Citations

Summary Long-read nanopore sequencing enables simultaneous detection of germline variation and native DNA base modifications on individual DNA molecules, providing a unique opportunity to investigate allele-specific epigenetic regulation. Here, we performed whole-genome nanopore sequencing on normal and tumor prostate tissues to characterize differential methylation, methylation entropy, and allele-specific methylation (ASM) associated with noncoding genetic variants. Genome-wide analysis identified extensive cancer-associated differentially methylated regions (DMRs), with hypermethylated DMRs significantly enriched near transcription start sites and transcriptional regulatory regions. Integration with transcriptomic datasets revealed strong inverse relationships between promoter methylation and gene expression, while 5-hydroxymethylcytosine (5hmC) levels positively correlated with transcriptional activity across gene bodies. Using fragment-level methylation patterns enabled by long-read sequencing, we further quantified methylation entropy incorporating both 5mCG and 5hmCG states. Cancer-hypermethylated DMRs exhibited markedly reduced entropy, consistent with clonal fixation of methylation states during tumor progression. Entropy profiling across chromatin annotations demonstrated maximal epigenetic heterogeneity at partially modified enhancer-associated regions. To investigate cis-regulatory genetic effects, we developed a simple ASM framework (nanoASM) that can partition sequencing reads by allelic state and identifies allele-specific DMRs directly from long-read data. Compared with conventional population-level mQTL analysis, ASM demonstrated substantially improved statistical efficiency by leveraging within-individual contrasts and reducing sample-level heterogeneity. Although germline single nucleotide polymorphisms (SNPs) were largely shared between normal and tumor tissues, ASM patterns differed substantially, with tumor-associated ASM regions displaying significantly larger genomic span and stronger allelic methylation differences. Comparative analysis with TCGA prostate mQTL and GTEx prostate eQTL datasets demonstrated substantial concordance between ASM directionality and downstream transcriptional effects, particularly for variants located within DMRs and near transcription start sites. At the IRX4 prostate cancer risk locus, ASM identified an androgen-responsive regulatory domain overlapping AR ChIP-seq and H3K27ac peaks, nominating rs6885084 as a candidate functional variant. At the PSCA locus, ASM anchored by rs4736369 was associated with allele-specific methylation, chromatin activation, transcript abundance, and isoform usage. Together, these findings establish nanopore-based ASM analysis as a powerful approach for resolving functional noncoding variants and their regulatory domains they control in prostate cancer.

Key Points Circulating proteins independently associated with kidney stone diagnosis are related to kidney stone matrix. Lower circulating uromodulin and higher scavenger receptor cysteine-rich domain-containing group B protein levels were associated with higher kidney stone risk, whereas kidney stone presence elevates plasma matrix metalloproteinase 7 level. The protective effect of uromodulin against kidney stones was independent of kidney function. Background Kidney stones are increasingly recognized as a systemic disorder with a high global prevalence. However, large proteomics studies are lacking. Methods An individual-level proteomics study with rigorous adjustments was performed on 35,331 UK Biobank participants to uncover the independent associations between 2922 circulating proteins and prevalent kidney stone disease. Mendelian randomization analysis was used to assess causal relationships. Findings were validated using genomic data from the Mayo Clinic Biobank ( N =43,744), concentration-response analysis, transcriptomics analyses, and additional genome-wide association studies. Results Nine plasma proteins were independently associated with a kidney stone diagnosis, including reduced uromodulin (UMOD; β , −0.10; 95% confidence interval [CI], −0.15 to −0.05) and elevated scavenger receptor cysteine-rich domain-containing group B protein (SSC4D; β , 0.28; 95% CI, 0.17 to 0.38) and were enriched in extracellular matrix pathways. Mendelian randomization analysis revealed that the presence of kidney stone contributed to elevated levels of matrix metalloproteinase 7. Conversely, lower plasma UMOD (odds ratio [OR], 0.93; 95% CI, 0.90 to 0.97) and higher plasma SSC4D (OR, 1.10; 95% CI, 1.02 to 1.18) were associated with kidney stone risk. These associations were consistently replicated in the Mayo Clinic Biobank dataset (UMOD; OR, 0.92; 95% CI, 0.86 to 0.98; SSC4D; OR, 1.13; 95% CI, 1.01 to 1.27) and further validated by a concentration-response analysis. Single-nucleus RNA sequencing and quantitative trait loci analyses confirmed consistent associations between thick ascending limb UMOD expression and stone former status and with blood and urine UMOD concentrations. Genome-wide association study analysis, adjusted for eGFR, suggested that the protective role of UMOD against kidney stones was independent of kidney function. Conclusions This study highlights significant associations between concentrations of specific blood proteins and a history of kidney stones. Several implicated proteins are related to kidney stone matrix, with UMOD independently associated with lower risk and SSC4D with higher risk of kidney stones.

