2
Methods, Tools, and Resources Needed to Discover and Develop Biomarkers

OVERVIEW OF THE BIOMARKER DISCOVERY AND DEVELOPMENT PROCESS

Scientists have been searching for cancer biomarkers for many years, but the methods of discovery have changed as new technologies have been developed. Traditionally, scientists have relied on conventional laboratory research tools, such as gel electrophoresis and immunohistochemistry, to identify altered genes and changes in mRNA and protein expression (Ross et al., 2004). Progress in this work has been slow because researchers could examine only one or a small number of candidate markers at a time, and the methods required some prior knowledge and experience with the potential markers of interest. More recently, many novel high-throughput technologies (Kiviat and Critchlow, 2002; Fan et al., 2004; Aebersold et al., 2005; Weckwerth and Morgenthal, 2005; De Bortoli and Biglia, 2006; IOM, 2006a), especially in the fields of genomics and proteomics, have made it easier to interrogate hundreds or even thousands of potential biomarkers at once, without prior knowledge of the underlying biology or pathophysiology of the system being studied (Table 2-1). As a result, there has been a flood of new data and renewed interest in discovering novel biomarkers for use in drug development and patient care.

The goal of these discovery methods is to identify genetic variations or mutations as well as changes in gene or protein expression or activity that can be linked to a disease state or a response to a medical intervention.



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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment 2 Methods, Tools, and Resources Needed to Discover and Develop Biomarkers OVERVIEW OF THE BIOMARKER DISCOVERY AND DEVELOPMENT PROCESS Scientists have been searching for cancer biomarkers for many years, but the methods of discovery have changed as new technologies have been developed. Traditionally, scientists have relied on conventional laboratory research tools, such as gel electrophoresis and immunohistochemistry, to identify altered genes and changes in mRNA and protein expression (Ross et al., 2004). Progress in this work has been slow because researchers could examine only one or a small number of candidate markers at a time, and the methods required some prior knowledge and experience with the potential markers of interest. More recently, many novel high-throughput technologies (Kiviat and Critchlow, 2002; Fan et al., 2004; Aebersold et al., 2005; Weckwerth and Morgenthal, 2005; De Bortoli and Biglia, 2006; IOM, 2006a), especially in the fields of genomics and proteomics, have made it easier to interrogate hundreds or even thousands of potential biomarkers at once, without prior knowledge of the underlying biology or pathophysiology of the system being studied (Table 2-1). As a result, there has been a flood of new data and renewed interest in discovering novel biomarkers for use in drug development and patient care. The goal of these discovery methods is to identify genetic variations or mutations as well as changes in gene or protein expression or activity that can be linked to a disease state or a response to a medical intervention.

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment TABLE 2-1 Examples of Biomarker Categories and High-Throughput Methods of Discovery Biomarker Category Examples of Methods Genomics   DNA-based   Copy number/loss of heterozygosity Various DNA arrays Sequence variation Various sequencing methods Epigenetic variation   Genome rearrangements   RNA-based   mRNA signatures Various DNA arrays miRNA signatures   Proteomics Mass spectrometry Proteins Liquid chromatogrpahy Peptides Protein arrays Metabolomics   Metabolites Mass spectrometry Lipids Liquid chromatography Carbohydrates Nuclear magnetic resonance SOURCE: Derived from IOM, 2006a. Analysis of these large datasets requires sophisticated algorithms and bioinformatics to identify individual markers of interest or to derive signatures or patterns of many markers (reviewed by Cristoni and Bernardi, 2004; Englbrecht and Facius, 2005; Tinker et al., 2006). Although these methods are continually evolving and being improved, there is still a great need for novel approaches to data analysis, especially with regard to network oriented models that can incorporate many different types of data to fully integrate the vast complexity of biology in health and disease. However, identifying biomarker patterns or specific changes in genes or the products of gene expression in tumors is only the beginning of the process to develop cancer biomarkers. Before a candidate biomarker can be put into use, it must undergo several stages of confirmation, validation, and qualification for use (Wagner, 2002; Feng et al., 2004; Ransohoff, 2004, 2005; Simon, 2005; De Bortoli and Biglia, 2006). Analytical validation is the process of assessing the assay or measurement performance characteristics, while qualification is the evidentiary process of linking a biomarker with the biology and clinical end-

