Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

Although genome-wide association studies (GWAS) for prostate cancer (PrCa) have identified more than 100 risk regions, most of the risk genes at these regions remain largely unknown. Here we integrate the largest PrCa GWAS (N = 142,392) with gene expression measured in 45 tissues (N = 4458), including normal and tumor prostate, to perform a multi-tissue transcriptome-wide association study (TWAS) for PrCa. We identify 217 genes at 84 independent 1 Mb regions associated with PrCa risk, 9 of which are regions with no genome-wide significant SNP within 2 Mb. 23 genes are significant in TWAS only for alternative splicing models in prostate tumor thus supporting the hypothesis of splicing driving risk for continued oncogenesis. Finally, we use a Bayesian probabilistic approach to estimate credible sets of genes containing the causal gene at a pre-defined level; this reduced the list of 217 associations to 109 genes in the 90% credible set. Overall, our findings highlight the power of integrating expression with PrCa GWAS to identify novel risk loci and prioritize putative causal genes at known risk loci.

Prostate Cancer Germline Variations and Implications for Screening and Treatment

Prostate cancer (PCa) is a highly heritable disease, and rapid evolution of sequencing technologies has enabled marked progression of our understanding of its genetic inheritance. A complex polygenic model that involves common low-penetrance susceptibility alleles causing individually small but cumulatively significant risk and rarer genetic variants causing greater risk represent the current most accepted model. Through genome-wide association studies, more than 100 single-nucleotide polymorphisms (SNPs) associated with PCa risk have been identified. Consistent reports have identified germline mutations in the genes BRCA1, BRCA2, MMR, HOXB13, CHEK2, and NBS1 as conferring moderate risks, with some leading to a more aggressive disease behavior. Considering this knowledge, several research strategies have been developed to determine whether targeted prostate screening using genetic information can overcome the limitations of population-based prostate-specific antigen (PSA) screening. Germline DNA-repair mutations are more frequent in men with metastatic disease than previously thought, and these patients have a more favorable response to therapy with poly(adenosine diphosphate [ADP]-ribose) polymerase (PARP) inhibitors. Genomic information is a practical tool that has the potential to enable the concept of precision medicine to become a reality in all steps of PCa patient care.

Large-scale Analysis Demonstrates Familial Testicular Cancer to have Polygenic Aetiology

Testicular germ cell tumour (TGCT) is the most common cancer in young men. Multiplex TGCT families have been well reported and analyses of population cancer registries have demonstrated a four- to eightfold risk to male relatives of TGCT patients. Early linkage analysis and recent large-scale germline exome analysis in TGCT cases demonstrate absence of major high-penetrance TGCT susceptibility gene(s). Serial genome-wide association study analyses in sporadic TGCT have in total reported 49 independent risk loci. To date, it has not been demonstrated whether familial TGCT arises due to enrichment of the same common variants underpinning susceptibility to sporadic TGCT or is due to shared environmental/lifestyle factors or disparate rare genetic TGCT susceptibility factors. Here we present polygenic risk score analysis of 37 TGCT susceptibility single-nucleotide polymorphisms in 236 familial and 3931 sporadic TGCT cases, and 12 368 controls, which demonstrates clear enrichment for TGCT susceptibility alleles in familial compared to sporadic cases (p=0.0001), with the majority of familial cases (84-100%) being attributable to polygenic enrichment. These analyses reveal TGCT as the first rare malignancy of early adulthood in which familial clustering is driven by the aggregate effects of polygenic variation in the absence of a major high-penetrance susceptibility gene.

PATIENT SUMMARY

To date, it has been unclear whether familial clusters of testicular germ cell tumour (TGCT) arise due to genetics or shared environmental or lifestyle factors. We present large-scale genetic analyses comparing 236 familial TGCT cases, 3931 isolated TGCT cases, and 12 368 controls. We show that familial TGCT is caused, at least in part, by presence of a higher dose of the same common genetic variants that cause susceptibility to TGCT in general.

