Project description:Background: Numerous germline genetic variants are associated with prostate cancer risk, but their biological role is not well understood. One possibility is that these variants influence gene expression in prostate tissue. We therefore examined the association of prostate cancer risk variants with the expression of genes nearby and genome-wide. Methods: We generated mRNA expression data for 20,254 genes with the Affymetrix GeneChip Human Gene 1.0 ST microarray from normal prostate (N=160) and prostate tumor (N=264) tissue from participants of the Physicians’ Health Study and Health Professionals Follow-up Study. With linear models, we tested the association of 39 risk variants with nearby genes and all genes, and the association of each variant with canonical pathways using a global test. Results: In addition to confirming previously reported associations, we detected several new significant (p<0.05) associations of variants with the expression of nearby genes including C2orf43, ITGA6, MLPH, CHMP2B, BMPR1B, and MTL5. Genome-wide, four genes (MSMB, NUDT11, NEFM, KLHL33) were significantly associated after accounting for multiple comparisons for each SNP (p<2.5x10-6). Many more genes had a false discovery rate <10%, including SRD5A1 and PSCA, and we observed significant associations with pathways in tumor tissue. Conclusions: The risk variants were associated with several genes, including promising prostate cancer candidates and lipid metabolism pathways, suggesting mechanisms for their impact on disease. These genes should be further explored in biological and epidemiological studies. Impact: Determining the biological role of these variants can lead to improved understanding of prostate cancer etiology and identify new targets for chemoprevention.
Project description:Prostate cancer is a leading cause of cancer death amongst males. The main clinical dilemma in treating prostate cancer is the high number of indolent cases that confer a significant risk of over diagnosis and over treatment. In this study we have performed a genome expression profiling of tumor tissue specimens from 36 patients with prostate cancer to identify transcripts that delineate aggressive and indolent cancer. We included normal prostate biopsies from 14 patients in our analysis. Unsupervised hierarchical cluster analysis separated the cancer samples into two groups with a significant overrepresentation of tumors from patients with biochemical recurrence in one of the groups (Chi2, p=0.02). The samples were separated by basically three clusters of genes that showed differential expression between the two sample clusters - totaling 371 transcripts. Ingenuity Pathway Analysis revealed that one cluster contained genes associated with invasive properties of the tumor, another genes associated with the cell cycle, and the last cluster genes involved in several biological functions. We successfully validated the transcripts association with recurrence using two publicly available patient datasets totaling 669 patients. Twelve genes were found to be independent predictors of recurrence in multivariate logistical regression analysis. In this study we have performed a genome expression profiling of tumor tissue specimens from 36 patients with prostate cancer to identify transcripts that delineate aggressive and indolent cancer. We included normal prostate biopsies from 14 patients in our analysis.
Project description:Proteomics analysis of matched tumor and normal adjacent tumor regions of 40 patients with multiparametric magnetic resonance imaging (mpMRI) visible or invisible tumors. All patients have clinically significant intermediate-risk (pathological ISUP Grade Group 2), localized prostate cancer.
Project description:To date, genome-wide association studies (GWAS) have revealed over 200 genetic risk loci associated with prostate cancer; yet, true disease-causing variants in gene regulatory regions remain elusive. Identification of causal variants and their targets from association signals relevant to prostate cancer is complicated by high linkage disequilibrium and limited availability of functional genomics data for specific tissue/cell types. Here, we integrated statistical fine-mapping and functional annotation from prostate-specific epigenomic profiles, high resolution 3D genome features, and quantitative trait loci data to distinguish causal variants from associations and identify target genes they regulate. Our fine-mapping analysis yielded 1,892 likely causal variants, and multiscale functional annotation linked them to 406 target genes. We prioritized rs10486567, located in an enhancer, as a genome-wide top-ranked SNP and predicted HOTTIP as its target. Deletion of the rs10486567-associated enhancer in prostate cancer cells decreased their capacity for invasive migration. HOTTIP overexpression in an enhancer-KO cell line rescued defective invasive migration. Furthermore, we found that rs10486567 regulates HOTTIP through allele-specific long- range chromatin interaction.