracy of diagnostic test. ROC curve was introduced by the British during World War II in order to identify accurate radar detectors and was used later in performance evaluation of radiological tests [39]. ROC curve is simply defined as performance indicator of a test or biomarker by plotting its sensitivity along the y axis and its 1-specificity or FPR (false positive rate) along the x axis to assess the diagnostic ability of such biomarker and in discrimination of the diseased from the healthy subjects [40]. ROC curves have been extensively used for evaluation of the accuracy of diagnostic tests with meaningful interpretations. Several indices could be derived from it such as the area under the curve (AUC) that determines the average of the sensitivity values for all possible specificity values and includes whole area underneath the entire ROC curve [36]. AUC could have a range between one and zero because values of the x and y axes probably having values ranging from zero to one as well. The closer the value of AUC to one the better is the clinical performance of that test [40]. Comparing AUC areas of different tests can be used to compare their diagnostic performance as AUC is a measure of their overall performance. The test with bigger AUC value is of better overall performance. On comparison of two tests and if both AUC areas are equal, this indicates same diagnostic performance of both tests, but non-necessarily mean identical ROC curves [41]. Figure 2 represents the CB levels among cancer and non-cancer cases, while Figure 3 illustrates ROC curve and area under the curve.

Figure 3.

ROC curve analysis and comparison of area under the curve.

2.3. Ideal biomarker

Measurement of sensitivity and specificity of a biomarker at a range of cutoff values could be of an important impact for evaluation of CB as we may chose a definitive cutoff value that achieves the highest sensitivity and specificity. Increment of cutoff point will definitely lead to increase of specificity of the test or false negative patients but on the other hand, this will decrease number of false positives; this indicate a highly specific but low sensitive biomarker. Similarly, if the cutoff point is low that indicates a highly sensitive but low-specific biomarker, as there are fewer false negatives but more false positive subjects. Indeed, pairs of sensitivities and specificities may describe accuracy of the biomarker and its ability to discriminate between healthy (normal) and diseased. We can identify the threshold limit or cutoff value to a diagnostic sensitivity of 100% or less but considering the corresponding specificity for that threshold. The decision threshold must be chosen to be used in patient care, but not for assessment of accuracy. Indeed assessment for performance at definitive point may be misleading or this may results in bias for comparison between tests [42]. Ideal biomarker must be strictly able to differentiate between cancerous from benign cases, aggressive tumors from insignificant one; it should be of high specificity and sensitivity. Furthermore, it should be a noninvasive and inexpensive [30, 43]. The characteristic features of an ideal biomarker are variable and relay to some extent on the application and classification of CB. Mostly, CB have to fulfill the following general properties to be considered ideal. Obviously, no biomarker could meet these requirements all together, but these criteria should be highly considered for selection of diagnostic biomarker [44]:

  • High clinical sensitivity: produced by all patients with that specific cancer (100% TPR).

  • High clinical specificity: low false negative rate (100%True negative).

  • Organ or tissue specific.

  • Proportional to tumor burden or volume: quantitatively proportionate to tumor volume or disease progression.

  • Short half-life: reflecting quickly any early changes in tumor burden for proper monitoring of therapy.

  • Present (if any) at low levels in the serum of healthy individuals and those with benign disease.

  • Sharply discriminating metastasis.

  • Exist in quantitative, standardized, reproducible, and validated assay.

  • Inexpensive or low coasting method.

  • Obtained in a noninvasive manner: detected in serum, body fluids, or in easily accessible tissue.


3. Uses, clinical utility, and limitations of CB

Conventionally used tumor markers or CB may be either proteins or glycoproteins, being probably not involved in carcinogenesis or development of cancer process, rather are likely to be by-products of malignant transformation. Low molecular weight, small molecules or nucleic acids markers (as gene mutations or polymorphisms and quantitative gene expression analysis, peptides, proteins, lipids metabolites, and other small molecules are promising and recently being evaluated as potential clinically useful tumor markers, the patterns of gene expression and genetic alterations and defects may be the framework of the molecular classification of CB [11]. There are several classification s for CB depending on different aspects related to their chemical nature, proposed mechanisms for their release and applications. Six years ago, a unique classification proposed by Mishra and Verma [45] with an emphasis on clinical utility of CB. They classified CB into prediction biomarkers as DNA biomolecules, detection biomarkers as RNA molecules, diagnostic biomarkers as protein biomarkers, and prognosis biomarkers as glyco-biomarkers. Clinical applications and uses of CB, as simply illustrated in Figure 4 are screening and early detection, diagnostic confirmation, prognosis and prediction of therapeutic response, and monitoring disease and recurrence [46]. Another use of CB includes cancer susceptibility and risk assessment markers which include the identification of individuals who are at a high risk of developing cancer or candidates for screening programs and early preventive studies [47]. Risk or susceptibility assessment markers include markers of inflammation, oxidative stress and single-nucleotide polymorphisms (SNPs), and mutations in certain genes [48, 49]. Table 1 illustrates most of traditional, the FDA approved, and clinically relevant CB with their uses in various cancer types.

           Figure 4.

Clinical utility and uses of cancer biomarkers.

3.1. Screening/early detection

In 2008, Wald defined screening as “the systematic application of a test to identify subjects at sufficient risk of a specific disorder to benefit from further investigation or direct preventive action, among persons who have not sought medical attention on account of symptoms of that disorder” [50]. Earlier efficient treatment must lead to better outcome compared with the treatment available at later cancer stages or symptomatic patients. Screening aim was to detect disease when subjects are asymptomatic which differ from diagnosis of symptomatic patients. Objectives of screening and early detection of cancer were to detect cancer at curable and better outcome state and even before appearance of symptoms. Reports calculated a drop in the 5 years survival rate from being about 90%, in early localized breast cancer, to reach about 60% in local metastasizing and only 30% to distant metastasizing cases of breast cancer [51]. Therefore, screening CB should be able to detect cancer in an early stage or asymptomatic stage and consequently will result in increase of survival rate and decrease complications or morbidities. Screening test must be highly specific to minimize false positives as less as possible. High specificity is mandatory for screening biomarker because even a small false-positive rate could result in large number of unnecessary other invasive diagnostic procedures that may be unneeded with the associated psychological burden and excess costs. Ideal screening programs have to be noninvasive and inexpensive and definitely lead to obvious reduction in morbidity and mortality and increase in survival rate. Usually, screening programs are directed for highly prevalent cancers and further treatment and follow-up are mandatory [34]. Other limiting factors for screening biomarker are the low diagnostic sensitivity and specificity of most of the currently used biomarkers to serve as screening markers and being elevated later in the course of cancer. However, few biomarkers have been used as screening biomarkers as AFP in screening for hepatocellular cancer in high‐risk subjects, PSA in screening for prostate cancer, CA125 in screening for ovarian cancer, and fecal occult blood testing (FOBT) in screening for colorectal cancers (CRC) and vanillymandelic acid (VMA) in screening for neuroblastoma in newborns [52]. PSA was cleared by the FDA as a screening biomarker for prostate cancer; however, false positive elevation of PSA levels can be found in individuals with benign or inflammatory conditions as benign prostatic hyperplasia and prostatitis [53]. Contribution of PSA screening in decreasing mortality is still being a matter of contraverse [54, 55].

Cancer biomarker Organ specificity/cancer type Application/uses References
Prostate-specific antigen (PSA) Prostate/BPH Screening, diagnosis and monitoring [86, 133]
Carbohydrate antigen 125 (CA125) Ovarian Diagnosis, prognosis, detecting
recurrence and monitoring therapy
Carcinoembryonic antigen (CEA) Colorectal/hepatic Monitoring therapy [135137]
Detecting recurrence
Screening for hepatic metastases
Carbohydrate antigen 15.3 (CA 15-3) Breast Monitoring therapy [69, 138]
Estrogen, progesterone receptors
(ER and PgR)
Breast Stratification/select patients for endocrine therapy [139141]
HER2 Breast Monitoring trastuzumab therapy [18, 32, 33, 142]
Carbohydrate antigen 27.29 (CA27.29) Breast Monitoring [84]
Human chorionic gonadotropin-β
Testicular Diagnosis [143]
Detecting recurrence
Monitoring therapy
Alfa-fetoprotein Hepatocellular carcinoma Diagnosis [144146]
Detecting recurrence
Monitoring therapy
Calcitonin Medullary carcinoma of thyroid Diagnosis and monitoring therapy [147, 148]
Thyroglobulin Thyroid Monitoring [149]
CA 19-9 Pancreatic Monitoring therapy [76]
Nuclear matrix protein 22 (NMP-22) Bladder Screening, monitoring and prognosis [150]
Prostate cancer antigen 3 (PCA3) Prostate Prognostic [151]

Table 1.