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment points (that is, clinical validity and utility). Both are intended to ensure that a biomarker is fit for a specified purpose, but it is often not clear how best to prove the performance characteristics of a biomarker-based test, especially for those that employ newer technologies, since many lack a gold standard for comparison (IOM, 2006a). Ultimately, a test used to make clinical decisions must, in combination with an intervention, lead to a beneficial impact on patient outcomes. Thus, use as a clinical diagnostic also involves evaluations of benefit, harm, cost, and effort (Ransohoff, 2004). Once an appropriate method has been selected for measuring the biomarker or pattern of markers, the technical parameters of the test must be well defined to establish sensitivity, specificity, reproducibility, and reliability of the measurements. However, different technology platforms may be needed at different stages of biomarker development. Platforms for biomarker discovery generally need to process many biomarkers simultaneously, but they can be low throughput in terms of the samples processed. Platforms for clinical research need to be high throughput in terms of specimen processing, but they usually focus on a smaller number of markers. Platforms for clinical practice need to be inexpensive and robust, and ideally results should be easily and objectively quantifiable. The validity of the biomarker as an indicator of a biological, pathological, or pharmacological process must also be confirmed in carefully designed studies. Validation is necessary for each potential use of a biomarker, and the level of evidence needed to implement a biomarker varies with the intended use. For example, in the drug development process, biomarkers can play a role at many different stages, from early, exploratory research to surrogacy for a clinical endpoint in large clinical trials, and the required degree of validation increases along that continuum (Table 2-2; see also Table 1-2). In the drug development process, the highest level of evidence is required if the biomarker is to be used as a surrogate endpoint—it must be qualified for that specific use by clearly demonstrating in clinical studies that the marker accurately predicts the clinical endpoint of interest. Correlation and plausibility is not sufficient (Srivastava and Wagner, 2002; Wagner, 2002; Fleming, 2005). For instance, tumor shrinkage might seem to be a plausible surrogate for treatment efficacy, but in fact, tumor shrinkage in response to a drug does not necessarily lead to improved patient survival (Norton, 1997; Citron, 2004; Hudis, 2005). Similarly, inhibition of preinvasive abnormalities is widely thought to predict a reduction in invasive cancer (Kelloff et al., 2006), but the relationship has not been fully validated in clinical studies, and a recent study even showed that the antiestrogen

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment TABLE 2-2 Biomarker Validation and Qualification Requires Demonstration of Fitness for a Specified Purpose Type of Biomarker Definition Purpose Exploration Research and development tool Hypothesis generation Demonstration Probable or emerging biomarker Decision making, supporting evidence with primary clinical evidence Characterization Known or established biomarker Decision making, dose finding, secondary/ tertiary claims Surrogacy Biomarker can substitute for a clinical endpoint Regulatory approval NOTE: Shown are four categories of biomarkers used for drug development and their intended purpose. SOURCE: Adapted from Wagner, 2006. raloxifene can reduce the risk of invasive breast cancer without significantly reducing the incidence of ductal carcinoma in situ, a preinvasive lesion with potential to develop into invasive cancer (NSABP, 2006). Similarly, the criteria for validation vary among the many possible clinical uses of biomarkers (see Table 1-1). For example, validation of a biomarker for screening, which entails the systematic testing of an asymptomatic population to identify evidence of particular type of cancer, requires proof that the biomarker detects the disease with a high degree of sensitivity, specificity, and positive predictive value. Ultimately, the clinical value of a screening test will also also depend on whether the routine use of the test, combined with appropriate interventions, reduces the morbidity and mortality due to that disease. In contrast, the clinical validation criteria for a diagnostic biomarker, which is used to definitively determine the presence or absence of cancer in patients with symptoms or a known abnormality, are less onerous. The vast majority of candidate biomarkers never progress past the initial discovery phase, and very few become qualified as surrogate endpoints or useful clinical tests, in part because further evaluation is expensive and time-consuming, with uncertain outcome (and thus a risky endeavor)

