A novel computational analysis of heterogeneity in breast tissue

Abstract

Breast cancer presents as part of a heterogeneous mix of breast disease pathologies whose biological origins are poorly understood. A systematic and quantitative study of heterogeneity in breast tissue would enable us to characterize the disease states present, and use that characterization to guide further research into the complex pathologic associations within breast tissue and between patients. Initially we focus on characterizing the co-occurrence of breast pathology-related diagnoses. In particular, this abstract presents our initial results from characterizing the co-occurrence of double and triple diagnoses. We will expand this analysis to co-occurrence of larger diagnosis sets. Additionally, we plan to analyze co-occurrence with other types of patient information, including: socio-economic status, family history, lifestyle choices, co-morbidity with other diseases, and many other factors hypothesized to contribute to an increased risk for developing breast cancer.