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Background: Low-dose computed tomography (CT) lung cancer screening is known to have a high false positive rate. This study aims to survey biomarkers of angiogenesis for those capable of assigning clinical significance to indeterminate pulmonary nodules detected through CT imaging studies., Methods: An institutional database and specimen repository was used to identify 193 patients with stage I non-small cell lung cancer (T1N0M0) and 110 patients with benign solitary pulmonary nodules detected by CT imaging studies. All specimens were evaluated in a blinded manner for 17 biomarkers of angiogenesis using multiplex immunoassays. Biomarker performance was calculated through the Mann-Whitney rank sum U test and a receiver operator characteristic analysis. These data were used to refine our previously reported multi-analyte classification panel, which was then externally validated against an independent patient cohort (n = 80)., Results: A total of 303 patients were screened for 17 biomarkers of angiogenesis. Median nodule size was 1.2 cm for benign cases and 1.8 cm for non-small cell lung cancer, whereas median smoking histories were 25 and 40 pack-years, respectively. Differences in serum concentrations of heparin-binding epidermal growth factor (HB-EGF), epidermal growth factor (EGF), vascular (V)EGF-A, VEGF-C, and VEGF-D were strongly significant (p ≤ 0.001) while follistatin, placental growth factor (PLGF), and bone morphogenic protein (BMP)-9 were significant (p ≤ 0.05) between patients with benign and malignant nodules. Our previously reported multi-analyte classification panel was refined to include interleukin (IL)-6, IL-10, IL-1 receptor antagonist (RA), tumor necrosis factor (TNF)-α, insulin-like growth factor binding protein (IGFBP)-5, IGFBP-4, IGF-2, stromal cell-derived factor (SDF)-1(α+β), HB-EGF, and HGF resulting in improved accuracy and a validated negative predictive value of 96.4%., Conclusions: Angiogenesis biomarkers may be useful in discriminating stage I NSCLC from benign pulmonary nodules., (Copyright © 2015 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

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.)

Background: Insulin-like growth factor 1 (IGF-I), IGF binding proteins (IGFBP) 1 to 7, and C-peptide have been postulated to predict survival in non-small cell lung cancer (NSCLC). Studying serum levels in NSCLC patients treated with surgical resection may provide information on the aggressiveness of tumors and be predictive of disease recurrence., Methods: Immunobead assays were used to measure pretreatment serum levels of IGF-I, IGFBP1 to IGFBP7, and C-peptide in 100 NSCLC patients. Of these, 59 had no metastatic progression (T1 to T4 N0 M0), whereas 41 had positive lymph nodes (T1 to T4 N1 to N3 M0). Data were analyzed using the Mann-Whitney two-sided rank sum test or Kaplan-Meier curves., Results: Low serum IGFBP5 levels correlated strongly with a positive nodal status (p < 0.001) and any incidence of disease recurrence (p = 0.003). Low serum levels of IGFBP5 also predicted poor recurrence-free survivals in the overall cohort (p ≤ 0.001) and in patients with no nodal metastases (p = 0.027). Conversely, a high serum level of IGFBP7 correlated with positive nodal status (p = 0.008), but was not prognostic for recurrence-free survival. No significant correlations were found for IGFBP5 or IGFBP7 for sex, age, race, smoking history, tumor histology, or fasting state., Conclusions: IGFBP5 and IGFBP7 had value as biomarkers for identifying NSCLC progression and patient outcome., (Copyright © 2011 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

We recently reported the development of a multianalyte serum algorithm to identify nodal status in non-small cell lung cancer (NSCLC) patients facing an anatomic resection with curative intent. This study aims to enhance the overall performance characteristics of this test by adding autoantibody biomarkers identified through immunoproteomic discovery. More specifically, we used sera from 20 NSCLC patients to probe 2-D immunoblots of HCC827 lysates for tumor-associated autoantigens. Relevant differences in immunoreactivity associated with pathological nodal status were then identified via tandem mass spectrometry. Identified autoantigens were then developed into Luminex immunobead assays alongside a series of autoantigen targets relevant to early-disease detection. These assays were then used to measure circulating autoantibody levels in the identical cohort of NSCLC patients used in our original study. This strategy identified 11 autoantigens found primarily in patients with disease progression to the locoregional lymph nodes. Custom Luminex-based "direct-capture" assays (25 total; including autoantibody targets relevant to early-disease detection) were assembled to measure autoantibody levels in sera from 107 NSCLC patients. Multivariate classification algorithms were then used to identify the optimal combination of biomarkers when considered collectively with our original 6-analyte serum panel. The new algorithm resulting from this analysis consists of TNF-α, TNF-RI, MIP-1α and autoantibodies against Ubiquilin-1, hydroxysteroid-(17-β)-dehydrogenase, and triosephosphate isomerase. The inclusion of autoantibody biomarkers provided a dramatic improvement in the overall test performance characteristics, relative to the original test panel, including an 11% improvement in the classification efficiency., (Copyright © 2010 UICC.)

Introduction: In non-small cell lung cancer (NSCLC), the presence of locoregional lymph node metastases remains the most important prognostic factor and significantly guides treatment regimens. Unfortunately, currently-available noninvasive staging modalities have limited accuracy. The objective of this study was to create a multianalyte blood test capable of discriminating a patient's true (pathologic) nodal status preoperatively., Methods: Pretreatment serum specimens collected from 107 NSCLC patients with localized disease were screened with 47 biomarkers implicated in disease presence or progression. Multivariate statistical algorithms were then used to identify the optimal combination of biomarkers for accurately discerning each patient's nodal status., Results: We identified 15 candidate biomarkers that met our criteria for statistical relevance in discerning a patient's preoperative nodal status. A 'random forest' classification algorithm was used with these parameters to define a 6-analyte panel, consisting of macrophage inflammatory protein-1alpha, carcinoembryonic antigen, stem cell factor, tumor necrosis factor-receptor I, interferon-gamma, and tumor necrosis factor-alpha, that was the optimum combination of biomarkers for identifying a patient's pathologic nodal status. A Classification and Regression Tree analysis was then created with this panel that was capable of correctly classifying 88% of the patients tested, relative to the pathologic assessments. This value is in contrast to our observed 85% classification rate using conventional clinical methods., Conclusions: This study establishes a serum biomarker panel with efficacy in discerning preoperative nodal status. With further validation, this blood test may be useful for assessing nodal status (including occult disease) in NSCLC patients facing tumor resection therapy.