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment (Hayes et al., 1998; Wagner, 2002; Srivastava and Wagner, 2002; Feng et al., 2004; Altman and Riley, 2005; Fleming, 2005; Simon, 2005; IOM, 2006a, 2006b). However, it has also been argued that the rules of evidence to assess the validity of biomarkers are both underdeveloped and not routinely applied (Ransohoff, 2004; Altman and Riley, 2005; LaBaer, 2005). The mechanisms of disease and pharmacologic response are complex and challenging to discern, and lack of appropriate study designs and analytic methods exacerbates the challenge. Perhaps as a result, both genomic and proteomic studies of the same cancer types have often identified discordant biomarker candidates and patterns (reviewed by Diamandis, 2004; Dalton and Friend, 2006; Quackenbush, 2006). Because of the enormous number of genes and proteins analyzed in genomic and proteomic studies, many false findings can be expected unless appropriate statistical methods are used (Simon, 2005). For example, in the discovery setting, overfitting can lead to erroneous identification of markers or patterns in association with disease. This occurs when multivariate analysis is used to assess associations between large numbers of possible predictors and an outcome—that is, a pattern is found that fits perfectly, but by chance (Ransohoff, 2004, 2005; Simon, 2005, 2006). Overfitting can be easily identified by checking reproducibility in a separate, independent group of individuals, but most published studies do not report this essential assessment of reproducibility (Ransohoff, 2004, 2005). Sample bias can also render conclusions drawn from a biomarker study invalid (Ransohoff, 2005). Bias can be defined broadly as the systematic but unintentional erroneous association of some characteristic with a group in a way that distorts a comparison with another group. The design, conduct, and interpretation of randomized clinical trials to assess medical interventions place high importance on ensuring that the treated and untreated patient populations are similar in every respect except for the treatment to avoid biases that could affect the outcome and thus the conclusions drawn from the results. However, most research on molecular markers for diagnosis or prognosis entails observational studies, in which it is difficult or impossible to ensure or even fully assess the similarity of the comparison groups, and which are much more likely to result in biased conclusions as a result. In fact, Ransohoff has suggested that bias can be so powerful in non-experimental observational research that a study should be presumed biased until proven otherwise (2005). He notes that a single bias might produce errors sufficiently large to invalidate results. Thus, great care must be taken in the design, conduct, interpretation, and reporting of such research.

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment The validity of biomarker research also depends on the generalizability of the results. Generalizability concerns how broadly the results can be applied and depends on the characteristics of study participants and how they were selected (Ransohoff, 2005). Initial studies often establish proof of principle, but they have limited generalizability. Subsequent larger studies then aim to assess broader generalizability. However, the developmental sequence for biomarker development is much less well defined than for drug development. In drug development, the phases of research are clearly delineated in a step-wise fashion to examine several key issues, including generalizability. Phase I studies aim to establish appropriate doses and to identify potential side effects, while phase II studies begin to address biological activity and adverse events. Phase III studies are larger and seek more definitive conclusions on efficacy and safety. Patients in phase I and II studies have often failed all available treatment options and have diverse characteristics, whereas phase III trials select patients who are more representative of how the drug will actually be used in clinical practice (Ransohoff, 2005). Delineating the phases for biomarker development is more difficult, in part because biomarker tests can be used for many different purposes, and thus research to assess the usefulness of a test must be designed to examine specific applications. The variability in technologies used to identify biomarkers further complicates the situation. As a result, proposals to establish developmental phases for biomarkers have focused on specific uses, such as early detection or surrogate endpoints, or specific methods (Pepe et al., 2001; Baker et al., 2004; Zolg and Langen, 2004). Perhaps because of this variability in the development pathway, the level of assessment and oversight for biomarkers is also more variable, and usually less stringent, than for drugs. The process of developing and implementing biomarkers differs from that of drugs in other ways as well, including economically. These issues are covered in more detail in Chapters 3 and 4. THE NEED FOR NEW, INNOVATIVE TECHNOLOGIES If the full potential of cancer biomarker-based tools in early detection, treatment, and drug development is to be realized, it will be important to optimize efforts to discover and validate putative biomarkers. Progress in biomarker discovery and development is directly dependent on the capacities of the technologies available. Initially, high-throughput methods to discover biomarkers and expression patterns focused on nucleic acids,

