The prostate cancer susceptibility variant rs2735839 near KLK3 gene is associated with aggressive prostate cancer and can stratify gleason score 7 patients

Evaluation of HNF1B, KLK3, ELAC2, TMPRSS2-ERG, and CTNNB1 polymorphisms associated with prostate cancer in samples of patients from HUPE-UERJ

PURPOSE

Prostate cancer (PCa) is the leading cause of death among men in 48 countries. Genetic alterations play a significant role in PCa carcinogenesis. For the hypothesis of this research, five unique polymorphisms (SNP) were investigated in different genes that showed to be associated in different ways with PCa: rs4430796, rs2735839, rs4792311, rs12329760, and rs28931588, respectively for the genes HNF1B, KLK3, ELAC2, TMPRSS2-ERG, and CTNNB1.

PATIENTS AND METHODS

Blood samples from 426 subjects were evaluated: 290 controls (161 females and 129 males) and 136 PCa patients. SNP were determined by real-time polymerase chain reaction. TaqMan SNP genotyping assay. In the control samples, the SNPs were defined in association with the self-reported ethnicity, and in 218 control samples with markers with ancestry indicators. The genes were in Hardy-Weinberg equilibrium. One hundred and seventy control samples were matched by ethnicity for comparison with the PCa samples.

RESULTS

The G allele at rs28931588 was monomorphic in both patients and controls studied. Significant differences were observed in allelic and genotypic frequencies between the control and Pca samples in rs2735839 (KLK3; p = 0.002 and χ2  = 8.73 and p = 0.01, respectively), by the global frequency and in the dominant model rs2735839_GG (odds ratio [OR] = 0.51, p = 0.02). AA and GA genotypes at rs4792311 (ELAC2) were more frequent in patients with Gleason 7(4 + 3), 8, and 9 (n = 37%-59.7%) compared to patients with Gleason 6 and 7(3 + 4) (n = 26%-40.0%) conferring a protective effect on the GG genotype (OR = 0.45, p = 0.02). The same genotype showed an OR = 2.71 (p = 0.01) for patients with low severity. The HNF1B-KLK3-ELAC2-TMPRSS2-ERG haplotypes: GAAT, AAAT, GAGT, and AAGT were more frequent in patients with Pca with OR ranging from 4.65 to 2.48.

CONCLUSIONS

Higher frequencies of risk alleles were confirmed in the SNPs, KLK3 rs2735839_A, ELAC2 rs4792311_A, and TMPRSS2 rs12329760_T in patients with Pca. Rs2735839_A was associated with risk of Pca and rs4792311_A with severity and Gleason score of 7(4 + 3) or greater. There is a need for careful observation of rs2735839 and rs4792311 in association with the prostatic biopsy due to the increased risk of Pca.

Cumulative Effect Assessment of Common Genetic Variants on Prostate Cancer: Preliminary Studies

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation among people. Genome Wide Association studies (GWASs) have generated multiple genetic variants associated with prostate cancer (PC) risk. Taking into account previously identified genetic susceptibility variants, the purpose of our study was to determine the cumulative association between four common SNPs and the overall PC risk. A total of 78 specimens from both PC and benign prostate hyperplasia (BPH) patients were included in the study. Genotyping of all selected SNPs was performed using the TaqMan assay. The association between each SNP and the PC risk was assessed individually and collectively. Analysis of the association between individual SNPs and PC risk revealed that only the rs4054823 polymorphism was significantly associated with PC, and not with BPH (p < 0.001). Statistical analysis also showed that the heterozygous genotype of the rs2735839 polymorphism is more common within the BPH group than in the PC group (p = 0.042). The cumulative effect of high-risk alleles on PC was analyzed using a logistic regression model. As a result, the carriers of at least one risk allele copy in each particular region had a cumulative odd ratio (OR) of 1.42 times, compared to subjects who did not have any of these factors. In addition, the combination of these four genetic variants increased the overall risk of PC by 52%. Our study provides further evidence of the cumulative effects of genetic risk factors on overall PC risk. These results should encourage future research to explain the interactions between known susceptibility variants and their contribution to the development and progression of PC disease.

