Mol Diagn Ther DOI 10.1007/s40291-014-0102-7
REVIEW ARTICLE
The Economic Considerations and Implications of the Stratification of Future Oncology Therapeutics Maria Gazouli • Kyriakos Souliotis
Ó Springer International Publishing Switzerland 2014
Abstract Cancer accounts for approximately 13 % of all deaths worldwide. Development of stratification biomarkers, for cancer screening, diagnosis, monitoring, and treatment optimization, is a vital concept to facilitate disease prevention and drug development. The advent of stratified medicine should result in the safer, more effective use of therapeutic drugs to treat cancer, and in reducing the cost associated with inappropriate therapeutic regimens; however, many barriers delay the use of biomarkers in drug development and clinical practice. Since the incorporation of biomarkers in clinical practice might have additional initial costs, the question arises regarding whether the improvement in outcomes is reached at a realistic additional cost. This review presents an overview of economic issues surrounding biomarkers in cancer treatment optimization.
Key Points There is a growing necessity to modernize the drug development process by incorporating new techniques that can predict the safety and effectiveness of new drugs faster, with more certainty, and at lower cost. Stratified cancer therapies might improve patient outcomes and economic effectiveness due to the increased possibility that those expected to benefit are exposed to the intervention, and decreased probability that those who will not benefit would be subject to costly and potentially risky interventions. The lack of cost-effectiveness or comparativeeffectiveness data is an important issue that needs to be considered though, highlighting the demand for better evidence regarding the economic impact of the use of cancer biomarkers.
1 Introduction
M. Gazouli (&) Department of Basic Medical Science, Laboratory of Biology, School of Medicine, University of Athens, Michalakopoulou 176, 11527 Athens, Greece e-mail: [email protected] K. Souliotis Faculty of Social Sciences, University of Peloponnesus, Korinth, Greece K. Souliotis Medical School, University of Athens, Athens, Greece
Cancer is one of the main causes of death worldwide (about one-quarter of all deaths). More than 5 million new cases of cancer are diagnosed every year in OECD (Organisation for Economic Co-operation and Development) countries and 7.6 million people worldwide died from cancer in 2008 [1, 2]. The cost of cancer care globally is around US$900 billion [3]. In Greece, the cost of cancer treatment is estimated to be about 6.5 % of total expenditure on healthcare [4]. Advances in healthcare technology, such as medical devices, biomarkers, and other in vitro diagnostics, have increased health expenditure [5]. Internationally, there is a
M. Gazouli, K. Souliotis
requirement to contain healthcare costs, which contradicts the necessary use of new technologies which, prima facie, appear to increase costs. It becomes evident that costeffectiveness research is necessary, to allow more efficient allocation of healthcare resources [6]. The main objective of this article was to provide an overview of the health economic issues of biomarkers with a focus on cancer care and treatment optimization.
2 Biomarkers in Cancer Biomarkers are molecules that have either a prognostic or a predictive role. A predictive biomarker indicates the likelihood of response to a particular therapy, whereas a prognostic biomarker provides information on the outcome irrespective of the treatments used [7]. Some biomarkers may be both prognostic and predictive, as is the case for the human epidermal growth factor receptor 2 (HER2) in breast cancer. Biomarkers must have several characteristics, including high sensitivity and specificity. Moreover, the biomarker measurement should be relatively easy to perform and the procedure needs to be demonstrably cost effective. Predictive biomarkers may help guide and target therapy towards those most likely to benefit, in addition to providing complex high-dimensional biological data that lead to better patient outcomes through more accurate diagnoses and optimal treatment routing [8]. Prognostic biomarkers can help to estimate the chance of recovery or recurrence of a cancer, and determine whether a person’s cancer will respond to a specific treatment [9]. The great need for prevention and better treatment in cancer led to the development of cancer biomarkers. Numerous cancer biomarkers have been discovered and are currently used in clinical practice, such as prostate-specific antigen (PSA; KLK3) (prostate cancer), ERBB2 (breast cancer), MUC16 [also known as CA-125] (ovarian cancer), a-fetoprotein (AFP), and b-human chorionic gonadotropin [CGB] (testicular cancer).
3 Cost-Effectiveness Analysis Cost-effectiveness analysis (CEA) is a tool that can compare the relative costs and outcomes of the use of biomarkers [10]. CEA helps to identify ways to redirect resources to better use. It demonstrates not only the utility of allocating resources from ineffective to effective interventions, but also the utility of allocating resources from less to more cost-effective interventions [11]. In recent years, the necessity of CEA has increased as the overall healthcare expenditure increases due to the rising costs associated with the relevant tests. Thus, economic analysis
should focus on the cost of the test and the infrastructure and additional resources required for the test. ‘Cost per quality-adjusted life-year (QALY) gained’ from the use of the new technology is an additional parameter estimated for the results of CEA. In several countries, the use of the new technology is evaluated according to the ‘cost per QALY gained’ [11]. For example, in UK the use of two targeted therapies, bevacizumab and cetuximab, for colorectal cancer was not approved since the average cost per QALY was higher than the £30,000 threshold [12]. However, recently Mittman et al. [13] have shown that restricting cetuximab to advanced colorectal cancer patients with wild-type KRAS reduces the incremental cost-effectiveness ratio (ICER) of cetuximab over best supportive care alone from CAN$199,742 per QALY to CAN$120,061 per QALY; consequently, cetuximab has been approved for reimbursement for wildtype KRAS advanced colorectal cancer patients in Canada. Cancer biomarkers affect health economics because the use of specific biomarkers may require screening in large population or targeted screening. Other biomarker applications such as diagnosis, monitoring of effects during treatment, and surveillance of patients during or following treatment, as well as treatment optimization, derive economic value from guiding cancer treatment approaches via selection of the most appropriate treatment among potential alternatives, and minimizing the likelihood and cost of adverse events. Nevertheless, it is a reality that the use of cancer biomarkers to guide treatment can confer both clinical and economic benefits. Thus, the focus of the economic aspects surrounding biomarkers should not just address the cost of the test, but should also examine the potential costs saved from interventions avoided and outcomes improved.
