Background, current role, and potential applications of radiogenomics

Abstract

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors.

Level of evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.

Keywords: brain; breast; gynecological cancer; liver; prostate; radiogenomics; radiomics.

Figure 1

Pearson’s correlation analysis of imaging features and 65 genes from commercially available prostate cancer classifiers. Hierarchical clustering on Pearson’s correlation distance between radiomic features and genes from commercially available prostate cancer classifiers: CCP (Cell Cycle Progression), Decipher and GPS (Genomic Prostate Score). Genes in these signatures that are up-expressed in aggressive cancers are indicated by a dark red box over the gene’s column while those that are down-expressed are indicated with a blue box. Groups of radiomic features are indicated along the dendrogram on the left. Group1 (left) connects the radiomic feature with location (TZ, PZ and ROI); Group 2 is related to the image modality/function: T2w, ADC and DCE-MRI. Reprinted under a creative commons license from: Buerki C, Castillo R, Jorda M, Ashab HA, Kryvenko ON, Punnen S, Parekh D, Abramowitz MC, Gillies RJ, Davicioni E, Erho N, Ishkanian A. Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostatebiopsies. Oncotarget. 2016 Aug 16;7(33):53362–53376. doi: 10.18632/oncotarget.10523.

Figure 2

The relationship between the DCE-MRI hypoxia gene signature, A_Brix and clinical outcome of 46 cervical cancer patients with both DCE-MRI and gene expression data. A: Hierarchical clustering (left) was performed based on the expression of 31 genes that were upregulated in tumors with low A_Brix and extracted to construct the DCE-MRI hypoxia gene signature. Box plot of A_Brix (middle) and Kaplan-Meier curves for progression-free survival (right) show patients with high (red) expression cluster had lower A_Brix and poorer outcome than patients with low (black) expression cluster. B: Box plot of A_Brix (left) and Kaplan-Meier curves for progression-free survival (right) show patients with high (blue) hypoxia score had lower A_Brix and poorer outcome than patients with low (green) hypoxia score. The hypoxia score was calculated by averaging the median centered expression levels for the 31 genes. Reprinted by permission from the American Association for Cancer Research: Halle C, Andersen E, Lando M, Aarnes E-K, Hasvold G, Holden M, Syljuåsen RG, Sundfør K, Kristensen GB, Holm R, Malinen E, Lyng H, Hypoxia-Induced Gene Expression in Chemoradioresistant Cervical Cancer Revealed by Dynamic Contrast-Enhanced MRI, Cancer Research, 2012, 72(20);5285–5295. doi: 10.1158/0008-5472.can-12-1085.

Figure 3

Figure 2 illustrates the computer segmentation method in example cases of one estrogen receptor positive tumor and one estrogen receptor negative tumor. The tumor segmentation outlines are shown along with computer-extracted image phenotype (CEIP) values (and ranges) for size, irregularity, and contrast enhancement heterogeneity. Reprinted from: NPJ Breast Cancer. 2016;2. pii: 16012. Epub 2016 May 11. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. Li H, Zhu Y, Burnside ES, Huang E, Drukker K, Hoadley KA, Fan C, Conzen SD, Zuley M, Net JM, Sutton E, Whitman GJ, Morris E, Perou CM, Ji Y, Giger ML.

Figure 4

The best-fit linear regression model allows imaging features to differentiate tumors with different Oncotype Dx Recurrence Score (ODxRS). A: Sagittal T1-weighted fat-suppressed post-contrast MRI of an invasive ductal nuclear grade 1 carcinoma with an ODxRS of 10 and B: corresponding kurtosis histogram, which demonstrates the frequency of MR intensity. C: Sagittal T1-weighted fat-suppressed postcontrast MRI of an invasive ductal nuclear grade 2 carcinoma with an ODxRS of 21 and D: corresponding kurtosis histogram. E: Sagittal T1- weighted fat-suppressed postcontrast MRI of an invasive ductal nuclear grade 3 carcinoma with an ODxRS of 43 and F: corresponding kurtosis histogram. Reprinted by permission from: J Magn Reson Imaging. 2015 Nov;42(5):1398–406. doi: 10.1002/jmri.24890 Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay. Sutton EJ, Oh JH, Dashevsky BZ, Veeraraghavan H, Apte AP, Thakur SB, Deasy JO, Morris EA.

Figure 5

Correlation heat map based on univariate linear regression analysis between each individual MR imaging phenotype and the recurrence predictor models of MammoPrint, Oncotype DX, PAM50 ROR-S, and PAM50 ROR-P. In this color scale yellow indicates higher correlation as compared with blue and the different gene assays served as the “reference standard” in this study. Some phenotypes correlate similarly (ie, similar color on the color scale) across the risk estimate models, while others do not. Reprinted by permission from: Radiology. 2016 Nov;281(2):382–391. Epub 2016 May 5. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y, Giger ML.

Figure 6

Box and whisker plots show the relationship of the MRI based phenotypes of, A: size (effective diameter) and, B: enhancement texture (maximum correlation coefficient) with the recurrence predictor models of MammaPrint, Oncotype DX, PAM50 ROR-S, and PAM50 ROR-P. A positive correlation between the selected MR imaging phenotypes of size (effective diameter) and negative correlation with enhancement texture (maximum correlation coefficient) and increasing levels of risk of recurrence for MammaPrint, Oncotype DX, PAM50 ROR-S, and PAM50 ROR-P were observed. A low value of this enhancement texture feature indicates a more heterogeneous enhancement pattern. Reprinted by permission from: Radiology. 2016 Nov;281(2):382–391. Epub 2016 May 5. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y, Giger ML.