Identifying cancer tissue-of-origin by a novel machine learning method based on expression quantitative trait loci

Cancer of unknown primary (CUP) refers to cancer with primary lesion unidentifiable by regular pathological and clinical diagnostic methods. This kind of cancer is extremely difficult to treat, and patients with CUP usually have a very short survival time. Recent studies have suggested that cancer treatment targeting primary lesion will significantly improve the survival of CUP patients. Thus, it is critical to develop accurate yet fast methods to infer the tissue-of-origin (TOO) of CUP. In the past years, there are a few computational methods to infer TOO based on single omics data like gene expression, methylation, somatic mutation, and so on. However, the metastasis of tumor involves the interaction of multiple levels of biological molecules. In this study, we developed a novel computational method to predict TOO of CUP patients by explicitly integrating expression quantitative trait loci (eQTL) into an XGBoost classification model. We trained our model with The Cancer Genome Atlas (TCGA) data involving over 7,000 samples across 20 types of solid tumors. In the 10-fold cross-validation, the prediction accuracy of the model with eQTL was over 0.96, better than that without eQTL. In addition, we also tested our model in an independent data downloaded from Gene Expression Omnibus (GEO) consisting of 87 samples across 4 cancer types. The model also achieved an f1-score of 0.7-1 depending on different cancer types. In summary, eQTL was an important information in inferring cancer TOO and the model might be applied in clinical routine test for CUP patients in the future.

Genetic variants in epithelial-mesenchymal transition genes as predictors of clinical outcomes in localized prostate cancer

BACKGROUND

Epithelial-mesenchymal transition (EMT) plays a pivotal role in the progression of prostate cancer (PCa). However, little is known about genetic variants in the EMT pathway as predictors of aggressiveness, biochemical recurrence (BCR) and disease reclassification in localized PCa.

PATIENTS AND METHODS

In this multistage study, we evaluated 5186 single nucleotide polymorphisms (SNPs) from 264 genes related to EMT pathway to identify SNPs associated with PCa aggressiveness and BCR in the MD Anderson PCa (MDA-PCa) patient cohort (N = 1762), followed by assessment of the identified SNPs with disease reclassification in the active surveillance (AS) cohort (N = 392).

RESULTS

In the MDA-PCa cohort, 312 SNPs were associated with high D'Amico risk (P < 0.05), among which, 14 SNPs in 10 genes were linked to BCR risk. In the AS cohort, 2 of 14 identified SNPs (rs76779889 and rs7083961) in C-terminal Binding Proteins 2 gene were associated with reclassification risk. The associations of rs76779889 with different endpoints were: D'Amico high versus low, odds ratio [95% confidence interval (CI)] = 2.89 (1.32-6.34), P = 0.008; BCR, hazard ratio (HR) (95% CI) = 2.88 (1.42-5.85), P = 0.003; and reclassification, HR (95% CI) = 2.83 (1.40-5.74), P = 0.004. For rs7083961, the corresponding risk estimates were: D'Amico high versus low, odds ratio (95% CI) = 1.69 (1.12-2.57), P = 0.013; BCR, HR (95% CI) = 1.87 (1.15-3.02), P = 0.011 and reclassification, HR (95% CI) = 1.72 (1.09-2.72), P = 0.020. There were cumulative effects of these two SNPs on modulating these endpoints.

CONCLUSION

Genetic variants in EMT pathway may influence the risks of localized PCa's aggressiveness, BCR and disease reclassification, suggesting their potential role in the assessment and management of localized PCa.

KLK3 SNP-SNP interactions for prediction of prostate cancer aggressiveness

Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP-SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P < 3.5 × 10-9) and 3145 (P < 1 × 10-5) SNP-SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene-gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP-SNP interactions were supported by gene expression and protein-protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.

Immune checkpoint-related serum proteins and genetic variants predict outcomes of localized prostate cancer, a cohort study

BACKGROUND

The clinical predictors and biological mechanisms for localized prostate cancer (PCa) outcomes remain mostly unknown. We aim to evaluate the role of serum immune-checkpoint-related (ICK) proteins and genetic variations in predicting outcomes of localized PCa.

METHODS

We profiled the serum levels of 14 ICK-related proteins (BTLA, GITR, HVEM, IDO, LAG-3, PD-1, PD-L1, PD-L2, Tim-3, CD28, CD80, 4-1BB, CD27, and CTLA-4) in 190 patients with localized PCa. The genotypes of 97 single nucleotide polymorphisms (SNPs) from 19 ICK-related genes were analyzed in an extended population (N = 1762). Meta-data from ArrayExpress and TCGA was employed to validate and to probe functional data. Patients were enrolled and tumor aggressiveness, biochemical recurrence (BCR), and progression information were obtained. Statistical analyses were performed analyzing associations between serum biomarkers, genotypes, mRNA and outcomes.

