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A workshop sponsored by the National Cancer Institute and the US Food and Drug Administration addressed past lessons learned and ongoing challenges faced in biomarker development and drug and biomarker codevelopment. Participants agreed that critical decision points in the product life cycle depend on the level of understanding of the biology of the target and its interaction with the drug, the preanalytical and analytical factors affecting biomarker assay performance, and the clinical disease process. The more known about the biology and the greater the strength of association between an analytical signal and clinical result, the more efficient and less risky the development process will be. Rapid entry into clinical practice will only be achieved by using a rigorous scientific approach, including careful specimen collection and standardized and quality-controlled data collection. Early interaction with appropriate regulatory bodies will ensure studies are appropriately designed and biomarker test performance is well characterized.
Remarkable advances in the understanding of neoplastic progression at the cellular and molecular levels have spurred interest in molecularly targeted cancer therapeutics. New imaging and bioassay technologies are providing the basis for developing biomarkers that will facilitate development of these molecularly targeted drugs. Biomarkers may be used in early drug development to elucidate the mechanism of action of a drug and provide preliminary evidence of its effect. As the relationship between a drug or class of drugs and a biomarker becomes better understood, there is hope that clinical assays can be developed to identify patients most likely to benefit from the drug. These biomarkers are termed predictive biomarkers. Although prognostic biomarkers that provide information on the natural course of disease after standard treatments are useful, predictive biomarkers are of greater value in clinical decision making and will be essential tools for tailoring treatments.
Drug and assay developers, regulators, and clinical investigators face many dilemmas in the course of developing targeted drugs and associated predictive biomarkers. Difficult choices must be made regarding use of precious resources (eg, biospecimens and funds) in selecting appropriate candidate biomarkers and determining optimal study design. These choices will be influenced by many factors, including the anticipated business model for the biomarker assay (eg, development as a commercial kit or as a service laboratory test) and the inherent tension between rapidly determining whether any patient group benefits from the new drug vs accurately defining individual patients most likely to benefit. Perhaps the most difficult scientific and business decisions in drug and predictive biomarker development involve whether to use biomarkers to determine patient eligibility for inclusion in clinical studies assessing benefit from a new agent. Using a predictive biomarker to select patients can lead to efficient clinical studies if the biomarker is highly sensitive and specific for benefit. But these studies may not produce the information required to demonstrate efficacy of the drug in an unselected patient population or to adequately characterize the performance of the biomarker. Recognition of the fact that single biomarkers may not adequately reflect the biology of cells has led to increasing use of panels of markers or multianalyte markers. Development and evaluation of these multianalyte biomarkers are more complicated than for single biomarkers, but the principles of development are much the same; for this reason, we do not specifically discuss them in this report.
The National Cancer Institute, the US Food and Drug Administration (FDA), and representatives of the drug and biomarker industry convened a workshop on October 29–30, 2007, in Bethesda, Maryland, to address the challenges facing biomarker development and drug and biomarker codevelopment. The goal of this meeting was to consider strategies to assist the research and development community in identifying and addressing issues in predictive biomarker development. This article builds on the discussions that took place at the meeting and presents a set of issues for consideration and proposed development paths. These issues and concepts have been organized graphically in a figure (Figure 1).
The workshop began with an overview session that included presentation of two case studies with different clinical study design strategies for evaluating a targeted therapy and its companion predictive biomarker. The presentations reviewed some of the lessons learned from past efforts to develop a targeted drug and companion biomarker and highlighted some critical issues to be addressed. The first case study described development of HER2 as a predictive biomarker for patients who were likely to respond to trastuzumab (Herceptin). The second case study described ongoing research to identify and evaluate predictive biomarkers and biomarker assays for response to epidermal growth factor receptor (EGFR) inhibitors. These case studies are summarized in Boxes 1 (HER2) and 2 (EGFR).
