Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

Abstract

Background. Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods. We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results. We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A,

Imaging biomarkers in oncology: Basics and application to MRI

“…Radiomic studies have used a number of techniques including statistical methods (histogram; gray‐level co‐occurrence matrix [GLCM]; gray‐level difference matrix [GLDM], run length matrix [RLM], gray level size zone matrix [GLSZM], and neighborhood gray tone difference matrix [NGTDM]) with or without Gaussian or Wavelet transformation; and fractal‐based methods across different sequences including T 2 ‐weighted, diffusion‐weighted, and DCE sequences. Initial radiogenomic studies including MRI have been performed in breast cancer renal cell carcinoma and glioma . Variable reproducibility has been shown across different classes of features and further validation work is still required for radiomic biomarkers.…”

Background, current role, and potential applications of radiogenomics

“…In a study of 48 image‐guided biopsies obtained in 13 tumors, Hu et al demonstrated correlations between conventional, diffusion tensor imaging (DTI), and dynamic susceptibility contrast (DSC) perfusion metrics, and commonly implicated alterations in EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53 ( P < 0.03)—with accuracies ranging from 87.5% for RB1 to 37.5% for TP53. A similar study of spatial diversity texture features was able to characterize local EGFR mutation status as well as patient survival in 65 glioblastomas .…”

Improving diagnosis and management of primary brain tumors

“…Multidisciplinary cancer meeting, now enriched by molecular tumor board, constitutes the main pillar for appropriate decision making. In a near future, this multidisciplinary approach will benefit from the ongoing development of new diagnosis strategies such as radiomics, integrating imaging characteristics and genomic data, to refine treatment choice [19]. Addressing patients to clinical trials is critical but major efforts are needed to better select innovating agents at the preclinical level and to carefully design selective clinical trials to evaluate the full potential of targeted-therapies and immune-therapies.…”