4
Case Studies

INTRODUCTION

The committee undertook case studies to illustrate the use of the recommended biomarker evaluation framework. Five case studies are presented in this chapter, each highlighting one or more aspects of the framework. The first case study is tumor volume in cancer, which highlights the need for rigorous analytical validation. The second case study is C-reactive protein (CRP), which highlights that data are crucial to ascertaining whether a biomarker can be more than a prognostic factor. The third case study is troponin, which highlights the utility of biomarkers for which sufficient data for use of the biomarker as a surrogate endpoint do not exist. The fourth case study is on low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, which highlights how even biomarkers frequently used as surrogate endpoints need to be carefully evaluated prior to each use. Finally, the fifth case study is on beta-carotene, which contains lessons for each step of the qualification framework. In particular, beta-carotene highlights the importance of biomarkers in nutrition-related settings.

Table 4-1 gives a brief summary of the results of the case studies. As can be seen, biomarkers are useful for a variety of purposes. In order for a biomarker to be used as a surrogate endpoint, however, a strong understanding of the causal pathways of the disease process and of an intervention’s intended and unintended effects are usually needed. Achieving such understanding is a daunting challenge, and the committee acknowledges that it is infrequent that this understanding is achieved. The case



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4 Case Studies INTRODuCTION The committee undertook case studies to illustrate the use of the recommended biomarker evaluation framework. Five case studies are presented in this chapter, each highlighting one or more aspects of the framework. The first case study is tumor volume in cancer, which high - lights the need for rigorous analytical validation. The second case study is C-reactive protein (CRP), which highlights that data are crucial to ascertaining whether a biomarker can be more than a prognostic factor. The third case study is troponin, which highlights the utility of biomark - ers for which sufficient data for use of the biomarker as a surrogate end - point do not exist. The fourth case study is on low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, which highlights how even biomarkers frequently used as surrogate endpoints need to be carefully evaluated prior to each use. Finally, the fifth case study is on beta-carotene, which contains lessons for each step of the qualification framework. In particular, beta-carotene highlights the importance of bio - markers in nutrition-related settings. Table 4-1 gives a brief summary of the results of the case studies. As can be seen, biomarkers are useful for a variety of purposes. In order for a biomarker to be used as a surrogate endpoint, however, a strong under- standing of the causal pathways of the disease process and of an inter- vention’s intended and unintended effects are usually needed. Achieving such understanding is a daunting challenge, and the committee acknowl- edges that it is infrequent that this understanding is achieved. The case 

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 TABLE 4-1 Brief Summary of the Results of the Case Studies Utilization: Utilization: Surrogate Biomarker Analytical Validity Qualification Possible Uses Endpoint Use Tumor Size No Numerous trials exist Data needed to Current data do not support with inconsistent findings improve analytical use as a surrogate endpoint on tumor shrinkage and validity of one or clinical benefit more test CRP High-sensitivity tests RCTs and observational Risk prediction; Current data does not support available studies available; limited potential expansion use as a surrogate endpoint data on CRP’s biological of statin treatment to role in disease progression specific populations Troponin Validated tests are Extensive data for acute Safety uses Current data does not support available for many troponin, limited data on use as a surrogate endpoint uses. More sensitive chronic troponin; data tests are also being collection is ongoing developed

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LDL Validated tests are Extensive data on Risk prediction Data supports use of LDL as available for many LDL, both RCTs and a surrogate endpoint for some uses. More accurate observational studies; cardiovascular outcomes for tests are also being repeated use of LDL as a statin drug interventions, developed surrogate endpoint but not for all cardiovascular outcomes or other cardiovascular interventions, foods, or supplements HDL Validated tests are Limited data; the Risk prediction Current data does not support available for many biological role of HDL not use as a surrogate endpoint uses. More accurate fully understood tests are also being developed. Beta-carotene Validated measures Extensive RCTs and Uses include a Current data does not support of blood serum beta- observational trials are biomarker of use as a surrogate endpoint carotene levels are available intake of fruits and available for many vegetables and an uses effective intervention to address vitamin A deficiency NOTE: CRP = C-reactive protein; HDL = high-density lipoprotein cholesterol; LDL = low-density lipoprotein cholesterol; RCT = randomized controlled trial. 