RESULTS

We showed that serum (s)BTLA and sTIM3 levels were associated with PCa aggressiveness (P < 0.05). sCD28, sCD80, sCTLA4, sGITR, sHVEM and sIDO correlated with both BCR and progression risks (all P < 0.05). We further identified ICK variants were significantly associated with aggressiveness, BCR and progression. Among them, 4 SNPs located in CD80 (rs7628626, rs12695388, rs491407, rs6804441) were not only associated with BCR and progression risk, but also correlated with sCD80 level (P < 0.01). rs491407 was further validated in an independent cohort. The CD80 mRNA expression was associated with BCR (HR, 1.85, 95% CI 1.06-3.22, P = 0.03) in meta-analysis of validation cohorts.

CONCLUSION

We highlight the prognostic value of serum ICK-related proteins for predicting aggressiveness, BCR and progression of PCa. The genetic variations and mRNA expression in CD80 could be predictors and potential targets of localized PCa.

Identification of Germline Genetic Variants that Increase Prostate Cancer Risk and Influence Development of Aggressive Disease

Prostate cancer (PrCa) is a heterogeneous disease, which presents in individual patients across a diverse phenotypic spectrum ranging from indolent to fatal forms. No robust biomarkers are currently available to enable routine screening for PrCa or to distinguish clinically significant forms, therefore late stage identification of advanced disease and overdiagnosis plus overtreatment of insignificant disease both remain areas of concern in healthcare provision. PrCa has a substantial heritable component, and technological advances since the completion of the Human Genome Project have facilitated improved identification of inherited genetic factors influencing susceptibility to development of the disease within families and populations. These genetic markers hold promise to enable improved understanding of the biological mechanisms underpinning PrCa development, facilitate genetically informed PrCa screening programmes and guide appropriate treatment provision. However, insight remains largely lacking regarding many aspects of their manifestation; especially in relation to genes associated with aggressive phenotypes, risk factors in non-European populations and appropriate approaches to enable accurate stratification of higher and lower risk individuals. This review discusses the methodology used in the elucidation of genetic loci, genes and individual causal variants responsible for modulating PrCa susceptibility; the current state of understanding of the allelic spectrum contributing to PrCa risk; and prospective future translational applications of these discoveries in the developing eras of genomics and personalised medicine.

Proteomic Profiling of Two Distinct Populations of Extracellular Vesicles Isolated from Human Seminal Plasma

Body fluids contain many populations of extracellular vesicles (EV) that differ in size, cellular origin, molecular composition, and biological activities. EV in seminal plasma are in majority originating from prostate epithelial cells, and hence are also referred to as prostasomes. Nevertheless, EV are also contributed by other accessory sex glands, as well as by the testis and epididymis. In a previous study, we isolated EV from seminal plasma of vasectomized men, thereby excluding contributions from the testis and epididymis, and identified two distinct EV populations with diameters of 50 and 100 nm, respectively. In the current study, we comprehensively analyzed the protein composition of these two EV populations using quantitative Liquid Chromatography-Mass Spectrometry (LC-MS/MS). In total 1558 proteins were identified. Of these, ≈45% was found only in the isolated 100 nm EV, 1% only in the isolated 50 nm EV, and 54% in both 100 nm and 50 nm EV. Gene ontology (GO) enrichment analysis suggest that both originate from the prostate, but with distinct biogenesis pathways. Finally, nine proteins, including KLK3, KLK2, MSMB, NEFH, PSCA, PABPC1, TGM4, ALOX15B, and ANO7, with known prostate specific expression and alternate expression levels in prostate cancer tissue were identified. These data have potential for the discovery of EV associated prostate cancer biomarkers in blood.

