Modeling Cellular Response in Large-Scale Radiogenomic Databases to Advance Precision Radiotherapy

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• Radiotherapy is a cornerstone of cancer treatment, yet patient responses vary significantly.
• Current radiation dose prescriptions lack individual tailoring, limiting treatment efficacy.
• Understanding molecular underpinnings of radiation response is crucial for developing predictive biomarkers.

Purpose of the Study:

• To introduce RadioGx, a computational platform for analyzing radiogenomic data.
• To identify molecular factors and biological processes associated with cellular radiation response.
• To explore tissue-specific determinants and drug-radiation interactions in cancer.

Main Methods:

• Development of the RadioGx computational platform for integrative analysis.
• Fitting dose-response data to the linear-quadratic model and calculating Area Under the Curve (AUC).
• Analysis of genomic, transcriptomic, and drug response data in large-scale cancer cell line databases.

Main Results:

• AUC emerged as a robust indicator of radioresponse, correlating with known radiation response pathways.
• Radiation sensitivity was linked to mutations in nonhomologous end joining genes.
• Hypoxia and tissue type were identified as significant modulators of radioresponse.
• Synergistic therapeutic effects were observed between ionizing radiation and drugs targeting cytoskeleton, DNA replication, and mitosis.

Conclusions:

• RadioGx offers a powerful computational tool for hypothesis generation in radiation oncology.
• The platform facilitates the discovery of predictive biomarkers for personalized cancer therapy.
• Findings highlight the importance of genetic mutations, tumor microenvironment, and tissue context in radiation response.