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 EVALUATION OF BIOMARKERS AND SURROGATE ENDPOINTS studies chosen are generally ones for which a great deal of data already exist, and in a number of the case studies, the biomarkers have been dis- cussed for several decades. However, the case studies illustrate that even in these situations the lack of sufficient data for surrogate endpoint status for the biomarkers is evident. Readers of these case studies may wonder what lessons can be gained toward prospective evaluation of biomarkers. For a newly discovered biomarker, it is likely that very little data will be available for review in the analytical validation and qualification steps of the evaluation framework. In these situations, the lack of data should be noted. During the utilization step of the framework, then, needs for further data are identified. After these data are collected, the evaluation process can be revisited until the data available support the use for which the biomarker is proposed. It should be emphasized that these case studies are illustrative. Com - plete, rigorous, systematic reviews of the evidence base were not con- ducted by the committee. Each case study first introduces general infor- mation about the biomarker itself. Analytical validation, qualification, and utilization analyses are then discussed. Finally, a summary of the lessons learned through each case is given. Biomarker Discovery and Development Although many candidate biomarkers have been reported, few have been sufficiently evaluated to justify their use in developing drugs or making treatment decisions. This slow pace has been attributed to the challenges posed by the discovery and development processes. The dis- covery process is dependent on the technologies available to interrogate complex biochemistry of health and disease, and identifying differences that can be detected consistently in diverse populations (IOM, 2007). Advances in the fields of genomics and proteomics have made it easier to interrogate hundreds or even thousands of potential biomarkers at once, leading to large datasets requiring sophisticated analyses to identify individual biomarkers of interest, or patterns of markers. A recent IOM committee determined that realizing the full potential of biomarker-based tools is dependent on progress in biomarker discovery (IOM, 2007). How- ever, technologies to identify and quantify proteins and metabolites have lagged behind methods to assess nucleic acids because of the diverse biochemical characteristics of the protein and metabolic products of the human genome. Beyond technology platforms, the committee also dis - cussed the need to develop new software packages, algorithms, and statis- tical and computational models capable of integrating data from multiple inputs, such as proteomic or genomic data from the same samples. Drug and diagnostic industries, along with academic researchers,

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 CASE STUDIES are involved in biomarker discovery activities. In drug development, biomarkers may be used in target validation, or in demonstrating that a potential drug target plays a key role in the disease process; early com- pound screening, identifying compounds with the most promise for safety and efficacy; pharmacodynamic assays to assess drug activity and select schedule/dose; patient selection; and surrogacy (IOM, 2007). Because therapeutics are generally only effective in a subset of patients, drug and diagnostic industries may develop (or in some cases, codevelop therapeu- tics and diagnostics) assays to assess which subset of patients would most benefit from a therapeutic. However, once a drug is approved, there is less financial incentive to develop biomarkers to guide treatment decisions because it would likely restrict the number of patients taking the drug. TuMOR SIzE AS BIOMARkER FOR CANCER CLINICAL ENDPOINTS Biomarkers play several roles in patient care in the context of can- cer, as discussed in the Institute of Medicine’s (IOM’s) Cancer Biomarkers report (IOM, 2007). In patients who do not have a cancer diagnosis, bio - markers can be used for risk stratification, prevention of carcinogenesis in precancerous tissues, and screening for early-stage tumors. Biomark- ers aid in making a diagnosis of cancer, classifying a particular patient’s disease, and determining disease prognosis. In the context of a particular treatment, biomarkers are used for treatment stratification (treatment decisions based on patient characteristics), risk management (regard - ing adverse effects of a therapy), monitoring effectiveness or side effects of a therapy, and post-treatment disease surveillance. One metric used as a biomarker in cancer care, in the absence of or in conjunction with molecular markers, is tumor size measured with anatomic imaging, most meaningfully expressed in terms of tumor volume (Lin et al., 2008; Van Beers and Vilgrain, 2008). Tumor response rates, defined by a change in tumor bulk, were com- monly used for making decisions regarding approval of anticancer drugs in the 1970s, but in the mid-1980s, the Food and Drug Administration (FDA) added a requirement that a clinical survival benefit or quality- of-life benefit should be demonstrated. Because long trials are usually needed to demonstrate significant survival benefit and the demand for new anticancer drugs is always urgent, in 1996 the FDA extended Accel- erated Approval under subpart H of the New Drug Application for drugs that are effective against serious or life-threatening diseases as measured by surrogate endpoints to anticancer drugs (HHS, 1996).1 This included 1 See http://www.fda.gov/ForConsumers/ByAudience/ForPatientAdvocates/Speeding AccesstoImportantNewTherapies/ucm128291.htm#accelerated.

