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The choice of appropriate physical quantities to characterize the biological effects of ionizing radiation has evolved over time coupled with advances in scientific understanding. The basic hypothesis in radiation dosimetry is that the energy deposited by ionizing radiation initiates all the consequences of exposure in a biological sample (e.g., DNA damage, reproductive cell death). Physical quantities defined to characterize energy deposition have included dose, a measure of the mean energy imparted per unit mass of the target, and linear energy transfer (LET), a measure of the mean energy deposition per unit distance that charged particles traverse in a medium. The primary advantage of using the "dose and LET" physical system is its relative simplicity, especially for presenting and recording results. Inclusion of additional information such as the energy spectrum of charged particles renders this approach adequate to describe the biological effects of large dose levels from homogeneous sources. The primary disadvantage of this system is that it does not provide a unique description of the stochastic nature of radiation interactions. We and others have used dose-averaged LET (LETd) as a correlative physical quantity to the relative biological effectiveness (RBE) of proton beams. This approach is based on established experimental findings that proton RBE increases with LETd. However, this approach might not be applicable to intensity-modulated proton therapy or other applications in which the proton energy spectrum is highly heterogeneous. In the current study, we irradiated cancer cells with scanning proton beams with identical LETd (3.4 keV/µm) but arising from two different proton energy/LET spectra (a narrow spectrum in group 1 and a widespread heterogeneous spectrum in group 2). Clonogenic survival after irradiation revealed significant differences in RBE at any cell surviving fraction: e.g., at a surviving fraction of 0.1, the RBE was 0.97 ± 0.03 in group 1 and 1.16 ± 0.04 in group 2 (p≤0.01), validating our hypothesis that LETd alone may not adequately indicate proton RBE. Further analysis showed that microdosimetric spectrum (the probability density function of the stochastic physical quantity lineal energy y) was helpful for interpreting observed differences in biological effects. However, more accurate use of microdosimetric spectrum to quantify RBE requires a cell line–specific mechanistic model. [ABSTRACT FROM AUTHOR]

In current treatment plans of intensity-modulated proton therapy, high-energy beams are usually assigned larger weights than low-energy beams. Using this form of beam delivery strategy cannot effectively use the biological advantages of low-energy and high-linear energy transfer (LET) protons present within the Bragg peak. However, the planning optimizer can be adjusted to alter the intensity of each beamlet, thus maintaining an identical target dose while increasing the weights of low-energy beams to elevate the LET therein. The objective of this study was to experimentally validate the enhanced biological effects using a novel beam delivery strategy with elevated LET. We used Monte Carlo and optimization algorithms to generate two different intensity-modulation patterns, namely to form a downslope and a flat dose field in the target. We spatially mapped the biological effects using high-content automated assays by employing an upgraded biophysical system with improved accuracy and precision of collected data. In vitro results in cancer cells show that using two opposed downslope fields results in a more biologically effective dose, which may have the clinical potential to increase the therapeutic index of proton therapy. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

The physical properties of particles used in radiation therapy, such as protons, have been well characterized, and their dose distributions are superior to photon-based treatments. However, proton therapy may also have inherent biologic advantages that have not been capitalized on. Unlike photon beams, the linear energy transfer (LET) and hence biologic effectiveness of particle beams varies along the beam path. Selective placement of areas of high effectiveness could enhance tumor cell kill and simultaneously spare normal tissues. However, previous methods for mapping spatial variations in biologic effectiveness are time-consuming and often yield inconsistent results with large uncertainties. Thus the data needed to accurately model relative biological effectiveness to guide novel treatment planning approaches are limited. We used Monte Carlo modeling and high-content automated clonogenic survival assays to spatially map the biologic effectiveness of scanned proton beams with high accuracy and throughput while minimizing biological uncertainties. We found that the relationship between cell kill, dose, and LET, is complex and non-unique. Measured biologic effects were substantially greater than in most previous reports, and non-linear surviving fraction response was observed even for the highest LET values. Extension of this approach could generate data needed to optimize proton therapy plans incorporating variable RBE. [ABSTRACT FROM AUTHOR]