
Butner Laboratory
Joseph Butner, Ph.D.
Principal Investigator
Areas of Research
- Artificial Intelligence
- Bioinformatics
- Cancer Biology
- Computational Biology
- Immunotherapy
- Mathematical Modeling
- Precision Medicine
- Radiation Oncology
- Radiation Therapy
- Systems Biology
Welcome to the Butner Lab. We develop mathematical descriptions of the key biological and physical mechanisms underlying cancer behavior, which can explain how cancer develops and responds to treatment. Our goal is to discover robust, calculable relationships between the unique characteristics of a patient and their disease and how these affect treatment outcomes.
Scroll Ahead
- Departments, Labs and Institutes
- Labs
- Butner Laboratory
Research Focus
- Equation-based modeling of immunotherapy and its interaction with other therapies
- Quantitative understanding of radiation-based immune modulation
- Methods to hybridize equation-based models with artificial intelligence methods to maximize performance while maintaining physical descriptions of biological systems
Ongoing research projects in the Butner Laboratory include:
- Determining robust modeling-based approaches for early-time prediction of brain metastases immunotherapeutic failure to identify lesions that may be salvaged through the addition of radiosurgery
- Predicting patient survival under immunotherapy by combining mechanistic modeling and deep learning methods
- Quantitatively describing the key biophysical underpinnings of the complex interplay between checkpoint inhibitor immunotherapy and radiotherapy
- Studying how molecular signaling and phenotypic hierarchies affect the early stages of tumor development at the cellular level
Robust mathematical descriptions of the relationships between treatment outcomes and patient/disease characteristics allow us to study how tumors are able to evade treatment and how treatment strategies may be adapted to overcome this resistance. Understanding these mathematical relationships should allow for dynamic, adaptive optimization of personalized treatment planning to maximize outcomes for individual patients.
Funding
- Andrew M. McDougall Brain Metastasis Clinic and Research Program
- The University of Texas MD Anderson Start-Up Funds Program
- MD Anderson Cancer Center Support Grant (CCSG) Development Funds (NIH P30CA016672)
Featured Publications
-
Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy
NPJ Syst Biol Appl. 2024 Aug 14;10(1):88. doi: 10.1038/s41540-024-00415-8. PMID: 39143136; PMCID: PMC11324794. Opens a new window
Butner JD, Dogra P, Chung C, Koay EJ, Welsh JW, Hong DS, Cristini V, Wang Z
-
Dedifferentiation-mediated stem cell niche maintenance in early-stage ductal carcinoma in situ progression: insights from a multiscale modeling study
Cell Death Dis. 2022 May 21;13(5):485. doi: 10.1038/s41419-022-04939-x. PMID: 35597788; PMCID: PMC9124196. Opens a new window
Butner JD, Dogra P, Chung C, Ruiz-Ramírez J, Nizzero S, Plodinec M, Li X, Pan PY, Chen SH, Cristini V, Ozpolat B, Calin GA, Wang Z
-
A mathematical model for the quantification of a patient's sensitivity to checkpoint inhibitors and long-term tumour burden
Nat Biomed Eng. 2021 Apr;5(4):297-308. doi: 10.1038/s41551-020-00662-0. Epub 2021 Jan 4. PMID: 33398132; PMCID: PMC8669771. Opens a new window
Butner JD, Wang Z, Elganainy D, Al Feghali KA, Plodinec M, Calin GA, Dogra P, Nizzero S, Ruiz-Ramírez J, Martin GV, Tawbi HA, Chung C, Koay EJ, Welsh JW, Hong DS, Cristini V
-
Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy
Sci Adv. 2020 Apr 29;6(18):eaay6298. doi: 10.1126/sciadv.aay6298. PMID: 32426472; PMCID: PMC7190324. Opens a new window
Butner JD, Elganainy D, Wang CX, Wang Z, Chen SH, Esnaola NF, Pasqualini R, Arap W, Hong DS, Welsh J, Koay EJ, Cristini V
Contact Us