Development path and clinical trial design(s)
Assays (trade name of assay, what was measured, method, manufacturer)
Development path and clinical trial design(s)
Assays (trade name of assay, what was measured, method, and manufacturer)
A key difference in the development paths followed in these two examples is that a biomarker-enrichment design strategy was used in the case of HER2-trastuzumab (only patients whose tumors tested positive for HER2 were entered into the pivotal phase III trial of trastuzumab and in most of the earlier trials). An enrichment strategy was not uniformly applied in the clinical trials evaluating the therapeutic agents targeting EGFR. Some trials of EGFR antibodies enrolled patients based on EGFR expression detected by immunohistochemical analysis but many trials of EGFR inhibitors enrolled patients without preselection by EGFR status.
Workshop participants divided into three breakout groups to consider different aspects of predictive biomarker development: 1) feasibility studies and choice of assay and biomarker, 2) study design and clinical utility evaluation—for drug, biomarker, drug and biomarker, and 3) decision points and their implications. Summary reports from the three groups were presented to all participants and recommendations were then discussed. These reports form the basis for this article and its recommendations.
A recurring theme in the workshop's discussions was the need to increase understanding of the biology associated with the chosen biomarkers, including their role in tumor behavior and their interplay with the drugs’ mechanisms of action. This biological understanding must be integrated into the clinical context in which the biomarker would be used (Boxes 1 and 2). Critical factors to this understanding are access to well-annotated biospecimens, early attention to characterization of the biomarker and standardization of assay methods, clinical trial designs that allow early evaluation of the effectiveness of the biomarker in predicting responsiveness to the drug, and ability to incorporate advancing technology and emerging data suggesting new or additional biomarkers that better characterize the target populations.
Sound biomarker and drug codevelopment depends on preclinical data that would support use of a biomarker assay in therapeutic decision making and reliable information that would allow development of a robust and reproducible assay. The clinical and laboratory data providing a rationale for codevelopment should show consistent associations between biomarker status and the drug's activity. The method(s) of detecting the biomarker in clinical specimens should show reasonable analytical and clinical performance. Furthermore, the proportion of patients who are likely to benefit from treatment, and the magnitude of the benefit, should be sufficient to warrant use of the biomarker. If only a fraction of the patients with a particular cancer that is diagnosed by standard methods may benefit from treatment but the magnitude of benefit is clinically important, then the codevelopment of a biomarker to identify those patients, if feasible, would be warranted.
A biological rationale supported by research or observational data is a critical factor in selecting candidate predictive biomarkers for codevelopment with a cancer therapeutic. This would include evidence that the biomarker(s) chosen is meaningfully correlated with the activity of the targeted agent (41). The evidence may derive from existing preclinical and clinical literature; data collected during phase I, expanded phase II, or even early phase III clinical studies of the agent; or modeling and biological inferences using incomplete or partially complete provisional datasets and data mining. All available information on cellular pathways relevant to drug action, mechanisms, or drug interactions should be included in decision making and in generation of study hypotheses.
A robust technology for testing in clinical samples must be available or it must be technically and economically feasible to develop an analytically reliable testing system on a timeline consistent with the development timeline for the drug. Definitive work on establishing even preliminary hypotheses may be at risk if reliable assay performance has not been achieved early in the decision-making process. In pilot work, real-world test performance and cost should all be considered important factors in determining the viability of biomarker testing. The anticipated strength of the association between biomarker status and drug benefit and the impact that use of the biomarker would have on the size of further clinical studies required for the drug development and approval process will likely play a role in the decision regarding whether to pursue codevelopment of a drug and a biomarker.
There is increasing appreciation that assay performance can be greatly affected by preanalytic factors, including methods used for sample collection, handling, processing, transport, and storage. Physiological, pharmacological, and pathological features of patients, ranging from underlying patient comorbidities to patient drug treatments, can be the source of spurious biomarker results. Information on these potential confounders should be gathered as comprehensively as possible, and attention should be paid to whether the assay will be robust enough in the real world of sample procurement.
