Biomarkers to the Rescue in a Lung Nodule Epidemic

In the article that accompanies this editorial, Sozzi et al address whether a microRNA (miRNA) biomarker signature can discriminate between benign and malignant pulmonary nodules discovered by lung cancer screening. After the encouraging results of the National Lung Screening Trial, we are facing an impending epidemic of pulmonary nodules rising from a combination of incidental findings and the proliferation of computed tomography (CT) screening programs targeting high-risk individuals for lung cancer. The expected frequency of lung nodule detection (20% to 60% of high-risk individuals), and the fact that the large majority of these lung nodules are benign (up to 96%), make the management of such indeterminate pulmonary nodules (IPNs) challenging. Although controversy remains about the definition of IPNs, most define these lesions as noncalcified lung nodules, solid, partially solid, or ground-glass opacities, which, assuming a spherical nodule, have diameters ranging from 5 to 15 mm in diameter. The probability that such nodules represent cancer, along with patient and physician experiences, will drive the diagnostic workup. Although most IPNs represent benign disease, significant morbidity and cost are associated with their management. The strong rationale for the evaluation of biomarkers in the context of indeterminate pulmonary nodules is further supported by the potential risks associated with the invasive evaluation of these nodules. These risks include patient anxiety, pneumothorax requiring chest tube (approximately 6.6%) after CT-guided fine-needle aspiration, and a high rate (24%) of nontherapeutic operations that result in a benign diagnosis—all of which must be balanced against missing a potentially curable lung cancer that would ultimately result in death. Thus, ruling out malignancy noninvasively with a biomarker would be a useful strategy for dealing with this lung nodule epidemic. The work presented by Sozzi et al is commendable on several fronts. The cancer biomarker signature was carefully designed and provides refinement to the specificity of the original signature in 84 samples from the Multicentric Italian Lung Detection (MILD) screening trial and validation in an independent data set of 939 samples (including 85 lung cancers) collected longitudinally in the MILD study. The integrated miRNA classifier signature provides a risk score that was then categorized into high, intermediate, and low risk. These three risk groups were prespecified on the basis of predefined cut points for four different signatures. Emphasis was given to predictive values as opposed to sensitivity and specificity, which are known to be unstable between populations given the time-varying nature of the biomarker and the clinical onset time of the disease. Positive and negative predictive values, however, are central to individual counseling. The test scores were blinded to clinical outcomes and analyzed by an independent research group. miRNAs are a large family of single-stranded, noncoding RNAs that direct the post-transcriptional repression of protein-coding genes. In human cells, more than 1,700 miRNAs have been identified and are predicted to regulate the activity of numerous protein-coding genes (www.mirbase.org). miRNAs have many desirable features as biomarkers. They are stable, quantifiable, and relatively abundant in the circulation. They also associate in circulation with lipids and vesicles. Although caution has been raised about the reproducibility of the measurements, the approach taken by this group attempts to circumvent this problem by using ratios of miRNAs, providing a normalization method of those measurements. Of the samples, 13% were not evaluable because of hemolysis. Two hundred microliters of plasma were required, which renders future validation studies feasible. The authors addressed the value of an miRNA risk score that is the result of the integration of four separate signatures developed in different clinical contexts: a biomarker of risk of developing cancer, a biomarker of risk of developing aggressive cancer, a diagnostic biomarker (presence of cancer), and a prognostic biomarker (presence of aggressive cancer). The clinical characteristics from the two groups were balanced well; yet the size of the nodules between benign and malignant groups varies inevitably. Focusing on the 652 individuals who entered the CT arm, 59% of the nodules were less than 5 mm in diameter, 26% were between 5 and 10 mm in diameter, and 15% were larger than 10 mm in diameter. When selecting highand intermediate-risk groups, the biomarker provided a negative predictive value (NPV) of 98% and a false positive rate (FPR) of 19%. The signature has encouraging characteristics in the validation set with an NPV of 99.7% and an FPR of 19.4% as compared with low-dose CT (LDCT) alone with an NPV of 99% and an FPR of 59%. When combined, the test achieved an NPV of 97% and an FPR of 4%. The combination of the LDCT and the biomarker is therefore associated with a fivefold reduction in FPR for lung cancer. The research followed most of the reporting standards proposed in the Standards for Reporting of Diagnostic Accuracy initiative. Time dependency analysis confirmed that the biomarker was remarkably stable up to 24 months before diagnosis. The velocity (rate of increase) of the biomarker was not reported. The results of this study are encouraging. The authors refined the signature, tested it in a most informative population of screeningdetected cancers and matched controls, and found quite strong performance characteristics, particularly driven by the high NPV. Although this represents a step towards an investigation of clinical JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 32 NUMBER 8 MARCH 1