Metastasis is the leading cause of cancer-related death, and liver metastases account for a substantial fraction of this burden, particularly in colorectal cancer. Despite their clear clinical importance, liver metastases exhibit striking histopathological heterogeneity that strongly predicts patient outcomes and therapy response, yet the mechanisms driving this diversity remain poorly understood. Current treatment strategies largely focus on tumor genetics and fail to account for microenvironmental and physiological factors, leaving a critical gap in the ability to treat metastatic disease effectively. This Endeavor Award addresses the fundamental question of how interactions between cancer cells and the liver microenvironment, as well as host physiology, shape the evolution and heterogeneity of liver metastases.
The team will leverage a unique combination of clinically relevant mouse models, patient-derived samples, and advanced technologies to dissect these mechanisms. Using intravital imaging, spatial multi-omics, and lineage tracing, they will track how cancer cells interact with liver-resident cells and how these interactions shape metastatic growth patterns. In parallel, they will investigate how liver physiology, particularly steatosis, alters the evolutionary trajectories and cellular states of metastatic tumors. Integrative analyses across large patient cohorts will complement these studies to validate findings in human disease.
Building on these mechanistic insights, the team will identify and prioritize therapeutic targets that drive aggressive metastatic phenotypes. Candidate pathways will be tested in organoid systems and patient-derived xenograft models, followed by evaluation of pharmacologic strategies to selectively impair metastatic growth.
By redefining metastatic heterogeneity as a function of cellular interactions and host physiology, this work establishes a new framework for precision oncology. The findings could enable patient stratification by metastatic phenotype and physiological context and guide the development of therapies tailored to these features. Ultimately, this approach could lead to more effective treatments for patients with metastatic cancer and improve outcomes in a disease setting where current options remain limited.