Global Gene Expression Characterization of Circulating Tumor Cells in Metastasic Castration-Resistant Prostate Cancer Patients

Background: Current therapeutic options in the course of metastatic castration-resistant prostate cancers (mCRPC) reinforce the need for reliable tools to characterize the tumor in a dynamic way. Circulating tumor cells (CTCs) have emerged as a viable solution to the problem, whereby patients with a variety of solid tumors, including PC, often do not have recent tumor tissue available for analysis. The biomarker characterization in CTCs could provide insights into the current state of the disease and an overall picture of the intra-tumor heterogeneity. Methods: in the present study, we applied a global gene expression characterization of the CTC population from mCRPC (n = 9), with the goal to better understand the biology of these cells and identify the relevant molecules favoring this tumor progression. Results: This analysis allowed the identification of 50 genes specifically expressed in CTCs from patients. Six of these markers (HOXB13, QKI, MAOA, MOSPD1, SDK1, and FGD4), were validated in a cohort of 28 mCRPC, showing clinical interest for the management of these patients. Of note, the activity of this CTC signature was related to the regulation of MYC, a gene strongly implicated in the biology of mCRPC. Conclusions: Overall, our results represent new evidence on the great value of CTCs as a non-invasive biopsy to characterize PC.

Aggressive prostate cancer phenotype and genome-wide association studies: where are we now?

The majority of prostate cancer (PCa) is indolent, however, a percentage of patients are initially diagnosed with metastatic disease, for which there is a worse prognosis. There is a lack of biomarkers to identify men at greater risk for developing aggressive PCa. Genome-wide association studies (GWAS) scan the genome to search associations of SNPs with specific traits, like cancer. To date, eight GWAS have resulted in the reporting of 16 SNPs associated with aggressive PCa (p < 5.00 × 10^-2). Still, validation studies need to be conducted to confirm the obtained results as GWAS can generate false-positive results. Furthermore, post-GWAS studies provide a better understanding of the functional consequences.

Association study between common variations in some candidate genes and prostate adenocarcinoma predisposition through multi-stage approach in Iranian population

BACKGROUND

Prostate cancer is one of the five common cancers and has the second incidence rate and the third mortality rate in Iranian population. The purpose of this study was to evaluate the association of rs16901979, rs4242382 and rs1447295 on 8q24 locus, rs2735839 (KLK3 gene) and rs721048 (EHBP1 gene) with prostate adenocarcinoma through multi-stage approach to identify the polymorphisms associated with prostate cancer and use them as screening factors. Screening tests can identify people who may have a chance of developing the disease before detection and any symptoms.

METHODS

The case-control study included 103 cases (prostate adenocarcinoma) and 100 controls (benign prostatic hyperplasia). Tetra-primer ARMS-PCR was used to genotyping of each participant. A Multi-stage approach was used for efficient genomic study. In this method, a smaller number of people can be used. Chi-squared, Fisher's exact test and logistic regression were used to investigate the SNPs associated with prostate cancer and Gleason score.

RESULTS

In the first stage (59 men), the frequency of polymorphisms rs16901979, rs4242382, rs1447295, rs2735839 and rs721048 in the prostate adenocarcinoma group was evaluated compared to the control group (P-value < 0.3) in order to select meaningful polymorphisms. There was not any significant difference between genotype frequency rs16901979 (P = 0.671) and rs721048 (P = 0.474) in the case group compared to BPH. Therefore, these polymorphisms were eliminated, and in the second step (144 men), rs4242382, rs2735839 and rs1447295 were evaluated (P-value < 0.05). According to the total population (203 men), there was significant difference between genotype frequency rs4242382 (P = 0.001), rs2735839 (P = 0.000) and rs1447295 (P = 0.005) even after using Bonferroni correction (p = 0.016). The effect of these three polymorphisms on prostate cancer was not modified by age and PSA. There was a significant difference between the allelic frequency of A vs G (rs4242382, rs2735839) at all classes of Gleason score and A vs C (rs1447295) at Gleason score ≥ 8.

CONCLUSIONS

The results of this study for rs2735839, rs4242382 and rs1447295 indicate the association of these polymorphisms with prostate adenocarcinoma predisposition in Iranian population. Exposure effect is homogeneous between different ages and PSA level categories. These three polymorphisms should be studied in a larger population to confirm these results.

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.

