Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Mol Oncol. Author manuscript; available in PMC 2013 Apr 1.
Published in final edited form as:
Mol Oncol. 2012 Apr; 6(2): 260–266.
Published online 2012 Mar 6. doi:  10.1016/j.molonc.2012.02.006
PMCID: PMC3593045
NIHMSID: NIHMS362678

Paying for personalized care: Cancer biomarkers and comparative effectiveness

Abstract

Genomic-based diagnostics can play a key role in creating a more efficient healthcare system by directing patients toward beneficial therapies and away from therapies that pose substantial risk or are unlikely to improve outcomes for the patient. We outline how the value provided by diagnostics is closely linked to a range of factors including magnitude of health outcome improvement, avoiding adverse effect, diagnostic parameters, process of care, resource utilization, and costs. Comparative effectiveness approaches to evidence generation, including health outcome measurements, quality of life, economic analyses, decision modeling, and pragmatic clinical trials, can be used to provide stakeholders with a range of information to inform treatment, guidelines, coverage, and reimbursement decisions. Evidence of comparative effectiveness can also help support value-based reimbursement of cancer biomarkers and treatment strategies as means of paying for personalized medicine.

Keywords: Personalized genomics, Diagnostics, Value, Comparative effectiveness

1. Introduction

Targeted anticancer drugs and diagnostics have the goal of providing stratified treatment based on an individual's unique inherited or tumor genomic profile. (Herbst and Lippman, 2007; Willard et al., 2005; Chakravarti, 2001) These targeted interventions are especially important and applicable in the realm of oncology due to the life-threatening nature of the disease, the role of genomic variation in tumor development, and molecular classification of cancer subtypes. The number of targeted therapies available in oncology continues to increase (Gerber, 2008), but many of these therapies are expensive. For example, Erbitux (cetuximab) treatment costs over $70,000 per patient with colorectal cancer, (Shankaran et al., 2009) whereas Xalkori (crizotinib) for non-small cell lung cancer for 48 weeks is estimated at $115,000 (Oncology Times, 2011). However, expensive therapies may offer good value (i.e., an efficient use of healthcare resources relative to alternative treatments) if they provide substantial clinical benefits. The application of diagnostics can improve the average benefit for these drugs by identifying the specific population subgroups most likely to benefit or least likely to experience a serious adverse event. Figure 1 illustrates examples of the impact of targeted therapies with response rates in subgroups identified with diagnostics. As evidenced from Figure 1, many targeted therapies have a much higher response in specific subgroups.

Figure 1
Response metrics for selected targeted therapies in group with targeted genetic profiles/therapies. Sources: Trastuzumab (Seidman et al., 2001), Erlotinib (Cappuzzo et al., 2010), Cetuximab (De Roock et al., 2008).

Comparative effectiveness research (CER) is defined as “the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care” (Medicine, 2009). It is important to note that comparative effectiveness research is a broad ranging term that includes a variety of methods which seem to compare different interventions and that these comparisons may or may not include economic factors. Cost-effectiveness analysis (CEA), which we discuss extensively here, is one often used technique falling under CER which incorporates costs in its analysis, in addition to healthcare outcomes. Comparative effectiveness research can play a key role in elucidating the relative effectiveness of competing approaches to diagnosis or therapy by transparently defining and quantifying comparative clinical (and economic) outcomes including survival, quality of life, resource utilization (and costs). (Veenstra and Ramsey, 2009; Sullivan et al., 2009).

The objective of this paper is to discuss key drivers that influence the value provided by diagnostics in oncology and demonstrate how CER-based approaches can help to provide valid evidence before adopting costly new cancer therapies and diagnostics. This can lead to more efficient resource utilization and improved effectiveness in our healthcare system.

2. Predictive and prognostic diagnostics

Cancer genomic diagnostics are often considered as having predictive and prognostic capabilities, although some tests can be both predictive and prognostic in nature (Walther et al., 2009). Predictive diagnostics are those associated with a particular therapy whereas prognostic tests identify subgroups with (or without) particular risk factors that are not associated with a specific therapy (Walther et al., 2009; Bonanno et al., 2010). Predictive diagnostics typically present a clear decision structure for patient management: if the test indicates that the patient is a responder, administer target therapy, if non-responder, administer alternative therapy (or none). For example, a medical professional may decide to prescribe erlotinib as a 1st line treatment to an advanced NSCLC patient testing positive for an EGFR mutation, and standard chemotherapy to a patient testing negative (Zhou et al., 2011). A predictive diagnostic can also identify patients at increased risk for adverse events when given a particular therapy (Lecomte et al., 2004).