A Review of Prostate Cancer Genome-Wide Association Studies (GWAS)

Prostate cancer is the most common cancer in men in Europe and the United States. The genetic heritability of prostate cancer is contributed to by both rarely occurring genetic variants with higher penetrance and moderate to commonly occurring variants conferring lower risks. The number of identified variants belonging to the latter category has increased dramatically in the last 10 years with the development of the genome-wide association study (GWAS) and the collaboration of international consortia that have led to the sharing of large-scale genotyping data. Over 40 prostate cancer GWAS have been reported, with approximately 170 common variants now identified. Clinical utility of these variants could include strategies for population-based risk stratification to target prostate cancer screening to men with an increased genetic risk of disease development, while for those who develop prostate cancer, identifying genetic variants could allow treatment to be tailored based on a genetic profile in the early disease setting. Functional studies of identified variants are needed to fully understand underlying mechanisms of disease and identify novel targets for treatment. This review will outline the GWAS carried out in prostate cancer and the common variants identified so far, and how these may be utilized clinically in the screening for and management of prostate cancer. Cancer Epidemiol Biomarkers Prev; 27(8); 845-57. ©2018 AACR.

Abstract 227: Germline variation at 8q24 and prostate cancer risk in men of European ancestry

We performed an in-depth and well-powered investigation of genetic variation across the cancer susceptibility region at chromosome 8q24 (127.6-129.0 Mb) to search for novel risk variants associated with prostate cancer (PCa) risk in the European ancestry population. We combined genotyped and imputed data from the PRACTICAL/ELLIPSE OncoArray and iCOGS consortia consisting of 71,535 PrCa cases and 52,935 controls of European ancestry. Variants with high imputation quality score (>0.8) were retained for a total of 5,600 overlapping variants between the two datasets. Associations of genetic variants with PCa risk were evaluated using unconditional logistic regression with adjustment for country and ten principal components. The marginal risk estimates for the 5,600 variants that passed quality control were combined by a fixed effects meta-analysis. A meta-stepwise selection was performed on variants marginally associated with PCa risk from the meta results (P<0.05). A polygenic risk score and the contribution to the familial relative risk of PCa were estimated for variants from the final model. Of the 5,600 variants at 8q24 retained for analysis, 1,268 (23%) were associated with PCa risk at P<5x10-8 while 2,772 (49%) were marginally associated at P<0.05. In the stepwise model, 12 variants remained statistically significantly associated with PCa risk with conditional meta p-values between 2.93x10-137 and 4.28x10-15. The independent stepwise signals were confirmed by Joint Analysis of Marginal (JAM) summary statistics, which defined the credible sets of variants driving those signals. Three of the variants (rs1914295, rs190257175, rs12549761) were weakly correlated (r2≤0.17) with any known PCa risk marker, and may define novel association signals. Men in the top 1% of the polygenic risk score distribution had a 3.97-fold relative risk (95%CI=3.87-4.07) compared to men with "average risk" (25th-75th percentiles). The 12 independent signals at 8q24 capture 11.54% (95%CI=9.86-13.65) of the familial relative risk of PCa, which is approximately one quarter of the total PCa familial relative risk explained by known genetic risk factors. Most of the independently associated signals have good evidence for biologic functionality; in particular, many reside within putative transcriptional enhancers and/or binding sites for AR and FOXA1 transcription factors in prostate cell lines. In summary, we defined 12 independent association signals among men of European ancestry, with three of the risk variants representing putative novel association signals. Whereas the individual associations of these variants with PCa risk are relatively modest (ORs<2.0), their cumulative effects are substantial, and their contribution to the overall familial relative risk of PCa is substantially greater than any other known prostate cancer risk locus.

Citation Format: Marco Matejcic, Edward J. Saunders, Tokhir Dadaev, Mark Brook, Ali Amin Al Olama, Fredrick R. Schumacher, Sonja I. Berndt, Sara Benlloch, Kenneth Muir, Koveela Govindasami, Victoria L. Stevens, Susan M. Gapstur, Catherine M. Tangen, Jyotsna Batra, Judith Clements, APCB (Australian Prostate Cancer Bio Resource), Henrik Gronberg, Nora Pashayan, Johanna Schleutker, Demetrius Albanes, Alicja Wolk, Catharine West, Lorelei Mucci, Peter Kraft, Géraldine Cancel-Tassin, Stella Koutros, Karina Dalsgaard Sorensen, Lovise Maehle, Eli Marie Grindedal, Sara Strom, David E. Neal, Freddie C. Hamdy, Jenny L. Donovan, Ruth C. Travis, Robert J. Hamilton, Sue Ann Ingles, Barry Rosenstein, Yong-Jie Lu, Graham G. Giles, Adam S. Kibel, Ana Vega, Jeanette Bensen, Manolis Kogevinas, Fredrik Wiklund, Stephen Chanock, Douglas F. Easton, Rosalind A. Eeles, Zsofia Kote-Jarai, David V. Conti, Christopher A. Haiman. Germline variation at 8q24 and prostate cancer risk in men of European ancestry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 227.

