New Spatial Phenotypes from Imaging Uncover Survival Differences for Breast Cancer Patients

Mahmudul Hasan, Ariadna Kim Silva, Shahira Abousamra, Shao-Jun Tang, Prateek Prasanna, Joel H. Saltz, Kevin Gardner, Chao Chen, Alisa Yurovsky

Abstract: Imaging technologies have revolutionized the study of the tumor microenvironment (TME) by leveraging spatial analysis, which enables the exploration of tissue organization and cellular communication, as well as aiding cancer diagnosis and prognosis. However, while many advanced spatial analysis methods have been recently published, they are enmeshed with specific imaging technology. An opportunity exists to develop a technology-agnostic methodology that captures complex spatial patterns in the TME as phenotypes to use in downstream tasks. In this paper, we present a novel variation of spatial g-function and a comprehensive imaging-technologyagnostic framework that identifies rich spatial phenotypes that can be used in survival analysis and classification tasks. Applying our methodology to breast cancer, we uncover spatial phenotypes with significance to survival across racial groups and molecular subtypes of breast cancer. We find other phenotypes that are significant to the survival of specific patient categories (such as African American). We also demonstrate that our phenotypes reflect specific biological contexts. These results highlight the relevance of our proposed spatial analysis and phenotype discovery pipeline and demonstrate the benefits of the systematic exploration of spatial phenotypes for more personalized diagnosis and treatments.