4 Cancer Therapy and Economic Benefits Personalized medicine is the use of biomarkers to provide effective and safer targeted therapy, improving health outcomes. From an economic perspective, personalized medicine is promising because only patients who are likely to be benefited by treatment will finally receive that treatment. Economic analysis is necessary, especially for high-cost treatments with specific biomarker analysis results. A systematic literature review of MEDLINE, EMBASE, SciSearch, Cochrane, and nine other databases was conducted from 2000 to January 2014 using mainly the search terms ‘cost-effectiveness’, ‘biomarkers’, ‘KRAS’, ‘EGFR’, and ‘economic analysis’ in order to review the economic benefits by biomarker testing. An example of genetic biomarker used for personalized therapy is oncogene KRAS. Patients with metastatic colorectal cancer
Economics and Cancer Biomarkers
receive anti-epidermal growth factor receptor (EGFR) therapy, but patients having tumors with mutated KRAS are not likely to respond to anti-EGFR therapy. Limiting anti-EGFR therapy to those without KRAS mutations will reserve treatment, such as cetuximab, for those likely to benefit. This treatment will avoid unnecessary costs and harm in those who do not respond [14]. In such examples, economic evaluation in relation to the clinical benefits is useful. Several studies have shown that mutations in KRAS are not detected in 60 % of patients with colorectal cancer. These data reveal the necessity of mutation detection in patients with metastatic colorectal cancer, who do respond to therapy. The American Society of Clinical Oncology (ASCO) suggests testing all patients for detection of KRAS mutations [15]. A recent study showed that screening for both KRAS and BRAF mutations compared with the base strategy of no anti-EGFR therapy increases expected overall survival by 0.034 years at a cost of $22,033, yielding an ICER of approximately $650,000 per additional year of life. Compared with anti-EGFR therapy without screening, adding KRAS testing saves approximately $7,500 per patient; adding BRAF testing saves another $1,023, with little reduction in expected survival [16]. Although screening for both KRAS and BRAF brings economic benefits compared with anti-EGFR therapy, the cost-effectiveness ratio of the former compared with the base strategy of no anti-EGFR therapy remains above the generally accepted threshold for the acceptable costeffectiveness ratio of $100,000/QALY [16]. Yet there are doubts from other scientists concerning the methodology used in this study [17]. In the US, the National Comprehensive Cancer Network (NCCN) changed its guidelines by recommending the KRAS test in primary cancer or local metastasis be part of pretreatment control of all patients with metastatic colorectal cancer [18]. The same recommendations have also been reported in Europe [19]. Economic evaluation of KRAS testing in the US and Germany showed that $7,500–12,400 and €3,900–9,600 is saved, respectively [20]. Another study, from Switzerland, showed that cetuximab treatment guided by KRAS/BRAF achieved gains of 0.491 QALYs compared with the reference strategy. The KRAS testing strategy achieved an additional gain of 0.002 QALYs compared with KRAS/ BRAF. KRAS/BRAF testing was the most cost-effective approach when compared with the reference strategy (ICER: €62,653/QALY) [21]. In Greece, the KRAS test will reduce the total treatment cost by about 41.7–42.5 % [22]. Finally, in a recent systematic review Lange et al. [23] concluded that even if treatment with bevacizumab, cetuximab, and panitumumab is not considered to be cost effective in patients with metastatic colorectal cancer, testing for KRAS mutation prior to treatment with
cetuximab or panitumumab is found to be clearly cost effective compared with no testing.
5 Economic Evaluation and Possible Problems (Factors Affecting the Economic Value) Personalized medicine could be beneficial in economic terms if the total cost of diagnosis and treatment for patients diagnosed with early-stage cancer is less than those diagnosed in later stages, or if it leads to improved outcome and minimizes the costs of adverse events [24– 26]. However, economic assessment is needed to provide data on the potential added value of the technology providing the personalized approach to medicine. The benefit of technologies of personalized medicine and the incremental costs can be optimized if guidelines are better designed. For this reason, some specific issues require careful consideration because several factors affect the economic value of screening (see Table 1). The first step is to define the scope of the economic evaluation. In practice, this means defining the research question and being clear about the technology to be evaluated [27]. The test that will finally be selected has to be sensitive and specific. It is well-known that diagnostic errors, i.e. diagnostic tests giving false-positive or falsenegative results, can affect treatment outcomes and costs of personalized medicine. For example, patients with breast cancer, carrying HER2 mutations, have improved survival and reduced the risk of distant metastases if trastuzumab is added to chemotherapy [28]. The different tests and methods vary in sensitivity and specificity, therefore a false-positive patient will receive trastuzumab treatment. This means unnecessary costs and possible health loss from the side effects of trastuzumab, and inconvenience. Also, inappropriate secondary testing and unnecessary costs increase the cost at population level. Therefore, health economic evaluations of biomarkers in personalized Table 1 Factors affecting economic value of screening Factors
Economic considerations
Biomarker sensitivity and specificity
Costs arising out of false-positive screening can be very costly at the population level
Disease prevalence
More false-negative tests occur if the disease is relatively common
Clinical utility of the test
The biomarker can be consider as cost effective only if the tests alter treatment practice and lead to improved patient outcome
Economic model
Uncertainty in an economic model is greater if the analysis is complex. Health economic modeling is often fraught with difficulties associated with lack of data
M. Gazouli, K. Souliotis
medicine need to incorporate input parameters such as test sensitivity and specificity. In some cases, a combination of tests is required, thus complicating the economic evaluation and requiring the introduction of combined sensitivities and specificities of tests in health economic modeling. Economic evaluation in this case requires a larger decision list and a good understanding about which tests will be performed after the initial test, which treatment decisions will be made after the tests are performed, and the impact of these tests and treatments on costs and health outcomes [29]. Another issue is that uncertainty in an economic model is greater if the analysis is more complex. A number of factors (some mentioned above) can potentially affect the degree of uncertainty observed when conducting an economic evaluation of personalized medicine. The degree of uncertainty is a key factor affecting the decision on whether the technology is likely to result in a cost effective use of resources [30]. Lack of an evidence base sufficient for informed decision making opens up opportunities for manufacturers of tests and pharmaceuticals to close the evidence gap and produce data to feed into reimbursement decision-making processes. Furthermore, the impact of data and evidence gaps is challenging with regards to economic evaluation [31]. Health economic modeling is fraught with difficulties associated with lack of data, and methods are being developed to try and deal with this enduring issue, such as using mathematical approaches to elicit expert opinion and identify parameter values and distributions [32]. Current economic evaluations of companion diagnostic medicines focus on the health gain from the technology and assume perfect uptake and use in terms of prescribing practice [26, 33, 34]. Important gaps in information are especially evident with standalone tests. Information on treatment patterns and on its costs and outcomes is often lacking, especially for false-positive and false-negative patients. As an example, in the case of prognostic tests that can identify patients with good or bad prognosis, the way these patients are managed can largely differ between centers. A key requirement in the development of many technologies is to conduct early health economic models, i.e. models conducted in the early stages of the development of a technology. Based on an anticipated clinical profile of the technology, its potential cost effectiveness can be estimated. For instance, if a new treatment that is in development phase I or II is expected to reduce the number of events by 20 %, this anticipated clinical feature can be inserted into such a model to estimate the potential savings that can be made and the potential number of QALYs that can be gained. Hence, the manufacturers will better understand whether the new technology has the potential to become cost effective, given its anticipated price and expected benefits.