Current cancer biomarkers and uses in clinical practice.

3.2. Diagnosis/differential diagnosis

A diagnostic biomarker would be applied only for symptomatic patients in contrast to screening biomarker that would be applicable only for symptomatic individuals. Interestingly, the characteristics of an ideal diagnostic biomarker are similar to the characteristics for screening. Notably, most of well-established biomarkers for screening could be used as diagnostic markers and PSA is well-recognized example. PSA, in combination with a digital rectal examination (DRE), is the most commonly used diagnostic tool for prostate cancer [56]. Regarding encountered limitations for diagnostic biomarkers, current available cancer biomarkers are still having low diagnostic sensitivity and specificity; however, diagnostic biomarkers must be of high sensitivity in order to be a good diagnostic biomarker [57]. For example, Bence-Jones protein in urine remains one of the strongest, well-established diagnostic indicators of multiple myeloma [29]. Nevertheless, some CB have proved to be useful in confirming diagnosis, often in conjunction with a panel of other markers especially to identify primary tumor in metastatic cases with unknown primary and/or other clinical, imaging tools [58]. Use of panel of CB in order to increase sensitivity and specificity of CB in diagnosis has been used to confirm diagnosis of certain cancers. In 2005, Mor et al. [59] reported that a panel, consisting of 4 biomarkers: leptin, osteopontin, prolactin, and insulin-like growth factor 2, collectively had a sensitivity of 95% and a specificity of 95% for the detection of ovarian cancer. In another report, addition of two biomarkers to the previously studied panel included macrophage inhibitory factor and CA125, sensitivity was 95% and a specificity increase to 99.4% for the detection of ovarian cancer. Other attempts to improve diagnostic sensitivity and specificity included combination of CA125 with ultrasonography for diagnosis of ovarian cancer [60].

3.3. Prognosis/prediction

Prognosis is the probability of cure or likely outcome of any patient. A prognostic marker is a disease or patient characteristic feature at the time of diagnosis independent upon therapy; hence, prognostic marker will provide information about the natural history of the disease or the likely outcome. Meanwhile, a predictive biomarker predicts the response to different therapeutic modalities; hence, predictive biomarker is the basic concept for personalized medicine [57]. Magnitude of elevation or levels of CB usually reflects tumor burden, or mass hence higher elevation of CB level mostly reflects bad prognosis and vice versa. By reflecting the tumor burden, CB can be used in staging system for cancer or the tumor–node–metastasis (TNM) classification. For example, in testicular germ cell tumors, very high levels of a CB such as AFP, LDH, and HCG-β may indicate an aggressive cancer with poor prognosis and outcome so such biomarkers may be used for staging in TNMS system in place with a site-specific prognostic factor (S is for site-specific prognostic factors) [61]. LDH alone has been used for staging of lymphoma as well [62]. However, the accuracy of the marker in determining tumor stage is poor. Estrogen receptor (ER) is one of the widely used prognostic and predictive tissue biomarker; as a predictive tissue biomarker, ER is used for selecting the patients likely to respond to hormonal therapy. Therefore, patients with ER positive tumors will mostly respond to selective ER modulators or aromatase inhibitors independent upon stage of breast cancer weather early or advanced [63]. ER is considered a prognostic marker as well, once ER is negative, that indicate a poor prognosis and when positive a good prognosis is likely the outcome for such patients. In spite of most of CB have some prognostic values which their specific therapeutic impact cannot be applied because of their poor predication accuracy [64]. In the same context, high serum levels of HER2 in serum of breast cancer patients correlate with poor prognosis in such patients [24]. Targeted therapy for HER-2 positive breast cancer patients, trastuzumab (Herceptin), is a recombinant monoclonal antibody against HER-2. Herceptin has been used in women with metastatic breast cancer that overexpressed HER2 and reported to increases the clinical benefit of first-line chemotherapy in those patients [65]. KRAS is a predictive biomarker for colorectal cancer, because patients with somatic mutations in KRAS have poor response to anti-epidermal growth factor receptor (EGFR)-targeting therapies [66].

3.4. Therapeutic monitoring/follow-up/evidence of metastasis or recurrence

Therapeutic monitoring may constitute the most common applications of CB markers in clinical practice [67]. Clinically useful biomarkers usually fluctuate in accordance with tumor behavior, size, or burden changes that are best elicited by increase in levels of CB with progressive disease, decrease with remission, and do not change significantly with stable disease. Kinetics of CB are more important than single measurement or elevated values [68]. Recurrence of cancer may be detected biochemically via rise in CB levels even before appearance of any clinical or radiological evidence of cancer recurrence. Continues follow-up for cancer patients during and after therapy can mirror their condition if the levels of CB were not elevated or remain at basal level, indicating successful therapy or remission. On the other hand, rising of CB level above the basal level indicates recurrence of the disease. CB can be a warning sign of recurrence earlier by 3–12 months before any other diagnostic methods. Many CB could be used for monitoring therapy or detection of recurrence or metastasis, for example, CEA in colorectal cancers, cancer antigen 125 (CA 125) in ovarian cancers, or PSA in prostatic cancer [69]. Some patients who encountered resistance to therapeutic modalities will experience increasing levels of CB, and in that case, reconsideration of alternative therapy is mandatory. Monitoring CB, as screening and diagnostic biomarker needs to be both diagnostically sensitive and specific to ensure proper assessment of effective therapy and continuation of such beneficial therapies and early discontinuation/replacement of ineffective therapy or resistant cancer to those therapies. A representing example of monitoring CB is carbohydrate antigen 19-9 (CA19-9) which has been used in pancreatic in CRC [70]. CA19-9 has been approved by the FDA in 2002 as a monitoring marker for pancreatic cancer. However, it is not recommended as a screening biomarker [71, 72]. Monitoring biomarkers have been extensively used in clinical practice with few limitations perhaps related to detectors’ biomarkers of recurrence rather than monitoring ones. Limitations of those biomarkers probably related to short lead time and poor affection to the outcome [29].


4. Applications of CB in most common cancers

Cancer is an enormous health problem all over the world, over years cancer was indicated as one of the leading causes of death among males and females; an estimated 8.2 million deaths among cancer patients occurred in 2012 worldwide [73]. Over 11 million patients are diagnosed with cancer every year, and 16 million new cases will be expected yearly by 2020 [2]. According to the latest report of the International Agency for Research on Cancer (IARC), the GLOBOCAN worldwide estimates of cancer incidence and mortality published on 2015 and the most common cancers’ types among males were lung, prostate, colorectal, liver, and urinary bladder. Meanwhile, breast cancer, lung, liver, ovarian cancers were among the most common cancers in females worldwide [1]. For many years ago, few CB have been used as an effective tool in clinical practice, while also promising CB were extensively studied for their clinical utility. As previously discussed, traditionally used or promising CB may be used for risk assessment for cancer, screening among asymptomatic population, confirming diagnosis or differentially discriminate benign from malignant, prediction of outcome or prognosis, and monitoring of therapy or staging of cancer applications [58].

