Genetic associations of breast and prostate cancer are enriched for regulatory elements identified in disease-related tissues

A genome-wide gene-based gene-environment interaction study of breast cancer in more than 90,000 women

Background

Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene-environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this.

Methods

We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P<0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test.

Results

After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE=4.44×10-6).

Conclusion

In this transcriptome-informed genome-wide gene-environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk.

Impact

Our study suggests a limited role of gene-environment interactions in breast cancer risk.

OUP accepted manuscript

In the last years, many studies were able to identify associations between common genetic variants and complex diseases. However, the mechanistic biological links explaining these associations are still mostly unknown. Common variants are usually associated with a relatively small effect size, suggesting that interactions among multiple variants might be a major genetic component of complex diseases. Hence, elucidating the presence of functional relations among variants may be fundamental to identify putative variants’ interactions. To this aim, we developed Polympact, a web-based resource that allows to explore functional relations among human common variants by exploiting variants’ functional element landscape, their impact on transcription factor binding motifs, and their effect on transcript levels of protein-coding genes. Polympact characterizes over 18 million common variants and allows to explore putative relations by combining clustering analysis and innovative similarity and interaction network models. The properties of the network models were studied and the utility of Polympact was demonstrated by analysing the rich sets of Breast Cancer and Alzheimer's GWAS variants. We identified relations among multiple variants, suggesting putative interactions. Polympact is freely available at bcglab.cibio.unitn.it/polympact.

SNPs associated with colorectal cancer at 15q13.3 affect risk enhancers that modulate GREM1 gene expression

Several genome wide association studies of colorectal cancer (CRC) have identified single nucleotide polymorphisms (SNPs) on chromosome 15q13.3 associated with CRC risk. To identify functional variant(s) underlying this association, we investigated SNPs in linkage disequilibrium with the risk‐associated SNP rs4779584 that overlapped regulatory regions/enhancer elements characterized in colon‐related tissues and cells. We identified several SNP‐containing regulatory regions that exhibited enhancer activity in vitro, including one SNP (rs1406389) that correlated with allele‐specific effects on enhancer activity. Deletion of either this enhancer or another enhancer that had previously been reported in this region correlated with decreased expression of GREM1 following CRISPR/Cas9 genome editing. That GREM1 is one target of these enhancers was further supported by an expression quantitative trait loci correlation between rs1406389 and GREM1 expression in the transverse but not sigmoid colon in the Genotype‐Tissue Expression dataset. Taken together, we conclude that the 15q13.3 region contains at least two functional variants that map to distinct enhancers and impact CRC risk through modulation of GREM1 expression.

SNP-adjacent super enhancer network mediates enhanced osteogenic differentiation of MSCs in ankylosing spondylitis

Ankylosing spondylitis (AS) is a rheumatic disease with pathological osteogenesis that causes bony ankylosis and even deformity over time. Mesenchymal stem cells (MSCs) are multipotent stem cells that are the main source of osteoblasts. We previously demonstrated that enhanced osteogenic differentiation of MSCs from AS patients (ASMSCs) is related to pathological osteogenesis in AS. However, the more concrete mechanism needs further exploration. Super enhancers (SEs) are dense clusters of stitched enhancers that control cell identity determination and disease development. Single-nucleotide polymorphisms (SNPs) regulate the formation and interaction of SEs and denote genes accounting for AS susceptibility. Via integrative analysis of multiomic data, including histone 3 lysine 27 acetylation (H3K27ac), chromatin immunoprecipitation sequencing (ChIP-seq), SNPs and RNA sequencing (RNA-seq) data, we discovered a transcription network mediated by AS SNP-adjacent SEs (SASEs) in ASMSCs and identified key genes, such as Toll-like receptor 4 (TLR4), interleukin 18 receptor 1 (IL18R1), insulin-like growth factor binding protein 4 (IGFBP4), transportin 1 (TNPO1) and proprotein convertase subtilisin/kexin type 5 (PCSK5), which are pivotal in osteogenesis and AS pathogenesis. The SASE-regulated network modulates the enhanced osteogenic differentiation of ASMSCs by synergistically activating the PI3K-Akt, NF-kappaB and Hippo signaling pathways. Our results emphasize the crucial role of the SASE-regulated network in pathological osteogenesis in AS, and the preferential inhibition of ASMSC osteogenic differentiation by JQ1 indicates that SEs may be attractive targets in future treatment for new bone formation in AS.

Super-enhancer in prostate cancer: transcriptional disorders and therapeutic targets

Abnormal activity of oncogenic and tumor-suppressor signaling pathways contributes to cancer and cancer risk in humans. Transcriptional dysregulation of these pathways is commonly associated with tumorigenesis and the development of cancer. Genetic and epigenetic alterations may mediate dysregulated transcriptional activity. One of the most important epigenetic alternations is the non-coding regulatory element, which includes both enhancers and super-enhancers (SEs). SEs, characterized as large clusters of enhancers with aberrant high levels of transcription factor binding, have been considered as key drivers of gene expression in controlling and maintaining cancer cell identity. In cancer cells, oncogenes acquire SEs and the cancer phenotype relies on these abnormal transcription programs driven by SEs, which leads to cancer cells often becoming addicted to the SEs-related transcription programs, including prostate cancer. Here, we summarize recent findings of SEs and SEs-related gene regulation in prostate cancer and review the potential pharmacological inhibitors in basic research and clinical trials.