Gene expression-based prognostic signatures in lung cancer: ready for clinical use?

A substantial number of studies have reported the development of gene expression-based prognostic signatures for lung cancer. The ultimate aim of such studies should be the development of well-validated clinically useful prognostic signatures that improve therapeutic decision making beyond current practice standards. We critically reviewed published studies reporting the development of gene expression-based prognostic signatures for non-small cell lung cancer to assess the progress made toward this objective. Studies published between January 1, 2002, and February 28, 2009, were identified through a PubMed search. Following hand-screening of abstracts of the identified articles, 16 were selected as relevant. Those publications were evaluated in detail for appropriateness of the study design, statistical validation of the prognostic signature on independent datasets, presentation of results in an unbiased manner, and demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines. Based on this review, we found little evidence that any of the reported gene expression signatures are ready for clinical application. We also found serious problems in the design and analysis of many of the studies. We suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic signature studies. These guidelines emphasize the importance of focused study planning to address specific medically important questions and the use of unbiased analysis methods to evaluate whether the resulting signatures provide evidence of medical utility beyond standard of care-based prognostic factors.

[1]  Avrum Spira,et al.  Guidelines: Expression profiling — best practices for data generation and interpretation in clinical trials , 2004 .

[2]  David M Jablons,et al.  Genomic prognostic models in early-stage lung cancer. , 2009, Clinical lung cancer.

[3]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[4]  Jeremy J. W. Chen,et al.  A five-gene signature and clinical outcome in non-small-cell lung cancer. , 2007, The New England journal of medicine.

[5]  D. Botstein,et al.  Diversity of gene expression in adenocarcinoma of the lung , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[6]  H. Dienemann,et al.  Prognostic assessment after surgical resection for non-small cell lung cancer: experiences in 2083 patients. , 2007, Lung cancer.

[7]  Douglas G Altman,et al.  Reporting recommendations for tumor marker prognostic studies. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  Guidel Ines,et al.  Expression profiling — best practices for data generation and interpretation in clinical trials , 2004, Nature Reviews Genetics.

[9]  David E. Misek,et al.  Gene-expression profiles predict survival of patients with lung adenocarcinoma , 2002, Nature Medicine.

[10]  Zhifu Sun,et al.  A Gene Expression Signature Predicts Survival of Patients with Stage I Non-Small Cell Lung Cancer , 2006, PLoS medicine.

[11]  Rainer Spang,et al.  Microarray Based Diagnosis Profits from Better Documentation of Gene Expression Signatures , 2008, PLoS Comput. Biol..

[12]  P. Ravdin,et al.  Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. , 2001, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[13]  Marcin Skrzypski,et al.  A Multigene Assay Is Prognostic of Survival in Patients with Early-Stage Lung Adenocarcinoma , 2008, Clinical Cancer Research.

[14]  Jeffrey T. Chang,et al.  Oncogenic pathway signatures in human cancers as a guide to targeted therapies , 2006, Nature.

[15]  Masahiro Tsuboi,et al.  The present status of postoperative adjuvant chemotherapy for completely resected non-small cell lung cancer. , 2007, Annals of thoracic and cardiovascular surgery : official journal of the Association of Thoracic and Cardiovascular Surgeons of Asia.

[16]  N. Hayward,et al.  Expression profiling defines a recurrence signature in lung squamous cell carcinoma. , 2006, Carcinogenesis.

[17]  A. Jemal,et al.  Cancer Statistics, 2008 , 2008, CA: a cancer journal for clinicians.

[18]  L. V. van't Veer,et al.  Clinical application of the 70-gene profile: the MINDACT trial. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[19]  M. Tyers,et al.  Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. , 2002, Cancer research.

[20]  Igor Jurisica,et al.  Prognostic gene signatures for non-small-cell lung cancer , 2009, Proceedings of the National Academy of Sciences.

[21]  Xianglin Shi,et al.  Constructing Molecular Classifiers for the Accurate Prognosis of Lung Adenocarcinoma , 2006, Clinical Cancer Research.

[22]  John M Drake,et al.  Limits to Forecasting Precision for Outbreaks of Directly Transmitted Diseases , 2005, PLoS medicine.

[23]  Marcin Skrzypski,et al.  An Immune Response Enriched 72-Gene Prognostic Profile for Early-Stage Non–Small-Cell Lung Cancer , 2009, Clinical Cancer Research.

[24]  Chaya S Moskowitz,et al.  Quantifying and comparing the accuracy of binary biomarkers when predicting a failure time outcome. , 2004, Statistics in medicine.

[25]  M. Pepe,et al.  Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. , 2004, American journal of epidemiology.

[26]  Michael W Kattan,et al.  Evaluating a New Marker’s Predictive Contribution , 2004, Clinical Cancer Research.

[27]  A. Dupuy,et al.  Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. , 2007, Journal of the National Cancer Institute.

[28]  M. Radmacher,et al.  Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. , 2003, Journal of the National Cancer Institute.

[29]  Zhifu Sun,et al.  Non-overlapping and non-cell-type-specific gene expression signatures predict lung cancer survival. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  E. Lander,et al.  Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[31]  S. Mukherjee,et al.  A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. , 2006, The New England journal of medicine.

[32]  L. Tanoue,et al.  Three-Gene Prognostic Classifier for Early-Stage Non–Small-Cell Lung Cancer , 2009 .

[33]  Shuta Tomida,et al.  Gene expression-based, individualized outcome prediction for surgically treated lung cancer patients , 2004, Oncogene.

[34]  Igor Jurisica,et al.  Gene expression–based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study , 2008, Nature Medicine.

[35]  S. Paik,et al.  Development of the 21-gene assay and its application in clinical practice and clinical trials. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[36]  Yi Zhang,et al.  Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung. , 2006, Cancer research.

[37]  L. Tanoue Staging of non-small cell lung cancer. , 2008, Seminars in respiratory and critical care medicine.

[38]  N. Hayward,et al.  Gene Expression Signature Predicts Recurrence in Lung Adenocarcinoma , 2007, Clinical Cancer Research.

[39]  Marcin Skrzypski,et al.  Three-Gene Expression Signature Predicts Survival in Early-Stage Squamous Cell Carcinoma of the Lung , 2008, Clinical Cancer Research.

[40]  Peter Dayan,et al.  Serotonin, Inhibition, and Negative Mood , 2007, PLoS Comput. Biol..