The economic value of screening also depends on the prevalence of disease which affects the predictive value of the test. In general, more false-negative results occur if the disease is relatively common, and more false positives occur if the disease if relatively uncommon [35]. The clinical utility of the test is another important issue that needs to be considered because if test results do not alter treatment practice then the biomarker cannot be considered cost effective. Finally, average costs per screening are very important at the population level; screening methods that are inexpensive at the margin can be very expensive when aggregated across an entire subpopulation (e.g. women aged C50 years). Thus, the comparatively low cost of biomarkers (compared with, for example, colonoscopies or mammograms) has the potential to lower aggregate screening costs, which may in turn alter the economic properties of screenings that have relied on more costly diagnostic approaches in the past [36].
6 The Value of Personalized Medicine At this point it should be noted that in the previous paragraphs we have drawn on the economic value of personalized medicine, yet appropriately assessing new diagnostic/screening technologies applied in personalized medicine requires just more than that. Information on economic value and affordability, but also on clinical utility and patient value derived from the application of the technology should be accounted for [37]. In this context, the value of personalized medicine can be defined in a twofold manner, i.e. from a societal perspective and from a patient perspective [38]. The societal perspective is concerned with the allocation of limited resources to maximize health benefits (QALY), and also by the fact that genetic testing can increase costs without offering any benefits if it has no clinical utility. These issues refer to the economic value and affordability of the new technology, and are more or less covered by what has already been previously discussed. The patient perspective refers to the clinical utility of the technology, i.e. to the fact that therapeutic outcomes may improve and risks may be reduced if treatment is limited only to responders, but also to the value that a diagnosis brings to the patient, i.e. the value of testing information, also referred to as the value of ‘knowing’ [38, 39, 41]. Most patients prefer to have information with regards to their health status, even if this information cannot direct treatment, while in the case of a negative, ‘no disease’ diagnosis, reassurance value is generated for them [38, 39, 41]. She estimation of patient preferences using methods such as contingent valuation, conjoint analysis, and discrete choice experiments plays an important role in determining the value of
Economics and Cancer Biomarkers
‘knowing’ and therefore, in part, the value of personalized medicine [39–41].
7 Conclusions Nowadays, the use of cancer biomarkers in patient care contributes to early and more accurate disease detection and targeted cancer therapy, enhancing treatment efficacy for stratified groups of patients. Furthermore, targeted cancer therapies apart from improving patient outcomes, contribute also to economic competence by increasing the chance that those most likely to benefit are exposed to the intervention, and by decreasing the probability that those who will not benefit will be subject to costly and potentially risky therapeutic approaches. The lack of costeffectiveness or comparative-effectiveness data is an important issue that needs to be considered though, highlighting the demand for better evidence regarding the economic impact of the use of cancer biomarkers. Acknowledgements and Disclosures The authors have no conflicts of interest that are directly relevant to the content of this article.
References 1. Organisation for Economic Co-operation and Development (OECD) Health at a glance. Health status, Chap. 2. France; 2007. http://www.oecd.org/document/. 2. WHO. Projections of mortality and burden of disease, 2004–2030. http://www.who.int/healthinfo/global_burden_disease/projections 2004/en/. 3. American Cancer Society. Economic impact of cancer. http:// www.cancer.org/Cancer/CancerBasics/economic-impact-of-cancer. Accessed 2012. 4. Ministry of Health. 2011–2015. http://www.anticancer.gov.gr/ catalogue/SxedioDrasis_24selido.pdf. 5. US Congress. Technological change and the growth of health care spending. DC: Congressional Budget Office; 2008. 6. Yao L. Institute offers blueprint for U.S. effort to compare medical treatments. Wall Street Journal; 1 Jul 2009. http://online. wsj.com/news/articles/SB124640564984476653. 7. Dienstmann R, Vilar E, Tabernero J. Molecular predictors of response to chemotherapy in colorectal cancer. Cancer J. 2011;17:114–26. 8. Wilson CL, Schultz S, Waldman SA. Cancer biomarkers where medicine, business and public policy intersect. Biotechnol Healthc. 2007;4:33–42. 9. Jaffe CC. Pathology and imaging in biomarker development. MD: NCI; 2001. 10. Drummond M, McGuire A. Economic evaluation in health care: merging theory with practice. New York: Oxford University Press; 2001. 11. Appleby J, Devlin N, Parkin D, Buxton M, Chalkidou K. Searching for cost effectiveness thresholds in the NHS. Health Policy. 2009;91:239–45. 12. Nass SJ, Moses HL. Cancer biomarkers: the promises and challenges of improving detection and treatment. DC: The National Academies Press; 2007.
