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REVIEW

Cancer biomarkers: knowing the present and predicting the future Sabarni K Chatterjee & Bruce R Zetter† †Author

for correspondence Program in Vascular Biology Children’s Hospital 300 Longwood Avenue, Boston, MA 02115, USA Tel.: +1 617 919 2320 [email protected] rd.edu

In recent years the discovery of cancer biomarkers has become a major focus of cancer research. The widespread use of prostate-specific antigen in prostate cancer screening has motivated researchers to identify suitable markers for screening different types of cancer. Biomarkers are also useful for diagnosis, monitoring disease progression, predicting disease recurrence and therapeutic treatment efficacy. With the advent of new and improved genomic and proteomic technologies such as DNA and tissue microarray, twodimensional gel eletrophoresis, mass spectrometry and protein assays coupled with advanced bioinformatic tools, it is possible to develop biomarkers that are able to reliably and accurately predict outcomes during cancer management and treatment. In years to come, a serum or urine test for every phase of cancer may drive clinical decision making, supplementing or replacing currently existing invasive techniques.

History of biomarkers

Keywords: biomarkers, mass spectrometry, microarray, neoplasia, prostate-specific antigen, proteomics

Over the past several decades, considerable investment has been made in the early detection of cancer. An increasing number of early cancers diagnosed as asymptomatic malignancies, or in some instances even as premalignant lesions, can be attributed to more efficient screening programs and changes in clinical practice. The definitive diagnosis of cancer, has however relied on histological evaluation of tissues. An ideal tumor marker would be a protein or protein fragment that can be easily detected in the patient’s blood or urine, but not detected in a healthy person. Today, the most common use of tumor biomarkers is for detection of early disease and recurrent disease. In the future, better tests that may predict tumor outcome in advance and predict the response of individual tumors to particular therapeutic drugs may be developed. The first recognized test for a type of common cancer was reported in 1965 by Dr Joseph Gold [1]. He found a substance in the blood of patients with colon cancer that was normally found in fetal tissues and named it carcinoembryonic antigen (CEA). By the end of the 1970s, potential serum tests had been developed for a variety of cancers [2]. Additional biomarkers developed in the 1980s were CA 19-9 for colorectal and pancreatic cancer, CA 15-3 for breast cancer and CA-125 for ovarian cancer. However, these early markers have proven to be reliable indicators of early disease as they are present in basal levels in normal individuals and are substantially higher only when there is a considerable amount of cancer present. Furthermore, these markers are for the most part not specific for a single cancer. For example, patients with lung or

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breast cancer can often have elevated CEA, and CA-125 can be high in women with noncancerous gynecological conditions [3]. The best-known cancer biomarker that has been used by physicians to detect early disease is the prostate-specific antigen (PSA). The serum PSA test has been widely used in screening for prostate cancer in the last decade, and has brought about a dramatic change increase in early detection of the disease [4]. The upper limit of normal PSA level was considered to be 4 ng/ml. Nevertheless, 33% of tumors spread beyond the prostate in men, with PSA values between 4 and 10 ng/ml, rendering many of these tumors refractory to treatment. Between 1989 and 1996 prostate cancer incidence rates increased steadily with a parallel decrease (2.5% per year) in mortality rate. The above observations have been attributed to the dramatic increase in the use of serum PSA which allowed earlier diagnosis of asymptomatic prostate cancer [5,6]. Although PSA screening may provide a suspicion of prostate cancer, a clinical diagnosis still relies on a pathological tissue examination. Of 15 million men screened in 1998 with the PSA test, 15% or approximately 2.25 million had PSA levels higher than normal and thus faced the prospect of biopsy [7]. Any healthy individual with a PSA between 4.0 ng/ml and 10.0 ng /ml is recommended for biopsy although the lower limit for suspicion of prostate cancer has been dropped recently to 2.5 ng/ml. Since PSA elevation is also associated with benign prostatic hyperplasia (BPH), elevated PSA levels do not always indicate the presence of cancer. The resultant specificity of PSA less than 4.0 ng/ml in detecting early disease Future Oncology (2005) 1(1), 37–50 37

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is just 25%. Consequently, many individuals undergo an unnecessary biopsy [8]. Despite the advances made in serum PSA screening, it is still difficult to reliably detect the early stages of prostate cancer without histological examination. Evaluating the Gleason score from a tissue specimen taken at the time of biopsy is still the most widely used diagnostic and prognostic tool for human prostate cancer [9]. Serum PSA is, perhaps, more reliable as a marker of prostate cancer recurrence or as an indicator of treatment efficacy. Levels of PSA in the blood fall to less than measurable levels (PSA nadir) after surgical removal of the prostate, radical prostatectomy, or treatment of prostate cancer by radiation [10,11]. This reduction of PSA blood levels can be monitored over time, and the finding of a later increase in the level of serum PSA is considered evidence of a clinical recurrence of prostate cancer (PSA recurrence), thus triggering additional treatment [10]. These values often predate clinical evidence of prostate cancer recurrence as determined by radiographic or physical examination, and are often the only initial indication of prostate tumor progression [12]. Despite the success of PSA as a prostate cancer biomarker, useful diagnostic or predictive markers do not exist for most other tumors. Even in prostate cancer, the greatest success of PSA is in detecting recurrent disease. The lack of availability of biomarkers with high specificity and sensitivity limits our ability to screen for most cancers. Sensitivity refers to the percentage of individuals with disease who are marker positive (validity of a positive result), whereas specificity refers to the likelihood that a given marker will be elevated only in individuals with the disease (validity of a negative result). A perfect marker for a specific cancer should have 100% sensitivity and 100% specificity. PSA for example is a very sensitive marker but has low specificity. New screening markers with high specificity and sensitivity are still required for virtually all cancers.

importantly, with the clinical appearance of many new therapeutic agents, appropriate markers can be used to determine which tumors will respond to which treatments in order to predict the likelihood of drug resistance. Widespread screening

One of the most important roles of cancer biomarkers will be to utilize them in widespread screening so that asymptomatic individuals can be detected with disease at a very early stage. Surprisingly, there are virtually no molecular cancer markers that are widely recognized as effective for screening large segments of the population for the presence of one or more cancers. To date, the only tumor biomarker approved by US Food and Drug Administration (FDA) for widespread screening purposes is PSA along with digital rectal examination (DRE) as discussed earlier. Despite of the success of PSA in detecting early stage prostate cancer in some individuals, its use to screen patients for prostate cancer remains controversial. Furthermore, it is unclear whether the benefits of the PSA test outweigh the risk of follow-up diagnostic tests and treatment. Overdiagnosis of prostate cancer by PSA screening can occur where patients with non-life-threatening small cancers undergo unnecessary complications resulting from surgery or radiation. Recent refinements in the PSA test method has made it possible to distinguish between slow and fast growing cancers to make the test more specific. PSA velocity

PSA velocity is the change in PSA level over time. A steep rise in PSA level increases the likelihood of malignant prostate cancer. A recent study demonstrated a correlation between the PSA velocity and time to death from prostate cancer after radical prostatectomy. Patients whose PSA level increased by more than 2.0 ng/ml during the year prior to diagnosis of prostate cancer, were shown to be at higher risk of dying from the disease despite undergoing radical prostatectomy [13].

Use of biomarkers in cancer

The future of cancer management is expected to be profoundly dependent upon the use of biomarkers that will guide physicians at every step of disease management. Cancer biomarkers can be used for the accurate evaluation and management of the disease in different stages. They can be useful for predicting several outcomes during the course of disease including early detection, outcome prediction and detection of disease recurrence. Most 38

PSA density

PSA density considers the relationship of the PSA level to the size of the prostate. An elevated PSA might not arouse suspicion in a patient with a pre-existing enlarged prostate. Thus, consideration of prostate density may avoid unnecessary biopsy in men with elevated PSA due to benign prostate hypertrophy. The method has a disadvantage, however, in that some aggressive cancers may be missed in this cohort. Future Oncology (2005) 1(1)

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Free vs bound PSA

Advances in biomarker discovery

Circulating PSA in the serum has been identified in two forms, free PSA or PSA bound to protein. The ratio of free to bound PSA decreases from benign to cancer i.e., there is more free PSA in benign conditions whilst more bound PSA in cancer. Therefore, the ratio of bound:free PSA can be used as an adjunct to the total PSA level to provide an additional indication of the presence of clinically relevant prostate cancer [14].

