Despite significant progress in the treatment of breast cancer, the transition of pre-invasive ductal carcinoma in situ (DCIS) to more aggressive invasive ductal carcinoma (IDC) remains unknown. No prognostic biomarkers can reliably predict the risk of progression from DCIS to IDC. Similar genomic profiles of matched pre-invasive DCIS and IDC suggests that the progression is not driven by genetic aberrations in DCIS cells, but microenvironmental factors, such as hypoxia and metabolic stress prevalent in DCIS, may drive the transition. However, lack of experimental models with controlled microenvironments to capture the DCIS-IDC progression is impeding progress in the field.
This collaborative study seeks to develop three-dimensional (3D) organoid models with controlled hypoxic microenvironments to recapitulate DCIS-IDC transition in the same parent tumors. The goal is to discover key hypoxia-induced factors involved in initiation, maintenance and spatial distribution of invasive breast cancer cells. The proposed work will utilize microfabricated hydrogel microwells to generate 3D organoids of DCIS cell lines and patient-derived cells with the goal to recapitulate three key hallmarks of DCIS-IDC transition observed in vivo: tumor-size induced hypoxia and metabolic stress, tumor heterogeneity, and spontaneous emergence of migratory phenotype in the same parent cells without any external stimulus. The microwell platform provides unique opportunity to define tumor-intrinsic mechanisms of DCIS-IDC transition. We will use integrated experimental and computational approaches coupled with CRISPR-based gene knock-in (fusion protein-labeling) and deep learning-based image analysis, to delineate how hypoxia, tumor secretome, and intracellular signaling networks work together to induce the migratory phenotype and drive progression to invasive disease.
The research team consists of investigators from the University of Pittsburgh with expertise in biomaterials and tissue engineering (Sant, School of Pharmacy), mathematical and computational modeling and deep learning-based image analysis (Xing, Computational and Systems Biology), high resolution live cell imaging (Watkins, Center for Biological Imaging), tumor biology and proteomics (Donnenberg, School of Medicine), and clinical tumor biology and surgical oncology (McAuliffe, Magee-Womens Research Institute).
The successful completion of the proposed work will lead to development of organoid models that will allow us to explore fundamental mechanisms responsible for emergence of migratory and invasive phenotypes. The mechanistic understanding gained from these studies will improve diagnosis, lead to the development of treatment strategies to halt tumor progression at pre-invasive stage, and thus prevent patient overtreatment. The organoid platform can be generalized to other tumor types and will be disseminated to other cancer researchers.
Shilpa Sant is an Assistant Professor at University of Pittsburgh School of Pharmacy in the Department of Pharmaceutical Sciences and holds secondary appointment in the Department of Bioengineering. She is a faculty member at UPMC-Hillman Cancer Center and McGowan Institute of Regenerative Medicine. After earning her Bachelors in Pharmaceutical Sciences and Masters in Pharmacology from University of Mumbai, Shilpa received her PhD in Pharmaceutical Technology from the University of Montreal, Canada. Before joining Pitt, she was interdisciplinary training fellow at the Wyss Institute for Biologically Inspired Engineering and the Center for Bioengineering at Brigham and Women's Hospital, Harvard Medical School.
Dr. Sant has extensive research experience in biomaterials, drug delivery, tissue engineering, and microfabrication. The central theme in her lab is to develop physiologically relevant three-dimensional microenvironments with the goal to elucidate how microenvironmental factors drive cellular behaviors in disease progression as well as in tissue repair/regeneration. Her research team combines interdisciplinary biomaterial-, molecular/cell biology- and micro/nanotechnology-based approaches to build biomimetic microenvironments.