Due to poor compliance and uptake of LDCT screening among high-risk populations, lung cancer is often diagnosed in advanced stages where treatment is rarely curative. Based upon the American College of Radiology’s Lung Imaging and Reporting Data System (Lung-RADS) 80–90% of patients screened will have clinically “non-actionable” nodules (Lung-RADS 1 or 2), and those harboring larger, clinically “actionable” nodules (Lung-RADS 3 or 4) have a significantly greater risk of lung cancer. The development of a companion diagnostic method capable of identifying patients likely to have a clinically actionable nodule identified during LDCT is anticipated to improve accessibility and uptake of the paradigm and improve early detection rates. Using protein microarrays, we identified 501 circulating targets with differential immunoreactivities against cohorts characterized as possessing either actionable (n = 42) or non-actionable (n = 20) solid pulmonary nodules, per Lung-RADS guidelines. Quantitative assays were assembled on the Luminex platform for the 26 most promising targets. These assays were used to measure serum autoantibody levels in 841 patients, consisting of benign (BN; n = 101), early-stage non-small cell lung cancer (NSCLC; n = 245), other early-stage malignancies within the lung (n = 29), and individuals meeting United States Preventative Screening Task Force (USPSTF) screening inclusion criteria with both actionable (n = 87) and non-actionable radiologic findings (n = 379). These 841 patients were randomly split into three cohorts: Training, Validation 1, and Validation 2. Of the 26 candidate biomarkers tested, 17 differentiated patients with actionable nodules from those with non-actionable nodules. A random forest model consisting of six autoantibody (Annexin 2, DCD, MID1IP1, PNMA1, TAF10, ZNF696) biomarkers was developed to optimize our classification performance; it possessed a positive predictive value (PPV) of 61.4%/61.0% and negative predictive value (NPV) of 95.7%/83.9% against Validation cohorts 1 and 2, respectively. This panel may improve patient selection methods for lung cancer screening, serving to greatly reduce the futile screening rate while also improving accessibility to the paradigm for underserved populations.

Background/Objectives: Lung cancer screening can reduce patient mortality. Multiple issues persist including timely management of patients with a radiologically defined indeterminate pulmonary nodule (IPN), which carries unknown pathological significance. This pilot study focused on combining demographic, clinical, radiographic, and common circulating biomarkers for their ability to aid in IPN risk of malignancy prediction. Methods: A case-control cohort consisting of 379 patients with IPNs (251 stage I lung tumors and 128 nonmalignant nodules) was used for this effort, divided into training (70%) and testing (30%) sets. Demographic variables (age, sex, race, ethnicity), radiographic information (nodule size and location), smoking pack-years, and plasma biomarker levels of CA-125, SCC, CEA, HE4, ProGRP, NSE, Cyfra 21-1, IL-6, PlGF, sFlt-1, hs-CRP, Ferritin, IgG, IgE, IgM, IgA, and Kappa and Lambda Free Light Chains were assessed for this purpose. Results: Multivariable analyses of biomarker, demographic, and radiographic variables yielded a model consisting of age, lesion size, pack-years, history of extrathoracic cancer, upper lobe location, spiculation, hs-CRP, NSE, Ferritin, and CA-125 (AUC = 0.872 in training, 0.842 in testing) with superior performance over the Mayo Score model, which consists of age, lesion size, history of smoking, history of extrathoracic cancer, upper lobe location, and spiculation (AUC = 0.816 in training, 0.787 in testing). Conclusions: In conclusion, a simple reduced algorithm consisting of biomarkers, clinical information, and demographic variables may have value for malignancy prediction of screen-detected IPNs. Upon further validation, this method stands to reduce the need for serial radiographic studies and the risks of diagnostic delay.

Background: Familial dysbetalipoproteinemia (FDB) is a genetic lipoprotein disorder that can develop in patients homozygous for the APOE2 genotype (ε2/ε2). It is associated with decreased clearance of remnant lipoproteins and increased atherosclerotic cardiovascular disease (ASCVD) risk disproportionate to their level of LDL-C. A goal of this study was to develop a screening test for the ε2/ε2 genotype based on routinely available lipid tests and to determine those at most risk for ASCVD. Methods: After assembly of a primary prevention cohort from the UK Biobank (n= 269,895), gene array and exome data was utilized to classify patients as being ε2/ε2 genotype positive or negative. Lipid profiles and APOB levels were extracted and the number of ASCVD events was tabulated during a 15-year follow-up period. Results: Using a newly developed equation for estimating APOB (eAPOB) with lipid panel test results, the ratio of measured APOB to eAPOB was better than any other individual lipid test or ratio for identifying patients with the ε2/ε2 genotype (AUC: APOB/eAPOB: 0.990 (0.986-0.994), nonHDL-C/APOB: 0.961 (0.952-0.970), APOB: 0.955 (0.949-0.961), VLDL/TG: 0.788 (0.771-0.804)). The majority of ε2/ε2 patients could be identified with the APOB/eAPOB ratio even before they expressed the FDB phenotype with elevated TG and nonHDL-C. The PCE or PREVENT risk equations were the most accurate method for identifying higher risk patients (AUC: PREVENT: 0.690 (0.637-0.742), PCE: 0.697 (0.645-0.749)). Conclusion: The APOB/eAPOB ratio can be used to accurately identify the ε2/ε2 genotype and conventional risk equations are the best method for determining those at risk for ASCVD.

Background: Familial dysbetalipoproteinemia (FDB) is a genetic lipoprotein disorder that can develop in patients homozygous for the apolipoprotein 2 (APOE2) genotype (APOE ɛ2/ɛ2). It is associated with decreased clearance of remnant lipoproteins and increased atherosclerotic cardiovascular disease (ASCVD) risk disproportionate to low-density lipoprotein-cholesterol level. A goal of this study was to develop a screening test for the ɛ2/ɛ2 genotype based on routinely available lipid tests and to determine those at most risk for ASCVD. Materials and Methods: After assembly of a primary prevention cohort from the UK Biobank (n = 298 249), gene array and exome data were utilized to classify patients as being ɛ2/ɛ2 genotype positive or negative. Lipid profiles and apolipoprotein B‎ (APOB) levels were extracted and the number of ASCVD events was tabulated during a 15-year follow-up period. Results: Using a newly developed equation for estimating APOB (eAPOB) with lipid panel test results, the ratio of measured APOB to eAPOB was better than any other individual lipid test or ratio for identifying patients with the ɛ2/ɛ2 genotype [area under the curve‎ (AUC): APOB/eAPOB: 0.990 (0.986–0.994), nonhigh-density lipoprotein cholesterol/APOB: 0.961 (0.952–0.970), APOB: 0.955 (0.949–0.961), very low-density lipoproteins/triglycerides: 0.788 (0.771–0.804)]. Most ɛ2/ɛ2 patients could be identified with the APOB/eAPOB ratio regardless of their FDB phenotype status. The PCE or PREVENT risk equations were the most accurate method for identifying higher risk patients [AUC: PREVENT: 0.690 (0.637–0.742), PCE: 0.697 (0.645–0.749)]. Conclusion: The APOB/eAPOB ratio can be used to accurately identify the ɛ2/ɛ2 genotype and conventional risk equations are the best method for determining those at risk for ASCVD.