Association Studies Of Cancer Research Articles

Uncovering immune cell-associated genes in breast cancer: based on summary data-based Mendelian randomized analysis and colocalization study

BackgroundBreast cancer, which is the most prevalent form of cancer among women globally, encompasses various subtypes that demand distinct treatment approaches. The tumor microenvironment and immune response are of crucial significance in the development and progression of breast cancer. Nevertheless, there has been scant evidence concerning the genes within breast cancer - specific immune cells.MethodsWe utilized summary data-based Mendelian randomization (SMR) to identify genes associated with breast cancer by utilizing expression quantitative trait loci (eQTL) datasets for 14 different immune cell types and genome-wide association studies (GWAS) for overall breast cancer and its subtypes. Furthermore, colocalization analysis was carried out to evaluate whether the observed association in SMR analyses is influenced by the same causal variant. Replication analysis and bulk RNA sequencing (bulkRNA-seq) analysis were employed to validate promising immune genes as potential drug targets.ResultsAfter correcting for the rate of false discovery, we discovered a total of 17 genes in 9 immune cell types that were significantly associated with overall breast cancer and its subtypes. The genes KCNN4, L3MBTL3, ZBTB38, MDM4, and TNFSF10 were identified in overall breast cancer and its subtypes. Colocalization analyses provided robust evidence in support of these associations. Notably, the KCNN4 gene in non-classical MONOcytes (MONOnc) was further validated through replication analysis and bulkRNA-seq analysis.ConclusionIn summary, our research has revealed a repertoire of genes within diverse immune cells associated with breast cancer. KCNN4 gene in non-classical MONOcytes (MONOnc) exhibited a negative association with overall breast cancer and its subtypes, which was identified as a potential drug target for breast cancer, opening up new avenues for therapeutic interventions.

Systematic prioritization of functional variants and effector genes underlying colorectal cancer risk

Genome-wide association studies of colorectal cancer (CRC) have identified 170 autosomal risk loci. However, for most of these, the functional variants and their target genes are unknown. Here, we perform statistical fine-mapping incorporating tissue-specific epigenetic annotations and massively parallel reporter assays to systematically prioritize functional variants for each CRC risk locus. We identify plausible causal variants for the 170 risk loci, with a single variant for 40. We link these variants to 208 target genes by analyzing colon-specific quantitative trait loci and implementing the activity-by-contact model, which integrates epigenomic features and Micro-C data, to predict enhancer–gene connections. By deciphering CRC risk loci, we identify direct links between risk variants and target genes, providing further insight into the molecular basis of CRC susceptibility and highlighting potential pharmaceutical targets for prevention and treatment.

Nonadditive Effects of Common Genetic Variants Have a Negligent Contribution to Cancer Heritability.

Contribution of dominance effects to cancer heritability is unknown. We leveraged existing genome-wide association data for seven cancers to estimate the contribution of dominance effects to the heritability of individual cancer types. We estimated the proportion of phenotypic variation caused by dominance genetic effects using genome-wide association data for seven cancers (breast, colorectal, lung, melanoma, nonmelanoma skin, ovarian, and prostate) in a total of 166,772 cases and 284,824 controls. We observed no evidence of a meaningful contribution of dominance effects to cancer heritability. By contrast, additive effects ranged between 0.11 and 0.34. In line with studies of other human traits, the dominance effects of common genetic variants play a minimal role in cancer etiology. These results support the assumption of an additive inheritance model when conducting cancer association studies with common genetic variants.

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

BackgroundThe genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.ResultsWe studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.ConclusionWe show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Polygenic inheritance and its interplay with smoking history in predicting lung cancer diagnosis: a French-Canadian case-control cohort

The most near-term clinical application of genome-wide association studies in lung cancer is a polygenic risk score (PRS). A case-control dataset was generated consisting of 4002 lung cancer cases from the LORD project and 20,010 ethnically matched controls from CARTaGENE. A genome-wide PRS including >1.1 million genetic variants was derived and validated in UK Biobank (n=5419 lung cancer cases). The predictive ability and diagnostic discrimination performance of the PRS was tested in LORD/CARTaGENE and benchmarked against previous PRSs from the literature. Stratified analyses were performed by smoking status and genetic risk groups defined as low (<20th percentile), intermediate (20-80th percentile) and high (>80th percentile) PRS. The phenotypic variance explained and the effect size of the genome-wide PRS numerically outperformed previous PRSs. Individuals with high genetic risk had a 2-fold odds of lung cancer compared to low genetic risk. The PRS was an independent predictor of lung cancer beyond conventional clinical risk factors, but its diagnostic discrimination performance was incremental in an integrated risk model. Smoking increased the odds of lung cancer by 7.7-fold in low genetic risk and by 11.3-fold in high genetic risk. Smoking with high genetic risk was associated with a 17-fold increase in the odds of lung cancer compared to individuals who never smoked and with low genetic risk. Individuals at low genetic risk are not protected against the smoking-related risk of lung cancer. The joint multiplicative effect of PRS and smoking increases the odds of lung cancer by nearly 20-fold. This work was supported by the CQDM and the IUCPQ Foundation owing to a generous donation from Mr. Normand Lord.

