Biology:Radiogenomics
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.[1][2][3] It is also used in the context of studying the genomics of tumor response to radiation therapy.[4][5]
The term radiogenomics was coined in 2002 by Andreassen et al. (2002)[6] as an analogy to pharmacogenomics, which studies the genetic variation associated with drug responses. See also West et al. (2005)[7] and Bentzen (2006).[8]
The Radiogenomics Consortium
In 2009,[9][10] 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 (http://epi.grants.cancer.gov/radiogenomics/) is a Cancer Epidemiology Consortium supported by the Epidemiology and Genetics Research Program of the National Cancer Institute of the National Institutes of Health.[11] 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.
Imaging genomics [12]
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
References
- ↑ "Individual patient data meta-analysis shows no association between the SNP rs1800469 in TGFB and late radiotherapy toxicity.". Radiother Oncol 105 (3): 289–95. 2012. doi:10.1016/j.radonc.2012.10.017. PMID 23199655.
- ↑ "Independent validation of genes and polymorphisms reported to be associated with radiation toxicity: a prospective analysis study.". Lancet Oncol 13 (1): 65–77. 2012. doi:10.1016/S1470-2045(11)70302-3. PMID 22169268.
- ↑ "A replicated association between polymorphisms near TNFα and risk for adverse reactions to radiotherapy.". Br J Cancer 107 (4): 748–53. 2012. doi:10.1038/bjc.2012.290. PMID 22767148.
- ↑ Das, AK; Bell MH; Nirodi CS; Story MD; Minna JD (2010). "Radiogenomics predicting tumor responses to radiotherapy in lung cancer.". Sem Radiat Oncol 20 (3): 149–55. doi:10.1016/j.semradonc.2010.01.002. PMID 20685577.
- ↑ Yard, Brian D.; Adams, Drew J.; Chie, Eui Kyu; Tamayo, Pablo; Battaglia, Jessica S.; Gopal, Priyanka; Rogacki, Kevin; Pearson, Bradley E. et al. (2016-04-25). "A genetic basis for the variation in the vulnerability of cancer to DNA damage". Nature Communications 7: 11428. doi:10.1038/ncomms11428. ISSN 2041-1723. PMID 27109210. Bibcode: 2016NatCo...711428Y.
- ↑ Andreassen, CN; Alsner J; Overgaard J (2002). "Does variability in normal tissue reactions after radiotherapy have a genetic basis--where and how to look for it?". Radiother Oncol 64 (2): 131–40. doi:10.1016/s0167-8140(02)00154-8. PMID 12242122.
- ↑ "Molecular markers predicting radiotherapy response: report and recommendations from an International Atomic Energy Agency technical meeting.". Int J Radiat Oncol Biol Phys 62 (5): 1264–73. 2005. doi:10.1016/j.ijrobp.2005.05.001. PMID 16029781.
- ↑ Bentzen, SM (2006). "Preventing or reducing late side effects of radiation therapy: radiobiology meets molecular pathology.". Nat Rev Cancer 6 (9): 702–13. doi:10.1038/nrc1950. PMID 16929324.
- ↑ "Establishment of a Radiogenomics Consortium". International Journal of Radiation Oncology, Biology, Physics 76 (5): 1295–1296. 2010. doi:10.1016/j.ijrobp.2009.12.017. PMID 20338472.
- ↑ West, C; Rosenstein BS (2010). "Establishment of a radiogenomics consortium". Radiother Oncol 94 (1): 117–8. doi:10.1016/j.radonc.2009.12.007. PMID 20074824.
- ↑ "National Cancer Institute of Health - Epidemiology and Genomics Research Program". http://epi.grants.cancer.gov/Consortia/single/rgc.html.
- ↑ Radiomics
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. et al. (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. 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. doi:10.1371/journal.pone.0041522. PMID 22870228. Bibcode: 2012PLoSO...741522Z.
- 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.
- "Conducting radiogenomic research - Do not forget careful consideration of the clinical data.". Radiother Oncol 105 (3): 337–40. 2012. 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. 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. doi:10.1038/srep43381. PMID 28233873. Bibcode: 2017NatSR...743381O.
- Hall, William A.; Bergom, Carmen; Thompson, Reid F.; Baschnagel, Andrew M.; Vijayakumar, Srinivasan; Willers, Henning; Li, X. Allen; Schultz, Christopher J. et al. (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. PMID 29502932.
- Johnson, K; Chang-Claude, J; Critchley, AM; Kyriacou, C; Lavers, S; Rattay, T; Seibold, P; Webb, A et al. (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. PMID 30389261.
- Mbah, C; De Ruyck, K; De Schrijver, S.; De Sutter, C.; Schiettecatte, K.; Monten, C.; Paelinck, L.; De Neve, W. et al. (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.
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