13. Mittmann N, Au HJ, Tu D, O’Callaghan CJ, Isogai PK, et al. Prospective cost-effectiveness analysis of cetuximab in metastatic colorectal cancer: evaluation of National Cancer Institute of Canada Clinical Trials Group CO.17 trial. J Natl Cancer Inst. 2009;101:1182–92. 14. ImClone LLC. Prescribing information for Erbitux. In: Company ELa, editor. Indianapolis: ImClone LLC; 2012. 15. Allegra CJ, Jessup JM, Somerfield MR, Hamilton SR, Hammond EH, Hayes DF, et al. 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. J Clin Oncol. 2009;27:2091–6. 16. Behl AS, Goddard KA, Flottemesch TJ, Veenstra D, Meenan RT, Lin JS, et al. Cost-effectiveness analysis of screening for KRAS and BRAF mutations in metastatic colorectal cancer. J Natl Cancer Inst. 2012;104:1785–95. 17. Hess LM, Carter G, Smolen L, Bowman L, von Hohnhorst P, Seagle C. Re: cost-effectiveness analysis of screening for KRAS and BRAF mutations in metastatic colorectal cancer. J Natl Cancer Inst. 2014;106:djt370. 18. National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines in OncologyTM: colon cancer v2.2010. 19. European Society of Pathology. Who participated so far in the ESP KRAS EQA schemes with a genotyping score of C90%? http:// kras.eqascheme.org/info/public/eqa/previous_participants.xhtml. 20. Vijayaraghavan A, Efrusy MB, Go¨ke B, Kirchner T, Santas CC, Goldberg RM. Cost-effectiveness of KRAS testing in metastatic colorectal cancer patients in the United States and Germany. Int J Cancer. 2012;131:438–45. 21. Blank PR, Moch H, Szucs TD, Schwenkglenks M. KRAS and BRAF mutation analysis in metastatic colorectal cancer: a costeffectiveness analysis from a Swiss perspective. Clin Cancer Res. 2011;17:6338–46. 22. Papagianopoulou V, Christodoulopoulou A, Bracco A, Yfantopoulos I. Estimating the cost saving from the introduction of KRAS testing in the management of metastatic colorectal cancer (MCRC) patients receiving panitumumab in Greece. Value Health. 2008;11:A464–5. 23. Lange A, Prenzler A, Frank M, Kirstein M, Vogel A, von der Schulenburg JM. A systematic review of cost-effectiveness of monoclonal antibodies for metastatic colorectal cancer. Eur J Cancer. 2014;50:40–9. 24. Schneider JE, Sidhu MK, Doucet C, Kiss N, Ohsfeldt RL, Chalfin D. Economic of cancer biomarkers. Pers Med. 2012;9:829–37. 25. Wong WB, Carlson JJ, Thariani R, Veenstra DL. Cost effectiveness of pharmacogenomics: a critical and systematic review. Pharmacoeconomics. 2010;28:1001–13. 26. Trusheim MR, Berndt ER, Douglas FL. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat Rev Drug Discov. 2007;6:287–93. 27. Drummond MF, Jefferson TO. Guidelines for authors and peer reviewers of economic submissions to the BMJ. The BMJ Economic Evaluation Working Party. BMJ. 1996;313:275–83. 28. Moja L, Tagliabue L, Balduzzi S, Parmelli E, Pistotti V, Guarneri V, et al. Trastuzumab containing regimens for early breast cancer. Cochrane Database Syst Rev. 2012;4:CD006243. 29. Valk PJ, Verhaak RG, Beijen MA, Erpelinck CA, Barjesteh van Waalwijk van Doorn-Khosrovani S, Boer JM, et al. Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med. 2004;350:1617–28. 30. Bilcke J, Beutels P, Brisson M, Jit M. Accounting for methodological, structural, and parameter uncertainty in decision-analytic models: a practical guide. Med Decis Making. 2011;31:675–92. 31. Payne K, Shabaruddin FH. Cost-effectiveness analysis in pharmacogenomics. Pharmacogenomics. 2010;11:643–6.
M. Gazouli, K. Souliotis 32. Soares MO, Bojke L, Dumville J, Iglesias C, Cullum N, Claxton K. Methods to elicit experts’ beliefs over uncertain quantities: application to a cost effectiveness transition model of negative pressure wound therapy for severe pressure ulceration. Stat Med. 2011;30:2363–80. 33. Phillips KA, Van Bebber SL. A systematic review of costeffectiveness analyses of pharmacogenomic interventions. Pharmacogenomics. 2004;5:1139–49. 34. Vegter S, Boersma C, Rozenbaum M, Wilffert B, Navis G, Postma MJ. Pharmacoeconomic evaluations of pharmacogenetic and genomic screening programmes: a systematic review on content and adherence to guidelines. Pharmacoeconomics. 2008;26:569–87. 35. Dennis LK, Lynch CF, Smith EM. Cancer. In: Wallace RB, editor. Public health and preventive medicine. 15th ed. NY: McGraw-Hill Medical; 2008. 36. Kumar S, Mohan A, Guleria R. Biomarkers in cancer screening, research and detection: present and future: a review. Biomarkers. 2006;11:385–405.
37. Phillips KA, Ann Sakowski J, Trosman J, Douglas MP, Liang SY, Neumann P. The economic value of personalized medicine tests: what we know and what we need to know. Genet Med. 2014;16(3):251–7. 38. IJzerman MJ. Is there a need for a comprehensive research program in health economics and personalized medicine? IP12, ISPOR 16th Annual European Congress, Dublin; 2013. 39. Marshall D. Patient preferences have important implications for determining the value of personalized medicine. IP12, ISPOR 16th Annual European Congress, Dublin; 2013. 40. Neumann PJ, Cohen JT, Hammitt JK, Concannon TW, Auerbach HR, Fang C, Kent DM. Willingness-to-pay for predictive tests with no immediate treatment implications: a survey of US residents. Health Econ. 2012;21:238–51. 41. Towse A, Garrison L. Economic incentives for evidence generation: promoting an efficient path to personalized medicine. Value Health. 2014;16:S39–43.
