Joseph Butner

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.