Recent advancements in biomarker development using gene arrays in addition to proteomic technologies, including twodimensional electrophoresis (2-DE) and mass spectrometry, have facilitated the discovery of several new biomarkers. Recently, the FDA has approved a small number of new urine-based biomarkers including bladder tumor antigen (BTA) and nuclear matrix protein-22 as diagnostic markers for bladder cancer [17,18]. Celis and colleagues [19] executed an elaborate proteome analysis using 2-DE and developed a comprehensive database for bladder cancer. Survivin, an inhibitor of apoptosis, has also recently been identified as a urinary diagnostic biomarker for bladder cancer [20]. Another potential diagnostic biomarker for bladder cancer, calreticulin (CRT), has been identified very recently [21]. These new discoveries provide hope for an increase in the number of new diagnostic markers, however the number of biomarkers approved in the past decade still vastly trails the number of new therapies that have been brought to the clinic in that time. Physicians still depend on invasive techniques including biopsy or radiologic methods such as mammography for early detection of disease. Recent developments in genomics and proteomics technologies including mass spectrometry have provided hope of discovering patterns of multiple biomarkers and/or ‘signature’ protein/gene profiles specific to each particular cancer [22,23]. In this case, the eventual clinical ‘marker’ may more accurately be a pattern of genes or proteins that provide an indication of the presence of cancer in an individual. The coming decade should see the continued development of novel biomarkers that will detect early cancers as well as predict the risk of early tumors by screening for invasive cancer. Conceptually, the major difficulty in utilizing circulating molecular markers as cancer screening tools is that very small tumors, which need to be detected and removed prior to metastasis to other organs, may not produce sufficient markers for detection in serum or urine. To make such an early detection possible it is necessary to first develop new ultrasensitive methods for detecting very low circulating levels of these analytes. Detecting cancer may also be compromised if the marker is produced by any normal tissue and released into the bloodstream where it would contribute to a high background signal.

Diagnostic & prognostic biomarkers

Although few new markers have reached the clinic in recent years, technological advances in genomics and proteomics have produced candidate markers that may have potential for cancer screening. Calcitonin is one of the new tumor markers that may be used to help early cancer diagnosis. Calcitonin is elevated in the serum of a thyroid medullary carcinoma patient and may with further clinical evaluations, be useful in screening for this cancer [15]. Calcitonin is a hormone produced by parafollicular C cells in the thyroid gland that helps to regulate blood calcium levels. Levels of this hormone are elevated in cancers of the parafollicular C cells, termed medullary carcinoma of the thyroid. Since medullary carcinoma of the thyroid is often inherited, blood calcitonin can be measured to detect the cancer in its earliest stages in family members who are at risk. Other cancers, particularly lung cancers, can produce calcitonin, but measurement of its level in the blood is not usually used to follow these cancers. Several reported cancer biomarkers have been found to have low sensitivity in that they are found only in a small subset of patients with a particular type of cancer. Although these markers are not useful for general screening, they can be useful in detecting recurrent disease in those patients whose tumors produce that particularmarker. One such biomarker is CA125, which is present in a subset of ovarian cancers [16]. CA-125 is also elevated in endometriosis and some other benign conditions, and fails to identify more than 50% of the early cancers, thus it is not recommended for use in a general screening. However, in patients whose primary tumor is CA-125 positive, postsurgical elevation of CA-125 levels reflect recurrent disease. CEA is a colon cancer marker, which has low specificity and insufficient senitivity to be used as a screening marker but is useful in follow-up. www.futuremedicine.com

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Predictive biomarkers

In recent years, an increasing number of markers have been identified that aim to predict cancer outcome rather than to detect it early. This progress stems from the ability of new techniques in genomics and proteomics to discover genes and proteins associated with distinct cancer stages. In addition, these markers are often present in the circulation in detectable amounts, perhaps due to the larger size of tumors that require a prognostic test. Such biomarkers can distinguish between invasive and noninvasive tumors, between metastatic and nonmetastatic tumors and between indolent and life-threatening tumors. Prostate and breast cancer are two specific cancers that are diagnosed relatively early compared to other tumors, as a result of widespread screening programs. Monitoring the prognosis of these tumors can eventually lead to a high treatment success rate by providing an indication as to which patient would benefit from no treatment, localized treatment such as surgery or radiation, or early systemic therapy. Potential prognostic biomarkers have been proposed for several tumors. Prostate cancer

As discussed above, PSA has been the major marker associated with diagnosis of prostate cancer for the past several years. Despite improvements to the test, PSA does not provide a prediction of disease outcome in newly diagnosed patients and cannot, by itself, determine the course of treatment. The great promise of better prognostic markers is that patients who are known to be at high risk of future cancer recurrence, due to the presence of metastatic markers at the time of diagnosis, could be given systemic therapy at the time of diagnosis, or treatment and not wait for the later rise in PSA which indicative full blown recurrent disease. Recent interest in the authors’ laboratory and by other researchers has focused on identifying biomarkers that distinguish tumors that have the capacity to metastasize, from those that are likely to be confined to the primary organ. In the last decade, the authors’ laboratory has identified several potential predictive biomarkers including thymosin β-15, antizyme, antizyme inhibitor and collagen XXIII that may help to distinguish metastatic prostate cancer [24–27]. The authors’ early experiments revealed overexpression of these genes in metastatic prostate cancer cell lines. Recently, they tested the presence of thymosin β-15 in both prostate 40

cancer tissue specimens as well as patient urine, and established that thymosin β-15 is elevated in metastatic prostate cancer. In addition, levels of thymosin β-15 in combination with PSA can predict recurrence of prostate cancer with more sensitivity and specificity than PSA alone [95]. Other promising prognostic biomarkers of prostate cancer include the p53 tumor suppressor gene, bcl-2 proto-oncogene, and Ki-67 proliferation labeling index. Presence of mutated p53 in prostate cancer tissue and overexpression of bcl2 and Ki-67 are all associated with poor prognosis [28,29]. Other potential prognostic markers of prostate cancer include osteopontin, osteocalcin, metalloproteinases (MMPs) and MMP inhibitors [30,31]. Another recent study has identified propyl isomerase Pin1 as a potential prognostic marker of prostate cancer. Pin1 expression is directly related to recurrence of disease. Patients with higher Pin1 expression have approximately eight times greater probability of recurrence than those with low Pin1 expression [32]. Combination of selected prognostic biomarkers, for example thymosin β 15 + PSA, may ultimately provide the most effective means for accurately predicting disease outcome. Breast cancer

Although mammography is widely used to detect incipient breast cancer, this technique does not provide any indication of eventual disease outcome. Consequently, several recent studies have sought to identify proteins that contribute to the ‘metastatic signature of breast cancer’ [23]. Two breast cancer susceptibility genes BRCA1 and BRCA2 were discovered after research on families with very strong patterns of breast cancer in 1994 and 1995 [33,34]. These genes are present in all men and women and are considered to be tumor suppressors. Women who have a mutation in the BRCA1 or BRCA2 gene have a significantly elevated risk of developing breast cancer. Genetic testing for mutations in BRCA1 and BRCA2 genes can be used for predicting the risk of an individual developing breast cancer. The major concern regarding the use of these two genes in accurate prediction of the eventual onset of breast cancer lies in the variability between individuals. Identical genetic alterations can lead to a different disease outcome in different populations of women, emphasizing modifying factors, such as diet, smoking and environmental exposures in determining the final outcome. Furthermore, women who do not have any of these mutations Future Oncology (2005) 1(1)