REVIEW ARTICLE
The Economic Considerations and Implications of the Stratification of Future Oncology Therapeutics Maria Gazouli • Kyriakos Souliotis
Ó Springer International Publishing Switzerland 2014
Abstract Cancer accounts for approximately 13 % of all deaths worldwide. Development of stratification biomarkers, for cancer screening, diagnosis, monitoring, and treatment optimization, is a vital concept to facilitate disease prevention and drug development. The advent of stratified medicine should result in the safer, more effective use of therapeutic drugs to treat cancer, and in reducing the cost associated with inappropriate therapeutic regimens; however, many barriers delay the use of biomarkers in drug development and clinical practice. Since the incorporation of biomarkers in clinical practice might have additional initial costs, the question arises regarding whether the improvement in outcomes is reached at a realistic additional cost. This review presents an overview of economic issues surrounding biomarkers in cancer treatment optimization.
Key Points There is a growing necessity to modernize the drug development process by incorporating new techniques that can predict the safety and effectiveness of new drugs faster, with more certainty, and at lower cost. Stratified cancer therapies might improve patient outcomes and economic effectiveness due to the increased possibility that those expected to benefit are exposed to the intervention, and decreased probability that those who will not benefit would be subject to costly and potentially risky interventions. The lack of cost-effectiveness or comparativeeffectiveness data is an important issue that needs to be considered though, highlighting the demand for better evidence regarding the economic impact of the use of cancer biomarkers.
1 Introduction
M. Gazouli (&) Department of Basic Medical Science, Laboratory of Biology, School of Medicine, University of Athens, Michalakopoulou 176, 11527 Athens, Greece e-mail: [email protected] K. Souliotis Faculty of Social Sciences, University of Peloponnesus, Korinth, Greece K. Souliotis Medical School, University of Athens, Athens, Greece
Cancer is one of the main causes of death worldwide (about one-quarter of all deaths). More than 5 million new cases of cancer are diagnosed every year in OECD (Organisation for Economic Co-operation and Development) countries and 7.6 million people worldwide died from cancer in 2008 [1, 2]. The cost of cancer care globally is around US$900 billion [3]. In Greece, the cost of cancer treatment is estimated to be about 6.5 % of total expenditure on healthcare [4]. Advances in healthcare technology, such as medical devices, biomarkers, and other in vitro diagnostics, have increased health expenditure [5]. Internationally, there is a
M. Gazouli, K. Souliotis
requirement to contain healthcare costs, which contradicts the necessary use of new technologies which, prima facie, appear to increase costs. It becomes evident that costeffectiveness research is necessary, to allow more efficient allocation of healthcare resources [6]. The main objective of this article was to provide an overview of the health economic issues of biomarkers with a focus on cancer care and treatment optimization.
2 Biomarkers in Cancer Biomarkers are molecules that have either a prognostic or a predictive role. A predictive biomarker indicates the likelihood of response to a particular therapy, whereas a prognostic biomarker provides information on the outcome irrespective of the treatments used [7]. Some biomarkers may be both prognostic and predictive, as is the case for the human epidermal growth factor receptor 2 (HER2) in breast cancer. Biomarkers must have several characteristics, including high sensitivity and specificity. Moreover, the biomarker measurement should be relatively easy to perform and the procedure needs to be demonstrably cost effective. Predictive biomarkers may help guide and target therapy towards those most likely to benefit, in addition to providing complex high-dimensional biological data that lead to better patient outcomes through more accurate diagnoses and optimal treatment routing [8]. Prognostic biomarkers can help to estimate the chance of recovery or recurrence of a cancer, and determine whether a person’s cancer will respond to a specific treatment [9]. The great need for prevention and better treatment in cancer led to the development of cancer biomarkers. Numerous cancer biomarkers have been discovered and are currently used in clinical practice, such as prostate-specific antigen (PSA; KLK3) (prostate cancer), ERBB2 (breast cancer), MUC16 [also known as CA-125] (ovarian cancer), a-fetoprotein (AFP), and b-human chorionic gonadotropin [CGB] (testicular cancer).
3 Cost-Effectiveness Analysis Cost-effectiveness analysis (CEA) is a tool that can compare the relative costs and outcomes of the use of biomarkers [10]. CEA helps to identify ways to redirect resources to better use. It demonstrates not only the utility of allocating resources from ineffective to effective interventions, but also the utility of allocating resources from less to more cost-effective interventions [11]. In recent years, the necessity of CEA has increased as the overall healthcare expenditure increases due to the rising costs associated with the relevant tests. Thus, economic analysis
should focus on the cost of the test and the infrastructure and additional resources required for the test. ‘Cost per quality-adjusted life-year (QALY) gained’ from the use of the new technology is an additional parameter estimated for the results of CEA. In several countries, the use of the new technology is evaluated according to the ‘cost per QALY gained’ [11]. For example, in UK the use of two targeted therapies, bevacizumab and cetuximab, for colorectal cancer was not approved since the average cost per QALY was higher than the £30,000 threshold [12]. However, recently Mittman et al. [13] have shown that restricting cetuximab to advanced colorectal cancer patients with wild-type KRAS reduces the incremental cost-effectiveness ratio (ICER) of cetuximab over best supportive care alone from CAN$199,742 per QALY to CAN$120,061 per QALY; consequently, cetuximab has been approved for reimbursement for wildtype KRAS advanced colorectal cancer patients in Canada. Cancer biomarkers affect health economics because the use of specific biomarkers may require screening in large population or targeted screening. Other biomarker applications such as diagnosis, monitoring of effects during treatment, and surveillance of patients during or following treatment, as well as treatment optimization, derive economic value from guiding cancer treatment approaches via selection of the most appropriate treatment among potential alternatives, and minimizing the likelihood and cost of adverse events. Nevertheless, it is a reality that the use of cancer biomarkers to guide treatment can confer both clinical and economic benefits. Thus, the focus of the economic aspects surrounding biomarkers should not just address the cost of the test, but should also examine the potential costs saved from interventions avoided and outcomes improved.
