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Sunday, December 22, 2024

ASU researchers develop AI tool for personalized cancer treatment

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Dr. James Rund Senior Vice President for Educational Outreach and Student Services/Interim AD | Arizona State Sun Devils Website

Dr. James Rund Senior Vice President for Educational Outreach and Student Services/Interim AD | Arizona State Sun Devils Website

Arizona State University researchers have developed an artificial intelligence-based tool, HLA-Inception, which could lead to personalized cancer treatments. The tool provides insights into how an individual's immune system responds to foreign cells by focusing on Major Histocompatibility Complex-1 (MHC-1) proteins. This AI-based tool can classify specific protein groups unique to a person and predict whether their immune defenses may recognize viruses and cancers.

"We are able to make predictions on pathological outcomes of patients, such as the survival against certain cancer medicines, based on the molecular details that a human is born with," said Abhishek Singharoy, an assistant professor in ASU’s School of Molecular Sciences who led the study. "Now with this tool, something that was taking days only takes seconds."

The study's findings were published in the journal Cell Systems on March 29. MHC-1 proteins act as guards on cell surfaces, alerting the immune system to invaders by presenting foreign protein fragments for recognition and attack. Each person's MHC-1 proteins have specific preferences for protein fragments they interact with, making prediction challenging due to thousands of different MHC-1 versions in humans.

By analyzing nearly 6,000 MHC-1 complexes, researchers identified patterns predicting immune responses across diverse populations. HLA-Inception uses electrostatic signatures to classify proteins into 11 types, aiding in distinguishing self from non-self peptides monitored by MHC-1.

The research found that patients with diverse MHC-1 proteins covering more classes had better chances of surviving certain cancer therapies. "The continued integration of machine learning in health care will help de-risk and personalize treatments," said Eric Wilson, an author on the paper and postdoctoral fellow at Icahn School of Medicine at Mount Sinai.

HLA-Inception is freely available for academic use, promoting collaboration and innovation in immunotherapy. "I am excited to use these tools for the development of better cancer therapeutic vaccines and immunotherapies," said Karen Anderson, a co-author and professor in ASU’s School of Life Sciences.

Singharoy highlighted the potential impact beyond academia: "Our technique now is the fastest out there." Additional contributors include John Kevin Cava from ASU’s School of Computing and Augmented Intelligence; Diego Chowell from The Precision Immunology Institute at Icahn School of Medicine; and Remya Raja, Kiran K. Mangalaparthi, Akhilesh Pandey, and Marion Curtis from Mayo Clinic.

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