4.1. Breast cancer

Breast cancer is the most common malignancy among females and the first leading cause of cancer mortality worldwide; its prevalence is surprisingly increasing at a rapid rate lately [74]. Therefore, it is critical to use all available tools for early diagnosis and proper management of cases. Clinically, symptoms are mainly breast lump, nipple discharge, or skin or nipple changes. Screening guidelines by The American Cancer Society recommend that women over 40 have to perform mammography and a yearly or every other year clinical breast exam [75]. Diagnosis mainly relies on pathological examination; however, the role of CB in breast cancer is mainly helpful with prognosis, monitoring of therapy, and for follow-up. Notably, CB does not show great utility for early diagnosis [76]. Assessment of ER and progesterone receptors (PR) in tissue for newly diagnosed breast cancers has been recommended by European Society of Medical Oncology, for predicting response to hormone therapy in early and advanced breast cancer cases [63, 77, 78]. HER-2 is another prognostic marker, most useful for selecting patients with either early or metastatic breast cancer for the treatment with Trastuzumab (Herceptin) [79] or predicting resistance to tamoxifen therapy in early stage of breast cancer [63]. Determination of risk groups for the development of breast cancer, who must be included in screening program, involves the detection of genetic mutation of BRCA 1 or BRCA 2 genes, which account for up to 5% of breast cancer cases. Due to their high susceptibility to breast and ovarian cancer, it is strongly recommended that women carrying BRCA1 or BRCA2 mutations undergo routine cancer screening [80]. It was reported that low levels of urokinase plasminogen activator (uPA) and plasminogen activator inhibitor-1 (PAI-1) correlate with a reduced risk of recurrence of breast cancer and shown to be strong independent prognostic factors of newly diagnosed lymph node-negative breast cancers [81, 82]. Serum biomarkers are mainly applicable as monitoring markers during therapy or to less extent prognostic markers and usually assisted in post-operative surveillance, and CB included under that category include CA15.3, CEA, and BR 27-29 [83, 84]. They are used in conjugation with other tools of radiological and clinical assessments to monitor chemotherapy in advanced breast cancer cases. Elevation of serum levels of these markers may indicate recurrence or progression of the disease [85].

4.2. Prostate cancer

Prostate cancer (PCa) is one of the most common cancer in men and most common causes of male cancer-related deaths [74]. Strong evidences suggested that PSA test revolutionized the prostate cancer screening and diagnosis landscape, and the introduction of PSA as a screening test has led to a sharp increase in the incidence of prostate cancer because there has been a shift to diagnosis at earlier stages, consequently reducing mortality from prostate cancer [86]. Later, many studies demonstrated significant improvement sensitivity of PSA as a diagnostic marker using a PSA subtractions and isoforms [−2] (proPSA) and its percentage derivative % proPSA (percent value relative to PSA) as these fraction may help for the discrimination between benign and malignant prostatic tumors in patients with PSA values ranging from 4 to 10 μg/L [87, 88]. Other novel and promising biomarkers under investigation include human kallikrein type 2, prostate cancer antigen 3 (PSA 3), and prostate stem cell antigen (PSCA) [89]. PCA3 urine assay has promising role in improving the accuracy of diagnosis in prostate cancer [90]. Elevated levels of metalloproteinase 2 and 9 (MMP-2 and MMP-9) members of protease family have been associated with prostate cancer diagnosis [91]. MMPs have been studied as biomarkers of therapeutic monitoring in prostate cancer [92].

4.3. Ovarian cancer

Most of the patients with epithelial ovarian cancer are diagnosed late and they have clinically advanced stage III and IV on diagnosis; therefore, ovarian cancer needs a sensitive and specific diagnostic biomarkers [93]. CA 125 is one of the most widely and conventionally used CB. It is recommended as a screening biomarker for women who have positive family history or are high risk for the development of ovarian cancer, beside CA125 has been used in conjugation with vaginal ultrasound as a well-established, diagnostic biomarker [94]. CA125 is also been used as monitoring biomarker, being decreased after starting of chemotherapy or surgery, that correlates with favorable response basal level of CA125, two weeks before starting any therapeutic intervention then follow ups and continues monitoring of its level at regular intervals are highly recommended [95]. Other biomarkers were extensively studied in monitoring of ovarian cancer and in prediction of prognosis but further studies are needed for proper confirmation of their exact role. This panel includes kallikreins (5–9), osteopontin, Her-2/neu, tumor-associated inhibin, CEA, trypsin inhibitor, hCG, interleukin-6 (IL-6), prostasin, TPA, lysophosphatidic acid, plasminogen activator inhibitor-1 (PAI-1) [9597].

4.4. Colorectal cancer

CRC is ranked third among all cancers all over the world. An estimated one million new cases are diagnosed and half of a million cases died each year [1]. The most common site for colorectal carcinoma is the rectum encountering 38% of all cases followed by sigmoid accounting 29% of cases [98]. Screening program for CRC should be directed to all asymptomatic individuals above 50 years as recommended [99]. National Academy of Clinical Biochemistry (NACB) recommends that all subjects 50 years or older should undergo screening for colorectal cancer. Multiple screening procedures exist [100]. Fecal occult blood test (FOBT) is the most widely used CB in stool [101]. Testing for blood in the stools involves either detecting globin fraction of blood (hemoglobin) by fecal immunochemical test or the guaiac test which measures pseudo-peroxidase activity of heme fraction of hemoglobin. CEA was characterized and introduced into clinical practice in 1965 [76]. It is widely used as universal or non-organ, non-tissue-specific tumor marker. CEA is not used in screening of CRC due to its low sensitivity and specificity, beside the low prevalence of CRC among asymptomatic population; however, it is very efficient prognostic and therapy monitoring biomarker [102]. CEA estimation is recommended at the beginning of therapy then every 1–3 months all through the therapeutic regimen, it is also the marker of choice for metastatic cases of CRC [103]. CA19-9 has been used as prognostic marker, in surveillance of CRC after surgical resection and as monitoring marker for therapeutic intervention in advanced cases [104]. Other CB under investigation are CA242 and tissue inhibitor of metalloproteinases type 1(TIMP-1) and both may complement CEA in the surveillance of patients with colorectal cancer [105].


5. Discovery of new biomarkers/validation/technologies (omics)

Among hundreds of thousands of cancer biomarkers have been discovered, only few of them have been approved during the past two decades by the FDA for monitoring response, surveillance, or recurrence of cancer [106]. To be a clinically applicable and reliable biomarker, it must be of value for informing clinical decision-making to improve the patient outcome [107]. Initially, CB have to distinguish between people with cancer and those without. In fact, many biomarkers do not achieve beyond this point because the investigators are either unable to develop robust, accurate assay methods, or this biomarker lacks sufficient sensitivity and/or specificity [108]. Actually, there was very low rate (0.1%) of successful clinical translation of biomarker [109]. Developing new cancer biomarkers has been formulated in stepwise manner. About 15 years ago, Hammond and Taube proposed an approach for CB development starting from discovering the marker, developing an assay method for assessment, analyzing its clinical potential preliminarily, standardization of its assay, and finally validation of such biomarker for clinical use [110]. Structured phased model for the development evaluation, and validation of biomarkers, (shown in Table 2) has been proposed by Pepe et al. [111] and has been adopted and modified by others [112, 113]. This model was similar to another model commonly used in drug development strategy including five phases: preclinical exploratory studies, clinical assay and validation, retrospective longitudinal repository studies, prospective screening studies, and finally cancer control studies. Novel biomarkers must bypass an analytical validation step concerned mainly with testing and assay methods of the biomarker (technical aspects). After that, the biomarker has to be analyzed for its clinical validity for discriminating between groups independently. Finally, candidate biomarker must be assessed for clinical utility for providing additional input for patient management or aid to provide additional information helping in decision-making for patients in order to improve patient outcome [114].

Phases Type of studies Outcome
Phase I Preclinical exploration Promising directions are explored and potential
biomarkers identified
Phase II Clinical assay and validation Determination of the potential capacity of the biomarker
to established disease
Phase III Retrospective longitudinal Determine how well biomarkers detect preclinical disease through
retrospectively testing
Phase IV Prospective screening Identify the characteristics of the disease detected by the biomarker
and determine the false positive rate
Phase V Cancer control Quantification of the role of the biomarkers in the
reduction of disease burden through Phase 5
population screening

Table 2.

