Biomarkers of risk to develop lung cancer in the new screening era

Introduction: why develop new biomarkers for early detection of lung cancer?

The rationale for developing biomarkers for the early detection of lung cancer is very strong and well established. It stems from the fact that, at the population level, the earlier we detect the disease, the better the outcome and the lower the health care cost. The impetus for biomarker development has grown stronger since the NLST trial demonstrated that early detection via chest CT screening reduced the relative risk for lung cancer death in the high risk individuals (1). Low dose chest CT in this group alone may save up to 12,000 lives a year, but it represents only about 8% of individuals dying of this disease every year. Thus, much is to be done to capture these lung cancers that escape chest CT screening as currently recommended despite its high sensitivity and specificity (2). The reason for limited detection relates to how many at-risk individuals are studied with CT and to how we best define this risk. Detection and careful management of indeterminate pulmonary nodules are integral parts of this effort. Lung cancer screening using chest CT also raises many questions, some of which could be addressed with well poised biomarkers. For example, who is at utmost risk for lung cancer? How do we expand the screening criteria from the NLST without causing more harm than good? Once the CT screening studies are done, how do we approach a non-invasive diagnosis of lung cancer? How do we prevent the overdiagnosis bias? In a previous report, we discussed the difference between biomarkers of risk for developing disease and diagnostic biomarkers (3). Here we focus on biomarkers that could be used in a risk assessment evaluation for screening programs (Figure 1). We do not discuss diagnostic biomarkers, predictive biomarkers of disease behavior, or biomarkers that could be used as intermediate endpoint for chemoprevention.

Figure 1 Schematic representation of the clinical setting of biomarkers of risk for developing lung cancer.

An ideal biomarker to assess the risk of developing lung cancer

Biomarkers are usually understood as molecular entities quantifiable in biological specimens. An ideal biomarker would be easily measurable (of sufficient abundance), accurate, quantitative, reproducible, biological plausible, cheap and adopted by practitioners to modify the management of high risk individuals (4). None of the candidate molecular biomarkers have demonstrated thus far reduction in lung cancer mortality. Regardless, there is tremendous growth in biomarker research because of the incredible potential it would offer. In this context, we are looking for biomarkers that are typically better at providing strong positive predictive value (PPV) (usually with great specificity) or negative predictive value (NPV) (usually with greater sensitivity). The ideal biomarker will provide insights into the management of high risk individuals. Several investigators have developed models to predict individual’s risk of developing lung cancer (5-15). These models differ in the number and type of predictor variables and most models do not include molecular biomarkers. To date, no single model is systematically applied to high-risk individuals as a screening tool. Yet CMS recommends the use of decision aids as part of shared decision-making during the required visit prior to screening (16). None of the decision aids have included molecular biomarkers, and of those have made the guidelines recommendations for Components Necessary for High-Quality Lung Cancer Screening (17) neither in the ATS ACCP guidelines for implementation (16). Biomarkers are likely to get there as evidence for clinical utility is being tested. The evidence will include stage shift, added value to existing clinical tools, cost effectiveness and hopefully cancer control.

Biomarkers of risk of developing lung cancer

In this section we will discuss current molecular biomarkers of risk assessment in those without measurable disease and before a chest CT has been done. Consideration of the use of such biomarkers should trigger a discussion with the patient before ordering it to address the intent of the test and the implications of the possible results. Many biomarkers have been developed over the years to predict tumor development (18).

Let us consider the characteristics of such a biomarker to assess the risk of lung cancer. For screening purposes, given the low prevalence of disease, a strong NPV of a test is a very attractive feature. Thus, high sensitivity with low false negative could rule out disease and reassure individuals. In this respect a negative chest CT provides great risk reduction (19).

Some biomarkers may be quite sensitive at the risk of overcalling the disease. This is common with inflammatory markers and miRNAs, which are typically elevated during pathogenesis and yet not associated with measurable disease. These markers are associated with increased odds of developing the disease at five years but with relatively small odds ratios. But it is unknown at what odds ratios it is worth incorporating these biomarkers into screening programs remains unknown.