Search for genetic factor association with cancer-free prostate-specific antigen level elevation on the basis of a genome-wide association study in the Korean population

We investigated the genetic markers associated with elevated serum prostate-specific antigen (sPSA) levels to improve the predictive power of sPSA in screening for prostate cancer. A genome-wide association study was carried out among 4124 healthy Korean male adults using the Affymetrix Axiom Customized Biobank Genotyping Arrays for sPSA levels. A subgroup analysis for increased sPSA levels who underwent a prostate biopsy (n=64) was also carried out. We detected 11 single nucleotide polymorphisms (SNPs) near the Solute carrier family 45member 3, AGAP7P, MSMB, LOC101929917, and KLK3 genes associated with sPSA levels. The top SNP associated with the log of the sPSA levels was rs72434280 in the Solute carrier family 45 member 3 gene (P value, discovery set=2.98×10, replication set=7.31×10). A case-control study utilizing available biopsy reports (49 patients with normal biopsies vs. 15 patients with biopsies indicating cancer) for the sPSA more than 3 ng/ml group was carried out for the respective SNPs after adjusting for age. Only the SNPs near the KLK3 gene were associated with prostate cancer. In the model of the predictive elevation of sPSA level, adding the genetic risk score [area under the curve (AUC)=0.697] to age and BMI (AUC=0.602) significantly improved the results of the AUC (P<0.0001). We found seven SNPs associated with elevated prostate-specific antigen levels in healthy Korean men. Four SNPs were a novel marker in the Korean population. In men with increased prostate-specific antigen levels, genotyping SNP related to cancer-free elevation of sPSA level could be informative to decide the indication of prostate biopsy.

Genetic associations of T cell cancer immune response with tumor aggressiveness in localized prostate cancer patients and disease reclassification in an active surveillance cohort

Determining prostate cancer (PCa) aggressiveness and reclassification are critical events during the treatment of localized disease and for patients undergoing active surveillance (AS). Since T cells play major roles in cancer surveillance and elimination, we aimed to identify genetic biomarkers related to T cell cancer immune response which are predictive of aggressiveness and reclassification risks in localized PCa. The genotypes of 3,586 single nucleotide polymorphisms (SNPs) from T cell cancer immune response pathways were analyzed in 1762 patients with localized disease and 393 who elected AS. The aggressiveness of PCa was defined according to pathological Gleason score (GS) and D'Amico criteria. PCa reclassification was defined according to changes in GS or tumor characteristics during subsequent surveillance biopsies. Functional characterization and analysis of immune phenotypes were also performed. In the localized PCa cohort, seven SNPs were significantly associated with the risk of aggressive disease. In the AS cohort, another eight SNPs were identified as predictors for aggressiveness and reclassification. Rs1687016 of PSMB8 was the most significant predictor of reclassification. Cumulative analysis showed that a genetic score based on the identified SNPs could significantly predict risk of D'Amico high risk disease (P-trend = 2.4E-09), GS4 + 3 disease (P-trend = 1.3E-04), biochemical recurrence (P-trend = 0.01) and reclassification (P-trend = 0.01). In addition, the rs34309 variant was associated with functional somatic mutations in the PI3K/PTEN/AKT/MTOR pathway and tumor lymphocyte infiltration. Our study provides plausible evidence that genetic variations in T cell cancer immune response can influence risks of aggressiveness and reclassification in localized PCa, which may lead to additional biological insight into these outcomes. Abbreviations: PCa, prostate cancer; AS, active surveillance; GS, Gleason score; PSA, prostate specific antigen; TCGA, The Cancer Genome Atlas; SNP, single nucleotide polymorphisms; UFG, unfavorable genotype.

Clinical Proof-of-concept of a Novel Platform Utilizing Biopsy-derived Live Single Cells, Phenotypic Biomarkers, and Machine Learning Toward a Precision Risk Stratification Test for Prostate Cancer Grade Groups 1 and 2 (Gleason 3 + 3 and 3 + 4)

OBJECTIVE

To examine the ability of a novel live primary-cell phenotypic (LPCP) test to predict postsurgical adverse pathology (P-SAP) features and risk stratify patients based on SAP features in a blinded study utilizing radical prostatectomy (RP) surgical specimens.

METHODS

Two hundred fifty-one men undergoing RP were enrolled in a prospective, multicenter (10), and proof-of-concept study in the United States. Fresh prostate samples were taken from known areas of cancer in the operating room immediately after RP. Samples were shipped and tested at a central laboratory. Utilizing the LPCP test, a suite of phenotypic biomarkers was analyzed and quantified using objective machine vision software. Biomarkers were objectively ranked via machine learning-derived statistical algorithms (MLDSA) to predict postsurgical adverse pathological features. Sensitivity and specificity were determined by comparing blinded predictions and unblinded RP surgical pathology reports, training MLDSAs on 70% of biopsy cells and testing MLDSAs on the remaining 30% of biopsy cells across the tested patient population.

