The identification of biomarkers and the development of genomics-based assays predictive of outcomes following radiotherapy, in an effort to help guide the treatment of patients with cancer, is an area of increasing research interest. Here, we discuss the validity of one such classifier, ARTIC, in the context of complementary genomic approaches designed to predict both tumour response and the susceptibility of nonmalignant tissues to toxicities resulting from radiotherapy.
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References
van Leeuwen, C. M. et al. The alfa and beta of tumours: a review of parameters of the linear-quadratic model, derived from clinical radiotherapy studies. Radiat. Oncol. 13, 96 (2018).
Yard, B. D. et al. A genetic basis for the variation in the vulnerability of cancer to DNA damage. Nat. Commun. 7, 11428 (2016).
Azria, D. et al. Data-based radiation oncology: design of clinical trials in the toxicity biomarkers era. Front. Oncol. 7, 83 (2017).
Sjostrom, M. et al. Clinicogenomic radiotherapy classifier predicting the need for intensified locoregional treatment after breast-conserving surgery for early-stage breast cancer. J. Clin. Oncol. https://doi.org/10.1200/JCO.19.00761 (2019).
Ahmed, K. A. et al. Utilizing the genomically adjusted radiation dose (GARD) to personalize adjuvant radiotherapy in triple negative breast cancer management. EBioMedicine 47, 163–169 (2019).
Rosenstein, B. S. Radiogenomics: identification of genomic predictors for radiation toxicity. Semin. Radiat. Oncol. 27, 300–330 (2017).
Azria, D. et al. Radiation-induced CD8 T-lymphocyte apoptosis as a predictor of breast fibrosis after radiotherapy: results of the prospective multicenter french trial. EBioMedicine 2, 1965–1973 (2015).
West, C. et al. The REQUITE project: validating predictive models and biomarkers of radiotherapy toxicity to reduce side-effects and improve quality of life in cancer survivors. Clin. Oncol. (R. Coll. Radiol.) 26, 739–742 (2014).
Kerns, S. L. et al. Radiogenomics consortium genome-wide association study meta-analysis of late toxicity after prostate cancer radiotherapy. J. Natl. Cancer Inst. https://doi.org/10.1093/jnci/djz075 (2019).
Kang, J. et al. Machine learning and radiogenomics: lessons learned and future directions. Front. Oncol. 8, 228 (2018).
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David Azria is a founder of NovaGray. B.R. declares no competing interests.
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Azria, D., Rosenstein, B.S. Use of genomics to balance cure and complications. Nat Rev Clin Oncol 17, 9–10 (2020). https://doi.org/10.1038/s41571-019-0306-1
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DOI: https://doi.org/10.1038/s41571-019-0306-1
