Proteomics in the Forefront of Cancer Biomarker Discovery† Sudhir Srivastava*,‡ and Rashmi-Gopal Srivastava§ Cancer Biomarkers Research Group of National Cancer Institute, Organ Systems Branch of National Cancer Institute Received January 27, 2005
Introduction Biomarkers hold great promise for cancer detection, diagnosis, and prognosis because of their potential ability in identifying unique molecular signatures detrimental to certain pathophysiological states. However, the progress has been slow in bringing biomarkers to clinical fruition. In the last several decades, only a few biomarkers, such as PAP Smears, PSA, and CA125, have found their way to clinical application. Since 1998, only two protein tests have been introduced in Food and Drug Administration-approved clinical tests.1 It is said that the biomarker pipeline is becoming dry due to the lack of discovery while the validation of existing biomarkers is slow. It seems ironic because most of the high throughput technologies, such as genomics and proteomics were supposed to be transforming the biomarker pipeline and clinical landscape through rapid discovery of biomarkers for clinical application, especially for early detection of cancer. So, what is the reason for such a dismal state-of-the-science for biomarkers in clinical application? Among many reasons affecting the introduction of protein-based tests are the lack of required specificity, sensitivity, and other performance characteristics for a requested clinical purpose, such as diagnosis, prognosis, or monitoring. Another reason for the lack of clinically relevant biomarkers is the lack of an organized effort to move biomarkers from discovery to development to validation to clinical application. As a consequence, much work in this area is fragmented into numerous small and disconnected studies without complete evaluation. Usually, the results of these studies cannot even be generalized to the population as a whole. In 1999, realizing the need for such an effort, the National Cancer Institute (NCI) established a consortium, the Early Detection Research Network (EDRN), to accelerate the development of biomarkers for cancer detection and diagnosis with the hope that some of these biomarkers may also serve as predictive markers for treatment and chemoprevention. The EDRN is based on the premise that a vertical approach to biomarker research in an integrated, multidisciplinary environment will facilitate collaboration among technology developers, basic scientists, clinicians, epidemiologists, biostatisticians, and other health professionals, and therefore expedite clinical applications. When this research is conducted within a consortium of †
Part of the Biomarkers special issue. * To whom correspondence should be addressed. Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, EPN 3142, 6130 Executive Boulevard, Rockville, MD 20852. Tel: (301) 435-1594. Fax: (301) 402-8990. E-mail:
[email protected]. ‡ Cancer Biomarkers Research Group of National Cancer Institute. § Organ Systems Branch of National Cancer Institute.
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collaborating investigators in a systematic and concerted fashion, translations of basic scientific discoveries in genomics, and proteomics into medical benefits are expedited. Structured around four main components, the Network comprises a group of Biomarkers Developmental Laboratories (BDL), Biomarkers Reference Laboratories (BRL), Clinical Epidemiology and Validation Centers (CEVC), and a single Data Management and Coordinating Center (DMCC). BDLs develop and characterize new biomarkers, or refine existing biomarkers. BRLs serve as a resource for clinical and laboratory validation of biomarkers, including technological development, standardization of assay methods, and refinement. CEVCs conduct the early phases of clinical and epidemiological research on the application of biomarkers. Statistical, logistics and informatics support is provided through a DMCC. This Center develops the theoretical statistical approaches to pattern analysis of multiple markers simultaneously. Also in 1999, a formal definition was given for a biomarker for a variety of applications. According to this definition, a biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. A biomarker can serve as a clinical endpoint, surrogate endpoint, or both. Clinical endpoint is defined as a characteristic or variable that reflects how a patient feels, functions or survives. Surrogate endpoint is defined as a biomarker intended to substitute for a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit (or harm, or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic or other scientific evidence.2 In proteomics, such a biomarker could be a protein or peptide (native or modified) identified the through use of various technologies, or a pattern of peaks identified through time-offlight based technologies as discussed in the latter part of this article. To this end, biomarkers are proposed to identify unique molecular signatures detrimental to certain disease states. Why proteomics? The reason for the use of protein-based assays is straightforward. Mammalian systems are much more complex than can be deciphered by their genes alone. Expression analysis directly at the protein level is necessary to unravel the critical changes that occur as part of disease pathogenesis. This is because proteins are often expressed at levels and forms that cannot be predicted from DNA or mRNA analysis. Proteins represent the dynamic states of cells and therefore indicate any abnormal activity in real-time. Protein-based assays have remained the mainstay of the diagnostic field for the past several decades and are likely to be expanded by the emergence of newer, high throughput technologies in coming years. 10.1021/pr050016u CCC: $30.25
