Farhad Kosari

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

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.

Detection and Quantification of Methylation in DNA using Solid-State Nanopores

Epigenetic modifications in eukaryotic genomes occur primarily in the form of 5-methylcytosine (5 mC). These modifications are heavily involved in transcriptional repression, gene regulation, development and the progression of diseases including cancer. We report a new single-molecule assay for the detection of DNA methylation using solid-state nanopores. Methylation is detected by selectively labeling methylation sites with MBD1 (MBD-1x) proteins, the complex inducing a 3 fold increase in ionic blockage current relative to unmethylated DNA. Furthermore, the discrimination of methylated and unmethylated DNA is demonstrated in the presence of only a single bound protein, thereby giving a resolution of a single methylated CpG dinucleotide. The extent of methylation of a target molecule could also be coarsely quantified using this novel approach. This nanopore-based methylation sensitive assay circumvents the need for bisulfite conversion, fluorescent labeling, and PCR and could therefore prove very useful in studying the role of epigenetics in human disease.

High expression levels of survivin protein independently predict a poor outcome for patients who undergo surgery for clear cell renal cell carcinoma.

In a previous study of gene array data, the authors identified survivin as a candidate marker of aggressiveness in clear cell renal cell carcinoma (ccRCC). What remained in question was whether survivin expression at the protein level is an independent predictor of disease progression and cancer‐specific survival.

Gene Panel Model Predictive of Outcome in Men at High-Risk of Systemic Progression and Death From Prostate Cancer After Radical Retropubic Prostatectomy

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.

Optimization of laser capture microdissection and RNA amplification for gene expression profiling of prostate cancer

BackgroundTo discover prostate cancer biomarkers, we profiled gene expression in benign and malignant cells laser capture microdissected (LCM) from prostate tissues and metastatic prostatic adenocarcinomas. Here we present methods developed, optimized, and validated to obtain high quality gene expression data.ResultsRNase inhibitor was included in solutions used to stain frozen tissue sections for LCM, which improved RNA quality significantly. Quantitative PCR assays, requiring minimal amounts of LCM RNA, were developed to determine RNA quality and concentration. SuperScript II™ reverse transcriptase was replaced with SuperScript III™, and SpeedVac concentration was eliminated to optimize linear amplification. The GeneChip® IVT labeling kit was used rather than the Enzo BioArray™ HighYield™ RNA transcript labeling kit since side-by-side comparisons indicated high-end signal saturation with the latter. We obtained 72 μg of labeled complementary RNA on average after linear amplification of about 2 ng of total RNA.ConclusionUnsupervised clustering placed 5/5 normal and 2/2 benign prostatic hyperplasia cases in one group, 5/7 Gleason pattern 3 cases in another group, and the remaining 2/7 pattern 3 cases in a third group with 8/8 Gleason pattern 5 cases and 3/3 metastatic prostatic adenocarcinomas. Differential expression of alpha-methylacyl coenzyme A racemase (AMACR) and hepsin was confirmed using quantitative PCR.

USP44 regulates centrosome positioning to prevent aneuploidy and suppress tumorigenesis.

Most human tumors have abnormal numbers of chromosomes, a condition known as aneuploidy. The mitotic checkpoint is an important mechanism that prevents aneuploidy by restraining the activity of the anaphase-promoting complex (APC). The deubiquitinase USP44 was identified as a key regulator of APC activation; however, the physiological importance of USP44 and its impact on cancer biology are unknown. To clarify the role of USP44 in mitosis, we engineered a mouse lacking Usp44. We found that USP44 regulated the mitotic checkpoint and prevented chromosome lagging. Mice lacking Usp44 were prone to the development of spontaneous tumors, particularly in the lungs. Additionally, USP44 was frequently downregulated in human lung cancer, and low expression correlated with a poor prognosis. USP44 inhibited chromosome segregation errors independent of its role in the mitotic checkpoint by regulating centrosome separation, positioning, and mitotic spindle geometry. These functions required direct binding to the centriole protein centrin. Our data reveal a new role for the ubiquitin system in mitotic spindle regulation and underscore the importance of USP44 in the pathogenesis of human cancer.

The role of desmoglein-3 in the diagnosis of squamous cell carcinoma of the lung.

