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Objective: Circulating biomarkers related to insulin-like growth factor (IGF) signaling are associated with disease progression in multiple carcinomas, but their potential diagnostic value for lung cancer screening has been inadequately examined. We evaluated 9 circulating IGF-related factors for their ability to assign clinical significance to indeterminate pulmonary nodules identified via computed tomography-based radiologic studies., Methods: Patients (n = 224 stage I non-small cell lung cancer; n = 123 benign) were enrolled by Rush University and the Mayo Clinic and had pretreatment serum evaluated for levels of IGF-1, IGF-2, and insulin-like growth factor binding proteins (IGFBPs) 1-7. The Mann-Whitney rank-sum test and receiver-operator characteristics curves were used to assess differences in biomarker concentrations relevant to malignant versus benign pathology. These targets were used to help refine our companion blood test for assigning clinical significance to computed tomography-detected solitary nodules (discovery cohort, n = 94) and were validated against an independent cohort from the Mayo Clinic (n = 81)., Results: Patients with benign pulmonary nodules were found to have serum concentrations of IGFBP-3, IGFBP-5, IGF-1, and IGF-2 that were higher (P = .001, P < .001, P = .002, and P = .011, respectively) than those with non-small cell lung cancer, with distinct associations with histologic subtypes observed. Refinement of our multianalyte classification algorithm using IGF-related factors provided a new panel consisting of interleukin-6, interleukin-1 receptor antagonist, interleukin-10, stromal cell-derived factor-1(α + β), IGFBP-4, IGFBP-5, and IGF-2 with improved assay performance-achieving a (validated) negative predictive value of 100%., Conclusions: Our findings suggest a divergent role for IGF signaling in the biology of benign and malignant pulmonary nodules. Upon further validation, these observations may help identify cases of false positives resulting from computed tomography-based screening studies., (Copyright © 2015 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)

Objective: Molecular diagnostics capable of prognosticating disease recurrence in stage I non-small cell lung cancer (NSCLC) patients have implications for improving survival. The objective of the present study was to develop a multianalyte serum algorithm predictive of disease recurrence in stage I NSCLC patients., Methods: The Luminex immunobead platform was used to evaluate 43 biomarkers against 79 patients with resectable NSCLC, with the following cohorts represented: stage I (T(1)-T(2)N(0)M(0)) NSCLC without recurrence (n = 37), stage I (T(1)-T(2)N(0)M(0)) NSCLC with recurrence (n = 15), and node-positive (T(1)-T(2)N(1)-N(2)M(0)) NSCLC (n = 27). Peripheral blood was collected before surgery, with all patients undergoing anatomic resection. Univariate statistical methods (receiver operating characteristics curves and log-rank test) were used to evaluate each biomarker with respect to recurrence and outcome. Multivariate statistical methods were used to develop a prognostic classification panel for disease recurrence., Results: No relationship was found between recurrence and age, gender, smoking history, or histologic type. Analysis for all stage I patients revealed 28 biomarkers significant for recurrence. Of these, the log-rank test identified 10 biomarkers that were strongly (P < .01) prognostic for recurrence. The Random Forest algorithm created a 6-analyte panel for preoperative classification that accurately predicted recurrence in 77% of stage I patients tested, with a sensitivity of 74% and specificity of 79%., Conclusions: We report the development of a serum biomarker algorithm capable of preoperatively predicting disease recurrence in stage I NSCLC patients. Refinement of this panel might stratify patients for adjuvant therapy or aggressive recurrence monitoring to improve survival., (Copyright © 2012 The American Association for Thoracic Surgery. Published by Mosby, Inc. All rights reserved.)