Extracting biologically relevant data from radiology images can enable better monitoring of disease progression and therapy response. The field of radiogenomics is providing new approaches for such genomic/radiology correlations. However, there are several challenges in validation and clinical translation in that few DNA mutations are shared between tumors from different individuals and the differences in scale between imaging and genomic features can limit interpretation of underlying mechanisms. The goals of this work were to i) analyze correlations between low grade glioma (LGG) DNA somatic mutations, using a novel DNA impact scoring approach, and MRI derived imaging features; and ii) to interpret results in context of cancer hallmarks1. Multi-parametric MRI and corresponding DNA data from 32 LGG patients were extracted from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). The cohort included 18 males (56%), with mean age of 44 years (range: 21-74 years). An expert radiologist outlined the normal and tumor regions of interest using ITK-Snap tool. The normal region was used as a reference to normalize image intensities in the tumor region. Tumor mean intensity and mean variance were computed from Apparent Diffusion Coefficient (ADC), T1 enhancement ratio (derived from T1 pre- and post- contract MRI), and Fluid-Attenuated Inversion Recovery (FLAIR) images. A novel algorithm was used to compute DNA impact scores for each somatic mutation. The score represents the probability of a DNA variant being pathogenic vs. nonpathogenic. First, the scoring algorithm computes a score for nucleotide base insertions, deletions, or single base changes and then computes the consequence of such changes on amino acid coding, binding sites, splice sites and protein phosphorylation sites. An impact score was then computed based on the individual DNA impact scores of mutations within the gene. Finally, an average DNA impact score was computed at the Cancer Hallmark level using a gene-cancer hallmark map. At gene level, significant positive correlations were found between the ATRX (p=0.0002), TP53 (p=0.02) and ADC mean intensity. At pathway level, regulation of TP53 expression and degradation, and DNA damage response, signal transduction by p53 class mediator, and DNA translocase activity were found to be enriched with genes that correlated with ADC and FLAIR. These pathways also contained genes that were enriched in the following cancer hallmarks: replicative immortality, evading growth suppression and genome instability. The ATRX gene is a member of all three hallmarks and TP53 a member of two. Since ADC is a measure of water diffusion and hence an indirect measure of cellularity, these findings demonstrate that mutations in replication and repair pathways are contributing to imaging features at the tumor level.1 Hanahan, D. and Weinberg, R.A. (2011). Hallmarks of cancer: the next generation. Cell 144(5):646-74.

Citation Format: John Graf, Mirabela Rusu, Yunxia Sui, Dattesh Shanbhag, Uday Patil, Jeffrey Kiefer, Jill Barnholtz-Sloan, Michael Berens, Fiona Ginty, Sandeep Gupta, Chinnappa Kodira, Lee Newberg, Sushravya Raghunath, Anup Sood. Interpreting glioma MR imaging and somatic mutations in a cancer hallmark context [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 882. doi:10.1158/1538-7445.AM2017-882