Supercomputers help researchers design cancer models and predict treatments outcomes based on patient-specific conditions
Researchers have developed computer models to predict how cancer will progress in a specific individual, based on tissue, cellular and subcellular protein signaling responses. The models can predict how brain tumors (gliomas) will grow with much greater accuracy than previous models. Recently, the group began a clinical study to predict how an individual’s cancer will progress after one cycle of therapy, and to use that prediction to plan the course of treatment.
Model of tumor growth in a rat brain before radiation treatment (left) and after one session of radiotherapy (right). The different colors represent tumor cell concentration, with red being the highest. The treatment reduced the tumor mass substantially.
Credit: Lima et. al. 2017, Hormuth et. al. 2015
Attempts to eradicate cancer are often compared to a “moonshot” — the successful effort that sent the first astronauts to the moon.
But imagine if, instead of Newton’s second law of motion, which describes the relationship between an object’s mass and the amount of force needed to accelerate it, we only had reams of data related to throwing various objects into the air.
This, says Thomas Yankeelov, approximates the current state of cancer research: data-rich, but lacking governing laws and models.
The solution, he believes, is not to mine large quantities of patient data, as some insist, but to mathematize cancer: to uncover the fundamental formulas that represent how cancer, in its many varied forms, behaves.
“We’re trying to build models that describe how tumors grow and respond to therapy,” said Yankeelov, director of the Center for Computational Oncology at The University of Texas at Austin (UT Austin) and director of Cancer Imaging Research in the LIVESTRONG Cancer Institutes of the Dell Medical School. “The models have parameters in them that are agnostic, and we try to make them very specific by populating them with measurements from individual patients.”
Computer models for personalized medicine
The Center for Computational Oncology (part of the broader Institute for Computational Engineering and Sciences, or ICES) is developing complex computer models and analytic tools to predict how cancer will progress in a specific individual, based on their unique biological characteristics.
In December 2017, writing in Computer Methods in Applied Mechanics and Engineering, Yankeelov and collaborators at UT Austin and Technical University of Munich, showed that they can predict how brain tumors (gliomas) will grow and respond to X-ray radiation therapy with much greater accuracy than previous models. They did so by including factors like the mechanical forces acting on the cells and the tumor’s cellular heterogeneity. The paper continues research first described in the Journal of The Royal Society Interface in April 2017.
“We’re at the phase now where we’re trying to recapitulate experimental data so we have confidence that our model is capturing the key factors,” he said.
To develop and implement their mathematically complex models, the group uses the advanced computing resources at the Texas Advanced Computing Center (TACC). TACC’s supercomputers enable researchers to solve bigger problems than they otherwise could and reach solutions far faster than with a single computer or campus cluster.
According to ICES Director J. Tinsley Oden, mathematical models of the invasion and growth of tumors in living tissue have been “smoldering in the literature for a decade,” and in the last few years, significant advances have been made.
“We’re making genuine progress to predict the growth and decline of cancer and reactions to various therapies,” said Oden, a member of the National Academy of Engineering.
Date:
January 3, 2018
Source:
University of Texas at Austin, Texas Advanced Computing Center
Link: https://www.sciencedaily.com/releases/2018/01/180103123054.htm
Story Source:
Materials provided by University of Texas at Austin, Texas Advanced Computing Center. Original written by Aaron Dubrow. Note: Content may be edited for style and length.
Journal Reference:
E.A.B.F. Lima, J.T. Oden, B. Wohlmuth, A. Shahmoradi, D.A. Hormuth, T.E. Yankeelov, L. Scarabosio, T. Horger. Selection and validation of predictive models of radiation effects on tumor growth based on noninvasive imaging data. Computer Methods in Applied Mechanics and Engineering, 2017; 327: 277 DOI: 10.1016/j.cma.2017.08.009
Cite This Page:
University of Texas at Austin, Texas Advanced Computing Center. “Tailoring cancer treatments to individual patients: Supercomputers help researchers design cancer models and predict treatments outcomes based on patient-specific conditions.” ScienceDaily. ScienceDaily, 3 January 2018. <www.sciencedaily.com/releases/2018/01/180103123054.htm>.
Nutrigenomics Institute is not responsible for the comments and opinions included in this article
Cite This Page:
University of Texas at Austin, Texas Advanced Computing Center. “Tailoring cancer treatments to individual patients: Supercomputers help researchers design cancer models and predict treatments outcomes based on patient-specific conditions.” ScienceDaily. ScienceDaily, 3 January 2018. <www.sciencedaily.com/releases/2018/01/180103123054.htm>.
Nutrigenomics Institute is not responsible for the comments and opinions included in this article
