Optimizing Clinical Outcome and Toxicity in Lung Cancer Using a Genomic Marker of Radiosensitivity

Abstract

ABSTRACTCancer sequencing efforts have demonstrated that cancer is the most complex and heterogeneous disease that affects humans. However, radiation therapy, one of the most common cancer treatments, is prescribed based on an empiric 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 We propose that the field of radiation oncology is operating under an outdated null hypothesis: that all patients are biologically similar and should uniformly respond to the same dose of radiation. We have previously developed the Genomic Adjusted Radiation Dose (GARD), a method which accounts for biological heterogeneity and can be utilized to predict optimal RT dose for an individual patient. In this article, we utilize GARD to characterize the biological imprecision of one-size-fits-all RT dosing schemes which result in both over- and under-dosing for the majority of patients treated with RT. To elucidate this inefficiency, and therefore the opportunity for improvement using a personalized dosing scheme, we develop a patient-specific competing hazards-style mathematical model combining the canonical equations for tumor control (TCP) and normal tissue complication probabilities (NTCP). This model simultaneously optimizes tumor control and toxicity by personalizing RT dose using patient-specific genomics. Using data from two prospectively collected cohorts of patients with non-small-cell lung cancer, we validate the competing hazards model by demonstrating that it predicts the results of RTOG 0617. We show how 0617 failure can be explained by the biological imprecision of empiric uniform dose escalation which results in 80% of patients being over-exposed to normal tissue toxicity without potential tumor control benefit. In conclusion, our data reveals a tapestry of radiosensitivity heterogeneity, provides a biological framework that explains the failure of empiric RT dose escalation, and quantifies the opportunity to improve clinical outcomes in lung cancer by incorporating genomics into RT.