It is critical that standards for test performance be developed and adopted. This need has been addressed by FDA in a concept article on drug and diagnostic codevelopment and in a series of guidance documents (42–47). To ensure success, there must be access to systematically collected, well-annotated, well-preserved patient specimens with linked outcome data. This requires that sufficient sample collection be prospectively built into studies and that standard protocols for collecting the samples and clinical information be implemented. Use of a certification process or reimbursement incentives to promote standardization of sample collection, handling, and preservation might help address these issues. Complete and consistent reporting of the data and analysis of results generated in studies for development of biomarker assays and evaluation of their clinical use are also critical to allow appropriate interpretation and design of subsequent clinical trials. In this regard, the REMARK Guidelines prepared by the National Cancer Institute–European Organization for Research and Treatment of Cancer Working Group on Cancer Diagnostics (48) and the Standards for Reporting of Diagnostic Accuracy initiative (49) may be of considerable value.
There are limitations imposed by informed consent documents and institutional review boards on patient sample collection, storage, and use. An effective, clear, and simplified informed consent process needs to be developed and widely adopted for the purpose of permitting the use of archived biospecimens and associated clinical information in biomarker research.
Because advances in understanding the biology of cancer and in technologies for biomarker assays take place continuously, the decision to develop a companion biomarker may occur at any time during drug development, that is, from preclinical investigations to postmarketing studies conducted after approval of the drug. In ideal circumstances, the biomarker assay should be defined and validated by the end of phase II for definitive evaluation in phase III. However, this may often not be possible. There is also some risk to investing too much on biomarker development early in the drug development process because the biomarker may be irrelevant if the drug does not effectively inhibit the target or if the target is not related to cancer progression in the clinical setting in which it is tested. It is also possible that the putative target of the agent may be shown to be wrong in early clinical testing, which could result in effort and resources being wasted on development of a biomarker that would not be relevant. Alternative approaches for iterative biomarker discovery, design, and validation may need to be considered (see “Study Design and Clinical Utility Evaluation” section below).
The HER2 and EGFR inhibitor case studies highlight two extremes in the spectrum of potential strategies for biomarker and drug codevelopment. As described in Box 1, a wealth of preclinical and clinical biological data were available to provide the rationale for codeveloping trastuzumab and an assay for HER2 in the treatment of breast cancer, including information on use of HER2 as a prognostic marker, data from laboratory experiments linking amplification and/or expression of HER2 to development of breast cancer, and sensitivity of HER2 to blocking antibodies (3). In addition, adequate, although not optimal, assays for HER2 existed, and it was economically advantageous to initiate agent development in the smaller subpopulation of HER2-amplified breast cancer patients. Thus, for trastuzumab, there was a critical mass of information to support the development of the agent in a patient subpopulation identifiable with a biomarker assay.
As suggested in Box 2, the situation for EGFR inhibitors has been less straightforward. Conflicting data exist on the frequency of expression or overexpression of EGFR and its association with clinical outcomes (20,21). Laboratory data do not suggest that EGFR protein overexpression is tightly associated with the anticancer effects of EGFR inhibitors (35,36). For example, some cell lines with low levels of protein expression are sensitive to EGFR inhibition and others with high expression are resistant.
Although cut points and scoring systems based on EGFR protein expression have often been used for patient selection for EGFR inhibitor therapy in clinical trials, this practice is not well supported by clear and consistent clinical or laboratory data. The pivotal positive trial of erlotinib that led to its approval for treatment of patients with locally advanced or metastatic non–small cell lung cancer after failure of at least one prior chemotherapy regimen was conducted in unselected patients (50). Retrospective analyses of results from this study examining a variety of EGFR assays failed to identify subsets of patients that did or did not benefit from treatment with this EGFR inhibitor (23).
