Gene panel model predictive of outcome in men at high-risk of systemic progression and death from prostate cancer after radical retropubic prostatectomy

BAZ2A-RNA mediated association with TOP2A and KDM1A represses genes implicated in prostate cancer

BAZ2A represses rRNA genes (rDNA) that are transcribed by RNA polymerase I. In prostate cancer (PCa), BAZ2A function goes beyond this role because it represses genes frequently silenced in metastatic disease. However, the mechanisms of this BAZ2A-mediated repression remain elusive. Here, we show that BAZ2A represses genes through its RNA-binding TAM domain using mechanisms differing from rDNA silencing. Although the TAM domain mediates BAZ2A recruitment to rDNA, in PCa, this is not required for BAZ2A association with target genes. Instead, the BAZ2A-TAM domain in association with RNA mediates the interaction with topoisomerase 2A (TOP2A) and histone demethylase KDM1A, whose expression positively correlates with BAZ2A levels in localized and metastatic PCa. TOP2A and KDM1A pharmacological inhibition up-regulate BAZ2A-repressed genes that are regulated by inactive enhancers bound by BAZ2A, whereas rRNA genes are not affected. Our findings showed a novel RNA-based mechanism of gene regulation in PCa. Furthermore, we determined that RNA-mediated interactions between BAZ2A and TOP2A and KDM1A repress genes critical to PCa and may prove to be useful to stratify prostate cancer risk and treatment in patients.

Signature for Prostate Cancer Based on Autophagy-Related Genes and a Nomogram for Quantitative Risk Stratification

Background. Prostate cancer (PCa) ranks as the most common malignancy and the second leading cause of cancer-related death among males worldwide. The essential role of autophagy in the progression of PCa and treatment resistance has been preliminarily revealed. However, comprehensive molecular elucidations of the correlation between PCa and autophagy are rare. Method. We obtained transcription information and corresponding clinicopathological profiles of PCa patients from TCGA, MSKCC, and GEO datasets. LAASO analysis was employed to select gene signatures and estimate the autophagy score for each patient. Correlations between the signature and prognosis of PCa were investigated by K-M and multivariate Cox regression analyses. A nomogram was established on the basis of the above results. Further validations relied on ROC, calibration analysis, decision curve analysis, and external cohorts. Variable activated signaling pathways were revealed using GSVA algorithms, and the genetic alteration landscape was elucidated via the oncodrive module from the “maftools” R package. In addition, we also examined the therapeutic role of the signature based on phenotype data from GDSC 2016. Result. Six autophagy-related genes were eventually selected to establish the signature, including ULK1, CAPN10, FKBP5, UBE2T, NLRC4, and BNIP3L. We used these genes and corresponding coefficients to calculate an autophagy score (AutS) for each patient in this study. A high AutS group and a low AutS group were divided on the mean AutS of the patients. Longer overall survival, higher Gleason score and PSA, and better response to ADT were observed in patients with high AutS. Meanwhile, we found that high AutS PCa was related to more proliferation-associated signaling activation and higher genetic mutation frequencies, manifesting a poor prognosis. A nomogram was constructed based on GS, T stage, PSA, and AutS as covariates. Its discriminative efficacy and clinical value were validated using robust statistical methods. Finally, we tested its prognostic value through two external cohorts and six published signatures. Conclusion. The autophagy-related gene signature is a highly discriminative model for risk stratification and drug therapy in PCa, and a nomogram incorporating AutS might be a promising tool for precision medicine.

Antizyme Inhibitor 1 regulates matrikine expression and enhances the metastatic potential of aggressive primary prostate cancer

Molecular drivers of metastasis in patients with high-risk localized prostate cancer (PCa) are poorly understood. Therefore, we aim to study molecular drivers of metastatic progression in high-risk PCa patients. A retrospective matched case-control study of two clinico-pathologically identical groups of high-risk PCa patients was undertaken. One group developed metastatic recurrence (n=19) while the other did not (n=25). The primary index tumor was identified by a uro-pathologist, followed by DNA and RNA extraction for somatic copy number aberration (CNA) analysis and whole-transcriptome gene expression analysis. In vitro and in vivo studies included cell line manipulation and xenograft models. The integrative CNA and gene expression analyses identified an increase in AZIN1 gene expression within a focal amplification of 8q22.3, which was associated with metastatic recurrence of high-risk PCa patients in four independent cohorts. The effects of AZIN1 knockdown were evaluated, due to its therapeutic potential. AZIN1 knockdown effected proliferation and metastatic potential of PCa cells and xenograft models. RNA sequencing after AZIN1 knockdown in PCa cells revealed upregulation of genes coding for collagen subunits. The observed effect on cell migration after AZIN1 knockdown was mimicked when exposing PCa cells to bio-active molecules deriving from COL4A1 and COL4A2. Our integrated CNA and gene expression analysis of primary high-risk PCa identified the AZIN1 gene as a novel driver of metastatic progression, by altering collagen subunit expression. Future research should further investigate its therapeutic potential in preventing metastatic recurrence. Implications: AZIN1 was identified as driver of metastatic progression in high-risk PCa through matrikine regulation.

Limitations of Explainability for Established Prognostic Biomarkers of Prostate Cancer

High-throughput technologies do not only provide novel means for basic biological research but also for clinical applications in hospitals. For instance, the usage of gene expression profiles as prognostic biomarkers for predicting cancer progression has found widespread interest. Aside from predicting the progression of patients, it is generally believed that such prognostic biomarkers also provide valuable information about disease mechanisms and the underlying molecular processes that are causal for a disorder. However, the latter assumption has been challenged. In this paper, we study this problem for prostate cancer. Specifically, we investigate a large number of previously published prognostic signatures of prostate cancer based on gene expression profiles and show that none of these can provide unique information about the underlying disease etiology of prostate cancer. Hence, our analysis reveals that none of the studied signatures has a sensible biological meaning. Overall, this shows that all studied prognostic signatures are merely black-box models allowing sensible predictions of prostate cancer outcome but are not capable of providing causal explanations to enhance the understanding of prostate cancer.

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature

Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.

Etoposide and topoisomerase II inhibition for aggressive prostate cancer: Data from a translational study

BACKGROUND

Etoposide phosphate (VP-16) is a topoisomerase 2 (TOP2) inhibitor that demonstrated activity in patients with metastatic castration-resistant prostate cancer (mCRPC). We investigated the sensitivity of prostate cancer (PCa) cells (LNCaP, 22Rv1, PC3, DU145, PDB and MDB) to VP-16 and the possible relationship between VP-16 activity and TOP2 expression. The activity of VP-16 was compared with that of docetaxel, enzalutamide and olaparib. The prevalence and clinical significance of TOP2 genetic and transcriptomic alterations was also explored in mCRPC.

METHODS

Cell cultures and crystal violet cell proliferation assays were performed. Specific antibodies were used in western blots analyses of cell protein extracts. Datasets were analyzed in cBioportal.

RESULTS

VP-16 was active in all PCa cell lines analyzed and demonstrated increased activity in PC3 and DU145 cells. VP-16 was more cytotoxic compared to the other treatments, except for LNCaP and 22Rv1, which were more sensitive to docetaxel. Maintenance of antiandrogen treatment in MDB and PDB increased sensitivity to VP-16, docetaxel and enzalutamide. TOP2A was found overexpressed in 22Rv1, DU145 and PC3, whereas TOP2B was overexpressed in 22Rv1 and PDB. In the mCRPC datasets analysis, TOP2A mRNA overexpression was associated with worse patients' prognosis, with the molecular features of neuroendocrine prostate cancer (NEPC) and with lower androgen receptor (AR) score. Patients overexpressing TOP2A mRNA were more likely to harbor RB1 loss.

