(NewsNation) — A landmark study from the University of Southern California could hold the key to improving the efficacy of cancer treatment.
Published in Nature Communications, the study revealed how specific gene mutations could help doctors better individually treat cancer patients.
Researchers found more than 700 genetic alters that directly impacted survival rates. They then created an AI tool to predict the responses of lung cancer patients to immunotherapy.
“These discoveries highlight how genetic profiling can play a crucial role in personalizing cancer care,” Ruishan Liu said in a news release.
“By understanding how different mutations influence treatment response, doctors can select the most effective therapies—potentially avoiding ineffective therapies and focusing on those most likely to help.”
According to the CDC, around 10% of ovarian cancer and 3% of breast cancer cases are caused by BRCA gene mutation.
“Our goal was to find patterns that might not be obvious at first glance, and then translate these insights into real-world tools that can expand access to immunotherapy for people with cancer,” Lui added.
“One key innovation lies in integrating huge amounts of data with advanced statistical and machine learning techniques to uncover previously unrecognized mutation-treatment interactions.
“This research shows the power of computational science in transforming complex clinical and genomic data into actionable insights. It’s deeply fulfilling to contribute to tools and knowledge that can directly improve patient care.”
