Machine learning tool predicts cancer treatment response based on genetic mutations

A groundbreaking study led by USC Assistant Professor of Computer Science Ruishan Liu has uncovered how specific genetic mutations influence cancer treatment outcomes-insights that could help doctors tailor treatments more effectively. The largest study of its kind, the research analyzed data for more than 78,000 cancer patients across 20 cancer types. Patients received immunotherapies, chemotherapies and targeted therapies.

Using advanced computational analysis, the researchers identified nearly 800 genetic changes that directly impacted survival outcomes. They also discovered 95 genes significantly associated with survival in cancers such as breast, ovarian, skin, and gastrointestinal cancers.

Building on these insights, the team developed a machine learning tool to predict how patients with advanced lung cancer might respond to immunotherapy.

These discoveries highlight how genetic profiling can play a crucial role in personalizing cancer care. 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."

Ruishan Liu, USC Assistant Professor of Computer Science

Published in Nature Communications, the study highlights the critical roles of genes such as TP53, CDKN2A, and CDKN2B in influencing treatment outcomes, validating these associations with real-world data.

Study co-authors are Shemra Rizzo, Lisa Wang, Nayan Chaudhary, Sophia Maund, and Sarah McGough and Ryan Copping of Genentech; Marius Rene Garmhausen of Roche; and James Zou of Stanford University.

Why do mutations matter?

Genetic mutations-changes in DNA-can influence how cancer develops and how a patient responds to treatment. Some mutations occur randomly, while others are inherited.

In cancer, mutations can determine whether a tumor is more aggressive or how it might respond to certain treatments. Today, genetic testing is increasingly used in cancer care to identify these mutations, allowing doctors select treatments more precisely.

For example, Patients diagnosed with non-small cell lung cancer (NSCLC) often receive genomic testing for mutations in genes like KRAS, EGFR and ALK to determine whether targeted therapies or immunotherapies might be effective.

Key findings from the study include:

• KRAS mutations in advanced non-small cell lung cancer were linked to poorer response to a common treatment (EGFR inhibitors), suggesting alternative treatments may be needed.
• NF1 mutations improved responses to immunotherapy and worsened responses to certain targeted therapies, highlighting their complex role in treatment.
• PI3K pathway mutations, which regulate cell growth, had varying effects depending on cancer type, with different responses in breast, melanoma and renal cancers.
• DNA repair pathway mutations improved immunotherapy effectiveness in lung cancer by increasing tumor instability.
• Mutations in immune-related pathways were associated with better survival rates for lung cancer patients treated with immunotherapy, suggesting not all mutations hinder treatment success.

A powerful predictive tool

While cancer treatments have traditionally followed a one-size-fits-all approach, where patients with the same type of cancer receive the same standard therapies, the study underscores the importance of precision medicine, which tailors treatment based on a patient's unique genetic makeup.

Yet while vast amounts of mutation data exist, only a small number have clinically validated treatments, limiting potential real-world impact and patient benefit. To bridge this gap, based on their findings, Liu's team used machine learning to analyze how multiple mutations interact to influence treatment outcomes.

"Our goal was to find patterns that might not be obvious at first glance." Ruishan Liu

They developed a Random Survival Forest (RSF) model, a predictive tool designed to refine treatment recommendations for lung cancer patients. By integrating large-scale real-world data with machine learning, the model identified new mutation-treatment interactions. 

"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 said. "One key innovation lies in integrating huge amounts of data with advanced statistical and machine learning techniques to uncover previously unrecognized mutation-treatment interactions."

While further clinical trials are needed, Liu sees this study as an important step toward making cancer treatment more precise and personalized.

"This research shows the power of computational science in transforming complex clinical and genomic data into actionable insights," she said. "It's deeply fulfilling to contribute to tools and knowledge that can directly improve patient care."