Lung Cancer Diagnosis from Proteomic Analysis of Preinvasive Lesions

Abstract

Early detection may help improve survival from lung cancer. In this study our goal was to derive and validate a signature from the proteomic analysis of bronchial lesions that could predict the diagnosis of lung cancer. Using previously published studies of bronchial tissues we selected a signature of 9 matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) mass to charge ratio features to build a prediction model diagnostic of lung cancer. The model was based on MALDI MS signal intensity (MALDI score) from bronchial tissue specimens from our 2005 published cohort of 51 patients. The performance of the prediction model in identifying lung cancer was tested in an independent cohort of bronchial specimens from 60 patients. The probability of having lung cancer based on the proteomic analysis of the bronchial specimens was characterized by an area under the receiver operating characteristic curve of 0.77 (95% CI 0.66 to 0.88) in this validation cohort. Eight of the 9 features were identified and validated by Western blotting and immunohistochemistry. These results demonstrate that proteomic analysis of endobronchial lesions may facilitate the diagnosis of lung cancer and the monitoring of high risk individuals for lung cancer in surveillance and chemoprevention trials.

Clinical Significance and Prognostic Value of Pentraxin-3 as Serologic Biomarker for Lung Cancer

“…Recently, many discoveries of non-protein serum markers have been documented-eg, mutated DNAs, methylated DNAs, RNAs (Keller et al, 2011;Rabman et al, 2011;Sehramm et al, 2011), which were regarded as potential tool for non-invasive lung cancer diagnosis and significantly improved diagnostic accuracy for lung cancer. However, protein markers measurable in serum are the most applicable for clinical routine assessments and large-scale population studies (Locker et al, 2006), because generally such tests are non-invasive, require less than 100 μL serum, have low dependence on operator expertise and special equipments, are low cost, have high reproducibility, and samples need no pretreatment (eg, extraction, purification or reverse transcription).…”

Biophysical technologies for understanding circulating tumor cell biology and metastasis

“…The advantage of MALDI IMS over other in situ molecular analysis techniques is the lack of target-specific reagents required, as is needed in immunohistochemistry (73). This technology has already been used to characterize diagnostic and prognostic markers in tumors such as breast (74), brain (75), colon (76), gastric (77), lung (78)(79)(80), and prostate cancer (81). In a large retrospective study by Taguchi et al, blood collected from patients with NSCLC before treatment with an EGFR TKI was analyzed by MALDI IMS and compared against patient outcome, and an algorithm was developed to stratify patients into "good" or "poor" risk groups based on possible clinical benefit from EGFR TKI treatment (82).…”

Implementation of Proteomics for Cancer Research: Past, Present, and Future

“…Lung cancer (Yanagisawa et al, 2007;Yang et al, 2007;Rahman et al, 2011;Yousefi et al, 2012) TEF1 α; A 25-signal Proteomic Signature; Autoantibodies against triosephosphate isomerase and superoxide dismutase (MsSOD); HSP27; Aminopeptidase-P; eIF-5A; 15 distinct MS peaks; PGP 9.5 autoantibody Liver cancer (Orvisky et al, 2006;Sun et al, 2007;Gray et al, 2009;Ressom et al, 2012;Xiao et al, 2012) HOP, hnRNP C1/C2; eIF1A; Multiplex serum markers; Ferritin-light-unit; Adenylate kinase-3a-like1; biliverdin reductase B; Tissue ferritin light chain; V10 fragment of vitronectin; Brain-derived neurotrophic factor…”