CONCLUSIONS

Specific subpopulations of patients with aggressive variant prostate cancer (AVPC) could benefit from VP-16 treatment. TOP2A overexpression, rather than TOP2B, might be a good biomarker to predict response to VP-16.

G9a-mediated repression of CDH10 in hypoxia enhances breast tumour cell motility and associates with poor survival outcome

Rationale: Epigenetic mechanisms are fundamental processes that can modulate gene expression, allowing cellular adaptation to environmental conditions. Hypoxia is an important factor known to initiate the metastatic cascade in cancer, activating cell motility and invasion by silencing cell adhesion genes. G9a is a histone methyltransferase previously shown to accumulate in hypoxic conditions. While its oncogenic activity has been previously reported, not much is known about the role G9a plays in the hypoxia-mediated metastatic cascade.

Methods: The role of G9a in cell motility in hypoxic condition was determined by inhibiting G9a either by short-hairpin mediated knock down or pharmacologically using a small molecule inhibitor. Through gene expression profiling, we identified CDH10 to be an important G9a target that regulates breast cancer cell motility. Lung metastasis assay in mice was used to determine the physiological significance of G9a.

Results: We demonstrate that, while hypoxia enhances breast cancer migratory capacity, blocking G9a severely reduces cellular motility under both normoxic and hypoxic conditions and prevents the hypoxia-mediated induction of cellular movement. Moreover, inhibition of G9a histone methyltransferase activity in mice using a specific small molecule inhibitor significantly reduced growth and colonisation of breast cancer cells in the lung. We identify the type-II cadherin CDH10 as being a novel hypoxia-dependent gene, directly repressed by G9a through histone methylation. CDH10 overexpression significantly reduces cellular movements in breast cancer cell lines and prevents the hypoxia-mediated increase in cell motility. In addition, we show that CDH10 expression is prognostic in breast cancer and that it is inversely correlated to EHMT2 (G9a) transcript levels in many tumor-types, including breast cancer.

Conclusion: We propose that G9a promotes cellular motility during hypoxic stress through the silencing of the cell adhesion molecule CDH10 and we describe CDH10 as a novel prognostic biomarker for breast cancer.

A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer

Prostate cancer (PCa) is a genetically heterogeneous cancer entity that causes challenges in pre-treatment clinical evaluation, such as the correct identification of the tumor stage. Conventional clinical tests based on digital rectal examination, Prostate-Specific Antigen (PSA) levels, and Gleason score still lack accuracy for stage prediction. We hypothesize that unraveling the molecular mechanisms underlying PCa staging via integrative analysis of multi-OMICs data could significantly improve the prediction accuracy for PCa pathological stages. We present a radiogenomic approach comprising clinical, imaging, and two genomic (gene and miRNA expression) datasets for 298 PCa patients. Comprehensive analysis of gene and miRNA expression profiles for two frequent PCa stages (T2c and T3b) unraveled the molecular characteristics for each stage and the corresponding gene regulatory interaction network that may drive tumor upstaging from T2c to T3b. Furthermore, four biomarkers (ANPEP, mir-217, mir-592, mir-6715b) were found to distinguish between the two PCa stages and were highly correlated (average r = ± 0.75) with corresponding aggressiveness-related imaging features in both tumor stages. When combined with related clinical features, these biomarkers markedly improved the prediction accuracy for the pathological stage. Our prediction model exhibits high potential to yield clinically relevant results for characterizing PCa aggressiveness.

Frequently rearranged and overexpressed δ-catenin is responsible for low sensitivity of prostate cancer cells to androgen receptor and β-catenin antagonists

The mechanism of prostate cancer (PCa) progression towards the hormone refractory state remains poorly understood. Treatment options for such patients are limited and present a major clinical challenge. Previously, δ-catenin was reported to promote PCa cell growth in vitro and its increased level is associated with PCa progression in vivo. In this study we show that re-arrangements at Catenin Delta 2 (CTNND2) locus, including gene duplications, are very common in clinically significant PCa and may underlie δ-catenin overexpression. We find that δ-catenin in PCa cells exists in a complex with E-cadherin, p120, and α- and β-catenin. Increased expression of δ-catenin leads to its further stabilization as well as upregulation and stabilization of its binding partners. Resistant to degradation and overexpressed δ-catenin isoform activates Wnt signaling pathway by increasing the level of nuclear β-catenin and subsequent stimulation of Tcf/Lef transcription targets. Evaluation of responses to treatments, with androgen receptor (AR) antagonist and β-catenin inhibitors revealed that cells with high levels of δ-catenin are more resistant to killing with single agent treatment than matched control cells. We show that combination treatment targeting both AR and β-catenin networks is more effective in suppressing tumor growth than targeting a single network. In conclusion, targeting clinically significant PCa with high levels of δ–catenin with anti-androgen and anti β-catenin combination therapy may prevent progression of the disease to a castration-resistant state and, thus, represents a promising therapeutic strategy.

Pixelated spatial gene expression analysis from tissue

Here, we present a technique that performs on-chip picoliter real-time reverse transcriptase loop mediated isothermal amplification (RT-LAMP) reactions on a histological tissue section without any analyte purification while preserving the native spatial location of the nucleic acid molecules. We demonstrate this method by amplifying TOP2A messenger RNA (mRNA) in a prostate cancer xenograft with 100 µm spatial resolution and by visualizing the variation in threshold time of amplification across the tissue. The on-chip reaction was validated by mRNA fluorescence in situ hybridization (mFISH) from cells in the tissue section. The entire process, from tissue loading on microchip to results from RT-LAMP can be carried out in less than 2 h. We anticipate that this technique, with its ease of use, fast turnaround, and quantitative molecular outputs, would become an invaluable tissue analysis tool for researchers and clinicians in the biomedical arena.

Spatial localization of genetic information is important for tissue heterogeneity but difficult to capture with current analytical techniques. Here the authors present “Pixelated RT-LAMP”, an approach that uses parallel on-chip reactions to provide the distribution of target sequences directly from tissue.

TOP2A and EZH2 Provide Early Detection of an Aggressive Prostate Cancer Subgroup

Purpose: Current clinical parameters do not stratify indolent from aggressive prostate cancer. Aggressive prostate cancer, defined by the progression from localized disease to metastasis, is responsible for the majority of prostate cancer-associated mortality. Recent gene expression profiling has proven successful in predicting the outcome of prostate cancer patients; however, they have yet to provide targeted therapy approaches that could inhibit a patient's progression to metastatic disease.Experimental Design: We have interrogated a total of seven primary prostate cancer cohorts (n = 1,900), two metastatic castration-resistant prostate cancer datasets (n = 293), and one prospective cohort (n = 1,385) to assess the impact of TOP2A and EZH2 expression on prostate cancer cellular program and patient outcomes. We also performed IHC staining for TOP2A and EZH2 in a cohort of primary prostate cancer patients (n = 89) with known outcome. Finally, we explored the therapeutic potential of a combination therapy targeting both TOP2A and EZH2 using novel prostate cancer-derived murine cell lines.Results: We demonstrate by genome-wide analysis of independent primary and metastatic prostate cancer datasets that concurrent TOP2A and EZH2 mRNA and protein upregulation selected for a subgroup of primary and metastatic patients with more aggressive disease and notable overlap of genes involved in mitotic regulation. Importantly, TOP2A and EZH2 in prostate cancer cells act as key driving oncogenes, a fact highlighted by sensitivity to combination-targeted therapy.Conclusions: Overall, our data support further assessment of TOP2A and EZH2 as biomarkers for early identification of patients with increased metastatic potential that may benefit from adjuvant or neoadjuvant targeted therapy approaches. Clin Cancer Res; 23(22); 7072-83. ©2017 AACR.