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may be given a false sense of security, since women with no mutation can still be at risk. Knowing a possible outcome often helps in early surgical intervention, but it also increases the psychological trauma associated with a prophylactic surgical intervention. Consequently, BRCA1 and BRCA2 genetic analysis can best serve as a prognostic indicator but not as a decision maker. There are several other factors that can help monitor prognosis of breast cancer. There is some evidence that the apoptotic index (the percentage of dying cells) may be a better predictor of 5-year disease-free survival than tumor volume, mitotic index or status of organ confinement. Vascular density, the marker of blood vessels in a given volume of tumor, is another important factor that influences tumor growth and dissemination [35]. Microvessel density (MVD) has been shown to increase with the declining pathological stage of the tumor and can predict the likelihood of extracapsular extension especially in breast cancer [36]. MMPs, which are critical for angiogenesis and tumor invasion, have also been indicated as prognostic biomarkers for breast cancer [37]. Absence of estrogen and progesterone receptors, and upregulation of Ki-67 (Mib-1) antigen are additional indicators of poor prognosis in breast cancer patients [38–40]. Another major protein that can serve as a prognostic marker in several cancers including breast cancer is osteopontin (OPN) [41]. OPN is a transformation-associated protein and has been considered to play a significant role as a prognostic biomarker in several cancers. High OPN serum levels have been detected in patients with metastatic cancer of the prostate, breast and lung [42]. Consequently, patients with a variety of cancers could benefit from knowing their level of circulating OPN at the time of diagnosis as an indicator of the likelihood of metastatic disease. Her-2 oncogene expression in tissues or serum is the most commonly used predictive biomarker for breast cancer. Expression of Her-2 is significantly related to positive lymph nodes, poor nuclear grade, lack of steroid receptors and high proliferative activity [43,44]. Her-2 expression in association with proliferative activity has been shown to identify a subgroup of node-specific breast cancer patients with poorer prognosis [45]. Her-2 is a true predictive marker in that its presence not only provides an indication of disease outcome, but also can lead to the selection of an appropriate cancer treatment such as the use of Herceptin® to treat patients with Her-2 positive tumors. The combination of Her-2 and www.futuremedicine.com

estrogen/progesterone receptor status can provide a greater indication of disease outcome in breast cancer. Additional indicators of breast cancer outcome include urokinase-type plasminogen activator (uPA) and its inhibitors plasminogen activator inhibitor (PAI) 1 and -2, [46,47] as well as levels of lysosomal cysteine proteases cathepsin B and cathepsin L have also been positively correlated to relapse-free survival and overall survival after treatment of primary breast tumor [48]. The situation with breast cancer highlights the current status of biomarker application today. Discovery efforts have revealed several new biomarkers, however, few are FDA approved and it is not yet completely understood how these markers can be used best alone or in combination to detect or predict outcome in most breast cancer patients. The next decade should see the improved application of these and other new biomarkers to the diagnosis and prognosis of a variety of tumors. Ovarian cancer

Ovarian cancer is a target of intense biomarker research because it is often not discovered until the disease is quite advanced. CA-125 has been tested as both a diagnostic and prognostic marker in ovarian cancer, but it has several limitations including both low specificity and low selectivity. Barbieri and colleagues have shown that cyclin D (CD) 1 overexpression is related to a more aggressive tumor phenotype and poor prognosis in ovarian carcinoma even though CD1 expression is not limited to ovarian cancer [49]. Increased serum concentration of carboxyterminal telopeptide of Type I collagen (ICTP) reflects the aggressiveness of invasive ovarian cancer [50]. Tumor-associated trypsin inhibitor is another potentially important predictor of disease stage in ovarian cancer. Elevated serum levels and tissue expression in preoperative patients can be used to predict the stage and also future prognosis and disease-free survival [51,52]. Surface-enhanced laser desorption ionization (SELDI) mass spectrometry technology has been used to compare serum protein profiles of different stages of ovarian cancer [53]. This results in a profile of a large number of serum peptides that must be sorted and interpreted using complex bioinformatic algorithms. Despite the intense efforts that have been carried out, to date there is not a reliable biomarker strategy that allows improved early detection of this life-threatening disease. 41

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Colorectal cancer

There have been several studies to identify genetic markers or signature genetic profiles for the diagnosis and prognosis of colorectal cancer. Microsatellite instability that results from mutations in DNA mismatch-repair genes such as MLH1, MSH2, or MSH6 is associated with prognosis of colorectal cancer [54,55]. Increases in the levels of D-dimer, a fibrin degradation product, in the serum has also been proposed as a prognostic biomarker for colorectal carcinoma [56]. Use of colorectal cancer markers is not widespread because of their low specificity and microsatellite instability, which is used as an indicator for colorectal cancer has minimal specificity. Rather, it serves as a surrogate marker for deficiencies in DNA repair that may be altered in other cancers and disease, rather than being specific for colorectal cancer [57]. Other cancers

Poor survival rates in non-small cell lung cancer have also been ascribed to p53 alterations [58]. Other less commonly used prognostic biomarkers include β-2 microglobulin (β-2M), which has been used to determine prognosis in multiple myeloma and lymphomas [59]. Caspase-3 has also been shown to be a novel, independent prognostic factor in gastric carcinoma [60]. Recent advances in genomic and proteomic technologies have identified several potential marker candidates or protein profiles from physiological fluids [61]. In spite of these great efforts, there is still a lack of biomarkers that can accurately predict the outcome for a specific cancer. Future emphasis should be placed on identifying biomarkers that predict the capacity of the primary tumor to metastasize, the presence or absence of micrometastatic disease and, where relevant, the time course of disease recurrence. It seems unlikely that any single marker will possess 100% sensitivity and specificity. As new biomarkers are tested, the use of multiple independently predictive parameters or signature protein markers for cancer will become indispensable in our efforts to improve cancer management. Biomarkers for tumor recurrence

Most of the biomarkers in current clinical use are best applied to demonstrating recurrence. In many cases, patients who emerge from cancer surgery and/or chemotherapy will have a 30–70% statistical chance of a recurrent cancer, due to 42

either local growth or distant metastasis. This leaves the patient in a state of not knowing whether they have been cured, or whether they harbor silent tumors that will grow over time. Until the promise of prognostic markers is realized, the best choice for these patients is to utilize markers that act as harbingers of recurrent disease. PSA is a classic example of a biomarker used for prostate cancer recurrence after initial nonsurgical treatment. Patients whose PSA levels fall to almost zero after surgery or radiation can be monitored over time to determine whether PSA levels remain stable. An increase in PSA levels at a later time is indicative of recurrent disease and is generally followed by systematic therapy. Additional markers have been discovered that are associated with the biological behavior of prostate cancer, helping to move beyond the reliance on PSA. Interleukin-6 soluble receptor and transforming growth factor β1 levels are both measured in patient serum and ‘recurrence is predicted with the help of a nomogram [62]. In a separate study it was shown that two biomarkers, enhancer of zeste homolog (EZH) 2 and Ecadherin (ECAD), when found together in prostate tumor tissue, predicted a threefold increase in risk of cancer recurrence after surgery [63]. In other cancers, calcitonin, a biomarker for thyroid medullary carcinoma is widely accepted to be a biomarker for thyroid cancer recurrence. Elevation of calcitonin levels in the serum after treatment indicates disease recurrence. Thyroglobulin is also used to determine cancer recurrence after the thyroid is removed. The future of cancer biomarkers

The future of clinical cancer management belongs to the prognostic and predictive biomarkers of cancer. These markers are of utmost importance as they will be the used to make clinical decisions that will eventually save lives. In the future, biomarkers will guide decision making during cancer management. Biomarker(s) that correctly predict outcome in a specific disease and allow physicians and patients to make informed treatment decisions need to be developed. The dogma with regard to cancer markers has been that in the absence of effective treatments, the best approach to cancer was early diagnosis, followed by surgical intervention before the tumor had spread. Although a few effective techniques such as DRE for prostate cancer, Future Oncology (2005) 1(1)

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needle biopsy and mammography for breast cancer have been successfully used in the early diagnosis of cancer, few new diagnostic markers have emerged. There remains a need for a noninvasive urine or serum marker test that can accurately predict the outcome of cancer. Patients are still diagnosed too late and treated without knowing whether their tumor has spread beyond the primary site. Over the next few decades, biomarkers will not only help screen, detect, diagnose, help in prognostic evaluation, monitor treatment and predict recurrence, but also play a major role in clinical decision making. Markers that predict the response for a given treatment are indeed needed. The biomarker discovery approach should include the development of predictors to determine: • • • •

