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To identify novel candidate genes, we performed a GWAS analysis in a breast cancer dataset from UK Biobank (UKB) and combined the results with the GWAS results of the Breast Cancer Association Consortium (BCAC) by a meta-analysis. Using the summary statistics from the meta-analysis, we performed a jointTWAS analysis that combined TWAS signals from multiple tissuesusing theaggregated Cauchy association test (ACAT).We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression data. The fileGWAS_meta_analysis_summary_code.tar.gzincludes meta-analysis results and related code;multi-tissue_TWAS_acat_summary.tar.gzincludes multi-tissue TWAS results by ACAT method and related code;S-predixcan-by-tissue_summary.tar.gzincludes single tissue TWAS results for 11 tissuesand related code. &nbsp

Genome-wide association studies (GWAS) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWAS) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify novel candidate genes, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 47 tissues from the Genotype-Tissue Expression data using a multivariate adaptive shrinkage method along with association summary statistics from the Breast Cancer Association Consortium and UK Biobank data. We identified 380 genes at 129 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold (p < 2.36 × 10−6). Of them, 29 genes were located in 11 novel regions that were at least 1Mb away from published GWAS hits. The rest of TWAS-significant genes were located in 118 known genomic loci from previous GWAS of breast cancer. After conditioning on previous GWAS index variants, we found that 22 genes located in known GWAS loci remained statistically significant. Our study maps potential target genes in more than half of known GWAS loci and discovers multiple new loci, providing new insights into breast cancer genetics.

Our study describes breast cancer risk loci using a cross-ancestry GWAS approach. We first identify variants that are associated with breast cancer at P, GWAS have enhanced our understanding for the genetic basis of breast cancer, but the majority of them were performed for European ancestry populations. Here, the authors use a cross-ancestry approach and report seven new variants associated with breast cancer risk among women of African ancestry.

The genetic risk for prostate cancer has been governed by a few rare variants with high penetrance and over 150 commonly occurring variants with lower impact on risk; however, most of these variants have been identified in studies containing exclusively European individuals. People of non-European ancestries make up less than 15% of prostate cancer GWAS subjects. Across the globe, incidence of prostate cancer varies with population due to environmental and genetic factors. The discrepancy between disease incidence and representation in genetics highlights the need for more studies of the genetic risk for prostate cancer across diverse populations. To better understand the genetic risk for prostate cancer across diverse populations, we performed PrediXcan and GWAS in a cohort of 4,769 self-identified African American (2,463 cases and 2,306 controls), 2,199 Japanese American (1,106 cases and 1,093 controls), and 2,147 Latin American (1,081 cases and 1,066 controls) individuals from the Multiethnic Genome-wide Scan of Prostate Cancer. We used prediction models from 46 tissues in GTEx version 8 and five models from monocyte transcriptomes in the Multi-Ethnic Study of Artherosclerosis. Across the three populations, we predicted 19 gene-tissue pairs, including five unique genes, to be significantly (lfsr < 0.05) associated with prostate cancer. One of these genes, NKX3-1, replicated in a larger European cohort. At the SNP level, 110 SNPs met genome-wide significance in the African American cohort while 123 SNPs met significance in the Japanese American cohort. Fine mapping revealed three significant independent loci in the African American cohort and two significant independent loci in the Japanese American cohort. These identified loci confirm findings from previous GWAS of prostate cancer in diverse cohorts while PrediXcan-identified genes suggest potential new directions for prostate cancer research in populations across the globe.

INTRODUCTION AND OBJECTIVES:The main dysregulated pathway of clear cell renal cell carcinoma (ccRCC), the von Hippel Lindau (VHL)/hypoxia inducible factor-α (HIF-α) axis, is a key regulator and reg...

BACKGROUND AND OBJECTIVES: Accumulating data implicate iron accumulation in tumorigenesis of the kidney, particularly the clear cell renal cell carcinoma (ccRCC) subtype. The von Hippel Lindau (VHL)/hypoxia inducible factor-α (HIF-α) axis is uniquely dysregulated in ccRCC and is a major regulator and regulatory target of iron metabolism, yet the role of iron in ccRCC tumorigenesis and its potential interplay with VHL inactivation remains unclear. We investigated whether ccRCC iron accumulation occurs due to increased cell dependency on iron for growth and survival as a result of VHL inactivation. MATERIALS AND METHODS: Free iron levels were compared between four VHL-mutant ccRCC cell lines (786–0, A704, 769-P, RCC4) and two benign renal tubule epithelial cell lines (RPTEC, HRCEp) using the Phen Green SK fluorescent iron stain. Intracellular iron deprivation was achieved using two clinical iron chelator drugs, deferasirox (DFX) and deferoxamine (DFO), and chelator effects were measured on cell line growth, cell cycle phase, apoptosis, HIF-1α and HIF-2α protein levels and HIF-α transcriptional activity based on expression of target genes CA9, OCT4/POU5F1 and PDGFβ/PDGFB. Similar assays were performed in VHL-mutant ccRCC cells with and without ectopic wild-type VHL expression. RESULTS: Free iron levels were significantly higher in ccRCC cell lines than benign renal cell lines. DFX depleted cellular free iron more rapidly than DFO and led to greater growth suppression of ccRCC cell lines (>90% at ~30–150 μM) than benign renal cell lines (~10–50% at up to 250 μM). Similar growth responses were observed using DFO, with the exception that a prolonged treatment duration was necessary to deplete cellular iron adequately for differential growth suppression of the less susceptible A704 ccRCC cell line relative to benign renal cell lines. Apoptosis and G1-phase cell cycle arrest were identified as potential mechanisms of chelator growth suppression based on their induction in ccRCC cell lines but not benign renal cell lines. Iron chelation in ccRCC cells but not benign renal cells suppressed HIF-1α and HIF-2α protein levels and transcriptional activity, and the degree and timing of HIF-2α suppression correlated with the onset of apoptosis and differential growth suppression. Restoration of wild-type VHL function in ccRCC cells was sufficient to prevent chelator-induced apoptosis and G1 cell cycle arrest, indicating that ccRCC susceptibility to iron deprivation is VHL inactivation-dependent. CONCLUSION: ccRCC cells are characterized by high free iron levels and a cancer-specific dependency on iron for HIF-α overexpression, cell cycle progression and apoptotic escape. This iron dependency is introduced by VHL inactivation, revealing a novel interplay between VHL/HIF-α dysregulation and ccRCC iron metabolism. Future study is warranted to determine if iron deprivation using chelator drugs provides an effective therapeutic strategy for targeting HIF-2α and suppressing tumor progression in ccRCC patients.