Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

Leland S Hu; Shuluo Ning; Jennifer M Eschbacher; Leslie C Baxter; Nathan Gaw; Sara Ranjbar; Jonathan Plasencia; Amylou C Dueck; Sen Peng; Kris A Smith; Peter Nakaji; John P Karis; C Chad Quarles; Teresa Wu; Joseph C Loftus; Robert B Jenkins; Hugues Sicotte; Thomas M Kollmeyer; Brian P O'Neill; William Elmquist; Joseph M Hoxworth; David Frakes; Jann Sarkaria; Kristin R Swanson; Nhan L Tran; Jing Li; J Ross Mitchell

doi:10.1093/neuonc/now135

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

Neuro Oncol. 2017 Jan;19(1):128-137. doi: 10.1093/neuonc/now135. Epub 2016 Aug 8.

Authors

Leland S Hu¹, Shuluo Ning¹, Jennifer M Eschbacher¹, Leslie C Baxter¹, Nathan Gaw¹, Sara Ranjbar¹, Jonathan Plasencia¹, Amylou C Dueck¹, Sen Peng¹, Kris A Smith¹, Peter Nakaji¹, John P Karis¹, C Chad Quarles¹, Teresa Wu¹, Joseph C Loftus¹, Robert B Jenkins¹, Hugues Sicotte¹, Thomas M Kollmeyer¹, Brian P O'Neill¹, William Elmquist¹, Joseph M Hoxworth¹, David Frakes¹, Jann Sarkaria¹, Kristin R Swanson¹, Nhan L Tran¹, Jing Li¹, J Ross Mitchell¹

Affiliation

¹ Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.).

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, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).

Conclusion: MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Keywords: genetic; glioblastoma; heterogeneity; radiogenomics; texture.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Biomarkers, Tumor / genetics*
DNA Copy Number Variations / genetics*
Feasibility Studies
Genomics / methods*
Glioblastoma / genetics*
Glioblastoma / pathology*
Glioblastoma / radiotherapy
Humans
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging / methods*
Neoplasm Staging
Prognosis

Substances

Biomarkers, Tumor

Abstract

Publication types

MeSH terms

Substances

Grants and funding