Introduction

Results from the 10-year National Lung Screening Trial (NLST) show low dose CT screening confers a survival benefit in the at-risk population [1]. Although radiographic imaging is the de-facto screening modality, circulating biomarkers have potential to enhance early detection initiatives and further improve outcomes [2]–[6]. Our group and others have been developing autoantibody assays that could complement CT scanning in lung cancer diagnosis and management [4]–[9]. It is now well established that cancer patients produce autoantibodies to tumor proteins that are mutated, misfolded, ectopically presented, over-expressed, aberrantly degraded or anomalously glycosylated [4]–[13]. Assays comprised of panels of robust and complementary markers selected from an extensive repertoire of tumor-associated antibodies are designed to compensate for tumor heterogeneity. Biological amplification of low frequency cellular aberrancy makes autoantibodies logical biomarkers for early detection and a prevailing strategy for detecting occult malignancy [2]–[15]. Six markers from prior discovery were analyzed in a comparative study using samples from two independent CT screening studies. Integrity and relative comparability of two screening sample cohorts from, each with a high percentage of cancer samples drawn prior to radiographic detection, offered a unique opportunity to test principles, precepts, and dominant objectives of investigation to date [7]–[9], [16]–[18].

A panel of six autoantibody markers were used to assay samples from the Mayo Clinic CT screening trial, to gather normal distribution values, and generate a cutoff value that might be used to improve efficiency of lung cancer screening. Established cutoff values were applied to 285 samples from 95 participants of a regional CT screening study in the 5^th district of Kentucky (Appalachia). The primary objective of the study was to determine the ability of an autoantibody profile to detect lung cancers at the time of or before CT scan. The uniformity of sample collection and study entry criteria was an important standard for analysis within and between the two screening sample cohorts. Class prediction in sample sets comprised predominantly of occult lung cancers (prior to radiographic detection) is a unique aspect of this analysis. Accurate classification of stage I screening detected cancers was a secondary metric.

Materials and Methods

Kentucky cohort

The Marty Driesler Lung Screening Project was a community-based CT screening study that accrued 254 at risk subjects from Eastern Kentucky between 2005 and 2008 [18]. Eligibility criteria included age 55 to 75 years, 30 pack-years history of smoking, and no other malignancy within five years of study entry. Cancer rate was 2.6%. All subjects provided written informed consent prior to any research procedures.

Since analysis of all available samples was cost prohibitive, a sample set of two hundred fifty six samples from ninety-five participants was constructed by an independent investigator and analyzed in a blinded fashion. The test cohort of nineteen lung cancer samples included five stage I screening detected lung cancers (three prevalence, two incidence), and four lung cancers diagnosed clinically one to five years after the last serial screening CT and corresponding blood sample. One case of head and neck cancer was diagnosed during the screening period, and six other non-thoracic malignancies were diagnosed up to five years from the last lung cancer screening CT. All cancer cases are summarized in Table 1. One or more non-malignant pulmonary nodules were noted in 56% of the study cohort. Dominant non-malignant radiographic findings included emphysema, mediastinal adenopathy and granulomatous disease.

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Table 1. Characteristics of cancers associated with the KY screening cohort.

https://doi.org/10.1371/journal.pone.0087947.t001

Results

The additive sum of absolute fluorescence from six markers was used as an intuitive measure of overall autoantibody reactivity to provide a single value point for each sample, define distribution in the at risk population, and assign cutoffs for cancer prediction in an independent cohort. The median value across 180 non-cancer samples from the Mayo Clinic sample cohort was 1126 fluorescent units (FU), with 25%/75% quartile values of 640 and 2076 FU respectively; there was one extreme outlier. A cutoff of 640 fluorescent units offered 88% sensitivity across fifty cancer samples in the Mayo cohort, which included accurate classification of 6/6 established stage I cancers and 38/44 samples drawn one to five years prior to radiographic appearance. By comparison the median value across 237 non-cancer samples from the Kentucky cohort was 726 fluorescent units (FU), with 25%/75% quartile values of 461 and 1249 FU respectively, which is roughly one third lower than measured in the Mayo Clinic sample cohort. A contingency chart (table 2) shows class prediction in the Kentucky cohort at the predetermined cutoff of 640 FU, and also bares the effect of inflated cutoff values on sensitivity and specificity that resulted from the discrepancy between the training and testing cohorts. The cutoff of 640 FU accurately classified 102/237 non-lung cancer samples (43%) and 11/19 cancer samples (58%), which included 4/5 stage I lung cancers (80%), and 7/14 of occult cancer samples (50%) one to five years prior to radiographic appearance. Class prediction and temporal relationship of sample draw to cancer diagnosis is summarized in table 1.