Abstract 2969: Genetically predicted blood protein biomarkers and prostate cancer risk: an analysis in over 140,000 European descendants

Prostate cancer (PrCa) is the second most frequently diagnosed malignancy among males in many countries. Several protein markers measured in blood have been found to be associated with PrCa risk. However, most previous studies assessed only a small number of biomarkers or included a small sample size. To search for novel protein biomarkers for PrCa risk, we performed a large study in 79,194 prostate cancer cases and 61,112 controls of European ancestry included in PRACTICAL/ELLIPSE consortia by using genetic instruments.

Protein quantitative trait loci (pQTLs) for 1,478 plasma proteins identified in a large study of 3,301 European descendants were used as instruments to evaluate associations between genetically predicted protein levels and PrCa risk. For proteins showing a significant association with PrCa risk, we further evaluated whether genetically predicted mRNA expression levels of the corresponding genes were associated with PrCa risk, by using mRNA expression prediction models for blood, prostate and cross tissue built using data of the Genotype-Tissue Expression Project.

We identified 31 proteins showing an association of their predicted levels with PrCa risk at a false discovery rate < 0.05, including 22 encoded by genes located more than 500 Kb away from any reported PrCa risk variants. These include proteins encoded by GSTP1, whose methylation was identified as a potential biomarker for PrCa detection, and MSMB, SPINT2, and CTSS, which were previously implicated as potential target genes of PrCa risk variants identified in genome-wide association studies. Eighteen of the proteins showed an inverse association and 13 showed a positive association. Among the 23 of the genes with mRNA expression prediction models built, seven showed an mRNA-PrCa association at p < 0.05 with a same direction of effect as the protein-PrCa association. For 28 of the identified genes, somatic changes of short indels, splice site, nonsense, or missense mutations were detected in PrCa patients in The Cancer Genome Atlas (enrichment p<0.0001). Pathway enrichment analysis showed that these genes were significantly enriched in cancer related pathways, including STAT3 Pathway, Glutathione Redox Reactions I, Glutathione-mediated Detoxification, Endoplasmic Reticulum Stress Pathway, and tRNA Splicing.

Our study has identified multiple proteins significantly associated with PrCa risk. Further research is needed to evaluate potential utility of the identified proteins in early detection of PrCa.

Citation Format: Lang Wu, Xiang Shu, Jiandong Bao, Xingyi Guo, the PRACTICAL, CRUK, BPC3, CAPS, PEGASUS consortia, Zsofia Kote-Jarai, Christopher A. Haiman, Rosalind A. Eeles, Wei Zheng. Genetically predicted blood protein biomarkers and prostate cancer risk: an analysis in over 140,000 European descendants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2969.

Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci

Genome-wide association studies (GWAS) and fine-mapping efforts to date have identified more than 100 prostate cancer (PrCa)-susceptibility loci. We meta-analyzed genotype data from a custom high-density array of 46,939 PrCa cases and 27,910 controls of European ancestry with previously genotyped data of 32,255 PrCa cases and 33,202 controls of European ancestry. Our analysis identified 62 novel loci associated (P < 5.0 × 10-8) with PrCa and one locus significantly associated with early-onset PrCa (≤55 years). Our findings include missense variants rs1800057 (odds ratio (OR) = 1.16; P = 8.2 × 10-9; G>C, p.Pro1054Arg) in ATM and rs2066827 (OR = 1.06; P = 2.3 × 10-9; T>G, p.Val109Gly) in CDKN1B. The combination of all loci captured 28.4% of the PrCa familial relative risk, and a polygenic risk score conferred an elevated PrCa risk for men in the ninetieth to ninety-ninth percentiles (relative risk = 2.69; 95% confidence interval (CI): 2.55-2.82) and first percentile (relative risk = 5.71; 95% CI: 5.04-6.48) risk stratum compared with the population average. These findings improve risk prediction, enhance fine-mapping, and provide insight into the underlying biology of PrCa1.