RESULTS

The LPCP test predicted adverse pathologies post-RP with area under the curve (AUC) via receiver operating characteristics analysis of greater than 0.80 and distinguished between Prostate Cancer Grade Groups 1, 2, and 3/Gleason Scores 3 + 3, 3 + 4, and 4 + 3. Further, LPCP derived-biomarker scores predicted Gleason pattern, stage, and adverse pathology with high precision-AUCs>0.80.

CONCLUSION

Using MLDSA-derived phenotypic biomarker scores, the LPCP test successfully risk stratified Prostate Cancer Grade Groups 1, 2, and 3 (Gleason 3 + 3 and 7) into distinct subgroups predicted to have surgical adverse pathologies or not with high performance (>0.85 AUC).

Systematic identification of functionally relevant risk alleles to stratify aggressive versus indolent prostate cancer

Novel approaches for classification, including molecular features, are needed to direct therapy for men with low-grade prostate cancer (PCa), especially men on active surveillance. Risk alleles identified from genome-wide association studies (GWAS) could improve prognostication. Those risk alleles that coincided with genes and somatic copy number aberrations associated with progression of PCa were selected as the most relevant for prognostication.

In a systematic literature review, a total of 698 studies were collated. Fifty-three unique SNPs residing in 29 genomic regions, including 8q24, 10q11 and 19q13, were associated with PCa progression. Functional studies implicated 21 of these single nucleotide polymorphisms (SNPs) as modulating the expression of genes in the androgen receptor pathway and several other oncogenes. In particular, 8q24, encompassing MYC, harbours a high density of SNPs conferring unfavourable pathological characteristics in low-grade PCa, while a copy number gain of MYC in low-grade PCa was associated with prostate-specific antigen recurrence after radical prostatectomy.

By combining GWAS data with gene expression and structural rearrangements, risk alleles were identified that could provide a new basis for developing a prognostication tool to guide therapy for men with early prostate cancer.

Testis specific Y-like 5: gene expression, methylation and implications for drug sensitivity in prostate carcinoma

BACKGROUND

TSPYL5, a putative tumor suppressor gene, belongs to the nucleosome assembly protein family. The chromosomal location of the TSPYL5 gene is 8Q22.1, and its exact role in prostate cancer etiology remains unclear. Further TSPYL5 gene and protein expression in prostate carcinoma cells and diseased tissues including its susceptibility for epigenetic silencing is unknown. Also, not known is the variation in TSPYL5 protein expression with regards to progression of prostatic carcinoma and its possible role in drug sensitivity.

METHODS

TSPYL5, DNMT-1 and DNMT-B gene expression in DU145, LNCaP and RWPE-1 cells and prostate tumor tissues was analyzed by qRT-PCR and RT-PCR. Demethylation experiments were done by treating DU145 and LNCaP cells with 5-aza-2'-deoxycytidine in vitro. Methylation analysis of TSPYL5 gene was performed by methylation specific PCR and pyrosequencing. TSPYL5 protein expression in benign and diseased prostate tumor tissues was performed by immunohistochemistry and in the cells by Western blotting.

RESULTS

TSPYL5 was differentially expressed in non-tumorigenic prostate epithelial cells (RWPE-1), androgen independent (DU145), dependent (LNCaP) prostate carcinoma cells and tissues. Methylation-specific PCR and pyrosequencing analysis identified an inverse relationship between DNA methylation and expression leading to the silencing of TSPYL5 gene. Treatment of prostate carcinoma cells in which TSPYL5 was absent or low (DU145 and LNCaP) with the demethylating agent 5-aza-2'-deoxycytidine upregulated its expression in these cells. Immunohistochemical studies clearly identified TSPYL5 protein in benign tissue and in tumors with Gleason score (GS) of 6 and 7. TSPYL5 protein levels were very low in tumors of GS ≥ 8. TSPYL5 overexpression in LNCaP cells increased the cell sensitivity to chemotherapy drugs such as docetaxel and paclitaxel, as measured by the cellular viability. Furthermore, the cells also exhibited reduced CDKN1A expression with only marginal reduction in pAKT.

