Klee, Eric W.

Abstract DNA methylation abnormalities, either gain or loss of methylation in promoters or other regulatory regions, contribute to tumorigenesis by altering the activity of tumor suppressor or oncogenes. To identify the molecular alterations in prostate cancer, we have profiled gene methylation using Infinium Human Methylation27 bead chips in benign prostate tissue, primary prostate tumor tissue from patients who developed metastasis within 5 years of prostatectomy, and serum from patients who developed metastasis. Tissue DNA isolation and bisulfite conversion was done using ZR Genomic DNA kit and EZ methylation kit. Serum DNA isolation and bisulfite conversion was done using ZR serum DNA kit and EZ methylation kit. Methylation data was processed using the Beadstudio average normalization algorithm and differentially methylated genes identified using a two-group T-test p-value < 0.05. To evaluate the impact of methylation on mRNA expression levels, we isolated RNA from LCM-derived epithelial cells of 124 patients using Picopure RNA isolation kit and measured expression using HG-U133+2 microarrays. The gene expression data was preprocessed using GCRMA background correction, FASTLO normalization, affinities-only PM correction, and median polish summarization, and differential gene expression was defined by a two-group T-test p-value < 0.05, a log2 fold change > 1, and a maximum average expression value > log2 7. Comparison of the methylation and gene expression data identified 321 differentially expressed Affymetrix probesets which mapped to genes differentially methylated genes; 272 with differential methylation in the prostate cancer tissue and 97 with differential methylation in the prostate cancer serum. In general, we expected a complementary effect between gene methylation and expression, with hyper-methylation driving decreased expression, and hypo-methylation driving increased expression. This was seen in the prostate cancer tissue methylation data, where 228/272 (84%) probesets possessed this methylation-expression complementarity; 212/238 hyper-methylated genes and 16/34 hypo-methylated genes. The effect was not as strong in the prostate cancer serum methylation data, where 30/97 (31%) probesets displayed the methylation-expression complementarity; 19/79 hyper-methylated genes and 11/18 hypo-methylated genes. Overall, our results revealed a significant correlation of the DNA methylation with the down regulation in the mRNA expression levels of the genes, which could play potential roles in prostate cancer progression. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4950.

BACKGROUND Next-generation DNA sequencing (NGS) techniques have the potential to revolutionize molecular diagnostics; however, a thorough evaluation of these technologies is necessary to ensure their performance meets or exceeds that of current clinical sequencing methods. METHODS We evaluated the NimbleGen Sequence Capture 385K Human Custom Arrays for enrichment of 22 genes. We sequenced each sample on both the Roche 454 Genome Sequencer FLX (GS-FLX) and the Illumina Genome Analyzer II (GAII) to compare platform performance. RESULTS Although the sequence capture method allowed us to rapidly develop a large number of sequencing assays, we encountered difficulty enriching G+C–rich regions. Although a high proportion of reads consistently mapped outside of the targeted regions, >80% of targeted bases for the GAII and >30% of bases for the GS-FLX were covered by a read depth of ≥20, and >90% of bases for the GAII and >80% of bases for the GS-FLX were covered by a read depth of ≥5. We observed discrepancies among sequence variants identified by the different platforms. CONCLUSIONS Although oligonucleotide arrays are quick and easy to develop, some problematic regions may evade capture, necessitating sequential redesigning for complete optimization. Neither sequencing technology was able to detect every variant identified by Sanger sequencing because of well-known drawbacks of the NGS technologies. The rapidly decreasing error rates and costs of these technologies, however, coupled with advancing bioinformatic capabilities, make them an attractive option for molecular diagnostics in the very near future.