Rare germline variants in DNA repair genes and the angiogenesis pathway predispose prostate cancer patients to develop metastatic disease

BACKGROUND

Prostate cancer (PrCa) demonstrates a heterogeneous clinical presentation ranging from largely indolent to lethal. We sought to identify a signature of rare inherited variants that distinguishes between these two extreme phenotypes.

METHODS

We sequenced germline whole exomes from 139 aggressive (metastatic, age of diagnosis < 60) and 141 non-aggressive (low clinical grade, age of diagnosis ≥60) PrCa cases. We conducted rare variant association analyses at gene and gene set levels using SKAT and Bayesian risk index techniques. GO term enrichment analysis was performed for genes with the highest differential burden of rare disruptive variants.

RESULTS

Protein truncating variants (PTVs) in specific DNA repair genes were significantly overrepresented among patients with the aggressive phenotype, with BRCA2, ATM and NBN the most frequently mutated genes. Differential burden of rare variants was identified between metastatic and non-aggressive cases for several genes implicated in angiogenesis, conferring both deleterious and protective effects.

CONCLUSIONS

Inherited PTVs in several DNA repair genes distinguish aggressive from non-aggressive PrCa cases. Furthermore, inherited variants in genes with roles in angiogenesis may be potential predictors for risk of metastases. If validated in a larger dataset, these findings have potential for future clinical application.

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.

Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets

Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials.

Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array

This corrects the article DOI: 10.1038/bjc.2016.50.

Height, selected genetic markers and prostate cancer risk: results from the PRACTICAL consortium

This corrects the article DOI: 10.1038/bjc.2017.231.

Prostate-specific antigen velocity in a prospective prostate cancer screening study of men with genetic predisposition

This corrects the article DOI: 10.1038/bjc.2017.429.

Validation of loci at 2q14.2 and 15q21.3 as risk factors for testicular cancer

Testicular germ cell tumor (TGCT), the most common cancer in men aged 18 to 45 years, has a strong heritable basis. Genome-wide association studies (GWAS) have proposed single nucleotide polymorphisms (SNPs) at a number of loci influencing TGCT risk. To further evaluate the association of recently proposed risk SNPs with TGCT at 2q14.2, 3q26.2, 7q36.3, 10q26.13 and 15q21.3, we analyzed genotype data on 3,206 cases and 7,422 controls. Our analysis provides independent replication of the associations for risk SNPs at 2q14.2 (rs2713206 at P = 3.03 × 10-2; P-meta = 3.92 × 10-8; nearest gene, TFCP2L1) and rs12912292 at 15q21.3 (P = 7.96 × 10-11; P-meta = 1.55 × 10-19; nearest gene PRTG). Case-only analyses did not reveal specific associations with TGCT histology. TFCP2L1 joins the growing list of genes located within TGCT risk loci with biologically plausible roles in developmental transcriptional regulation, further highlighting the importance of this phenomenon in TGCT oncogenesis.

DNA repair gene panel mutations in young onset prostate cancer cases in the

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Background: Prostate cancer (PrCa) is the most common solid tumour in men in the Western world. There is substantial evidence that PrCa predisposition is due both to common and rare germline variation. Methods: We screened 167 genes from DNA damage response and repair pathways, within a UK based cohort of young onset cases (diagnosed at < 65 years) and controls. Samples were sequenced using a custom Agilent SureSelectXT bait library and Illumina HiSeq technology and processed using a BWA/GATK 2.8 pipeline. Following sample QC, data were analysed from 1,285 PrCa cases and 1,163 controls. Results: We identified 5,086 single nucleotide variants (SNVs) and 175 indels; 233 unique protein truncating variants (PTVs) with MAF < 0.5% in controls were found in 97 genes of the screening panel. The total proportion of PTV carriers in cases was higher than in controls (14.5% vs. 11.6%, P = 0.036; OR = 1.29, 95% CI 1.01-1.64). This enrichment was greater within the previously reported BROCA gene set of 22 tumour suppressor genes (4.5% vs 2.2%, P = 2.5x10-3; OR = 2.07, 95% CI 1.28-3.34). To identify genes which best to distinguish PrCa cases from controls, we applied the adaptive combination of P values algorithm, ADA, for genes with at least 2 carriers of PTVs. This analysis selected 10 genes, (OR = 3.37, 95% CI 2.05-5.66, PADA= 5.99x10-3); men with PTVs in these were about 3.4-fold more likely to have PrCa (5.8% vs. 1.8%). We subsequently compared aggressive cases (Gleason score ≥ 8, n = 204) with non-aggressive cases (Gleason score ≤ 7, n = 1049) and lethal PrCa cases (cause of death PrCa, n = 183) with indolent cases (Gleason score ≤ 6, n = 563) to evaluate genes associated with poor clinical prognosis. Using ADA, 4 genes were selected for aggressive PrCa ( PADA= 0.006) and 2 of these also for lethal PrCa ( PADA= 0.057). Conclusions: These gene sets provide an 11 gene panel which could be used for clinical testing and will help to facilitate the development of a PrCa specific sequencing panel with both predictive and prognostic potential.

