During the past 50 years, clinical advances have substantially reduced the mortality rate for people with cancer, but new breakthroughs often require years of trial and error in the lab. An innovative partnership between The University of Texas at Austin’s Machine Learning Lab, Oden Institute for Computational Engineering and Sciences, and Dell Medical School aims to speed up those discoveries, saving lives in the process, according to a news release.
What would have previously taken years in the lab can potentially be accomplished in days with the appropriate computing simulations.
Cancer is arguably the greatest health challenge of our time.
“The biggest promise of computational oncology is personalized medicine,” Dheeraj Pandey said. “The ability for us to answer questions that save precious lives. More importantly, the field is attempting to break silos between physics, biology, and computing researchers who are fighting indefatigably against cancer.”
UT researchers will integrate two emerging disciplines — computational oncology and machine learning — to transform the future of cancer care. Machine learning applies algorithms to large data sets to build classifiers that can make accurate predictions, even in complex biological and chemical domains. Computational oncology uses physics-based and data-driven advanced mathematical and computational approaches to model tumors, calibrate patient-specific models, and simulate patient responses to potential treatment options.
Modeling and simulation occur across a spectrum of scales, from the cellular level to the organ level of the human body. The models can be theory-driven, knowledge-driven, or data-driven. Or, increasingly, a combination of all three. Substantial computational skills and capabilities, as well as medical knowledge, are required to capture the individuality of each cancer patient’s situation for accurate decision making at all levels.
“UT Austin has a unique environment that enables the interdisciplinary research critical to tackling societal grand challenges such as personalized care for cancer patients,” said Karen Willcox, Director of the Oden Institute. “We are thrilled to build a new partnership with the Machine Learning Lab, building on the Oden Institute’s strength in computational oncology and our existing partnerships with Dell Med, MD Anderson Cancer Center and the Texas Advanced Computing Center. Computational medicine is a top priority for the Oden Institute, and the generosity of the Pandey family is a game changer in taking our efforts to a new level.”
The Oden Institute and its Center for Computational Oncology sit at the forefront of developing mechanism-based modeling techniques that optimize treatment and outcomes for an individual patient. The Machine Learning Laboratory is the university’s headquarters for machine learning and artificial intelligence.
“A new wave of machine learning is creating predictive models that are transforming science,” said Adam Klivans, Director of the Machine Learning Lab and NSF-funded Institute for Foundations of Machine Learning. “Our technologies can anticipate new biological and chemical interactions to advance the automated discovery of new treatments.”
Currently, cancer biologists and chemists rely on trial and error to determine what treatments will be most effective. Connecting university research with community providers is central to the mission of Dell Med. Through initiatives such as the Livestrong Cancer Institutes, Dell Med translates leading-edge research into high-quality clinical trials and patient-focused precision medicine.
“Time is critical when treating cancer,” said Gail Eckhardt, Director of the Livestrong Cancer Institutes at Dell Med. “This brings us that much closer to the day when clinicians and researchers can integrate patient data and computational methods to individualize therapy, thereby improving the lives of patients with cancer.”
“Computational approaches are the key to accelerating progress against cancer,” said David Jaffray, Chief Technology and Digital officer at The University of Texas MD Anderson Cancer Center. “This investment will further the collaborative, team science approach we have developed with the leadership at UT Austin. Together, we are building a critical mass of talent to use the power of data and computing to make real progress against this terrible disease.”
