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Appendix C Introduction to Biomarkers The purpose of this appendix is to introduce readers to biomarkers, uses of biomarkers, and requirements for determining clinical utility of biomarkers. This material is intended for readers who wish to learn more about general biomarker concepts. This appendix explains how specific terms are used in the report and also illustrates several common misconcep- tions about biomarkers and the terminology used to describe them. BIOMARKERS Investigators seek to discover new biomarkers for many purposes, including the guidance of clinical decision making to determine how best to select agents to treat individual patients. The scientific literature provides definitions of the term biomarker as well as some of the principal uses of biomarkers. A widely used definition of a biomarker is “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a[n] . . . intervention” (Biomarkers Definitions Working Group, 2001). A recent Institute of Medicine (IOM) report on Evaluation of Biomarkers and Sur- rogate Endpoints in Chronic Disease provided the following description of biomarkers (IOM, 2010): Biomarkers can be measurements of macro- molecules (DNA, RNA, proteins, lipids), cells, or processes that describe a normal or abnormal biological state in an organism. Biomarkers may be detected and analyzed in tissue, in circulation (blood, lymph), and in body fluids (urine, stool, sputum, breast nipple aspi- ration, etc.). Biomarkers may be identified in cells of concern (such as pre- 281
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282 EVOLUTION OF TRANSLATIONAL OMICS malignant or even existing cancer cells) or in tissues surrounding the area of interest (such as evidence of neo-vascularization or inflammation sur- rounding a cancer). Biomarkers may be in exfoliated cells, or they may be soluble or suspended molecules (for example proteins, DNA, microRNA) in circulation or in secretions. Finally, inherited germline biomarkers can be evaluated from circulating leukocytes or exfoliated cells from easily acces- sible tissues, such as from a cheek swab. CLINICAL USES OF BIOMARKERS Biomarkers have many important potential roles in clinical research and in clinical practice (IOM, 2010; see also Tables 1-1 and 1-2 in Chapter 1). These include prognosis, prediction of response to therapy (effect modifiers), prediction of clinical outcome (surrogate endpoints), risk assessment, screen- ing, diagnosis, pharmacogenetics, and patient monitoring during and after treatment. Although these uses are applicable to most if not all disease pro- cesses, this appendix refers to oncologic examples because most of the case studies for this report arose from the field of oncology. Prognostic Factors and Effect Modifiers1 Prognostic Factors Prognostic factors, used to estimate the risk of or time to clinical outcomes such as disease recurrence or progression, may be useful even though these biomarkers are simply correlated with the causal mecha- nisms of the disease process (Fleming, 2005). In oncology, biomarkers may have roles as both prognostic factors and effect modifiers, and, in fact, these may be mixed. For example, overexpression and/or amplifica- tion of the human epidermal growth factor receptor 2 (HER2) portend a poor prognosis in breast cancer patients who do not receive any adjuvant systemic therapy. Moreover, this same biomarker appears to be associ- ated with poorer response to endocrine therapy in patients with estrogen receptor (ER)-positive breast cancer (compared to those who have ER- positive, HER2-negative cancers), but it has been associated with higher response rates to various chemotherapies (e.g., anthracyclines, taxanes) and it is especially related to benefit from anti-HER2 therapies, such as trastuzumab and lapatinib (Wolff et al., 2007). Therefore, any study addressing the prognostic role of a biomarker needs to take into account the specific, intended clinical use of the biomarker and the potential con- founding effects of how patients in the study cohort are treated. Perhaps 1Also see the discussion in Chapter 1.