Treatment or no treatment Surgery or no surgery Surgery and/or radiation Extent of surgical intervention (as in the choice between mastectomy or lumpectomy in breast cancer) and most importantly, when to employ systemic drug therapy

Today, most breast cancer patients in countries around the world do not receive systemic therapy until there is evidence of metastatic disease and this is often too late. In addition, many new cancer treatments have emerged in the past decade, but are effective for only a small fraction of people who have a particular type of cancer. Thus, the development of these new treatments has increased the need for markers that predict outcome and those that direct which treatment options are most likely to be effective for a particular patient with a particular tumor. New marker development

Concern remains as to whether the tools available are well suited to provide the technological support to meet the demands of new biomarker development. Until recently, the discovery of cancer biomarkers has been a slow approach to identify proteins that are dysregulated as a consequence of the disease and shed into the body fluids such as serum, urine or saliva. Unfortunately, this approach is arduous and prolonged as each candidate marker(s) must be identified among thousands of proteins. The recent advancements in genomic and proteomic technologies including gene array technology, serial analysis of gene expression (SAGE) improved 2-DE and new www.futuremedicine.com

mass spectrometric techniques coupled with advancements in bioinformatic tools, shows great promise of meeting the demand for the discovery of a variety of new biomarkers that are both sensitive and specific. Genomic approaches Microarray technology

The use of DNA microarrays has provided one of the most powerful tools to investigate global gene expression in all aspects of human cancer. Microarrays have been used to obtain major insights into progression, prognosis and response to therapy on the basis of gene expression profiles. Microarray methods were initially developed to study differential gene expression. Improvement of the initial methods now permits more intricate studies that can identify small deletions or insertions in tumor-suppressor genes [64], and has been employed to the systematic analysis of gene expression [65]. One microarray technology that has drawn widespread use is GeneChip® (Affymetrix, USA). High-density oligonucleotide GeneChips are produced by synthesizing several thousands of short oligonucleotides in situ on glass wafers, using a combination of photolithography and lightdirected solid-phase DNA synthesis [66,67]. The GeneChip series comprises of several different oligonucleotides to represent each gene on the array. Each oligonucleotide has a complimentary sequence with a single base mismatch to account for nonspecific binding [101]. The obvious advantage of the GeneChip technology is the ability to measure levels of gene expression in cells and tissues. Its sensitivity allows the detection of very low abundance mRNA [66]. The major limitation of this microarray technology is that it requires prior knowledge of the sequence of the genes to be analyzed. This makes gene prediction a technical challenge. SAGE technology is a recent development that is not only sensitive and comprehensive but can also analyze gene expression in organisms with uncharacterized genomes [68]. Several scientists have utilized microarray technology to monitor altered gene expression. Several genes that were overexpressed following induced expression of BRCA1 in MDA435 breast cancer cells included DNA-damage inducible gene (GADD45) and early growth response 1 (EGR1) gene. Among the repressed genes were Ki67 and the prothymosin α gene, which have been previously been identified as prognostic markers of breast cancer [69]. Cancer researchers are using 43

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SAGE and microarray technology to study several thousand genes at the same time to provide insights into diagnosis, prognosis, therapeutic targets and clinical outcome [66,70]. Furthermore, there is a significant focus from the scientific community to identify subsets of differentially expressed genes between healthy and tumor tissues to identify potential biomarker panels for several cancers including ovarian cancer [71], oral cancer [72] and colorectal cancer [73]. Despite serious efforts to minimize variability, the number of differentially expressed genes that have been, and will be identified, presents a challenge in managing and interpreting the data. Bioinformatic data analysis tools that can integrate microarray data with clinical data are being developed. This allows investigators to focus on a smaller subset of genes with direct relevance to tumor biology. A legitimate concern regarding mRNA expression study is whether the changes in mRNA are a true reflection of the expression of the protein they encode. To date, the most significant biomarkers that have emerged from microarray analysis include estrogen/progesterone receptor protein expression, HER2 gene/protein alterations, 17q23 genomic amplifications and cyclooxygenase-2 protein expression, all for breast cancer [74]; insulin-like growth factor binding protein 2 protein expression for prostate cancer [75,76]; vimentin protein expression for kidney cancer [77]; and Myc and A1B1 protein expression for hepatocellular carcinoma [78]. However, due to the lack of a unifying bioinformatic resource, most of the data generated from these studies has remained disorderly. Recently, a major collaborative effort has created ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses [79]. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. A metaanalysis study of the database, which includes approximately 90 datasets from more than 7000 microarray experiments, revealed a signature of 67 genes that appear to be required to change normal human cells into cancerous ones [80]. Proteomic approaches Two-dimesional electrophoresis

Previously, two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectrometry had been the primary proteomic technology used for biomarker discovery. This is 44

a well-suited technique for direct comparison of differentially expressed proteins between normal and tumor tissues and has been used in various cancers including prostate and breast [81,82]. However, the specificity of the 2D gel technique remains a concern. Contamination from surrounding tissues present in tissue specimens can confuse the detection of tumor-specific markers. Invention of improved tissue-capturing techniques such as laser capture microdissection has greatly improved the specificity of 2D-PAGE for biomarker discovery [83,84]. Introduction of techniques that significantly enrich the proteome for a subset of proteins markedly improves the sensitivity of 2D-PAGE based detection [85]. 2DPAGE analyses of normal and malignant tissues of ovarian cancer identified several overexpressed proteins including glyoxalase-I and FK506BP [86]. Recent development of the differential in-gel electrophoresis facilitates analyses of protein expression by labeling different populations of proteins with fluorescent dyes. This technique has recently been used to identify differentially expressed proteins in squamous cell carcinoma and breast cancer [87]. The major limitations of 2D-PAGE methodology are its inability to detect low abundance proteins and the difficulty of its application to high-throughput assay. Mass spectrometry

Mass spectrometry has been used traditionally with 2D-PAGE as a means to identify the spots that appear on the gel. Recent technological developments make mass spectrometry an important tool on its own for the rapid identification of cancer biomarkers. Mass spectrometry analysis of a complex proteome is sensitive, robust and quantitative. Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry and tandem mass spectrometry are routinely used in the identification of biomarkers coupled with 2D electrophoresis. Recent technological improvements in mass spectrometry instrumentation have increased their use in biomarker discovery immensely. Direct analysis of biological samples using mass spectrometry is becoming more popular due to its high-throughput nature and increased sensitivity. The authors’ laboratory have recently identified over 400 proteins differentially regulated in metastatic prostate cancer cells by the quantitative method of stable isotope labeling with amino acids in cell culture (SILAC) mass spectrometry [88]. Using SILAC, cells representing two different biological conditions are cultured in amino-acid-deficient Future Oncology (2005) 1(1)

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growth media supplemented with 12C (light) or 13C (heavy) labelled amino acids and proteins. Fractions from each culture are then mixed and subjected to traditional tandem mass spectrometry. This method quantifies the difference in expression of individual proteins in the two different conditions by calculating the ratio of the intensities of corresponding peaks containing heavy and light amino acid. Using another mass spectrometry based technique, isotope-coded affinity tag (ICAT) clinically distinct samples can be quantitatively analyzed by chemically labeling the proteins with heavy or light tags that bind to cysteine residues in the proteins. This technology has been used to identify and quantify expression of proteins from cells, tissues or other biological fluids [89]. The ICAT technique has been successfully used for quantitative studies of markers associated with androgen stimulated and unstimulated cancer cells [90]. Another addition to the mass spectrometry technology is the surface-enhanced laser desorption ionization time-of-flight (SELDITOF) mass spectrometry. This technology has the potential to very rapidly compare biomarker patterns in tissues and body fluids. Proteomic profiles of body fluids such as serum or urine of cancer patients can be analyzed in a time efficient manner by the use of SELDI-TOF mass spectrometry. This technology has been effectively used in the validation of serum prostate-specific membrane antigen as one of the markers that distinguishes prostate cancer from benign disease [91]. A proteomic pattern that distinguished different stages of ovarian cancer was identified by this technology [92]. Its wide acceptance is still in question due to some concerns regarding reproducibility and reduced sensitivity at high molecular weight range [93]. Table 1 summarizes the important biomarkers and their use as discussed in this article. Sources for marker testing

