Beschreibung:
<jats:title>Abstract</jats:title>
<jats:p>A key challenge in radiotherapy is to maximize radiation doses to cancer while minimizing damage to surrounding healthy tissues. As toxicity in a minority of patients limits the doses that can be safely given to the majority, there is interest in developing a test to measure an individual’s radiosensitivity before treatment and predict their likelihood of developing toxicity. A biomarker that predicts a cancer patient’s risk of toxicity could be used to personalize dose prescriptions or to offer alternative treatments. Many approaches have been studied to measure radiosensitivity. The development of omics technologies underpinned genome wide association studies (GWAS) attempting to identify genetic variants reported as single nucleotide polymorphisms (SNPs). The advantages of the approach include: a genetic test will be easier to implement clinically than a functional assay; a genetic test will not suffer from the poor reproducibility associated with some radiosensitivity testing methods; and SNPs are the most common type of genetic variation and so easiest to identify. Omics technologies offer promise, but to have an impact on radiotherapy practice research must identify biomarkers that replicate across cohorts. Robust replication needs big data, which is only possible with large collaborative efforts. The need for big data was addressed by establishing an international Radiogenomics Consortium. The latest results from the consortium will be presented including the largest radiogenomics study carried out to date involved ~4,000 men with prostate cancer.</jats:p>
<jats:p>Citation Format: Catharine M.L. West. Predicting radiotherapy outcomes: Radiogenomics and risk of radiotherapy toxicity [abstract]. In: Proceedings of the AACR Virtual Special Conference on Radiation Science and Medicine; 2021 Mar 2-3. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(8_Suppl):Abstract nr IA-021.</jats:p>