Structured phased model for the development evaluation, and validation of biomarkers modified from Pepe et al. [111] and Paradiso et al. [113].

5.1. Challenges for discovery of novel biomarkers

Development of biomarkers for cancer screening, early detection, and monitoring of treatment has both biological and economic challenges. Most detection methods currently in use identify mostly late stage or fully developed cancer, not in the premalignant or early lesions, which are amenable to resection and cure. In spite of the fact that a screening test might detect cancer at the preclinical stage, at the same time, not applicable for follow-up so it could fail to detect micrometastasis, therefore limiting the benefit of early detection and treatment [115]. Another challenge is that in many organs, for example; prostate or colon, preneoplastic lesions are much more common than aggressive cancers [116]. This creates the question of whether any screening method should just focus on early lesions or whether it should also analyze the behavior of the tumor. Another challenge for the development of CB is the nature of the cancer as being a heterogeneous disease; it is composed of many biologically different phenotypes with different responses to intervention. The nature of its heterogeneity is found between cells of a single macroscopic cancer. This heterogeneity may complicate the development of biomarkers. Therefore, the development of biomarker by genomic and proteomic means might carefully address the heterogeneity issues [117]. Detailed and comprehensive knowledge of cancer at the cellular and molecular levels has grown dramatically and exponentially in the past two decades and has resulted in significant improvement in the characterization of human tumors which in turn has catalyzed a shift toward the development of targeted therapies, the basic concept for personalized medicine [118]. Therefore, it has been recently postulated that the emergence of highly powerful “omics” technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics [119]. Omics technologies may be the backbone toward the discovery of novel CB and/or panels, with distinct advantages over the currently used biomarkers. Omics have increased the number of potentially investigated biomarkers as DNA, RNA, or other protein biomolecules. The former concept of single biomarker discovery was replaced recently by multi-biomarkers discovery of panel of genes or proteins whereby, rising the query of whether the heterogeneous and multifactorial cancer may have single fingerprint.

5.2. Genomic technologies

Genomic technologies have been used extensively for the characterization of cancers at the molecular level hence providing better comprehensive understanding of cancer and may provide scientists the basic concepts for designing drugs that could target specific molecules or the fundamental of personalized medicine [120]. Personalized medicine has been defined by The US National Cancer Institute (NCI) as “a form of medicine that uses information about a person’s genes, proteins, and environment to prevent, diagnose, and treat disease.” [50]. Genomic alterations that may be associated with cancer include gene amplification, mutation, chromosomal rearrangements, and aberrant methylation. Molecular alterations are evolved in the content or sequence of DNA, its transcriptions mRNA or microRNA, the production of proteins, or the synthesis of various metabolites. Genomic alterations can be assessed through genome sequencing technologies or microarray for gene expression [29]. Mutation screening can be assessed by sequencing technique, while assessment of DNA copy numbers could be analyzed by DNA microarrays and DNA expression profile via PCR [120]. Genomic microarrays represent a highly powerful and sensitive technique; it can predict the clinical behavior of tumors [121]. Genomics has been extensively used for biomarker discovery and identification. Human genome accounts approximately 30,000 genes, the availability of omics techniques allows researchers to move another step further, which is designing and manufacturing of a biological drug with better understanding of pharmacogenomics, thus biomarkers allow the studying of the influence of genetic variation, providing new methods for treating patients on an individual basis. The outcome of such researches is known as personalized medicine [122].

5.3. Epigenomics

Epigenetics refers to heritable changes in gene expression that are not attributable to alterations in the sequence of DNA. Epigenetic changes include DNA methylation, histone modifications, and non-coding RNAs. These alterations may be present ubiquitously human malignancies and may appear in early cancer development. Therefore, they provide particularly attractive markers with broad applications in diagnostics [123]. Methylated DNA (meDNA) is a various stable carrier of epigenetic information that is directly occurred in tumor formation and progression. In fact, the inherent stability of DNA is one of the major advantages of detecting methylation. Genes that are often methylated in tumors are termed tumor biomarkers because their methylation can be used to detect the disease. Utilization of meDNA markers is superior comparing to other types of tumor biomarkers for numerous reasons including: The analysis of DNA methylation can be achieved with a wide range of methods using different types of biological material such as tissue, plasma, serum, sputum, and urine, among others [124]. Methodology of DNA methylation measurement has progressed gradually through the years. Assessment techniques for epigenetic changes may include: The bisulphate conversion of DNA followed by PCR amplification allows gene-specific methylation analysis (methylation-specific PCR, i.e., MSP), which is based on using primers and probes specific to the corresponding methylated DNA sequence [125]. This technology makes the detection of hundreds of thousands of DNA methylation signals a reality. These signals can be digitized into a long string of ones and zeros, creating a digital phenotype that reflects genetic activity in a particular cell or tissue, that is, whether it is functioning normally or whether it is abnormal. Around 200 such biomarkers have been discovered through a large-scale genome-wide screening effort of all major human tumors for DNA methylation biomarkers in bio-specimen; tissue and serum [126].

5.4. Proteomics

Proteomics-based strategy diseases identification is considered as one of the dynamic and innovative tools that could confirm, complement, or quite often supply more elaborate information beyond that obtained by other high-throughput approaches such as genomic, transcriptomics, and epigenomics. Despite genomic expression profiling is a highly reliable method for cancer classification and prognostication [127, 128]. The function of such genes and the data interpretation in the context of functional networks require their translation into active proteins and their analysis through the power of proteomics. Moreover, although studies focusing on detecting the differential expression of mRNA have been extremely informative, they do not necessarily correlate with the functional protein concentrations. Therefore, post genomic “proteomic” projects correlating protein expression profiles to cancer are essential for a complementary and comprehensive representation of cancer biology. Moreover, targeting-specific protein pathways involved in tumorigenesis present a realistic aim in cancer treatment, as proteins exert their effects through specific pathways rather than functioning individually [120]. Macromolecules, in general, and proteins, in particular, are highly dynamic molecules. Mechanistically, proteins can be subjected to extensive functional regulation by various processes such as proteolytic degradation, posttranslational modification, involvement in complex structures, and compartmentalization. Proteomics is concerned with studying the whole protein repertoire of a defined entity in a biological fluid, an organelle, a cell, a tissue, an organ, a system, or the whole organism. Therefore, in-depth studying of proteomics profiles of various bio-specimens obtained from cancer patients is expected to increase our understanding of tumor pathogenesis, monitoring, and the identification of novel targets for cancer therapy. In a simple way, proteins may be actively secreted or released by the tumor cells as a result of necrosis or apoptosis and released into the circulation [76]. This changes the protein profile. The difference in signal intensities may be detected by comparison with sera from normal individuals. Secretomics, a subfield of proteomics that studies secreted proteins and secretion pathways using proteomic approaches, has recently emerged as an important tool for the discovery of biomarkers. In what is now commonly referred to as proteogenomics, and proteomic technologies are further used for improving gene annotations. Parallel analysis of the genome and the proteome facilitates discovery of post-translational modifications and proteolytic events (comparative proteogenomics).

5.5. Metabolomics

A cancer biomarker can be a metabolite, secreted by tumor, metabolic pathway or process, and may be employed to diagnose cancer and predict patient response towards therapies and monitor recurrence. Though proteins are the key tumor markers that can be as diverse as molecular, biochemical, physiological, or anatomical [129]. Markers can be utilized for diagnosis (to identify early stage), prognosis (assess the lethality), and prediction (of patient’s response to treatment) of cancer. The markers can be detected in body fluids (blood, urine, serum, stool, saliva), or tissues (tissue samples or biopsies of the cancer). Moreover, it has been shown recently that cancer volatile organic compounds (VOC) markers can be detected in breath [130]. However, detecting the markers is a sophisticated process and metabolomics is one of the omic technologies. Among genome, transcriptome, proteome, and metabolome, the latter is the powerful representative of the phenotype [131]. Exploring the cancer metabolome seems to be an effective way to study the phenotypic changes associated with tumor. Screening biomarkers by recruiting an array of analytical techniques has been emphasized [132]. Rather than a single metabolite, a pattern is believed to be more indicative of cancer status. Metabolomic approach makes it feasible to detect an array of metabolites in a single assay. The principal analytical tools employed for metabolome analysis are mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR).