Once comprehensive information is gathered on the biology of a biomarker and the analytical performance and practical aspects of using a particular assay (eg, cost, accessibility, stability of analyte under conditions of collection, and use), a decision can be made whether to incorporate the biomarker into further clinical studies.
The intended use of a biomarker must be clearly defined, and the effectiveness and safety data should support the biomarker and its assay for this use. In general, the safety of a biomarker assay is related to the clinical impact of false-positive or false-negative test results (51). Limitations and uncertainties should be described.
There are several strategies for assessing biomarkers and assays for their ability to predict effectiveness of treatment and several choices of trial designs for demonstrating that the biomarker is informative in making treatment decisions. To most efficiently achieve success in meeting regulatory requirements for safety and effectiveness of the biomarker and drug combination, dialog with FDA early in the development of the biomarker and coordination of regulatory work across multiple FDA centers may be helpful (42).
Clinical trial strategies for biomarker and therapeutic development may be broadly divided into enrichment and all-comers designs. Enrichment studies (eg, studies performed solely in biomarker-positive patients or only in biomarker-negative patients for toxicity or resistance signals) may expedite collection of data to demonstrate safety and efficacy of the diagnostic–drug combination compared with existing treatments or placebo. This approach can facilitate rapid and cost-effective market entry. Enrichment studies may be preferred when the biomarker of interest is well understood biologically and analytically, and the supporting data suggest little benefit of the drug in biomarker-negative populations or suggest substantial drug toxicity or resistance in biomarker-positive patients. When efficacy is tested only in biomarker-positive patients, postmarketing studies of biomarker-negative patients to understand the full spectrum of drug utility are valuable but may be challenging. There will always be limitations on how far preclinical and early clinical trial results can be extrapolated to justify later steps in the drug and biomarker development paths.
Clinical trials in enriched populations must be designed with appropriate control groups to identify both the prognostic and predictive value of the biomarker to determine the potential clinical utility of the test for selecting a specific treatment (52). If the biomarker is prognostic for clinical outcomes independent of treatment, then results from single-arm studies may not have a historical reference for comparison. In some cases, imperfect understanding of the biomarker could lead to enrichment studies creating misleading information on the utility of the drug. If only biomarker-positive patients are studied, the sole performance characteristic of the biomarker that can be evaluated is whether or not the drug benefits biomarker-positive patients, and the behavior of the drug in biomarker-negative patients would be undefined. This leaves open the possibility that a new drug could be introduced into the market with restrictions. For example, although cetuximab was initially evaluated in enriched populations and approved for use only in patients with EGFR-positive tumors, it is becoming apparent that some patients with EGFR-negative tumors, as assessed by immunohistochemistry, may benefit (29). Thus, the biological background on the biomarker suggesting that treatment benefit will be limited to those positive for the biomarker must be very strong if one is to consider an enrichment strategy.
An “all-comers” study design does not restrict entry into the clinical trial on the basis of biomarker status. Although the accrual of patients is prospective, the biomarker evaluation may be conducted either prospectively or retrospectively. The biomarker can be measured at trial entry and used for stratified randomization of patients to the treatment groups, or patients can be randomly assigned to treatment groups without stratification by marker status and biomarker assays can be conducted later on the banked biospecimens. For the latter situation, it is even possible that the biomarker assays could be conducted many years later. If biomarker evaluation is not required for study entry and will be conducted later, it is important that the proportion of patients from whom specimens are collected for assay is high. Otherwise, there may be questions about whether the group of patients for whom specimens and assay results are available is representative of the full trial cohort. An advantage of requiring biomarker status for stratified randomization is that it ensures that biomarker results will be available for all patients who enter the trial, and it ensures balanced marker distribution between treatment groups. However, trials requiring up-front biomarker assay results can be logistically more difficult to conduct. For larger studies, major imbalances in biomarker distributions between treatment arms are unlikely to occur, and stratification becomes less important.