Trends in Gene Expression Profiling for Prostate Cancer Risk Assessment: A Systematic Review

Objectives

The aim of the study is to review biotechnology advances in gene expression profiling on prostate cancer (PCa), focusing on experimental platform development and gene discovery, in relation to different study designs and outcomes in order to understand how they can be exploited to improve PCa diagnosis and clinical management.

Methods

We conducted a systematic literature review on gene expression profiling studies through PubMed/MEDLINE and Web of Science between 2000 and 2016. Tissue biopsy and clinical gene profiling studies with different outcomes (e.g., recurrence, survival) were included.

Results

Over 3,000 papers were screened and 137 full-text articles were selected. In terms of technology used, microarray is still the most popular technique, increasing from 50 to 70% between 2010 and 2015, but there has been a rise in the number of studies using RNA sequencing (13% in 2015). Sample sizes have increased, as well as the number of genes that can be screened all at once, but we have also observed more focused targeting in more recent studies. Qualitative analysis on the specific genes found associated with PCa risk or clinical outcomes revealed a large variety of gene candidates, with a few consistent cross-studies.

Conclusions

The last 15 years of research in gene expression in PCa have brought a large volume of data and information that has been decoded only in part, but advancements in high-throughput sequencing technology are increasing the amount of data that can be generated. The variety of findings warrants the execution of both validation studies and meta-analyses. Genetic biomarkers have tremendous potential for early diagnosis of PCa and, if coupled with other diagnostics (e.g., imaging), can effectively be used to concretize less-invasive, personalized prediction of PCa risk and progression.

A four gene signature predictive of recurrent prostate cancer

Prostate cancer is the most common form of non-dermatological cancer among US men, with an increasing incidence due to the aging population. Patients diagnosed with clinically localized disease identified as intermediate or high-risk are often treated by radical prostatectomy. Approximately 33% of these patients will suffer recurrence after surgery. Identifying patients likely to experience recurrence after radical prostatectomy would lead to improved clinical outcomes, as these patients could receive adjuvant radiotherapy. Here, we report a new tool for prediction of prostate cancer recurrence based on the expression pattern of a small set of cooperation response genes (CRGs). CRGs are a group of genes downstream of cooperating oncogenic mutations previously identified in a colon cancer model that are critical to the cancer phenotype. We show that systemic dysregulation of CRGs is also found in prostate cancer, including a 4-gene signature (HBEGF, HOXC13, IGFBP2, and SATB1) capable of differentiating recurrent from non-recurrent prostate cancer. To develop a suitable diagnostic tool to predict disease outcomes in individual patients, multiple algorithms and data handling strategies were evaluated on a training set using leave-one-out cross-validation (LOOCV). The best-performing algorithm, when used in combination with a predictive nomogram based on clinical staging, predicted recurrent and non-recurrent disease outcomes in a blinded validation set with 83% accuracy, outperforming previous methods. Disease-free survival times between the cohort of prostate cancers predicted to recur and predicted not to recur differed significantly (p = 1.38x10-6). Therefore, this test allows us to accurately identify prostate cancer patients likely to experience future recurrent disease immediately following removal of the primary tumor.

Novel Nine-Exon AR Transcripts (Exon 1/Exon 1b/Exons 2-8) in Normal and Cancerous Breast and Prostate Cells

Nearly 20 different transcripts of the human androgen receptor (AR) are reported with two currently listed as Refseq isoforms in the NCBI database. Isoform 1 encodes wild-type AR (type 1 AR) and isoform 2 encodes the variant AR45 (type 2 AR). Both variants contain eight exons: they share common exons 2-8 but differ in exon 1 with the canonical exon 1 in isoform 1 and the variant exon 1b in isoform 2. Splicing of exon 1 or exon 1b is reported to be mutually exclusive. In this study, we identified a novel exon 1b (1b/TAG) that contains an additional TAG trinucleotide upstream of exon 1b. Moreover, we identified AR transcripts in both normal and cancerous breast and prostate cells that contained either exon 1b or 1b/TAG spliced between the canonical exon 1 and exon 2, generating nine-exon AR transcripts that we have named isoforms 3a and 3b. The proteins encoded by these new AR variants could regulate androgen-responsive reporters in breast and prostate cancer cells under androgen-depleted conditions. Analysis of type 3 AR-GFP fusion proteins showed partial nuclear localization in PC3 cells under androgen-depleted conditions, supporting androgen-independent activation of the AR. Type 3 AR proteins inhibited androgen-induced growth of LNCaP cells. Microarray analysis identified a small set of type 3a AR target genes in LNCaP cells, including genes known to modulate growth and proliferation of prostate cancer (PCGEM1, PEG3, EPHA3, and EFNB2) or other types of human cancers (TOX3, ST8SIA4, and SLITRK3), and genes that are diagnostic/prognostic biomarkers of prostate cancer (GRINA3, and BCHE).

Novel Gene Expression Signature Predictive of Clinical Recurrence After Radical Prostatectomy in Early Stage Prostate Cancer Patients

BACKGROUND

Current clinical tools have limited accuracy in differentiating patients with localized prostate cancer who are at risk of recurrence from patients with indolent disease. We aimed to identify a gene expression signature that jointly with clinical variables could improve upon the prediction of clinical recurrence after RP for patients with stage T2 PCa.

METHODS

The study population includes consented patients who underwent a radical retropubic prostatectomy (RP) and bilateral pelvic lymph node dissection at the University of Southern California in the PSA-era (1988-2008). We used a nested case-control study of 187 organ-confined patients (pT2N0M0): 154 with no recurrence ("controls") and 33 with clinical recurrence ("cases"). RNA was obtained from laser capture microdissected malignant glands representative of the overall Gleason score of each patient. Whole genome gene expression profiles (29,000 transcripts) were obtained using the Whole Genome DASL HT platform (Illumina, Inc). A gene expression signature of PCa clinical recurrence was identified using stability selection with elastic net regularized logistic regression. Three existing datasets generated with the Affymetrix Human Exon 1.0ST array were used for validation: Mayo Clinic (MC, n = 545), Memorial Sloan Kettering Cancer Center (SKCC, n = 150), and Erasmus Medical Center (EMC, n = 48). The areas under the ROC curve (AUCs) were obtained using repeated fivefold cross-validation.

RESULTS

A 28-gene expression signature was identified that jointly with key clinical variables (age, Gleason score, pre-operative PSA level, and operation year) was predictive of clinical recurrence (AUC of clinical variables only was 0.67, AUC of clinical variables, and 28-gene signature was 0.99). The AUC of this gene signature fitted in each of the external datasets jointly with clinical variables was 0.75 (0.72-0.77) (MC), 0.90 (0.86-0.94) (MSKCC), and 0.82 (0.74-0.91) (EMC), whereas the AUC for clinical variables only in each dataset was 0.72 (0.70-0.74), 0.86 (0.82-0.91), and 0.76 (0.67-0.85), respectively.