A very important aspect of marker development is to translate its usefulness to the clinic. A potential marker can be tested in different sources, including tumor tissues and body fluids such as serum and urine. The methodologies should be of rapid execution, reliable and possibly not very expensive. Several techniques that are used in biomarker assays will be discussed. Immunohistochemistry

Immunohistochemistry is a technique that utilizes the staining of histological specimens for www.futuremedicine.com

a particular marker. Immunohistochemistry is widely used to predict outcomes among patients with different grades of disease by staining tissues from biopsy samples taken from cancer patients at the time of diagnosis. Major disadvantages of this method are that it depends on biopsy samples that are collected using invasive techniques, and the small percentage of tumor tissue obtained during biopsy sometimes misses more aggressive tumor populations that reside nearby. Enzyme-linked immunosorbent assay

A marker is applicable as a fluid analyte when at least two requirements are met: the potential marker must circulate in the serum or urine and a quantitative high-throughput assay must be available to detect the marker. Enzyme-linked immunosorbent assay (ELISA) is a sensitive, high-throughput technique that quantitatively measures the amount of analyte present in a physiological fluid such as serum or urine. The advantage of ELISA is that it can be utilized with body fluids that are collected noninvasively. In brief, the primary antibody binds to the analyte, and an enzyme-linked secondary antibody binds to the previous complex. The enzyme activity is quantitatively measured by the addition of an appropriate substrate, and it is proportional to the amount of analyte present in the fluid. Variations include the competitive ELISA and the sandwich ELISA. This technique is widely accepted as a clinical tool and is very sensitive. Chip technology

With the emergence of genomics and proteomics research in the last few years, it has become necessary to develop techniques that can provide comparative information between several different models simultaneously. With the discovery of several markers in recent years and the growing belief that a panel of markers rather than one marker alone will predict a more accurate outcome, development of new detection systems have become necessary. There is a need for tools that can do for protein expression profiling what DNA chips have done for a RNA expression analysis. Analyzing protein expression can aid researchers in understanding the molecular basis of disease, including disease susceptibility, diagnosis, progression and potential points of therapeutic interference. This has led to a surge in the development of ‘protein chips’. The basic format of most protein chips is similar to DNA chips, such as use of a glass or plastic printed 45

REVIEW – Chatterjee & Zetter

Table 1. Cancer biomarkers and their use. Biomarkers

Cancers

Use

PSA

Prostate

Screening, diagnostic, predict recurrence

Refs [4]

CEA

Several cancers including colorectal, lung, breast, liver, pancreatic, thyroid,bladder

Determine recurrence, Monitor treatment efficacy

[1]

CA 125

Ovarian

Diagnostic, monitor treatment, predict recurrence

[16]

BTA

Bladder

Diagnosis, predict recurrence.

[17]

NMP22

Bladder

Diagnosis, predict recurrence

[18]

Calreticulin

Bladder

Diagnosis

[21]

Limitations of biomarker development

Survivin

Bladder

Diagnosis

[20]

Calcitonin

Thyroid

Diagnosis, monitor treatment and predict recurrence

[24]

Antizyme

Prostate

Prognosis

[25]

Antizyme inhibitor

Prostate

Prognosis

[26]

Collagen XXIII

Prostate, breast several others

Prognosis

[27]

MMP

Prostate, breast

Prognosis

[41,42]

MMP inhibitors

Prostate, breast

Prognosis

[30]

Her-2

Breast

Prognosis, response to therapy

[31]

Urokinase-type plasminogen activator

Breast

Recurrence

[47]

PAI-1, PAI-2

Recurrence

[47,48]

Cathepsin B and L

Breast

Recurrence

[50]

Cyclin D1

Ovarian

Prognosis, recurrence

[51]

ICTP

Ovarian

Prognosis, stage

[60]

β-2 microglobulin

Multiple myeloma and lymphoma

Prognosis

[61]

Caspase-3

Gastric carcinoma

Prognostic

EZH2

Prostate

Recurrence

[64]

Vimentin

Kidney

Prognosis

[78]

Myc and A1B1

Hepatocellular carcinoma

Prognosis

[79]

SELDI pattern

Ovarian cancer

Diagnosis, prognosis, stage

[93]

BTA: Bladder tumor antigen; CEA: Carcinoembryonic antigen; EZH: Enhancer of zeste homolog; ICTP: Carboxy terminal telopeptide of type I collagen; MMP: Matrix metalloproteinases; PAI: Plasminogen acitvator inihibitor; PSA: Prostate-specific antigen; SELDI: Surface-enhanced laser desorption ionization.

with an array of molecules, such as antibodies that can capture proteins. Ideally, a protein chip 46

containing a panel of molecules such as antibodies would be able to predict a cancer state by a simple serum or urine test. This technology is likely to see considerable application and development in coming years.

The critical limitation in biomarker development is the lack of a proper structure in the biomarker discovery process as is present in testing a new drug. Recently, the establishment of the Early Detection Research Network (EDRN) by the National Cancer Institute, USA has led to the improved coordination between biomarker research laboratories. Pepe and colleagues proposed a five-phase formal categorization that would guide the development of a biomarker [94]. The structure of the biomarker development process has been outlined in Table 2. Most of the biomarkers currently known are of limited clinical use. Early studies lacked epidemiological validity or statistical power and thus lacked universal application to populations. Lack of pre-analytical studies and standardized protocols across laboratories adds to diminished reproducibility. These problems render many markers insufficiently sensitive or specific enough for clinical use. The application of uniform standards as suggested by Pepe should facilitate the translation of newly discovered biomarkers to the clinic [94]. Future perspective

As more potential biomarkers are discovered, the limitations in clinical use of these new markers, in the discovery phase, are reduced and more in the validation of the markers and rapid application to clinical practice. Oncology practice in the next decade will be ruled by cost effectiveness. Biomarkers that detect cancers, predict cancer outcome and influence treatment choice will have a major role in determining cost effectiveness in clinical cancer management. Biomarkers will be of greatest importance if they can focus the use of expensive cancer treatments on those who are most likely to benefit. This requires the methods to be simple, inexpensive, robust and reliable. To determine disease outcome, no single biomarker is likely to have the appropriate degree of certainty to dictate treatment decisions. Consequently, the future of cancer prognosis may rely on small panels of 6–10 markers that can give an accurate molecular staging that will indicate the likelihood of metastatic involvement and the need for rapid systemic therapy. The highthroughput technology platforms will help in the Future Oncology (2005) 1(1)

Cancer biomarkers – REVIEW

Table 2. Phases of biomarker development. Preclinical exploratory

Phase 1

Promising directions identified

Clinical assay and validation

Phase 2

Clinical assay detects established disease

Retrospective longitude

Phase 3

Biomarker detects disease early before it becomes clinical and a "screen positive" rule is defined

Prospective screening

Phase 4

Extent and characteristics of disease detected by the test and the false referral rate are identified

Cancer control

Phase 5

Impact of screening on reducing the burden of disease on the population is quantified

discovery of multiple new biomarkers for a particular disease simultaneously. At present, the

microarray-chip technology as well as the 2D gel and mass-spectrometry, are not easily applicable to the clinical setting and require well equipped laboratories and well-trained personnel. Simple, rapid and sensitive microarray-based protein chips, label-free detection systems and antibodybased protein chip systems that will bring the advancements of biomarker discovery into clinical practice are in development. We are approaching a time when the use of proper biomarkers will help detect cancer, monitor and manage progression of the disease and its therapeutic treatment. Development of simple diagnostic kits that will accurately and reliably predict cancer and can be used in the clinic or, by potential patients themselves is a crucial goal for the future of oncology.

Executive summary Biomarker history • Biomarkers developed in the 1980's were CA 19-9 for colorectal and pancreatic cancer, CA 15-3 for breast cancer and CA-125 for ovarian cancer. • Prostate-specific antigen (PSA) is the best-known cancer biomarker that has been used by physicians to detect early disease. • Serum PSA is, perhaps, more reliable as a marker of prostate cancer recurrence or as an indicator of treatment efficacy. • Levels of PSA in the blood drop to less than measurable levels (PSA nadir) after surgical removal of the prostate (radical prostatectomy) or treatment of prostate cancer by radiation. • Even in prostate cancer the greatest success of PSA is in detecting recurrent disease.

