Transcriptional profiles in peripheral blood mononuclear cells prognostic of clinical outcomes in patients with advanced renal cell carcinoma.

PURPOSE Given their accessibility, surrogate tissues, such as peripheral blood mononuclear cells (PBMC), may provide potential predictive biomarkers in clinical pharmacogenomic studies. In leukemias and lymphomas, the prognostic value of peripheral blast expression profiles is clear; however, it is unclear whether circulating mononuclear cells of patients with solid tumors might yield profiles with similar prognostic associations. EXPERIMENTAL DESIGN In this study, we evaluated the association of expression profiles in PBMCs with clinical outcomes in patients with advanced renal cell cancer. Transcriptional patterns in PBMCs of 45 renal cell cancer patients were compared with clinical outcome data at the conclusion of a phase II study of the mTOR kinase inhibitor CCI-779 to determine whether pretreatment transcriptional patterns in PBMCs were correlated with eventual patient outcomes. RESULTS Unsupervised hierarchical clustering of the PBMC profiles using all expressed genes identified clusters of patients with significant differences in survival. Cox proportional hazards modeling showed that the expression levels of many PBMC transcripts were predictors for the patient outcomes of time to progression and overall survival (time to death). Supervised class prediction approaches identified multivariate expression patterns in PBMCs capable of assigning favorable outcomes of time to death and time to progression in a test set of renal cancer patients, with overall performance accuracies of 72% and 85%, respectively. CONCLUSIONS The present study provides the first example of gene expression profiling in peripheral blood, a clinically accessible surrogate tissue, for identifying patterns of gene expression associated with higher likelihoods of positive outcome in patients with a solid tumor.

[1]  M. Mita,et al.  Mammalian target of rapamycin: a new molecular target for breast cancer. , 2003, Clinical breast cancer.

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

[3]  S. Liou,et al.  Randomized phase II study of multiple dose levels of CCI-779, a novel mammalian target of rapamycin kinase inhibitor, in patients with advanced refractory renal cell carcinoma. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[4]  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.

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

[6]  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.

[7]  D W SMITHERS,et al.  Clinical Cancer Research , 1941, Lancet.

[8]  Shile Huang,et al.  Inhibitors of mammalian target of rapamycin as novel antitumor agents: from bench to clinic. , 2002, Current opinion in investigational drugs.

[9]  R Simon,et al.  Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data , 2003, British Journal of Cancer.

[10]  Manuel Hidalgo,et al.  Disease-associated expression profiles in peripheral blood mononuclear cells from patients with advanced renal cell carcinoma. , 2003, Cancer research.

[11]  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.

[12]  J. Welsh,et al.  Molecular classification of human carcinomas by use of gene expression signatures. , 2001, Cancer research.

[13]  E. Lander,et al.  MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia , 2002, Nature Genetics.

[14]  D. Slonim,et al.  Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls , 2001, Genome Biology.

[15]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[16]  M Mazumdar,et al.  Survival and prognostic stratification of 670 patients with advanced renal cell carcinoma. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

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

[18]  T. Poggio,et al.  Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Van,et al.  A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.

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

[21]  D. Lockhart,et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays , 1996, Nature Biotechnology.