CONCLUSIONS

We report a novel gene-expression based classifier identified using agnostic approaches from whole genome expression profiles that can improve upon the accuracy of clinical indicators to stratify early stage localized patients at risk of clinical recurrence after RP. Prostate 76:1239-1256, 2016. © 2016 Wiley Periodicals, Inc.

Clinical variability and molecular heterogeneity in prostate cancer

Prostate cancer is a clinically heterogeneous disease, with some men having indolent disease that can safely be observed, while others have aggressive, lethal disease. Over the past decade, researchers have begun to unravel some of the genomic heterogeneity that contributes to these varying clinical phenotypes. Distinct molecular sub-classes of prostate cancer have been identified, and the uniqueness of these sub-classes has been leveraged to predict clinical outcomes, design novel biomarkers for prostate cancer diagnosis, and develop novel therapeutics. Recent work has also elucidated the temporal and spatial heterogeneity of prostate cancer, helping us understand disease pathogenesis, response to therapy, and progression. New genomic techniques have provided us with a window into the remarkable clinical and genomic heterogeneity of prostate cancer, and this new perspective will increasingly impact patient care.

Hypoxia on the Expression of Hepatoma Upregulated Protein in Prostate Cancer Cells

Hepatoma upregulated protein (HURP) is a multifunctional protein with clinical promise. This protein has been demonstrated to be a predictive marker for the outcome in high-risk prostate cancer (PCa) patients, besides being a resistance factor in PCa. Although changes in oxygen tension (pO₂) are associated with PCa aggressiveness, the role of hypoxia in the regulation of tumor progression genes such as HURP has not yet been described. We hypothesized that pO₂ alteration is involved in the regulation of HURP expression in PCa cells. In the present study, PCa cells were incubated at 2% O₂ (hypoxia) and 20% O₂ (normoxia) conditions. Hypoxia reduced cell growth rate of PCa cells, when compared to the growth rate of cells cultured under normoxia (p < 0.05). The decrease in cell viability was accompanied by fivefold (p < 0.05) elevated rate of vascular endothelial growth factor (VEGF) release. The expression of VEGF and the hypoxia-inducible metabolic enzyme carbonic anhydrase 9 were elevated maximally nearly 61-fold and 200-fold, respectively (p < 0.05). Noted in two cell lines (LNCaP and C4-2B) and independent of the oxygen levels, HURP expression assessed at both mRNA and protein levels was reduced. However, the decrease was more pronounced in cells cultured under hypoxia (p < 0.05). Interestingly, the analysis of patients’ specimens by Western blot revealed a marked increase of HURP protein (fivefold), when compared to control (cystoprostatectomy) tissue (p < 0.05). Immunohistochemistry analysis showed an increase in the immunostaining intensity of HURP and the hypoxia-sensitive molecules, hypoxia-inducible factor 1-alpha (HIF-1α), VEGF, and heat-shock protein 60 (HSP60) in association with tumor grade. The data also suggested a redistribution of subcellular localization for HURP and HIF-1α from the nucleus to the cytoplasmic compartment in relation to increasing tumor grade. Analysis of HURP Promoter for HIF-1-binding sites revealed presence of four putative HIF binding sites on the promoter of DLGAP5/HURP gene in the non-translated region upstream from the start codon, suggesting association between HIF-1α and the regulation of HURP protein. Taken together, our findings suggest a modulatory role of hypoxia on the expression of HURP. Additionally our results provide basis for utilization of tumor-associated molecules as predictors of aggressive PCa.

Biomarkers for prostate cancer: present challenges and future opportunities

Prostate cancer (PCa) has variable biological potential with multiple treatment options. A more personalized approach, therefore, is needed to better define men at higher risk of developing PCa, discriminate indolent from aggressive disease and improve risk stratification after treatment by predicting the likelihood of progression. This may improve clinical decision-making regarding management, improve selection for active surveillance protocols and minimize morbidity from treatment. Discovery of new biomarkers associated with prostate carcinogenesis present an opportunity to provide patients with novel genetic signatures to better understand their risk of developing PCa and help forecast their clinical course. In this review, we examine the current literature evaluating biomarkers in PCa. We also address current limitations and present several ideas for future studies.

Topoisomerase 2 Alpha Cooperates with Androgen Receptor to Contribute to Prostate Cancer Progression

Overexpression of TOP2A is associated with risk of systemic progression in prostate cancer patients, and higher levels of TOP2A were found in hormone-resistant cases. To elucidate the mechanism by which high levels of TOP2A contribute to tumor progression we generated TOP2A overexpressing prostate cancer cell lines. We show that TOP2A promotes tumor aggressiveness by inducing chromosomal rearrangements of genes that contribute to a more invasive phenotype. Anti-androgen treatment alone was ineffective in killing TOP2A overexpressing cells due to activation of an androgen receptor network. TOP2A poisons killed tumor cells more efficiently early in the progression course, while at later stages they provided greater benefit when combined with anti-androgen therapy. Mechanistically, we find that TOP2A enhances androgen signaling by facilitating transcription of androgen responsive genes, thereby promoting tumor cell growth. These studies revealed a relationship between TOP2A and androgen receptor signaling pathway that contributes to prostate cancer progression and confers sensitivity to treatments.

Expression profiling of prostate cancer tissue delineates genes associated with recurrence after prostatectomy

Prostate cancer is a leading cause of cancer death amongst males. The main clinical dilemma in treating prostate cancer is the high number of indolent cases that confer a significant risk of overtreatment. In this study, we have performed gene expression profiling of tumor tissue specimens from 36 patients with prostate cancer to identify transcripts that delineate aggressive and indolent cancer. Key genes were validated using previously published data and by tissue microarray analysis. Two molecular subgroups were identified with a significant overrepresentation of tumors from patients with biochemical recurrence in one of the groups. We successfully validated key transcripts association with recurrence using two publically available datasets totaling 669 patients. Twelve genes were found to be independent predictors of recurrence in multivariate logistical regression analysis. SFRP4 gene expression was consistently up regulated in patients with recurrence in all three datasets. Using an independent cohort of 536 prostate cancer patients we showed SFRP4 expression to be an independent predictor of recurrence after prostatectomy (HR = 1.35; p = 0.009). We identified SFRP4 to be associated with disease recurrence. Prospective studies are needed in order to assess the clinical usefulness of the identified key markers in this study.

Tissue-based Genomics Augments Post-prostatectomy Risk Stratification in a Natural History Cohort of Intermediate- and High-Risk Men

BACKGROUND

Radical prostatectomy (RP) is a primary treatment option for men with intermediate- and high-risk prostate cancer. Although many are effectively cured with local therapy alone, these men are by definition at higher risk of adverse pathologic features and clinical disease recurrence. It has been shown that the Decipher test predicts metastatic progression in cohorts that received adjuvant and salvage therapy following RP.

OBJECTIVE

To evaluate the Decipher genomic classifier in a natural history cohort of men at risk who received no additional treatment until the time of metastatic progression.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective case-cohort design for 356 men who underwent RP between 1992 and 2010 at intermediate or high risk and received no additional treatment until the time of metastasis. Participants met the following criteria: (1) Cancer of the Prostate Risk Assessment postsurgical (CAPRA-S) score ≥3; (2) pathologic Gleason score ≥7; and (3) post-RP prostate-specific antigen nadir <0.2 ng/ml.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary endpoint was defined as regional or distant metastases. Time-dependent receiver operating characteristic (ROC) curves, extension of decision curve analysis to survival data, and univariable and multivariable Cox proportional-hazards models were used to measure the discrimination, net benefit, and prognostic potential of genomic and pathologic risk factors. Cumulative incidence curves were constructed using Fine-Gray competing-risks analysis with appropriate weighting of the controls to account for the case-cohort study design.