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Figure 1. Building blocks of clinical proteomics. These blocks comprise technologies (separation as well as fractionation technologies), analytical specific reagents, biorepository and bioinformatics. Separation technologies refer to instruments or devices that enable separation and identification of proteins and also include various types of fractionation technologies for depleting most abundant proteins or enriching less abundant proteins for their subsequent identification on mass-spectrometerbased technologies. Reagents refer to analytes or affinity reagents, such as antibodies, aptamers, and single chain antibodies to capture molecules of interest. Biorepository refers to the collection of clinically annotated biological specimens, including the serum and plasma. Bioinformatics refers to the collection of statistical and computational tools in preprocessing and postprocessing of data generated from high throughput platforms, such as microarrays, protein profiles (intensity vs m/z) for identification of candidate biomarkers or the development of disease classifiers.
A universal definition of proteomics remains elusive. However, the word ‘proteomics’ derives from a Greek word “proteus” meaning an ancient Greek god, regarded by some as a symbol of the original matter from which we are created. For the purpose of this article, proteomics refers to the study of proteins at both the structural and functional levels. Distinct changes occur in proteins during the transformation of a normal cell into a neoplastic cell that range from altered expression, differential protein modification, changes in specific activity, to aberrant localizationsall of which affect cellular function. Additionally, there are more than 200 post-translational modifications that proteins could undergo that affect function, protein-protein and nucleic acid-protein interaction, stability, targeting, half-life etc.,3 all contributing to a potentially large number of protein products from one gene. Identifying and understanding these changes is the underlying theme in clinical proteomics with necessary infrastructures and resources (Figure 1). The deliverables include identification of biomarkers that have utility both for early detection and for determining therapy. This article cites many examples of proteomics-based biomarker discoveries, and systematic approaches to address challenges in evaluating these discoveries.
Proteomics in Cancer-Specific Biomarkers Discovery There are two approaches in proteomics: (1) a global, nondirected approach, and (2) a target-specific approach. In the global approach, the protein profile, i.e., expression or appearance of proteins using profile-enabler technologies, such as surface-enhanced laser desorption-time-of-flight (SELDI) or matrix-assisted laser desorption ionization (MALDI), is studied.
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Figure 2. Life cycle of biomarker. This proposed life cycle depicts the stages in biomarker development and validation. In the Discovery Phase, a promising lead (biomarker) is identified through basic research in animal models, tissue, or cell cultures. In the Development Phase, a biomarker is identified in human specimens; an assay for the biomarker is developed and/or refined; analytical sensitivity of the assay for the biomarker is tested; and the clinical sensitivity of the assay measured to meet specific biological questions such as detecting mutations for biological events, or specific clinical questions such as detecting precancerous lesions. In the Evaluation Phase, a biomarker is subjected to broad questions to verify the intended use, such as whether the biomarker is applicable to biological and clinical settings in a variety of conditions. Biomarker is subjected to rigorous evaluation for precision, reproducibility, accuracy and other performance characteristics, including sensitivity and specificity in controlled study designs, such as a case-control or a large prospective trial. In the Application Phase, a biomarker test is field-tested for a specific clinical use, such as screening of cancer, and for its cost and effectiveness in reducing disease burden and mortality due to the disease. An unsuccessful verification or benefit may lead to return to the discovery phase and to cycle re-entry.
