Molecular profiles of tumor contrast enhancement: A radiogenomic analysis in anaplastic gliomas

Abstract

The presence of contrast enhancement (CE) on magnetic resonance (MR) imaging is conventionally regarded as an indicator for tumor malignancy. However, the biological behaviors and molecular mechanism of enhanced tumor are not well illustrated. The aim of this study was to investigate the molecular profiles associated with anaplastic gliomas (AGs) presenting CE on postcontrast T1‐weighted MR imaging. In this retrospective database study, RNA sequencing and MR imaging data of 91 AGs from the Cancer Genome Atlas (TCGA) and 64 from the Chinese Glioma Genome Atlas (CGGA) were collected. Gene set enrichment analysis (GSEA), significant analysis of microarray, generalized linear models, and Least absolute shrinkage and selection operator algorithm were used to explore radiogenomic and prognostic signatures of AG patients. GSEA indicated that angiogenesis and epithelial‐mesenchymal transition were significantly associated with post‐CE. Genes driving immune system response, cell proliferation, and focal adhesions were also significantly enriched. Gene ontology of 237 differential genes indicated consistent results. A 48‐gene signature for CE was identified in TCGA and validated in CGGA dataset (area under the curve = 0.9787). Furthermore, seven genes derived from the CE‐specific signature could stratify AG patients into two subgroups based on overall survival time according to corresponding risk score. Comprehensive analysis of post‐CE and genomic characteristics leads to a better understanding of radiology‐pathology correlations. Our gene signature helps interpret the occurrence of radiological traits and predict clinical outcomes. Additionally, we found nine prognostic quantitative radiomic features of CE and investigated the underlying biological processes of them.

Patient‑derived orthotopic xenograft glioma models fail to replicate the magnetic resonance imaging features of the original patient tumor

“…Since certain mesenchymal components in the growth process of xenografts were derived from NOD-SCID nude mice (34,35), the expression levels of immune-associated genes were decreased in the xenografts. The contrast enhancement and increased K trans value indicated that the original tumors exhibited significant angiogenic activity and high vascular permeability (36), which may be attributed to the increased expression levels of the angiogenesis-associated genes in the original tumors (37)(38)(39). The rADC values of the original tumors were significantly lower compared with the xenografts, which may be attributed with the higher expression levels of the cell adhesion and extracellular matrix-associated genes noted in the original tumors, which in turn resulted in a higher cell density (40,41).…”

Radiomic biomarkers of tumor immune biology and immunotherapy response

“…2), texture feature associations did not demonstrate similar consistency. For example, measures of heterogeneity (GLCM information measure of correlation 1, GLRLM run length nonuniformity, GLSZM gray level nonuniformity) associated negatively with expression of immune gene signatures in head and neck cancer [73], whereas the opposite association appeared to be true of breast cancer and glioma [53,62] (Fig. 3).…”

“…1), we identified 54 studies in which imaging features were associated with tumor immune phenotypes (Table 1 and Supplemental Table 1) or response to immunotherapy (Table 2 and Supplemental Table 2). The following types of cancers were investigated: lung [3,[26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] (20), brain [45][46][47][48][49][50][51][52][53][54] (10), breast [55][56][57][58][59][60][61][62][63] (9), liver [9,41,[64][65]…”

Survival Prediction in Gliomas: Current State and Novel Approaches

“…By far the most common theme in computational glioma survival prediction involving imaging relates to glioma tumor grading. Numerous publications have employed tumor grading from imaging features as a starting point to help develop prognostic biomarkers (21,(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60). Some publications have focused strictly on glioma grading (31,50,58) and T2FLAIR (T2 fluid-attenuated inversion recovery) images, and the risk factors and OS were explored by Kaplan-Meier survival analysis by stratifying patients into low-and high-risk groups (C-Index 0.707 and 0.711 in training and test cohorts respectively) (31).…”