Results from several microarray-based studies have led to the identification of up-regulated expression levels of the DSG3 gene in pulmonary squamous cell carcinomas (SQCCs). The purpose of this study was to determine the role of DSG3 expression in the diagnosis of SQCCs of the lung and to compare DSG3 with p63, CK5, and CK6, as markers of squamous cell differentiation. Expression of DSG3 mRNA was evaluated in bulk laser capture microdissection-derived microarray data and by quantitative reverse transcription PCR on both SQCCs and adenocarcinomas. Expression levels of p63, CK5, and CK6 were evaluated in microarray data from the same set. An immunohistochemical study using antibodies directed against DSG3, p63, and CK5/6 was also performed. DSG3 was over-expressed in SQCCs but had very limited expression in both adenocarcinomas and non-neoplastic lungs. The microarray data showed that DSG3 had a sensitivity and specificity of 88% and 98%, respectively, in detecting SQCC versus adenocarcinoma. In comparison, sensitivity and specificity was 92% and 82% for p63, and 85% and 96% for CK5, respectively. The correlation coefficient between the microarray and immunohistochemical data for these genes was greater than or equal to 0.9. Using immunohistochemistry, sensitivity and specificity of DSG3 for lung cancers were 98% and 99%, respectively. Therefore, DSG3 can be a useful ancillary marker to separate SQCC from other subtypes of lung cancer.

The ability of biomarkers to predict systemic progression in men with high-risk prostate cancer treated surgically is dependent on ERG status.

The objective of this study was to assess the relationship of the tumor protein levels of TOP2A and MIB-1 and ERG status with cancer-specific outcomes in men with high-risk prostate cancer treated by radical prostatectomy (RP). A 150-pair case-control study was designed from RP patients who developed systemic progression (SP) within 6 years of RP (cases) and men who were free of disease at least 8 years after RP (controls). The cases and controls were matched on conventional prognostic clinical parameters. TOP2A and MIB-1 levels were assessed by immunohistochemical methods, and ERG status was assessed by quantitative reverse transcription-PCR. The prognostic abilities of TOP2A and MIB-1 were significantly better in ERG(-) patients, and TOP2A was superior to MIB-1. In receiver operating characteristic analysis, the TOP2A and MIB-1 scores exhibited AUCs of 0.81 and 0.78 for ERG(-) patients, versus 0.67 and 0.68 for ERG(+) patients, respectively. Clinical parameters attained an AUC of 0.65 in ERG(-) patients and 0.54 in ERG(+) patients. When both markers were incorporated into a model for ERG(-) patients, the AUC increased to 0.83, with TOP2A showing a stronger association with SP than MIB-1. The time to SP was significantly associated with TOP2A; higher 5-year SP rates were observed in patients with higher TOP2A protein levels. In addition, although patient numbers are small, the response to adjuvant androgen deprivation therapy is associated with ERG status, showing more significant treatment effect in ERG(+) patients.

Identification of Prognostic Biomarkers for Prostate Cancer

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.

Mate Pair Sequencing of Whole-Genome-Amplified DNA Following Laser Capture Microdissection of Prostate Cancer

High-throughput next-generation sequencing provides a revolutionary platform to unravel the precise DNA aberrations concealed within subgroups of tumour cells. However, in many instances, the limited number of cells makes the application of this technology in tumour heterogeneity studies a challenge. In order to address these limitations, we present a novel methodology to partner laser capture microdissection (LCM) with sequencing platforms, through a whole-genome amplification (WGA) protocol performed in situ directly on LCM engrafted cells. We further adapted current Illumina mate pair (MP) sequencing protocols to the input of WGA DNA and used this technology to investigate large genomic rearrangements in adjacent Gleason Pattern 3 and 4 prostate tumours separately collected by LCM. Sequencing data predicted genome coverage and depths similar to unamplified genomic DNA, with limited repetition and bias predicted in WGA protocols. Mapping algorithms developed in our laboratory predicted high-confidence rearrangements and selected events each demonstrated the predicted fusion junctions upon validation. Rearrangements were additionally confirmed in unamplified tissue and evaluated in adjacent benign-appearing tissues. A detailed understanding of gene fusions that characterize cancer will be critical in the development of biomarkers to predict the clinical outcome. The described methodology provides a mechanism of efficiently defining these events in limited pure populations of tumour tissue, aiding in the derivation of genomic aberrations that initiate cancer and drive cancer progression.