Kube, Dagmar Marie

Abstract Purpose: This paper describes a process for the identification of genes that can report on the aggressiveness of prostate tumors and thereby add to the information provided by current pathologic analysis. Materials and Methods: Expression profiling data from over 100 laser capture microdissection derived samples from nonneoplastic epithelium; Gleason patterns 3, 4, and 5 and node metastasis prostate cancer were used to identify genes at abnormally high levels in only some tumors. These variably overexpressed genes were stratified by their association with aggressive phenotypes and were subsequently filtered to exclude genes with redundant expression patterns. Selected genes were validated in a case-control study in which cases (systemic progression within 5 years) and controls (no systemic progression at 7 years of follow-up) were matched for all clinical and pathologic criteria from time of prostatectomy (n = 175). Both cases and controls, therefore, could have nodal invasion or seminal vesicle involvement at the time of initial treatment. Results: A number of candidate variably overexpressed genes selected for their association with aggressive prostate cancer phenotype were evaluated in the case control study. The most prominent candidates were SSTR1 and genes related to proliferation, including TOP2A. Conclusions: The process described here identified genes that add information not available from current clinical measures and can improve the prognosis of prostate cancer.

AbstractWe describe a method to identify candidate cancer biomarkers by analyzing numeric approximations of tissue specificity of human genes. These approximations were calculated by analyzing predicted tissue expression distributions of genes derived from mapping expressed sequence tags (ESTs) to the human genome sequence using a binary indexing algorithm. Tissue-specificity values facilitated high-throughput analysis of the human genes and enabled the identification of genes highly specific to different tissues. Tissue expression distributions for several genes were compared to estimates obtained from other public gene expression datasets and experimentally validated using quantitative RT-PCR on RNA isolated from several human tissues. Our results demonstrate that most human genes (∼98%) are expressed in many tissues (low specificity), and only a small number of genes possess very specific tissue expression profiles. These genes comprise a rich dataset from which novel therapeutic targets and novel diagnostic serum biomarkers may be selected.Contact: vasm@mayo.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Abstract Purpose: The objective of this study was to use gene expression profiling to identify novel biomarkers that are predictive of aggressive behavior in clear cell renal cell carcinoma (CCRCC). Experimental Design: Candidate genes were discovered using Human Genome U133 Plus 2 Arrays and validated on independent samples by quantitative reverse transcription-PCR (RT-PCR). Both the discovery and the validation cohorts included nonaggressive primary CCRCC, aggressive primary CCRCC, metastatic CCRCC, and nonneoplastic kidney adjacent to tumor. Results: Aggressive primary and metastatic CCRCC displayed no significant differences in gene expression. In contrast, we identified significant differences in gene expression between nonaggressive and aggressive CCRCC (including metastatic CCRCC). Thirty-four of the 35 transcripts that displayed the most significant differential expression by microarray analysis also displayed significant differential expression in independent validation studies using quantitative RT-PCR (P < 0.001 for 31 candidates and P < 0.005 for the remaining three candidates). Hierarchical clustering of the quantitative RT-PCR data using our candidate markers accurately grouped 88% (23 of 26) of aggressive and metastatic CCRCC samples, 100% (14 of 14) of nonaggressive CCRCC samples, and 100% (15 of 15) of nonneoplastic samples into separate clusters. Finally, we evaluated the ability of protein expression levels of one of our candidate markers (survivin) to predict survival among a cohort of 183 CCRCC patients treated surgically at Mayo Clinic from 1990 to 1992. In multivariate analysis, expression of survivin (BIRC5) was inversely associated with cancer-specific survival (P = 0.017). Conclusion: We used a combination of genomic profiling and validation by quantitative PCR to identify a panel of candidate biomarkers for determining CCRCC aggressiveness. Our data also indicate that the gene expression alterations that result in aggressive behavior and metastatic potential can be identified in the primary tumor.