Javier F. Torres-Roca , G. Daniel Grass , Jacob G. Scott , Steven A. Eschrich
{"title":"Towards Data Driven RT Prescription: Integrating Genomics into RT Clinical Practice","authors":"Javier F. Torres-Roca , G. Daniel Grass , Jacob G. Scott , Steven A. Eschrich","doi":"10.1016/j.semradonc.2023.03.007","DOIUrl":null,"url":null,"abstract":"<div><p><span>The genomic era has significantly changed the practice of clinical oncology<span><span>. The use of genomic-based molecular diagnostics including prognostic </span>genomic signatures<span> and new-generation sequencing has become routine for clinical decisions regarding cytotoxic chemotherapy, targeted agents and </span></span></span>immunotherapy<span>. In contrast, clinical decisions regarding radiation therapy (RT) remain uninformed about the genomic heterogeneity of tumors. In this review, we discuss the clinical opportunity to utilize genomics to optimize RT dose. Although from the technical perspective, RT has been moving towards a data-driven approach, RT prescription dose is still based on a one-size-fits all approach, with most RT dose based on cancer diagnosis and stage. This approach is in direct conflict with the realization that tumors are biologically heterogeneous, and that cancer is not a single disease. Here, we discuss how genomics can be integrated into RT prescription dose, the clinical potential for this approach and how genomic-optimization of RT dose could lead to new understanding of the clinical benefit of RT.</span></p></div>","PeriodicalId":49542,"journal":{"name":"Seminars in Radiation Oncology","volume":"33 3","pages":"Pages 221-231"},"PeriodicalIF":2.6000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seminars in Radiation Oncology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1053429623000218","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The genomic era has significantly changed the practice of clinical oncology. The use of genomic-based molecular diagnostics including prognostic genomic signatures and new-generation sequencing has become routine for clinical decisions regarding cytotoxic chemotherapy, targeted agents and immunotherapy. In contrast, clinical decisions regarding radiation therapy (RT) remain uninformed about the genomic heterogeneity of tumors. In this review, we discuss the clinical opportunity to utilize genomics to optimize RT dose. Although from the technical perspective, RT has been moving towards a data-driven approach, RT prescription dose is still based on a one-size-fits all approach, with most RT dose based on cancer diagnosis and stage. This approach is in direct conflict with the realization that tumors are biologically heterogeneous, and that cancer is not a single disease. Here, we discuss how genomics can be integrated into RT prescription dose, the clinical potential for this approach and how genomic-optimization of RT dose could lead to new understanding of the clinical benefit of RT.
期刊介绍:
Each issue of Seminars in Radiation Oncology is compiled by a guest editor to address a specific topic in the specialty, presenting definitive information on areas of rapid change and development. A significant number of articles report new scientific information. Topics covered include tumor biology, diagnosis, medical and surgical management of the patient, and new technologies.