6. Conclusion and prospective

Cancer biomarkers play an important role in the field of oncology and in clinical practice for risk assessment, screening, diagnosis integrated with other diagnostic tools and mostly for the determination of prognosis and response to treatment and/or relapse. Cancer biomarkers can also facilitate the molecular definition of cancer. It is necessary for clinicians and researchers to have a comprehensive understanding of molecular aspects, clinical utility, and reliability of biomarkers in order to determine whether and in what setting a biomarker is clinically useful for the patient care, or additional evaluation is required before integration into routine medical practice. The challenge and future prospective of biomarkers, by facilitating the combination of therapeutics with diagnostics, promise to play an important role in the development of personalized medicine.


  1. 1. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International Journal of Cancer Journal International du Cancer 2015;136:E359–86.
  2. 2. Cho WC. Contribution of oncoproteomics to cancer biomarker discovery. Molecular Cancer 2007;6:25.
  3. 3. Ilyin SE , Belkowski SM, Plata-Salaman CR. Biomarker discovery and validation: technologies and integrative approaches. Trends in Biotechnology 2004;22:411–6.
  4. 4. Goossens N , Nakagawa S, Sun X, Hoshida Y. Cancer biomarker discovery and validation. Translational Cancer Research 2015;4:256–69.
  5. 5. Rhea JM , Molinaro RJ. Cancer biomarkers: surviving the journey from bench to bedside. MLO: Medical Laboratory Observer 2011;43:10–2, 6, 8; quiz 20, 2.
  6. 6. Pandha HS , Waxman J. Tumour markers. QJM: Monthly Journal of the Association of Physicians 1995;88:233–41.
  7. 7. Kyle RA. Multiple myeloma: how did it begin? Mayo Clinic Proceedings 1994;69:680–3.
  8. 8. Sinclair D , Dagg JH, Smith JG, Stott DI. The incidence and possible relevance of Bence-Jones protein in the sera of patients with multiple myeloma. British Journal of Haematology 1986;62:689–94.
  9. 9. Schwartz MK. Enzymes in cancer. Clinical Chemistry 1973;19:10–22.
  10. 10. Pritzker KP. Cancer biomarkers: easier said than done. Clinical Chemistry 2002;48:1147–50.
  11. 11. Johnson PJ. A framework for the molecular classification of circulating tumor markers. Annals of the New York Academy of Sciences 2001;945:8–21.
  12. 12. Suresh MR. Classification of tumor markers. Anticancer Research 1996;16:2273–7.
  13. 13. Hanahan D , Weinberg RA. The hallmarks of cancer. Cell 2000;100:57–70.
  14. 14. Baylin SB , Ohm JE. Epigenetic gene silencing in cancer—a mechanism for early oncogenic pathway addiction? Nature Reviews Cancer 2006;6:107–16.
  15. 15. Weissleder R , Ntziachristos V. Shedding light onto live molecular targets. Nature Medicine 2003;9:123–8.
  16. 16. Sidransky D. Emerging molecular markers of cancer. Nature Reviews Cancer 2002;2:210–9.
  17. 17. Vogelstein B , Kinzler KW. Cancer genes and the pathways they control. Nature Medicine 2004;10:789–99.
  18. 18. Bhatt AN , Mathur R, Farooque A, Verma A, Dwarakanath BS. Cancer biomarkers—current perspectives. The Indian Journal of Medical Research 2010;132:129–49.
  19. 19. Welsh JB , Zarrinkar PP, Sapinoso LM, Kern SG, Behling CA, Monk BJ,. Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proceedings of the National Academy of Sciences of the United States of America 2001;98:1176–81.
  20. 20. Hellstrom I , Raycraft J, Hayden-Ledbetter M, Ledbetter JA, Schummer M, McIntosh M,. The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Research 2003;63:3695–700.
  21. 21. Chang X , Ye X, Dong L, Cheng H, Cheng Y, Zhu L,. Human epididymis protein 4 (HE4) as a serum tumor biomarker in patients with ovarian carcinoma. International Journal of Gynecological Cancer: Official Journal of the International Gynecological Cancer Society 2011;21:852–8.
  22. 22. Galgano MT , Hampton GM, Frierson HF Jr. Comprehensive analysis of HE4 expression in normal and malignant human tissues. Modern Pathology: An Official Journal of the United States and Canadian Academy of Pathology, Inc 2006;19:847–53.
  23. 23. Abelev GI , Eraiser TL. Cellular aspects of alpha-fetoprotein reexpression in tumors. Seminars in Cancer Biology 1999;9:95–107.
  24. 24. Molina R , Jo J, Filella X, Zanon G, Pahisa J, Munoz M,. C-erbB-2 oncoprotein in the sera and tissue of patients with breast cancer. Utility in Prognosis. Anticancer Research 1996;16:2295–300.
  25. 25. Stacker SA , Achen MG, Jussila L, Baldwin ME, Alitalo K. Lymphangiogenesis and cancer metastasis. Nature Reviews Cancer 2002;2:573–83.
  26. 26. Wulfkuhle JD , Liotta LA, Petricoin EF. Proteomic applications for the early detection of cancer. Nature Reviews Cancer 2003;3:267–75.
  27. 27. Xylinas E , Kluth LA, Rieken M, Karakiewicz PI, Lotan Y, Shariat SF. Urine markers for detection and surveillance of bladder cancer. Urologic Oncology 2014;32:222–9.
  28. 28. Locke JA , Black PC. Next generation biomarkers in prostate cancer. Frontiers in Bioscience 2016;21:328–42.
  29. 29. Kulasingam V , Diamandis EP. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nature Clinical Practice Oncology 2008;5:588–99.
  30. 30. Levenson VV. Biomarkers: diagnostic highlights and surrogate end points. Cambridge Healthtech Institute’s biomarker series: biomarker validation: bringing discovery to the clinic & cancer biomarkers: from discovery to clinical practice. May 3–5, 2004, Philadelphia, Pennsylvania, USA. Pharmacogenomics 2004;5:459–61.
  31. 31. Nolen B. The expansion and advancement of cancer biomarkers. Cancer Biomarkers: Section A of Disease Markers 2011;10:61–2.
  32. 32. Ross JS , Slodkowska EA, Symmans WF, Pusztai L, Ravdin PM, Hortobagyi GN. The HER-2 receptor and breast cancer: ten years of targeted anti-HER-2 therapy and personalized medicine. The Oncologist 2009;14:320–68.
  33. 33. Span PN , Sweep FC, Wiegerinck ET, Tjan-Heijnen VC, Manders P, Beex LV,. Survivin is an independent prognostic marker for risk stratification of breast cancer patients. Clinical Chemistry 2004;50:1986–93.
  34. 34. Duffy MJ. Clinical uses of tumor markers: a critical review. Critical Reviews in Clinical Laboratory Sciences 2001;38:225–62.
  35. 35. Swets JA. Measuring the accuracy of diagnostic systems. Science 1988;240:1285–93.
  36. 36. Zweig MH , Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 1993;39:561–77.
  37. 37. Begg CB. Advances in statistical methodology for diagnostic medicine in the 1980s. Statistics in Medicine 1991;10:1887–95.
  38. 38. Camp BW. What the clinician really needs to know: questioning the clinical usefulness of sensitivity and specificity in studies of screening tests. Journal of Developmental and Behavioral Pediatrics: JDBP 2006;27:226–30.
  39. 39. Obuchowski NA. Receiver operating characteristic curves and their use in radiology. Radiology 2003;229:3–8.
  40. 40. Kumar R , Indrayan A. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatrics 2011;48:277–87.
  41. 41. DeLong ER , DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–45.
  42. 42. Robertson EA , Zweig MH, Van Steirteghem AC. Evaluating the clinical efficacy of laboratory tests. American Journal of Clinical Pathology 1983;79:78–86.
  43. 43. Biomarkers Definitions Working G. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clinical Pharmacology and Therapeutics 2001;69:89–95.
