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Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial Patterns

  • Samar S. M. ElsheikhEmail author
  • Spyridon Bakas
  • Nicola J. Mulder
  • Emile R. Chimusa
  • Christos Davatzikos
  • Alessandro Crimi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

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.

Keywords

Glioblastoma Gene expression Brain tumor Radiomics Radiogenomics 

Notes

Acknowledgement

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health SciencesUniversity of Cape TownCape TownSouth Africa
  2. 2.Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  4. 4.Division of Human Genetics, Institute of Infectious Disease and Molecular Medicine, Faculty of Health SciencesUniversity of Cape TownCape TownSouth Africa
  5. 5.University Hospital of ZürichZürichSwitzerland
  6. 6.African Institute for Mathematical SciencesBiriwaGhana

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