1
Introduction

A long-standing goal of cancer research has been to identify the molecular mechanisms by which cancers develop, and then to detect those molecular markers of cancers early and to target those mechanisms with drugs specifically designed to attack them. A few remarkable strides have been made toward achieving that goal of “personalized medicine” in some cancers (The Royal Society, 2005). For example, the discovery of a chromosomal translocation in chronic myelogenous leukemia led to the development of a drug (imatinib) that targets the enzyme produced as a result of that translocation (reviewed by Druker, 2004; Baselga, 2006). In breast cancer, expression of the estrogen receptor serves as a biomarker for prognosis and identifies women who are likely to benefit from antiestrogen therapy (reviewed by Duffy, 2005; Ariazi et al., 2006). Similarly, the over-expression of HER2 (a growth factor receptor) in breast cancer serves as a biomarker for prognosis and for treatment with trastuzumab, a drug that targets that receptor’s function (reviewed by Yeon and Pegram, 2005; Duffy, 2005; Baselga, 2006).

However, cancer is a collection of more than 100 different diseases, and, for most cancers, the molecular characteristics have not been fully classified and there are no known or validated markers for early detection, treatment planning, or targeted therapy. The diagnosis of cancers is still based largely on morphological examination of tumor biopsy specimens, as it has been for decades, but this approach has significant limitations for predicting a given tumor’s potential for progression and response to treatment.



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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment 1 Introduction A long-standing goal of cancer research has been to identify the molecular mechanisms by which cancers develop, and then to detect those molecular markers of cancers early and to target those mechanisms with drugs specifically designed to attack them. A few remarkable strides have been made toward achieving that goal of “personalized medicine” in some cancers (The Royal Society, 2005). For example, the discovery of a chromosomal translocation in chronic myelogenous leukemia led to the development of a drug (imatinib) that targets the enzyme produced as a result of that translocation (reviewed by Druker, 2004; Baselga, 2006). In breast cancer, expression of the estrogen receptor serves as a biomarker for prognosis and identifies women who are likely to benefit from antiestrogen therapy (reviewed by Duffy, 2005; Ariazi et al., 2006). Similarly, the over-expression of HER2 (a growth factor receptor) in breast cancer serves as a biomarker for prognosis and for treatment with trastuzumab, a drug that targets that receptor’s function (reviewed by Yeon and Pegram, 2005; Duffy, 2005; Baselga, 2006). However, cancer is a collection of more than 100 different diseases, and, for most cancers, the molecular characteristics have not been fully classified and there are no known or validated markers for early detection, treatment planning, or targeted therapy. The diagnosis of cancers is still based largely on morphological examination of tumor biopsy specimens, as it has been for decades, but this approach has significant limitations for predicting a given tumor’s potential for progression and response to treatment.

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment Recharacterization of cancers and other diseases in pathophysiological terms is key to the future of medicine. Considerable progress has been made in the molecular classification of some cancers, such as hematological malignancies (Box 1-1). More recently, the systematic analysis of genomic alterations in a small set of breast and prostate cancers revealed that individual tumors accumulate an average of approximately 90 mutant genes but that only a subset of these contribute to the neoplastic process. The authors identified 189 genes (average of 11 per tumor) that were mutated at BOX 1-1 Biomarkers of Hematologic Cancers The diagnosis of hematologic cancers presents an enormous challenge. The numerous stages of hematopoietic differentiation give rise to many biologically and clinically distinct cancers, most often via acquired genetic alterations. Knowledge of the biology underlying hematological malignancies has greatly increased in recent decades, leading to a much more sophisticated classification system that incorporates not only the traditional morphologic characteristics, but also immunophenotypic, genetic, and clinical features. However, even with this added information, considerable heterogeneity still exists within identified subtypes, with different clinical presentations and outcomes. Researchers have long sought a classification system based on molecular pathogenesis, and DNA microarrays have been recently used to survey the expression of thousands of genes in parallel. Studies have identified novel disease subtypes and have also uncovered relationships between diseases previously considered to be unrelated. The results show great promise for refining diagnosis and prognosis, predicting response to treatment, and identifying potential targets for novel therapeutic interventions, although much work remains to be done before such tests can be routinely used to aid clinical decisions. For example, further clinical validation in larger cohorts and independent studies are needed, as well as test platform standardization and analytical validation. It is also not yet clear whether whole gene expression patterns are required, or whether a small set of genes will be sufficient to predict prognosis. SOURCES: Reviewed by Staudt, 2003; Levene et al., 2003; Bullinger, 2005; Bullinger et al., 2005.

