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Mini-Reviews Clinical Chemistry 59:1 60–67 (2013) The Early Detection Research Network: 10-Year Outlook Sudhir Srivastava1* BACKGROUND: The National Cancer Institute’s Early Detection Research Network (EDRN) has made significant progress in developing an organized effort for discovering and validating biomarkers, building resources to support this effort, demonstrating the capabilities of several genomic and proteomic platforms, identifying candidate biomarkers, and undertaking multicenter validation studies. In its first 10 years, the EDRN went from a groundbreaking concept to an operational success. CONTENTS: The EDRN has established clear milestones for reaching a decision of “go” or “no go” during the biomarker development process. Milestones are established on the basis of statistical criteria, performance characteristics of biomarkers, and anticipated clinical use. More than 300 biomarkers have been stopped from further development. To date, the EDRN has prioritized more than 300 biomarkers and has completed more than 10 validation studies. The US Food and Drug Administration has now cleared 5 biomarkers for various clinical endpoints. SUMMARY: The EDRN today combines numerous collaborative and multidisciplinary investigator-initiated projects with a strong national administrative and data infrastructure. The EDRN has created a rigorous peerreview system that ensures that preliminary data—analytical, clinical, and quantitative—are of excellent quality. The process begins with an internal review with clinical, biostatistical, and analytical expertise. The project then receives external peer review and, finally, National Cancer Institute program staff review, resulting in an exceptionally robust and high-quality validation trial. © 2012 American Association for Clinical Chemistry The Early Detection Research Network (EDRN)2 is a pioneering effort by the National Cancer Institute (NCI) that is designed to discover and validate biomarkers for assessment of cancer and cancer risk. First launched in 2000, the EDRN provides a vertically integrated network of academic- and industry-based scientists collaborating to meet the challenge of developing new cancer-screening and early detection products. The mission of the EDRN is to implement biomarker research through systematic evidence-based discovery, development, and validation of biomarkers for identification of cancer risk, early detection, diagnosis, and prognosis determination to reduce cancer morbidity and mortality (Tables 1, 2, and 3). The identification of biomarkers involves a rigorous process that begins with discovery, which leads to development, validation, and application. EDRN has fulfilled these expectations by establishing a process for biomarker development by using a multidisciplinary and multiinstitutional approach. This infrastructure, combined with the development of highly interactive databases and informatics systems, serves as a model for the conduct of translational research that is fully aligned with the goals and objectives of the NCI and the NIH communities (1 ). The EDRN has implemented pioneering solutions to enable data sharing between laboratories using common data elements, thus ensuring and expediting consistent data description across institutions. Research collaborations occur within an environment of teamwork across different disciplines and laboratories focused on achieving the following common goals (2 ): • Developing and testing promising biomarkers and technologies to obtain preliminary information to guide further testing; • Evaluating promising, analytically proven biomarkers and technologies, such as measures of accuracy, sensitivity, and specificity and, when possible, poten- 1 Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Rockville, MD. * Address correspondence to this author at: Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, 6130 Executive Blvd., Suite 3142, MSC 7362, Rockville, MD 20852. E-mail [email protected]. Received July 10, 2012; accepted October 25, 2012. Previously published online at DOI: 10.1373/clinchem.2012.184697 60 2 Nonstandard abbreviations: EDRN, Early Detection Research Network; NCI, National Cancer Institute; HUPO, Human Proteome Organisation; MS, mass spectrometry; TSP1, thrombospondin 1; EPCA-2, early prostate cancer antigen-2; proPSA, prostate-specific antigen precursor; CA125, cancer antigen 125; DCP, des-␥ carboxyprothrombin; AFP, ␣-fetoprotein; HCC, hepatocellular carcinoma; HE4, human epididymis