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283 APPENDIX C the best example of a successful omics-based test for prognosis is the development of Oncotype Dx. The case study for this omics-based test is described in Appendix A. Effect Modifiers An effect modifier may relate to a class of therapy (such as chemother- apy in general) or a specific agent within a class (such as an anthracycline, or, more specifically, doxorubicin). Indeed, the origin of this report stems from development of omics-based tests designed to be effect modifiers for chemotherapy in several types of cancers, including lung, breast, and ovar- ian cancer (see Chapter 6). With the advent of targeted cancer therapies that are directed toward a specific, somatic molecular abnormality, biomarkers may provide direct guidance for selection of individual agents. Examples of these types of biomarkers include the expression status of ER and HER2 for selection of endocrine or anti-HER2 therapies, respectively, in breast cancer (Hammond et al., 2010; Wolff et al., 2007), K-ras mutations for selection of antibody therapy against the epidermal growth factor receptor (Allegra et al., 2009), and ALK (anaplastic lymphoma kinase) mutations for selection of crizotinib in lung cancer (Shaw et al., 2010). Surrogate Endpoints A third important category of biomarkers is that of surrogate end- points. A surrogate endpoint is “a biomarker that is intended to substitute for a clinical endpoint. A surrogate endpoint is expected to predict clini- cal benefit (or harm or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence” (Biomarkers Definitions Working Group, 2001). In standard clinical practice, a surro- gate endpoint may provide the clinician either increased certainty that an event is already, or is likely to be, occurring. For example, if the level of a tumor biomarker in the bloodstream is rising in a patient with previously established cancer, that may be an indication of an impending relapse, and might guide earlier intervention than if the clinician waits for the relapse to be detectable by other means. Such a surrogate endpoint may have clini- cal/biological validity but may or may not have clinical utility. This issue is described in greater detail below. Validating a biomarker as a surrogate endpoint or effect modifier is complex. Correlation of patient tumor response with longer survival does not allow one to conclude that does not allow one to conclude that a given treatment that induces a tumor response will also lead to an increase in survival. In other words, “a correlate does not a surrogate make” (Fleming
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284 EVOLUTION OF TRANSLATIONAL OMICS and DeMets, 1996). Likewise, when using biomarkers in the development of omics-based tests to guide decisions about when to use available agents, it is not enough to establish that the biomarker is a prognostic factor. It is important to recognize that “a prognostic factor does not an effect modifier/ predictive factor make” (Fleming and Powers, in press). Risk Assessment Biomarkers can be used to assess a patient’s risk for a future diagnosis of disease. Risk assessment is particularly valuable if preventive measures and/or early detection and intervention have been shown to effectively reduce morbidity or mortality. For example, prophylactic surgery and/or chemoprevention with the selective estrogen receptor modulators (SERMs; tamoxifen, raloxifene) have been shown to reduce the incidence and, in the case of surgery, associated mortality of breast cancer (Newman and Vogel, 2007). However, application of these strategies is only applied to subjects with appropriately high risk: in particular, women with either an inherited genetic risk for breast cancer development, such as those who harbor muta- tions in the BRCA1 and 2 genes, or those with a sufficiently high calculated risk of a new breast cancer using well-validated instruments such as the National Cancer Institute (NCI) Breast Cancer Risk Assessment Tool (Gail et al., 2007). Screening For some diseases, screening permits diagnosis of disease at an earlier, more treatable point. Screening strategies have been implemented for a number of cancers, including breast, colorectal, lung, prostate, and cervical cancer, and have been proposed for others, such as ovarian cancer. Of these, reduction of mortality has only been demonstrated for breast (USPSTF, 2009), colorectal (AHRQ, 2012; Zauber et al., 2012), lung (Aberle et al., 2011), and cervical cancers (Gates, 2001; NCI, 2012). Radiological or endoscopic methods have been employed in the first three examples, while cytologic examination—and more recently, detection of chronic human papillomavirus (HPV) infection—is used to screen for cervical cancer. For colorectal cancer screening, biomarker stool tests are also used for screening (IOM, 2008). No omics-based tests have been implemented for screening as yet. Diagnosis Diagnostic biomarkers are used to confirm whether a patient has a particular disease. In an oncology setting, a patient may present with clini-
