Combining molecular and imaging metrics in cancer: radiogenomics

Abstract

Background: Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing.

Main body: In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis.

Conclusion: Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.

Keywords: Molecular profiling; Precision medicine; Radiogenomics; Radiomics.

Fig. 1

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 under a Creative Commons license 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

Fig. 2

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 with 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.

Fig. 3

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 (i.e., similar color on the color scale) across the risk estimate models, while others do not. Reprinted with 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

Fig. 4

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 with 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

Fig. 5

Kaplan–Meier survival curves which illustrate that the presence of necrosis, satellite nodules, and vascular encasement were all associated with decreased survival. Reprinted with permission from: Aherne EA, Pak LM, Goldman DA, Gonen M, Jarnagin WR, Simpson AL, and Do RK. Intrahepatic cholangiocarcinoma: can imaging phenotypes predict survival and tumor genetics? Abdom Radiol, 2018, 43 [10]:2665

Fig. 6

Kaplan–Meier survival curves which illustrate that the presence of satellite nodules and vascular encasement were associated with decreased disease-free survival. Reprinted with permission from: Aherne EA, Pak LM, Goldman DA, Gonen M, Jarnagin WR, Simpson AL, and Do RK. Intrahepatic cholangiocarcinoma: can imaging phenotypes predict survival and tumor genetics? Abdom Radiol, 2018, 43 [10]:2665

Fig. 7

Univariate Cox regression analysis of prognostic factors for overall survival is summarized. Reprinted with permission from: Cen et al. [116] PubMed PMID: 30877466

Fig. 8

Hierarchical clustering yielded distinct groups of RUNX3 promoter methylation status and CT features. Red positive, green negative. Reprinted with permission from: Cen et al. [116] PubMed PMID: 30877466

Fig. 9

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]:53362–53,376. doi: 10.18632/oncotarget.10523