Some serum-based inflammation marker levels are associated with prospective lung cancer risk. A panel of 11 proteins was associated with lung cancer risk in the PLCO trial where four markers (CRP, BCA-1/CXCL13, MDC/CCL22, and IL-1RA) provided an overall estimated 10-year cumulative risks of lung cancer of 0.16% in never smokers, 1.8% in former smokers, and 5.0% in current smokers. Such a profile is a good candidate for validation in other cohorts and registry studies (20).

Circulating miRNA profiles are associated with malignancy, including lung cancer (21). They also are stable in blood despite high levels of circulating RNases, making them prime candidates to serve as biomarkers of disease (22,23). Although the strength of these signatures seems to reside in the diagnostic setting (24-26) by potentially reducing the need for additional diagnostic evaluation, it remains to be determined whether these may add value to a true risk assessment strategy.

High specificity on the other hand is always desirable so we do not overcall cancers (false positive). Should such a test be positive, it would push individuals into a higher risk group to consider appropriate surveillance. A few examples of such candidates include autoantibodies, epithelial chromosomal imbalances, and cfDNA, and they are summarized here.

Autoantibodies to tumor associated antigens have been found to precede clinical presentation s of cancer by months to years (27-29), and they may actually circulate before the disease is measurable on CT of the chest (30-33). The difficulty with using autoantibodies as a screening tool is that the sensitivity is around 40%, thereby missing quite a few cancers. A positive test may be useful as an adjunct to detection of the disease by CT or bronchoscopy and could inform the decision of surveillance versus further intervention. By using a panel of antigens, autoantibodies can be detectable 1–5 years before detection of lung cancer on incidence screening (27). The robust specificity of this approach indicates that autoantibody panels may make a significant contribution in the future to the diagnosis and screening of individuals at risk for lung cancer. As a screen, such autoantibody test may be more appropriate for populations at high risk for lung cancer.

Chromosomal imbalances have been tested in the sputum of high risk individuals and provide a candidate biomarker of tumor development (34,35). Using probes to EGFR, MYC, 5p15, and CEP 6 by multicolor FISH, Varella-Garcia et al. demonstrated CA-FISH as a potential marker for incident lung cancer (36). Whether this approach may assess the lung cancer risk of high risk individuals in a screening context needs to be demonstrated.

Another example of a high specificity test is circulating tumor DNA (ctDNA). The presence of cell free DNA circulating in plasma or serum has been described in patients with cancer (37,38). The analysis of ctDNA may give valuable information about the underlying genomic alterations of individual’s tumor. Although the precise mechanism of DNA release into the blood remains unknown, it is likely to be derived from apoptotic and necrotic tumor cells. ctDNA is exquisitely specific but its sensitivity is limited by the number of circulating molecules, and as a screening tool suffers the risk of missing too many cancers (39). Such markers, however, could be helpful when positive and provide diagnostic and prognostic information. The diagnostic accuracy of quantitative analysis of circulating tumor DNA is not very different than conventional serum biomarkers for lung cancer screening. Yet the sensitivity of the assay remains the major limitation of the test even in stage 1 lung cancer. A multigene panel analysis of ctDNA may lead to increased sensitivity, but for now this marker is more likely to apply to a diagnostic setting.

What would such biomarker of risk really measure?

The biomarker could measure a genetic risk (e.g., altered metabolism of carcinogens, DNA repair machinery abnormalities, predisposition to inflammation, or germline mutations) or the influence of the environment on tumor development (exposure to carcinogens or surrogates of risk such as epigenetic changes in the airway epithelium or the prevalence of preinvasive lesions). There has been recent interest in the potential for genetic variants to give insight into the pathogenesis of lung cancer. These variants indicate that there is great heterogeneity in mechanisms of disease development that is modulated by inherited genetic variation (40). With these come the opportunity to improve models predicting lung cancer risk. SNP genotype signature data may add value to the performance of clinical variables for risk prediction by re-assigning risk in 26% of the screening participants (41). Yet most risk assessment predictive models have shown little improvement with the addition of genetic factors (42-44). While genetics are not yet incorporated into lung cancer malignancy prediction models beside a family history of lung cancer, it is likely that a profile of many genetic markers will be necessary to be clinically useful biomarkers for risk assessment. Future development of predictive models will incorporate previously identified predictors and newly identified biomarkers, genetic or otherwise.

