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Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

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Science Translational Medicine  02 Sep 2015:
Vol. 7, Issue 303, pp. 303ra138
DOI: 10.1126/scitranslmed.aaa7582
  • Fig. 1. Consensus matrix, CDF curve, and delta curve for all clusters.

    (A to C) The Stanford cohort as the development cohort and the TCGA cohort as the validation cohort. (D to F) The TCGA cohort as the development cohort and the Stanford cohort as the validation cohort. (A and D) Consensus matrices represented as heat maps for k = 3 (clusters 1, 2, and 3). Subjects are both rows and columns, and consensus values range from 0 (never clustered together, white) to 1 (always clustered together, dark blue). The matrices are ordered by consensus-clustered groups, depicted as a dendrogram above the heat map. (B and E) The CDF curve was one diagnostic tool used to select the optimal number of clusters in consensus clustering. The bottom left of the graph represents sample pairs rarely clustered together, whereas the upper right contains those almost always paired together. The middle segment represents sample pairs with ambiguous assignments across different clustering runs. The goal was to identify the lowest rate of ambiguous assignments (flat middle segment). (C and F) The delta curve depicts the CDF progression graph, plotting the relative change in area under the CDF curve, comparing k with k + 1. The goal was to select the largest k that induced the smallest incremental change in the AUC.

  • Fig. 2. GBM subtypes cluster by phenotypic MRI characteristics, correlate with survival, and associate with molecular pathways.

    Three distinct image-based subtypes were derived from a development cohort (Stanford cohort) and validated in an independent validation cohort (TCGA cohort). (A) Imaging phenotypes are illustrated as simplified, representative pictograms for each cluster, although the multivariate combination of quantitative images features that characterize each cluster (table S4) cannot be fully visually exemplified. (B) Aggregate multislice 2D renditions of the three imaging subtypes (clusters). (C) Kaplan-Meier survival curves (solid lines) with 95% confidence intervals (CIs) (dotted lines) derived from TCGA survival data are shown for each cluster in the TCGA validation cohort. Survival differences across the clusters: P = 0.004, log-rank test (n = 37 subjects across the clusters who underwent the same Stupp protocol treatment regimen: cluster 1, n = 6; cluster 2, n = 22; cluster 3, n = 9). (D) Molecular changes associated with each cluster. Arrows indicate up- or down-regulation of sample pathways identified using PARADIGM. Table S6 provides a comprehensive list of significant regulatory pathways associated with each cluster at FDR <5%.

  • Fig. 3. Tumor volumes by cluster for the development cohort (Stanford cohort).

    The largest tumors were in cluster 3, and the smallest tumors were in cluster 2 (P < 0.001, Kruskal-Wallis test). An overlap in tumor volume is observed between clusters 1 and 2.

  • Table 1. Clinical and molecular characteristics of the development (Stanford) and validation (TCGA) cohorts.

    Clinical data for the validation cohort were available on 114 subjects, and molecular data were available on 107 subjects (missing data are noted). KPS is shown as three categories and a mean value. Tumor location, EGFR amplification, IDH1 mutation, and MGMT promoter hypermethylation are tabulated as number (n) and percentage.

    CharacteristicStanford
    development
    cohort
    (n = 121)
    TCGA
    validation
    cohort
    (n = 144)
    Age, mean (SD)64.6 (14.1)58.5 (15.1)
    Sex, n male (%)69 (57)73 (64)
    KPS, n (%)
      >70%65 (54)78 (68)
      50–70%44 (37)19 (17)
      <50%11 (9)1 (1)
      Missing116
      Mean ± SD71.7 ± 1778.6 ± 12.3
    Location, n (%)
      Frontal42 (35)
      Parietal23 (19)
      Basal ganglia3 (3)
      Temporal49 (41)
      Occipital4 (3)
    EGFR amplification, n (%)21 (17)100 (69)
      Missing7426
    IDH1 mutation, n (%)4 (4)
      Missing22
    MGMT hypermethylation, n (%)27 (22)25 (23)
      Missing7360
    Molecular subgroups, n (%)
      Proneural32 (30)
      Neural18 (17)
      Mesenchymal31 (29)
      Classical25 (23)
      Missing1
  • Table 2. Clinical and molecular characteristics of the three imaging subtypes in both cohorts.

    (A) The development (Stanford) cohort. (B) The validation (TCGA) cohort. For continuous values, data are means (SD). P values are derived from comparisons across the three clusters using the Kruskal-Wallis test (*) for continuous variables or the Fisher’s exact test () for categorical variables. Where noted as missing, molecular features were not available for analysis.