Cancer research in Lebanon: Scope of the most recent publications of an academic institution (Review).

Cancer may be considered one of the most interesting areas of study, and although oncology research has grown markedly over the last decade, there is as yet no known cure for cancer. The objective of the present review is to examine various approaches to cancer research from a single institution, summarize their key conclusions and offer recommendations for future evaluations. The review examined 72 cancer-associated studies that were published within six years from 2017 to 2022. Published works in the subject fields of 'cancer' or 'oncology' and 'research' that were indexed in Scopus and Web of Science were retrieved and sorted according to article title, author names, author count, citation count and key words. After screening, a total of 28 in vitro/animal studies and 46 patient-associated published studies were obtained. A large proportion of these studies comprised literature reviews (20/72), while 20 studies were observational in nature. The 72 publications included 23 in which various types of cancer were examined, while the remaining studies focused on specific types of cancer, including lung, breast, colon and brain cancer. These studies aimed to investigate the incidence, prevalence, treatment and prevention mechanisms associated with cancer. Despite the existence of extensive cancer research, scientists seldom contemplate an ultimate cure for cancer. However, it is crucial to continuously pursue research on cancer prevention and treatment in order to enhance the effectiveness and minimize potential side effects of cancer therapy.

Statins inhibit paclitaxel-induced PD-L1 expression and increase CD8+ T cytotoxicity for better prognosis in breast cancer.

In recent years, the widespread use of lipid-lowering drugs, especially statins, has attracted people's attention. Statin use may be potentially associated with a reduced risk of breast cancer. To explore the relationship between statin use and cancer risk. And further explore the potential role of statins in the adjuvant treatment of breast cancer. Data for the Mendelian randomization portion of the study were obtained from genome-wide association studies of common cancers in the UK Biobank and FinnGen studies and from the Global Lipid Genetics Consortium's low density lipoprotein (LDL). In addition, the impacts of statins and chemotherapy drugs on breast cancer were examined using both in vitro and in vivo models, with particular attention to the expression levels of the immune checkpoint protein PD-L1 and its potential to suppress tumor growth. Data from about 3.8 million cancer patients and ~1.3 million LDL-measuring individuals were analyzed. Genetically proxied HMGCR inhibition (statins) was associated with breast cancer risk reduction (P=0.0005). In vitro experiments showed that lovastatin significantly inhibited paclitaxel-induced PD-L1 expression and assisted paclitaxel in suppressing tumor cell growth. Furthermore, the combination therapy involving lovastatin and paclitaxel amplified CD8+ T-cell infiltration, bolstering their tumor-killing capacity and enhancing in vivo efficacy. The utilization of statins is correlated with improved prognoses for breast cancer patients and may play a role in facilitating the transition from cold to hot tumors. Combination therapy with lovastatin and paclitaxel enhances CD8+ T-cell activity and leads to better prognostic characteristics.

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

Abstract PO5-09-03: Multi-tissue transcriptome-wide association studies identified genes for intrinsic subtypes of breast cancer

