Elsheikh Samar S M, Bakas Spyridon, Mulder Nicola J, Chimusa Emile R, Davatzikos Christos, Crimi Alessandro
Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Brainlesion. 2019;11383:239-250. doi: 10.1007/978-3-030-11723-8_24. Epub 2019 Jan 26.
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 genomewide 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.
胶质母细胞瘤是最具侵袭性的原发性恶性脑肿瘤，预后较差。胶质母细胞瘤的异质性神经影像学、病理学和分子特征为亚分类、预后评估以及靶向治疗的发展提供了机会。磁共振成像能够量化这些肿瘤的特定表型成像特征。通过探索遗传学基础，可以进一步深入了解疾病机制。在此，我们使用基因表达来评估与从磁共振成像中提取的各种定量成像表型特征之间的关联。我们通过一个非参数相关框架在基因水平上进行多阶段全基因组关联测试，突出了一种新的相关性，该框架能够更高效且计算成本更低地测试关于成像表型 - 基因型综合关系的多个假设。我们的结果显示了几个先前与胶质母细胞瘤和其他类型癌症相关的新基因，如LRRC46（17号染色体）、EPGN（4号染色体）和TUBA1C（12号染色体），它们均与我们的影像学肿瘤特征相关。
