• By Dana Sparks

Science Saturday: Mayo Clinic, NASA team up to test AI algorithm on colorectal cancer

August 29, 2020
illustrated graphic of cancer cells

Mayo is working with NASA to sequence the path of cancer — from what causes it to what drives it, and potentially how to prevent it.

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Mayo researchers and NASA Frontier Development Lab data scientists are embarked on a research sprint this summer to optimize an artificial intelligence (AI) algorithm for colorectal cancer, and possibly, other cancers.

The algorithm was developed by researchers within the Mayo Clinic Center for Individualized Medicine and shows potential in detecting spatio-temporal patterns of colorectal cancer progression solely from tumor snapshots.

“Our research shows the algorithm is able to predict the evolutionary trajectory by which colorectal cancer is going to occur,” says Nicholas Chia, Ph.D., the Bernard and Edith Waterman co-director for the Mayo Clinic Center for Individualized Medicine’s Microbiome Program,. “We have information from this algorithm in terms of what event came first, what events are most important and exactly what the path to cancer was or what it will be.”

For eight weeks, Mayo is working with a team of NASA engineers, computer scientists and software developers in order to get the algorithm optimized for multi-omics data integration and causal modeling.

Dr. Chia says the project is using complex multi-omics data, including the microbiome, to sequence the path of cancer — starting with what causes it, to what drives it, and potentially, how to prevent it. 

John Kalantari Ph.D., a machine learning scientist within the Center’s Microbiome Program, says he was inspired to develop the algorithm by contemporary applications of an AI technique known as reinforcement learning — popularized by its use in autonomous driving and defeating human experts in computer games, such as chess, Go, and StarCraft.

"We had a eureka moment when we realized that if we viewed our patient cancers as the result of an optimal game of cell evolution, then we could use inverse reinforcement learning techniques to learn the optimal 'moves' and environmental conditions that enable cancer progression, metastasis, recurrence, immune system evasion, and/or changes in treatment efficacy,” Dr. Kalantari explains. “By reverse-engineering how a tumor survived and thrived to become cancer in each individual patient, we are able to enhance our understanding of cancer systems biology and also improve our ability to predict treatment outcomes, discover early biomarkers of progression and identify new therapeutic/preventative targets in a more holistic manner."

Read the rest of the article on the Individualized Medicine blog.

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