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment significant frequency, the vast majority of which were not previously known to be altered in tumors and are predicted to affect a wide range of cellular functions, including transcription, adhesion, and invasion, thus providing potential new targets for diagnosis and therapy, as well as new directions for basic research in tumor biology (Sjoblom et al., 2006). However, much work remains to be done. Developing drugs and determining appropriate therapy for most diseases is still largely empirical and lacking well-defined molecular targets, and most medicines have been shown to be effective in less than 60 percent of patients in the disease populations that they address (Spear et al., 2001; Austin and Babbiss, 2006). In oncology, that figure is much lower, with an average drug response rate of less than 25 percent, due to the tremendous heterogeneity among patients with a given type of cancer. In addition, most cancer drugs are toxic agents that affect cell growth, so they often have significant side effects due to their activity against normal tissues in the body. In oncology, then, there is considerable opportunity for improving the drug development process as well as improving prevention, early detection, diagnosis, and treatment of cancers. In principle, biomarkers should improve patient outcomes by ensuring that each patient receives the drugs that are most likely to be effective for his or her particular tumor, thereby enhancing the drug response rate and limiting toxicity. In addition to improving the effectiveness of therapy, biomarkers have the potential to improve the cost-effectiveness of treatment, both by avoiding the use of costly therapies to which a cancer will not respond and by avoiding the need to manage associated side effects of such treatments. Biomarkers that detect cancers at their earliest and most treatable stages should also improve patient outcome and the cost-effectiveness of therapy. Yet despite years of research, the number of cancer biomarkers in clinical use is quite small (Duffy, 2005; Hayes, 2005; Gasparini et al., 2006). Although in recent decades, knowledge about the biology of cancers has increased greatly, and many candidate biomarkers have been reported, very few have been sufficiently validated to justify their use in developing drugs or making patient care decisions. Why is this so? The discovery and development of useful biomarkers pose enormous challenges, and many different factors contribute to the slow pace of biomarker development (FDA, 2004). A discussion of how to develop and use biomarkers should start with a definition of the term. In its broadest sense, a biomarker is any biological,

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment chemical, or biophysical indicator of an underlying biological process. From a medical perspective, a biomarker is a physiological characteristic that is indicative of health and disease; it has been explicitly defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic response(s) to a therapeutic intervention” (Biomarkers Definitions Working Group, 2001). A cancer biomarker has been defined as “a molecular, cellular, tissue, or process-based alteration that provides indication of current, or more importantly, future behavior of cancer” (Hayes et al., 1996). These biological and physiological indicators could include a broad range of biochemical entities, such as nucleic acids, proteins, sugars, lipids, and small metabolites, as well as whole cells, in either specific tissues of interest or in the circulation. Detection of biomarkers, either individually or as larger sets or patterns, can be accomplished by a wide variety of methods, ranging from biochemical analysis of blood or tissue samples to biomedical imaging. In fact, there are strong interconnections between biomedical imaging and the development of biomarkers. For example, biomarkers are increasingly needed to expand the capabilities of imaging, but imaging is also an important tool for validating biomarkers for specific uses. In addition, imaging will be necessary to identify the location and extent of tumors whose presence might be indicated by future biomarker tests. Although imaging is likely to play an increasingly important role in the future of cancer detection and therapy, the primary focus of this report is in vitro diagnostics.1 Many of the challenges in biomarker development are relevant to both biomedical imaging and in vitro diagnostics, but in vivo imaging also entails a set of unique considerations (reviewed by Chandra et al., 2005), in part because it often requires injection of chemical agents, and thus it faces some of the same challenges as drug development but lacks the financial incentives of the drug industry. These issues are not addressed in the report, but this topic will be covered in more detail in a future workshop on drug development that is tentatively being planned by the IOM’s National Cancer Policy Forum. Biomarkers can be useful at any point in the biomedical continuum, from basic biomedical research through pharmaceutical discovery and preclinical development, clinical trials, and patient care (Kiviat and Critchlow, 1 In this report, “diagnostic” is often used synonymously with “biomarker test.” These terms refer to any laboratory-based test that can be used in drug discovery and development as well as in patient care and clinical decision making.