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment because the methods were more advanced and fully characterized than for other cellular components. The Human Genome Project1 spurred interest in the development and application of methods to access nucleic acids, and although there is still a need for standardization of reagents, platforms, and analyses, considerable progress has been made in the field. In addition, ongoing work to sequence the genomes of individual tumors as well as other organisms continues to spur the development of new technologies. For example, single-molecule sequencing2 is likely to lower the cost of sequencing significantly, and should reduce the problems that arise from normal cell contamination of tumor samples (IOM, 2006a). However, there are limitations to what can be learned from genomics approaches to assess DNA and RNA. Although RNA assays can detect dynamic changes in gene expression as well as identify upstream DNA-level abnormalities in cancers, it is more costly and difficult to work with than the comparatively stable DNA. It can be argued that proteins, which perform many essential functions in cells, may provide more meaningful biomarkers than either type of nucleic acid because changes in DNA and RNA are not always directly linked to altered protein expression, modification, or function. But progress in the identification of protein biomarkers has lagged, in part because proteins are more numerous and far more subject to quantitative and post-translational structural changes than genes, and in part because of the limitations of current technologies (Tyers and Mann, 2003; Aebersold et al., 2005; Hartwell, 2005; Cottingham, 2006; ). Technologies used to examine other types of biomarkers, such as metabolomics, are even less developed and characterized. Metabolomics entails the study of metabolic responses to drugs, environmental changes, and diseases via identification of small-molecule metabolite profiles; that is, it attempts to measure the metabolic consequences of altered genes and protein expression. 1 The Human Genome Project was an international research project to map each human gene and to completely sequence human DNA. Approximately $2.7 billion were invested in the project between 1990 and 2003. 2 Single-molecule sequencing, also called nanopore sequencing, is a method for sequencing DNA that involves passing the DNA through small pores about 1 nanometer in diameter. The size of the pore ensures that the DNA is forced through the hole as a long string, one base at a time. The base (i.e., adenine, guanine, cytosine, or thymine) is identified by the characteristic obstruction it creates in the pore, which is detected electrically. Single-molecule sequencing can be a more sensitive technique for identifying relatively rare genetic strands in a sample, without the need for replicating them with a polymerase chain reaction.

