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Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.

ABSTRACT: Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

SUBMITTER: Hu LS

PROVIDER: S-EPMC10539500 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.

Hu Leland S LS D'Angelo Fulvio F Weiskittel Taylor M TM Caruso Francesca P FP Fortin Ensign Shannon P SP Blomquist Mylan R MR Flick Matthew J MJ Wang Lujia L Sereduk Christopher P CP Meng-Lin Kevin K De Leon Gustavo G Nespodzany Ashley A Urcuyo Javier C JC Gonzales Ashlyn C AC Curtin Lee L Lewis Erika M EM Singleton Kyle W KW Dondlinger Timothy T Anil Aliya A Semmineh Natenael B NB Noviello Teresa T Patel Reyna A RA Wang Panwen P Wang Junwen J Eschbacher Jennifer M JM Hawkins-Daarud Andrea A Jackson Pamela R PR Grunfeld Itamar S IS Elrod Christian C Mazza Gina L GL McGee Sam C SC Paulson Lisa L Clark-Swanson Kamala K Lassiter-Morris Yvette Y Smith Kris A KA Nakaji Peter P Bendok Bernard R BR Zimmerman Richard S RS Krishna Chandan C Patra Devi P DP Patel Naresh P NP Lyons Mark M Neal Matthew M Donev Kliment K Mrugala Maciej M MM Porter Alyx B AB Beeman Scott C SC Jensen Todd R TR Schmainda Kathleen M KM Zhou Yuxiang Y Baxter Leslie C LC Plaisier Christopher L CL Li Jing J Li Hu H Lasorella Anna A Quarles C Chad CC Swanson Kristin R KR Ceccarelli Michele M Iavarone Antonio A Tran Nhan L NL

Nature communications 20230928 1

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, i ...[more]

PMID: 37770427

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Project description:PurposeWe aimed to explore whether multiparametric magnetic resonance imaging (MRI)-based radiomics combined with selected blood inflammatory markers could effectively predict the grade and proliferation in glioma patients.MethodsThis retrospective study included 152 patients histopathologically diagnosed with glioma. Stratified sampling was used to divide all patients into a training cohort (n=107) and a validation cohort (n=45) according to a ratio of 7:3, and five-fold repeat cross-validation was adopted in the training cohort. Multiparametric MRI and clinical parameters, including age, the neutrophil-lymphocyte ratio and red cell distribution width, were assessed. During image processing, image registration and gray normalization were conducted. A radiomics analysis was performed by extracting 1584 multiparametric MRI-based features, and the least absolute shrinkage and selection operator (LASSO) was applied to generate a radiomics signature for predicting grade and Ki-67 index in both training and validation cohorts. Statistical analysis included analysis of variance, Pearson correlation, intraclass correlation coefficient, multivariate logistic regression, Hosmer-Lemeshow test, and receiver operating characteristic (ROC) curve.ResultsThe radiomics signature demonstrated good performance in both the training and validation cohorts, with areas under the ROC curve (AUCs) of 0.92, 0.91, and 0.94 and 0.94, 0.75, and 0.82 for differentiating between low and high grade gliomas, grade III and grade IV gliomas, and low Ki-67 and high Ki-67, respectively, and was better than the clinical model; the AUCs of the combined model were 0.93, 0.91, and 0.95 and 0.94, 0.76, and 0.80, respectively.ConclusionBoth the radiomics signature and combined model showed high diagnostic efficacy and outperformed the clinical model. The clinical factors did not provide additional improvement in the prediction of the grade and proliferation index in glioma patients, but the stability was improved.

Dataset Information

Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.

Publications

Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.

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