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Ann Oncol. 2017 Apr 1;28(4):733-740. doi: 10.1093/annonc/mdw683.

Comprehensive evaluation of published gene expression prognostic signatures for biomarker-based lung cancer clinical studies.

Author information

Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, People's Republic of China.
Department of Medical Oncology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, P.R. China.
Department of Thoracic Surgery and Oncology, the Second Department of Thoracic Surgery, Cancer Center, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China.
Inova Schar Cancer Institute, Falls Church, VA, USA.
Department of Thoracic/Head & Neck Medical Oncology, The University of Texas, MD Anderson Cancer Center, 1515 Holcombe Blvd. Unit 0085, Houston, TX, USA.
Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, USA.
Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, USA.
Hamon Center for Therapeutic Oncology, University of Texas Southwestern Medical Center, Dallas, USA.
Department of Translational Molecular Pathology, MD Anderson Cancer Center, University of Texas, Houston, USA.
Department of Oncology, First Affiliated Hospital, Soochow University, Suzhou, China.
Departments of Head and Neck and Mammary Gland Oncology and Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Laboratory of Molecular Diagnosis of Cancer, West China Hospital, Sichuan University, Chengdu, Sichuan, China.



A more accurate prognosis for non-small-cell lung cancer (NSCLC) patients could aid in the identification of patients at high risk for recurrence. Many NSCLC mRNA expression signatures claiming to be prognostic have been reported in the literature. The goal of this study was to identify the most promising mRNA prognostic signatures in NSCLC for further prospective clinical validation.

Experimental design:

We carried out a systematic review and meta-analysis of published mRNA prognostic signatures for resected NSCLC. The prognostic performance of each signature was evaluated via a meta-analysis of 1927 early stage NSCLC patients collected from 15 studies using three evaluation metrics (hazard ratios, concordance scores, and time-dependent receiver-operating characteristic curves). The performance of each signature was then evaluated against 100 random signatures. The prognostic power independent of clinical risk factors was assessed by multivariate Cox models.


Through a literature search, we identified 42 lung cancer prognostic signatures derived from genome-wide expression profiling analysis. Based on meta-analysis, 25 signatures were prognostic for survival after adjusting for clinical risk factors and 18 signatures carried out significantly better than random signatures. When analyzing histology types separately, 17 signatures and 8 signatures are prognostic for adenocarcinoma and squamous cell lung cancer, respectively. Despite little overlap among published gene signatures, the top-performing signatures are highly concordant in predicted patient outcomes.


Based on this large-scale meta-analysis, we identified a set of mRNA expression prognostic signatures appropriate for further validation in prospective clinical studies.


meta-analysis; non-small-cell lung cancer; prognostic gene signatures

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