    CharacteristicCluster 1, pre-multifocalCluster 2, sphericalCluster 3, rim-enhancingP value
    (A) Development cohort
    Age, mean (SD)64.7 (17.7)66.0 (11.7)62.0 (13.0)0.324*
    Sex, n (%)
      Female142414
      Male2227200.736
    Mean KPS (SD)67.7 (19.2)73.7 (13.9)72.9 (18.1)0.439*
    Tumor volume, cm3 (SD)31.2 (30.1)22.6 (18.9)55.9 (32.7)4.49 × 10−7*
    MGMT hypermethylation, n81270.747†
      Missing252919
    EGFR amplification, n5880.702†
      Missing243317
    Location, n0.0532
      Basal ganglia021
      Frontal92013
      Occipital400
      Parietal7610
      Temporal162311
    (B) Validation cohort
    Age, mean (SD)58.6 (14.5)58.9 (16.5)57.6 (13.4)0.506*
    Sex, n (%)
      Female15 (37)15 (24)11 (28)
      Male15 (37)37 (59)21 (52)0.171
      Missing11118
    Mean KPS ± SD78.3 ± 10.380 ± 12.676.6 ± 13.20.488*
      Missing763
    Molecular subgroups, n (%)
      Classical4 (10)13 (21)8 (20)0.075
      Mesenchymal7 (17)19 (30)5 (13)
      Neural7 (17)3 (5)8 (20)
      Proneural7 (17)12 (19)7 (18)
      Missing161612
    CIMP, n (%)4 (10)1 (2)1 (3)0.121
      Missing122
    IDH1 mutation, n (%)3 (10)1 (2)0 (0)0.216
      Missing2119
    MGMT hypermethylation, n (%)6 (21)13 (27)6 (21)0.496
      Missing172815
    EGFR amplification, n (%)27 (66)43 (68)30 (75)0.662
      Missing10158
  • Table 3. Selected pathways associated with each image-based cluster.

    From the complete set of pathways significantly associated with each image-based cluster (table S6) are selected pathways that are either specifically differentially expressed among the three clusters or among the top 10 for the cluster by fold change.

    Signaling pathwayFold
    change
    q value
    (%)
    Cluster 1
    Signaling events mediated by stem cell
    factor receptor (c-Kit)
    1.0330
    Cluster 2
    Signaling events mediated by c-Kit0.9760
    Signaling events mediated by prolactin (PRL)0.9974.5
    Platelet-derived growth factor receptor–α
    (PDGFR-α) signaling pathway
    0.9970
    Forkhead box protein A2 (FOXA2) and FOXA3
    transcription factor networks
    0.9910
    Vascular endothelial growth factor receptor 1
    (VEGFR1)–specific signals
    0.9964.5
    Angiopoietin (Ang) receptor Tie2-mediated signaling0.9880
    Regulation of nuclear mothers against
    decapentaplegic homolog 2/3 (SMAD2/3) signaling
    0.9954.5
    Signaling events mediated by protein tyrosine
    phosphatase 1B (PTP1B)
    0.9954.5
    Osteopontin-mediated events0.9954.5
    Signaling events activated by hepatocyte growth
    factor receptor (c-Met)
    0.9944.5
    Cluster 3
    FOXA2 and FOXA3 transcription factor networks1.0130
    PDGFR-β signaling pathway1.0130
    Canonical Wnt signaling pathway1.0033.6197
    VEGFR1-specific signals1.0040
    Ang receptor Tie2-mediated signaling1.0093.6197
    Syndecan-1–mediated signaling events1.0110
    Interleukin-6 (IL-6)–mediated signaling events1.0103.6197
    Osteopontin-mediated events1.0100
    Fc-ε receptor I signaling in mast cells1.0090
    αMβ2 integrin signaling1.0090

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/7/303/303ra138/DC1

    Methods

    Fig. S1. Relative contributions of the top 24 imaging features in characterizing each of the clusters.

    Table S1. Clinical characteristics of the Stanford cohort before and after selection of the development cohort.

    Table S2. Clinical characteristics of the TCGA cohort before and after selection of the validation cohort.

    Table S3. All 2D and multislice 2D quantitative MR image features used for analysis.

    Table S4. Two-dimensional and multislice 2D quantitative MR image features significantly associated with each cluster.

    Table S5. Cluster assignment by subjects in the development cohort with and without midline-crossing lesions.

    Table S6. Regulatory signaling pathways significantly associated with each cluster.

    References (4547)

  • Supplementary Material for:

    Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

    Haruka Itakura, Achal S. Achrol, Lex A. Mitchell, Joshua J. Loya, Tiffany Liu, Erick M. Westbroek, Abdullah H. Feroze, Scott Rodriguez, Sebastian Echegaray, Tej D. Azad, Kristen W. Yeom, Sandy Napel, Daniel L. Rubin, Steven D. Chang, Griffith R. Harsh IV, Olivier Gevaert*

    *Corresponding author. E-mail: olivier.gevaert{at}stanford.edu

    Published 2 September 2015, Sci. Transl. Med. 7, 303ra139 (2015)
    DOI: 10.1126/scitranslmed.aaa7582

    This PDF file includes:

    • Methods
    • Fig. S1. Relative contributions of the top 24 imaging features in characterizing each of the clusters.
    • Table S1. Clinical characteristics of the Stanford cohort before and after selection of the development cohort.
    • Table S2. Clinical characteristics of the TCGA cohort before and after selection of the validation cohort.
    • Table S3. All 2D and multislice 2D quantitative MR image features used for analysis.
    • Table S4. Two-dimensional and multislice 2D quantitative MR image features significantly associated with each cluster.
    • Table S5. Cluster assignment by subjects in the development cohort with and without midline-crossing lesions.
    • Table S6. Regulatory signaling pathways significantly associated with each cluster.
    • References (4547)

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