In the target-specific approach, known molecular targets, such as antigens or antibodies, are probed using cross-reacting analytes, such as antigens, antibodies or other affinity reagents. In addition, the field of proteomics is extended to encompass the structural analysis of proteins.4 Protein profiling strives to investigate changes in protein expression in different stages of cancer. Every aspect of proteomics tends to benefit the discovery of protein-based biomarkers; however, the development of proteomics-enabled biomarkers should proceed in a systematic way and follow the phases of development and evaluation (Figure 2). Unique ionization techniques such as electrospray ionization (ESI) and matrix assisted laser desorption ionization (MALDI) have facilitated the characterization of proteins by mass spectrometry. These techniques have enabled the transfer of proteins into the gas-phase making it conducive for their analysis in the mass spectrometer and a number of technologies are using these ionization techniques.5-8 An effective platform for clinical biomarker discovery would be revolutionary, permitting detection of disease early when it is curable, distinguishing responders from nonresponders of therapeutic intervention quickly, and ultimately engineering disease prevention through identifying individuals at risk and determining their response to prevention interventions. The best platform for reproducible feature identification and quantification is Journal of Proteome Research • Vol. 4, No. 4, 2005 1099
perspectives currently debatable. For example, many believe that the high throughput of MALDI-based instruments is more suited to clinical diagnostics, and they envision the mass spectrometer sitting in the clinical laboratory. In contrast, others believe that the mass spectrometer will be a discovery tool, but not a diagnostic instrument, and hence they favor ESI-based instruments and the ability for online separations. Most of these technologies use some forms of fractionation through selective surface-binding chips, chromatography, or depletion of most abundant proteins. However, numerous issues arise when one uses fractionation. For example, what techniques for fractionating plasma simplify the proteome sufficiently to allow significant depth of coverage given the dynamic range of conventional mass spectrometers? Also, what techniques for fractionating plasma are capable of the level of reproducibility (tested on multiple repeats of the same sample) required to allow biomarkers to be detected analyzing samples from a large number of cases and controls in high dimensional data? Can the best conventional fractionation schemes and mass spectrometry instrumentation interrogate a large enough “space” of the plasma proteome to discover diagnostic biomarkers? However, it is beyond the scope of this review to discuss the various technologic challenges in details.
Promise of Proteomics in Cancer Detection Global protein profiling has only recently become possible due to advances in mass spectrometry, separation technologies, and the catalog of the human genome. A number of investigators have used SELDI-TOF (surface enhanced laser desorptiontime-of-flight) to evaluate a serum protein pattern as a biomarker for ovarian,9 lung,10 breast,11 and prostate12 cancer detection. Recently, Chan and colleagues, in a five-center casecontrol study, analyzed the serum proteomic pattern of 153 patients with invasive epithelial ovarian cancer, 42 with other ovarian cancers, 166 with benign pelvic masses, and 142 healthy women. They identified three biomarkers: apolipoprotein, a truncated form of transthyretin, and a cleavage fragment of inter-alpha-trypsin inhibitor.13 This study is noteworthy because the investigators not only studied the differential protein expression, but also identified the proteins of interest, using SDS-PAGE separation. Many investigators have identified the proteins from the peaks of interest, obtained through MALDI or SELDI.14 Key limitations of technology as currently applied are the lack of reproducible feature identification, quantification, and the limitation of any single study to a small fraction of the proteome. There are a number of efforts being made to standardize the technology, reagents, and tools to effectively analyze samples and data. Recently, investigators from the NCI Early Detection Research Network (EDRN) cross-validated six sites using SELDI-TOF mass spectrometry to analyze a reference set of sera from prostate cancer patients and normal subjects. The instrumentation at all sites were calibrated and standardized in parallel. Each site was then presented with the same set of 14 normal sera and 14 case sera. Based on these “known” samples, all six sites were able to discriminate between normal versus cancer when applying certain classifier algorithms. Then all sites were presented with a different set of 28 “blinded” samples from which they were challenged to determine which samples were normal and which were cancer. Four sites classified all 28 correctly, one site called 26 of 28 correctly, and one site did not pass after correctly classifying just 19 samples. The results from this multi-institutional study 1100
