Abstract
Glioblastoma, the most frequent primary malignant brain neoplasm, is genetically diverse and classified into four transcriptomic subtypes, i. e., classical, mesenchymal, proneural, and neural. Currently, detection of transcriptomic subtype is based on ex vivo analysis of tissue that does not capture the spatial tumor heterogeneity. In view of accumulative evidence of in vivo imaging signatures summarizing molecular features of cancer, this study seeks robust non-invasive radiographic markers of transcriptomic classification of glioblastoma, based solely on routine clinically-acquired imaging sequences. A pre-operative retrospective cohort of 112 pathology-proven de novo glioblastoma patients, having multi-parametric MRI (T1, T1-Gd, T2, T2-FLAIR), collected from the Hospital of the University of Pennsylvania were included. Following tumor segmentation into distinct radiographic sub-regions, diverse imaging features were extracted and support vector machines were employed to multivariately integrate these features and derive an imaging signature of transcriptomic subtype. Extracted features included intensity distributions, volume, morphology, statistics, tumors' anatomical location, and texture descriptors for each tumor sub-region. The derived signature was evaluated against the transcriptomic subtype of surgically-resected tissue specimens, using a 5-fold cross-validation method and a receiver-operating-characteristics analysis. The proposed model was 71% accurate in distinguishing among the four transcriptomic subtypes. The accuracy (sensitivity/specificity) for distinguishing each subtype (classical, mesenchymal, proneural, neural) from the rest was equal to 88.4% (71.4/92.3), 75.9% (83.9/72.8), 82.1% (73.1/84.9), and 75.9% (79.4/74.4), respectively. The findings were also replicated in The Cancer Genomic Atlas glioblastoma dataset. The obtained imaging signature for the classical subtype was dominated by associations with features related to edge sharpness, whereas for the mesenchymal subtype had more pronounced presence of higher T2 and T2-FLAIR signal in edema, and higher volume of enhancing tumor and edema. The proneural and neural subtypes were characterized by the lower T1-Gd signal in enhancing tumor and higher T2-FLAIR signal in edema, respectively. Our results indicate that quantitative multivariate analysis of features extracted from clinically-acquired MRI may provide a radiographic biomarker of the transcriptomic profile of glioblastoma. Importantly our findings can be influential in surgical decision-making, treatment planning, and assessment of inoperable tumors.
Highlights
Glioblastoma is the most frequent primary malignant brain tumor with grim prognosis, despite aggressive combination of therapies (Stupp et al, 2017), and is characterized by inter- and intra-patient heterogeneity at radiographic, histologic, and molecular fronts, thereby providing opportunities for sub-classification, prognostication, and adoption of targeted therapeutic approaches (Aum et al, 2014; Lemée et al, 2015).There is mounting evidence that different glioblastoma patients show different levels of sensitivity to therapeutic approaches depending on their distinct genetic characterization
This study evaluates a group of 112 primary glioblastoma patients, diagnosed between 2006 and 2013 at the Hospital of the University of Pennsylvania (HUP), having pre-operative multi-parametric magnetic resonance imaging (mpMRI)
The main characteristics of the obtained imaging signature show that the mesenchymal subtype have lower T2 and T2-FLAIR signal in peritumoral edematous/invaded region, enhancing tumor (ET) of lower eccentricity, non-enhancing tumor (NET) of higher eccentricity, and higher volumes of ET, edema region (ED) and whole tumor (WT)
Summary
Glioblastoma is the most frequent primary malignant brain tumor with grim prognosis, despite aggressive combination of therapies (Stupp et al, 2017), and is characterized by inter- and intra-patient heterogeneity at radiographic, histologic, and molecular fronts, thereby providing opportunities for sub-classification, prognostication, and adoption of targeted therapeutic approaches (Aum et al, 2014; Lemée et al, 2015).There is mounting evidence that different glioblastoma patients show different levels of sensitivity to therapeutic approaches depending on their distinct genetic characterization. It has been suggested earlier that glioblastoma should not be considered a single disease, but rather should be categorized into four transcriptomic subtypes, i.e., classical, mesenchymal, proneural, and neural (Verhaak et al, 2010) These subtypes present very distinct molecular biomarkers such as collective loss in chromosome 10 and amplification of chromosome 7 in classical subtype, largest occurrence of focal hemizygous deletions of a region at 17q11.2, encompassing NF1 gene, in mesenchymal subtype, aberrations in PDGFRA and mutations in IDH1 in proneural subtype, and presence of GABRA1, SYT1, NEFL, and SLC12A5 in neural subtype (Verhaak et al, 2010). Such assessment has inherent limitations of: (i) tissue sampling error that sometimes leads to missing the tumor mutation, and (ii) inability to acquire multiple specimens over the course of the disease due to invasiveness of the tissue collection procedure, thereby leading to the failure in determining molecular subtype of the tumor over the course of the treatment
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