Use of biomarkers in cancer • Most important role of cancer biomarkers is to utilize them in widespread screening so that asymptomatic individuals can be detected with disease at a very early stage. • Prostate-specific antigen (PSA) along with digital rectal examination (DRE) is the only FDA approved screening biomarker. • PSA velocity, PSA density and Free versus bound PSA are better predictors than PSA alone. • Calcitonin, a thyroid cancer marker can be a potential screening biomarker. • Recently, the FDA has approved a few diagnostic bladder cancer biomarkers such as NMP 22 and BTA. • Advancements in genomics and proteomics technologies facilitated the discovery new potential markers. • Genetic and proteomic signature profiles have been used as potential predictors of disease outcome.

Future of biomarkers • The future of clinical cancer management belongs to the prognostic and predictive biomarkers of cancer. • Development of new treatments has increased the need for markers that predict outcome and those that direct which treatment options are most likely to be effective for a particular patient with a particular tumor.

New marker development • The recent advancements in genomic and proteomic technologies including gene array technology, improved two-dimensional gel electrophoresis and new mass spectrometric techniques coupled with advancements in bioinformatic tools shows great promise of meeting the demand for the discovery of a variety of new biomarkers that are both sensitive and specific.

Sources of biomarker discovery • Several techniques that are used in biomarker assays include immunohistochemistry (IHC), enzyme-linked immunosorbent assay (ELISA) and Protein Chip Technology.

Limitations of biomarker development • The critical limitation in biomarker development is the lack of a proper structure in biomarker discovery process as is present in testing a new drug. • The establishment of Early Detection Research Network (EDRN) by the National Cancer Institute of USA has led to the improved coordination between biomarker research laboratories. • Phases of biomarker discovery have been outlined by the EDRN.

Future perspective • Biomarkers that detect cancers, predict cancer outcome and influence treatment choice will have a major role in determining the costeffectiveness in clinical cancer management. • A small panel of biomarkers collectively will predict accurate molecular staging of disease. • Development of simple diagnostic kits that will accurately and reliably predict cancer and can be used in the clinic or, by potential patients themselves is a crucial goal for the future of oncology. www.futuremedicine.com

47

REVIEW – Chatterjee & Zetter Bibliography

13.

Papers of special note have been highlighted as either of interest (•) or of considerable interest (••) to readers. 1.

2.

3.

4.

••

5. 6.

7.

8.

9.

•

10.

11.

12.

48

Gold P, Freedman SO: Demonstration of tumor-specific antigens in human colonic carcinomata by immunological tolerance and absorption techniques. J. Exp. Med. 121, 439 (1965). Gold P, Freedman SO: Specific carcinoembyronic antigens of the human digestive system. J. Exp. Med. 122, 467 (1965). Yilmaz A, Ece F, Bayramgurler B, Akkaya E, Baran R: The value of Ca-125 in the evaluation of tuberculosis activity. Respir. Med. 95, 666– 669 (2001). Barry MJ: Clinical practice. Prostate-specific antigen testing for early diagnosis of prostate cancer. N. Engl. J. Med. 344, 1373 (2001). Review article that talks about the PSA test and increase in prostate cancer incidence rate. It also addresses issues regarding the clinical approach towards prostate cancer management. 1996, National Cancer Institute Annual Report, Bethesda, MD. Bartsch G, Horninger W et al.: Prostate cancer mortality after introducing of Prostate-specific antigen mass screening in the Federal State of Tyrol, Austria. Urology 58, 417–424 (2001). Potter SR, Partin AW: Prostate cancer: detection, staging and treatment of localized disease. Semin. Roentgenol. 34, 269–283 (1999). Harris R, Lohr KN: Screening for Prostate cancer: an update of the evidence for the U.S. Preventive Service Task Force. Ann. Intern. Med. 137, 917–929 (2002). Stamey TA: Preoperative serum prostate-specific antigen (PSA) below 10 microg/l predicts neither the presence of prostate cancer nor the rate of postoperative PSA failure. Clin. Chem. 47, 631–634 (2001). Limitations of preoperative serum PSA below 10 ng/ml as a marker to predict prostate cancer and need for better serum marker for prostate cancer. Partin AW, Pound CR, Clemens JQ, Epstein JI, Walsh PC: Serum PSA after anatomic radical prostatectomy The Johns Hopkins experience after 10 years. Urol. Clin. North Am. 20, 713– 725 (1993). Chodak GW, Neumann J, Blix G, Sutton H, Farah R: Effect of external beam radiation therapy on serum prostate-specific antigen. Urology 35, 288–294 (1990). Moul JW: Prostate specific antigen only progression of prostate cancer. J. Urol. 163, 1632–1642 (2000).

•

14.

15.

16.

• 17.

18.

19.

•• 20.

21.

22.

•

D’Amico AV, Chen MH, Roehl KA, Catalona WJ: Preoperative PSA velocity and the risk of death from prostate cancer after radical prostatectomy N. Engl. J. Med. 351, 125–135 (2004). Men whose PSA level increases by more than 2.0 ng per milliliter during the year before the diagnosis of prostate cancer may have a relatively high risk of death from prostate cancer despite undergoing radical prostatectomy Jung K, Elgeti U, Lein M et al.: Ratio of free or complexed prostate-specific antigen (PSA) to total PSA: which ratio improves differentiation between benign prostate hyperplasia and prostate cancer? Clin. Chem. 46, 55–62 (2000). Cox CE, VanVickle J, Froome LC, Mendelsohn G, Baylin SB, Wells SA Jr: Carcinoembryonic antigen and calcitonin as markers of malignancy in medullary thyroid carcinoma Surg. Forum 30, 120–121 (1979). Diamandis EP: Tumor Markers: past, present and future. In: Tumor markers: physiology, pathology, technology, and clinical applications. (Eds) Diamandis EP, Fritsche HA, Lilja, H, Chan, DW, Schwartz, ML. AACC. Washington, DC. Press, 3–8 (2002). A very comprehensive review on biomarkers. Konety BR, Getzenberg RH: Urine based biomarkers of urological malignancy. J. Urol. 165, 600–611 (2001). Mungan NA, Vriesema JLJ, Thomas CMG, Kiemeney LALM, Witjes JA: Urinary bladder cancer test: a new urinary tumor marker in the follow-up of superficial bladder cancer. Urology 56, 787–792 (2000). Celis JE, Østergaard M, Rasmussen HH, Gromov P, Gromova I, Varmark H et al.: A comprehensive protein resource for the study of bladder cancer: http://biobase.dk/cgi-bin/celis. Electrophoresis 20, 300–309 (1999). A two-dimensional urinary proteomics profile for the discovery of bladder cancer markers. Shariat SF, Casella R, Khoddami SM et al.: Urine detection of survivin is a sensitive marker for the noninvasive diagnosis of bladder cancer. J. Urol. 171(2 Pt. 1), 626–30 (2004). Kageyama S, Isono T, Iwari H et al.: Identification by proteomic analysis of calreticulin as marker for bladder cancer and evaluation of the diagnostic accuracy of its detection in urine. Clin. Chem. 50, 857–866 (2004). Ramaswamy S, Tamayo P, Mukherjee S et al.: Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl Acad. Sci. USA 98, 15149–15154 (2001). The feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.

23.

24.

•

25.

•

26.

27.

28.

29.

30.

31.

32.

33.

•• 34.