protein 4. Early Detection Research Network: 10-Year Outlook Mini-Reviews Table 1. List of standard and protocols developed by EDRN.a Assays Validation of bleomycin-induced chromosomal breakage in lymphocytes Biomarker of lung cancer susceptibility Validation of 3.4-kb mitochondrial DNA deletion Biomarker for prostate cancer risk Development of high-density breast and prostate tissue microarrays Testing of in situ hybridization and other molecular probes Validation of SOPsb for microsatellite instability and DNA methylation assays a b Applications Biomarkers for bladder cancer Validation of saliva-based mRNA assay Biomarkers of oral cancer Validation of proteomic prostate-specific biomarkers, including percent proPSA and other PSA isoforms Biomarkers for improving PSA screening of prostate cancer Urine PCA3 assay Detection of prostate cancer Urine/TMA assay for T2S:ERG fusion Detection of prostate cancer FISH assay for T2S:ERG fusion Detection of prostate cancer Aptamer-based assay Detection of lung cancer Proteomic panel for Lung Cancer Detection of lung cancer OVA1TM test for ovarian cancer Differential diagnosis of benign pelvic mass from ovarian cancer ROMA for ovarian cancer Differential diagnosis of benign pelvic mass from ovarian cancer Vimentin methylation in stool Detection of colon cancer SOPs for blood, sera, plasma, urine, stool Standard reference sample sets 8-oxyguanine DNA glycosylase (8OGG); alkyl-adenine DNA gycosylase; APE1 endonuclease Measuring DNA repair capacity for lung cancer risk Retrieved from the EDRN 5th report [NCI (3 )]. SOP, standard operating procedure; PCA, prostate cancer; TMA, transcription-mediated amplification; ERG, v-ets erythroblastosis Virus E26 oncogene homolog; FISH, fluorescence in-situ hybridization; ROMA, risk of ovarian malignancy algorithm; APE1, apurinic endonuclease 1. tial predictors of outcomes or surrogate endpoints for clinical trials; • Analyzing biomarkers and their expression patterns to serve as a background for large, definitive validation studies; • Conducting early phases of clinical and epidemiological biomarker studies; • Encouraging collaboration and dissemination of information to ensure progress and avoid fragmentation of effort. In its first 10 years, the EDRN evolved from a groundbreaking concept to an operational success. With the primary mission to discover and scrupulously validate biological markers that signal the earliest stages of cancer (e.g., premalignant lesions, genetic variations, and risk indicators) and reagents for risk assessment in primary organ systems (e.g., prostate, breast, ovary, lung, colon, pancreas, and liver). The EDRN combines collaborative, multidisciplinary, investigator-initiated projects with a strong administrative and data infrastructure (3 ). 1998 –2000: Inception and Inauguration In 1997, a 20-member Cancer Prevention Program Review Group, seeking a means to revitalize the National Cancer Prevention and Control Program, recommended the concept of the EDRN. The NCI’s Board of Scientific Advisors and the National Cancer Advisory Board approved the concept on November 13, 1998 (4 ). The EDRN structure emerged (Fig. 1) with working components that comprised laboratories, validation centers, a data management center, and 2 oversight components including a steering committee and a network consulting team. 2001–2003: Meeting the Scientific Challenges The network developed systematic, comprehensive guidelines to develop, evaluate, and validate biomarkers. This 5-phase approach established both a scientific standard and a roadmap for successfully translating biomarker research from the laboratory to the clinic (5 ). Clinical Chemistry 59:1 (2013) 61 Mini-Reviews Table 2. Adoption of EDRN-supported assays.a Detection/biomarker assay Clinical translation validation Blood FDAb IVD pending review proPSA Urine a b PCA3 FDA cleared Urine/TMA assay for T2S:ERG fusion for PCA CLIA in process FISH for T2S:ERG fusion for prostate cancer In CLIA Lab Aptamer-based markers for lung cancer In CLIA Lab Proteomic panel for lung cancer In CLIA Lab OVA1TM for ovarian cancer FDA cleared SOPs for blood (serum, plasma), urine, stool Frequently used by biomarker research community Vimentin methylation marker for colon cancer In CLIA Lab ROMA algorithm for CA125 and HE4 tests for pelvic mass malignancies FDA cleared Blood/DCP and AFP-L3 for HCC FDA cleared Blood GP73 for HCC Together with AFP-L3 used in China for monitoring/ risk assessment of cirrhotic patients for HCC Retrieved from the EDRN 5th report [NCI (3 )]. FDA, US Food and Drug Administration; IVD, in vitro diagnostic. For other abbreviations see the text and the Table 1 footnote. Phase 1: Discovery, involving exploratory study, to identify potentially useful biomarkers. Phase 2: Validation, in which biomarkers are studied to determine their capacity for distinguishing people with cancer from those without. Phase 3: Determination of the capacity of a biomarker to detect preclinical disease by testing the marker against tissues collected longitudinally from research cohorts. Phase 4: Prospective screening studies on biomarker performance in large populations, and determination of false-referral rates. Phase 5: The penultimate period, in which largescale population studies are performed to evaluate the biomarker both for its role in cancer detection and its overall screening impact. Although the network’s focus is mainly on phases 1 through 3, researchers have welcomed the 5-phase structure because it provides an orderly succession of studies that build upon each other to yield an efficient and thorough approach to biomarker development. 2003–2005: The Network Surges Ahead in Real Time In late 2006, the EDRN’s Program for Rapid Independent Diagnostic Evaluation was established as an administrative means to assist extramural investigators in successfully conducting cross-laboratory validation of biomarkers (6 ). The network implemented the follow62 Clinical Chemistry 59:1 (2013) ing guiding principles for biomarker validation and used criteria for the prioritization of collaborative projects: 1. 2. 3. 4. 5. 6. Biologic rationale/strength of hypothesis; Strength of study design; Technical parameters; Clinical or scientific impact; Practicality; Collaborative strength/team effort. To broaden the opportunities for scientific interactions and coordinated research, collaborative groups were formed. These organ-specific research groups were structured to promote information exchange on organ-related biomarkers and to identify research priorities within the EDRN. One major role of the collaborative groups was to serve as advisors and liaisons with associate members. The associate membership component was designed for investigators not affiliated with the EDRN but who wished to join the network by proposing collaborative studies within its scope and objectives. More than 200 collaborative projects spanned the various organ sites. These projects are monitored through the EDRN’s electronic Study Information System. Useful bioinformatics tools were developed to maximize standardization of information and data management across multiple activities and research Early Detection Research Network: 10-Year Outlook Table 3. Candidate biomarkers tested for early detection of ovarian cancer. Marker a Diagnostic sensitivity at 95%, diagnostic specificity (95% CI) CA125 0.73 (0.64–0.84) HE4 0.57 (0.50–0.70) Transthyretin 0.47 (0.38–0.56) CA15.3 0.46 (0.39–0.54) CA72.4 0.40 (0.33–0.52) IGFBP2a 0.38 (0.30–0.45) Cytokeratin 19 0.37 (0.25–0.45) IGF2 0.36 (0.28–0.49) Kallikrein 6 0.36 (0.26–0.43) Mesothelin 0.35 (0.27–0.46) B7-H4 0.35 (0.27–0.42) Apolipoprotein A1 0.34 (0.26–0.43) sV-CAM 0.34 (0.22–0.42) Prolactin 0.34 (0.15–0.49) Chitinase 0.31 (0.23–0.38) Spondin-2 0.28 (0.18–0.36) MIC1 0.24 (0.14–0.30) CA19.9 0.23 (0.18–0.30) Transferrin 0.23 (0.15–0.32) IGFBP1 0.21 (0.15–0.31) Hepcidin 0.21 (0.12–0.28) TSH 0.19 (0.13–0.25) CTAP-III 0.19 (0.12–0.24) MMP3 0.17 (0.11–0.27) MMP2 0.17 (0.08–0.28) GH 0.16 (0.10–0.23) MIF 0.15 (0.09–0.24) Eotaxin 0.15 (0.07–0.23) MPO 0.14 (0.09–0.22) EGFR 0.10 (0.05–0.17) ITIH4 0.09 (0.05–0.16) B2M 0.05 (0.02–0.12) IGFBP2, insulin-like growth factor binding protein-2; IGF2, insulin-like growth factor-2; sV-CAM, Soluble vascular cell adhesion molecule; MIC1, macrophage inhibitory cytokine 1; TSH, thyroid stimulating hormone; CTAP-III, connective tissue activating peptide-III; MMP3, matrix metalloproteinase-3; GH, growth hormone; MIF, macrophage migration inhibitory factor; MPO, myeloperoxidase; EGFR, epidermal growth factor receptor; ITIH4, inter-alpha-trypsin inhibitor heavy chain family, member 4; B2M, -2 microglobulin. sites. These tools include the EDRN Virtual Specimen Bank, also known as the ERNE Knowledge System, which was deployed in early 2003 to allow a common web-based query to search for available samples across Mini-Reviews the EDRN clinical validation centers. The Validation Study Information Management System, another of these tools, encompasses all the security features of the auditing systems required by the US Food and Drug Administration. A number of public–private partnerships were established since the inauguration of the EDRN. The most notable partnerships are with the Canary Foundation, the Lustgarten Foundation, and the international Human Proteome Organisation (HUPO). The premise of the HUPO-sponsored study was an initiative to evaluate multiple proteomic technology platforms, develop bioinformatics tools and standards for protein identification, and to create a database of the plasma proteome. The entire study was published in the August 2005 issue of the Journal of Proteomics (7 ). 