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment 2002; Srivastava and Wagner, 2002; Nakamura and Grody, 2004; Park et al., 2004; Floyd and McShane, 2004; Caprioli, 2005; Ludwig and Weinstein, 2005; Dalton and Friend, 2006, Kelloff et al., 2006; Weissleider, 2006). Clinical applications include disease risk stratification, chemoprevention, disease screening, diagnosis and prognosis/prediction, treatment planning and monitoring, and posttreatment surveillance (Table 1-1). For drug development, biomarkers may be used to assess drug candidates for evidence of safety and efficacy at each step of the development process (Table 1-2). Two primary challenges to developing cancer biomarkers are the discovery of candidate markers and the validation of those candidates for specific uses. The discovery process depends on the technologies available to interrogate the complex biochemistry of health and disease in order to identify differences that can be detected consistently in diverse populations. TABLE 1-1 Use of Cancer Biomarkers in Patient Care Clinical Biomarker Use Clinical Objective Risk stratification Assess the likelihood that cancers will develop (or recur) Chemoprevention Identify and target molecular mechanisms of carcinogenesis in precancerous tissues Screening Detect and treat early-stage cancers in the asymptomatic population Diagnosis Definitively establish the presence of cancer Classification Classify patients by disease subset Prognosis Predict the probable outcome of cancer regardless of therapy, to determine the aggressiveness of treatment Prediction/ treatment stratification Predict response to particular therapies and choose the drug that is mostly likely to yield a favorable response in a given patient Risk management Identify patients with a high probability of adverse effects of a treatment Therapy monitoring Determine whether a therapy is having the intended effect on a disease and whether adverse effects arise Posttreatment surveillance Early detection and treatment of recurrent disease

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment TABLE 1-2 Use of Biomarkers in Drug Development Biomarker Use Drug Development Objective Target validation Demonstrate that a potential drug target plays a key role in the disease process Early compound screening Identify compounds with the most promise for efficacy and safety Pharmacodynamic assays Determine drug activity; select dose and schedule Patient selection In clinical trials, patient selection (inclusion/exclusion) by disease subset or probability of response/adverse events Surrogate endpoint Use of a short-term outcome measure in place of the long-term primary endpoint to determine more quickly whether the treatment is efficacious and safe in drug regulatory approval Recent technological developments, especially in genomics and proteomics, have made it much easier to examine a large number of potential markers at once. Nonetheless, progress is still limited by the sensitivity and specificity of the current technologies, as well as the methods and tools used to analyze the enormous pools of data generated by high-throughput technologies, and there is still a need for new and improved technologies to discover potential biomarkers. The validation process is also arduous and costly, often requiring collection of or access to many patient samples with extensive clinical annotation and long-term follow-up. In addition, a biomarker must be validated for each specific application (as in Tables 1-1 and 1-2) for which it will be used. For example, the criteria for validating a biomarker for use as a screening test in asymptomatic populations will be quite different from those used to validate a biomarker for use as a surrogate end point in clinical trials of a drug, since the applications are so fundamentally different. There must be convincing evidence that a surrogate end point accurately predicts the clinical endpoint of interest. In the case of screening, a test must have sufficient sensitivity, specificity, and positive predictive value2 to accurately identify a disease in the general population. 2 The probability that an individual with a positive test has a particular disease, or characteristic, that the test is designed to detect. It is a measure of the ratio of true positives to (false + true positives).

