Seminar Detail

Abstract: Growing data links breast cancer with circadian disruption. However, time course studies on intact human breast tumors are difficult. Breast tumor rhythms and their influence on clinical breast cancer biology have remained largely unknown. I will describe our work to overcome this gap – using machine learning to order transcriptomic data from hundreds of patients and testing predictions in patient derived organoids. We used the same machine learning framework to define global metrics of circadian rhythm strength and organization in each sample. Breast cancer subtypes show differences in circadian function. Clock-competent, Luminal A tumor samples display a marked variation in rhythm strength. Strikingly, patients with higher rhythm strength Luminal A tumors had an increased 5-year mortality. The same tumors showed increased cycling of metastasis linked EMT pathway genes. Correspondingly, molecular clock disruption reduced Luminal A cell invasiveness.This work adds to the foundation in developing personalized breast cancer chronotherapy and metrics of molecular circadian function.

Bio: Ron is an Assistant Professor of Medicine at the University of Pennsylvania. As an undergraduate Ron studied biomedical engineering and philosophy. He traveled north for the University of Minnesota’s MD/PhD program where he completed his graduate degree in engineering mechanics. He still cannot fix his car. After an internal medicine residency at the University of Vermnot, he moved to the University of Pennsylvania for clinical training in sleep medicine where he did a postdoctoral fellowship with Dr. John Hogenesch focusing on circadian bioinformatics. His lab uses techniques from machine learning, engineering, and systems biology to understand how sleep and molecular rhythms influence physiology in the brain and body.