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Background: TITF1/NKX2-1 is a master transcription factor of peripheral airway cells and a known lineage-specific oncogene in lung cancer cells, whereas it also suppresses tumor progression and is associated with favorable prognosis of lung adenocarcinoma. In our previous study, a protein signature for TITF1 was found in plasmas of mouse models of lung adenocarcinoma, leading to the identification of concordant circulating protein signatures in human non-small cell lung cancer (NSCLC). Following study demonstrated one of TITF1 signature proteins, pro-SFTPB, as a predictive biomarker for NSCLC. To identify additional biomarkers which would complement the performance of pro-SFTPB, we studied secretome from forty NSCLC cell lines in terms of TITF1 expression. Materials and Methods: Forty NSCLC cell lines were cultured according to the standard SILAC protocol. The same batch of cells was used for analysis of whole cell extract and conditioned media. Samples were fractionated and subjected to mass spectrometric analysis. Gene expression data for the same set of NSCLC cell lines were obtained using the Illumina Human WG-6 v3.0 Expression BeadChips. Results: NSCLC cell lines were divided into two groups by median of TITF1 gene expression (TITF1-positive and negative). In secretome analysis, overall 10,399 proteins were identified and semi-quantified with using total number of spectral counts. Interestingly, top abundant secretome proteins in TITF1-negative NSCLC cell lines contain many cytokine/chemokines (IL6, CXCL1, CXCL2 and CXCL5) and growth factors (CTGF, PDGFA, PDGFC, and VEGFC). Among top abundant secretome proteins, gene expression of SRGN was most significantly and inversely correlated with TITF1 gene expression. SRGN is a proteoglycan and physiologically associated with some proteases and cytokine secretion in hematopoietic cells. In addition recent studies have indicated that SRGN is also associated with aggressive phenotype in some cancers, thus considered as a promising biomarker candidate for TITF1-negative NSCLC. Then we determined whether SRGN would be directly regulated by TITF1. Suppression of TITF1 by siRNA or TNF alpha treatment increased SRGN expression, suggesting potential transcriptional regulation of SRGN by TITF1. Finally we studied functional roles of SRGN in TITF1-negative cell lines. Knockdown of SRGN by siRNA did not affect cell proliferation. However two chemokines, CXCL1 and CXCL5, which are abundant in secretome of TITF1-negative cell lines, were decreased by SRGN knockdown. In addition, macrophage migration was also decreased in the conditioned media of SRGN knockdown cells compared with control cells. Conclusion: Our findings suggest functional relevance of SRGN in tumor microenvironment and tumor aggressiveness in TITF1/NKX2-1-negative NSCLC. Validation study for plasma levels of CXCL1 and CXCL5 in NSCLC subjects is now on-going. Citation Format: Ayumu Taguchi, Muge Celiktas, Dhillon Dilsher, Qing Zhang, Chee-Hong Wong, Alice Chin, Adi Gazdar, Samir Hanash. Secretome signature associated with TITF1/NKX2-1-negative non-small cell lung cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2485. doi:10.1158/1538-7445.AM2014-2485

IMPORTANCE: There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases. OBJECTIVE: To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria. DESIGN, SETTING, AND PARTICIPANTS: Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS). MAIN OUTCOMES AND MEASURES: Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity). RESULTS: In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model. CONCLUSIONS AND RELEVANCE: This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.