2006 –2008: An Investment in Prevention The EDRN is one of the first organizations to recognize the importance of standardized, prospective collection of biologic samples in the context of clinically relevant circumstances to discover and accelerate the validation of biomarkers for cancer detection and prognosis. Samples from cancer cases and controls are collected prospectively for each major cancer organ site, including cancers of the ovary, breast, lung, colon, liver, prostate, and pancreas (2 ). The sample sets contain comprehensive clinical, demographic, and epidemiologic information that help in the rapid evaluation of technologies and biomarkers in prevalidation before the initiation of prospective, large, and expensive validation trials. Over the past 10 years, the EDRN has collected a wide range of biological samples with a primary focus on noninvasively available samples. These samples include serum, plasma, urine, DNA, and sputum from major epithelial cancers, e.g., colon, breast, prostate, pancreas, bladder, and lung. All samples were collected using the NCI Biospecimens First Generation Guidelines. The samples are clinically annotated with EDRN-developed common data elements and accessible through an online query using the EDRN Resource Network Exchange system. Samples collected by the EDRN are provided to EDRN and non-EDRN principal investigators. EDRN investigators at the Data Management and Coordinating Center have developed a study design for phase 2 and 3 biomarker validation trials. They have termed this the PRoBE (Prospective Specimen Collection Retrospective Blinded Evaluation) design (5 ). A key feature of this design is the avoidance of bias by collecting all samples before diagnosis, so that cases and controls are enrolled under the same conditions and all samples are collected and processed identically. This design can require large numbers of study particiClinical Chemistry 59:1 (2013) 63 Mini-Reviews Fig. 1. Structural organization of the EDRN. pants because the incidence of most cancers are low; most of those study participants enrolled will not have cancer. 2008 to the Present: Delivery of Clinically Useful Biomarkers The EDRN plays very important roles as a “brake” and “accelerator.” A number of biomarkers undergo rigorous validation tests before they are adopted for larger validation studies. Biomarkers that are not effective for the intended clinical objective are not considered further. Each biomarker is tested against the intended goal and then considered for a validation study. A few examples with the intended clinical objectives are illustrated below. • Is the biomarker assay reproducible in an independent laboratory? If not, then it is a “no go.” Example: the EDRN pioneered a systematic evaluation of protein profiles as a candidate biomarker using SELDI-TOF-mass spectrometry (MS) and MALDI platforms for the diagnosis of prostate cancer. The study was initiated in 2003 to test published claims that 64 Clinical Chemistry 59:1 (2013) MS-based protein profiling in ovarian, prostate, lung, and other cancers could serve as a source of reliable biomarkers. Network investigators designed a 3-stage study to validate the reproducibility of the platform (stage 1), validate the diagnostic use of protein patterns (stage 2), and conduct a clinical validation (stage 3) using well-annotated, prospective samples from stratified risk groups and prostate cancer cases and controls. The stage 1 study confirmed the portability and reproducibility of the SELDI-TOF-MS platforms among the 8 participating institutes. The stage 2 study could not substantiate previous claims that protein profiling significantly discriminates between prostate cancer and noncancer. Thus, the approach that used protein expression profiling based on SELDI/MALDI-TOF-MS did not perform well enough to warrant the launch of the stage 3 prospective study. It was a no go. (8 –9 ). • Is the biomarker’s performance reproducible