4 Cancer Therapy and Economic Benefits Personalized medicine is the use of biomarkers to provide effective and safer targeted therapy, improving health outcomes. From an economic perspective, personalized medicine is promising because only patients who are likely to be benefited by treatment will finally receive that treatment. Economic analysis is necessary, especially for high-cost treatments with specific biomarker analysis results. A systematic literature review of MEDLINE, EMBASE, SciSearch, Cochrane, and nine other databases was conducted from 2000 to January 2014 using mainly the search terms ‘cost-effectiveness’, ‘biomarkers’, ‘KRAS’, ‘EGFR’, and ‘economic analysis’ in order to review the economic benefits by biomarker testing. An example of genetic biomarker used for personalized therapy is oncogene KRAS. Patients with metastatic colorectal cancer
Economics and Cancer Biomarkers
receive anti-epidermal growth factor receptor (EGFR) therapy, but patients having tumors with mutated KRAS are not likely to respond to anti-EGFR therapy. Limiting anti-EGFR therapy to those without KRAS mutations will reserve treatment, such as cetuximab, for those likely to benefit. This treatment will avoid unnecessary costs and harm in those who do not respond [14]. In such examples, economic evaluation in relation to the clinical benefits is useful. Several studies have shown that mutations in KRAS are not detected in 60 % of patients with colorectal cancer. These data reveal the necessity of mutation detection in patients with metastatic colorectal cancer, who do respond to therapy. The American Society of Clinical Oncology (ASCO) suggests testing all patients for detection of KRAS mutations [15]. A recent study showed that screening for both KRAS and BRAF mutations compared with the base strategy of no anti-EGFR therapy increases expected overall survival by 0.034 years at a cost of $22,033, yielding an ICER of approximately $650,000 per additional year of life. Compared with anti-EGFR therapy without screening, adding KRAS testing saves approximately $7,500 per patient; adding BRAF testing saves another $1,023, with little reduction in expected survival [16]. Although screening for both KRAS and BRAF brings economic benefits compared with anti-EGFR therapy, the cost-effectiveness ratio of the former compared with the base strategy of no anti-EGFR therapy remains above the generally accepted threshold for the acceptable costeffectiveness ratio of $100,000/QALY [16]. Yet there are doubts from other scientists concerning the methodology used in this study [17]. In the US, the National Comprehensive Cancer Network (NCCN) changed its guidelines by recommending the KRAS test in primary cancer or local metastasis be part of pretreatment control of all patients with metastatic colorectal cancer [18]. The same recommendations have also been reported in Europe [19]. Economic evaluation of KRAS testing in the US and Germany showed that $7,500–12,400 and €3,900–9,600 is saved, respectively [20]. Another study, from Switzerland, showed that cetuximab treatment guided by KRAS/BRAF achieved gains of 0.491 QALYs compared with the reference strategy. The KRAS testing strategy achieved an additional gain of 0.002 QALYs compared with KRAS/ BRAF. KRAS/BRAF testing was the most cost-effective approach when compared with the reference strategy (ICER: €62,653/QALY) [21]. In Greece, the KRAS test will reduce the total treatment cost by about 41.7–42.5 % [22]. Finally, in a recent systematic review Lange et al. [23] concluded that even if treatment with bevacizumab, cetuximab, and panitumumab is not considered to be cost effective in patients with metastatic colorectal cancer, testing for KRAS mutation prior to treatment with
cetuximab or panitumumab is found to be clearly cost effective compared with no testing.
5 Economic Evaluation and Possible Problems (Factors Affecting the Economic Value) Personalized medicine could be beneficial in economic terms if the total cost of diagnosis and treatment for patients diagnosed with early-stage cancer is less than those diagnosed in later stages, or if it leads to improved outcome and minimizes the costs of adverse events [24– 26]. However, economic assessment is needed to provide data on the potential added value of the technology providing the personalized approach to medicine. The benefit of technologies of personalized medicine and the incremental costs can be optimized if guidelines are better designed. For this reason, some specific issues require careful consideration because several factors affect the economic value of screening (see Table 1). The first step is to define the scope of the economic evaluation. In practice, this means defining the research question and being clear about the technology to be evaluated [27]. The test that will finally be selected has to be sensitive and specific. It is well-known that diagnostic errors, i.e. diagnostic tests giving false-positive or falsenegative results, can affect treatment outcomes and costs of personalized medicine. For example, patients with breast cancer, carrying HER2 mutations, have improved survival and reduced the risk of distant metastases if trastuzumab is added to chemotherapy [28]. The different tests and methods vary in sensitivity and specificity, therefore a false-positive patient will receive trastuzumab treatment. This means unnecessary costs and possible health loss from the side effects of trastuzumab, and inconvenience. Also, inappropriate secondary testing and unnecessary costs increase the cost at population level. Therefore, health economic evaluations of biomarkers in personalized Table 1 Factors affecting economic value of screening Factors
Economic considerations
Biomarker sensitivity and specificity
Costs arising out of false-positive screening can be very costly at the population level
Disease prevalence
More false-negative tests occur if the disease is relatively common
Clinical utility of the test
The biomarker can be consider as cost effective only if the tests alter treatment practice and lead to improved patient outcome
Economic model
Uncertainty in an economic model is greater if the analysis is complex. Health economic modeling is often fraught with difficulties associated with lack of data
M. Gazouli, K. Souliotis
medicine need to incorporate input parameters such as test sensitivity and specificity. In some cases, a combination of tests is required, thus complicating the economic evaluation and requiring the introduction of combined sensitivities and specificities of tests in health economic modeling. Economic evaluation in this case requires a larger decision list and a good understanding about which tests will be performed after the initial test, which treatment decisions will be made after the tests are performed, and the impact of these tests and treatments on costs and health outcomes [29]. Another issue is that uncertainty in an economic model is greater if the analysis is more complex. A number of factors (some mentioned above) can potentially affect the degree of uncertainty observed when conducting an economic evaluation of personalized medicine. The degree of uncertainty is a key factor affecting the decision on whether the technology is likely to result in a cost effective use of resources [30]. Lack of an evidence base sufficient for informed decision making opens up opportunities for manufacturers of tests and pharmaceuticals to close the evidence gap and produce data to feed into reimbursement decision-making processes. Furthermore, the impact of data and evidence gaps is challenging with regards to economic evaluation [31]. Health economic modeling is fraught with difficulties associated with lack of data, and methods are being developed to try and deal with this enduring issue, such as using mathematical approaches to elicit expert opinion and identify parameter values and distributions [32]. Current economic evaluations of companion diagnostic medicines focus on the health gain from the technology and assume perfect uptake and use in terms of prescribing practice [26, 33, 34]. Important gaps in information are especially evident with standalone tests. Information on treatment patterns and on its costs and outcomes is often lacking, especially for false-positive and false-negative patients. As an example, in the case of prognostic tests that can identify patients with good or bad prognosis, the way these patients are managed can largely differ between centers. A key requirement in the development of many technologies is to conduct early health economic models, i.e. models conducted in the early stages of the development of a technology. Based on an anticipated clinical profile of the technology, its potential cost effectiveness can be estimated. For instance, if a new treatment that is in development phase I or II is expected to reduce the number of events by 20 %, this anticipated clinical feature can be inserted into such a model to estimate the potential savings that can be made and the potential number of QALYs that can be gained. Hence, the manufacturers will better understand whether the new technology has the potential to become cost effective, given its anticipated price and expected benefits.