Investigators have explored the possibility of a two-step procedure in which drug effect is evaluated first in the total population without knowledge of biomarker results followed by evaluation of drug effect in a biomarker-defined subset, depending on the outcome in the full group (53,54). If a two-step procedure is to be used, it should be specified prospectively in the protocol. The plan should include a precise definition of the subset, specification of the rule that will determine whether testing terminates with the full group or continues to the subset, and an indication of the statistical operating characteristics of the procedure. If a positive drug effect is found when analyzing the full patient cohort, this would be interpreted as indicating benefit of the drug in the entire unselected population. A negative study outcome in the full group would be followed by another statistical test for treatment benefit according to biomarker status. If a statistically significant treatment benefit was observed in biomarker-positive patients, the results would indicate benefit of the drug in this biomarker-selected population. To account for the multiple tests and control the overall probability of reaching incorrect conclusions, a standard approach would be to distribute the typical testing type I error of .05 over the multiple tests. For example, an α-level of .04 could be allocated to the test of treatment effect in the overall patient cohort, and if biomarker exploration is subsequently needed, the remainder of the α-level (.01) can be applied to the test of treatment effect in the biomarker-selected subgroup. This means that the size of the study will have to be larger than it would have been if an enrichment design had been used up front with the full α-level of .05 allocated to the treatment comparison in the biomarker-selected subgroup. However, the two-step design offers valuable flexibility if there is some uncertainty about how to best define the biomarker-positive subgroup or if there is a possibility that the treatment has broad effects resulting in some benefit to all patients, regardless of biomarker status. A potential danger noted in this two-step approach is that a drug benefit observed in the entire population might be a consequence of the strength of response in the biomarker-positive subset rather than truly being a benefit in the entire population. Nonetheless, an advantage of this design is that data will have been collected on biomarker-negative patients, and one could perform exploratory analyses to examine for possible treatment effects in the biomarker-negative subgroup.
To date, no studies using this two-step approach have been reported. Most reports of biomarker and therapeutic studies in the literature and most FDA-approved drug and biomarker assay combinations have been based on enrichment designs. These have been pragmatic and facile but have not occurred without surprises. For example, as noted above, cetuximab appears to benefit both EGFR-positive and EGFR-negative patients when EGFR is assessed by immunohistochemistry (29). Given the specificity of the antibody for target, it is possible that the apparent benefit to the immunohistochemically defined EGFR-negative group might reflect the limitations of the assay or biomarker in defining the patients most likely to benefit, although other biological explanations continue to be explored.
Other approaches using adaptive biomarker enrichment have been suggested (55,56). Adaptive clinical trial designs permit midtrial modifications, which are based on incorporating new interim information either from inside or outside the trial. Adaptive designs may be Bayesian (using previous information gathered outside of the trial updated with information collected in the trial), but do not have to be. The flexibility of adaptive designs permits changes during the conduct of the trial in features such as sample size, treatment allocation ratios, and the number of interim analyses, as well as the selection of treatments and doses. For example, using adaptive designs, patients enrolled in a drug intervention clinical trial could be classified initially on the basis of the presence or absence of selected sets of biomarkers. As new drug efficacy and biomarker data become available, those data could be used to change the probability that newly accrued patients receive certain treatments depending on their biomarker profiles. Adaptive designs, like any other design, must adhere to regulatory requirements for maintaining study integrity and patient safety. They need to be designed with specific operating characteristics in mind. For most study designs, this requires specification of type I and type II error rates; for Bayesian designs, more extensive evaluations, often through a series of computer simulation studies, are required to fully evaluate their operating characteristics.
Pharmaceutical and device manufacturers working to market codeveloped products are urged to bring all the needed expertise to the development process, including FDA regulatory staff and third-party payers, to make the appropriate scientific and business decisions. These experts should be convened near the beginning of the process to ensure that challenges and obstacles are identified and addressed early in the product life cycle with minimum surprises and to consider novel business and marketing arrangements to share both risks and rewards for development of these products.