Abstract Background: Though several genome-wide association studies (GWAS) of breast cancer (BC) have identified common variants which differ between intrinsic subtypes, the genes through which these variants act through to impact BC risk have not been fully established. Furthermore, transcriptome-wide association studies (TWAS) have thus far been primarily employed to identify genes associated with overall BC risk, while overlooking how the influence of common variation on gene expression may contribute to subtype-specific differences. Methods: In this study, we performed two complementary multi-tissue TWASs for each of the following BC intrinsic subtypes: Luminal A-like, Luminal B-like, Luminal B/HER2-negative-like, HER2-enriched-like, Triple Negative BC. These two approaches included 1) an expression-based approach that collated TWAS signals from expression quantitative trait loci (eQTLs) across multiple tissues using the Aggregated Cauchy Association Test (ACAT) and 2) a splicing-based approach that utilized two applications of ACAT to collate splicing QTLs (sQTLs) for a given gene in a tissue and then across tissues. To perform these two TWASs, we utilized e/sQTL models trained in 11 tissues from the Genotype-Tissue Expression Project including breast, ovary, uterus, vagina, EBV-transformed lymphocytes, whole blood, spleen, liver, subcutaneous adipose, visceral adipose, and cell-cultured fibroblasts. GWAS summary statistics were previously generated from 133,384 BC cases and 113,789 controls who were participants in the Breast Cancer Association Consortium (BCAC). We further performed our TWAS while conditioning e/sQTL effect sizes on nearby GWAS index SNPs. Additionally, we utilized gene-based fine-mapping of eQTLs and sQTL to identify candidate causal genes for each intrinsic subtype. Results: Overall, we identified 164 genes in 69 loci that were associated with Luminal A-like, 19 genes in 9 loci with Luminal B-like, 18 genes in 11 loci with Luminal B/HER2-negative-like, 10 genes in 7 loci with HER2-enriched-like, and 29 genes in 12 loci with TNBC. Among these genes, 17 genes had not been reported in previous TWAS of BC, and 140 genes, 1 gene, 2 genes, 2 genes, and 16 genes were uniquely associated with each of the intrinsic subtypes, respectively. Additionally, we identified one gene associated with Luminal A-like and one gene with Luminal B-like BC that were each in a locus located at least 1.4 Mb from published GWAS hits. Furthermore, we identified 106, 11, 10, 5, and 21 candidate causal genes for each of the intrinsic subtypes, respectively, that had a posterior inclusion probability of 0.9 in at least one e/sQTL. Conclusion: In summary, our multi-tissue TWAS corroborated previous GWAS loci for overall BC risk and intrinsic subtypes, while underscoring how common variation impacts BC etiology by modulating the expression and splicing of genes in multiple tissue types. Citation Format: James Li, Julian McClellan, Haoyu Zhang, Guimin Gao, Dezheng Huo. Multi-tissue transcriptome-wide association studies identified genes for intrinsic subtypes of breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-09-03.

Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction.

We performed genome-wide association studies of breast cancer including 18,034 cases and 22,104 controls of African ancestry. Genetic variants at 12 loci were associated with breast cancer risk (P < 5 × 10-8), including associations of a low-frequency missense variant rs61751053 in ARHGEF38 with overall breast cancer (odds ratio (OR) = 1.48) and a common variant rs76664032 at chromosome 2q14.2 with triple-negative breast cancer (TNBC) (OR = 1.30). Approximately 15.4% of cases with TNBC carried six risk alleles in three genome-wide association study-identified TNBC risk variants, with an OR of 4.21 (95% confidence interval = 2.66-7.03) compared with those carrying fewer than two risk alleles. A polygenic risk score (PRS) showed an area under the receiver operating characteristic curve of 0.60 for the prediction of breast cancer risk, which outperformed PRS derived using data from females of European ancestry. Our study markedly increases the population diversity in genetic studies for breast cancer and demonstrates the utility of PRS for risk prediction in females of African ancestry.

Multi-tissue transcriptome-wide association studies identified 235 genes for intrinsic subtypes of breast cancer.

Although genome-wide association studies (GWAS) of breast cancer (BC) identified common variants which differ between intrinsic subtypes, genes through which these variants act to impact BC risk have not been fully established. Transcriptome-wide association studies (TWAS) have identified genes associated with overall BC risk, but subtype-specific differences are largely unknown. We performed two multi-tissue TWAS for each BC intrinsic subtype, including an expression-based approach that collated TWAS signals from expression quantitative trait loci (eQTLs) across multiple tissues and a novel splicing-based approach that collated signals from splicing QTLs (sQTLs) across intron clusters and subsequently across tissues. We used summary statistics for five intrinsic subtypes including Luminal A-like, Luminal B-like, Luminal B/HER2-negative-like, HER2-enriched-like, and triple-negative BC, generated from 106 278 BC cases and 91 477 controls in the Breast Cancer Association Consortium. Overall, we identified 235 genes in 88 loci that were associated with at least one of the five intrinsic subtypes. Most genes were subtype-specific, and many have not been reported in previous TWAS. We discovered common variants that modulate expression of CHEK2 confer increased risk to Luminal A-like BC, in contrast to the viewpoint that CHEK2 primarily harbors rare, penetrant mutations. Additionally, our splicing-based TWAS provided population-level support for MDM4 splice variants that increased the risk of triple-negative BC. Our comprehensive, multi-tissue TWAS corroborated previous GWAS loci for overall BC risk and intrinsic subtypes, while underscoring how common variation that impacts expression and splicing of genes in multiple tissue types can be used to further elucidate the etiology of BC.