  44. 44. Cooner WH. Definition of the ideal tumor marker. The Urologic Clinics of North America 1993;20:575–9.
  45. 45. Mishra A , Verma M. Cancer biomarkers: are we ready for the prime time? Cancers 2010;2:190–208.
  46. 46. Schrohl AS , Holten-Andersen M, Sweep F, Schmitt M, Harbeck N, Foekens J,. Tumor markers: from laboratory to clinical utility. Molecular & Cellular Proteomics: MCP 2003;2:378–87.
  47. 47. Greenwald P. Cancer risk factors for selecting cohorts for large-scale chemoprevention trials. Journal of Cellular Biochemistry Supplement 1996;25:29–36.
  48. 48. Fagan P , Moolchan ET, Pokhrel P, Herzog T, Cassel KD, Pagano I,. Biomarkers of tobacco smoke exposure in racial/ethnic groups at high risk for lung cancer. American Journal of Public Health 2015;105:1237–45.
  49. 49. Goodman M , Bostick RM, Kucuk O, Jones DP. Clinical trials of antioxidants as cancer prevention agents: past, present, and future. Free Radical Biology & Medicine 2011;51:1068–84.
  50. 50. Wald NJ. Guidance on terminology. Journal of Medical Screening 2008;15:50.
  51. 51. Weigelt B , Peterse JL, van’t Veer LJ. Breast cancer metastasis: markers and models. Nature Reviews Cancer 2005;5:591–602.
  52. 52. Duffy MJ. Use of biomarkers in screening for cancer. Advances in Experimental Medicine and Biology 2015;867:27–39.
  53. 53. Catalona WJ , Smith DS, Ratliff TL, Dodds KM, Coplen DE, Yuan JJ,. Measurement of prostate-specific antigen in serum as a screening test for prostate cancer. The New England Journal of Medicine 1991;324:1156–61.
  54. 54. Andriole GL , Crawford ED, Grubb RL 3rd, Buys SS, Chia D, Church TR,. Mortality results from a randomized prostate-cancer screening trial. The New England Journal of Medicine 2009;360:1310–9.
  55. 55. Schroder FH , Hugosson J, Roobol MJ, Tammela TL, Ciatto S, Nelen V,. Screening and prostate-cancer mortality in a randomized European study. The New England Journal of Medicine 2009;360:1320–8.
  56. 56. Heidenreich A , Bellmunt J, Bolla M, Joniau S, Mason M, Matveev V,. EAU guidelines on prostate cancer. Part I: screening, diagnosis, and treatment of clinically localised disease. Actas urologicas espanolas 2011;35:501–14.
  57. 57. Pavlou MP , Diamandis EP, Blasutig IM. The long journey of cancer biomarkers from the bench to the clinic. Clinical Chemistry 2013;59:147–57.
  58. 58. Henry NL , Hayes DF. Cancer biomarkers. Molecular Oncology 2012;6:140–6.
  59. 59. Mor G , Visintin I, Lai Y, Zhao H, Schwartz P, Rutherford T,. Serum protein markers for early detection of ovarian cancer. Proceedings of the National Academy of Sciences of the United States of America 2005;102:7677–82.
  60. 60. Visintin I , Feng Z, Longton G, Ward DC, Alvero AB, Lai Y,. Diagnostic markers for early detection of ovarian cancer. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 2008;14:1065–72.
  61. 61. Szymendera JJ , Zborzil J, Sikorowa L, Kaminska JA, Gadek A. Value of five tumor markers (AFP, CEA, hCG, hPL and SP1) in diagnosis and staging of testicular germ cell tumors. Oncology 1981;38:222–9.
  62. 62. Swan F Jr. , Velasquez WS, Tucker S, Redman JR, Rodriguez MA, McLaughlin P,. A new serologic staging system for large-cell lymphomas based on initial beta 2-microglobulin and lactate dehydrogenase levels. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 1989;7:1518–27.
  63. 63. Duffy MJ. Predictive markers in breast and other cancers: a review. Clinical Chemistry 2005;51:494–503.
  64. 64. Hanstein B , Djahansouzi S, Dall P, Beckmann MW, Bender HG. Insights into the molecular biology of the estrogen receptor define novel therapeutic targets for breast cancer. European Journal of Endocrinology/European Federation of Endocrine Societies 2004;150:243–55.
  65. 65. Burstein HJ , Kuter I, Campos SM, Gelman RS, Tribou L, Parker LM,. Clinical activity of trastuzumab and vinorelbine in women with HER2-overexpressing metastatic breast cancer. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 2001;19:2722–30.
  66. 66. Allegra CJ , Jessup JM, Somerfield MR, Hamilton SR, Hammond EH, Hayes DF,. American Society of Clinical Oncology provisional clinical opinion: testing for KRAS gene mutations in patients with metastatic colorectal carcinoma to predict response to anti-epidermal growth factor receptor monoclonal antibody therapy. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 2009;27:2091–6.
  67. 67. Sharma S. Tumor markers in clinical practice: general principles and guidelines. Indian Journal of Medical and Paediatric Oncology: Official Journal of Indian Society of Medical & Paediatric Oncology 2009;30:1–8.
  68. 68. Basuyau JP , Leroy M, Brunelle P. Determination of tumor markers in serum. Pitfalls and good practice. Clinical Chemistry and Laboratory Medicine 2001;39:1227–33.
  69. 69. Bast RC Jr. , Ravdin P, Hayes DF, Bates S, Fritsche H Jr., Jessup JM,. 2000 update of recommendations for the use of tumor markers in breast and colorectal cancer: clinical practice guidelines of the American Society of Clinical Oncology. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 2001;19:1865–78.
  70. 70. Koprowski H , Steplewski Z, Mitchell K, Herlyn M, Herlyn D, Fuhrer P. Colorectal carcinoma antigens detected by hybridoma antibodies. Somatic Cell Genetics 1979;5:957–71.
  71. 71. Rosty C , Goggins M. Early detection of pancreatic carcinoma. Hematology/Oncology Clinics of North America 2002;16:37–52.
  72. 72. Locker GY , Hamilton S, Harris J, Jessup JM, Kemeny N, Macdonald JS,. ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 2006;24:5313–27.
  73. 73. Bray F , Jemal A, Grey N, Ferlay J, Forman D. Global cancer transitions according to the Human Development Index (2008–2030): a population-based study. The Lancet Oncology 2012;13:790–801.
  74. 74. Pisani P , Bray F, Parkin DM. Estimates of the world-wide prevalence of cancer for 25 sites in the adult population. International Journal of Cancer Journal International du Cancer 2002;97:72–81.
  75. 75. Sabatino SA , White MC, Thompson TD, Klabunde CN, Centers for Disease C, Prevention. Cancer screening test use—United States, 2013. MMWR Morbidity and Mortality Weekly Report 2015;64:464–8.
  76. 76. Ludwig JA , Weinstein JN. Biomarkers in cancer staging, prognosis and treatment selection. Nature Reviews Cancer 2005;5:845–56.
  77. 77. Mirza AN , Mirza NQ, Vlastos G, Singletary SE. Prognostic factors in node-negative breast cancer: a review of studies with sample size more than 200 and follow-up more than 5 years. Annals of Surgery 2002;235:10–26.
  78. 78. Duffy MJ. Estrogen receptors: role in breast cancer. Critical Reviews in Clinical Laboratory Sciences 2006;43:325–47.
  79. 79. Ross JS , Fletcher JA, Linette GP, Stec J, Clark E, Ayers M,. The Her-2/neu gene and protein in breast cancer 2003: biomarker and target of therapy. The Oncologist 2003;8:307–25.
  80. 80. Vietri MT , Molinari AM, Laura De Paola M, Cantile F, Fasano M, Cioffi M. Identification of a novel in-frame deletion in BRCA2 and analysis of variants of BRCA1/2 in Italian patients affected with hereditary breast and ovarian cancer. Clinical Chemistry and Laboratory Medicine 2012;50:2171–80.
