Relative biological effectiveness variation along monoenergetic and modulated Bragg peaks of a 62-MeV therapeutic proton beam: a preclinical assessment.

Abstract

The biological optimization of proton therapy can be achieved only through a detailed evaluation of relative biological effectiveness (RBE) variations along the full range of the Bragg curve. The clinically used RBE value of 1.1 represents a broad average, which disregards the steep rise of linear energy transfer (LET) at the distal end of the spread-out Bragg peak (SOBP). With particular attention to the key endpoint of cell survival, our work presents a comparative investigation of cell killing RBE variations along monoenergetic (pristine) and modulated (SOBP) beams using human normal and radioresistant cells with the aim to investigate the RBE dependence on LET and intrinsic radiosensitvity. Human fibroblasts (AG01522) and glioma (U87) cells were irradiated at 6 depth positions along pristine and modulated 62-MeV proton beams at the INFN-LNS (Catania, Italy). Cell killing RBE variations were measured using standard clonogenic assays and were further validated using Monte Carlo simulations and the local effect model (LEM). We observed significant cell killing RBE variations along the proton beam path, particularly in the distal region showing strong dose dependence. Experimental RBE values were in excellent agreement with the LEM predicted values, indicating dose-averaged LET as a suitable predictor of proton biological effectiveness. Data were also used to validate a parameterized RBE model. The predicted biological dose delivered to a tumor region, based on the variable RBE inferred from the data, varies significantly with respect to the clinically used constant RBE of 1.1. The significant RBE increase at the distal end suggests also a potential to enhance optimization of treatment modalities such as LET painting of hypoxic tumors. The study highlights the limitation of adoption of a constant RBE for proton therapy and suggests approaches for fast implementation of RBE models in treatment planning.