Breast Cancer Risk Analysis Using Deep Learning on Multi-omics Data Combined with Epigenetic Factors

Cancer is a major threat to humankind and a leading cause of mortality worldwide. According to World Health Organization estimates in 2020, breast cancer is a top-tier cancer and a substantial cause of death in women. The early identification of breast cancer can effectively reduce risk factors and mortality. Recent studies in risk analysis were focused on the stand-alone impact of genomic data ignoring the influence of environmental factors. In our proposed method, we studied the breast cancer risk assessment using multi-omics with epigenetic factors and a deep learning model. Our model outperformed existing breast cancer detection methods and stage identification on data collected from TCGA-GDC datahub. However, the vitality analysis model could not produce significant results mainly due to non-availability of sufficient quality data on survival information. The proposed model validates the crucial role of DNA methylation in pre-symptomatic breast cancer risk analysis.