Key Points Loss-of-function ALG8 and ALG9 variants were enriched in polycystic kidney/liver groups and International Classification of Diseases–coded cystic individuals in population cohorts. The ALG8 and ALG9 kidney phenotypes were usually mild to moderate, and lower eGFR or kidney failure was rare. ALG8 pathogenic variants sometimes resulted in severe polycystic liver disease. Background Autosomal dominant polycystic kidney disease (ADPKD) is a common, inherited nephropathy often resulting in kidney failure. It is genetically heterogeneous; along with the major genes, PKD1 and PKD2 , at least eight others have been suggested. ALG8 pathogenic variants have been associated with autosomal dominant polycystic liver disease and implicated in ADPKD, while ALG9 has been suggested as an ADPKD gene, but details of the phenotypes and penetrance are unclear. Methods We screened >3900 families with cystic kidneys and/or livers using global approaches to detect ALG8 or ALG9 pathogenic variants. In addition, population cohorts with sequence data (Genomics England 100K Genomics Project, UK Biobank, and Mayo Clinic Biobank [MCBB]) were screened for ALG8 / ALG9 pathogenic variants. Results Multicenter screening of individuals with polycystic kidney and/or liver disease identified 51 (1.3%) ALG8 (7 multiplex) and 23 (0.6%) ALG9 (5 multiplex) families—frequencies that were approximately 10× and approximately 24× greater than nonpolycystic kidney disease controls. Analysis of individuals with polycystic kidney disease phenotypes in 100K Genomics Project, UK Biobank, and MCBB identified nine ALG8 (0.39%) and nine ALG9 (0.39%) families, an enriched frequency over controls. Two individuals had PKD1 and ALG8 pathogenic changes. Eighty-nine percent of individuals with ALG8 mutations with imaging in the entire MCBB had kidney cysts (50%, >10 cysts), with greater median kidney and liver cyst numbers than controls. For ALG9, 78% had kidney cysts (27%, >10 cysts). Individuals with ALG8 mutations typically had mild cystic kidneys with limited enlargement. Liver cysts were common (71%), with enlarged livers (>2L) found in 11 of 62 patients, although surgical intervention was rare. The ALG9 kidney phenotype was also of mild cystic kidneys, but enlarged livers were rare; for both genes, CKD or kidney failure were rare. Conclusions ALG8 and ALG9 are defined as cystic kidney/liver genes but with limited penetrance for lower eGFR.

Key Points Common kidney stones are unlikely to be an independent and direct cause of CKD in the general population. CKD may protect against kidney stones because of changes in key urinary factors critical for stone formation. Background Kidney stones and CKD are common disorders with a substantial interaction. Although observational studies have suggested a potential for enhanced CKD risk after prior kidney stones, the exact relationship remains ambiguous. Methods Shared comorbidities between two diseases were identified using unbiased screening. Genome-wide association study summary statistics were obtained from the UK Biobank (UKBB), FinnGen, and CKDGen, followed by genetic association analyses across various traits. Bidirectional Mendelian randomization (MR) analyses were performed to define causal links, complemented by multivariable MR that included the shared comorbidities including hypertension, diabetes, and obesity. Observational analyses were undertaken using cohorts from the Mayo Clinic and a UKBB subset. Results Despite identifying a total of 123 conditions as shared comorbidities, there was no significant genetic correlation between kidney stones and CKD. Unadjusted MR analysis revealed no significant association between kidney stones and CKD risk (UKBB [exposure]/FinnGen [outcome]: odds ratio [OR]=0.97, 95% confidence interval [CI], 0.88 to 1.06; FinnGen/UKBB: OR=1.17, 95% CI, 0.98 to 1.39). Kidney stones did significantly associate with a higher urinary albumin-creatinine ratio ( β =0.014, 95% CI, 0.002 to –0.025), but this association disappeared in the multivariable MR model ( β =0.009, 95% CI, −0.003 to 0.020). Furthermore, in a cross-sectional analysis limited to the UKBB cohort, a robust regression model did not detect an independent association between kidney stones and urinary albumin-creatinine ratio ( β =0.16, 95% CI, −0.04 to 0.35) or eGFR ( β =0.10, 95% CI, −0.07 to 0.28). Conversely, CKD associated with a diminished risk of kidney stones in multivariable MR models (UKBB/FinnGen: OR=0.77, 95% CI, 0.69 to 0.87; FinnGen/UKBB: OR=0.73, 95% CI, 0.66 to 0.81). Furthermore, in the Mayo Clinic cohort with available urinary biochemistries, lower eGFR was associated with lower urinary calcium excretion and urinary calcium oxalate/phosphate supersaturation. Conclusions In this study, kidney stones were not independently associated with CKD. Conversely, CKD was associated with a lower risk of calcium kidney stones likely via changes in key urinary traits, including lower calcium excretion.

