A five-gene signature and clinical outcome in non-small-cell lung cancer.

BACKGROUND Current staging methods are inadequate for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We developed a five-gene signature that is closely associated with survival of patients with NSCLC. METHODS We used computer-generated random numbers to assign 185 frozen specimens for microarray analysis, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis, or both. We studied gene expression in frozen specimens of lung-cancer tissue from 125 randomly selected patients who had undergone surgical resection of NSCLC and evaluated the association between the level of expression and survival. We used risk scores and decision-tree analysis to develop a gene-expression model for the prediction of the outcome of treatment of NSCLC. For validation, we used randomly assigned specimens from 60 other patients. RESULTS Sixteen genes that correlated with survival among patients with NSCLC were identified by analyzing microarray data and risk scores. We selected five genes (DUSP6, MMD, STAT1, ERBB3, and LCK) for RT-PCR and decision-tree analysis. The five-gene signature was an independent predictor of relapse-free and overall survival. We validated the model with data from an independent cohort of 60 patients with NSCLC and with a set of published microarray data from 86 patients with NSCLC. CONCLUSIONS Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.

[1]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[2]  L. Schwarzfischer,et al.  Molecular cloning of a novel macrophage maturation-associated transcript encoding a protein with several potential transmembrane domains. , 1995, Biochemical and biophysical research communications.

[3]  Rakesh Agrawal,et al.  SPRINT: A Scalable Parallel Classifier for Data Mining , 1996, VLDB.

[4]  Aseem Kumar,et al.  Defective TNF-α-Induced Apoptosis in STAT1-Null Cells Due to Low Constitutive Levels of Caspases , 1997 .

[5]  J. J. Chen,et al.  Profiling expression patterns and isolating differentially expressed genes by cDNA microarray system with colorimetry detection. , 1998, Genomics.

[6]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[7]  I. Bièche,et al.  Real-time reverse transcription-PCR assay for future management of ERBB2-based clinical applications. , 1999, Clinical chemistry.

[8]  Yih-Leong Chang,et al.  Collapsin response mediator protein-1 and the invasion and metastasis of cancer cells. , 2001, Journal of the National Cancer Institute.

[9]  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.

[10]  J. Shih,et al.  Global analysis of gene expression in invasion by a lung cancer model. , 2001, Cancer research.

[11]  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.

[12]  M. Xiong,et al.  Recursive partitioning for tumor classification with gene expression microarray data , 2001, Proceedings of the National Academy of Sciences of the United States of America.

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

[14]  S. Ramaswamy,et al.  Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. , 2002, Cancer research.

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

[16]  F. Motoi,et al.  Potential tumor suppressive pathway involving DUSP6/MKP-3 in pancreatic cancer. , 2003, The American journal of pathology.

[17]  G. Kundu,et al.  Tyrosine Kinase p56lck Regulates Cell Motility and Nuclear Factor κB-mediated Secretion of Urokinase Type Plasminogen Activator through Tyrosine Phosphorylation of IκBα following Hypoxia/Reoxygenation* , 2003, Journal of Biological Chemistry.

[18]  G. Legname,et al.  The influence of the src‐family kinases, Lck and Fyn, on T cell differentiation, survival and activation , 2003, Immunological reviews.

[19]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[20]  E. Lander,et al.  A molecular signature of metastasis in primary solid tumors , 2003, Nature Genetics.

[21]  James A Koziol,et al.  Recursive partitioning as an approach to selection of immune markers for tumor diagnosis. , 2003, Clinical cancer research : an official journal of the American Association for Cancer Research.

[22]  R. Bast,et al.  Individualized care for patients with cancer - a work in progress. , 2004, The New England journal of medicine.

[23]  Bengt Bergman,et al.  Cisplatin-based adjuvant chemotherapy in patients with completely resected non-small-cell lung cancer. , 2004, The New England journal of medicine.

[24]  Hua Yu,et al.  The STATs of cancer — new molecular targets come of age , 2004, Nature Reviews Cancer.

[25]  G. Kundu,et al.  Tyrosine Kinase, p56lck-induced Cell Motility, and Urokinase-type Plasminogen Activator Secretion Involve Activation of Epidermal Growth Factor Receptor/Extracellular Signal Regulated Kinase Pathways* , 2004, Journal of Biological Chemistry.

[26]  Y. Yatabe,et al.  Prognostic model of pulmonary adenocarcinoma by expression profiling of eight genes as determined by quantitative real-time reverse transcriptase polymerase chain reaction. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[27]  Sridhar Ramaswamy,et al.  Translating cancer genomics into clinical oncology. , 2004, The New England journal of medicine.

[28]  Ash A. Alizadeh,et al.  Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. , 2004, The New England journal of medicine.

[29]  Jeremy J. W. Chen,et al.  The transcriptional factor YY1 upregulates the novel invasion suppressor HLJ1 expression and inhibits cancer cell invasion , 2005, Oncogene.

[30]  J. Weinstein,et al.  Biomarkers in Cancer Staging, Prognosis and Treatment Selection , 2005, Nature Reviews Cancer.

[31]  P. Dennis,et al.  An overview of lung cancer genomics and proteomics. , 2005, American journal of respiratory cell and molecular biology.

[32]  Yi-Chen Lin,et al.  Tumor-associated macrophages: the double-edged sword in cancer progression. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[33]  Michael Thomas,et al.  Identification of metastasis-associated receptor tyrosine kinases in non-small cell lung cancer. , 2005, Cancer research.

[34]  J. Hoheisel.  Microarray technology: beyond transcript profiling and genotype analysis , 2006, Nature Reviews Microbiology.

[35]  Zhong Wen-zhao,et al.  Erlotinib in Lung Cancer - Molecular and Clinical Predictors of Outcome , 2006 .

[36]  A. Jemal,et al.  Cancer Statistics, 2006 , 2006, CA: a cancer journal for clinicians.

[37]  D.,et al.  Regression Models and Life-Tables , 2022 .