Prostate-specific antigen velocity in a prospective prostate cancer screening study of men with genetic predisposition

BACKGROUND

Prostate-specific antigen (PSA) and PSA-velocity (PSAV) have been used to identify men at risk of prostate cancer (PrCa). The IMPACT study is evaluating PSA screening in men with a known genetic predisposition to PrCa due to BRCA1/2 mutations. This analysis evaluates the utility of PSA and PSAV for identifying PrCa and high-grade disease in this cohort.

METHODS

PSAV was calculated using logistic regression to determine if PSA or PSAV predicted the result of prostate biopsy (PB) in men with elevated PSA values. Cox regression was used to determine whether PSA or PSAV predicted PSA elevation in men with low PSAs. Interaction terms were included in the models to determine whether BRCA status influenced the predictiveness of PSA or PSAV.

RESULTS

1634 participants had ⩾3 PSA readings of whom 174 underwent PB and 45 PrCas diagnosed. In men with PSA >3.0 ng ml-l, PSAV was not significantly associated with presence of cancer or high-grade disease. PSAV did not add to PSA for predicting time to an elevated PSA. When comparing BRCA1/2 carriers to non-carriers, we found a significant interaction between BRCA status and last PSA before biopsy (P=0.031) and BRCA2 status and PSAV (P=0.024). However, PSAV was not predictive of biopsy outcome in BRCA2 carriers.

CONCLUSIONS

PSA is more strongly predictive of PrCa in BRCA carriers than non-carriers. We did not find evidence that PSAV aids decision-making for BRCA carriers over absolute PSA value alone.

Appraising the relevance of DNA copy number loss and gain in prostate cancer using whole genome DNA sequence data

A variety of models have been proposed to explain regions of recurrent somatic copy number alteration (SCNA) in human cancer. Our study employs Whole Genome DNA Sequence (WGS) data from tumor samples (n = 103) to comprehensively assess the role of the Knudson two hit genetic model in SCNA generation in prostate cancer. 64 recurrent regions of loss and gain were detected, of which 28 were novel, including regions of loss with more than 15% frequency at Chr4p15.2-p15.1 (15.53%), Chr6q27 (16.50%) and Chr18q12.3 (17.48%). Comprehensive mutation screens of genes, lincRNA encoding sequences, control regions and conserved domains within SCNAs demonstrated that a two-hit genetic model was supported in only a minor proportion of recurrent SCNA losses examined (15/40). We found that recurrent breakpoints and regions of inversion often occur within Knudson model SCNAs, leading to the identification of ZNF292 as a target gene for the deletion at 6q14.3-q15 and NKX3.1 as a two-hit target at 8p21.3-p21.2. The importance of alterations of lincRNA sequences was illustrated by the identification of a novel mutational hotspot at the KCCAT42, FENDRR, CAT1886 and STCAT2 loci at the 16q23.1-q24.3 loss. Our data confirm that the burden of SCNAs is predictive of biochemical recurrence, define nine individual regions that are associated with relapse, and highlight the possible importance of ion channel and G-protein coupled-receptor (GPCR) pathways in cancer development. We concluded that a two-hit genetic model accounts for about one third of SCNA indicating that mechanisms, such haploinsufficiency and epigenetic inactivation, account for the remaining SCNA losses.