RESULTS AND LIMITATIONS

Ninety six patients had unavailable tumor blocks or failed microarray quality control. Decipher scores were then obtained for 260 patients, of whom 99 experienced metastasis. Decipher correlated with increased cumulative incidence of biochemical recurrence, metastasis, and prostate cancer-specific mortality (p<0.01). The cumulative incidence of metastasis was 12% and 47% for patients with low and high Decipher scores, respectively, at 10 yr after RP. Decipher was independently prognostic of metastasis in multivariable analysis (hazard ratio 1.26 per 10% increase; p<0.01). Decipher had a c-index of 0.76 and increased the c-index of Eggener and CAPRA-S risk models from 0.76 and 0.77 to 0.86 and 0.87, respectively, at 10 yr after RP. Although the cohort was large, the single-center retrospective design is an important limitation.

CONCLUSIONS

In a patient population that received no adjuvant or salvage therapy after prostatectomy until metastatic progression, higher Decipher scores correlated with clinical events, and inclusion of Decipher scores improved the prognostic performance of validated clinicopathologic risk models. These results confirm the utility already reported for Decipher.

PATIENT SUMMARY

The Decipher test improves identification of patients most at risk of metastatic progression and death from prostate cancer after radical prostatectomy.

Genomic Predictors of Outcome in Prostate Cancer

CONTEXT

Given the highly variable behavior and clinical course of prostate cancer (PCa) and the multiple available treatment options, a personalized approach to oncologic risk stratification is important. Novel genetic approaches offer additional information to improve clinical decision making.

OBJECTIVE

To review the use of genomic biomarkers in the prognostication of PCa outcome and prediction of therapeutic response.

EVIDENCE ACQUISITION

Systematic literature review focused on human clinical studies reporting outcome measures with external validation. The literature search included all Medline, Embase, and Scopus articles from inception through July 2014.

EVIDENCE SYNTHESIS

An improved understanding of the genetic basis of prostate carcinogenesis has produced an increasing number of potential prognostic and predictive tools, such as transmembrane protease, serine2:v-ets avian erythroblastosis virus E26 oncogene homolog (TMPRSS2:ERG) gene fusion status, loss of the phosphatase and tensin homolog (PTEN) gene, and gene expression signatures utilizing messenger RNA from tumor tissue. Several commercially available gene panels with external validation are now available, although most have yet to be widely used. The most studied commercially available gene panels, Prolaris, Oncotype DX Genomic Prostate Score, and Decipher, may be used to estimate disease outcome in addition to clinical parameters or clinical nomograms. ConfirmMDx is an epigenetic test used to predict the results of repeat prostate biopsy after an initial negative biopsy. Additional future strategies include using genetic information from circulating tumor cells in the peripheral blood to guide treatment decisions at the initial diagnosis and at subsequent decision points.

CONCLUSIONS

Major advances have been made in our understanding of PCa biology in recent years. Our field is currently exploring the early stages of a personalized approach to augment traditional clinical decision making using commercially available genomic tools. A more comprehensive appreciation of value, limitations, and cost is important.

PATIENT SUMMARY

We summarized current advances in genomic testing in prostate cancer with a special focus on the estimation of disease outcome. Several commercial tests are currently available, but further understanding is needed to appreciate the potential benefits and limitations of these novel tests.

Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error

Background:

Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation.

Methods:

Prostatectomy samples from a large patient cohort with long follow-up were blindly assessed by expert pathologists who identified the tissue regions with the highest and lowest Gleason grade from each patient. To simulate biopsy-sampling error, a core from a high- and a low-Gleason area from each patient sample was used to generate a ‘high' and a ‘low' tumour microarray, respectively.

Results:

Using a quantitative proteomics approach, we identified from 160 candidates 12 biomarkers that predicted prostate cancer aggressiveness (surgical Gleason and TNM stage) and lethal outcome robustly in both high- and low-Gleason areas. Conversely, a previously reported lethal outcome-predictive marker signature for prostatectomy tissue was unable to perform under circumstances of maximal sampling error.

Conclusions:

Our results have important implications for cancer biomarker discovery in general and development of a sampling error-resistant clinical biopsy test for prediction of prostate cancer aggressiveness.

ERG protein expression as a biomarker of prostate cancer

TMPRSS2-ERG is a recurrent rearrangement specific for prostate cancer, leading to the overexpression of a truncated ERG protein product that is amenable to immunohistochemical detection. Two monoclonal anti-ERG antibodies have currently been validated, with comparable sensitivity and specificity for detecting ERG rearrangement. ERG immunostaining has been applied in different settings to elucidate the role of ERG rearrangement and overexpression in prostate cancer tumorigenesis and progression, as well as to investigate potential diagnostic and prognostic applications. In this article we review the literature on the topic and suggest potential future applications.

Antisense transcription at the TRPM2 locus as a novel prognostic marker and therapeutic target in prostate cancer

Overwhelming evidence indicates that cancer is a genetic disease caused by the accumulation of mutations in oncogenes and tumor suppressor genes. It is also increasingly apparent, however, that cancer depends not only on mutations in these coding genes but also on alterations in the large class of non-coding RNAs. Here, we report that one such long non-coding RNA, TRPM2-AS, an antisense transcript of TRPM2, which encodes an oxidative stress-activated ion channel, is overexpressed in prostate cancer (PCa). The high expression of TRPM2-AS and its related gene signature were found to be linked to poor clinical outcome, with the related gene signature working also independently of the patient's Gleason score. Mechanistically, TRPM2-AS knockdown led to PCa cell apoptosis, with a transcriptional profile that indicated an unbearable increase in cellular stress in the dying cells, which was coupled to cell cycle arrest, an increase in intracellular hydrogen peroxide and activation of the sense TRPM2 gene. Moreover, targets of existing drugs and treatments were found to be consistently associated with high TRPM2-AS levels in both targeted cells and patients, ultimately suggesting that the measurement of the expression levels of TRPM2-AS allows not only for the early identification of aggressive PCa tumors, but also identifies a subset of at-risk patients who would benefit from currently available, but mostly differently purposed, therapeutic agents.

A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling

BACKGROUND

Prostate tumor heterogeneity and biopsy undersampling pose challenges to accurate, individualized risk assessment for men with localized disease.

OBJECTIVE

To identify and validate a biopsy-based gene expression signature that predicts clinical recurrence, prostate cancer (PCa) death, and adverse pathology.

DESIGN, SETTING, AND PARTICIPANTS

Gene expression was quantified by reverse transcription-polymerase chain reaction for three studies-a discovery prostatectomy study (n=441), a biopsy study (n=167), and a prospectively designed, independent clinical validation study (n=395)-testing retrospectively collected needle biopsies from contemporary (1997-2011) patients with low to intermediate clinical risk who were candidates for active surveillance (AS).

OUTCOME MEASURES AND STATISTICAL ANALYSIS

The main outcome measures defining aggressive PCa were clinical recurrence, PCa death, and adverse pathology at prostatectomy. Cox proportional hazards regression models were used to evaluate the association between gene expression and time to event end points. Results from the prostatectomy and biopsy studies were used to develop and lock a multigene-expression-based signature, called the Genomic Prostate Score (GPS); in the validation study, logistic regression was used to test the association between the GPS and pathologic stage and grade at prostatectomy. Decision-curve analysis and risk profiles were used together with clinical and pathologic characteristics to evaluate clinical utility.