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demonstrate that validation is in fact feasible for protein profiling where screening and assessment of cancer can be performed in a reproducible manner by a multitude of clinical centers in a standardized manner. To this end, Phase II validation studies are continuing the development of serum protein-profiling for prostate cancer to distinguish between people with cancer and those without.15 Global protein profiling, while useful for biomarker discovery, has inherent limitations. First, it does not provide or has limited information on specific proteins, such as identity and biological role of the proteins. Second, there is great difficulty in getting the noise out from hundreds of thousands of protein peaks observed on mass spectra. Therefore, simultaneous efforts are being made to identify proteins and develop antibody assays for useful proteins. Among the target-specific approaches, immunoassays are the fastest-growing technologies for the analysis of biomolecules and are used widely for diagnostic purposes.16 The development of a competitive binding assay using radioisotopes, and then later enzyme-labeled probes, was a major milestone in antibody-based immunoassays. These developments have paved the way for an enormous expansion in application of technologies, particularly in biomedical research and clinical chemistry. The assays are limited, however, to the analysis of a few thousand assays per day, even with the adaptation of the microtiter plate format. Furthermore, the presence of only known analytes that can be screened for becomes a serious bias in proteome analysis, where unknown gene products are significant. Newer technologies, such as protein and antibody arrays, are further advancing the ability to perform tens of thousands of assays in parallel. Protein arrays are composed of multiple recombinant proteins that are arranged in an orderly fashion on a small surface. The protein and antibody arrays are mostly miniaturized immunoassays for multiple proteins and antibodies, respectively.17-21 They can also be used to detect proteinprotein, protein-ligand, protein-DNA, and protein-RNA interactions. Their production could be automated using either pinbased or liquid microdispenser robots. Protein and antibody microarrays will be the approach of choice to close the information gap between genomics and proteomics in the development of new markers for the early detection, diagnosis, and classification of cancer. Ordered microarrays of proteins, protein ligands, or antibodies directed at well-defined antigens are the desirable platforms for the high-throughput, precise analysis of quantitative differences between the proteomic maps of healthy and cancer-stricken individuals. This emerging technology can be applied to identify differences at the cellular level, to compare normal cells and cancer cells from the same organ, and to compare body fluids such as plasma and urine from healthy donors and cancer patients. Like DNA, protein microarrays are also subject to potential bias and reproducibility problems.22,23,24 All discovery-oriented approaches that generate enormous datasets from a limited number of patients are subject to chance and overfitting unless it can be shown that the exact mode of analysis gets similar results in a different dataset from many independent studies. Microarray data, therefore, must be interpreted with caution. The humoral response against tumor antigens that occurs in cancer is being exploited to develop a screening test for early detection analogous to diagnosing HIV exposure based on seropositivity to HIV proteins.25 Several tumor antigens that induce an antibody response in lung cancer have been identified and are currently going through NCI EDRN validation. If
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successfully validated, these markers would be highly beneficial for developing strategies for early lung cancer detection either as a stand alone screening procedure, or to complement other modalities, such as computed tomography (CT) screening. While initially the Hanash lab utilized a standard 2-D gel/ Western blot approach for antigen identification,26,27 more recently, an innovative strategy that allows the microarray based display of tumor lysate proteins has been implemented. The microarray based approach has a much higher throughput than 2-D gels with improved quantitation of antigen/antibody reactions.28 In addition, antibody array is more suitable for clinical use, because antibodies persist from months to years in the serum and the chemistry of antibody is very well-known.