  81. 81. Harbeck N , Kruger A, Sinz S, Kates RE, Thomssen C, Schmitt M,. Clinical relevance of the plasminogen activator inhibitor type 1—a multifaceted proteolytic factor. Onkologie 2001;24:238–44.
  82. 82. Andres SA , Edwards AB, Wittliff JL. Expression of urokinase-type plasminogen activator (uPA), its receptor (uPAR), and inhibitor (PAI-1) in human breast carcinomas and their clinical relevance. Journal of Clinical Laboratory Analysis 2012;26:93–103.
  83. 83. Molina R , Jo J, Filella X, Zanon G, Pahisa J, Mu noz M, et al. c-erbB-2 oncoprotein, CEA, and CA 15.3 in patients with breast cancer: prognostic value. Breast Cancer Research and Treatment 1998;51:109–19.
  84. 84. Jensen JL , Maclean GD, Suresh MR, Almeida A, Jette D, Lloyd S,. Possible utility of serum determinations of CA 125 and CA 27.29 in breast cancer management. The International Journal of Biological Markers 1991;6:1–6.
  85. 85. Sparano JA. TAILORx: trial assigning individualized options for treatment (Rx). Clinical Breast Cancer 2006;7:347–50.
  86. 86. Bjartell AS. Next-generation prostate-specific antigen test: ready to use? European Urology 2013;64:700–2.
  87. 87. Gao CL , Rawal SK, Sun L, Ali A, Connelly RR, Banez LL,. Diagnostic potential of prostate-specific antigen expressing epithelial cells in blood of prostate cancer patients. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 2003;9:2545–50.
  88. 88. Steuber T , Niemela P, Haese A, Pettersson K, Erbersdobler A, Felix Chun KH,. Association of free-prostate specific antigen subfractions and human glandular kallikrein 2 with volume of benign and malignant prostatic tissue. The Prostate 2005;63:13–8.
  89. 89. Haese A , Vaisanen V, Lilja H, Kattan MW, Rittenhouse HG, Pettersson K,. Comparison of predictive accuracy for pathologically organ confined clinical stage T1c prostate cancer using human glandular kallikrein 2 and prostate specific antigen combined with clinical stage and Gleason grade. The Journal of Urology 2005;173:752–6.
  90. 90. Marks LS , Fradet Y, Deras IL, Blase A, Mathis J, Aubin SM,. PCA3 molecular urine assay for prostate cancer in men undergoing repeat biopsy. Urology 2007;69:532–5.
  91. 91. Moses MA , Wiederschain D, Loughlin KR, Zurakowski D, Lamb CC, Freeman MR. Increased incidence of matrix metalloproteinases in urine of cancer patients. Cancer Research 1998;58:1395–9.
  92. 92. Morgia G , Falsaperla M, Malaponte G, Madonia M, Indelicato M, Travali S,. Matrix metalloproteinases as diagnostic (MMP-13) and prognostic (MMP-2, MMP-9) markers of prostate cancer. Urological Research 2005;33:44–50.
  93. 93. Coticchia CM , Yang J, Moses MA. Ovarian cancer biomarkers: current options and future promise. Journal of the National Comprehensive Cancer Network: JNCCN 2008;6:795–802.
  94. 94. Duffy MJ , Bonfrer JM, Kulpa J, Rustin GJ, Soletormos G, Torre GC,. CA125 in ovarian cancer: European Group on Tumor Markers guidelines for clinical use. International Journal of Gynecological Cancer: Official Journal of the International Gynecological Cancer Society 2005;15:679–91.
  95. 95. Morgan RJ Jr., Alvarez RD, Armstrong DK, Boston B, Chen LM, Copeland L, . Ovarian cancer. Clinical Practice Guidelines in Oncology. Journal of the National Comprehensive Cancer Network: JNCCN 2008;6:766–94.
  96. 96. Chambers SK , Gertz RE Jr., 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;75:1627–33.
  97. 97. Coppola D , Szabo M, Boulware D, Muraca P, Alsarraj M, Chambers AF,. Correlation of osteopontin protein expression and pathological stage across a wide variety of tumor histologies. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 2004;10:184–90.
  98. 98. Davies RJ , Miller R, Coleman N. Colorectal cancer screening: prospects for molecular stool analysis. Nature Reviews Cancer 2005;5:199–209.
  99. 99. Smith RA , von Eschenbach AC, Wender R, Levin B, Byers T, Rothenberger D,. American Cancer Society guidelines for the early detection of cancer: update of early detection guidelines for prostate, colorectal, and endometrial cancers. Also: update 2001–testing for early lung cancer detection. CA: A Cancer Journal for Clinicians 2001;51:38–75; quiz 7–80.
  100. 100. Allison JE. Colon Cancer Screening Guidelines 2005: the fecal occult blood test option has become a better FIT. Gastroenterology 2005;129:745–8.
  101. 101. Allison JE , Sakoda LC, Levin TR, Tucker JP, Tekawa IS, Cuff T,. Screening for colorectal neoplasms with new fecal occult blood tests: update on performance characteristics. Journal of the National Cancer Institute 2007;99:1462–70.
  102. 102. Duffy MJ. Carcinoembryonic antigen as a marker for colorectal cancer: is it clinically useful? Clinical Chemistry 2001;47:624–30.
  103. 103. Benson AB 3rd , Schrag D, Somerfield MR, Cohen AM, Figueredo AT, Flynn PJ, . American Society of Clinical Oncology recommendations on adjuvant chemotherapy for stage II colon cancer. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 2004;22:3408–19.
  104. 104. Reiter W , Stieber P, Reuter C, Nagel D, Lau-Werner U, Lamerz R. Multivariate analysis of the prognostic value of CEA and CA 19-9 serum levels in colorectal cancer. Anticancer Research 2000;20:5195–8.
  105. 105. Holten-Andersen MN , Christensen IJ, Nielsen HJ, Stephens RW, Jensen V, Nielsen OH,. Total levels of tissue inhibitor of metalloproteinases 1 in plasma yield high diagnostic sensitivity and specificity in patients with colon cancer. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 2002;8:156–64.
  106. 106. Anderson NL , Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. Molecular & Cellular Proteomics: MCP 2002;1:845–67.
  107. 107. Sawyers CL , van’t Veer LJ. Reliable and effective diagnostics are keys to accelerating personalized cancer medicine and transforming cancer care: a policy statement from the American association for cancer research. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 2014;20:4978–81.
  108. 108. Srivastava S , Verma M, Henson DE. Biomarkers for early detection of colon cancer. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 2001;7:1118–26.
  109. 109. Poste G. Bring on the biomarkers. Nature 2011;469:156–7.
  110. 110. Hammond ME , Taube SE. Issues and barriers to development of clinically useful tumor markers: a development pathway proposal. Seminars in Oncology 2002;29:213–21.
  111. 111. Pepe MS , Etzioni R, Feng Z, Potter JD, Thompson ML, Thornquist M,. Phases of biomarker development for early detection of cancer. Journal of the National Cancer Institute 2001;93:1054–61.
  112. 112. Bensalah K , Montorsi F, Shariat SF. Challenges of cancer biomarker profiling. European Urology 2007;52:1601–9.
  113. 113. Paradiso A , Mangia A, Orlando C, Verderio P, Belfiglio M, Marchetti A,. The Integrated Oncology Program of the Italian Ministry of Health. Analytical and clinical validation of new biomarkers for early diagnosis: network, resources, methodology, quality control, and data analysis. The International Journal of Biological Markers 2009;24:119–29.
  114. 114. Teutsch SM , Bradley LA, Palomaki GE, Haddow JE, Piper M, Calonge N,. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genetics in Medicine: Official Journal of the American College of Medical Genetics 2009;11:3–14.
  115. 115. Pollak MN , Foulkes WD. Challenges to cancer control by screening. Nature Reviews Cancer 2003;3:297–303.