The economic value of screening also depends on the prevalence of disease which affects the predictive value of the test. In general, more false-negative results occur if the disease is relatively common, and more false positives occur if the disease if relatively uncommon [35]. The clinical utility of the test is another important issue that needs to be considered because if test results do not alter treatment practice then the biomarker cannot be considered cost effective. Finally, average costs per screening are very important at the population level; screening methods that are inexpensive at the margin can be very expensive when aggregated across an entire subpopulation (e.g. women aged C50 years). Thus, the comparatively low cost of biomarkers (compared with, for example, colonoscopies or mammograms) has the potential to lower aggregate screening costs, which may in turn alter the economic properties of screenings that have relied on more costly diagnostic approaches in the past [36].
6 The Value of Personalized Medicine At this point it should be noted that in the previous paragraphs we have drawn on the economic value of personalized medicine, yet appropriately assessing new diagnostic/screening technologies applied in personalized medicine requires just more than that. Information on economic value and affordability, but also on clinical utility and patient value derived from the application of the technology should be accounted for [37]. In this context, the value of personalized medicine can be defined in a twofold manner, i.e. from a societal perspective and from a patient perspective [38]. The societal perspective is concerned with the allocation of limited resources to maximize health benefits (QALY), and also by the fact that genetic testing can increase costs without offering any benefits if it has no clinical utility. These issues refer to the economic value and affordability of the new technology, and are more or less covered by what has already been previously discussed. The patient perspective refers to the clinical utility of the technology, i.e. to the fact that therapeutic outcomes may improve and risks may be reduced if treatment is limited only to responders, but also to the value that a diagnosis brings to the patient, i.e. the value of testing information, also referred to as the value of ‘knowing’ [38, 39, 41]. Most patients prefer to have information with regards to their health status, even if this information cannot direct treatment, while in the case of a negative, ‘no disease’ diagnosis, reassurance value is generated for them [38, 39, 41]. She estimation of patient preferences using methods such as contingent valuation, conjoint analysis, and discrete choice experiments plays an important role in determining the value of
Economics and Cancer Biomarkers
‘knowing’ and therefore, in part, the value of personalized medicine [39–41].
7 Conclusions Nowadays, the use of cancer biomarkers in patient care contributes to early and more accurate disease detection and targeted cancer therapy, enhancing treatment efficacy for stratified groups of patients. Furthermore, targeted cancer therapies apart from improving patient outcomes, contribute also to economic competence by increasing the chance that those most likely to benefit are exposed to the intervention, and by decreasing the probability that those who will not benefit will be subject to costly and potentially risky therapeutic approaches. The lack of costeffectiveness or comparative-effectiveness data is an important issue that needs to be considered though, highlighting the demand for better evidence regarding the economic impact of the use of cancer biomarkers. Acknowledgements and Disclosures The authors have no conflicts of interest that are directly relevant to the content of this article.
References 1. Organisation for Economic Co-operation and Development (OECD) Health at a glance. Health status, Chap. 2. France; 2007. http://www.oecd.org/document/. 2. WHO. Projections of mortality and burden of disease, 2004–2030. http://www.who.int/healthinfo/global_burden_disease/projections 2004/en/. 3. American Cancer Society. Economic impact of cancer. http:// www.cancer.org/Cancer/CancerBasics/economic-impact-of-cancer. Accessed 2012. 4. Ministry of Health. 2011–2015. http://www.anticancer.gov.gr/ catalogue/SxedioDrasis_24selido.pdf. 5. US Congress. Technological change and the growth of health care spending. DC: Congressional Budget Office; 2008. 6. Yao L. Institute offers blueprint for U.S. effort to compare medical treatments. Wall Street Journal; 1 Jul 2009. http://online. wsj.com/news/articles/SB124640564984476653. 7. Dienstmann R, Vilar E, Tabernero J. Molecular predictors of response to chemotherapy in colorectal cancer. Cancer J. 2011;17:114–26. 8. Wilson CL, Schultz S, Waldman SA. Cancer biomarkers where medicine, business and public policy intersect. Biotechnol Healthc. 2007;4:33–42. 9. Jaffe CC. Pathology and imaging in biomarker development. MD: NCI; 2001. 10. Drummond M, McGuire A. Economic evaluation in health care: merging theory with practice. New York: Oxford University Press; 2001. 11. Appleby J, Devlin N, Parkin D, Buxton M, Chalkidou K. Searching for cost effectiveness thresholds in the NHS. Health Policy. 2009;91:239–45. 12. Nass SJ, Moses HL. Cancer biomarkers: the promises and challenges of improving detection and treatment. DC: The National Academies Press; 2007.
