Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor - PubMed (original) (raw)

. 2003 Jun 10;100(12):6958-63.

doi: 10.1073/pnas.1131754100. Epub 2003 May 30.

Joanna H Shih, Shuba R Iyengar, Jodi Maranchie, Joseph Riss, Robert Worrell, Carlos Torres-Cabala, Ray Tabios, Andra Mariotti, Robert Stearman, Maria Merino, McClellan M Walther, Richard Simon, Richard D Klausner, W Marston Linehan

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

James R Vasselli et al. Proc Natl Acad Sci U S A. 2003.

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.

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Figures

Fig. 1.

Fig. 1.

Unsupervised dendrogram and resulting survival curves. (a) Dendrogram created by unsupervised clustering demonstrating two major groups of patients. (b) Kaplan–Meier survival curve based on the two groups of patients from a. (c) Survival curve from b depicting patients with <0.5 Pearson correlation between arrays removed.

Fig. 2.

Fig. 2.

Cluster matrix of 45 genes most significantly associated with survival. The colors in the matrix represent a maximum of 2-fold up-regulation (brightest yellow) or 2-fold down-regulation (brightest blue) relative to the median for each gene. The patients (columns) are labeled by patient number followed by the number of days alive after nephrectomy. The bar graph below the matrix graphically depicts the length of patient survival; red bars represent patients who are still alive. The dendrogram above the patient list graphically depicts the two major patient groups and minor subgroups within the two larger groups.

Fig. 3.

Fig. 3.

Survival curves based on supervised analysis. (a) Kaplan–Meier plots of overall survival of 58 patients grouped into good prognosis and poor prognosis by their gene-expression outcome predictor generated by the leave-one-out cross-validated procedure. (b) Survival curves of patients grouped by the median of log ratio +0.5 and –0.5 for VCAM-1.

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