UCSF

Fibrosis is a risk factor for cancer in many tissues and accompanies tumor progression. Fibrosis-associated stiffening of breast tumors is linked with increased tumor growth, invasion, and metastasis. However, the mechanisms through which tissue stiffness imparts these phenotypes on a tumor are still poorly understood, due in part to limitations in existing in vitro culture systems and methods to discern mechanically soft and stiff regions within tumors. In this dissertation, I sought to better identify physiologically-relevant mechanisms through which fibrosis drives tumor aggression by emphasizing unbiased screening approaches and utilizing recently available clinical transcriptomic datasets. These projects identified metabolic state, glycocalyx content and bulkiness, and immune cell infiltration and phenotypes as a network of changes in the tumor microenvironment driven by and feeding back into tissue fibrosis. Moreover, my collaborators and I developed a novel method for characterizing tissue rigidity and identified shortcomings in existing cell culture models, thus facilitating future research into the impact of tissue mechanics in breast cancer. Altogether, these works apply state-of-the-art sequencing and machine learning methods to shed light on the impact of tissue fibrosis and mechanics in breast cancer development and aggression.