Abstract 29: Integrating computational epigenetic and statistical approaches to investigate how genome-wide transcription factor (TF)-DNA bindings affect breast cancer risk
Authors: Wen Wanqing , Chen Zhishan , Long Quan , Zheng Wei , Guo Xingyi 
Published: September 2020

Abstract
Background: Fine-mapping and functional genomic studies of risk loci identified by genome wide association studies (GWAS) of breast cancer suggest that risk-associated regulatory variants may disrupt DNA binding affinities of transcription factors (TFs), such as FOXA1 and ESR1, thus altering gene expression and affecting breast cancer risk. To date, however, no studies have directly investigated how genome-wide TF-DNA bindings in a tissue-specific context affect breast cancer risk.
Methods: We recently developed an analytic framework using computational epigenetic approaches and generalized mixed models to identify breast cancer risk associated cis-regulatory elements that are occupied by TFs. Summary statistics derived from GWAS of imputed data for approximately 11 million genetic variants were obtained from the Breast Cancer Association Consortium (BCAC). A total of 113 ChIP-seq datasets for TFs and chromatin features were annotated from the Encyclopedia of DNA Elements (ENCODE) and Roadmap projects and the Cistrome database (http://cistrome.org/) in multiple breast cancer cell lines..Chi-square values reported in the BCAC dataset were used to measure breast cancer risk associated with a genetic variant. To investigate TF-DNA bindings for breast cancer risk, we constructed generalized mixed models to evaluate the associations between the Chi-square values and binding sites of single TF or co-occupied by multiple TFs, given linkage disequilibrium (LD) blocks of variants to handle their dependence. To define approximately independent LD blocks similar to other studies, we defined LD blocks using non-overlapping segments of 100k bps. In addition, we investigated the effect of putative cis-regulatory elements characterized by TF-DNA bindings and chromatin features on breast cancer risk.
Results: We identified a total of 22 TFs where their TF-DNA bindings were significantly associated with breast cancer risk at P &lt; 1 × 10−5, with the top five TFs being FOXA1, SIN3AK, ESR1, TCF7l2, and AR (P &lt; 1 × 10−10). We further observed stronger associations of breast cancer risk with co-occupied TF-TF DNA bindings, with the top five TF pairs (P &lt; 1 × 10−16) being FOXA1+E2F1, FOXA1+NR2F2, FOXA1+ESR1, FOXA1+SIN3, and FOXA1+TCF12. The interactions of TF-TF DNA bindings for these five TF pairs were highly significant (P &lt; 1 × 10−5). In addition, our results showed a significant interaction (P &lt; 1 × 10−5) between chromatin features and the TF-DNA bindings. As compared with quiescent/low chromatin features, more TF-DNA bindings in enhancers, strong or weak transcription, or heterochromatin were associated with lower breast cancer risk.
Conclusions: Our findings suggest that putative regulatory variants, which alter the TF-DNA binding affinities, particularly those located in TF-TF co-occupied sites, and TF-DNA binding colocalized with chromatin features, play an important role in breast cancer risk. Our approaches can be generalized to investigate the effect of genome-wide TF-DNA bindings on the risk of any human cancers that have comprehensive ChIP-seq and large-scale GWAS data.
Citation Format: Wanqing Wen, Zhishan Chen, Quan Long, Wei Zheng, Xingyi Guo. Integrating computational epigenetic and statistical approaches to investigate how genome-wide transcription factor (TF)-DNA bindings affect breast cancer risk [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 29.