Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy

Purpose

Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.

Methods

A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.

Results

Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67–0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.

Conclusion

A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.     

Stage-specific alterations of DNA methyltransferase expression, DNA hypermethylation, and DNA hypomethylation during prostate cancer progression in the transgenic adenocarcinoma of mouse prostate model

We analyzed DNA methyltransferase (Dnmt) protein expression and DNA methylation patterns during four progressive stages of prostate cancer in the transgenic adenocarcinoma of mouse prostate (TRAMP) model, including prostatic intraepithelial neoplasia, well-differentiated tumors, early poorly differentiated tumors, and late poorly differentiated tumors. Dnmt1, Dnmt3a, and Dnmt3b protein expression were increased in all stages; however, after normalization to cyclin A to account for cell cycle regulation, Dnmt proteins remained overexpressed in prostatic intraepithelial neoplasia and well-differentiated tumors, but not in poorly differentiated tumors. Restriction landmark genomic scanning analysis of locus-specific methylation revealed a high incidence of hypermethylation only in poorly differentiated (early and late) tumors. Several genes identified by restriction landmark genomic scanning showed hypermethylation of downstream regions correlating with mRNA overexpression, including p16INK4a, p19ARF, and Cacna1a. Parallel gene expression and DNA methylation analyses suggests that gene overexpression precedes downstream hypermethylation during prostate tumor progression. In contrast to gene hypermethylation, genomic DNA hypomethylation, including hypomethylation of repetitive elements and loss of genomic 5-methyldeoxycytidine, occurred in both early and late stages of prostate cancer. DNA hypermethylation and DNA hypomethylation did not correlate in TRAMP, and Dnmt protein expression did not correlate with either variable, with the exception of a borderline significant association between Dnmt1 expression and DNA hypermethylation. In summary, our data reveal the relative timing of and relationship between key alterations of the DNA methylation pathway occurring during prostate tumor progression in an in vivo model system.     

Expression signatures that correlated with Gleason score and relapse in prostate cancer

Predicting prognosis in prostate carcinoma remains a challenge when using clinical and pathologic criteria only. We used an array-based DASL assay to identify molecular signatures for predicting prostate cancer relapse in formalin-fixed, paraffin-embedded (FFPE) prostate cancers, through gene expression profiling of 512 prioritized genes. Of the 71 patients that we analyzed, all but 3 had no evidence of residual tumor (defined as negative surgical margins) following radical prostatectomy and no patient received adjuvant therapy following surgery. All of the 71 patients had an undetectable serum PSA following radical prostatectomy. Follow-up period was 44+/-15 months. Highly reproducible gene expression patterns were obtained with these samples (average R(2)=0.99). We identified a panel of 11 genes that correlated positively and 5 genes that correlated negatively with Gleason grade. A gene expression score (GEX) was derived from the expression levels of the 16 genes. We assessed the prognostic value of these genes and found the GEX significantly correlated with disease relapse (p=0.007). These results suggest that the approach we used is effective for expression profiling in heterogeneous FFPE tissues for cancer diagnosis/prognosis biomarker discovery and validation.     

PC-１基因表达增强C4-2B前列腺癌细胞生存

建立稳定表达外源PC-１基因的人前列腺癌骨转移C4-2B细胞模型,初步探讨PC- １基因表达对前列腺癌发展的影响.通过脂质体介导的方法,将融合PC-１基因的真核表达载体pcDNA3.1PC-１稳定转染C4-2B细胞,Western 印迹和RT-PCR技术,分别从蛋白水平和RNA水平确定外源PC-１基因表达. MTT和软琼脂集落形成能力等一系列方法,研究PC-１基因的功能,RT-PCR和实时定量PCR检测前列腺癌发生发展相关基因表达的变化. 结果表明,PC-１基因的高表达能够诱导雄激素受体（AR）调控基因和一系列重要的信号通路成员基因PSA、PSMA、NKX31、Jagged1、EphA3、SGEF和 NOTCH3等表达发生变化. 实验结果初步证明,PC-１基因表达在晚期前列腺癌中,以及在雄激素非依赖的转变中可以发挥作用,PC-１基因表达可调控一些重要信号通路.对PC-１基因功能深入研究将有可能为发现新的前列腺癌的诊断治疗分子靶标提供线索.     

Coupled analysis of gene expression and chromosomal location

Microarray technology can be used to assess simultaneously global changes in expression of mRNA or genomic DNA copy number among thousands of genes in different biological states. In many cases, it is desirable to determine if altered patterns of gene expression correlate with chromosomal abnormalities or assess expression of genes that are contiguous in the genome. We describe a method, differential gene locus mapping (DIGMAP), which aligns the known chromosomal location of a gene to its expression value deduced by microarray analysis. The method partitions microarray data into subsets by chromosomal location for each gene interrogated by an array. Microarray data in an individual subset can then be clustered by physical location of genes at a subchromosomal level based upon ordered alignment in genome sequence. A graphical display is generated by representing each genomic locus with a colored cell that quantitatively reflects its differential expression value. The clustered patterns can be viewed and compared based on their expression signatures as defined by differential values between control and experimental samples. In this study, DIGMAP was tested using previously published studies of breast cancer analyzed by comparative genomic hybridization (CGH) and prostate cancer gene expression profiles assessed by cDNA microarray experiments. Analysis of the breast cancer CGH data demonstrated the ability of DIGMAP to deduce gene amplifications and deletions. Application of the DIGMAP method to the prostate data revealed several carcinoma-related loci, including one at 16q13 with marked differential expression encompassing 19 known genes including 9 encoding metallothionein proteins. We conclude that DIGMAP is a powerful computational tool enabling the coupled analysis of microarray data with genome location.