••

Van de Vijver MJ, He YD, Van’t veer LJ et al.: A gen-expession signature as a predictor of survival in breast cancer N. Engl. J. Med. 347, 1999– 2009 (2002). Bao L, Loda M, Zetter BR: Thymosin β15 expression in tumor cell lines with varying metastatic potential. Clin. Exp. Metastasis. 16(3), 227–233 (1998). Thymosin β-15 overexpression in metastatic prostate cancer cells, potential marker for prostate cancer. Bao L, Loda M, Janmey PA, Stewart R, AnandApte B, Zetter BR: Thymosin beta 15: a novel regulator of tumor cell motility upregulated in metastatic prostate cancer. Nature Med. 2(12), 1322–1328 (1996). Thymosin β−15 levels are elevated in human prostate cancer and correlate positively with the Gleason tumor grade. A potential biomarker for prostate caner metastasis. Koike C, Chao DT, Zetter BR: Sensitivity to polyamine-induced growth arrest correlates with antizyme induction in prostate carcinoma cells. Cancer Res. 59(24), 6109–6112 (1999). Banyard J, Bao L, Zetter BR: Type XXIII collagen, a new transmembrane collagen identified in metastatic tumor cells. J. Biol. Chem. 278(23), 20989–20994 (2003). Moul JW, Bettencourt MC, Sesterhenn et al.: Protein expression of p53, bcl-2 and Ki-67 (MIB-1) as biomarkers in patients with surgically treated, clinically localized prostate cancer. Surgery 120, 159–166 (1996). Aaltoma S, Lipponen P, Vesalainen S, Ala-opas M, Eskelinen M, Syrjanen K: Value of Ki-67 immunolabeling as a prognostic factor in prostate cancer. Eur. Urol. 32, 410–415 (1997). Tuck AB, Chambers AF: The role of osteopontin in breast cancer: clinical and experimental studies. J. Mammary Gland Biol. Neoplasia 6, 419–429 (2001). Gao X, Porter AT, Grignon DJ, Pontes JE, Honn KV: Diagnostic and prognostic markers for human prostate cancer. Prostate 31(4), 264– 281 (1997). Ayala G, Wang D, Wulf G et al.: The prolyl isomerase Pin1 is a novel prognostic marker in human prostate cancer. Cancer Res. 63, 6244– 6251 (2003). Miki Y, Swensen J, Shattuck-Eidens D et al.: A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 266, 66–71 (1994). Identification of BRCA1 gene and relation to breast cancer diagnosis. Wooster R, Bignell G, Lancaster J et al.: Identification of the breast cancer susceptibility gene BRCA2. Nature 378, 789–792 (1995). Identification of BRCA2 gene.

Future Oncology (2005) 1(1)

Cancer biomarkers – REVIEW 35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

45.

46.

47.

Weidner N, Folkman J, Pozza F et al.: Tumor angiogenesis: a new significant and independent prognostic indicator in earlystage breast carcinoma. J. Natl Cancer Inst. 84(24), 1875–1887 (1992). Weidner N, Gasparini G: Determination of epidermal growth factor receptor provides additional prognostic information to measuring tumor angiogenesis in breast carcinoma patients. Breast Cancer Res. Treat. 29, 97–107 (1994). Talvensaari-Mattila A, Paakko P, Hoyhtya M, Blanco-Sequeiros G, Turpeenniemi-Hujanen T: Matrix metalloproteinase-2 immunoreactive protein: a marker of aggressiveness in breast carcinoma. Cancer 83, 1153–1162 (1998). McGuire WL, Tandon AK, Allred DC, Chamness GC, Clark GM: How to use prognostic factors in axillary node-negative breast cancer patients. J. Natl Cancer Inst. 82, 1006–1015 (1990). Sahin AA, Ro J, Ro JY et al.: Ki-67 immunostaining in node-negative stage I/II breast carcinoma. Significant correlation with prognosis. Cancer 68, 549–557 (1991). Wintzer HO, Zipfel I, Schulte-Monting J, Hellerich U, von Kleist S: Ki-67 immunostaining in human breast tumors and its relationship to prognosis. Cancer 67, 421– 428 (1991). Rittling SR, Chambers AF: Role of osteopontin in tumor progression. Br. J. Cancer. 90, 1877–1881 (2004). Fedarko NS, Jain A, Karadag A, Van Eman MR, Fisher LW: Elevated serum bone sialoprotein and osteopontin in colon, breast, prostate, and lung cancer. Clin. Cancer Res. 7, 4060–4066 (2001). Tetu B, Brisson J: Prognostic significance of HER-2/neu oncoprotein expression in nodepositive breast cancer. The influence of the pattern of immunostaining and adjuvant therapy. Cancer 73:2359–2365 (1994). Muss HB, Thor AD, Berry DA et al.: c-erbB-2 expression and response to adjuvant therapy in women with node-positive early breast cancer. N. Engl. J. Med. 330, 1260–12666 (1994). Volpi A, Nanni O, De Paola F et al.: HER-2 expression and cell proliferation: prognostic markers in patients with node-negative breast cancer. J. Clin. Oncol. 21(14), 2708–2712 (2003). Choong PF, Nadesapillai AP: Urokinase plasminogen activator system: a multifunctional role in tumor progression and metastasis Clin. Orthop. 415 (Suppl.) S46–S58 (2003). Brostnar S, Vrhovec I, Svetic B, Cufer T: Prognostic value of the urokinase-type plasminogen activator, and its inhibitors and

www.futuremedicine.com

48.

49.

50.

51.

52.

53.

54.

55.

56.

57.

58.

59.

60.

receptor in breast cancer patients Clin. Breast Cancer 3, 138–146 (2002). Foekens JA, Kos J, Peters HA et al.: Prognostic significance of Cathepsins B and L in primary human breast cancer J. Clin. Oncol. 16 1013– 1021 (1998). Barbieri F, Lorenzi P, Ragni N et al.: Overexpression of cyclin D1 is associated with poor survival in epithelial ovarian cancer. Oncology 66(4), 310–5 (2004). Santala M, Risteli J, Kauppila A: Comparison of carboxyterminal telopeptide of Type I collagen (ICTP) and CA 125 as predictors of prognosis in ovarian cancer. Anticancer Res. 24, 1057–1062 (2004). Torre GC, Rembado R, Barbetti V et al.: Serum levels of tumor-associated trypsin inhibitor (TATI) in benign and malignant gynecological diseases. Scan. J. Clin. Lab. Invest. Suppl. 207, 15–18 (1991). Paju A, Vartiainen J, Haglund C et al.: Expression of trypsinogen-1, trypsinogen-2, and tumor-associated trypsin inhibitor in ovarian cancer: prognostic study on tissue and serum. Clin. Cancer Res. 10, 4761–4768 (2004). Ardekani AM, Liotta LA, Petricoin EF 3rd: Clinical potential of proteomics in the diagnosis of ovarian cancer. Expert Rev. Mol. Diagn. 2(4), 312–320 (2002). Bubb VJ, Curtis LJ, Cunningham C et al.: Microsatellite instability and the role of hMSH2 in sporadic colorectal cancer. Oncogene 12, 2641–2649 (1996). Halling KC, French AJ, McDonnell SK et al.: Microsatellite instability and 8p allelic imbalance in stage B2 and C colorectal cancers. J. Natl Cancer Inst. 91, 1295–1303 (1999). Blackwell K, Hurwitz H, Lieberman G et al.: Circulating D-dimer levels are better predictors of overall survival and disease progression than carcinoembryonic antigen levels in patients with metastatic colorectal carcinoma. Cancer 101(1), 77–82 (2004). Andreyev J, Cunnigham D: Markers, markers everywhere……Prognosis in colorectal cancer–time for a new approach. J. Clin. Oncol. 19, 286–288 (2001). Niklinski J, Niklinski W, Laudanski J, Chyczewska E, Chyczewska L: Prognostic molecular markers in non-small cell lung cancer. Lung Cancer 34, Suppl. 2: S53–S58 (2001). Aviles A, Narvaez BR: β-2 microglobulin and lactate dehydrogenase levels are useful prognostic markers in early stage primary gastric lymphoma. Clin. Lab. Haematol. 20, 297–302 (1998). Isobe N, Onodera H, Mori A et al.: Caspase-3 expression in human gastric carcinoma and its

61.

62.

63.

•

64.

••

65.

66.

67.

68.

69.