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment Proteomics research aims to interrogate extremely complex protein mixtures in blood and tissues. It has been estimated that blood contains more than 100,000 different protein forms with abundances that span 10–12 orders of magnitude (Anderson and Anderson, 2002; Jacobs et al., 2005). The leading high-throughput proteomics technology, mass spectrometry (MS), has limited ability to identify and quantify proteins in complex mixtures. Many known biomarkers occur at very low abundance and would not be identified by current technologies (Aebersold et al., 2005; Jacobs et al., 2005; Kolch et al., 2005). New fractionation methods (for depletion or enrichment) prior to MS could improve the process, as currently available methods are tedious and expensive, and are not amenable to high-throughput analysis. Furthermore, identification of the various peptides or proteins detected by the technology remains a difficult challenge. Antibodies raised against specific protein biomarkers could fill this gap, but currently antibodies are not available for most of the proteins that could be disease biomarkers (Anderson and Anderson, 2002; Aebersold et al., 2005; Hartwell, 2005; Jacobs et al., 2005; Cottingham, 2006). In addition to improving the sensitivity, specificity, and dynamic range of these technologies, it will be important to process the resultant data efficiently and effectively, by developing new software packages, algorithms, and statistical and computation models, including those that can integrate data from multiple inputs, such as proteomic and genomic data from the same samples (Cristoni and Bernardi, 2004; Englbrecht and Facius, 2005; Tinker et al., 2006). These approaches will also necessitate novel sample preparation procedures, from a variety of sources such as blood, plasma, tissues, and cells, and for a variety of analytic technologies, including metabolomics, proteomics, and genomics. Finally it will be necessary to develop new assays to translate discovery into viable clinical tests, and to develop novel approaches for real-time in vivo detection, via imaging and nanomaterials. The Human Proteome Organisation (HUPO), an international consortium of national proteomics research associations, government researchers, academic institutions, and industry partners, has begun to examine some of these issues in a pilot phase of its Plasma Proteome Project (Omenn et al., 2005), and progress is being made on several fronts (de Hoog and Mann, 2004; Ong and Mann, 2005), but much work remains to be done. There is a significant need for new and improved technologies for biomarker discovery and development, particularly in the field of proteomics. Such new technologies also will yield dividends in improved

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment capabilities for understanding fundamental cellular processes in cancers and systems biology in general. Although technology development and directed discovery have not traditionally been a primary focus of National Institutes of Health (NIH) funding, and the NIH peer review process generally does not favor high-risk projects (IOM, 2003), that has been changing in recent years and there are some precedents for funding initiatives that focus on large-scale discovery projects and also catalyze the development of improved technologies for biomedical research. As noted above, the Human Genome Project drove not only the development of new technologies, but also improvements in the existing technologies through automation, data standards, and quality control (Aebersold et al., 2005; Hartwell, 2005). More recently, the NIH Roadmap proposed a framework for the priorities NIH as a whole must address in order to optimize its entire research portfolio, laying out a vision for a more efficient and productive system of medical research (NIH 2006b). The NIH Roadmap identified opportunities in three main areas: new pathways to discovery, research teams of the future, and re-engineering the clinical research enterprise. The first main area, pathways to discovery, aims to advance the quantitative understanding of complex biological systems by deciphering the many interconnected networks of molecules that comprise cells and tissues, their interactions, and their regulation. In addition the program aims to increase access in the research community to new and better technologies, databases and other scientific resources that are more sensitive, more robust, and more easily adaptable to evolving needs. The Protein Structure Initiative (PSI), a $600 million, 10-year venture funded by the National Center for Research Resources of the National Institute of General Medical Sciences, is a recent example of an NIH program that explicitly funded technology development in the initial phase of the project. The overarching goal of the initiative is to determine the structure and function of thousands of proteins by 2010, with the final product serving as an inventory of all the protein structure families in nature. In the first phase of the initiative, PSI funded nine research centers that focused on developing novel and innovative approaches and technologies, such as robotic instruments, to determine protein 3-D structures from knowledge of their amino acid sequences (NIGMS, 2006). Technological innovations were developed for each step of the process, from the initial target selection, to the final poststructural analysis. According to NIH leadership, PSI succeeded in reducing costs four-fold from the initial year, and the techno-

