Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor

Abstract

To identify potential molecular determinants of tumor biology and possible clinical outcomes, global gene-expression patterns were analyzed in the primary tumors of patients with metastatic renal cell cancer by using cDNA microarrays. We used grossly dissected tumor masses that included tumor, blood vessels, connective tissue, and infiltrating immune cells to obtain a gene-expression "profile" from each primary tumor. Two patterns of gene expression were found within this uniformly staged patient population, which correlated with a significant difference in overall survival between the two patient groups. Subsets of genes most significantly associated with survival were defined, and vascular cell adhesion molecule-1 (VCAM-1) was the gene most predictive for survival. Therefore, despite the complex biological nature of metastatic cancer, basic clinical behavior as defined by survival may be determined by the gene-expression patterns expressed within the compilation of primary gross tumor cells. We conclude that survival in patients with metastatic renal cell cancer can be correlated with the expression of various genes based solely on the expression profile in the primary kidney tumor.

Clear Cell Renal Cell Carcinoma: Gene Expression Analyses Identify a Potential Signature for Tumor Aggressiveness

“…The median false discovery rate (FDR) by this process was 0.8% (one gene) and a 90th percentile of 3.9% (five genes). Second, expression values of probe sets (11,715) determined by the dChip program to be most variable across the aggressive and nonaggressive cases were exported to GeneCluster 2.0 (Whitehead/MIT for Genome Research) to identify 120 probe sets with highest signal to noise ratios (list 2). The signal to noise ratio estimate, also called the discriminate score (10), is computed as SNR = (l 1 À l 2 ) / (r 1 + r 2 ), where l and r refer to the mean and SD, respectively.…”

“…In recent years, gene expression profiling has emerged as a means of rapidly identifying candidate genes and gene expression patterns that are associated with aggressive tumors (8,9) including CCRCC (10,11). To date however, comprehensive gene expression profiling studies of well-characterized CCRCCs with the express purpose of identifying markers of aggressiveness are lacking.…”

Genomics of renal cell cancer — Does it provide breakthrough?

“…This study also defined an immunodiagnostic set of proteins (vimentin, CD74, parvalbumin and galectin-3) which can be used to differentiate between the classical RCC, its chromophobic variant and the oncocytoma. In a larger study on 58 cases of stage IV RCC (unfortunately, of various subtypes grouped together), Vasselli et al 53 described a 45-gene signature for poor prognosis, highlighting VCAM-1 as a potential prognostic marker. Although these two studies differed in many respects, the lack of any overlap in the significant genes is striking.…”

Using bioinformatics and genome analysis for new therapeutic interventions

“…A common unsupervised method is hierarchical pairwise clustering (33) based on averagelinkage between clusters to identify the most closely related classes in a tree representation of the relationships (33 -35). Hierarchical clustering has been successful in the molecular classification of signature expression profiles of distinct types of large B cell lymphoma (36), benign and malignant prostate cancer tissues (37), and kidney tumors that can predict survival in patients with metastatic renal cell cancer (38). There are many additional clustering methods that have been applied to cancer-related data sets; these methods provide the means to search for different types of patterns based on experimental objectives (39).…”