Height, selected genetic markers and prostate cancer risk: results from the PRACTICAL consortium

BACKGROUND

Evidence on height and prostate cancer risk is mixed, however, recent studies with large data sets support a possible role for its association with the risk of aggressive prostate cancer.

METHODS

We analysed data from the PRACTICAL consortium consisting of 6207 prostate cancer cases and 6016 controls and a subset of high grade cases (2480 cases). We explored height, polymorphisms in genes related to growth processes as main effects and their possible interactions.

RESULTS

The results suggest that height is associated with high-grade prostate cancer risk. Men with height >180 cm are at a 22% increased risk as compared to men with height <173 cm (OR 1.22, 95% CI 1.01-1.48). Genetic variants in the growth pathway gene showed an association with prostate cancer risk. The aggregate scores of the selected variants identified a significantly increased risk of overall prostate cancer and high-grade prostate cancer by 13% and 15%, respectively, in the highest score group as compared to lowest score group.

CONCLUSIONS

There was no evidence of gene-environment interaction between height and the selected candidate SNPs.Our findings suggest a role of height in high-grade prostate cancer. The effect of genetic variants in the genes related to growth is seen in all cases and high-grade prostate cancer. There is no interaction between these two exposures.

Abstract 1311: Germline variation at 8q24 and prostate cancer risk

The 8q24 region harbors multiple risk variants for distinct cancers including 7 for prostate cancer, the majority of which lie in separate linkage disequilbrium blocks. It is not known whether a common biological mechanism underlies the association of genetic variation with cancer risk at 8q24, or whether there are site-specific functions of regulatory elements that are affected in this region. Given the proximity, the MYC oncogene is a likely candidate as are multiple long non-coding RNAs in the region. To further understand the contribution of germline variation to prostate cancer risk we performed a comprehensive fine-mapping analysis of the region in men of European ancestry from the PRACTICAL/ELLIPSE Consortium. More specifically, we tested 1,731 genotype tag SNPs and 12,221 imputed variants spanning the risk region (127.3-129.0Mb) in 56,363 prostate cancer cases and 37,386 controls of European ancestry that were genotyped with the Illumina OncoArray. We performed stepwise logistic regression and identified 13 variants with risk allele frequencies between 0.006 and 0.998 that surpassed genome-wide statistical significance (p-values between 3.2x10-8 and 8.0 x10-78) and with per allele odds ratios ranging from 1.11(rs5013678) to 2.68(rs183373024). Ongoing analyses that will be presented include incorporating existing GWAS and fine-mapping data (iCOGs) for men of European and African ancestry (35,000 cases and 35,000 controls) using JAM, a Bayesian approach that investigates multi-SNP models using marginal meta-analysis statistics. Leveraging the power from the overall multiethnic meta-analysis (&gt;93,000 cases and &gt;72,000 controls) will provide further insight into the number of independent signals in the region and their contribution to prostate cancer risk in these populations.

Citation Format: Kan Wang, Ali Amin Al Olama, Rosalind Eeles, David Conti, Zsofia Kote-Jarai, Christopher A. Haiman. Germline variation at 8q24 and prostate cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1311. doi:10.1158/1538-7445.AM2017-1311

Abstract 1301: Identification of novel susceptibility loci and genes for prostate cancer risk: A large transcriptome-wide association study in over 143,000 subjects

Common genetic variants in over 150 loci have been found to be associated with prostate cancer (PrCa) risk through GWAS. These variants, however, explain only a small fraction of PrCa heritability, and the genes responsible for the detected associations remain largely unknown. It has been suggested that many GWAS-identified associations may be driven by the regulation of risk variants on the expression of disease causal genes. To identify novel PrCa risk loci and possible causal genes at known risk loci, we performed a transcriptome-wide association study (TWAS) to evaluate associations of genetically predicted gene expressions with PrCa risk.

We used RNA sequencing data from normal prostate tissues and high-density genotyping from 73 European descendants included in the Genotype-Tissue Expression Project and established genetic models to predict gene expression level. Given that the regulatory mechanisms for most genes are similar across most human tissues, we also built cross-tissue models using gene expression data generated in all tissues from 369 European descendants to increase the statistical power. Based on prediction performance, we selected 22,126 genes and conducted association analyses of their predicted expression with PrCa risk using GWAS data obtained from more than 143,000 subjects included in PRACTICAL/ELLIPSE consortia.

