Purpose

Epidemiological studies suggest that short sleep duration and poor sleep quality may increase breast cancer risk. However, whether sleep is associated with breast tumor aggressiveness characteristics has largely been unexplored.

Methods

The study included 4171 non-Hispanic whites (NHW) and 235 African Americans (AA) diagnosed with incident, primary, invasive breast cancer in the Women's Health Initiative (WHI) Observational Study (1994-2013). We used logistic regression to examine the association of baseline sleep (sleep duration, sleep quality, WHI Insomnia Rating Scale) with tumor grade, stage, hormone receptor status, HER2 status.

Results

In NHW, women who reported 6 h of sleep/night were more likely to have tumors classified as regional/distant stage at diagnosis compared to women who slept 7-8 h/night (adjusted odds ratio (OR): 1.25, 95% confidence interval (CI): 1.05-1.48). AA women who reported their typical night's sleep as 'average quality' or 'restless or very restless sleep' were more likely to be diagnosed with triple-negative tumors than those who reported 'sound or restful' sleep (adjusted ORs: 2.91 (1.11, 7.63) and 3.74 (1.10, 12.77), respectively).

Conclusions

Our findings provide indications that aspects of sleep (sleep duration and quality), partially modifiable health behaviors, may be associated with development of aggressive tumor characteristics in postmenopausal women. The role of these sleep attributes may differ for NHW and AA women; however, further study in robust, racial diverse samples is needed. This study provides evidence that facets of sleep behavior are associated with the development of aggressive tumor features and these associations differ by race.

Understanding the earth as a system requires integrating many forms of data from multiple fields. Builders and funders of the cyberinfrastructure designed to enable open data sharing in the geosciences risk a key failure mode: What if geoscientists do not use the cyberinfrastructure to share, discover and reuse data? In this study, we report a baseline assessment of engagement with the NSF EarthCube initiative, an open cyberinfrastructure effort for the geosciences. We find scientists perceive the need for cross-disciplinary engagement and engage where there is organizational or institutional support. However, we also find a possibly imbalanced involvement between cyber and geoscience communities at the outset, with the former showing more interest than the latter. This analysis highlights the importance of examining fields and disciplines as stakeholders to investments in the cyberinfrastructure supporting science.

Familial, sequencing, and genome-wide association studies (GWASs) and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWASs) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through application of a pleiotropy-guided conditional/conjunction false discovery rate (FDR) approach in the setting of a TWASs. This identified 14 candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were >1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. The 22 genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.