when checked by use of an independent set? Example: Thrombospondin 1 (TSP1); kallikreins 2, 3, 5, and 11; and early prostate cancer antigen-2 (EPCA-2) were no go for further validation studies af- Mini-Reviews Early Detection Research Network: 10-Year Outlook ter they failed in a blinded test on an EDRN prostate cancer reference set. Prostate-specific antigen precursor (proPSA), %[⫺2]proPSA, held its performance [area under the concentration curve (AUC) ⫽ 0.69] on the same set of samples (10 ). For TSP1 and EPCA-2, the AUC dropped from above 0.95, observed in preliminary data, to below 0.55 on the reference set. • Does the biomarker outperform currently used markers, or add significant value to it? If not, then it is a no go. Example: In the phase 3 validation study, 5 predictive models, each containing 6 – 8 biomarkers, were evaluated according to a predetermined analysis plan. The diagnostic sensitivity of each predictive model to distinguish ovarian cancer from controls at 98% specificity was first determined for the entire set of samples. In this study design, 1 model showed performance comparable with that of cancer antigen 125 (CA125) in terms of diagnostic sensitivity and specificity, whereas the remaining models had poorer performance than CA125. A second component of this study involved unblinding half of the samples to perform training with each model, followed by testing on the remaining samples. In this case again, only 1 model showed performance that was merely comparable with CA125 alone. Thus, all tested markers failed to show improvement over CA125 (11–12 ). • Does the biomarker have a clear potential clinical use? If not, then it is a no go. Example: des-␥ carboxyprothrombin (DCP) indicated better diagnostic sensitivity and specificity in preliminary data than did ␣-fetoprotein (AFP), the conventional clinical biomarker for hepatocellular carcinoma (HCC). Therefore, if the performance is true, it will replace or add to AFP as a better noninvasive blood test. A phase 2 validation study has just been completed, which found that that better performance holds only on later stage HCC but does not hold for early stage HCC. However, for early stage HCC, in particular HCC with viral etiology, DCP, when combined with AFP, increases the diagnostic specificity by 50% (from 24% to 36%) and holds diagnostic sensitivity at 95% (13 ). Four validation studies have already led to successful submission and clearance of clinically useful biomarkers by the US Food and Drug Administration: (a) validation of AFP and DCP for differentiating hepatocellular cancer from nonmalignant liver diseases; (b) validation of human epididymis protein 4 (HE4) in conjunction with CA125 for measuring the risk of ovarian cancer in high-risk patients; (c) a panel of 5 proteomics-based markers for the detection of ovarian cancer; and (d) a noncoding RNA test for measuring the risk of second biopsy in prostate cancer. These clearances support EDRN-based validation studies that are rapidly moving basic science discoveries to the bedside (3 ). As an example, we tested more than 68 biomarkers for the detection of ovarian cancer as part of the verification process before launching a large validation study. EDRN uses a verification process for biomarker performance by measuring the sensitivity, specificity, and ROC of each biomarker individually and as part of a panel. As previously mentioned, the EDRN ovarian cancer study tested more than 68 biomarkers in samples collected under stringent protocol and operating procedures. Individual biomarkers were then evaluated for their performance to determine if they warranted a full validation trial to determine the clinical utility. Except for CA125 and HE4, none of the biomarkers listed in Table 3 performed with adequate sensitivity and specificity for preclinical detection of ovarian cancer (11 ). The US Food and Drug Administration has now cleared a panel of CA125 and HE4 for monitoring women for early recurrence of ovarian cancer. Future Outlook Genome-wide global profiling is expected to lead to a molecular taxonomy of cancer that goes beyond organ and tissue types. These technologies will supersede and enhance the classifications based on histopathology or on the patterns of expression of genes of unknown biological significance uncovered by global transcriptomic profiling. Such an integrated systems approach may help us to find molecular features of cancer that are manifested at the earliest stages, leading to effective target-based screening and early