Consideration of the integration of biomarker development into the drug development process should occur as early as possible because of the implications for biospecimen and data collection. Early implementation of standardized procedures for biospecimen and clinical data collection and efforts devoted to characterizing biomarker targets can maximize the utility of the specimens and provide information on the targets useful in addressing future questions regarding variations in biomarker expression or modulation. Decisions on how to study a biomarker in a particular clinical trial depend on the context of the study. Key considerations include factors such as the need to collect safety data, availability of reliable assays for evaluating the biomarker and drug activity, and relationship of results to clinical benefit (see the “Preclinical and Nonclinical Development” and “Clinical Development” sections of Figure 1).
A long lead time may be needed to develop well-characterized biomarker assays. The goal is to be prepared for phase III by ensuring that appropriate biomarker-based selection strategies are in place. For example, better-developed selection strategies might have more efficiently identified the positive effects of EGFR inhibitors in early phase III trials in patients with lung (23,50) and colorectal carcinomas (31,33,34).
In some cases, practical business considerations may outweigh benefits of a science-based strategy because the incentives for producing biomarker assays may be substantially misaligned for the diagnostics and pharmaceutical companies during different stages of development. The long time to market is a financial risk and critical issue for both the pharmaceutical and diagnostics industries, but it is a higher risk for the diagnostics industry during early biomarker and assay development. At this stage, not much is known about the drug or the biomarker, and the chosen biomarker might not ultimately be suitable or the drug might not prove sufficiently effective to warrant full development. Well-annotated biospecimens collected using standard protocols and appropriate consents are critical to allow biomarker assay development to take place when there is sufficient knowledge available about the drug and its relationship to the biomarker. The development of a shared repository of specimens from patients receiving the treatment of interest could help to address this need.
The lack of a rigorously evaluated biomarker at the end of phase II and the large sample size required to validate a biomarker assay increase the risk to the pharmaceutical company in later development stages. Secondary approvals (cross-labeling studies)—for example, taking a drug and biomarker combination approved in a narrowly defined population into a second population or evaluating a second drug from the same chemical or mechanistic class in combination with a biomarker—are less risky for both drug and biomarker assay development.
The perceived market worth for drugs and biomarkers may also be mismatched. The drug developed with the biomarker is a value generator, whereas the diagnostic may be perceived as a commodity (sometimes offered at no cost by the pharmaceutical company). Higher reimbursement rates for diagnostic assays used in patient management would likely provide needed incentives to the diagnostics industry.
The FDA usually recommends validating biomarkers prospectively to ensure study of appropriate populations, to minimize sampling bias, and to maximize the likelihood of having adequate power to evaluate the hypothesis being tested. This ideal study approach is most appealing if it can be performed in a time- and cost-efficient manner. If timelines for studies are long, and if study size requirements are large, companies may be reluctant to involve themselves in the uncertain process of companion product development, particularly in a competitive market with rapid changes in drugs, diagnostics, and clinical decision making. If companies were able to access well-constructed biological specimen collections and clinical data in early decision making, more robust biomarker-drug candidates and more focused phase III clinical studies might result.
If companies were able to develop well-planned incremental evaluations of drug-companion diagnostic pairs across phase I, II, and III studies that provide for better product decision making or in terms of trial design and size, the cost–benefit of these incremental evaluations could potentially revolutionize the development pipeline for new drugs and diagnostics. Unfortunately, it is too early to fully understand the financial costs and risks of this approach.
