Towards Data Driven RT Prescription: Integrating Genomics into RT Clinical Practice.

Area of Science:

• Oncology
• Genomics
• Radiation Oncology

Background:

• Genomic diagnostics are standard in oncology for chemotherapy, targeted therapy, and immunotherapy decisions.
• Radiation therapy (RT) prescription currently lacks genomic information, relying on cancer type and stage.
• Tumor genomic heterogeneity is not addressed in current RT dose-setting practices.

Purpose of the Study:

• To review the potential of integrating genomics into radiation therapy (RT) dose optimization.
• To explore how genomic data can inform RT prescription for improved patient outcomes.
• To discuss the clinical implications and potential for novel insights into RT efficacy through genomic optimization.

Main Methods:

• Review of current practices in clinical oncology and radiation therapy.
• Analysis of the role of genomic-based diagnostics in cancer treatment decisions.
• Discussion of the integration of tumor genomics into RT dose optimization strategies.

Main Results:

• Genomic diagnostics routinely guide decisions for chemotherapy, targeted agents, and immunotherapy.
• Radiation therapy (RT) dose prescription remains largely based on cancer diagnosis and stage, ignoring genomic heterogeneity.
• Genomic optimization offers a pathway to personalize RT dose and enhance treatment efficacy.

Conclusions:

• Integrating genomics into RT prescription presents a significant clinical opportunity.
• Genomic-informed RT dose optimization can overcome the limitations of current "one-size-fits-all" approaches.
• This integration promises to advance our understanding of radiation therapy's clinical benefits and tumor biology.