John Prensner


Biological Sciences, Computer Science, Healthcare Research
Artificial Intelligence, Data Mining, Machine Learning

John Prensner

Assistant Professor


Assistant Professor of Pediatrics, Medical School

My research group uses molecular techniques and computational methods to dissect the biology of pediatric cancers. We are invested in fundamental genomic discovery of non-canonical open reading frames that are dysregulated in cancer. We use functional genomics techniques to facilitate biological analysis. We employ data science methodologies to model and predict the molecular biology of cancers. Our particular focus is on RNA translation and its regulation. We also focus on therapeutic interventions that may represent novel treatment strategies for cancer.

What are some of your most interesting projects?

I am fascinated by the ways in which cancer cells pattern RNA translation. We are currently performing large-scale -omic analyses to identify patterns in RNA translational control across pediatric brain cancers.

How did you end up where you are today?

I came to science late in life. I studied English Literature as an undergraduate at Tufts University before deciding to study biochemistry as a senior in college. This led me to join a cancer biology lab after college, which motivated me to go to medical school. I enrolled in the University of Michigan Medical School in 2006 but quickly became interested in the biology of cancer. I then joined the Medical Scientist Training Program and graduated with an MD/PhD dual degree in 2014. I pursued Pediatrics clinical training at Boston Children’s Hospital and pediatric hematology/oncology at Dana-Farber Cancer Institute. I then completed post-doctoral research at the Broad Institute of MIT and Harvard prior to joining the faculty of the University of Michigan.

What is the most significant scientific contribution you would like to make?

My goal is to cure childhood brain cancers. That is what motivates me every day!