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285 APPENDIX C cal, radiographic, and even histologic findings that are strongly suggestive, or confirm, the diagnosis of malignancy, but the primary source of the cancer is not easily discernible. Biomarkers might be used to distinguish either a diagnosis of cancer versus benign growth, or the site of origin of the cancer (for example, lymphoma versus solid malignancy, or a type of epithelial cancer, such as colon, versus another, such as breast). Indeed, one of the case studies presented in Appendix A provides an example of an omics-based test, the Pathworks Tissue of Origin test, which is used to help identify the primary site of a malignancy of unknown origin. Pharmacogenetics In many diseases, inherited germline DNA sequence variants, known as single nucleotide polymorphisms (SNPs), may determine individual differ- ences in drug distribution within the body, metabolism, or effect on target tissues. SNPs can be used as biomarkers, and the field of study of these individual differences is known as pharmacogenetics. Such biomarkers may be used to predict particular susceptibility to drug toxicities and/or activities (Wang et al., 2011; Weinshilboum, 2003). Monitoring Biomarkers may be used to periodically monitor subjects for a poten- tial recurrence or other event that may be difficult to detect at the present time. Although tissue-based biomarkers might be monitored, such a strat- egy is invasive, and a strategy making use of biomarkers that are secreted or circulating in the bloodstream is preferable and more frequently used. Monitoring biomarkers might be used for monitoring previously effected individuals who have been rendered disease free, to detect an event earlier than what might be possible with standard clinical approaches. OMICS-BASED BIOMARKERS In the past, most single-analyte biomarkers have been generated and studied because of a pre-conceived biological association between them and the associated disease. For example, ER was pursued as an effect modi- fier because of the known beneficial effect of endocrine therapy (ovarian ablation, pharmacologic estrogen therapy, tamoxifen) on some but not all breast cancers (Hammond et al., 2010; McGuire et al., 1975). On the other hand, discovery of associations between omics-based tests and disease biology and/or clinical outcomes may be more likely to be a result of math- ematical correlations between large numbers of high order factors (such as a 15,000 gene array expression chip). The resulting computational model,
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286 EVOLUTION OF TRANSLATIONAL OMICS which must then be developed into a clinically applicable omics-based test, requires data intensive methods to provide an evidence-based validation of the use of the biomarker. While still desirable and beneficial, it is not always possible to link omics-based biomarkers to biological rationale. Technologies Enabling Omics Research Technologies enabling omics research include gene expression micro- arrays, multiplex quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), sequence analysis of DNA, RNA, and proteins, and multiple mass spectrometry techniques. On a gene expression microarray, thousands of oligonucleotides are arranged on a surface and hybridize with corresponding nucleic acid sequences in complex biological samples from tissues or plasma. In qRT-PCR, fluorescent-labeled oligonucleotide probes are used to amplify, detect, and quantify the presence of multiple genetic sequences and track gene transcription into RNA. Analysis of the sequences of DNA, RNA, and proteins enables better understanding of fundamental biological function and interaction. Multiple mass spectrometry techniques enable analysis of complex biological samples to identify and quantify proteins and their numerous modified isoforms. Other mass spectrometry or molecular magnetic resonance imaging instruments are important for metabolomics studies. These tools could potentially enable development of omics-based tests that may help to improve treatment efficacy and help patients avoid adverse side effects of therapies. BIOMARKERS IN CLINICAL TRIALS The use of prognostic biomarkers and effect modifiers in clinical trials is particularly relevant to the statement of task that led to this report. A biomarker might be used for direct assignment of patients to different treat- ment regimens within a clinical trial. For example, enrolling only patients who are positive for a biomarker that is known or presumed to be associ- ated with a higher risk of subsequent events, or with a higher possibility of responding to a specific type of therapy, could reduce the size or duration of a clinical trial. In this case, the biomarker is used to enrich accrual to the trial with patients most likely to benefit from the treatment. For more information on clinical trial designs, see Chapter 4. REFERENCES Aberle, D. R., C. D. Berg, et al. 2011. The National Lung Screening Trial: Overview and study design. Radiology 258(1):243-253.