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment logical improvements are likely to have a broad impact on protein structure research, beyond the PSI-funded work (Norvell and Berg, 2005). NCI currently also sponsors development of novel nanotechnologies as tools to accelerate advances in biomarker research. Clinical applications of nanotechnologies include measurement and analysis of biomarkers in vivo for early cancer detection, prevention and monitoring of treatment response. NCI programs such as the Innovative Molecular Analysis Technologies Program provide funding for research projects to develop new and emerging technologies, including nanotechnology methods and tools, and for refining existing technologies through to development and commercialization. In September 2004, NCI announced a 5-year $144.3 million initiative to develop nanotechnologies to be used in cancer research (NCI, 2004). The goals of this initiative, the NCI Alliance for Nanotechnology in Cancer, include the development of research tools to identify new biological targets, agents to monitor predictive molecular changes and prevent precancerous cells from becoming malignant, imaging agents and diagnostics to detect cancers early, and systems to provide real-time assessments of therapeutic and surgical efficacy.3 Emerging nanotechnologies include quantum dots, gold nanoparticles, and cantilevers. Quantum dots and magnetic nanoparticles can be used for barcoding of specific analytes, and gold and magnetic nanoparticles are components of a possible alternative to PCR known as the bio-barcode assay. Nanotechnologies can be used to genotype at high-throughput, and some researchers believe that they have the potential to reduce cost for many diagnostic applications (Azzazy et al., 2006). In addition, the size of nanoparticles makes them compatible with in vivo molecular manipulation and measurement (Yezhelyev et al., 2006). Nanodiagnostic assays have already been used to detect Alzheimer biomarkers in cerebrospinal fluid (Azzazy et al., 2006). The National Cancer Institute (NCI) has also recently launched new funding initiatives for proteomics research, noting that “current proteomic technology approaches are insufficient to reliably and reproducibly discover, identify, and quantify peptides and proteins of clinical significance for cancer” from complex patient samples. The NCI’s Clinical Proteomic Technologies Initiative for Cancer program is a 5-year $104 million program that includes two funding opportunities for proteomics technology development: Advanced Proteomic Platforms and Computational Sciences (DHHS, 2005) and Clinical Proteomic Technology Assessment for 3 http://otir.cancer.gov/programs/ati_nano.asp.

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment Cancer (CPTAC) (DHHS, 2006b). The goal of the former is to improve technology for protein/peptide detection, recognition, measurement, and characterization in biological fluids and to develop computational, statistical, and mathematical approaches for the analysis, processing, and exchange of large proteomic datasets. The goal of the CPTAC is more specifically to improve measurements of proteins and peptides with mass spectrometry and affinity-based proteomics platforms. The CPTAC Request for Application notes that CPTAC teams will … be responsible for refining, comparing, and optimizing existing proteomics methods and applications. Improvements are sought in areas such as: sample collection and fractionation, and detection, identification, and quantification of proteins or peptides of interest. In addition, rigorous method/technology validations are needed to ensure reliable and reproducible results for proteomics analyses of complex biological mixtures. The CPTAC program is not designed for an explicit goal of developing new technologies and/or advanced applications. Nonetheless, since the priority in the initiative is the integration of the appropriate scientific expertise and infrastructure, the participating groups of scientists should be capable of efficiently implementing new technologies that might emerge during the life of the program. While these NIH initiatives are to be commended, a review of projects funded through them suggests that many projects focus primarily on improving existing technologies, rather than on the development of completely novel technologies. To achieve the latter, it might be better to undertake a highly directed contract-based program. An examination of the Defense Advanced Research Projects Agency (DARPA) might prove instructive in that regard. DARPA is the central research and development organization for the Department of Defense, and it has focused on research projects that are high risk but also have potential for high payoff if successful (Box 2-1; IOM, 2003). As such, this approach is particularly amenable to technology development, and past leaders of NIH and NCI have expressed interest in adopting some aspects of the DARPA model to spark technological innovation. In fact, under the leadership of former NCI director Richard Klausner, NCI launched a pilot program that was modeled in part after DARPA, as well as other agencies, including the National Aerospace and Space Administration. Established in 1999, the Unconventional Innovations Program (UIP) focused on the development of novel, long-range technologies to support cancer research. The Program funded research through contracts instead of grants, allowing for enforcement of deadlines for specific milestones along the research track in order for researchers to