Well-planned incremental evaluations of drug-companion diagnostic pairs across phase I, II, and III studies that provide for better decision making in terms of trial design would ideally increase the efficiency of the drug development process. However, limits to biological understanding and resources can make it difficult to identify or carry out an optimal sequence of evaluations in a prospective fashion. Retrospective biomarker analyses using specimens previously collected within treatment trials may also be helpful, either in the discovery process, in fine-tuning developmental plans, or in some cases in supporting submissions to the regulatory agencies. The potential biases, effects of multiplicity, missing data, and other deficiencies of such analyses must be addressed. In addition, the statistical power for detecting biomarker effects in such retrospective studies will often be low. However, the results might identify promising new or improved biomarker assays that could be introduced into phase III studies. The FDA continues to seek input from the research and development community on these issues, but there is still no clear strategy for the approval of a predictive diagnostic with therapeutic agent using the retrospective approach (59).
There are situations in which biomarker assay approvals may be based on retrospective analysis alone. For example, a safety biomarker assay might be approved based on retrospective analysis of biomarker-positive and biomarker-negative specimens from patients who have been treated with approved drugs and in whom the safety outcomes are known. Early consultation with FDA scientists from both drug and device offices is encouraged when considering these strategies.
The workshop participants addressed scientific, regulatory, and business-related issues that affect the success or lack thereof in developing biomarkers for clinical decision making. Discussions of lessons learned in the development processes for trastuzumab and the EGFR inhibitors made it clear that the more known about the biology of the biomarker and its relationship to the therapeutic agent, the easier it is to determine the clinical trial design most appropriate for evaluating the utility of the biomarker assay for predicting the clinical effects of the agent. Major challenges for biomarker assay and therapeutic agent codevelopment relate to the costs of standardizing and evaluating the utility of an assay when neither the clinical activity of the agent nor the relationship of the biomarker to the mechanism(s) of action of the agent being developed is clear.
The following recommendations were made to address the challenges identified:
Finally, the misalignment of incentives for production of biomarker assays vs therapeutic agents continues to hamper codevelopment of agents and biomarkers. The resolution of this problem is not straightforward but some suggestions were made. Higher reimbursement for diagnostic tests, particularly those that are required for choice of therapeutic, would probably provide additional incentive to the assay developer. The many challenges identified during the discussions at the workshop and catalogued in this commentary and in Figure 1 will take time and community effort to address. We focused on discussion of alternative trial designs, the need for early comprehensive planning, and consideration of factors affecting business decisions for assay and drug developers. We hope that this will serve as the beginning of a productive dialog in the community.
National Cancer Institute. C.S. also received National Cancer Institute support for scientific and editorial assistance.
This article was developed from the presentations and discussions at the workshop entitled, NCI/FDA/Industry Workshop on Development of Markers for Clinical Decision-Making, held at the DoubleTree Hotel Bethesda, Bethesda, MD, on October 29–30, 2007.
During the planning and implementation of the workshop and preparation of the manuscript, Dr Taube was Associate Division Director of the Division of Cancer Treatment and Diagnostics, Director of the Cancer Diagnosis Program, National Cancer Institute; Dr Dancey was in the Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute; and Dr Gutman was Director of the Office of In Vitro Diagnostic Device Safety and Evaluation at the US Food and Drug Administration.
The authors wish to thank the members of the Workshop Planning Committee (in addition to the authors) for their valuable contributions to the content and the agenda: Deborah Jaffe (NCI, Office of the Director), Francis Kalush (FDA, Center for Devices and Radiological Health, Office of the Center Director), Patricia Keegan (FDA, Center for Drug Evaluation and Review, Office of New Drugs, Office of Oncology Drug Products, Division of Biologic Oncology Products), Soon Myung Paik (NSABP), Raj Puri (FDA, Center for Biologics Evaluation and Research, Office of Cellular Tissue and Gene Therapies, Division of Cellular and Gene Therapies), Richard Schilsky (University of Chicago, Division of Biological Sciences, Department of Hematology/Oncology). The authors also thank the workshop participants for their thoughtful practical presentations and discussions and the several participants who reviewed and provided commentary on the manuscript.
Dr Clark holds stock in OSI Pharmaceuticals, Inc, the maker of erlotinib, an epidermal growth factor receptor inhibitor.