Promises and Challenges Promises of proteomics in disease detection, prevention, and treatment are many, but challenges are formidable. Proteomics has great potential for preclinical detection of disease, subclinical detection of toxic effects of new therapies and prognosis, and therapeutic monitoring of cancer. Proteomicsgenerated molecular profiles may help stratify patients into those who are likely to respond and benefit from treatment from those who are not. In practical terms, one must consider the following in deciding whether a given technology, such as proteomics, is suitable for diagnostic uses:
residue or residues and implicated in a specific pathway or biological networks leading to initiation or promotion of a tumor. Most cellular signal pathways involve protein phosphorylation.31-33 PTMs have been reported for the key steps in tumor progression, such as cell cycle check point, differentiation, and apoptosis. Identifying PTM markers at an early stage of cancer development and developing prevention strategies is a high priority research area at NCI’s Division of Cancer Prevention. Areas of challenge included PTMs of proteins and how to prioritize these events based on their use for early detection, risk assessment, and chemoprevention. Challenges also exist in studying in protein compartments; ligands/antibodies; early/ preneoplastic lesions; biomarkers/fluids; integrating protein data; and links to metabolism/nutrition. Creation of a “toolset” for different PTMs would facilitate research. The focus of developing technology should address how to best investigate the sub-cellular components or protein compartments, and macrocomplexities of protein-protein interactions. Development of ligands and antibodies for PTMs should be considered a research priority. There are tissue resources available that could be used in proteomics; an assessment of available tissue resources may be undertaken to determine what exists and how it could be procured. Issues of database development and quality control should be addressed before proteomic research moves further ahead for clinical applications.34
(1) Is the technology portable and reproducible? (2) Is the technology amenable to a large-scale screening of people? (3) Is the technology cost-effective? (4) Is the technology applicable to readily available bodily fluids? Serum and body fluids provide an easy source of biological materials for proteomic studies, however, the complexity of serum makes global profiling challenging. Different types of fractionation and depletion technologies for removing high abundance proteins and enriching low abundance proteins bring more variability to the results of proteomic analysis. Variations also arise from the lack of reliability, reproducibility and portability of a variety of instruments and associated accessories. Another source of variation stems from the fact that many of the proteins undergo post-translational modifications (PTMs), and are not detected through global profiling. PTMs may occur at different stages of tumor development providing clues indicative of early or late events of transformation. During PTM, a protein is modified due to phosphorylation, acetylation, glycosylation, ubiquitination, farensylation, methylation, sialylation, etc.29 Hanash30 reported the presence of glycosylated annexin 1 and annexin 2 in the sera from lung cancer patients. He solubilized proteins from a lung adenocarcinoma cell line (A549) and from lung tumors and subjected to two-dimensional PAGE, followed by Western blot analysis in which individual sera were tested for primary antibodies. Sera from 60% of patients with lung adenocarcinoma and 33% of patients with squamous cell lung carcinoma but none of the noncancer controls exhibited IgG-based reactivity against proteins identified as glycosylated annexins I and/or II. This indicates that the glycolsylated annexins I and II are biologically relevant to the humoral response and could serve as a potential biomarker. PTM-based biomarkers, therefore, are proteins or peptides modified on a specific amino acid
Future Directions Cancer is a complex disease. Its understanding will require a systems biology approach and leverages from diverse fields such as biology, chemistry, engineering, informatics, and computational modeling. Clinicians and health professionals need to be involved at the outset so that developments in proteomics are tailored to address specific clinical questions. In order for proteomics to be successful in public health, proteomic approaches should be compared and contrasted for their benefits over other medically accepted detection and screening modalities. For example, in a recent study by Campa et al.,35 MALDI-TOF MS protein expression profiling was used to probe the spiral-CT detected suspicious lesions for lung cancer. Two distinct protein peaks at m/z 12 338 and 17 882, identified as macrophase migration inhibitory factor and cyclophilin A, respectively, in the MALDI-TOF spectra were found to be indicative of cancer when present in serum individually or together. The lesions were later confirmed by 18 F fluorodeoxyglucose (FDG) positron emission tomography (PET). It is also desirable to integrate proteomics with imaging to enhance the special localization and temporal view of the disease. The development of both proteomics and imaging should proceed in tandem with the shared goal of fostering molecular-based assays not requiring biopsy or tissue samples. EDRN (www.cancer.gov/edrn), emphasizes collaboration in the discovery, development and delivery of biomarkers for cancer detection, diagnosis and risk assessment using multiple platforms, i.e., genomics, proteomics, and imaging. The Network has developed criteria for setting standards for reagents and study designs. The approach is intended to expedite methods in addition to clinical trials to evaluate and validate biomarkers for clinical application during the early stages of investigation. The concept of validation asks: Journal of Proteome Research • Vol. 4, No. 4, 2005 1101
