Breast tumor genetics can change during disease progression, leading to distinct tumor cell populations that often contribute to therapeutic resistance. We propose to perform state-of-the-art, single-cell genomic sequencing on breast cancer biopsies and circulating tumor DNA (also known as liquid biopsies) to evaluate genetic changes during the course of a patient's therapy.

As type-specific treatments are being developed for patients with epithelial ovarian cancer, it has become important to accurately diagnose the distinct cancer types. Our vision is to establish an international network for AI-based, privacy-protected pathology quality assurance. As proof of concept, we propose to develop and deploy a machine learning-based ovarian cancer histopathology classifier.