Assessing the relative contribution of DSB repair proteins as a function of LET

Purpose
Particle therapy is gaining popularity due to its dosimetric benefits. Particle radiation also has a higher linear energy transfer (LET) than X-rays, leading to more complex DNA damage and a higher relative biological effectiveness (RBE). While potentially beneficial, there remains significant uncertainty in how RBE depends on genetic features of irradiated cells. Understanding how cells respond to and repair these damages is crucial for optimising radiotherapy.

Materials and Methods
This study evaluates how loss of different DNA double strand break (DSB) repair genes impacts on radiosensitivity. CRISPR-modified RPE-1 cells were exposed to 6 different LETs using X-rays, protons, carbon ions, and alpha particles, following which clonogenic survival and DNA DSB repair kinetics were measured. Experimental data were then compared with predictions from a mechanistic model of radiation response (Medras).

Results
Clonogenic assays showed that cells lacking ATM and NHEJ repair genes were particularly radiosensitive, even for high LET exposures. While RBE increased with LET for all analysed knockout lines, RBE increased at a slower rate for cells that were more sensitive to X-rays, regardless of the affected pathway. Moreover, data showed no significant difference in DNA repair pathway dependence as a function of LET. Medras-predicted responses were in good agreement with both the genetic background and LET dependencies of radiosensitivity, without any assumption of a change in repair pathway dependence with LET.

Conclusion
This research further highlights the importance of DSB repair pathways, particularly NHEJ, in determining cellular sensitivity to different radiation qualities, but suggests that in this system there is little difference in pathway dependence between X-rays and high-LET radiation. Mechanistic approaches like Medras offer a promising approach to predict radiation responses, to support more personalised and effective cancer treatments based on genetic profiles.