AI Tool Flags Who Benefits from Radiotherapy in Nasopharyngeal Cancer

A machine learning model has been developed to predict which patients with nasopharyngeal carcinoma (NPC) are most likely to respond positively to radiotherapy (1^✔ ✔Trusted Source
A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning

Go to source). Created by teams at Zhujiang Hospital and Nanfang Hospital of Southern Medical University, the tool, called the Nasopharyngeal Carcinoma Radiotherapy Sensitivity Score (NPC-RSS)—uses transcriptomic data and 113 algorithm combinations to identify an 18-gene signature linked to radiosensitivity, demonstrating high accuracy across both internal and external validation datasets.

Radiotherapy Resistance and Patient Stratification

“Radiotherapy is the primary treatment for NPC, but up to 30% of patients relapse due to radiation resistance,” said lead author Dr. Jian Zhang. “Our model helps solve this problem by identifying patients who are most likely to benefit from radiotherapy, allowing for more tailored and effective treatment strategies.”

Gene Signature and Immune Pathway Connection

The model’s core genes—such as SMARCA2, DMC1, and CD9 were found to influence tumor immune infiltration and key signaling pathways like Wnt/β-catenin and JAK-STAT. Notably, the radiosensitive group showed higher levels of immune cell activity, suggesting an intimate connection between radiation response and immune dynamics.

Validation Using Cell and Single-Cell Analysis

The predictive power of the NPC-RSS was confirmed using cell lines and single-cell sequencing, showing that radiosensitive tumors have richer immune environments compared to resistant ones. According to co-author Dr. Hui Meng, “Our findings suggest that integrating gene scores with immune profiles could be a game-changer in NPC care.” The team believes the model could become a clinical tool for guiding treatment decisions, minimizing unnecessary radiation exposure, and optimizing therapeutic outcomes. They are now working to expand their sample size and collaborate with international partners to further validate and refine the model. Reference:

1. A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning - (https:elifesciences.org/articles/99849)

Source-Eurekalert