13. Mittmann N, Au HJ, Tu D, O’Callaghan CJ, Isogai PK, et al. Prospective cost-effectiveness analysis of cetuximab in metastatic colorectal cancer: evaluation of National Cancer Institute of Canada Clinical Trials Group CO.17 trial. J Natl Cancer Inst. 2009;101:1182–92. 14. ImClone LLC. Prescribing information for Erbitux. In: Company ELa, editor. Indianapolis: ImClone LLC; 2012. 15. Allegra CJ, Jessup JM, Somerfield MR, Hamilton SR, Hammond EH, Hayes DF, et al. 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. J Clin Oncol. 2009;27:2091–6. 16. Behl AS, Goddard KA, Flottemesch TJ, Veenstra D, Meenan RT, Lin JS, et al. Cost-effectiveness analysis of screening for KRAS and BRAF mutations in metastatic colorectal cancer. J Natl Cancer Inst. 2012;104:1785–95. 17. Hess LM, Carter G, Smolen L, Bowman L, von Hohnhorst P, Seagle C. Re: cost-effectiveness analysis of screening for KRAS and BRAF mutations in metastatic colorectal cancer. J Natl Cancer Inst. 2014;106:djt370. 18. National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines in OncologyTM: colon cancer v2.2010. 19. European Society of Pathology. Who participated so far in the ESP KRAS EQA schemes with a genotyping score of C90%? http:// kras.eqascheme.org/info/public/eqa/previous_participants.xhtml. 20. Vijayaraghavan A, Efrusy MB, Go¨ke B, Kirchner T, Santas CC, Goldberg RM. Cost-effectiveness of KRAS testing in metastatic colorectal cancer patients in the United States and Germany. Int J Cancer. 2012;131:438–45. 21. Blank PR, Moch H, Szucs TD, Schwenkglenks M. KRAS and BRAF mutation analysis in metastatic colorectal cancer: a costeffectiveness analysis from a Swiss perspective. Clin Cancer Res. 2011;17:6338–46. 22. Papagianopoulou V, Christodoulopoulou A, Bracco A, Yfantopoulos I. Estimating the cost saving from the introduction of KRAS testing in the management of metastatic colorectal cancer (MCRC) patients receiving panitumumab in Greece. Value Health. 2008;11:A464–5. 23. Lange A, Prenzler A, Frank M, Kirstein M, Vogel A, von der Schulenburg JM. A systematic review of cost-effectiveness of monoclonal antibodies for metastatic colorectal cancer. Eur J Cancer. 2014;50:40–9. 24. Schneider JE, Sidhu MK, Doucet C, Kiss N, Ohsfeldt RL, Chalfin D. Economic of cancer biomarkers. Pers Med. 2012;9:829–37. 25. Wong WB, Carlson JJ, Thariani R, Veenstra DL. Cost effectiveness of pharmacogenomics: a critical and systematic review. Pharmacoeconomics. 2010;28:1001–13. 26. Trusheim MR, Berndt ER, Douglas FL. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat Rev Drug Discov. 2007;6:287–93. 27. Drummond MF, Jefferson TO. Guidelines for authors and peer reviewers of economic submissions to the BMJ. The BMJ Economic Evaluation Working Party. BMJ. 1996;313:275–83. 28. Moja L, Tagliabue L, Balduzzi S, Parmelli E, Pistotti V, Guarneri V, et al. Trastuzumab containing regimens for early breast cancer. Cochrane Database Syst Rev. 2012;4:CD006243. 29. Valk PJ, Verhaak RG, Beijen MA, Erpelinck CA, Barjesteh van Waalwijk van Doorn-Khosrovani S, Boer JM, et al. Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med. 2004;350:1617–28. 30. Bilcke J, Beutels P, Brisson M, Jit M. Accounting for methodological, structural, and parameter uncertainty in decision-analytic models: a practical guide. Med Decis Making. 2011;31:675–92. 31. Payne K, Shabaruddin FH. Cost-effectiveness analysis in pharmacogenomics. Pharmacogenomics. 2010;11:643–6.
M. Gazouli, K. Souliotis 32. Soares MO, Bojke L, Dumville J, Iglesias C, Cullum N, Claxton K. Methods to elicit experts’ beliefs over uncertain quantities: application to a cost effectiveness transition model of negative pressure wound therapy for severe pressure ulceration. Stat Med. 2011;30:2363–80. 33. Phillips KA, Van Bebber SL. A systematic review of costeffectiveness analyses of pharmacogenomic interventions. Pharmacogenomics. 2004;5:1139–49. 34. Vegter S, Boersma C, Rozenbaum M, Wilffert B, Navis G, Postma MJ. Pharmacoeconomic evaluations of pharmacogenetic and genomic screening programmes: a systematic review on content and adherence to guidelines. Pharmacoeconomics. 2008;26:569–87. 35. Dennis LK, Lynch CF, Smith EM. Cancer. In: Wallace RB, editor. Public health and preventive medicine. 15th ed. NY: McGraw-Hill Medical; 2008. 36. Kumar S, Mohan A, Guleria R. Biomarkers in cancer screening, research and detection: present and future: a review. Biomarkers. 2006;11:385–405.
37. Phillips KA, Ann Sakowski J, Trosman J, Douglas MP, Liang SY, Neumann P. The economic value of personalized medicine tests: what we know and what we need to know. Genet Med. 2014;16(3):251–7. 38. IJzerman MJ. Is there a need for a comprehensive research program in health economics and personalized medicine? IP12, ISPOR 16th Annual European Congress, Dublin; 2013. 39. Marshall D. Patient preferences have important implications for determining the value of personalized medicine. IP12, ISPOR 16th Annual European Congress, Dublin; 2013. 40. Neumann PJ, Cohen JT, Hammitt JK, Concannon TW, Auerbach HR, Fang C, Kent DM. Willingness-to-pay for predictive tests with no immediate treatment implications: a survey of US residents. Health Econ. 2012;21:238–51. 41. Towse A, Garrison L. Economic incentives for evidence generation: promoting an efficient path to personalized medicine. Value Health. 2014;16:S39–43.
