Abstract 883: Elucidating cancer hallmark context from glioma MR imaging and RNA expression data
Authors: Yunxia Sui, Mirabela Rusu, Dattesh Shanbhag, Uday Patil, Jeffrey Kiefer, Jill Barnholtz-Sloan, Michael Berens, Fiona Ginty, Graf John, Sandeep Gupta, Chinnappa Kodira, Lee Newberg, Sushravya Raghunath, Anup Sood, Sushravya Raghunath
Journal: Cancer Research (2017)

Abstract Radiogenomics or radiomics is an emerging field where tumor genomic data is correlated with radiology image features, thereby potentially providing more biological information about the tumor phenotype. A central challenge is the potential for model over-fitting due to analysis of many thousands of genomic data-points with hundreds of corresponding patient image features. Biological interpretation of the imaging feature correlations is also challenged by overlapping pathways and common gene effects. Our goals were: i) to explore correlations between gene expression and corresponding Magnetic Resonance (MR) Apparent Diffusion Coefficient (ADC) derived imaging features in low grade glioma (LGG); ii) to classify significant gene and imaging correlates by cancer hallmark1. RNA expression data from 32 LGG patients were extracted from The Cancer Genome Atlas (TCGA) and matched with corresponding MR image data from The Cancer Imaging Archive (TCIA). Among 32 patients, 18 were males (56%), and ages ranged from 21 to 74 years (mean age 44). Tumor and normal regions in the MR images were annotated by an expert radiologist using ITK-Snap. The normal reference region was used normalize image intensities in corresponding tumor regions. Tumor texture features were computed on ADC Maps at each voxel location within the disease region (including first and second order statistics, Run Length and co-occurrence matrix derived measures features. The voxel features were finally aggregated within the tumor region using statistical measures of mean, variance, median, kurtosis, and skewness. ADC imaging features (n=310) were correlated with each single gene expression value (11614 genes after MAD&amp;gt;0.4 filtering). Only image features and genes with pairwise correlations higher than 0.68 (0.68 is the 3-standard deviation above average correlation) and FDR (False Discovery Rate) &amp;lt;0.1 were used for follow-up analyses. Significant genes and MR image features were aggregated into 3 groups based on gene expression and correlated with cancer hallmarks. Seven Haralick image features (reflecting the average level of image intensity heterogeneity) were independently, significantly correlated with the Angiogenesis Hallmark (FDR all &amp;lt; 0.001). Three Haralick image features (reflecting asymmetric distribution of intensity) were significantly correlated with the Activating Invasion and Metastasis Hallmark (FDR all &amp;lt; 0.001). Validation of these findings in additional LGG cases with additional imaging protocols and features is ongoing. Radiogenomics informed by genomic profiling may usher in processes to infer cancer hallmarks to aid treatment planning and prognosis of glioma patients.1 Hanahan D and Weinberg RA (2011). Hallmarks of cancer: the next generation. Cell 144(5):646-74. Citation Format: Yunxia Sui, Mirabela Rusu, Dattesh Shanbhag, Uday Patil, Jeffrey Kiefer, Jill Barnholtz-Sloan, Michael Berens, Fiona Ginty, Graf John, Sandeep Gupta, Chinnappa Kodira, Lee Newberg, Sushravya Raghunath, Anup Sood, Sushravya Raghunath. Elucidating cancer hallmark context from glioma MR imaging and RNA expression data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 883. doi:10.1158/1538-7445.AM2017-883