The term radiogenomics is used in two contexts: either to refer to the study of genetic variation associated with response to radiation (radiation genomics) or to refer to the correlation between cancer imaging features and gene expression (imaging genomics).
Radiation genomics In radiation genomics, radiogenomics is used to refer to the study of genetic variation associated with response to radiation therapy. Genetic variation, such as single nucleotide polymorphisms, is studied in relation to a cancer patient's risk of developing toxicity following radiation therapy. It is also used in the context of studying the genomics of tumor response to radiation therapy. One clinical application of radiogenomic principles is the genomic adjusted radiation dose (GARD), which uses tumor gene-expression signatures to estimate radiosensitivity.
The term radiogenomics was coined in 2002 by Andreassen et al. (2002) as an analogy to pharmacogenomics, which studies the genetic variation associated with drug responses. See also West et al. (2005) and Bentzen (2006).
The Radiogenomics Consortium In 2009, a Radiogenomics Consortium (RGC) was established to facilitate and promote multi-centre collaboration of researchers linking genetic variants with response to radiation therapy. The Radiogenomics Consortium is a Cancer Epidemiology Consortium supported by the Epidemiology and Genetics Research Program of the National Cancer Institute of the National Institutes of Health. RGC researchers have completed numerous clinical studies that identified genetic variants associated with radiation toxicities in patients with prostate, breast, lung, head and neck, and other cancers.
Past meetings 2009 - Manchester, UK. Consortium proposed. 2010 - New York, USA. 2011 - London, UK. 2012 - Boston, USA. 2013 - Cambridge (also REQUITE launch), UK. 2014 - Heidelberg, Germany. 2015 - Montpellier, France. 2016 - Maastricht, Netherlands. 2017 - Barcelona, Spain. 2018 - Manchester, UK. 2019 - Rochester, USA. 2020 - Online. 2021 - Online. 2022 - Groningen, Netherlands. 2023 - Manchester, UK. 2024 - Aarhus, Denmark. 2025 - Barcelona, Spain.
Imaging genomics Radiological images are used to diagnose disease on a large scale: tissue imaging correlates with tissue pathology. The addition of genomic data including DNA microarrays, miRNA, RNA-Seq allows new correlations to be made between cellular genomics and tissue-scale imaging.
See also Pharmacogenomics Radiation therapy Radiosensitivity
References
Further reading https://epi.grants.cancer.gov/radiogenomics/ Kerns, Sarah L.; Dorling, Leila; Fachal, Laura; Bentzen, Søren; Pharoah, Paul D.P.; Barnes, Daniel R.; Gómez-Caamaño, Antonio; Carballo, Ana M.; Dearnaley, David P.; Peleteiro, Paula; Gulliford, Sarah L.; Hall, Emma; Michailidou, Kyriaki; Carracedo, Ángel; Sia, Michael; Stock, Richard; Stone, Nelson N.; Sydes, Matthew R.; Tyrer, Jonathan P.; Ahmed, Shahana; Parliament, Matthew; Ostrer, Harry; Rosenstein, Barry S.; Vega, Ana; Burnet, Neil G.; Dunning, Alison M.; Barnett, Gillian C.; West, Catharine M.L.; Radiogenomics, Consortium. (August 2016). "Meta-analysis of Genome Wide Association Studies Identifies Genetic Markers of Late Toxicity Following Radiotherapy for Prostate Cancer". eBioMedicine. 10: 150–163. doi:10.1016/j.ebiom.2016.07.022. PMC 5036513. PMID 27515689. Zinn, Pascal O.; Sathyan, Pratheesh; Mahajan, Bhanu; Bruyere, John; Hegi, Monika; Majumder, Sadhan; Colen, Rivka R. (2012). Lesniak, Maciej S (ed.). "A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature". PLOS ONE. 7 (8) e41522. Bibcode:2012PLoSO...741522Z. doi:10.1371/journal.pone.0041522. PMC 3411674. PMID 22870228. Segal, Eran; Sirlin, Claude B; Ooi, Clara; Adler, Adam S; Gollub, Jeremy; Chen, Xin; Chan, Bryan K; Matcuk, George R; et al. (2007). "Decoding global gene expression programs in liver cancer by noninvasive imaging". Nature Biotechnology. 25 (6): 675–80. doi:10.1038/nbt1306. PMID 17515910. S2CID 10499664. Andreassen CN, Barnett GC, Langendijk JA, Alsner J, De Ruysscher D, Krause M, Bentzen SM, Haviland JS, Griffin C, Poortmans P, Yarnold JR (2012). "Conducting radiogenomic research - Do not forget careful consideration of the clinical data". Radiother Oncol. 105 (3): 337–40. doi:10.1016/j.radonc.2012.11.004. PMID 23245646. West, CM; Barnett GC (2011). "Genetics and genomics of radiotherapy toxicity: towards prediction". Genome Med. 3 (8): 52. doi:10.1186/gm268. PMC 3238178. PMID 21861849. Oh, JH; Kerns, S; Ostrer, H; Powell, SN; Rosenstein, B; Deasy, JO (2017). "Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes". Sci Rep. 7 43381. Bibcode:2017NatSR...743381O. doi:10.1038/srep43381. PMC 5324069. PMID 28233873. Hall, William A.; Bergom, Carmen; Thompson, Reid F.; Baschnagel, Andrew M.; Vijayakumar, Srinivasan; Willers, Henning; Li, X. Allen; Schultz, Christopher J.; Wilson, George D.; West, Catharine M.L.; Capala, Jacek; Coleman, C. Norman; Torres-Roca, Javier F.; Weidhaas, Joanne; Feng, Felix Y. (June 2018). "Precision Oncology and Genomically Guided Radiation Therapy: A Report From the American Society for Radiation Oncology/American Association of Physicists in Medicine/National Cancer Institute Precision Medicine Conference". International Journal of Radiation Oncology, Biology, Physics. 101 (2): 274–284. doi:10.1016/j.ijrobp.2017.05.044. PMID 28964588. Lee, S; Kerns, S; Ostrer, H; Rosenstein, B; Deasy, JO; Oh, JH (2018). "Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy". Int J Radiat Oncol Biol Phys. 101 (1): 128–135. doi:10.1016/j.ijrobp.2018.01.054. PMC 5886789. PMID 29502932. Johnson, K; Chang-Claude, J; Critchley, AM; Kyriacou, C; Lavers, S; Rattay, T; Seibold, P; Webb, A; West, C; Symonds, RP; Talbot, CJ; Consortium, Requite (Jan 2019). "Genetic variants predict optimal timing of radiotherapy to reduce side-effects in breast cancer patients". Clin Oncol (R Coll Radiol). 31 (1): 9–16. doi:10.1016/j.clon.2018.10.001. hdl:2381/43295. PMID 30389261. Mbah, C; De Ruyck, K; De Schrijver, S.; De Sutter, C.; Schiettecatte, K.; Monten, C.; Paelinck, L.; De Neve, W.; Thierens, H.; West, C.; Amorim, G.; Thas, O.; Veldeman, L. (2018). "A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients". Acta Oncologica. 57 (5): 604–12. doi:10.1080/0284186X.2017.1417633. PMID 29299946.