Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial Patterns
Glioblastoma is the most aggressive malignant primary brain tumor with a poor prognosis. Glioblastoma heterogeneous neuroimaging, pathologic, and molecular features provide opportunities for subclassification, prognostication, and the development of targeted therapies. Magnetic resonance imaging has the capability of quantifying specific phenotypic imaging features of these tumors. Additional insight into disease mechanism can be gained by exploring genetics foundations. Here, we use the gene expressions to evaluate the associations with various quantitative imaging phenomic features extracted from magnetic resonance imaging. We highlight a novel correlation by carrying out multi-stage genome-wide association tests at the gene-level through a non-parametric correlation framework that allows testing multiple hypotheses about the integrated relationship of imaging phenotype-genotype more efficiently and less expensive computationally. Our result showed several novel genes previously associated with glioblastoma and other types of cancers, as the LRRC46 (chromosome 17), EPGN (chromosome 4) and TUBA1C (chromosome 12), all associated with our radiographic tumor features.
KeywordsGlioblastoma Gene expression Brain tumor Radiomics Radiogenomics
Research reported in this publication was partly supported by the National Institutes of Health (NIH) under award numbers NIH/NINDS:R01NS042645, NIH/NCI:U24CA189523, NIH/NCATS:UL1TR001878, the ITMAT of the University of Pennsylvania as well as by the Swedish International Development Cooperation Agency (SIDA) through the Organization for Women in Science for the Developing World (OWSD). Computations were performed using facilities provided by the University of Cape Town’s ICTS High Performance Computing team: hpc.uct.ac.za.
- 3.Bakas, S., et al.: GLISTRboost: combining multimodal MRI segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 144–155. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30858-6_13CrossRefGoogle Scholar
- 5.Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
- 6.Scarpace, L., et al.: Radiology data from the cancer genome atlas glioblastoma multiforme [TCGA-GBM] collection. Cancer Imaging Arch. 11, 4 (2016)Google Scholar
- 10.Batmanghelich, N.K., Dalca, A.V., Sabuncu, M.R., Golland, P.: Joint modeling of imaging and genetics. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 766–777. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38868-2_64CrossRefGoogle Scholar
- 11.Elsheikh, S., et al.: Relating connectivity changes in brain networks to genetic information in Alzheimer patients. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE (2018)Google Scholar
- 17.Cancer Genome Atlas Research Network: Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N. Engl. J. Med. 372(26), 2481–2498 (2015)Google Scholar
- 38.Kendall, M.G.: The advanced theory of statistics. In: The Advanced Theory of Statistics, 2nd edn (1946)Google Scholar