On the coherence between mathematical models of DSB repair and physiological reality

A Bayesian Approach for Prediction of Patient Radiosensitivity

PURPOSE

A priori identification of the small proportion of radiation therapy patients who prove to be severely radiosensitive is a long-held goal in radiation oncology. A number of published studies indicate that analysis of the DNA damage response after ex vivo irradiation of peripheral blood lymphocytes, using the γ-H2AX assay to detect DNA damage, provides a basis for a functional assay for identification of the small proportion of severely radiosensitive cancer patients undergoing radiotherapy.

METHODS AND MATERIALS

We introduce a new, more rigorous, integrated approach to analysis of radiation-induced γ-H2AX response, using Bayesian statistics.

RESULTS

This approach shows excellent discrimination between radiosensitive and non-radiosensitive patient groups described in a previously reported data set.

CONCLUSIONS

Bayesian statistical analysis provides a more appropriate and reliable methodology for future prospective studies.

Mechanistic Modelling and Bayesian Inference Elucidates the Variable Dynamics of Double-Strand Break Repair

DNA double-strand breaks are lesions that form during metabolism, DNA replication and exposure to mutagens. When a double-strand break occurs one of a number of repair mechanisms is recruited, all of which have differing propensities for mutational events. Despite DNA repair being of crucial importance, the relative contribution of these mechanisms and their regulatory interactions remain to be fully elucidated. Understanding these mutational processes will have a profound impact on our knowledge of genomic instability, with implications across health, disease and evolution. Here we present a new method to model the combined activation of non-homologous end joining, single strand annealing and alternative end joining, following exposure to ionising radiation. We use Bayesian statistics to integrate eight biological data sets of double-strand break repair curves under varying genetic knockouts and confirm that our model is predictive by re-simulating and comparing to additional data. Analysis of the model suggests that there are at least three disjoint modes of repair, which we assign as fast, slow and intermediate. Our results show that when multiple data sets are combined, the rate for intermediate repair is variable amongst genetic knockouts. Further analysis suggests that the ratio between slow and intermediate repair depends on the presence or absence of DNA-PKcs and Ku70, which implies that non-homologous end joining and alternative end joining are not independent. Finally, we consider the proportion of double-strand breaks within each mechanism as a time series and predict activity as a function of repair rate. We outline how our insights can be directly tested using imaging and sequencing techniques and conclude that there is evidence of variable dynamics in alternative repair pathways. Our approach is an important step towards providing a unifying theoretical framework for the dynamics of DNA repair processes.