Unraveling the association between adiposity and breast cancer biology by in silico analysis of bulk and single-cell omics data

Breast cancer (BC) is one of the most common forms of cancer, affecting 2.1 million women each year globally, while also having a considerably high mortality rate – approximately 30% in 2018 according to WHO. Obesity is another medical problem that is becoming a great concern due to its increasing prevalence (3 times between 1975 and 2016 worldwide). The association between an increased risk and/or poor prognosis of BC and obesity has been established and its clinical relevance especially in postmenopausal overweight and obese women has been widely recognized. Over the years, lots of efforts have been committed to molecular characterization of BC tumors on genomic, transcriptomic, proteomic, epigenetic levels and profiling of immune cell populations. These findings demonstrated the valuable contribution of omics approaches in understanding the disease, guiding clinical decisions and discovery of new treatment options. However, up to this point the impact of adiposity has not been taken into consideration in previous studies, leaving knowledge gaps to be filled in terms of how molecular profiles and the tumor biology of BC differ according to adiposity, as well as how these differences are related to risk level, prognosis, treatment resistance and presence of potential treatment targets. Based on the general hypothesis that the molecular and immune landscape of BC tumors might differ according to patient adiposity, this project first aims to establish generalized differentiating patterns of tumor molecular and immune profiles in different groups of adiposity status. The next goal is to select specific adiposity-dependent features that have known or potential prognostic value and investigate how and to which extent their impact to disease progression and prognosis changes relative to adiposity. Finally, we aim to construct a spatial atlas of the BC tumor microenvironment according to adiposity. State-of-the-art bioinformatics methods will be used and we look to develop an efficient and comprehensive data analysis strategy to effectively mine available data and successfully answer the research questions. We also wish to share our findings to the community via a webtool dedicated to querying and visualization of the results of the study.