The term radiogenomics is used in two contexts: either to refer to the study of genetic variation associated with response to radiation or to refer to the correlation between cancer imaging features and gene expression.
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. The term radiogenomics was coined more than ten years ago by Andreassen et al. as an analogy to pharmacogenomics, which studies the genetic variation associated with drug responses. See also West et al. and Bentzen.
The Radiogenomics Consortium
In 2009, 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. RGC researchers have recently completed a meta-analysis that identified genetic variants associated with radiation toxicities in prostate cancer patients.
Imaging Genomics
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
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 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 Similar studies in liver cancer have successfully determined much of the liver cancer genome from non-invasive imaging features. 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. This publication was accompanied by an editorial discussing the synergy between imaging and genomics. More recently, Mu Zhou et al. at Stanford University have showed that multiple associations between semantic image features and metagenes that represented canonical molecular pathways, and it can result in noninvasive identification of molecular properties of non-small cell lung cancer. Several radiogenomic studies have now been carried out in prostate cancer, and while a consensus is not yet clear some have noted that genetic features correlated with MRI signal are often also associated with more aggressive prostate cancer. The radiogenomic approach has been also successfully applied in breast cancer. In 2014, Mazurowski et al. 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.