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment pathway. Broad applicability would be optimized by emphasizing the development of objectively quantifiable biomarkers, rather than qualitative or semi-quantitative assays such as immunohistochemistry. This broader applicability will increase the potential market and also reduce the risk associated with the development process, thus improving the odds of profitability for the company. One caveat is that the requirements for sensitivity, specificity, precision, and accuracy of a biomarker may vary among different diseases, so markers would not necessarily be directly transferable. But the secondary development process for another disease is likely to be shorter and less expensive than starting from scratch. THE NEED FOR SUPPORT OF TRANSLATIONAL RESEARCH ACTIVITIES NIH and NCI have both recently stressed the need to support and facilitate translational research to ensure that critical basic science discoveries move “from bench to bedside” and that unmet medical needs in turn drive further bench research. The NIH Roadmap noted that “growing barriers between clinical and basic research, along with the ever increasing complexities involved in conducting clinical research, are making it more difficult to translate new knowledge to the clinic” (NIH, 2006b). An annual report from the President’s Cancer Panel concluded that “the translational research infrastructure is inadequate to enable the work that needs to be done; resources must be committed to develop the tools and workforce required. Increased funding for translation-oriented research—particularly collaborative, team efforts—is urgently needed across the translation continuum. Targeted Federal funding for translation-oriented research is drastically out of balance relative to financial commitments to basic science. Ways must be found to increase human tissue and clinical research resources without slowing the discovery engine. Supplemental funding may offer a temporary solution, but will be inadequate in the long term” (President’s Cancer Panel, 2005). Similarly, the NCI’s Translational Research Working Group (TRWG), recently appointed to evaluate the status of NCI’s investment in translational research and to envision the future, concluded that translational research is not well coordinated across NCI and that the resulting fragmented efforts are often duplicative and could lead to missed opportunities (Goldberg, 2006; NCI, 2006d). Specifically, a draft report from the TRWG concluded that

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment the absence of clearly designated funding and adequate incentives for researchers threatens the perceived importance of translational research in NCI; the absence of a structured, consistent review and prioritization process tailored to the characteristics and goals of translational research makes it difficult to direct resources to critical needs and opportunities; translational research core activities are often duplicative and inconsistently standardized, with capacity poorly matched to need; the multidisciplinary nature of translational research and the need to integrate sequential steps in complex development pathways warrants dedicated project management resources; and insufficient collaboration and communication between basic and clinical scientists, and the paucity of effective training opportunities limits the supply of experienced translational researchers. Support for translational research activities will be critical for developing and validating putative biomarkers. Initial discoveries of potential biomarkers are often published in high-impact journals, but subsequent work to confirm and validate those findings often does not merit publication in those same journals. Furthermore, such validation work often takes many years to complete and can require an interdisciplinary team approach to science that is not the norm in academia (reviewed by IOM, 2003; Gray, 2006; Kaiser, 2006a). The academic culture traditionally has not been supportive of faculty that engage in team science or translational research; promotion and reward structures are designed to recognize individual initiative and accomplishment. Thus, it will be important to consider how academic organizational structures, metrics for academic promotion, and the cultures of biomedical research can better support team building and multidisciplinary science. Key factors for success will include providing sufficient time, resources, and rewards for faculty who undertake translational research (Gray, 2006). Training programs that specifically deal with the many complexities of this work are also needed to help new translational investigators get started and become established (Kaiser, 2006a). Although not a traditional NIH funding focus, several recent initiatives have been undertaken to foster translational research. For example, NIH’s new Clinical and Translational Science Award Program encourages institutions to develop new approaches to clinical and translation research, including new organizational models and training programs, and to develop novel clinical research methodologies (DHHS, 2006a; Gray, 2006; Kaiser,