Prognostic diagnostics can provide information on the natural progression of disease (such as microsatellite instability in colorectal cancer (Popat et al., 2005)) or risk factors of a disease (such as BRCA+ for breast cancer risk (Ford et al., 1998; Pharoah et al., 2008)). These prognostic tests provide information to the patient and can be useful to guide management of patients toward increased surveillance, aggressive preventative measures, or enrollment in clinical trials for novel therapies.

In most clinical scenarios, predictive diagnostics are likely to have a greater influence on patient management and outcomes, though prognostic diagnostics may also play a role. Figure 2 illustrates how prognostic and predictive diagnostics can be incorporated into a patient management cycle. For the purposes of this work, we mostly limit our discussion to predictive diagnostics although similar principles can be applied to prognostic diagnostics.

Figure 2
Decision tree indicating the value of diagnostic driven therapy choices in the management of disease. Treatment A represents a therapy with greater effectiveness in a biomarker identified subgroup. Treatment B is standard treatment. Prognostic and predictive ...

3. Framework for evaluating the value of a diagnostic

From an economic perspective, the value of a diagnostic can be thought of as the net benefit provided, i.e., the difference between the benefits and costs associated with the diagnostic compared to managing the patient without the diagnostic technology. Cost-effectiveness analysis provides a framework for comparing benefits and costs across a range of different interventions. On the benefit side, the magnitude of the health outcomes can be measured using metrics such as life years gained, but also incorporate quality-of-life parameters (using the quality-adjusted-life-year or QALY) (Marthe et al., 1996). Improved survival, increased quality of life, cases successfully diagnosed and reduced side effects all contribute to positive health outcomes. Resources utilized are taken into account on the cost side including direct costs such as cost of treatment, diagnostics, time of healthcare professionals, infrastructure usage etc. Depending upon the analysis, indirect measures such as cost to patient of time lost, impaired productivity etc. may also be incorporated.

3.1. Impact of associated therapy, and test parameters

The degree of benefit of a predictive diagnostic is closely linked to the magnitude of improved health outcomes in the associated therapy. The magnitude of net benefit afforded by the diagnostic is also associated with the severity of the adverse effects avoided and can be understood in terms of a risk/benefit ratio. Assume a scenario with a highly beneficial therapy that is effective on an unknown subgroup of the population with disease, but carries substantial adverse effects. In the absence of predictive information on how the individual patient is likely to respond, the physician may conclude that the risk/benefit ratio is too high to prescribe the therapy. On the other hand, the presence of a diagnostic result suggesting a patient is likely to respond to the therapy lowers the risk/benefit ratio, allowing use of the therapy for the particular patient.

The impact of false negatives and false positives on patients and the healthcare system for a diagnostic test driving therapy decisions must also be considered. Thus key parameters in evaluating a diagnostic are the sensitivity (fraction of true positives over all positives) and specificity (fraction of true negatives over all negatives). Ideally, a diagnostic should possess both high sensitivity and high specificity, but often times a trade-off between these factors exists. A high sensitivity is desirable when the potential health benefits of a positive test result and consequent treatment are substantial and the high sensitivity ensures that a wide net for the biomarker is cast, even though some false positives are captured. If, on the other hand, a biomarker linked therapy has substantial side effects then a high specificity test will ensure that the number of false positives is kept small.

Sensitivity and specificity need to be interpreted in the context of disease and biomarker prevalence. With high disease/biomarker prevalence, the patient population is larger, and the diagnostic and treatment will impact a greater number of people. Prevalence together with sensitivity and specificity yields the positive predictive value (PPV, the proportion of positive test values that are correct), and negative predictive value (NPV, proportion of negative test values that are correct). A critical point to understand is that for a given sensitivity with low disease prevalence, the number of false positives rises (PPV becomes lower), i.e., it takes a very good test to find the proverbial needle in the haystack.

The above-mentioned concepts must not be considered in isolation, but rather relative to existing alternatives: if patients are prescribed a particular therapy based on the result of a diagnostic, then they forego alternative treatments which may provide entirely different benefits and harms. Thus, evaluating benefits and harms is generally conducted relative to an alternative intervention.

3.2. Evidence in patient management, complementarity and logistics

For effective patient management, a diagnostic test must have high clinical utility in the real-world, i.e., produce actionable results to improve positive health outcomes or ameliorate adverse conditions (Grosse and Khoury, 2006; Khoury, 2003). Furthermore, the diagnostic benefit is associated with the position of the diagnostic in the management pathway: diagnostics influencing treatment and improving health outcomes upstream in the clinical pathway may impact a larger proportion of the target population and thus avoiding more futile treatments than diagnostics further downstream.