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 EVALUATION OF BIOMARKERS AND SURROGATE ENDPOINTS surrogate endpoints such as tumor size as it is represented in compos - ite endpoints such as progression-free survival and time to progression. Accelerated approval is granted with the understanding that confirmatory evidence gathered in postmarket trials will lead to traditional approval of the drug, and a lack of such evidence may result in its removal from the market by the FDA. Lathia et al. (2009) recently noted that “between 1992 and 2004, 22 applications for 18 anticancer drug or biologic agents were granted accel- erated approval in the United States. These approvals were generally granted on the basis of end points such as overall response rate, time to progression, and disease-free survival. Of the 22 applications that received accelerated approval before January 2004, 6 were converted to regular approvals (i.e., demonstrated an effect on survival/outcome) whereas the remaining 16 were not converted to regular approvals; all these agents remain on the market.” While the outcome measured in phase III cancer trials is often over- all survival, surrogate endpoints play a large role in evaluation of new therapeutic agents in phase II clinical trials (Ratain et al., 1993; Sargent et al., 2009; Scher et al., 2008; Seibert et al., 2007). A primary endpoint com - monly reported in phase II trials for cancer therapeutics is response rate, defined in its most primitive form as tumor shrinkage. Unfortunately, phase II results based on tumor shrinkage are not always predictive of outcomes in phase III trials. In the case of agents with low response rates in phase II that go on to show an increase in progression-free survival or overall survival in phase III trials, speculation has been that this result may be due to tumor stabilization rather than tumor shrinkage by these therapeutic agents. This would suggest that although tumor shrinkage is an important variable to monitor, the way response rates are measured in phase II trials is failing to capture all clinically meaningful changes that should be considered in the drug evaluation process (Dhani et al., 2009; Llovet et al., 2008; Stewart, 2008; Weber, 2009). Tumor size is an inconsistently defined biomarker often used for determining efficacy of cancer therapeutics (Marcus et al., 2009). Vali - dation, qualification, and utilization analyses are complicated by use of multiple imaging platforms (hardware), nonstandardized acquisition and analysis protocols (software), dissimilar contrast agents and targeted imaging agents across trials and institutions, and inconsistent methods for measuring, calculating, and reporting tumor size. Tumor Size: Analytical Validation Tumor size measurements reported include tumor diameter, volume, and mass, as measured using anatomic imaging modalities such as mag-