CONCLUSIONS

Decrease in TSPYL5 protein in advanced tumors might possibly function as an indicator of prostate tumor progression. Its absence due to methylation-induced silencing can lead to reduced drug sensitivity in prostate carcinoma.

Low serum testosterone is associated with tumor aggressiveness and poor prognosis in prostate cancer

Serum testosterone is a potential marker to distinguish between indolent and aggressive prostate cancer (PCa). The present study aimed to investigate whether low levels of total serum testosterone at diagnosis were associated with aggressive PCa and poor clinical outcomes. In total, 762 non-Hispanic Caucasian men with previously untreated PCa were recruited from The University of Texas MD Anderson Cancer Center (Houston, TX, USA). Patients were categorized into three groups based on their total serum testosterone levels according to clinical guidelines [low (<230 ng/dl), intermediate (230-350 ng/dl) and normal (>350 ng/dl)]. PCa aggressiveness (low-, intermediate- or high-risk, or metastatic) was compared using multinomial logistic regression. Rates of disease progression, mortality from any cause and PCa-specific mortality were compared using the multivariate Cox proportional hazards model. Testosterone levels significantly decreased as PCa aggressiveness increased (P<0.001). Compared with the normal testosterone group, the low testosterone group had 2.9-fold (OR, 2.92; 95% CI, 1.74-4.90; P<0.001), 5.6-fold (OR, 5.63; 95% CI, 3.14-10.12; P<0.001) and 72.4-fold (OR, 72.40; 95% CI, 20.89-250.89; P<0.001) increased risks of having intermediate-risk, high-risk and metastatic PCa, respectively. Furthermore, low levels of testosterone were significantly associated with a 10.7-fold (HR, 10.68; 95% CI, 1.35-84.44; P=0.03) increased risk of PCa-specific mortality. The results of the present study indicate that low levels of total serum testosterone at diagnosis are associated with aggressive PCa and predict poor PCa-specific survival.

Mitochondrial DNA copy number in peripheral blood leukocytes and the aggressiveness of localized prostate cancer

We investigated whether low mitochondrial DNA copy number (mtDNAcn) in peripheral blood leukocytes at diagnosis was associated with an increased risk of the aggressive form of the tumor and disease progression among localized prostate cancer (PCa) patients. We recruited 1,751 non-Hispanic white men with previously untreated PCa from The University of Texas MD Anderson Cancer Center. mtDNAcn was categorized into three groups according to tertiles. We used multivariate logistic regression to estimate the odds ratios (ORs) and 95 percent confidence intervals (95% CIs) for the association of mtDNAcn with the risk of having aggressive PCa at diagnosis. We used Cox proportional hazards model to estimate hazard ratios (HRs) and 95% CIs for disease progression. We observed an inverse association between aggressiveness of PCa and mtDNAcn (P < 0.001). In multivariate analysis, compared to patients in the highest tertile of mtDNAcn, those in the second and lowest tertiles had significantly increased risks of presenting with the high-risk form of PCa, as defined by the D'Amico criteria, with ORs of 1.33 (95% CI, 0.89–1.98; P = 0.17) and 1.53 (95% CI, 1.02–2.30; P = 0.04), respectively. Furthermore, PCa patients in the lowest and second tertiles combined relative to those in the highest tertile had a 56% increased risk of disease progression (HR, 1.56; 95% CI, 0.96–2.54; P = 0.07). In summary, our results suggested that low mtDNAcn in peripheral blood leukocytes was associated with aggressive PCa at diagnosis and might further predict poor progression-free survival among localized PCa patients.

Genetic variants of the Wnt signaling pathway as predictors of aggressive disease and reclassification in men with early stage prostate cancer on active surveillance