intervention. Strategies must be put to use in the most appropriate manner for integrating clinical and genomics data with other omics and imaging data for an effective tailored therapy in the future. Recent advances in clinical diagnostic imaging and screening have increased our ability to detect lesions, but our inability to differentiate aggressive vs indolent cancers has led to overdiagnosis and overtreatment. Moreover, overdiagnosis has also led to greatly increased healthcare costs due to the technologies used, the procedures performed, and the number of patients examined. To address these problems, the EDRN has begun discussing the challenges and the scientific opportunities involved in lowering costs, minimizing overdiagnosis, and improving cancer detection, diagnosis, and staging through the combined use of biomarkers and imaging. Clinical Chemistry 59:1 (2013) 65 Mini-Reviews EDRN investigators are proactively analyzing the data along with their own expression data to triage biomarker information relevant to early detection of cancer. An example is the use of data generated by the NCI’s The Cancer Genome Atlas to guide the discovery of ovarian cancer–related secretome genes that can be further prioritized and subjected to scrutiny for clinical validation. including tumor grade, migration, invasion, and apoptosis, may or may not predict the clinical outcomes under assessment, such as metastasis and death. Unfortunately, current studies often ignore the potential role of a tumor’s local environment. This is a major limitation. How can researchers design relevant studies or establish implementable strategies to determine which cancers will likely lead to an undesirable clinical outcome? INTEGRATION OF IMAGING WITH BIOMARKERS Coping with Challenges NEXT GENERATION SEQUENCING AND BIOMARKER DISCOVERY Moving promising molecular diagnostics into the clinic subsequent to validation is a major undertaking in terms of expense, hours, and resources required. In this regard, the merging of ex vivo and in vivo imaging methodologies with biomarker information offers: 1. Noninvasive near-simultaneous detection of multiple targets and surrogate endpoints in an efficient, robust, and cost-effective manner; 2. Noninvasive assessment of dynamic physiologic processes in near real time, such as rate constants of cellular processes, blood flow, oxygenation, pH, temperature, and diffusion; 3. Ability to locate biomarker origins in 3-dimensional space during a single exam. It is anticipated that imaging-based clinical validation studies could be used to significantly shorten the current timeline required for clinical validation and subsequent regulatory clearance by the US Food and Drug Administration and recommendations by the Centers for Medicare and Medicaid Services. INDOLENT VS AGGRESSIVE TUMORS: OVERDIAGNOSIS Many detectable lesions and cancers are asymptomatic and not life threatening. However, the current inability to discern whether lesions are aggressive cancers that will lead to clinically significant morbidity and death or benign, slow-growing cancers that will lead to excessive testing leads to a phenomenon known as overdiagnosis. Because overdiagnosis leads to unnecessary medical procedures and treatment and increased risk of adverse side effects, it is important to develop strategies to quantify and manage its occurrence. About 25% of breast cancers detected on mammograms and about 60% of prostate cancers detected with PSA tests could represent overdiagnosis. A lung cancer screening trial of chest x-rays and sputum tests led to the estimation that 50% of the suspicious abnormalities detected represented overdiagnosis. In future directions the EDRN’s aims to develop strategies for studying the natural history of cancer to aid in developing better tools for determining which cancers are clinically important. The surrogate markers of cancer aggression often used as evaluative criteria, 66 Clinical Chemistry 59:1 (2013) The EDRN has admirably overcome many obstacles and challenges, particularly in its vehement approach to reducing silo-oriented research structures. In overcoming these obstacles, the EDRN has created a growing army of crusading researchers who promote the collaboration that has essentially changed the EDRN’s overarching philosophy and guiding principles. A consensus of collaboration and credit sharing within and between organ-based collaborative groups appears to be emerging. Substantial attention is now paid to coping with problems that previously were considered mundane (for