Abstract Purpose: Atypical protein kinase Cι (PKCι) is an oncogene in non–small cell lung cancer (NSCLC). Here, we identify four functional gene targets of PKCι in lung adenocarcinoma (LAC), the most prominent form of NSCLC. Experimental Design: Three independent public domain gene expression data sets were interrogated to identify genes coordinately expressed with PKCι in primary LAC tumors. Results were validated by QPCR in an independent set of primary LAC tumors. RNAi-mediated knockdown of PKCι and the target genes was used to determine whether expression of the identified genes was regulated by PKCι, and whether these target genes play a role in anchorage-independent growth and invasion of LAC cells. Results: Meta-analysis identified seven genes whose expression correlated with PKCι in primary LAC. Subsequent QPCR analysis confirmed coordinate overexpression of four genes (COPB2, ELF3, RFC4, and PLS1) in an independent set of LAC samples. RNAi-mediated knockdown showed that PKCι regulates expression of all four genes in LAC cells, and that the four PKCι target genes play an important role in the anchorage-independent growth and invasion of LAC cells. Meta-analysis of gene expression data sets from lung squamous cell, breast, colon, prostate, and pancreas carcinomas, as well as glioblastoma, revealed that a subset of PKCι target genes, particularly COPB2 and RFC4, correlate with PKCι expression in many tumor types. Conclusion: Meta-analysis of public gene expression data are useful in identifying novel gene targets of oncogenic PKCι signaling. Our data indicate that both common and cell type–specific signaling mechanisms contribute to PKCι-dependent transformation.

Purpose In men who are at high-risk of prostate cancer, progression and death from cancer after radical retropubic prostatectomy (RRP), limited prognostic information is provided by established prognostic features. The objective of this study was to develop a model predictive of outcome in this group of patients. Methods Candidate genes were identified from microarray expression data from 102 laser capture microdissected prostate tissue samples. Candidates were overexpressed in tumor compared with normal prostate and more frequently in Gleason patterns 4 and 5 than in 3. A case control study of 157 high-risk patients, matched on Gleason score and stage with systemic progression or death of prostate cancer as the end point, was used to evaluate the expression of candidate genes and build a multivariate model. Tumor was collected from the highest Gleason score in paraffin-embedded blocks and the gene expression was quantified by real-time reverse transcription polymerase chain reaction. Validation of the final model was performed on a separate case-control study of 57 high-risk patients who underwent RRP. Results A model incorporating gene expression of topoisomerase-2a, cadherin-10, the fusion status based on ERG, ETV1, and ETV4 expression, and the aneuploidy status resulted in a 0.81 area under the curve (AUC) in receiver operating characteristic statistical analysis for the identification of men with systemic progression and death from high grade prostate cancer. The AUC was 0.79 in the independent validation study. Conclusion The model can identify men with high-risk prostate cancer who may benefit from more intensive postoperative follow-up and adjuvant therapies.

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.

AbstractBackground: Zn-α2 glycoprotein (ZAG) is a relatively abundant glycoprotein that has potential as a biomarker for prostate cancer. We present a high-flow liquid chromatography–tandem mass spectrometry (LC-MS/MS) method for measuring serum ZAG concentrations by proteolytic cleavage of the protein and quantification of a unique peptide.Methods: We selected the ZAG tryptic peptide 147EIPAWVPEDPAAQITK162 as the intact protein for quantification and used a stable isotope-labeled synthetic peptide with this sequence as an internal standard. Standards using recombinant ZAG in bovine serum albumin, 50 g/L, and a pilot series of patient sera were denatured, reduced, alkylated, and digested with trypsin. The concentration of ZAG was calculated from a dose–response curve of the ratio of the relative abundance of the ZAG tryptic peptide to internal standard.Results: The limit of detection for ZAG in serum was 0.08 mg/L, and the limit of quantification was 0.32 mg/L with a linear dynamic range of 0.32 to 10.2 mg/L. Replicate digests from pooled sera run during a period of 3 consecutive days showed intraassay imprecision (CV) of 5.0% to 6.3% and interassay imprecision of 4.4% to 5.9%. Mean (SD) ZAG was higher in 25 men with prostate cancer [7.59 (2.45) mg/L] than in 20 men with nonmalignant prostate disease [6.21 (1.65) mg/L, P = 0.037] and 6 healthy men [3.65 (0.71) mg/L, P = 0.0007].Conclusions: This LC-MS/MS assay is reproducible and can be used to evaluate the clinical utility of ZAG as a cancer biomarker.