perspectives • Whether a test is clearly described; • If the true presence or absence of disease can be established for all individuals; • Whether the spectrum of patients with and without disease is adequate; • Whether the assessment of test and disease status is conducted in an unbiased manner; and • If the test performance is summarized by the important terms of sensitivity and specificity. In the context of this article it is worth mentioning that the Network developed a study design for a systematic evaluation of protein profiling, in this case SELDI, for cancer diagnosis. This published model, which can be applied to any other profile-based proteomics platforms, has been extensively discussed and accepted.15 This activity represents the Network’s goal to provide the scientific community with its experience and share its findings to help accelerate technology platform evaluation in diagnostic research. The protocol discusses a stepwise evaluation of protein profiling as follows: (1) Stage 1: Examine to see if the technique can be executed at different Network sites and replicated based on the discrimination with algorithms. This stage is divided into the following substages: Stage 1a: Standardize SELDI methodology and synchronize SELDI among seven participating institutions using a single source of pooled normal sera with verified presence of diagnostic peaks. Stage 1b: Each site is blinded and sent 14 cases of normal and prostate cancer sera and asked to run the SELDI and submit data to the EDRN Data Management Coordinating Center. The data are analyzed to see if the algorithms correctly classify cases from controls. Stage 1c: Sera from each center are sent to all the other centers and SELDI runs are made. Data are then analyzed to see if the cases can be discriminated from controls. (2) Stage 2: Prove that the same results can be achieved with a number of prostate cancer cases and controls at the various Network sites. (3) Stage 3: This stage is concerned with measuring the sensitivity and specificity of SELDI in clinically well characterized cases drawn from prospectively collected retrospective samples. Stage 3 will only be conducted if the previous two phases are successful in addressing the questions. These stages of validation were developed to test the portability, reliability and accuracy of MS-based detection of protein profiles in cancer diagnosis. For the future of proteomics in cancer diagnosis and treatment, strategies must be put into the most appropriate manner for validating reagents, technologies, computational and statistical tools for capturing and interpreting data accurately. We need to: • Assess technologies central to biomarker discovery in order to provide laboratories with the best possible techniques and protocols. • Develop an open-source suite of analysis tools compatible with this standard data format to facilitate standardization of analysis across laboratories and allow meaningful comparisons of results. • Establish a well-structured database for store and organize the data for candidate biomarkers. • Establish a source for standardized reagents, including human specimens, mouse models, antibodies, and other reagents. 1102
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Finally, for an effective diagnosis and tailored therapy, there is a need to integrate data from a variety of platforms, such as genomics, imaging, and proteomics, for a particular cancer site from multiple studies. To date, only a few large-scale integrated molecular profiling efforts have combined data obtained from multiple studies, or combined data obtained through two different global profiling platforms (genomic and transcriptomic, or transcriptomic and proteomic) for the same set of study samples.
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(28) Qiu, J.; Madoz-Gurpide, J.; Misek, D. E.; Kuick, R.; Brenner, D. E.; Michailidis, G.; Haab, B. B.; Omenn, G. S.; Hanash, S. Development of natural protein microarrays for diagnosing cancer based on an antibody response to tumor antigens. J. Proteome Res. 2004, 2, 261-267. (29) Bode, A. M.; Dong, Z. Posttranslational modification of p53 in tumorigenesis. Nat. Rev. Cancer 2004, 4, 793-805. (30) Franck, M. B.; Misek, D. E.; Yim, Anne-Marie; Krause, M. C.; Giordano, T. J.; Beer, D. G.; Hanash, S. M. PNAS 2001, 98 (17), 9824-9829. (31) Johnson, L. N.; O’Rielly M. Control by phosphorylation. Curr. Opin. Struct. Biol. 1996, 6, 762-769. (32) Hunter, T. Oncoprotein networks. Cell 1997, 88, 333-346. (33) Parekh, R. B.; Rohlff, C. Posttranslational modification of proteins and the discovery on new medicine. Pharm. Biotechnol. pp 718722. (34) An NCI workshop on posttranslational protein modification: novel technologies and implications for cancer prevention workshop, August 28-29, 2002, Bethesda, Maryland, USA. (35) Campa, M. J.; Wang, M. Z.; Howard, B.; Fitzgerald, M. C.; Patz, E. F.; Protein expression profiling identifies macrophase migration inhibitory factor and cyclophilin A as potential molecular targets in nonsmall cell lung cancer. Cancer Res. 2003, 63, 1652-1656.
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