example, QC of sample collection and management). The EDRN has built a set of data elements for translational research that includes demographic, clinical, biosample, research, and clinical data. Development and use of these data elements have been critical factors in creating group cohesion and in linking resources from diverse units throughout the EDRN. Leveraging other government resources, such as the Jet Propulsion Laboratory, the EDRN has developed a novel informatics infrastructure that permits interrogation of diverse databases at long distances. The maturing culture of the network has engaged its participants in a unifying effort to leverage resources, maximize returns on investments, and crossfertilize and synthesize new ideas by exposing previously unrelated concepts to one another. Through these efforts the levels of some challenges have diminished considerably since the emergence of the EDRN. The culture of working with industry has presented challenging working relationships. Small companies are attracted to the EDRN because of its rich resource and expertise. However, there continues to be a lack of investment from industrial partners in biomarker discovery research, and occasionally there is resistance to disclosure of biomarkers to be tested using EDRN reference samples. Communication among scientists with diverse backgrounds (e.g., biochemists, epidemiologists, and statisticians) challenges individual views of scientific inquiry that narrowly focus on respective disciplines without efforts on a larger scale to move discovery to Mini-Reviews Early Detection Research Network: 10-Year Outlook translational research. The struggle to enable effective communication and mutual respect is a major challenge for any vertically integrated unit such as the EDRN. The EDRN has learned from these problems, grown scientifically and culturally, and transmitted lessons learned to other parts of the NCI and the broader scientific community. Obstacles should not be interpreted as weakness of the concept or the model, but rather should be interpreted as a process of evaluation and change as the commitment of the stakeholders and the scientific community alike continues to grow collectively. Conclusions The EDRN has made significant progress in developing an organized effort for biomarker discovery and validation, building resources to support this effort, demonstrating the capabilities of several genomic and proteomic platforms, identifying candidate biomarkers, and undertaking multicenter validation studies. In its first 10 years, the EDRN has gone from a groundbreaking concept to an operational success. To date, the EDRN has developed a rich pipeline of more than 300 biomarkers that have been subjected to rigorous phase 2 validation and are ready for large validation studies. The EDRN has generated more than 1450 publications, more than 28 patents, and more than 14 licenses (2). Without the EDRN, research into new biomarkers of early cancer detection and risk would have remained on the periphery of research, with a strong but fragmented laboratory presence and little translational interest in the academic scientific community. However, with the network, a new translational paradigm has defined organizations, approaches, and standards by which biomarkers are developed and assessed. The network has created substantial focus, energy, and new research in the field of early detection. Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: Employment or Leadership: S. Srivastava, EDRN. Consultant or Advisory Role: None declared. Stock Ownership: None declared. Honoraria: None declared. Research Funding: None declared. Expert Testimony: S. Srivastava, EDRN. Patents: None declared. References 1. Wagner PD, Srivastava S. New paradigms in translational science research in cancer biomarkers. Transl Res 2012;159:343–53. 2. Crichton DJ, Mattman CA, Tornquist IM, Anton K, Hughes JS. Bioinformatics: biomarkers of early detection. Cancer Biomark 2010;9:511–30. 3. NCI. The Early Detection Research Network. November 2011, 5th report, NIH publication number 10-7696. http://edrn.nci.nih.gov/docs (Accessed March 2012). 4. NCI. EDRN initial report: translational research to identify early cancer and cancer risk, October 2000, publication number 01-4852. http://edrn.nci.nih.gov/docs (Accessed March 2012). 5. Pepe MS, Feng Z, Janes H, Bossuyt, PM, Potter JD. Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. J Natl Cancer Inst 2008; 100:1432– 8. 6. NIH. 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