Potential Role of Zinc Finger 365 rs10822013 and rs10995190 in Mammographic Density, Sporadic Breast Cancer Risk, and Prognosis.

Despite suggesting many genetic risk markers as the outcome of Genome-wide association studies (GWAS) for breast cancer, replicating the results in different populations has remained the main issue. In this regard, this study assessed the association of two variations in Zinc Finger 365 (ZNF365) in an Iranian population. In a case-control study conducted at Mashhad University of Medical Sciences, Mashhad, Iran, between 2017 and 2020, ZNF365-rs10822013 and rs10995190 were genotyped using Allele-Specific PCR (AS-PCR). Breast density was assessed using mammography images. PHASE software module version 2 and SPSS version 16.0 were used for haplotype and statistical analyses. Quantitative and qualitative variables were compared between groups using independent t tests and Chi square tests, respectively. Binary logistic regression analysis was performed to calculate odds ratios. Multivariate analysis was then undertaken for the baseline variables, with a P<0.05 in the univariate analysis. The survival analysis was performed using the Kaplan-Meier method and the log-rank test. In this survey, 732 females, including 342 breast cancer patients and 390 healthy subjects, were enrolled. rs10822013-T allele (P=0.014), rs10995190-G allele (P=0.003), and TG haplotype (P=0.002) were significantly associated with the increased risk of breast cancer. Moreover, rs10995190-GG genotype (P=0.042) and C-G haplotype (P=0.019) revealed a significant association with better overall survival. However, considered polymorphisms and their haplotypes indicated no association with breast density and clinical features of breast cancer. ZNF365 variants might be a potential risk marker of breast cancer in the Iranian population. The interaction between alleles in haplotypes may modulate the amount of the risk conferred by these variants. Further studies on different ethnic groups can validate these results.

Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations

Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.

Evaluating interactions of polygenic risk scores and NAT2 genotypes with tobacco smoking in bladder cancer risk.

Tobacco smoking is the most important risk factor for bladder cancer. Previous studies have identified the N-acetyltransferase (NAT2) gene in association with bladder cancer risk. The NAT2 gene encodes an enzyme that metabolizes aromatic amines, carcinogens commonly found in tobacco smoke. In our study, we evaluated potential interactions of tobacco smoking with NAT2 genotypes and polygenic risk score (PRS) for bladder cancer, using data from the UK Biobank, a large prospective cohort study. We used Cox proportional hazards models to measure the strength of the association. The PRS was derived using genetic risk variants identified by genome-wide association studies for bladder cancer. With an average of 10.1 years of follow-up of 390 678 eligible participants of European descent, 769 incident bladder cancer cases were identified. Current smokers with a PRS in the highest tertile had a higher risk of developing bladder cancer (HR: 6.45, 95% CI: 4.51-9.24) than current smokers with a PRS in the lowest tertile (HR: 2.41, 95% CI: 1.52-3.84; P for additive interaction = <.001). A similar interaction was found for genetically predicted metabolizing NAT2 phenotype and tobacco smoking where current smokers with the slow NAT2 phenotype had an increased risk of developing bladder cancer (HR: 5.70, 95% CI: 2.64-12.30) than current smokers with the fast NAT2 phenotype (HR: 3.61, 95% CI: 1.14-11.37; P for additive interaction = .100). Our study provides support for considering both genetic and lifestyle risk factors in developing prevention measures for bladder cancer.

Machine learning reveals genetic modifiers of the immune microenvironment of cancer

Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets.

Association Between Telomere Length and Risk of Lung Cancer in an Asian Population: A Mendelian Randomization Study.