Little is known about the genetic predictors of prostate cancer aggressiveness and reclassification in men with localized prostate cancer undergoing active surveillance. The Wnt signaling pathway is important for prostate cancer development and progression. Identifying genetic variants associated with prostate cancer aggressiveness and reclassification may have a potential role in the management of localized patients. In this study, we used a three-phase design. In phases I and II prostate cancer patient cohort, 578 single nucleotide polymorphisms (SNPs) from 45 genes of the Wnt signaling pathway were analyzed in 1762 localized prostate cancer patients. Twelve SNPs from four regions were significantly associated with aggressive disease, among which, three linked SNPs in CSNK1A1 at 5q32 (represented by rs752822) may differentiate GS 4+3 from GS 3+4 patients (OR = 1.44, 95% CI = 1.12-1.87, P = 4.76×10(-3)). In phase III active surveillance (AS) cohort, genotyping of rs752822 (candidate from phases I and II) and previously identified rs2735839 were determined in 494 GS ≤7 patients. We found a significant association between rs2735839 and prostate cancer reclassification in the AS cohort (AG + AA versus GG, HR = 1.59, 95% CI = 1.11-2.28, P = 0.012) and a suggestive association of rs752822. Jointly, rs752822 and rs2735839 showed good potentials in risk-stratifying GS 7 patients and predicting disease reclassification (OR = 2.71, 95% CI = 1.62-4.51, P = 1×10(-4) in phase II; HR = 1.89, 95% CI = 1.13-3.18, P = 0.016 in phase III). In summary, rs752822 and rs2735839 may assist in risk-stratifying GS 7 patients and predict prostate cancer reclassification. The significant associations were independent from GS, T stage and PSA levels at baseline.

A large multiethnic genome-wide association study of prostate cancer identifies novel risk variants and substantial ethnic differences

A genome-wide association study (GWAS) of prostate cancer in Kaiser Permanente health plan members (7,783 cases, 38,595 controls; 80.3% non-Hispanic white, 4.9% African-American, 7.0% East Asian, and 7.8% Latino) revealed a new independent risk indel rs4646284 at the previously identified locus 6q25.3 that replicated in PEGASUS (N = 7,539) and the Multiethnic Cohort (N = 4,679) with an overall P = 1.0 × 10(-19) (OR, 1.18). Across the 6q25.3 locus, rs4646284 exhibited the strongest association with expression of SLC22A1 (P = 1.3 × 10(-23)) and SLC22A3 (P = 3.2 × 10(-52)). At the known 19q13.33 locus, rs2659124 (P = 1.3 × 10(-13); OR, 1.18) nominally replicated in PEGASUS. A risk score of 105 known risk SNPs was strongly associated with prostate cancer (P < 1.0 × 10(-8)). Comparing the highest to lowest risk score deciles, the OR was 6.22 for non-Hispanic whites, 5.82 for Latinos, 3.77 for African-Americans, and 3.38 for East Asians. In non-Hispanic whites, the 105 risk SNPs explained approximately 7.6% of disease heritability. The entire GWAS array explained approximately 33.4% of heritability, with a 4.3-fold enrichment within DNaseI hypersensitivity sites (P = 0.004).

SIGNIFICANCE

Taken together, our findings of independent risk variants, ethnic variation in existing SNP replication, and remaining unexplained heritability have important implications for further clarifying the genetic risk of prostate cancer. Our findings also suggest that there may be much promise in evaluating understudied variation, such as indels and ethnically diverse populations.

Associations of prostate cancer risk variants with disease aggressiveness: results of the NCI-SPORE Genetics Working Group analysis of 18,343 cases

Genetic studies have identified single nucleotide polymorphisms (SNPs) associated with the risk of prostate cancer (PC). It remains unclear whether such genetic variants are associated with disease aggressiveness. The NCI-SPORE Genetics Working Group retrospectively collected clinicopathologic information and genotype data for 36 SNPs which at the time had been validated to be associated with PC risk from 25,674 cases with PC. Cases were grouped according to race, Gleason score (Gleason ≤ 6, 7, ≥ 8) and aggressiveness (non-aggressive, intermediate, and aggressive disease). Statistical analyses were used to compare the frequency of the SNPs between different disease cohorts. After adjusting for multiple testing, only PC-risk SNP rs2735839 (G) was significantly and inversely associated with aggressive (OR = 0.77; 95 % CI 0.69-0.87) and high-grade disease (OR = 0.77; 95 % CI 0.68-0.86) in European men. Similar associations with aggressive (OR = 0.72; 95 % CI 0.58-0.89) and high-grade disease (OR = 0.69; 95 % CI 0.54-0.87) were documented in African-American subjects. The G allele of rs2735839 was associated with disease aggressiveness even at low PSA levels (<4.0 ng/mL) in both European and African-American men. Our results provide further support that a PC-risk SNP rs2735839 near the KLK3 gene on chromosome 19q13 may be associated with aggressive and high-grade PC. Future prospectively designed, case-case GWAS are needed to identify additional SNPs associated with PC aggressiveness.