RESULTS AND LIMITATIONS

Of the 732 candidate genes analyzed, 288 (39%) were found to predict clinical recurrence despite heterogeneity and multifocality, and 198 (27%) were predictive of aggressive disease after adjustment for prostate-specific antigen, Gleason score, and clinical stage. Further analysis identified 17 genes representing multiple biological pathways that were combined into the GPS algorithm. In the validation study, GPS predicted high-grade (odds ratio [OR] per 20 GPS units: 2.3; 95% confidence interval [CI], 1.5-3.7; p<0.001) and high-stage (OR per 20 GPS units: 1.9; 95% CI, 1.3-3.0; p=0.003) at surgical pathology. GPS predicted high-grade and/or high-stage disease after controlling for established clinical factors (p<0.005) such as an OR of 2.1 (95% CI, 1.4-3.2) when adjusting for Cancer of the Prostate Risk Assessment score. A limitation of the validation study was the inclusion of men with low-volume intermediate-risk PCa (Gleason score 3+4), for whom some providers would not consider AS.

CONCLUSIONS

Genes representing multiple biological pathways discriminate PCa aggressiveness in biopsy tissue despite tumor heterogeneity, multifocality, and limited sampling at time of biopsy. The biopsy-based 17-gene GPS improves prediction of the presence or absence of adverse pathology and may help men with PCa make more informed decisions between AS and immediate treatment.

PATIENT SUMMARY

Prostate cancer (PCa) is often present in multiple locations within the prostate and has variable characteristics. We identified genes with expression associated with aggressive PCa to develop a biopsy-based, multigene signature, the Genomic Prostate Score (GPS). GPS was validated for its ability to predict men who have high-grade or high-stage PCa at diagnosis and may help men diagnosed with PCa decide between active surveillance and immediate definitive treatment.

An expression signature at diagnosis to estimate prostate cancer patients' overall survival

Background:

This study aimed to identify biomarkers for estimating the overall and prostate cancer (PCa)-specific survival in PCa patients at diagnosis.

Methods:

To explore the importance of embryonic stem cell (ESC) gene signatures, we identified 641 ESC gene predictors (ESCGPs) using published microarray data sets. ESCGPs were selected in a stepwise manner, and were combined with reported genes. Selected genes were analyzed by multiplex quantitative polymerase chain reaction using prostate fine-needle aspiration samples taken at diagnosis from a Swedish cohort of 189 PCa patients diagnosed between 1986 and 2001. Of these patients, there was overall and PCa-specific survival data available for 97.9%, and 77.9% were primarily treated by hormone therapy only. Univariate and multivariate Cox proportional hazard ratios and Kaplan–Meier plots were used for the survival analysis, and a k-nearest neighbor (kNN) algorithm for estimating overall survival.

Results:

An expression signature of VGLL3, IGFBP3 and F3 was shown sufficient to categorize the patients into high-, intermediate- and low-risk subtypes. The median overall survival times of the subtypes were 3.23, 4.00 and 9.85 years, respectively. The difference corresponded to hazard ratios of 5.86 (95% confidence interval (CI): 2.91–11.78, P<0.001) for the high-risk subtype and 3.45 (95% CI: 1.79–6.66, P<0.001) for the intermediate-risk compared with the low-risk subtype. The kNN models that included the gene expression signature outperformed the one designed on clinical parameters alone.

Conclusions:

The expression signature can potentially be used to estimate overall survival time. When validated in future studies, it could be integrated in the routine clinical diagnostic and prognostic procedure of PCa for an optimal treatment decision based on the estimated survival benefit.

Prognostic value of discs large homolog 7 transcript levels in prostate cancer

Hypoxia has been associated with malignant progression, metastasis and resistance to therapy. Hence, we studied expression of hypoxia–regulated genes in 100 prostate cancer (CaP) bulk tissues and 71 adjacent benign tissues. We found 24 transcripts significantly overexpressed (p≤0.02). Importantly, higher transcript levels of disc large (drosophila) homolog-associated protein 5 (DLGAP5)/discs large homolog 7 (DLG7)/hepatoma up-regulated protein (HURP), hyaluronan-mediated motility receptor (HMMR) and cyclin B1 (CCNB1) were associated with higher Gleason score and more advanced systemic progression. Since the products of HMMR and CCNB1 have been identified recently as molecular markers of CaP progression, we postulated that DLG7 has prognostic value too. To test this hypothesis, we measured transcript levels for DLG7 in a 150-pair case-control cohort. The cases (progression to systemic disease within six years of surgery) and controls (no progression within eight years) were matched for clinical and pathologic prognostic variables, including grade, stage, and preoperative serum levels of PSA. The overall prognostic ability of DLG7, as tested in receiver operating characteristic analysis was of 0.74 (95% CI, 0.68 to 0.8). Overall, our data indicate that expression of DLG7, a hypoxia-controlled gene, holds prognostic potential in high-risk CaP; this also demonstrates that variation of oxygen tension may constitute a tool for identification of novel biomarkers for CaP.

Biomarkers in pancreatic cancer: diagnostic, prognostic, and predictive

Pancreatic cancer is the fourth leading cause of cancer-related death in the United States. There has been minimal progress with regard to cancer-specific outcomes in recent decades. Although effective therapies will undoubtedly change the natural history of the disease, effective biomarkers are a promising tool that will likely have a positive impact and will undoubtedly have an important role in the management of patients with pancreatic ductal adenocarcinoma (PDA) in the future. At present, serum CA-19-9 (carbohydrate antigen 19-9) is the only Food and Drug Administration-approved biomarker for PDA, and it has utility as a prognostic marker and as a marker of disease recurrence. There has been a recent explosion in the pancreatic cancer biomarker field with more than 2000 biomarker studies implicating thousands of informative genes as candidate biomarkers. In this review, we summarize the literature on CA-19-9 in PDA and highlight the most promising investigational biomarkers. Distinctions are made between diagnostic biomarkers (detection of disease), prognostic biomarkers (provide information on prognosis and recurrence pattern), and predictive biomarkers (predict treatment response).

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.

Gene panel model predictive of outcome in patients with prostate cancer

In men at high risk for prostate cancer, established clinical and pathological parameters provide only limited prognostic information. Here we analyzed a French cohort of 103 prostate cancer patients and developed a gene panel model predictive of outcome in this group of patients. The model comprised of a 15-gene TaqMan Low-Density Array (TLDA) card, with gene expressions compared to a standardized reference. The RQ value for each gene was calculated, and a scoring system was developed. Summing all the binary scores (0 or 1) corresponding to the 15 genes, a global score is obtained between 0 and 15. This global score can be compared to Gleason score (0 to 10) by recalculating it into a 0-10 scaled score. A scaled score ≥2 suggested that the patient is suffering from a prostate cancer, and a scaled score ≥7 flagged aggressive cancer. Statistical analyses demonstrated a strongly significant linear correlation (p=3.50E-08) between scaled score and Gleason score for this prostate cancer cohort (N=103). These results support the capacity of this designed 15 target gene TLDA card approach to predict outcome in prostate cancer, opening up a new avenue for personalized medicine through future independent replication and applications for rapid identification of aggressive prostate cancer phenotypes for early intervention.

