Simulating RTOG 0617 using Genomic Adjusted Radiation Dose: Comparing Apples With Oranges

In the article “Personalizing radiotherapy prescription dose using genomic markers of radiosensitivity and normal tissue toxicity in non–small cell lung cancer” by Scott et al.,1Scott J.G. Sedor G. Scarborough J.A. et al.Personalizing radiotherapy prescription dose using genomic markers of radiosensitivity and normal tissue toxicity in NSCLC.J Thorac Oncol. 2021; 16: 428-438Abstract Full Text Full Text PDF Scopus (12) Google Scholar the authors claim that the failure of RTOG 0617 is because of patients receiving uniform dose in each arm of the trial. A key part of their analysis hinges on using their modeling framework to simulate the RTOG 06172Bradley J.D. Hu C. Komaki R.R. et al.Long-term results of NRG oncology RTOG 0617: standard- versus high-dose chemoradiotherapy with or without cetuximab for unresectable stage III non-small-cell lung cancer.J Clin Oncol. 2020; 38: 706-714Crossref PubMed Scopus (144) Google Scholar trial using data from Total Cancer Care and a smaller cohort from Moffitt Cancer Center. The simulations performed by the authors did not, however, consider the following points and therefore raises questions around the conclusions drawn by the authors. First, RTOG 0617 radiotherapy was given concurrently with chemotherapy in all arms, and the simulations presented by Scott et al.1Scott J.G. Sedor G. Scarborough J.A. et al.Personalizing radiotherapy prescription dose using genomic markers of radiosensitivity and normal tissue toxicity in NSCLC.J Thorac Oncol. 2021; 16: 428-438Abstract Full Text Full Text PDF Scopus (12) Google Scholar have not considered the effect of chemotherapy, which will influence local progression times—their end point of interest. Furthermore, regarding chemotherapy treatment, there was variation in how much chemotherapy was received by patients likely owing to toxicity—again, this variation in chemotherapy given was not considered. Second, local control is an interval-censored event and there are also competing events,3Satagopan J.M. Ben-Porat L. Berwick M. Robson M. Kutler D. Auerbach A.D. A note on competing risks in survival data analysis.Br J Cancer. 2004; 91: 1229-1235Crossref PubMed Scopus (525) Google Scholar such as metastatic progression, which prevent one from seeing local progression. The simulations conducted by Scott et al.1Scott J.G. Sedor G. Scarborough J.A. et al.Personalizing radiotherapy prescription dose using genomic markers of radiosensitivity and normal tissue toxicity in NSCLC.J Thorac Oncol. 2021; 16: 428-438Abstract Full Text Full Text PDF Scopus (12) Google Scholar did not reflect these two points related to local progression. Their simulations assume local progression is not an interval-censored event—that is, they did not simulate follow-up as in the trial in which follow-up evaluations were done every 3 months in the first year, every 4 months in the second year, and every 6 months in years 3 to 5.4Gómez G. Calle M.L. Oller R. Langohr K. Tutorial on methods for interval-censored data and their implementation in R.Stat Modell. 2009; 9: 259-297Crossref Scopus (83) Google Scholar In addition, they have not allowed for competing events in their simulations, such as metastatic progression; that is, they compared Kaplan-Meier estimate of local control from their simulation with cumulative incidence values from RTOG 0617. Finally, in RTOG 0617, there were known prognostic factors, which may or may not be independent of the radiosensitivity index, that influenced the outcome of the trial. For example, institution accrual volume was found to be a prognostic factor, and this is not likely to correlate to radiosensitivity of the biology of a tumor. Prognostic factors that can influence outcome were not considered in the simulation study by Scott et al.1Scott J.G. Sedor G. Scarborough J.A. et al.Personalizing radiotherapy prescription dose using genomic markers of radiosensitivity and normal tissue toxicity in NSCLC.J Thorac Oncol. 2021; 16: 428-438Abstract Full Text Full Text PDF Scopus (12) Google Scholar In summary, the simulated trial by Scott et al.1Scott J.G. Sedor G. Scarborough J.A. et al.Personalizing radiotherapy prescription dose using genomic markers of radiosensitivity and normal tissue toxicity in NSCLC.J Thorac Oncol. 2021; 16: 428-438Abstract Full Text Full Text PDF Scopus (12) Google Scholar does not mimic the RTOG 0617. The authors should consider concepts such as the “Target Trial”5Hernán M.A. Robins J.M. Using big data to emulate a target trial when a randomized trial is not available.Am J Epidemiol. 2016; 183: 758-764Crossref PubMed Scopus (496) Google Scholar for guidance on how to use retrospective observational data to setup and simulate randomized control trials, which RTOG 0617 was. Personalizing Radiotherapy Prescription Dose Using Genomic Markers of Radiosensitivity and Normal Tissue Toxicity in NSCLCJournal of Thoracic OncologyVol. 16Issue 3PreviewCancer sequencing efforts have revealed that cancer is the most complex and heterogeneous disease that affects humans. However, radiation therapy (RT), one of the most common cancer treatments, is prescribed on the basis of an empirical one-size-fits-all approach. We propose that the field of radiation oncology is operating under an outdated null hypothesis: that all patients are biologically similar and should uniformly respond to the same dose of radiation. Full-Text PDF Open ArchiveLetter ResponseJournal of Thoracic OncologyVol. 16Issue 5PreviewIn our recent publication “Personalizing radiotherapy prescription dose using genomic markers of radiosensitivity and normal tissue toxicity in NSCLC,”1 the primary goal was to report that tumor radiosensitivity can be related to the physical dose delivered, an extension of previous work that details the use of the genomically adjusted radiation dose parameter.2 Unfortunately, this point and the validity of its clinical ramifications remain largely unacknowledged in a recent letter, in which it seems that the technical aspects of trial simulation distracted from this paramount conclusion. Full-Text PDF