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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[edit]

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 more than ten years ago 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[edit]

In 2009,[9][10] a Radiogenomics Consortium 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 ([11]

Imaging Genomics[edit]

Since the turn of the twentieth century, radiological images have been used to diagnose disease on a large scale, and has been used successfully to diagnose conditions affecting every organ and tissue type in the body. This is because tissue imaging correlates with tissue pathology. The addition of genomic data in the last twenty years, including DNA microarrays, miRNA, RNA-Seq allows new correlations to be made between cellular genomics and tissue-scale imaging.

Practice and Applications of Imaging Genomics[edit]

In imaging genomics, radiogenomics can be used to create imaging biomarkers that can identify the genomics of a disease, especially cancer without the use of a biopsy. Various techniques for dealing with high-dimensional data are used to find statistically significant correlations between MRI, CT, and PET imaging features and the genomics of disease, including SAM, VAMPIRE, and GSEA.

The imaging radiogenomic approach has proven successful[12] in determining the MRI phenotype associated genetics of glioblastoma, a highly aggressive type of brain tumor with low prognosis. The first large-scale MR-imaging microRNA-mRNA correlative study in GBM was published by Zinn et al. in 2011[13] Similar studies in liver cancer have successfully determined much of the liver cancer genome from non-invasive imaging features.[14] Gevaert et al. at Stanford University have shown the potential to link image features of non-small cell lung nodules in CT scans to predict survival by leveraging publicly available gene expression data.[15] This publication was accompanied by an editorial discussing the synergy between imaging and genomics.[16]

The radiogenomic approach has been also successfully applied in breast cancer. In 2014, Mazurowski et al.[17] showed that enhancement dynamics in MRI, computed using computer vision algorithms, are associated with gene expression-based tumor molecular subtype in breast cancer patients.

Programs that study the connections between radiology and genomics are active at the University of Pennsylvania, UCLA, MD Anderson Cancer Center, Stanford University and at Baylor College of Medicine in Houston, Texas.

See also[edit]