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 CASE STUDIES netic resonance imaging (MRI), computed tomography (CT), ultrasound (US), and mammography (Strassburg et al., 2008). Validating use of tumor size as a biomarker is difficult because it is measured and defined in different ways depending on the imaging modality, the type of tumor, and the institution (Tran et al., 2004). Tumor size is sometimes expressed as diameter of the tumor in one or two views. Such values can also be used to approximate tumor volume using a spherical, cuboidal, prolate spheroid, or oblate spheroid model. However, many solid tumors are of irregular shape, and their volume can be best approximated by measur- ing tumor diameter in three (if possible) orthogonal views and using an elliptoid model to estimate tumor volume. A growing body of literature is advocating for the use of elliptoid modeling of tumor volume as the most meaningful representation of tumor size in terms of its accurate reflection of changes in tumor bulk confirmed by other volumetric mea - surements, such as water displacement and its correlation to clinical end - points. However, some widely used standardized response criteria, such as the Response Evaluation Criteria in Solid Tumors (RECIST), employ a sum of the longest dimension recorded of each tumor when attempting to quantify disease burden (Eisenhauer et al., 2009; Gehan and Tefft, 2000). A newer and more accurate approach to estimating tumor volume involves using two-dimensional tumor contours on sequential imaging slices to calculate volume in three dimensions. This technique can be used with MRI, CT, and positron emission tomography–computed tomography (PET–CT) images. Tumors are outlined on each slice manually or with automatic model-based segmentation and compiled to estimate gross tumor volume. This technique, particularly with implementation of auto - matic model-based segmentation to reduce interobserver discordance, provides a platform for accurately measuring tumor volume in a way that is reproducible and can be standardized relatively easily (Galanis et al., 2006). Tumor mass can also be approximated using an estimation of tumor volume. This may be a useful metric in a laboratory setting where such quantities can be confirmed using ex vivo measures, or when tumor size is measured with anatomic imaging and tumor density is measured with functional imaging, such as PET, and the two measures are combined to estimate tumor mass. In cases, however, where mass is extrapolated from volume using an estimate of density, this calculation may introduce another source of error. To further complicate measurement of tumor size, tumor borders are often poorly demarcated in highly invasive cancers, resulting in ambigu - ity about diameter length and interobserver discordance. For treatment evaluation the most emphasis should be put on reproducibility and accu- racy of serial measurements; in this case reproducibility includes stan -

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 EVALUATION OF BIOMARKERS AND SURROGATE ENDPOINTS dardizing data collection across institutions and trials so that meaningful comparisons can be made between populations of patients. The vari - ability in imaging platforms and techniques makes it unlikely that step one of the qualification framework is fulfilled given the current lack of standardization in the field. Adherence to American College of Radiology Appropriateness Criteria regarding appropriate modalities for imaging various types of cancer at different points along disease progression and treatment is one effort that could decrease discrepancies in data collection across trials (Böhm-Vélez et al., 2000; Fishman et al., 2000; Javitt, 2007). Because standardizing the hardware used at individual institutions may be difficult, it may be more feasible to standardize imaging acquisition and analysis protocols within a multicenter trial, and certainly within institutions (Grossi et al., 2004). Finally, some have explored the use of Bayesian analysis techniques to improve the accuracy of conclusions drawn from tumor images and other clinical data (Vokurka et al., 2002; Yang et al., 2003). Tumor Size: Qualification Because the growth of local or metastatic cancer cells can lead to the death of the host, it is biologically plausible that shrinkage of the exist - ing tumor or prevention of further growth could serve as indications of biological and clinical benefit. However, many hypotheses exist regard- ing how cancer causes death in an organism (Lichtenstein, 2005). Some cancers cause death because cancer cells, much like parasites, compete with native tissue for nutrients, so that the organism essentially starves. Tumors frequently interfere with physiologic processes through mass effect, such as compression of vessels and other luminal structures or intracranial compression of brain tissue, or through invasion of normal tissue, which can result in clinical disease and death of the organism. Paraneoplastic syndromes and immune response to neoplastic cells also play a role in the mortality and morbidity of many types of cancer. Given the contributions of these and other factors, the biological plausibility of using tumor size as a surrogate endpoint for evaluating disease progression and therapeutic efficacy in cancer is not entirely obvi- ous. Smaller tumors tend to grow faster, so major shrinkage of tumor mass does not necessarily translate to prolonged survival (Citron, 2004; Hudis, 2005). Data have shown that tumor size may not correlate with long-term clinical outcome in some cancers, such as in locally advanced breast cancer, where lack of nodal involvement is predictive of disease- free survival and overall survival rates, but tumor size does not affect these rates (Beenken et al., 2003; Berruti et al., 2008). Additionally, real clinical benefit is not always accompanied by measurable reduction in