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment Furthermore, health care payors have begun to require more data on the clinical validity of tests when making decisions about coverage and reimbursement. However, there is a lack of standards and guidelines for how to assess biomarkers and determine appropriate usage. These topics and the associated challenges are covered in more detail in Chapters 2 and 3. Clearly, much remains to be done to achieve the vital goal of effective early detection and individualized therapy for all people with cancer. A major research investment will be required to accomplish that goal. The opportunity cost of further progress in the field is always a consideration, as there are many competing needs and goals in biomedical research. However, a considerable investment is already being made in this field of research, and much could be accomplished by improving the discovery and development process to make the most of both ongoing and future efforts. The recommendations put forth by the committee in this report strive to realize these advances. COMMITTEE CHARGE The Committee on Developing Biomarker-based Tools for Cancer Screening, Diagnosis, and Treatment was asked to address (1) the potential to improve cancer screening, diagnosis, and therapy through the use of emerging biomarker technologies; (2) current limitations of genomics and proteomics technologies for cancer detection, diagnosis, and drug development, as well as steps that could be taken to overcome these limitations; (3) the logistics and cost of coordinating the development of biomarkers and targeted therapies; (4) regulatory oversight of biomarker development and use; (5) the adoption of biomarker-based tests and therapeutics into clinical practice; and (6) some of the potential economic implications of adopting these emerging technologies. A workshop hosted by the National Cancer Policy Forum in March 2006 addressed a similar set of questions, and the proceedings of the meeting (IOM, 2006, see Appendix) served as a primary input to the committee’s deliberations. FRAMEWORK OF THE REPORT Following this introduction, Chapter 2 provides a brief overview of the technologies and methods used to discover and develop biomarkers for preclinical and clinical use. It describes several resources for and approaches to

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment biomarker discovery and development that the committee agreed warranted further attention, including biorepositories, consortia, and demonstration projects. Chapter 3 reviews current oversight of biomarker development and use by federal agencies (Food and Drug Administration and Centers for Medicare and Medicaid Services). It examines a variety of approaches to improve the development and evaluation process and to ensure the quality of biomarker tests used by patients and physicians while also fostering innovation. Chapter 4 provides a brief overview of the technology evaluation and adoption processes and examines possible ways to facilitate data collection and analysis to monitor and improve the value of biomarker tests. REFERENCES Ariazi EA, Ariazi JL, Cordera F, Jordan VC. 2006. Estrogen receptors as therapeutic targets in breast cancer. Current Topics in Medicinal Chemistry 6(3):181-202. Austin M, Babbiss L. 2006. Commentary: When and how biomarkers could be used in 2016. AAPS Journal 8(1):E185-E189. Baselga J. 2006. Targeting tyrosine kinases in cancer: The second wave. Science 312(5777): 1175-1178. Biomarkers Definitions Working Group. 2001. Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clinical Pharmacology and Therapeutics 69:89-95. Bullinger L. 2005. Gene expression profiling in acute myeloid leukemia. Haematologica 91(6):733-738. Bullinger L, Doner H, Pollack JR. 2005. Genomics in myeloid leukemias: An array of possibilities. Reviews in Clinical and Experimental Hematology 9(1):E2. Caprioli RM. 2005. Deciphering protein molecular signatures in cancer tissues to aid in diagnosis, prognosis, and therapy. Cancer Research 65(23):10642-10645. Chandra S, Muir C, Silva M, Carr S. 2005. Imaging biomarkers in drug development: an overview of opportunities and open issues. Journal of Proteome Research 4(4):1134-1137. Dalton WS, Friend SH. 2006. Cancer biomarkers—an invitation to the table. Science 312(5777): 1165-1168. Druker BJ. 2004. Imatinib as a paradigm of targeted therapies. Advances in Cancer Research 91:1-30. Duffy MJ. 2005. Predictive markers in breast and other cancers: A review. Clinical Chemistry 51(3):494-503. FDA (Food and Drug Administration). 2004. Challenge and Opportunity on the Critical Path to New Medical Products. [Online]. Available: http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html [accessed September 2006].