The existence of diagnostics and treatments based upon test results is not sufficient; rather, the test must be accessible and acceptable: physicians and patients must use the test, make decisions based upon the test, and have access to appropriate interventions (Grosse and Khoury, 2006). This requires a strong evidentiary base leading to clear guidelines in order to attain the benefits associated with a diagnostic test. The gold standard for evidence remains the randomized, controlled trial (RCT). However, these may not always be realistic given the rate of medical technology evolution, patient needs, length of time needed, and resource and funding availability. Retrospective studies may also be used for evidence generation, but care is necessary to avoid the impact of any confounding variables (Concato et al., 2000). For example, different tests often exist for the same clinical application: fluorescent in-situ hybridization (FISH, which measures the number of gene copies) and immunohistochemistry (IHC, which measures protein expression) tests are both available for HER2 testing in breast cancer (Dowsett et al., 2003; Hofmann et al., 2008). Evidence on diagnostic suitability (incorporating the characteristics of different diagnostics) within a clinical application can increase the benefit of a diagnostic by reducing uncertainty, thus improving patient management.

Diagnostic benefit is also a function of the degree of complementarity present. If multiple, different additional tests must be conducted in conjunction with a specific test to reach a clinical decision, the value obtained by each test is likely to be less than if only one test were necessary. Complementarity is closely related to the test's specificity. High levels of prostate – specific antigen (PSA), for example, can be can be indicative of benign prostate disease or prostate cancer, requiring the use of further tests before a definitive diagnosis can be made (Lilja et al., 2008; Lin et al., 2008).

Even in the presence of evidence suggesting a diagnostic has useful clinical utility, adoption may not be widespread. For example, studies suggest that lung cancer patients testing positive for EGFR mutations have superior progression free survival under tyrosine kinase inhibitors (TKIs) (Zhou et al., 2011; Cappuzzo et al., 2010). While the label of the TKI erlotinib does not require EGFR testing and erlotinib is reimbursed without the test, in the 1st line setting EGFR mutation status may be better than clinical predictors in selecting patients for TKI-based therapy (Jackman et al., 2009). This suggests that EGFR testing should be part of the clinical treatment pattern in lung cancer. Despite this, testing for EGFR is not widespread with experts estimating these tests are used 10–20% of the time in relevant patients (Ramsey, 2011). Potential issues with EGFR testing impacting the perceived value include test accuracy, the possible need for a repeated biopsy sample, difficulty in sample extraction, and interpretation of results (Varella-Garcia et al., 2009; Mino-Kenudson and Mark, 2011). Additionally, there can be a range of laboratories offering sample analysis with varying standards which can make interpretation of results challenging (Eberhard et al., 2008); Camajova et al., 2009). This underscores the need for wellestablished protocols, consistent and timely access to results, and guidelines for clinical decision making if the diagnostic information is to be obtained and to prove useful in patient management.

4. Comparative effectiveness research and cost- effectiveness analysis

Comparative effectiveness research is a wide ranging term which includes a variety of methods and techniques. The motivation behind CER is to better inform clinical and policy decisions, focusing on obtaining better performance from the healthcare system. Comparative effectiveness research does not have to include cost, although many CER-based methods do utilize economic measures such as costs. Cost-effectiveness analysis is one such technique falling under the umbrella of comparative effectiveness research in which both effectiveness and costs are considered: which intervention works better, by how much, and how much more (or less) does it cost? Specifically, cost-effectiveness analyses can help in evaluating if an intervention represents good value for healthcare resources used.

The price of a drug or diagnostic is both a reflection of its perceived value and cost to health plans. Under a patent system, device and drug manufacturers can enjoy a limited time exclusivity (and pricing power). The key question to ask in paying for personalized care is whether the intervention in question provides sufficient value at a specific price point, in terms of healthcare outcomes and resource utilization, relative to other interventions. Payers care about the net cost of both a drug and a diagnostic compared to the value of improved health benefits (Garrison and Austin, 2006). If a diagnostic offers the possibility of directing treatment more accurately, or is responsible for driving down substantial unnecessary usage, then the diagnostic may, within the range considered, provide good value. The magnitude of the health outcomes can be measured not only using a metric such as life years gained, but also incorporate quality-of-life parameters (using the quality-adjusted-life-year or QALY – in this case cost-effectiveness analysis is referred to as cost-utility analysis) (Marthe et al., 1996). A commonly used metric is the additional dollars spent per QALY gained, with estimates of good value ranging from $50,000/QALY to almost $300,000/QALY (Nadler et al., 2006; Braithwaite et al., 2008; Neumann et al., 2000). Thus, the economic value of a diagnostic-drug pair has several components: the improvement in health outcomes, including avoidance of adverse effects; costs incurred (both to the patient and the healthcare system); and the outcomes of the alternative foregone. Specifically, diagnostic economic value is likely to be greatly influenced by the cost of drugs or side effects avoided. For example, Oncotype Dx (for breast cancer), even with a relatively high cost of approximately $3,500, is widely used (over 63,000 test results in 2010 (Health, 2010)) ostensibly because it saves on resources such as chemotherapy and delivery of care, while at the same time limiting substantial adverse effects in patients unlikely to benefit from chemotherapy (Hornberger et al., 2005; Tsoi et al., 2010).