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 CASE STUDIES tumor size, as is the case with cytostatic drugs or agents that reduce the density of cells within a tumor but leave the tumor volume unchanged (Young et al., 1999). Even in the case of treatment with conventional cyto - toxic drugs, initial tumor shrinkage is nearly always followed by tumor cell repopulation (Kerbel, 2006). In the case of many biomarkers or surrogate endpoints, a causal role for the biomarker in the disease pathway is established. LDL, for example, is hypothesized to have a causal role in the atherosclerotic disease process, and while this has not been conclusively proven, LDL is measured as a biomarker of atherosclerotic cardiovascular disease and targeted pharma- ceutically. Clearly tumor size is a different brand of surrogate endpoint from most molecular biomarkers in that increasing tumor size is viewed as a result of disease progression, not a causative factor. The exception to this mode of thinking about tumor size is tumors that secrete biologically active factors that promote proliferation via autocrine or paracrine signal- ing; in this case tumor growth may beget tumor growth while adequate vascular supply exists to support it (Imamoto et al., 1991). In many studies tumor size is used as an indicator of response rate and for determining time to progression and disease-free survival (Ohara et al., 2002; Ollivier et al., 2007; Pugnale et al., 2003). While the link between tumor size and clinical benefit is less firm than what is tradi - tionally required for associating a biomarker with a particular clinical endpoint (Therasse et al., 2006), use of tumor size as a biomarker in cancer has been rationalized by the serious nature of the disease and a lack of more solidly linked prognostic indicators. It is important to emphasize, however, that this rationalization is not universally accepted (Fleming et al., 2009). As will be discussed in Chapter 5, the in situations where it is deemed reasonable to permit marketing of drugs before clinical out- come evidence is available, it is important that this data be collected and analyzed through postmarket studies. In cancers where tumors shrink predictably in response to efficacious cytotoxic therapy, serial tumor-size measurements can provide insight into whether a new therapeutic agent or technique warrants further study or whether a particular patient or patient population is likely to benefit from that therapy (Henson et al., 2005; Husband et al., 2004; Kamel and Bluemke, 2002; Karrison et al., 2007). For example, in the case of locally advanced breast carcinoma treated with cytotoxic agents, tumor volume calculated using measure- ments taken with US, mammography, or MRI have been demonstrated to be prognostic and can also aid in selecting an effective treatment regimen (Berruti et al., 2005; Buijs et al., 2007; Cheung et al., 2003; Dose Schwarz et al., 2005; Eng-Wong et al., 2008; Hylton, 2006; Noterdaeme et al., 2009). Similarly, tumor volume is a critical measurement for monitoring and