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment Floyd E, McShane TM. 2004. Development and use of biomarkers in oncology drug development. Toxicologic Pathology 32 Suppl 1:106-115. Gasparini G, Longo R, Torino F, Gattuso D, Morabito A, Toffoli G. 2006. Is tailored therapy feasible in oncology? Critical Reviews in Oncology/Hematology 57(1):79-101. Hayes DF. 2005. Prognostic and predictive factors revisited. Breast 14(6):493-499. Hayes DF, Bast RC, Desch CE, Fritsche H Jr, Kemeny NE, Jessup JM, Locker GY, Macdonald JS, Mennel RG, Norton L, Ravdin P, Taube S, Winn RJ. 1996. Tumor marker utility grading system: A framework to evaluate clinical utility of tumor markers. Journal of the National Cancer Institute 88(20):1456-1466. IOM (Institute of Medicine). 2006. Developing Biomarker-Based Tools for Cancer Screening, Diagnosis, and Treatment: The State of the Science, Evaluation, Implementation, and Economics. A Workshop Summary. Patlak M, Nass S, rapporteurs. Washington, DC: The National Academies Press. Kelloff GJ, Lippman SM, Dannenberg AJ, Sigman CC, Pearce HL, Reid BJ, Szabo E, Jordan VC, Spitz MR, Mills GB, Papadimitrakopoulou VA, Lotan R, Aggarwal BB, Bresalier RS, Kim J, Arun B, Lu KH, Thomas ME, Rhodes HE, Brewer MA, Follen M, Shin DM, Parnes HL, Siegfried JM, Evans AA, Blot WJ, Chow WH, Blount PL, Maley CC, Wang KK, Lam S, Lee JJ, Dubinett SM, Engstrom PF, Meyskens FL Jr, O’Shaughnessy J, Hawk ET, Levin B, Nelson WG, Hong WK. 2006. Progress in chemoprevention drug development: The promise of molecular biomarkers for prevention of intraepithelial neoplasia and cancer—A plan to move forward. Clinical Cancer Research 12(12):3661-3697. Kiviat NB, Critchlow CW. 2002. Novel approaches to identification of biomarkers for detection of early stage cancer. Disease Markers 18:73-81. Levene AP, Morgan GJ, Davies FE. 2003. The use of genetic microarray analysis to classify and predict prognosis in haematological malignancies. Clinical and Laboratory Haemotology 25:209-220. Ludwig JA, Weinstein JN. 2005. Biomarkers in cancer staging, prognosis and treatment selection. Nature Reviews 5:845-856. Nakamura R, Grody W. 2004. Cancer diagnostics: Current and future trends. In: Nakamura RM, Grody WW, Wu JT, Nagle RB, General Considerations in the Use and Application of Laboratory Tests for the Evaluation of Cancer. Totawa, NJ: Humana Press Inc. Pp. 3-14. Park JW, Kerbel RS, Kelloff GJ, Barrett JC, Chabner BA, Parkinson DR, Peck J, Ruddon RW, Sigman CC, Slamon DJ. 2004. Rationale for biomarkers and surrogate end points in mechanism-driven oncology drug development. Clinical Cancer Research 10(11):3885-3896. The Royal Society. 2005. Personalised Medicines: Hopes and Realities. London, UK: The Royal Society. Sjoblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber TD, Mandelker D, Leary RJ, Ptak J, Silliman N, Szabo S, Buckhaults P, Farrell C, Meeh P, Markowitz SD, Willis J, Dawson D, Willson JK, Gazdar AF, Hartigan J, Wu L, Liu C, Parmigiani G, Park BH, Bachman KE, Papadopoulos N, Vogelstein B, Kinzler KW, Velculescu VE. 2006. The consensus coding sequences of human breast and colorectal cancers. Science 314:268-274. Spear BB, Heath-Chiozzi M, Huff J. 2001. Clinical application of pharmacogenetics. Trends in Molecular Medicine 7:201-204.

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Cancer Biomarkers: The Promises and Challenges of Improving Detection and Treatment Srivastava S, Wagner J. 2002. Surrogate endpoints in medicine. Disease Markers 18:39-40. Stuadt LM. 2003. Molecular diagnosis of the haematologic cancers. New England Journal of Medicine 348:1777-1785. Weissleder R. 2006. Molecular imaging in cancer. Science 312:1168-1171. Yeon CH, Pegram MD. 2005. Anti-erbB-2 antibody trastuzumab in the treatment of HER2-amplified breast cancer. Investigational New Drugs 23(5):391-409.