Abstract Background: Large genome-wide association studies (GWAS) have identified hundreds of single-nucleotide polymorphisms (SNPs) associated with increased risk of prostate cancer (PRCA). The majority of these risk loci are intergenic and the underlying causal variants are presumed to confer regulatory effects on gene expression. While follow-up eQTL studies have begun to explore putative regulatory targets of PRCA risk SNPs, these have largely focused on protein-coding genes. Recent evidence demonstrates that pri-miRNA transcripts are susceptible to the same cis-acting regulatory effects as other long RNAs, and PRCA risk SNPs may confer systemic impacts on the prostate transcriptome through miRNA expression dysregulation. However, no miRNA transcriptome-wide association studies (TWAS) of PRCA risk have been conducted to date, and the role of miRNA dysregulation in PRCA risk is poorly understood. Methods: We performed a comprehensive miRNA TWAS of PRCA risk using small RNA sequencing and genome-wide genotyping data from N = 441 normal prostate tissue samples from Mayo Clinic along with N = 411 prostate adenocarcinoma (PRAD) tumor samples from the Cancer Genome Atlas (TCGA). Both miRNA expression datasets were uniformly processed separately using the CAP-miRSeq miRNA workflow and trimmed mean of M (TMM) normalization. Expressed mature miRNAs were identified as ≥ 5 reads in >25% of samples. PEER was used to remove known and latent factors on miRNA expression levels, and subsequent analyses were performed on residual values. Genetically regulated expression (GReX) prediction models were trained for all expressed miRNAs with evidence of cis-heritability (P<0.01) in the respective normal and tumor datasets using the FUSION TWAS software. TWAS for PRCA risk was performed with both sets of GReX models using single-SNP summary statistics from the recent PRACTICAL consortium PRCA case-control OncoArray GWAS meta-analysis. Significant TWAS associations were declared at a false discovery rate of 0.05. Results: A total of 615 and 571 distinct expressed miRNAs were identified in the normal and tumor tissue datasets, respectively (overlap: 480). Among these, 98 (16%) normal tissue miRNAs demonstrated significant cis-heritability (median cis-h2 = 0.09, range: 0.04-0.79) for GReX model training. Similar results were obtained from TCGA tumor samples, with 63 (11%) miRNA GReX models successfully trained (median cis-h2 = 0.11, range: 0.05-0.60). Using normal tissue GReX models, we identified three significant TWAS miRNA associations with PRCA risk: over-expression of mir-941 family miRNAs (PTWAS = 3.1E-04) and reduced expression of miR-3617-5p (PTWAS = 1.7E-04) and hsa-miR-16-2-3p (PTWAS = 9.1E-04). The TCGA tumor TWAS also identified a significant association with miR-941 overexpression (PTWAS = 9.7E-04). All associated miRNAs have previously reported oncogenic or tumor-suppressive associations in other cancer types. Conclusions: We identified significant associations with PRCA genetic risk and miRNA expression dysregulation using tumor and normal prostate tissue data. Future work will examine downstream regulatory effects of miRNA dysregulation as well as miRNA-mediated risk mechanisms via competing endogenous RNA relationships. Citation Format: Nicholas B. Larson, Shannon K. McDonnell, Zachary Fogarty, Stephen N. Thibodeau. An miRNA transcriptome-wide association study of prostate cancer risk [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 54.

Abstract Background: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening. Methods: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3–T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups. Results: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age. Conclusions: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS. Impact: Personalized genetic risk assessments could inform prostate cancer screening decisions.

5082 Background: We evaluated the association of germ-line variation in 10 candidate genes important in prostate cancer biology (NKX3-1, JAK2, SLCO2B1, STAT3, CYP19A1, HSD17B4, TRMT11, PRMT3, HSD17B12, NCOA4) with failure of AA in advanced prostate cancer patients. Methods: Patients (N=619) were enrolled for genotyping at the time of AA failure. Candidate genes were selected from the literature and previous pilot studies in smaller cohorts which had identified variation in TRMT11 and HSD17B12 to be associated with AA failure. Genes were tagged using single nucleotide polymorphisms (SNPs) from HapMap with minor allele frequency of >5% and r2≥0.8. DNA was extracted from peripheral blood and genotyped using Illumina Veracode platform. The primary endpoint was time to failure on AA, defined as time from initiating continuous AA to two serial PSA increases over the nadir or clinical or imaging based progression, whichever came first. Principal component analysis was used for gene-levels tests. Association with the primary endpoint was assessed using proportional hazards regression models at the gene-level and at the SNP level. For SNP level results we estimated per allele (ordinal model) hazard ratios (HR) and 95% confidence intervals (CI) using Cox regression, adjusted for Gleason score (GS). Results: We successfully genotyped 60 SNPs (from 10 genes) in 519 subjects. The median age of the cohort was 71 years (range 43, 92). The GS distribution was 51% subjects with GS≥8; 33% with GS=7 and 17% with GS<7. Median time to AA failure for the cohort was 2.3 years (IQ range: 1.0-4.5). In gene level analyses adjusting for GS, TRMT11 (p=0.004), HSD17B12 (p=0.016), and JAK2 (p=0.044) genes were associated with time to AA failure. Four of the 18 genotyped SNPs in JAK2 were associated with AA failure after adjustment for GS (Table). Conclusions: Variation in the JAK2 gene is significantly associated with time to AA failure. Validation is needed to develop these as predictive biomarkers for AA in advanced prostate cancer. [Table: see text]