Radiotherapy is a mainstay of cancer treatment, used in either a curative or palliative manner to treat approximately 50% of patients with cancer. Normal tissue toxicity limits the doses used in standard radiation therapy protocols and impedes improvements in radiotherapy efficacy. Damage to surrounding normal tissues can produce reactions ranging from bothersome symptoms that negatively affect quality of life to severe life-threatening complications. Improved ways of predicting, before treatment, the risk for development of normal tissue toxicity may allow for more personalized treatment and reduce the incidence and severity of late effects. There is increasing recognition that the cause of normal tissue toxicity is multifactorial and includes genetic factors in addition to radiation dose and volume of exposure, underlying comorbidities, age, concomitant chemotherapy or hormonal therapy, and use of other medications. An understanding of the specific genetic risk factors for normal tissue response to radiation has the potential to enhance our ability to predict adverse outcomes at the treatment-planning stage. Therefore, the field of radiogenomics has focused upon the identification of genetic variants associated with normal tissue toxicity resulting from radiotherapy. Innovative analytic methods are being applied to the discovery of risk variants and development of integrative predictive models that build on traditional normal tissue complication probability models by incorporating genetic information. Results from initial studies provide promising evidence that genetic-based risk models could play an important role in the implementation of precision medicine for radiation oncology through enhancing the ability to predict normal tissue reactions and thereby improve cancer treatment.,
Seminars in Radiation Oncology
Department of Epidemiology

Kerns, S.L, Kundu, S, Oh, J.H, Singhal, S.K, Janelsins, M, Travis, L.B, … Rosenstein, B.S. (2015). The Prediction of Radiotherapy Toxicity Using Single Nucleotide Polymorphism-Based Models: A Step Toward Prevention. Seminars in Radiation Oncology (Vol. 25, pp. 281–291). doi:10.1016/j.semradonc.2015.05.006