Value of circulating insulin-like growth factor–associated proteins for the detection of stage I non–small cell lung cancer

Abstract

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. 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). 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%. 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.