Novel dual-color immunohistochemical methods for detecting ERG-PTEN and ERG-SPINK1 status in prostate carcinoma

Identification of new molecular markers has led to the molecular classification of prostate cancer based on driving genetic lesions. The translation of these discoveries for clinical use necessitates the development of simple, reliable and rapid detection systems to screen patients for specific molecular aberrations. We developed two dual-color immunohistochemistry-based assays for the simultaneous assessment of ERG-PTEN and ERG-SPINK1 in prostate cancer. A total of 232 cases from 184 localized and 48 metastatic prostate cancers were evaluated for ERG-PTEN and 284 cases from 228 localized and 56 metastatic prostate cancers were evaluated for ERG-SPINK1. Of the 232 cases evaluated for ERG-PTEN, 81 (35%) ERG-positive and 77 (33%) PTEN-deleted cases were identified. Of the 81 ERG-positive cases, PTEN loss was confirmed in 35 (15%) cases by fluorescence in situ hybridization (FISH). PTEN status was concordant in 203 cases (sensitivity 90% and specificity 87%; P<0.0001) by both immunohistochemisty and FISH; however, immunohistochemisty could not distinguish between heterozygous and homozygous deletion status of PTEN. Of the 284 cases evaluated for ERG-SPINK1, 111 (39%) cases were positive for ERG. In the remaining 173 ERG-negative cases, SPINK1 was positive in 26 (9%) cases. SPINK1 expression was found to be mutually exclusive with ERG expression; however, we identified two cases, of which one showed concomitant expression of ERG and SPINK1 in the same tumor foci, and in the second case ERG and SPINK1 were seen in two independent foci of the same tumor nodule. Unlike the homogenous ERG staining in cancer tissues, heterogeneous SPINK1 staining was observed in the majority of the cases. Further studies are required to understand the molecular heterogeneity of cases with concomitant ERG-SPINK1 expression. Automated dual ERG-PTEN and ERG-SPINK1 immunohistochemisty assays are simple, reliable and portable across study sites for the simultaneous assessment of these proteins in prostate cancer.

Genetic markers associated with early cancer-specific mortality following prostatectomy

BACKGROUND

This study sought to identify novel effectors and markers of localized but potentially life-threatening prostate cancer (PCa), by evaluating chromosomal copy number alterations (CNAs) in tumors from patients who underwent prostatectomy and correlating these with clinicopathologic features and outcome.

METHODS

CNAs in tumor DNA samples from 125 patients in the discovery cohort who underwent prostatectomy were assayed with high-resolution Affymetrix 6.0 single-nucleotide polymorphism microarrays and then analyzed using the Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm.

RESULTS

The assays revealed 20 significant regions of CNAs, 4 of them novel, and identified the target genes of 4 of the alterations. By univariate analysis, 7 CNAs were significantly associated with early PCa-specific mortality. These included gains of chromosomal regions that contain the genes MYC, ADAR, or TPD52 and losses of sequences that incorporate SERPINB5, USP10, PTEN, or TP53. On multivariate analysis, only the CNAs of PTEN (phosphatase and tensin homolog) and MYC (v-myc myelocytomatosis viral oncogene homolog) contributed additional prognostic information independent of that provided by pathologic stage, Gleason score, and initial prostate-specific antigen level. Patients whose tumors had alterations of both genes had a markedly elevated risk of PCa-specific mortality (odds ratio = 53; 95% CI = 6.92-405, P = 1 × 10(-4)). Analyses of 333 tumors from 3 additional distinct patient cohorts confirmed the relationship between CNAs of PTEN and MYC and lethal PCa.

CONCLUSIONS

This study identified new CNAs and genes that likely contribute to the pathogenesis of localized PCa and suggests that patients whose tumors have acquired CNAs of PTEN, MYC, or both have an increased risk of early PCa-specific mortality.

Characterization of transcriptional changes in ERG rearrangement-positive prostate cancer identifies the regulation of metabolic sensors such as neuropeptide Y

ERG gene rearrangements are found in about one half of all prostate cancers. Functional analyses do not fully explain the selective pressure causing ERG rearrangement during the development of prostate cancer. To identify transcriptional changes in prostate cancer, including tumors with ERG gene rearrangements, we performed a meta-analysis on published gene expression data followed by validations on mRNA and protein levels as well as first functional investigations. Eight expression studies (n = 561) on human prostate tissues were included in the meta-analysis. Transcriptional changes between prostate cancer and non-cancerous prostate, as well as ERG rearrangement-positive (ERG+) and ERG rearrangement-negative (ERG−) prostate cancer, were analyzed. Detailed results can be accessed through an online database. We validated our meta-analysis using data from our own independent microarray study (n = 57). 84% and 49% (fold-change>2 and >1.5, respectively) of all transcriptional changes between ERG+ and ERG− prostate cancer determined by meta-analysis were verified in the validation study. Selected targets were confirmed by immunohistochemistry: NPY and PLA2G7 (up-regulated in ERG+ cancers), and AZGP1 and TFF3 (down-regulated in ERG+ cancers). First functional investigations for one of the most prominent ERG rearrangement-associated genes - neuropeptide Y (NPY) - revealed increased glucose uptake in vitro indicating the potential role of NPY in regulating cellular metabolism. In summary, we found robust population-independent transcriptional changes in prostate cancer and first signs of ERG rearrangements inducing metabolic changes in cancer cells by activating major metabolic signaling molecules like NPY. Our study indicates that metabolic changes possibly contribute to the selective pressure favoring ERG rearrangements in prostate cancer.

The role of treatment modality on the utility of predictive tissue biomarkers in clinical prostate cancer: a systematic review

BACKGROUND

Tissue biomarkers could pivotally improve clinical outcome prediction following prostate cancer therapy. Clinically, prostate cancer is managed by diverse treatment modalities whose individual influence on a biomarker's predictive ability is not well understood and poorly investigated in the literature.

OBJECTIVE

We conducted a systematic review to assess the predictive value of biomarkers in different treatment contexts in prostate cancer.

STUDY METHODOLOGY

A literature search was performed using the MeSH headings "prostate neoplasms" and "biological markers". Rigorous selection criteria identified studies correlating expression with clinical outcomes from primary androgen deprivation therapy (ADT), radical prostatectomy and radiotherapy (± neoadjuvant ADT).

STUDY RESULTS

Of 10,668 studies identified, 481 papers matched initial inclusion criteria. Following rescreening, 384 studies identified 236 individual tissue biomarkers, of which 29 were predictive on multivariate analysis in at least 2 independent cohorts. The majority were only tested in surgical cohorts. Only 8 predictive biomarkers were tested across all 3 treatments with Ki67 identified as universal predictive marker. p16 showed potential for treatment stratification between surgery and radiotherapy but needs further validation in independent studies.

CONCLUSIONS

Despite years of research, very few tissue biomarkers retain predictive value in independent validation across therapy context. Currently, none have conclusive ability to help treatment selection. Future biomarker research should consider the therapy context and use uniform methodology and evaluation criteria.

Current challenges in development of differentially expressed and prognostic prostate cancer biomarkers

Introduction. Predicting the aggressiveness of prostate cancer at biopsy is invaluable in making treatment decisions. In this paper we review the differential expression of genes and microRNAs identified through microarray analysis as potentially useful markers for prostate cancer prognosis and discuss some of the challenges associated with their development. Methods. A review of the literature was conducted through Medline. Articles were identified through searches of the following terms: "prostate cancer AND differential expression", "prostate cancer prognosis", and "prostate cancer AND microRNAs". Results. Though numerous differentially expressed genes and microRNAs were identified as possible prognostic markers, the significance of several of these genes is either debated due to conflicting results or is not validated in other study populations. A few of the articles constructed predictive nomograms using a panel of biomarkers which require further validation. Challenges to the development of useful markers include different methodology, cancer heterogeneity, and sampling error. These can be overcome by categorizing prognostic factors into particular gene pathways or by supplementing biopsy information with blood or urine-based biomarkers. Conclusion. Though biomarkers based on differential expression offer the potential to improve decision making concerning prostate cancer, further validation of their utility and accuracy at the biopsy level is needed.

