Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma

Background Conventional renal cell carcinoma (cRCC) accounts for most of the deaths due to kidney cancer. Tumor stage, grade, and patient performance status are used currently to predict survival after surgery. Our goal was to identify gene expression features, using comprehensive gene expression profiling, that correlate with survival. Methods and Findings Gene expression profiles were determined in 177 primary cRCCs using DNA microarrays. Unsupervised hierarchical clustering analysis segregated cRCC into five gene expression subgroups. Expression subgroup was correlated with survival in long-term follow-up and was independent of grade, stage, and performance status. The tumors were then divided evenly into training and test sets that were balanced for grade, stage, performance status, and length of follow-up. A semisupervised learning algorithm (supervised principal components analysis) was applied to identify transcripts whose expression was associated with survival in the training set, and the performance of this gene expression-based survival predictor was assessed using the test set. With this method, we identified 259 genes that accurately predicted disease-specific survival among patients in the independent validation group (p < 0.001). In multivariate analysis, the gene expression predictor was a strong predictor of survival independent of tumor stage, grade, and performance status (p < 0.001). Conclusions cRCC displays molecular heterogeneity and can be separated into gene expression subgroups that correlate with survival after surgery. We have identified a set of 259 genes that predict survival after surgery independent of clinical prognostic factors.

[1]  R. Tibshirani,et al.  Prediction by Supervised Principal Components , 2006 .

[2]  T. Eberlein A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer , 2006 .

[3]  Qiqin Yin-Goen,et al.  Molecular classification of renal tumors by gene expression profiling. , 2005, The Journal of molecular diagnostics : JMD.

[4]  H. Tabuchi,et al.  Gene expression analysis of renal carcinoma: adipose differentiation‐related protein as a potential diagnostic and prognostic biomarker for clear‐cell renal carcinoma , 2005, The Journal of pathology.

[5]  A. Jemal,et al.  Cancer Statistics, 2005 , 2005, CA: a cancer journal for clinicians.

[6]  Christophe Ambroise,et al.  Use of microarray data via model-based classification in the study and prediction of survival from lung cancer , 2005 .

[7]  M. Kattan,et al.  A postoperative prognostic nomogram predicting recurrence for patients with conventional clear cell renal cell carcinoma. , 2005, The Journal of urology.

[8]  M. Cronin,et al.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.

[9]  J. Patard,et al.  Use of the University of California Los Angeles integrated staging system to predict survival in renal cell carcinoma: an international multicenter study. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  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.

[11]  R. Tibshirani,et al.  Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. , 2004, The New England journal of medicine.

[12]  R. Tibshirani,et al.  Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data , 2004, PLoS biology.

[13]  J. Cheville,et al.  A multifactorial postoperative surveillance model for patients with surgically treated clear cell renal cell carcinoma. , 2003, The Journal of urology.

[14]  Joanna H Shih,et al.  Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[15]  M. Terris,et al.  Gene expression patterns in renal cell carcinoma assessed by complementary DNA microarray. , 2003, The American journal of pathology.

[16]  Steve Horvath,et al.  Carbonic anhydrase IX is an independent predictor of survival in advanced renal clear cell carcinoma: implications for prognosis and therapy. , 2003, Clinical cancer research : an official journal of the American Association for Cancer Research.

[17]  Kimberly F. Johnson,et al.  Methods of Microarray Data Analysis III , 2003, Springer US.

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

[19]  David Botstein,et al.  The Stanford Microarray Database: data access and quality assessment tools , 2003, Nucleic Acids Res..

[20]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[21]  Meland,et al.  The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. , 2002, The New England journal of medicine.

[22]  N. Munshi,et al.  Nephrectomy followed by interferon alfa-2b compared with interferon alfa-2b alone for metastatic renal-cell cancer. , 2001, The New England journal of medicine.

[23]  P. O’Farrell Faculty Opinions recommendation of HIFalpha targeted for VHL-mediated destruction by proline hydroxylation: implications for O2 sensing. , 2001 .

[24]  M Vingron,et al.  Identification and Classification of Differentially Expressed Genes in Renal Cell Carcinoma by Expression Profiling on a Global Human 31 , 500-Element cDNA Array , 2001 .

[25]  K. Furge,et al.  Gene expression profiling of clear cell renal cell carcinoma: Gene identification and prognostic classification , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[26]  M. Ivan,et al.  HIFα Targeted for VHL-Mediated Destruction by Proline Hydroxylation: Implications for O2 Sensing , 2001, Science.

[27]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[28]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[29]  A. Ravaud,et al.  Recombinant Human Interleukin-2, Recombinant Human Interferon Alfa-2a, or Both in Metastatic Renal-Cell Carcinoma , 1998 .

[30]  J. Brooks,et al.  Mutations of the VHL tumour suppressor gene in renal carcinoma , 1994, Nature Genetics.

[31]  N. Dubrawsky Cancer statistics , 1989, CA: a cancer journal for clinicians.