70.

clinical significance. Oncology 66(3), 201–209 (2004). Diamandis EP: Point: Proteomic patterns in biological fluids: do they represent the future of cancer diagnostics? Clin. Chem. 49(8), 1272–1275 (2003). Kattan MW, Shariat SF, Andrews B et al.: The addition of interleukin-6 soluble receptor and transforming growth factor beta1 improves a preoperative nomogram for predicting biochemical progression in patients with clinically localized prostate cancer. J. Clin. Oncol. 21(19), 3573–3579 (2003). Rhodes DR, Sanda MG, Otte AP, Chinnaiyan AM, Rubin MA: Multiplex biomarker approach for determining risk of prostatespecific antigen-defined recurrence of prostate cancer. J. Natl Cancer Inst. 95, 661–668 (2003). Identification of EZH2 and ECAD as potential markers of prostate cancer. EZH2: ECAD status was statistically significantly associated with prostate cancer recurrence after radical prostatectomy and may be useful in defining a cohort of high-risk patients. Frolov A, Prowse AH, Vanderveer L, Bove B, Wu H, Godwin AK: DNA array-based method for detection of large rearrangements in the BRCA1 gene. Genes Chromosomes Cancer 35(3), 232–41 (2002). DNA microarray method to detect rearrangements in the BRCA1 gene. The method described in this report has the potential to screen clinical tumor samples for genomic rearrangements simultaneously in a large number of cancer-associated genes. Haab BB: Advances in protein microarray technology for protein expression and interaction profiling. Curr. Opin. Drug Discov. Devel. 4(1), 116–123 (2001). Lipshutz RJ, Fodor SP, Gingeras TR, Lockhart DJ: High density synthetic oligonucleotide arrays. Nature Genet. 21(1 Suppl.), 20–24 (1999). Fodor SP, Read JL, Pirrung MC, Stryer L, Lu AT, Solas D: Light-directed, spatially addressable parallel chemical synthesis. Science 251(4995), 767–773 (1991). Velculescu VE, Zhang L, Vogelstein B, Kinzler KW: Serial analysis of gene expression. Science 270, 484–487 (1995). Harkin DP, Bean JM, Miklos D et al.: Induction of GADD45 and JNK/SAPKdependent apoptosis following inducible expression of BRCA1. Cell 97(5), 575–86 (1999). Khan J, Simon R, Bittner M et al.: Gene expression profiling of alveolar rhabdomyosarcoma with cDNA microarrays. Cancer Res. 58(22), 5009–5013 (1998).

49

REVIEW – Chatterjee & Zetter

71.

72.

73.

74.

75.

76.

77.

••

78.

79.

••

50

Ismail RS, Baldwin RL, Fang J et al.: Differential gene expression between normal and tumor-derived ovarian epithelial cells. Cancer Res. 60(23), 6744–6749 (2000). Alevizos I, Mahadevappa M, Zhang X et al.: Oral cancer in vivo gene expression profiling assisted by laser capture microdissection and microarray analysis. Oncogene 20(43), 6196– 6204 (2001). Hegde P, Qi R, Gaspard R et al.: Identification of tumor markers in models of human colorectal cancer using a 19,200-element complementary DNA microarray. Cancer Res. 61(21), 7792–7797 (2001). Torhorst J, Bucher C, Kononen J et al.: Tissue microarrays for rapid linking of molecular changes to clinical end points. Am. J. Pathol. 159(6), 2249–2256 (2001). Chaib H, Cockrell EK, Rubin MA, Macoska JA: Profiling and verification of gene expression patterns in normal and malignant human prostate tissues by cDNA microarray analysis. Neoplasia 3(1), 43–52 (2001). Bubendorf L, Kolmer M, Kononen J et al.: Hormone therapy failure in human prostate cancer: analysis by complementary DNA and tissue microarrays. J. Natl Cancer Inst. 91(20), 1758–1764 (1999). Moch H, Schraml P, Bubendorf L et al.: Highthroughput tissue microarray analysis to evaluate genes uncovered by cDNA microarray screening in renal cell carcinoma. Am. J. Pathol. 154(4), 981–986 (1999). Use of tissue microarray to validate the expression of genes discovered by cDNA microarray. The combination of tumor arrays and cDNA arrays is a powerful approach to rapidly identify and further evaluate genes that play a role in tumor biology. Wang Y, Wu MC, Sham JS, Zhang W, Wu WQ, Guan XY: Prognostic significance of cmyc and AIB1 amplification in hepatocellular carcinoma. A broad survey using highthroughput tissue microarray. Cancer 95(11), 2346–2352 (2002). Rhodes DR, Yu J, Shanker K et al.: ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6(1), 1–6 (2004). ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major

80.

81.

82.

83.

••

84.

85.

86.

•

87.

88.

•

types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinicalbased and pathology-based analyses are available for exploration. Rhodes DR, Yu J, Shanker K et al.: Largescale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc. Natl Acad. Sci. USA 101(25), 9309– 9314 (2004). Wulfkuhle JD, McLean KC, Paweletz CP et al.: New approaches to proteomic analysis of breast cancer. Proteomics 1(10), 1205–1215 (2001). Ornstein DK, Petricoin EF 3rd: Proteomics to diagnose human tumors and provide prognostic information. Oncology 18(4), 521–529; discussion 529–32 (2004). Fuller AP, Palmer-Toy D, Erlander MG, Sgroi DC: Laser capture microdissection and advanced molecular analysis of human breast cancer. J. Mammary Gland Biol. Neoplasia. 8, 335–345 (2003). A review article, outlines the current status of the adaptation of advanced molecular technologies to LCM and highlights recent studies in which this approach has been applied to human breast cancer. Wulfkuhle JD, Paweletz CP, Steeg PS, Petricoin EF 3rd, Liotta L: Proteomic approaches to the diagnosis, treatment, and monitoring of cancer. Adv. Exp. Med. Biol. 532, 59–68 (2003). Wulfkuhle JD, Liotta LA, Petricoin EF: Proteomic applications for the early detection of cancer. Nature Rev. Cancer 3(4), 267–275 (2003). Jones MB, Krutzsch H, Shu H et al.: Proteomic analysis and identification of new biomarkers and therapeutic targets for invasive ovarian cancer. Proteomics 2(1), 76–84 (2002). The analysis revealed that the 52 kDa FK506 binding protein, Rho G-protein dissociation inhibitor (RhoGDI), and glyoxalase I are found to be uniquely overexpressed in invasive human ovarian cancer when compared to the Low Malignant Potential form of this cancer. Zhou G, Li H, DeCamp D et al.: 2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markers. Mol. Cell Proteo. 1(2), 117–124 (2002). Everley PA, Krijgsveld J, Zetter BR, Gygi SP: Quantitative cancer proteomics: stable isotope labeling with amino acids in cell culture (SILAC) as a tool for prostate cancer research. Mol. Cell Proteo. 3(7), 729–735 (2004). SILAC based mass spectrometry reveals several proteins differentially regulated in high

89.

90.

91.

92.

•

93.

94.

••

95.

metastatic prostate cancer cells compared to its low metastatic prostate cancer variant. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R: Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nature Biotechnol. 17(10), 994–999 (1999). Meehan KL, Sadar MD: Quantitative profiling of LNCaP prostate cancer cells using isotopecoded affinity tags and mass spectrometry. Proteomics 4(4), 1116–1134 (2004). Grizzle WE, Adam BL, Bigbee WL et al.: Serum protein expression profiling for cancer detection: validation of a SELDI-based approach for prostate cancer. Dis. Markers 19 (4–5), 185–195 (2004). Petricoin EF, Ardekani AM, Hitt BA et al.: Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572–577 (2002). A prospective population-based assessment of proteomic pattern technology as a screening tool for all stages of ovarian cancer in high-risk and general populations. Baggerly KA, Morris JS, Coombes KR: Reproducibility of SELDI-TOF protein patterns in serum: comparing data sets from different experiments. Bioinformatics 20(5), 777–785 (2004). Sullivan-Pepe M, Etzioni R, Feng Z et al.: Phases of biomarker development for early detection of cancer. J. Natl Cancer Inst. 93, 1054–1061 (2001). Phases of biomarker discovery. The only paper describing the structure of biomarker development. Hutchinson et al.: Development of an ELISA for the detection of thymosin β-15 in prostate cancer patient urine and established that PSA and thymosin β-15 in combination predicts prostate cancer with high sensitivity and specificity. Prostate (2005) (In press).

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Affiliations • Bruce R Zetter Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA Tel.: +1 617 919 2320 [email protected] • Sabarni K Chatterjee Program in Vascular Biology, Children’s Hospital, Boston and Harvard Medical School, Boston, MA 02115, USA [email protected]

Future Oncology (2005) 1(1)