Diagnostic, prognostic, and predictive biomarkers in pancreatic cancer

Serum CA 19-9 is the only FDA approved biomarker recommended for use in the routine management of pancreatic ductal adenocarcinoma (PDA). Over 2,000 biomarker studies related to pancreatic cancer appear in the literature, highlighting the need to discover and develop improved tests. Diagnostic biomarkers have implications for early detection of PDA, prognostic markers predict patient survival and recurrence patterns, and predictive markers can help personalize treatment regimens.

A novel survival-based tissue microarray of pancreatic cancer validates MUC1 and mesothelin as biomarkers

BACKGROUND

One-fifth of patients with seemingly 'curable' pancreatic ductal adenocarcinoma (PDA) experience an early recurrence and death, receiving no definable benefit from a major operation. Some patients with advanced stage tumors are deemed 'unresectable' by conventional staging criteria (e.g. liver metastasis), yet progress slowly. Effective biomarkers that stratify PDA based on biologic behavior are needed. To help researchers sort through the maze of biomarker data, a compendium of ∼2500 published candidate biomarkers in PDA was compiled (PLoS Med, 2009. 6(4) p. e1000046).

METHODS AND FINDINGS

Building on this compendium, we constructed a survival tissue microarray (termed s-TMA) comprised of short-term (cancer-specific death <12 months, n = 58) and long-term survivors (>30 months, n = 79) who underwent resection for PDA (total, n = 137). The s-TMA functions as a biological filter to identify bona fide prognostic markers associated with survival group extremes (at least 18 months separate survival groups). Based on a stringent selection process, 13 putative PDA biomarkers were identified from the public biomarker repository. Candidates were tested against the s-TMA by immunohistochemistry to identify the best markers of tumor biology. In a multivariate model, MUC1 (odds ratio, OR = 28.95, 3+ vs. negative expression, p = 0.004) and MSLN (OR = 12.47, 3+ vs. negative expression, p = 0.01) were highly predictive of early cancer-specific death. By comparison, pathologic factors (size, lymph node metastases, resection margin status, and grade) had ORs below three, and none reached statistical significance. ROC curves were used to compare the four pathologic prognostic features (ROC area = 0.70) to three univariate molecular predictors (MUC1, MSLN, MUC2) of survival group (ROC area = 0.80, p = 0.07).

CONCLUSIONS

MUC1 and MSLN were superior to pathologic features and other putative biomarkers as predicting survival group. Molecular assays comparing cancers from short and long survivors are an effective strategy to screen biomarkers and prioritize candidate cancer genes for diagnostic and therapeutic studies.

Identification of markers of prostate cancer progression using candidate gene expression

BACKGROUND

Metastatic prostate cancer (PCa) has no curative treatment options. Some forms of PCa are indolent and slow growing, while others metastasise quickly and may prove fatal within a very short time. The basis of this variable prognosis is poorly understood, despite considerable research. The aim of this study was to identify markers associated with the progression of PCa.

METHODS

Artificial neuronal network analysis combined with data from literature and previous work produced a panel of putative PCa progression markers, which were used in a transcriptomic analysis of 29 radical prostatectomy samples and correlated with clinical outcome.

RESULTS

Statistical analysis yielded seven putative markers of PCa progression, ANPEP, ABL1, PSCA, EFNA1, HSPB1, INMT and TRIP13. Two data transformation methods were utilised with only markers that were significant in both selected for further analysis. ANPEP and EFNA1 were significantly correlated with Gleason score. Models of progression co-utilising markers ANPEP and ABL1 or ANPEP and PSCA had the ability to correctly predict indolent or aggressive disease, based on Gleason score, in 89.7% and 86.2% of cases, respectively. Another model of TRIP13 expression in combination with preoperative PSA level and Gleason score was able to correctly predict recurrence in 85.7% of cases.

CONCLUSION

This proof of principle study demonstrates a novel association of carcinogenic and tumourigenic gene expression with PCa stage and prognosis.

Increased gene copy number of ERG on chromosome 21 but not TMPRSS2-ERG fusion predicts outcome in prostatic adenocarcinomas

The role of TMPRSS2-ERG gene fusion in prostate cancer prognostication remains controversial. We evaluated the prognostic role of TMPRSS2-ERG fusion using fluorescence in situ hybridization analysis in a case-control study nested in The Johns Hopkins retropubic radical prostatectomy cohort. In all, 10 tissue microarrays containing paired tumors and normal tissues obtained from 172 cases (recurrence) and 172 controls (non-recurrence) matched on pathological grade, stage, race/ethnicity, and age at the time of surgery were analyzed. All radical prostatectomies were performed at our institution between 1993 and 2004. Recurrence was defined as biochemical recurrence, development of clinical evidence of metastasis, or death from prostate carcinoma. Each tissue microarray spot was scored for the presence of TMPRSS2-ERG gene fusion and for ERG gene copy number gains. The odds ratio of recurrence and 95% confidence intervals were estimated from conditional logistic regression. Although the percentage of cases with fusion was slightly lower in cases than in controls (50 vs 57%), the difference was not statistically significant (P=0.20). The presence of fusion due to either deletion or split event was not associated with recurrence. Similarly, the presence of duplicated ERG deletion, duplicated ERG split, or ERG gene copy number gain with a single ERG fusion was not associated with recurrence. ERG gene polysomy without fusion was significantly associated with recurrence (odds ratio 2.0, 95% confidence interval 1.17-3.42). In summary, TMPRSS2-ERG fusion was not prognostic for recurrence after retropubic radical prostatectomy for clinically localized prostate cancer, although men with ERG gene copy number gain without fusion were twice more likely to recur.

Protein-coding and microRNA biomarkers of recurrence of prostate cancer following radical prostatectomy

An important challenge in prostate cancer research is to develop effective predictors of tumor recurrence following surgery to determine whether immediate adjuvant therapy is warranted. To identify biomarkers predictive of biochemical recurrence, we isolated the RNA from 70 formalin-fixed, paraffin-embedded radical prostatectomy specimens with known long-term outcomes to perform DASL expression profiling with a custom panel that we designed of 522 prostate cancer-relevant genes. We identified a panel of 10 protein-coding genes and two miRNA genes (RAD23B, FBP1, TNFRSF1A, CCNG2, NOTCH3, ETV1, BID, SIM2, LETMD1, ANXA1, miR-519d, and miR-647) that could be used to separate patients with and without biochemical recurrence (P < 0.001), as well as for the subset of 42 Gleason score 7 patients (P < 0.001). We performed an independent validation analysis on 40 samples and found that the biomarker panel was also significant at prediction of biochemical recurrence for all cases (P = 0.013) and for a subset of 19 Gleason score 7 cases (P = 0.010), both of which were adjusted for relevant clinical information including T-stage, prostate-specific antigen, and Gleason score. Importantly, these biomarkers could significantly predict clinical recurrence for Gleason score 7 patients. These biomarkers may increase the accuracy of prognostication following radical prostatectomy using formalin-fixed specimens.