We identified 140 genes showing an association of their predicted expression levels with PrCa risk at P &lt; 2.26×10-6, a Bonferroni-corrected significance threshold, including 105 protein-coding genes, 33 long non-coding RNAs, and 2 processed transcripts. Seven of these associated genes are located more than 1Mb away from any of the risk variants identified in PrCa GWAS, representing potential novel risk loci. Of the remaining 133 genes located in known risk loci, 100 have not been reported from previous eQTL analyses. The associations for 25 of these genes remained significant at P &lt; 3.76×10-4 (0.05/133) after adjusting for the risk variants reported in the initial GWAS. Our study also identified 33 genes that were previously reported based on eQTL and fine-mapping analyses. For many of the identified genes, somatic changes of indels, nonsense mutations, splice site variants, translation start site variants, or missense mutations were detected in PrCa patients in the TCGA, including known PrCa driver genes NKX3-1 and PLXNA1. Pathway enrichment analysis showed that cancer related functions were significantly enriched for the identified genes. The top canonical pathways identified included prostate cancer signaling, ATM signaling, AMPK signaling, protein ubiquitination pathway, and antigen presentation pathway.

In summary, we conducted the first large PrCa TWAS and identified multiple novel susceptibility loci and genes for PrCa risk. Our study provided substantial new information towards the understanding of PrCa genetics and biology.

Citation Format: Lang Wu, Jirong Long, Yingchang Lu, Xingyi Guo, Bogdan Pasaniuc, Kathryn L. Penney, Zsofia Kote-Jarai, Christopher A. Haiman, Rosalind A. Eeles, Wei Zheng, the PRACTICAL consortium. Identification of novel susceptibility loci and genes for prostate cancer risk: A large transcriptome-wide association study in over 143,000 subjects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1301. doi:10.1158/1538-7445.AM2017-1301

Abstract 4956: Transcriptome-wide association study identifies new prostate cancer susceptibility genes in the OncoArray data

Genome-wide association studies (GWAS) have identified over 150 genomic regions harboring risk variants for prostate cancer which explain one third of all familial risk. However, with some notable exceptions, the causal variants and target susceptibility genes at these risk loci have yet to be identified. Recent work has shown a strong overlap between loci associated with gene expression levels (eQTLs) in prostate tissue and GWAS loci, which suggests that the causal mechanism at a significant proportion of risk loci includes causal alleles that regulate expression levels of nearby susceptibility genes. While overlapping eQTLs with GWAS is a powerful method to prioritize susceptibility genes, it is often the case that multiple eQTLs co-localize at the GWAS risk region (due to linkage disequilibrium (LD) and correlations across transcript levels). This prohibits the identification of the true susceptibility gene as opposed to spurious co-localization at the same locus.

We recently leveraged gene expression imputation to perform transcriptome-wide association studies (TWAS) as a principled approach to measure the strength of association between gene expression and disease status. Here, we use imputed expression to identify new susceptibility genes for prostate cancer in the OncoArray GWAS data. We integrate gene expression data from more than 44 tissues across ~4,000 individuals with GWAS of prostate cancer from the OncoArray in ~140,000 individuals. Our approach identified 118 susceptibility genes for prostate cancer that reside in 90 independent loci across the genome. Of these, we report 7 genes located more than 0.5 Megabases away from any previously reported GWAS loci for prostate cancer, thus providing new risk loci. Second, we use TWAS to investigate genes previously reported as susceptibility genes for prostate cancer through overlaps of eQTL and GWAS. We find 36 (out of 86 previously reported genes) to be significant in TWAS. Overall, our findings highlight the power of integrating gene expression data with GWAS and provide testable hypotheses for future functional validation of prostate cancer risk.

Citation Format: Nicholas Mancuso, Wei Zheng, Kathryn Penney, The PRACTICAL Consortium, Zsofia Kote-Jarai, Christopher Haiman, Simon Gayther, Matthew Freedman, Bogdan Pasaniuc. Transcriptome-wide association study identifies new prostate cancer susceptibility genes in the OncoArray data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4956. doi:10.1158/1538-7445.AM2017-4956

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns

Motivation

Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped.

Results

We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns.

Availability and Implementation

The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ .

Contact

hlin1@lsuhsc.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.