  1. ^ Barnett GC, Elliott RM, Alsner J, Andreassen CN, Abdelhay O, Burnet NG, Chang-Claude J, Coles CE, Gutiérrez-Enríquez S, Fuentes-Raspall MJ, Alonso-Muñoz MC, Kerns S, Raabe A, Symonds RP, Seibold P, Talbot CJ, Wenz F, Wilkinson J, Yarnold J, Dunning AM, Rosenstein BS, West CM, Bentzen SM (2012). "Individual patient data meta-analysis shows no association between the SNP rs1800469 in TGFB and late radiotherapy toxicity.". Radioth Oncol. 105 (3): 289–95. PMC 3593101Freely accessible. PMID 23199655. doi:10.1016/j.radonc.2012.10.017. 
  2. ^ Barnett GC, Coles CE, Elliott RM, Baynes C, Luccarini C, Conroy D, Wilkinson JS, Tyrer J, Misra V, Platte R, Gulliford SL, Sydes MR, Hall E, Bentzen SM, Dearnaley DP, Burnet NG, Pharoah PD, Dunning AM, West CM (2012). "Independent validation of genes and polymorphisms reported to be associated with radiation toxicity: a prospective analysis study.". Lancet Oncol. 13 (1): 65–77. PMID 22169268. doi:10.1016/S1470-2045(11)70302-3. 
  3. ^ Talbot CJ, Tanteles GA, Barnett GC, Burnet NG, Chang-Claude J, Coles CE, Davidson S, Dunning AM, Mills J, Murray RJ, Popanda O, Seibold P, West CM, Yarnold JR, Symonds RP (2012). "A replicated association between polymorphisms near TNFα and risk for adverse reactions to radiotherapy.". Br J Cancer. 107 (4): 748–53. PMC 3419947Freely accessible. PMID 22767148. doi:10.1038/bjc.2012.290. 
  4. ^ 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. PMC 2917342Freely accessible. PMID 20685577. doi:10.1016/j.semradonc.2010.01.002. 
  5. ^ Yard, Brian D.; Adams, Drew J.; Chie, Eui Kyu; Tamayo, Pablo; Battaglia, Jessica S.; Gopal, Priyanka; Rogacki, Kevin; Pearson, Bradley E.; Phillips, James (2016-04-25). "A genetic basis for the variation in the vulnerability of cancer to DNA damage". Nature Communications. 7: 11428. ISSN 2041-1723. PMC 4848553Freely accessible. PMID 27109210. doi:10.1038/ncomms11428. 
  6. ^ 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?". Radioth Oncol. 64 (2): 131–40. PMID 12242122. doi:10.1016/s0167-8140(02)00154-8. 
  7. ^ West CM, McKay MJ, Hölscher T, Baumann M, Stratford IJ, Bristow RG, Iwakawa M, Imai T, Zingde SM, Anscher MS, Bourhis J, Begg AC, Haustermans K, Bentzen SM, Hendry JH (2005). "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. PMID 16029781. doi:10.1016/j.ijrobp.2005.05.001. 
  8. ^ Bentzen, SM (2006). "Preventing or reducing late side effects of radiation therapy: radiobiology meets molecular pathology.". Nat Rev Cancer. 6 (9): 702–13. PMID 16929324. doi:10.1038/nrc1950. 
  9. ^ West C, Rosenstein BS, Alsner J, Azria D, Barnett G, Begg A, Bentzen S, Burnet N, Chang-Claude J, Chuang E, Coles C, De Ruyck K, De Ruysscher D, Dunning A, Elliott R, Fachal L, Hall J, Haustermans K, Herskind C, Hoelscher T, Imai T, Iwakawa M, Jones D, Kulich C; EQUAL-ESTRO, Langendijk JH, O'Neils P, Ozsahin M, Parliament M, Polanski A, Rosenstein B, Seminara D, Symonds P, Talbot C, Thierens H, Vega A, West C, Yarnold J (2010). "Establishment of a Radiogenomics Consortium". Int J Radiat Oncol Biol Phys. 76 (5): 1295–6. PMID 20338472. doi:10.1016/j.ijrobp.2009.12.017. 
  10. ^ West, C; Rosenstein BS (2010). "Establishment of a radiogenomics consortium". Radioth Oncol. 94 (1): 117–8. PMID 20074824. doi:10.1016/j.radonc.2009.12.007. 
  11. ^ "NCI EGRP". 
  12. ^ Diehn, Maximilian; Nardini, Christine; Wang, David S.; McGovern, Susan; Jayaraman, Mahesh; Liang, Yu; Aldape, Kenneth; Cha, Soonmee; Kuo, Michael D. (2008). "Identification of noninvasive imaging surrogates for brain tumor gene-expression modules". Proceedings of the National Academy of Sciences. 105 (13): 5213–8. PMC 2278224Freely accessible. PMID 18362333. doi:10.1073/pnas.0801279105. 
  13. ^ Zinn, Pascal O.; Mahajan, Bhanu; Sathyan, Pratheesh; Singh, Sanjay K.; Majumder, Sadhan; Jolesz, Ferenc A.; Colen, Rivka R. (2011). Deutsch, Eric, ed. "Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme". PLoS ONE. 6 (10): e25451. PMC 3187774Freely accessible. PMID 21998659. doi:10.1371/journal.pone.0025451. 
  14. ^ Rutman, Aaron M.; Kuo, Michael D. (2009). "Radiogenomics: Creating a link between molecular diagnostics and diagnostic imaging". European Journal of Radiology. 70 (2): 232–41. PMID 19303233. doi:10.1016/j.ejrad.2009.01.050. 
  15. ^ Gevaert, O.; Xu, J.; Hoang, C. D.; Leung, A.N.; Xu, Y.; Quon, A.; Rubin, D.L.; Napel, S.; Plevritis, S.K. (2012). "Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results". Radiology. 264 (2): 387–96. PMC 3401348Freely accessible. PMID 22723499. doi:10.1148/radiol.12111607. 
  16. ^ Jaffe, C. (2012). "Imaging and genomics: is there a synergy?". Radiology. 264 (2): 329–31. PMID 22821693. doi:10.1148/radiol.12120871. 
  17. ^ Mazurowski, M. A.; Zhang, J.; Grimm, L. J.; Yoon, S. C.; Silber, J. I. (2014). "Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging". Radiology. 273 (2): 365–72. PMID 25028781. doi:10.1148/radiol.14132641. 

Further reading[edit]