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287 APPENDIX C AHRQ (Agency for Healthcare Research and Quality). 2012. Colorectal Cancer Screening. http://www.ahrq.gov/clinic/colorsum.htm (accessed February 13, 2012). Allegra, C. J., J. M. Jessup, M. R. Somerfield, S. R. Hamilton, E. H. Hammond, D. F. Hayes, P. K. McAllister, R. F. Morton, and R. L. Schilsky. 2009. American Society of Clinical Oncology provisional clinical opinion: Testing for KRAS gene mutations in patients with metastatic colorectal carcinoma to predict response to anti-epidermal growth factor receptor monoclonal antibody therapy. Journal of Clinical Oncology 27(12):2091-2096. Biomarkers Definitions Working Group. 2001. Biomarkers and surrogate endpoints: Pre- ferred definitions and conceptual framework. Clinical Pharmacology and Therapeutics 69(3):89-95. Fleming, T. R. 2005. Surrogate endpoints and FDA’s accelerated approval process. Health Affairs 24(1):67-78. Fleming, T. R., and D. L. DeMets. 1996. Surrogate end points in clinical trials: Are we being misled? Annals of Internal Medicine 125(7):605-613. Fleming, T. R., and J. H. Powers. In press. Biomarkers and surrogate endpoints in clinical trials. Statistics in Medicine. Gail, M. H., J. P. Costantino, D. Pee, M. Bondy, L. Newman, M. Selvan, G. L. Anderson, K. E. Malone, P. A. Marchbanks, W. McCaskill-Stevens, S. A. Norman, M. S. Simon, R. Spirtas, G. Ursin, and L. Bernstein. 2007. Projecting individualized absolute invasive breast cancer risk in African American women. Journal of the National Cancer Institute 99(23):1782-1792. Gates, T. J. 2001. Screening for cancer: Evaluating the evidence. American Family Physician 63(3): 513-522. Hammond, M. E., D. F. Hayes, M. Dowsett, D. C. Allred, K. L. Hagerty, S. Badve, P. L. Fitzgibbons, G. Francis, N. S. Goldstein, M. Hayes, D. G. Hicks, S. Lester, R. Love, P. B. Mangu, L. McShane, K. Miller, C. K. Osborne, S. Paik, J. Perlmutter, A. Rhodes, H. Sasano, J. N. Schwartz, F. C. Sweep, S. Taube, E. E. Torlakovic, P. Valenstein, G. Viale, D. Visscher, T. Wheeler, R. B. Williams, J. L. Wittliff, and A. C. Wolff. 2010. American Society of Clinical Oncology/College Of American Pathologists guideline recommenda- tions for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. Journal of Clinical Oncology 28(16):2784-2795. Henry, N. L., and D. F. Hayes. 2006. Uses and abuses of tumor markers in the diagnosis, moni- toring, and treatment of primary and metastatic breast cancer. Oncologist 11(6):541-552. Hodgkinson, V. C., G. L. Eagle, P. J. Drew, M. J. Lind, and L. Cawkwell. 2010. Biomarkers of chemotherapy resistance in breast cancer identified by proteomics: Current status. Cancer Letters 294(1):13-24. IOM (Institute of Medicine). 2008. Implementing Colorectal Cancer Screening: Workshop Summary. Washington, DC: The National Academies Press. IOM. 2010. Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease. Wash- ington, DC: The National Academies Press. McGuire, W. L., G. C. Chamness, M. E. Costlow, and N. J. Richert. 1975. Steroids and human breast cancer. Journal of Steroid Biochemistry 6(5):723-727. McGuire, W. L., A. K. Tandon, D. C. Allred, G. C. Chamness, and G. M. Clark. 1990. How to use prognostic factors in axillary node-negative breast cancer patients. Journal of the National Cancer Institute 82(12):1006-1015. NCI (National Cancer Institute). 2012. Cervical cancer screening (PDQ®). Retrieved Feb- ruary 13, 2012, from http://www.cancer.gov/cancertopics/pdq/screening/cervical/ HealthProfessional/page2. Newman, L. A., and V. G. Vogel. 2007. Breast cancer risk assessment and risk reduction. Surgical Clinics of North America 87(2):307-316.
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