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Table 2. Contingency chart: class predictions by sample at various marker levels in the Kentucky screening cohort.

https://doi.org/10.1371/journal.pone.0087947.t002

Squamous and adenocarcinoma histologies were both represented among the true positives; there was nothing uniquely apparent about false negative samples. Other cancers accounted for 13/135 false positive measures (Table 1). Six of the seven independently diagnosed non-thoracic malignancies in the KY cohort measured positively in one or more annual samples. The single highest value was a subject lost to follow-up after prevalence screening who was diagnosed with extranodal marginal zone B-cell lymphoma (MALT) five years after enrollment. Benign intrathoracic findings were common to subjects with false positive and true negative measures. The majority of false positives represented persistent elevations across serial screening cycles. Among the 130 false positive samples (>640 FU) in subjects with at least two annual samples, only six (4.6%) were singular events within the series of two or more annual measures.

Discussion

Primary objectives were to confirm the principles and precepts of autoantibody profiling and assess the potential of an autoantibody profile to increase efficiency and diagnostic accuracy of screening CT. Samples from the Mayo Clinic CT screening trial were used to define range and distribution of a composite measure within a screening population, and assign a cutoff value that would allow maximum sensitivity for lung cancers at and below the detectable limits of CT scanning. Distribution measures and relative cutoffs for cancer detection were tested in an independent screening cohort from the 5^th district of Kentucky. A cutoff set on the lower quartile of 180 noncancer controls in the Mayo cohort provided reliable detection of established stage I cancers and capacity to detect a percentage of incidence cancers prior to radiographic appearance in both cohorts. Observed frequency of serially positive and serially negative values across annual repeats in the Kentucky screening cohort suggests that autoantibody levels have a specific biologic basis even when there is no clinically apparent significance to the measure. The assay does not appear specific for lung cancer, although the variety of non-thoracic malignancies precludes any conclusion about histologic specificity.

Inflated cutoff values that resulted from the notable discrepancy in the quartile distributions between the two cohorts skewed the dynamic range towards specificity in the Kentucky cohort. Although demographics, differences in eligibility criteria of the two studies and numerous independent clinical variables could account for this discrepancy, neither cohort is adequately sized for multivariable stratification. Conversely, observed differences in two independent but uniformly collected, moderately large and relatively comparable sample sets point strongly to sample age, processing, handling and/or storage as a source of preclinical error. Specifically, distribution analysis and assignment of cutoff values based on archived samples from two high-risk cohorts seems likely to have identified a biological effect that might not have been recognized with alternate study designs. Despite the presumption that autoantibodies are resilient biomarkers, there is a paucity of data on the consistency of autoantibody measures under various storage conditions and durations. Albeit limited, literature indicates serum antibody levels increase in cryopreserved samples over years of storage, possibly related to antigen-antibody complex dissociation and protein degradation [19], [20]. Importantly, the current data shows how the validation process can be encumbered by variables unique to archived sample sets, which must be considered when transitioning from laboratory-based analysis to implementation in population-based applications.

Even when given allowance for quantifiable preclinical error and the effect of inflated cutoff values on predictive accuracy in the validation set, the data discourage more advanced validation. The appeal of detecting occult disease with lead-time advantage over CT scanning is tempered by excessive false negative rates, and certainly restricts this assay’s utility in selecting individuals that most warrant serial imaging [5]. Interpretation of positive measures is further confounded by the apparent lack of specificity for thoracic malignancy.

Provisional assessment of the small number of radiographically detectable cancers in post hoc analysis approximates that of autoantibody profiles independently validated by other groups testing for established cancers [21]–[23]. If by extension we assume the best achievable sensitivity for stage I cancer is 80%, with a corresponding specificity of 40% expanding our analysis to sample sets with larger number of established cancers does not seem warranted. Also similar to other assays in the literature, a provisional sensitivity of 40% for established disease corresponds to specificity >90% [21]–[24]. Adjusting the cutoff for high specificity seems only to further deviate from a conventional screening paradigm. If used to further stratify cases by probability of cancer, however, a cutoff that favors specificity could mitigate inter-reader variability and reduce the number of false negative readings on screening CT scans [25], [26]. A highly specific assay might also help discriminate benign from malignant nodules identified during screening, even though predictive value will be compromised by the promiscuity of the assay for both occult and radiographically apparent disease [27]. In summary, this report does not draw conclusions about future utility of this approach, but this validation study does not seem to support use of this assay as a primary population-based screening tool. Combining additional investigation with knowledge of this assay’s performance may identify other logical areas for autoantibody profiling in lung cancer diagnosis and management.