Cost-effectiveness analyses offer the possibility of aiding decision-making by offering tools to quantitatively assess different clinical scenarios and synthesize existing evidence. For example mathematical models of clinical decision making can be constructed and analyses can be conducted to determine and to quantify the benefits and costs of using a diagnostic for patient stratification, driving therapeutic options to patients most likely to gain from using them (Elkin et al., 2004; Blank et al., 2011). These analyses can include pertinent information such as diagnostic sensitivity, specificity, and disease/biomarker prevalence. Health outcomes including quality of life parameters (utilizing QALYs) can also be explored. Analyses can be conducted for different population subgroups including factors such as sex, age and co-morbidities. Uncertainties in parameters can also be explored leading to a quantitative understanding of patient-intervention interactions. It is important to note that these approaches are comparative in nature i.e. they allow quantitative comparisons between different scenarios and interventions. With the use of transparent models, all assumptions are open to examination leading to a rich, broad-based evaluation of new and existing interventions.

Comparative effectiveness research though is a broader concept than cost-effectiveness analyses, and care must be taken to distinguish between them, especially in regards to the current discussions around healthcare reform (Weinstein and Skinner, 2010). Comparative effectiveness can offer substantial insights into a number of areas. For example, most clinical trials are often conducted under carefully controlled settings, seeking to measure treatment efficacy. These conditions may be quite different from those prevalent in routine clinical practice. CER methods in trial designs encourage the use of pragmatic or real-world trial designs which can deliver evidence of effectiveness in everyday clinical concepts (Macpherson, 2004; Helms, 2002). Pragmatic trial designs include elements such as including patients with commonly found comorbid conditions, standard treatment comparators (rather than placebos), community healthcare providers and diverse demographic characteristics (Luce et al., 2009; Sox and Greenfield, 2009). Logistical issues in adoption are an area of active research, (Ramsey et al., 2011) and pragmatic trial designs can play a key role in elucidating these issues and suggesting approaches to maximize accessibility.

Observational and retrospective databases can also be mined utilizing CER toward obtaining effectiveness data for different clinical interventions in real-world settings. The use of large electronic databases has made this task considerably easier with the opportunity to link large numbers of individual health outcomes to existing patterns in healthcare practice in a rapid, low-cost manner. However, these approaches can suffer from a paucity of data, since all relevant information may not be present (or accessible). For diagnostics, a key issue is the lack of identification of the particular test used, if it is not linked to a particular CPT code (Aspinall and Hamermesh, 2007; Ramsey et al., 2006). In addition, a key disadvantage in these studies is the presence of unmeasured factors that may bias findings (Sox and Greenfield, 2009).

CER-based methods can also be used to prioritize future research endeavors, identifying clinical questions regarding competing approaches to care which are likely to have the greatest benefit on society and the healthcare system. These CER-based approaches incorporate diverse stakeholder feedback, and can aid research prioritization by quantifying and valuing the potential information derived from clinical trials, and suggesting trial designs for maximal societal benefit (Thariani, 2011; Willan and Pinto, 2005).

Comparative effectiveness research can support appropriate use of cancer biomarkers by synthesizing existing evidence, quantitatively comparing different diagnostics, and outlining those scenarios where diagnostic-based interventions are likely to provide the greatest impact. Within a quantitative framework, cost-effectiveness analyses can guide reimbursement decisions to those diagnostics and therapies that provide the best value relative to other interventions, an area that will only continue to become more important under conditions of constrained healthcare and financial resources. CER-based approaches allow the comparison of these interventions in a transparent manner so stakeholders including industry, physicians, payers and providers can begin to understand the context, assumptions, limitations, implications and impacts of personalized care within the healthcare system.

5. Conclusions

Personalized therapies offer the promise of improved health outcomes in patients; however, high costs may prove a key limitation for accessibility. Through patient stratification, diagnostics can offer substantial value in oncology by guiding therapies toward those patients that will obtain maximal health benefits, and reduction of side effects and costs for those patients not likely to benefit from specific therapies.

Comparative effectiveness techniques can provide a comprehensive picture of the potential benefits, harms and costs of a diagnostic relative to patient management without the diagnostic. CER can evaluate different conditions in a quantitative manner, indicating scenarios where diagnostics can provide maximal value, comparing different alternatives for patient management, and ensuring efficient resource utilization. Incorporation of CER-elements in future clinical trial designs can aid in providing data for more accurate estimates for personalized interventions in typical clinical settings. Comparative effectiveness research and cost-effectiveness analysis offer substantial promise in directing personalized care and ensuring that value to the healthcare system is delivered by a quantitative weighting of benefits and costs across a range of potential interventions.

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