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 Dueck, Sen Peng, Kris A. Smith, Peter Nakaji, John P. Karis, C. Chad Quarles, Teresa Wu, Joseph C Loftus, Robert Brian Jenkins, Hugues Sicotte, Thomas M. Kollmeyer, Brian Patrick O'Neill, William Elmquist & 7 others Joseph M. Hoxworth, David Frakes, Jann N Sarkaria, Kristin Swanson, Nhan Tran, Jing Li, Joseph Ross Mitchell

Research output: Contribution to journalArticle

45 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)128-137
Number of pages10
JournalNeuro-Oncology
Volume19
Issue number1
DOIs
StatePublished - 2017

Fingerprint

Genetic Heterogeneity
Glioblastoma
erbB-1 Genes
Neoplasms
Biopsy
Brain Neoplasms
Image-Guided Biopsy
DNA Copy Number Variations
Genes
Decision Trees
Atlases
Feasibility Studies
Population
Genome
Therapeutics

Keywords

  • Genetic
  • Glioblastoma
  • Heterogeneity
  • Radiogenomics
  • Texture

ASJC Scopus subject areas

  • Oncology
  • Clinical Neurology
  • Cancer Research

Cite this

Hu, L. S., Ning, S., Eschbacher, J. M., Baxter, L. C., Gaw, N., Ranjbar, S., ... Mitchell, J. R. (2017). Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro-Oncology, 19(1), 128-137. https://doi.org/10.1093/neuonc/now135

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. / Hu, Leland S; Ning, Shuluo; Eschbacher, Jennifer M.; Baxter, Leslie C.; Gaw, Nathan; Ranjbar, Sara; Plasencia, Jonathan; Dueck, Amylou; Peng, Sen; Smith, Kris A.; Nakaji, Peter; Karis, John P.; Quarles, C. Chad; Wu, Teresa; Loftus, Joseph C; Jenkins, Robert Brian; Sicotte, Hugues; Kollmeyer, Thomas M.; O'Neill, Brian Patrick; Elmquist, William; Hoxworth, Joseph M.; Frakes, David; Sarkaria, Jann N; Swanson, Kristin; Tran, Nhan; Li, Jing; Mitchell, Joseph Ross.

In: Neuro-Oncology, Vol. 19, No. 1, 2017, p. 128-137.

Research output: Contribution to journalArticle

Hu, LS, Ning, S, Eschbacher, JM, Baxter, LC, Gaw, N, Ranjbar, S, Plasencia, J, Dueck, A, Peng, S, Smith, KA, Nakaji, P, Karis, JP, Quarles, CC, Wu, T, Loftus, JC, Jenkins, RB, Sicotte, H, Kollmeyer, TM, O'Neill, BP, Elmquist, W, Hoxworth, JM, Frakes, D, Sarkaria, JN, Swanson, K, Tran, N, Li, J & Mitchell, JR 2017, 'Radiogenomics to characterize regional genetic heterogeneity in glioblastoma', Neuro-Oncology, vol. 19, no. 1, pp. 128-137. https://doi.org/10.1093/neuonc/now135
Hu, Leland S ; Ning, Shuluo ; Eschbacher, Jennifer M. ; Baxter, Leslie C. ; Gaw, Nathan ; Ranjbar, Sara ; Plasencia, Jonathan ; Dueck, Amylou ; Peng, Sen ; Smith, Kris A. ; Nakaji, Peter ; Karis, John P. ; Quarles, C. Chad ; Wu, Teresa ; Loftus, Joseph C ; Jenkins, Robert Brian ; Sicotte, Hugues ; Kollmeyer, Thomas M. ; O'Neill, Brian Patrick ; Elmquist, William ; Hoxworth, Joseph M. ; Frakes, David ; Sarkaria, Jann N ; Swanson, Kristin ; Tran, Nhan ; Li, Jing ; Mitchell, Joseph Ross. / Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. In: Neuro-Oncology. 2017 ; Vol. 19, No. 1. pp. 128-137.
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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.",
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author = "Hu, {Leland S} and Shuluo Ning and Eschbacher, {Jennifer M.} and Baxter, {Leslie C.} and Nathan Gaw and Sara Ranjbar and Jonathan Plasencia and Amylou Dueck and Sen Peng and Smith, {Kris A.} and Peter Nakaji and Karis, {John P.} and Quarles, {C. Chad} and Teresa Wu and Loftus, {Joseph C} and Jenkins, {Robert Brian} and Hugues Sicotte and Kollmeyer, {Thomas M.} and O'Neill, {Brian Patrick} and William Elmquist and Hoxworth, {Joseph M.} and David Frakes and Sarkaria, {Jann N} and Kristin Swanson and Nhan Tran and Jing Li and Mitchell, {Joseph Ross}",
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TY - JOUR

T1 - Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

AU - Hu, Leland S

AU - Ning, Shuluo

AU - Eschbacher, Jennifer M.

AU - Baxter, Leslie C.

AU - Gaw, Nathan

AU - Ranjbar, Sara

AU - Plasencia, Jonathan

AU - Dueck, Amylou

AU - Peng, Sen

AU - Smith, Kris A.

AU - Nakaji, Peter

AU - Karis, John P.

AU - Quarles, C. Chad

AU - Wu, Teresa

AU - Loftus, Joseph C

AU - Jenkins, Robert Brian

AU - Sicotte, Hugues

AU - Kollmeyer, Thomas M.

AU - O'Neill, Brian Patrick

AU - Elmquist, William

AU - Hoxworth, Joseph M.

AU - Frakes, David

AU - Sarkaria, Jann N

AU - Swanson, Kristin

AU - Tran, Nhan

AU - Li, Jing

AU - Mitchell, Joseph Ross

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Genetic

KW - Glioblastoma

KW - Heterogeneity

KW - Radiogenomics

KW - Texture

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