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0 EVALUATION OF BIOMARKERS AND SURROGATE ENDPOINTS directing local control of non-small-cell lung cancer (NSCLC) with radia - tion therapy. The response rate often reported in phase II trials is based on an incor- rect premise that tumor size is analogous or proportional to the number of tumor cells, as described in RECIST (Desar et al., 2009; Park et al., 2003; Tuma, 2006). The Choi Criteria, which were originally developed to assess tumor progression in gastrointestinal stromal tumors (GISTs), incorporate tumor size and density (measured with contrast-enhanced CT) into a met - ric of tumor progression. The Choi Criteria are a more sensitive measure of responsiveness to a particular therapy and have been demonstrated to more accurately predict overall survival in GIST than reduction in tumor size (Benjamin et al., 2007; Choi, 2005; Choi et al., 2007; Hohenberger and Wardelmann, 2006; Sevinc and Turhal, 2008; Stacchiotti et al., 2009). The Southwest Oncology Group developed new criteria for evalua- tion of response in NSCLC that define response to therapy as anything other than progression. Patients who demonstrate a decrease in tumor size or who have stable disease are considered nonprogressive, and in NSCLC this measure of “disease control rate” is more predictive of over- all survival than tumor shrinkage. The North Central Cancer Treatment Group and National Surgical Adjuvant Breast and Bowel Project (NSABP) have similarly used nonprogression at a specific time point as a measure of response to therapy that is more predictive of overall survival than tumor shrinkage (Tuma, 2006). Tumor Size: utilization Cancer is a complex collection of diseases, which makes it difficult to make generalizations about how a particular surrogate endpoint should be used in trials for all types of cancer. One caveat to using tumor shrink- age as a surrogate endpoint is that it may not represent clinical benefit in all situations. In the case of GIST, progression usually occurs within the original tumor boundaries. Treatment of these tumors with Gleevec (imatinib) results in decreased cell density within the tumor and pro- longed patient survival, but rarely shrinks measurable diameters of exist - ing tumors to a significant degree. In this example Gleevec is thought to have both cytotoxic and cytostatic effects, and GIST cells are replaced by myxoid degeneration following cell death, both reasons why tumor size as a surrogate endpoint correlates poorly with clinical endpoints (Benjamin et al., 2007; Choi, 2005). Factors to consider for contextual analysis of tumor size as a sur- rogate endpoint include the following: (1) when in a patient’s treatment this variable is considered, and (2) for what purposes (Cademartiri et al., 2008; Christensen, 2008). In some cancers, tumor size is a useful diagnostic

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 CASE STUDIES and prognostic biomarker. For some cancers, though, imaging tumor size does not play a significant role in prognosis at the time of diagnosis. In the context of locally advanced breast cancer, for example, nodal involve - ment has a greater role in prognostication than tumor size (Beenken et al., 2003). NSABP has established criteria using pathologic complete response, which is defined as no evidence of malignancy on histologic analysis, instead of tumor shrinkage measured with anatomic imaging, to predict long-term prognosis over the course of disease. Obviously pathologic complete response cannot be evaluated for all cancers in all sites at all points along the history of the disease, which is why imaging has such an enormous role in monitoring response to therapy. In some types of breast cancer, for example, monitoring tumor size with imaging is tremendously useful for gauging efficacy of a particular therapy (Berruti et al., 2005, 2008; Buijs et al., 2007; Cheung et al., 2003; Eng-Wong et al., 2008; Hylton, 2006; Nicoletto et al., 2008). Tumor Size: Lessons Learned Although tumor shrinkage does not positively correlate with clinical benefit in all situations, the patchy qualification of tumor shrinkage as a surrogate endpoint for cancer trials has been tolerated by regulatory agen- cies for several reasons. Cancer as a family of diseases continues to result in high mortality and morbidity. Truly novel and efficacious therapeutics are not emerging as rapidly as society demands. Conditional approv- als based on tumor size are not always followed by full approvals, but when measured correctly and used in the appropriate context, perhaps in conjunction with other variables like tumor density, tumor size is a useful parameter for detecting clinical benefit (Jensen et al., 2008; Monteil et al., 2009; Specht et al., 2007). Even so, use of tumor size as a surrogate endpoint for regulatory approvals is decreasing and is being replaced by other, better qualified surrogate endpoints. These surrogates, including progression-free survival, also require postmarket studies to connect the interventions to beneficial changes in clinical outcomes. Tumor size as a surrogate endpoint highlights the many analytical validation issues of imaging biomarkers. Validation standards for imag - ing biomarkers should vary depending on their intended use as surrogate endpoints; criteria should be more stringent for the purposes of drug reg- istration than for earlier stages of drug development. Emerging molecular and functional imaging technologies will likely provide tools to address some of the deficiencies of anatomic imaging in cancer discussed here (Funaioli et al., 2007; Goldstein et al., 2005; Pantaleo et al., 2008a, 2008b; Schepkin et al., 2006; Ullrich et al., 2008; Wahl et al., 2009). Combined with functional imaging technologies like PET and targeted molecular

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