Modeling Age-Dependent Radiation-Induced Second Cancer Risks and Estimation of Mutation Rate: An Evolutionary Approach

TL;DR

This paper introduces an evolutionary stochastic model to estimate radiation-induced second cancer risks, integrating short-term and long-term effects, and fits it to clinical data to predict mutation rates and risks over time.

Contribution

A novel evolutionary framework that couples short-term and long-term effects to assess second cancer risks post-radiotherapy, estimating mutation rates from clinical data.

Findings

Model predicts increased mutation rates indicating genomic instability.

Results align with observed excess relative risk trends.

Model forecasts a negative correlation between ERR and age.

Abstract

Although the survival rate of cancer patients has significantly increased due to advances in anti-cancer therapeutics, one of the major side effects of these therapies, particularly radiotherapy, is the potential manifestation of radiation-induced secondary malignancies. In this work, a novel evolutionary stochastic model is introduced that couples short-term formalism (during radiotherapy) and long-term formalism (post treatment). This framework is used to estimate the risks of second cancer as a function of spontaneous background and radiation-induced mutation rates of normal and pre-malignant cells. By fitting the model to available clinical data for spontaneous background risk together with data of Hodgkins lymphoma survivors (for various organs), the second cancer mutation rate is estimated. The model predicts a significant increase in mutation rate for some cancer types, which may be a sign of genomic instability. Finally, it is shown that the model results are in agreement with the measured results for excess relative risk (ERR) as a function of exposure age, and that the model predicts a negative correlation of ERR with increase in attained age. This novel approach can be used to analyze several radiotherapy protocols in current clinical practice, and to forecast the second cancer risks over time for individual patients.