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment 2006b). Through this program, 12 institutions recently received 5-year awards totaling $108 million for the first year. The program is intended to eventually replace the 50-year old NIH program of General Clinical Research Centers, which currently consists of approximately 60 facilities with beds for patients participating in clinical studies. The NCI TRWG draft report also recommends a new organizational structure to coordinate NCI’s translational research, with designated leadership and budget and oversight by an external advisory committee. Some private funders, such as the Howard Hughes Medical Institute, the Burroughs Wellcome Fund, and the Doris Duke Charitable Foundation, have put a recent emphasis on translational research as well (Kaiser, 2006a). Nonetheless, there is concern that funding and other support will not be maintained well enough to sustain a nascent, growing field of translational specialists (Kaiser, 2006a). But continued funding from federal and private sponsors of this work is essential for progress in reaching the goal of personalized medicine. Indeed, NCI has noted that the development of new diagnostic tests, cancer treatments, and other interventions that benefit people with cancer and people at risk for cancer will rely on strong translational research collaborations between basic and clinical scientists to generate novel approaches (NCI, 2006d). SUMMARY AND CONCLUSIONS The discovery and development of biomarkers entails a complex, multistage process, with many challenges that must be overcome to make meaningful progress in the field. Despite a few spectacular successes, the number of biomarkers used in drug development or clinical practice is very small, and most putative biomarkers never advance beyond the discovery stage. Moreover, the limitations of current technology render many discovery efforts inefficient and inadequate. Changes are needed to streamline the process and make the most of limited resources available for biomarker research and development. First, a more organized, comprehensive approach to biomarker discovery is needed. Such an approach would more effectively foster technological innovation and could lead to more efficient, systematic searches for potential biomarkers. Second, international public–private consortia are needed to generate and share methods and precompetitive data on the validation and qualification of cancer biomarkers. Given the accomplishments of previous endeavors like The SNP Consortium, such collaborations are likely

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment to reduce the cost and risk of biomarker development and enable the field to move forward more efficiently. Funders of biomedical research should also place more emphasis on developing pathway biomarkers and on developing biomarkers for drugs already in use. A focus on quantifiable biomarkers of signaling pathways rather than individual cancers or drugs could increase the applicability of biomarkers and thus increase the potential for return on investments by sponsoring companies. Demonstration projects to develop biomarker tests that could determine which patients are most likely to benefit from drugs that are already in the clinic would not only improve treatment outcomes for patients, but also could catalyze industry and academia to undertake such studies by establishing a viable route to market and by delineating a viable business strategy. Ensuring the availability of high-quality and well-annotated patient samples that have been collected in prospective studies will be crucial to progress in discovering and developing biomarkers. Thus, funders of biomedical research funders should initiate and sustain funding for biorepositories of such patient samples collected in conjunction with large cohort studies and clinical trials, and use of these samples should be encouraged for validating biomarkers. NCI in particular should actively encourage and facilitate interaction between biomarker developers and clinical trials groups to enable this prospective collection of patient samples. Collectively, these strategies could lead to better biomarkers for the entire spectrum of cancer health care, from early detection and disease classification to drug development and treatment planning and monitoring, and they could bring personalized medicine closer to being a reality. REFERENCES Aebersold R, Anderson L, Caprioli R, Druker B, Hartwell L, Smith R. 2005. Perspective: A program to improve protein biomarker discovery for cancer. Journal of Proteome Research 4(4):1104-1109. AJHP (American Journal of Health-System Pharmacy) News. 2006. Genetics examined in tamoxifen’s effectiveness. [Online]. Available: http://www.ashp.org/news/ShowArticle.cfm?id=17501 [accessed November 2006]. Altman DG, Riley RD. 2005. Primer: An evidence-based approach to prognostic markers. Nature Clinical Practice Oncology 2(9):466-472. Anderson NL, Anderson NG. 2002. The human plasma proteome: History, character, and diagnostic prospects. Molecular and Cellular Proteomics 1(11):845-867. Azzazy HM, Mansour MM, Kazmierczak SC. 2006. Nanodiagnostics: A new frontier for clinical laboratory medicine. Clinical Chemistry 52(7):1238-1246.

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