  116. 116. Neugut AI , Jacobson JS, Rella VA. Prevalence and incidence of colorectal adenomas and cancer in asymptomatic persons. Gastrointestinal Endoscopy Clinics of North America 1997;7:387–99.
  117. 117. Wagner PD , Verma M, Srivastava S. Challenges for biomarkers in cancer detection. Annals of the New York Academy of Sciences 2004;1022:9–16.
  118. 118. Diamandis EP. Towards identification of true cancer biomarkers. BMC Medicine 2014;12:156.
  119. 119. Kulasingam V , Pavlou MP, Diamandis EP. Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer. Nature Reviews Cancer 2010;10:371–8.
  120. 120. Sawyers CL. The cancer biomarker problem. Nature 2008;452:548–52.
  121. 121. Tran B , Dancey JE, Kamel-Reid S, McPherson JD, Bedard PL, Brown AM,. Cancer genomics: technology, discovery, and translation. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 2012;30:647–60.
  122. 122. Arteaga CL , Baselga J. Impact of genomics on personalized cancer medicine. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 2012;18:612–8.
  123. 123. Gibney ER , Nolan CM. Epigenetics and gene expression. Heredity 2010;105:4–13.
  124. 124. Nogueira da Costa A , Herceg Z. Detection of cancer-specific epigenomic changes in biofluids: powerful tools in biomarker discovery and application. Molecular Oncology 2012;6:704–15.
  125. 125. Herman JG , Graff JR, Myohanen S, Nelkin BD, Baylin SB. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proceedings of the National Academy of Sciences of the United States of America 1996;93:9821–6.
  126. 126. Booth MJ , Branco MR, Ficz G, Oxley D, Krueger F, Reik W,. Quantitative sequencing of 5-methylcytosine and 5-hydroxymethylcytosine at single-base resolution. Science 2012;336:934–7.
  127. 127. Zeidan BA , Townsend PA. SELDI-TOF proteomic profiling of breast carcinomas identifies clinicopathologically relevant groups of patients similar to previously defined clusters from cDNA expression. Breast Cancer Research: BCR 2008;10:107.
  128. 128. Curtis C , Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ,. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012;486:346–52.
  129. 129. Winter PM , Caruthers SD, Kassner A, Harris TD, Chinen LK, Allen JS,. Molecular imaging of angiogenesis in nascent Vx-2 rabbit tumors using a novel alpha(nu)beta3-targeted nanoparticle and 1.5 tesla magnetic resonance imaging. Cancer Research 2003;63:5838–43.
  130. 130. Westhoff M , Litterst P, Freitag L, Urfer W, Bader S, Baumbach JI. Ion mobility spectrometry for the detection of volatile organic compounds in exhaled breath of patients with lung cancer: results of a pilot study. Thorax 2009;64:744–8.
  131. 131. Holmes E , Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell 2008;134:714–7.
  132. 132. Spratlin JL , Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: a review. Clinical Cancer Research An Official Journal of the American Association for Cancer Research 2009;15:431–40.
  133. 133. Vickers AJ , Brewster SF. PSA Velocity and Doubling Time in Diagnosis and Prognosis of Prostate Cancer. British Journal of Medical & Surgical Urology 2012;5:162–8.
  134. 134. van Nagell JR , Jr., DePriest PD, Reedy MB, Gallion HH, Ueland FR, Pavlik EJ, et al. The efficacy of transvaginal sonographic screening in asymptomatic women at risk for ovarian cancer. Gynecologic Oncology 2000;77:350–6.
  135. 135. Koizumi F , Odagiri H, Fujimoto H, Kawamura T, Ishimori A. Clinical evaluation of four tumor markers (CEA, TPA, CA50 and CA72-4) in colorectal cancer. Rinsho Byori: The Japanese Journal of Clinical Pathology 1992;40:523–8.
  136. 136. Matsuoka Y. Basic and clinical aspects of tumor markers–with special reference to CEA. Rinsho Byori: The Japanese Journal of Clinical Pathology 1990;38:31–4.
  137. 137. von Kleist S. The clinical value of the tumor markers CA 19/9 and carcinoembryonic antigen (CEA) in colorectal carcinomas: a critical comparison. The International Journal of Biological Markers 1986;1:3–8.
  138. 138. Molina R , Barak V, van Dalen A, Duffy MJ, Einarsson R, Gion M,. Tumor markers in breast cancer-European Group on Tumor Markers recommendations. Tumour Biology: The Journal of the International Society for Oncodevelopmental Biology and Medicine 2005;26:281–93.
  139. 139. McGuire WL , Horwitz KB, Pearson OH, Segaloff A. Current status of estrogen and progesterone receptors in breast cancer. Cancer 1977;39:2934–47.
  140. 140. Colleoni M , Viale G, Zahrieh D, Pruneri G, Gentilini O, Veronesi P,. Chemotherapy is more effective in patients with breast cancer not expressing steroid hormone receptors: a study of preoperative treatment. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 2004;10:6622–8.
  141. 141. Paik S , Tang G, Shak S, Kim C, Baker J, Kim W,. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 2006;24:3726–34.
  142. 142. Varadan V , Kamalakaran S, Gilmore H, Banerjee N, Janevski A, Miskimen KL,. Brief-exposure to preoperative bevacizumab reveals a TGF-beta signature predictive of response in HER2-negative breast cancers. International Journal of Cancer Journal International du Cancer 2016;138:747–57.
  143. 143. Kurtzman JT , Wilson H, Rao CV. A proposed role for hCG in clinical obstetrics. Seminars in Reproductive Medicine 2001;19:63–8.
  144. 144. Chen L , Ho DW, Lee NP, Sun S, Lam B, Wong KF,. Enhanced detection of early hepatocellular carcinoma by serum SELDI-TOF proteomic signature combined with alpha-fetoprotein marker. Annals of Surgical Oncology 2010;17:2518–25.
  145. 145. Johnson PJ , Williams R. Serum alpha-fetoprotein estimations and doubling time in hepatocellular carcinoma: influence of therapy and possible value in early detection. Journal of the National Cancer Institute 1980;64:1329–32.
  146. 146. Minami T , Tateishi R, Kondo M, Nakagomi R, Fujiwara N, Sato M,. Serum alpha-fetoprotein has high specificity for the early detection of hepatocellular carcinoma after hepatitis C virus eradication in patients. Medicine 2015;94:e901.
  147. 147. Bae YJ , Schaab M, Kratzsch J. Calcitonin as biomarker for the medullary thyroid carcinoma. Recent results in cancer research Fortschritte der Krebsforschung Progres dans les recherches sur le cancer 2015;204:117–37.
  148. 148. van Veelen W , de Groot JW, Acton DS, Hofstra RM, Hoppener JW, Links TP,. Medullary thyroid carcinoma and biomarkers: past, present and future. Journal of Internal Medicine 2009;266:126–40.
  149. 149. Mazzaferri EL , Robbins RJ, Spencer CA, Braverman LE, Pacini F, Wartofsky L,. A consensus report of the role of serum thyroglobulin as a monitoring method for low-risk patients with papillary thyroid carcinoma. The Journal of Clinical Endocrinology and Metabolism 2003;88:1433–41.
  150. 150. Kibar Y , Goktas S, Kilic S, Yaman H, Onguru O, Peker AF. Prognostic value of cytology, nuclear matrix protein 22 (NMP22) test, and urinary bladder cancer II (UBC II) test in early recurrent transitional cell carcinoma of the bladder. Annals of Clinical and Laboratory Science 2006;36:31–8.
  151. 151. van Gils MP, Cornel EB, Hessels D, Peelen WP, Witjes JA, Mulders PF, et al. Molecular PCA3 diagnostics on prostatic fluid. The Prostate 2007;67:881–7.

Written By

Hala Fawzy Mohamed Kamel, and Hiba Saeed Bagader Al-Amodi

Submitted: October 14th, 2015 Reviewed: February 8th, 2016 Published: August 17th, 2016