Several traditional observational studies and Mendelian randomization (MR) studies have indicated an association between leukocyte telomere length (LTL) and the risk of lung cancer in the European population. However, the results in the Asian population are still unclear. The objective was to reveal the genetic causal association between LTL and the risk of lung cancer in the Asian population. We conducted a two-sample MR analysis using summary statistics. Instrumental variables (IVs) were obtained from the genome-wide association studies (GWAS) of LTL (n = 23,096) and lung cancer (n = 212,453) of Asian ancestry. We applied the random-effects inverse-variance weighted (IVW) model as the main method. As well, several other models were performed as complementary methods to assess the impact of potential MR assumption violations, including MR-Egger regression, weighted median, and weighted mode models. We included eight single-nucleotide polymorphisms (SNPs) as IVs for LTL and found that LTL was significantly associated with the risk of lung cancer in the IVW model (odds ratio (OR): 1.60; 95% confidence interval (CI): 1.31 - 1.97; P = 5.96 × 10-6), which was in line with the results in the weighted median and weighted mode models. However, the relationship was not statistically significant in the MR-Egger regression model (OR: 1.44; 95% CI: 0.92 - 2.26; P = 0.160). Sensitivity analyses indicated the robustness of the results. This two-sample MR study confirmed that longer telomere length significantly increased the risk of lung cancer in the Asian population, which was in accord with findings in the Western population.

Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility.

Predicting protein levels from genotypes for proteome-wide association studies (PWAS) may provide insight into the mechanisms underlying cancer susceptibility. We performed PWAS of breast, endometrial, ovarian, and prostate cancers and their subtypes in several large European-ancestry discovery consortia (effective sample size: 237,483 cases/317,006 controls) and tested the results for replication in an independent European-ancestry GWAS (31,969 cases/410,350 controls). We performed PWAS using the cancer GWAS summary statistics and two sets of plasma protein prediction models, followed by colocalization analysis. Using Atherosclerosis Risk in Communities (ARIC) models, we identified 93 protein-cancer associations [false discovery rate (FDR) < 0.05]. We then performed a meta-analysis of the discovery and replication PWAS, resulting in 61 significant protein-cancer associations (FDR < 0.05). Ten of 15 protein-cancer pairs that could be tested using Trans-Omics for Precision Medicine (TOPMed) protein prediction models replicated with the same directions of effect in both cancer GWAS (P < 0.05). To further support our results, we applied Bayesian colocalization analysis and found colocalized SNPs for SERPINA3 protein levels and prostate cancer (posterior probability, PP = 0.65) and SNUPN protein levels and breast cancer (PP = 0.62). We used PWAS to identify potential biomarkers of hormone-related cancer risk. SNPs in SERPINA3 and SNUPN did not reach genome-wide significance for cancer in the original GWAS, highlighting the power of PWAS for novel locus discovery, with the added advantage of providing directions of protein effect. PWAS and colocalization are promising methods to identify potential molecular mechanisms underlying complex traits.

A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes

Genome-wide association studies (GWASs) 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 (TWASs) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify 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 joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression (GTEx) data. In the GWAS analysis, we identified eight loci distinct from those reported previously. In the TWAS analysis, we identified 309 genes at 108 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold. Of these, 17 genes were located in eight regions that were at least 1 Mb away from published GWAS hits. The remaining TWAS-significant genes were located in 100 known genomic loci from previous GWASs of breast cancer. We found that 21 genes located in known GWAS loci remained statistically significant after conditioning on previous GWAS index variants. Our study provides insights into breast cancer genetics through mapping candidate target genes in a large proportion of known GWAS loci and discovering multiple new loci.

Genome-wide association studies in advanced prostate cancer: KYUCOG-1401-A study.

Androgen deprivation therapy (ADT) has been widely used for the treatment of advanced prostate cancer. However, prognosis and adverse events vary among patients. This study aimed to identify genetic markers able to predict the outcome of ADT. Japanese patients treated with primary ADT for advanced prostate cancer in the KYUCOG-1401 trial were enrolled as a development set. A distinct population of advanced prostate cancer cases treated with ADT was included as a validation set. Single-nucleotide polymorphisms (SNPs) associated with radiographic progression-free survival (PFS) at 1 year and adverse events including de novo diabetes mellitus, arthralgia, and de novo dyslipidemia were identified in the development set by a genome-wide association study (GWAS). The SNPs associated with radiographic PFS in the development study were then genotyped in the validation set. GWAS followed by validation identified SNPs (rs76237622 in PRR27 and rs117573572 in MTAP) that were associated with overall survival in ADT. A genetic prognostic model using these SNPs showed excellent predictive efficacy for PFS and overall survival in ADT. In addition, GWAS showed that several SNPs were associated with de novo diabetes mellitus, arthralgia, and de novo dyslipidemia in ADT. This study identified novel multiple SNPs that correlated with outcomes in ADT. Future studies on correlations affecting the therapeutic efficacy of ADT-based combination therapies would make a valuable contribution to the development of personalized medicine.

Familial aggregation of esophageal cancer.

Familial aggregation of esophageal cancer.