What is the metric for success of these biomarkers of risk assessment?

A critical goal of biomarker research is to add value to existing risk assessment standards, and the biomarker should be designed to supplement the current diagnostic/management tools (45). The biomarker of interest for risk assessment for lung cancer should therefore provide added value to the clinical or risk models such as the PanCan, Bach or Spitz models. Odds ratios and post-test probabilities are metrics of most relevance for clinical practice, but these metrics are often not sufficient (46,47). A good predictive value of any biomarker or test result, by itself, is no guarantee for relevant added predictive value when combined with the standard predictors. Comparing area under the ROC curves, testing for the net reclassification improvement [how many times a sample is now reclassified in the correct group of cancer vs. no cancer (48) for categorical variables], or using the integrated discrimination improvement (IDI) for continuous variables may provide other valuable metrics of success. The positive likelihood ratio (PLR) indicates how much the odds of the disease increase when a test is positive, and the negative likelihood ratio (NLR) indicates how much the odds of the disease decrease when a test is negative. Likelihood ratios of >10 or <0.1 generate often conclusive changes from pretest to posttest probability. The ultimate metric of success for biomarker of risk in a screening program, however, is to reduce the number needed to screen and to increase the number of cancers found at an early stage.

Reasons for failure

Some of the reasons why the field has failed to deliver greater number of effective biomarkers of risk are discussed above in the section describing an ideal biomarker. The lack of analytical reproducibility or of biological variability is a common obstacle. Attempts to fit the biomarker with characteristics that are not inherently strong also often lead to lack of validation. Appropriate study design is crucial for the derivation of a potential biomarker for screening much like the successful validation of a promising biomarker for clinical use. Derivation of a biomarker useful for lung cancer screening may be more successful using a nested case-control study design within a prospective longitudinal cohort following the PRoBE design (49). These are difficult studies to design in some biological specimens given the limited number of prospective cohorts meeting the tissue collection required for the analytical method of interest, such as the analysis of RNA in airway specimens (bronchial or nasal) or volatile organic compounds in the exhaled breath. Many candidate biomarkers have been developed in a case control study design. Most of them have failed when applied to risk assessment because of the lack of sensitivity. One common mistake is to ignore the inherent characteristics of the biomarker and apply it to a clinical situation outside of the context in which it was designed.

Areas to explore

There is a clear need to evaluate the benefit of risk assessment biomarkers with repeated measures over time. The assumption is that as risk increases, molecular moieties should be more readily available (e.g., in the circulation) over time. This may be true for tumor specific antigens and ctDNA, but would not apply to genetic risk. Statistical models could test the ability of different biomarkers to complement each other in a single population, in order to eventually determine those that could be tested prospectively. Given biomarkers’ non-specificity and commonality in predicting diseases, modeling multiple markers of the same clinical diagnostic criteria can be used to develop more accurate individual and cumulative risk estimates for specific diseases. We should therefore consider a joint effects approach to determine individual biomarker associations as well as to ascertain the impact of simultaneous increases in multiple biomarker concentrations on the diagnosis of lung cancer. Biomarkers of risk would ideally be tested prospectively in a randomized clinical trial. However, given the relatively low prevalence of this disease, the number needed to screen may be prohibitive; therefore the development of registries is most appropriate. Registries are longitudinal cohort prospective studies where a biomarker is introduced but does not force providers to change their management. The lead time to diagnosis may be sufficient to cause a stage shift and therefore improve outcome. The discovery of other traits associated with lung cancer using PheWAS studies may be of relevance to the field in terms of increasing our ability to refine the at-risk population (50-53). Finally, it is through better understanding of the biology of cancer development and of preinvasive lesions that we will shed further light into the field of biomarker research.

Acknowledgements

Funding: The work was supported by the National Institutes of Health (CA152662 and CA186145 to PPM).

Footnote

Conflicts of Interest: The authors have no conflicts of interest to declare.

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