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

Annals of Oncology

Volumn 28, Issue 4, 2017, Pages 733-740

  Tang, H ^a   Wang, S ^a   Xiao, G ^a   Schiller, J ^a,b   Papadimitrakopoulou, V ^c   Minna, J ^a,d   Wistuba, I I ^c   Xie, Y ^a,b  

^a UNIVERSITY OF TEXAS AT DALLAS   (United States)

^b UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^c UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^d UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

Author keywords

Meta analysis; Non small cell lung cancer; Prognostic gene signatures

Indexed keywords

MESSENGER RNA; TUMOR MARKER; TRANSCRIPTOME;

CANCER PATIENT; CANCER PROGNOSIS; CANCER STAGING; CANCER SURVIVAL; CLINICAL OUTCOME; CLINICAL STUDY; COHORT ANALYSIS; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENOME ANALYSIS; HISTOPATHOLOGY; HUMAN; HUMAN GENOME; LUNG ADENOCARCINOMA; LUNG CANCER; META ANALYSIS; NON SMALL CELL LUNG CANCER; PREDICTION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; REVIEW; RISK ASSESSMENT; RISK FACTOR; SCORING SYSTEM; SYSTEMATIC REVIEW; TIME FACTOR; AGED; FEMALE; GENETICS; LUNG TUMOR; MALE; MORTALITY; PROGNOSIS; PROPORTIONAL HAZARDS MODEL;

AGED; BIOMARKERS, TUMOR; CARCINOMA, NON-SMALL-CELL LUNG; FEMALE; HUMANS; LUNG NEOPLASMS; MALE; PROGNOSIS; PROPORTIONAL HAZARDS MODELS; TRANSCRIPTOME;

EID: 85019128028     PISSN: 09237534     EISSN: 15698041     Source Type: Journal    
DOI: 10.1093/annonc/mdw683     Document Type: Review

References (22)

1. National Comprehensive Cancer Network I. In NCCN Clinical Practice Guidelines in Oncology. Non-small Cell Lung Cancer 2016 (Version 4.2016).
2. Tanoue LT. Staging of non-small cell lung cancer. Semin Respir Crit Care Med 2008; 29: 248-260.
3. Subramanian J, Simon R. Gene expression-based prognostic signatures in lung cancer: Ready for clinical use? J Natl Cancer Inst 2010; 102: 464-474.
4. Subramanian J, Simon R. What should physicians look for in evaluating prognostic gene-expression signatures? Nat Rev Clin Oncol 2010; 7: 327-334.
5. Bair E, Tibshirani R. Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol 2004; 2: E108.
6. Zhu CQ, Pintilie M, John T et al. Understanding prognostic gene expression signatures in lung cancer. Clin Lung Cancer 2009; 10: 331-340.
7. Collett D. Modelling Survival Data in Medical Research. Raton, Florida, USA: Chapman & Hall/CRC 2003.
8. Zheng Y, Cai T, Feng Z. Application of the time-dependent ROC curves for prognostic accuracy with multiple biomarkers. Biometrics 2006; 62: 279-287.
9. Chen DT, Hsu YL, Fulp WJ et al. Prognostic and predictive value of a malignancy-risk gene signature in early-stage non-small cell lung cancer. J Natl Cancer Inst 2011; 103: 1859-1870.
10. Shedden K, Taylor JM, Enkemann SA et al. Gene expression-based survival prediction in lung adenocarcinoma: A multi-site, blinded validation study. Nat Med 2008; 14: 822-827.
11. Tomida S, Takeuchi T, Shimada Y et al. Relapse-related molecular signature in lung adenocarcinomas identies patients with dismal prognosis. J Clin Oncol 2009; 27: 2793-2799.
12. Roepman P, Jassem J, Smit EF et al. An immune response enriched 72- gene prognostic prole for early-stage non-small-cell lung cancer. Clin Cancer Res 2009; 15: 284-290.
13. Herbst RS, Heymach JV, Lippman SM. Lung cancer. N Engl J Med 2008; 359: 1367-1380.
14. Venet D, Dumont JE, Detours V. Most random gene expression signatures are signicantly associated with breast cancer outcome. PLoS Comput Biol 2011; 7: e1002240.
15. Lynch TJ, Bell DW, Sordella R et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to getinib. N Engl J Med 2004; 350: 2129-2139.
16. Bell DW, Lynch TJ, Haserlat SM et al. Epidermal growth factor receptor mutations and gene amplication in non-small-cell lung cancer: Molecular analysis of the ideal/intact getinib trials. J Clin Oncol 2005; 23: 8081-8092.
17. Mok TS, Wu YL, Thongprasert S et al. Getinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med 2009; 361: 947-957.
18. Zhu CQ, da Cunha Santos G, Ding K et al. Role of KRAS and EGFR as biomarkers of response to erlotinib in National Cancer Institute of Canada Clinical Trials Group Study BR.21. J Clin Oncol 2008; 26: 4268-4275.
19. Byers LA, Diao L, Wang J et al. An epithelial-mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identies Axl as a therapeutic target for overcoming EGFR inhibitor resistance. Clin Cancer Res 2013; 19: 279-290.
20. Zhao Q, Shi X, Xie Y et al. Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA. Brief Bioinform 2015; 16: 291-303.
21. Ma S, Dai Y. Principal component analysis based methods in bioinformatics studies. Brief Bioinform 2011; 12: 714-722.
22. Ma S, Kosorok MR, Fine JP. Additive risk models for survival data with high-dimensional covariates. Biometrics 2006; 62: 202-210.