Joseph Butner
Assistant Professor
The University of Texas MD Anderson Cancer Center at Houston
Department of Radiation Oncology
Dr. Butner’s research is focused on using mathematical and computational methods to discover
causes of cancer treatment failure, identify new calculable biomarkers, and find methods to
engineer personalized treatment strategies. Research in his laboratory (dry lab only) is focused
on applying mechanistic mathematical modeling, computational biology, biostatistics, deep
machine learning, and physical oncology to study the mechanistic underpinning of the success or
failure of current standard of care therapy or combination therapies. These efforts seek to identify
new approaches to predict disease outcomes and reveal new methods to optimize personalized
treatment strategies or shift outcomes towards an individual’s treatment plan goals. Dr. Butner
collaborates with renowned oncologists across the MD Anderson Cancer Center to apply these
models to clinical data collected in-house from patients receiving cutting-edge cancer treatment.
Trainees in Dr. Butner’s laboratory will receive hands-on experience with mechanistic (equationbased) models and applying them to clinical data, gain valuable insights into different modeling
methods and how to select a modeling approach that is best suited to solve the problem of
interest, and learn valuable skillsets for overcoming the unique challenges inherent to working
with real-world patient data.
Ongoing research in the Butner laboratory includes mathematical modeling studies to better
understand the effects of high- and low-dose radiotherapy on immune priming and stromal
alterations in patients receiving checkpoint inhibitor immunotherapies in both lesions receiving
direct radiation treatment and in non-target lesions due to system-wide effects (e.g., the abscopal
effect), and studies on how deep-learning platforms may be leveraged with mechanistic modeling
to correlate a wide range of clinical measures with the mechanisms of treatment response.
Education & Training
PhD, University of New Mexico, 2017