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. 2020 Feb 24;12(2):520.
doi: 10.3390/cancers12020520.

Intratumoral Heterogeneity and Longitudinal Changes in Gene Expression Predict Differential Drug Sensitivity in Newly Diagnosed and Recurrent Glioblastoma

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Free PMC article

Intratumoral Heterogeneity and Longitudinal Changes in Gene Expression Predict Differential Drug Sensitivity in Newly Diagnosed and Recurrent Glioblastoma

Ella L Kim et al. Cancers (Basel). .
Free PMC article

Abstract

Background: Inevitable recurrence after radiochemotherapy is the major problem in the treatment of glioblastoma, the most prevalent type of adult brain malignancy. Glioblastomas are notorious for a high degree of intratumor heterogeneity manifest through a diversity of cell types and molecular patterns. The current paradigm of understanding glioblastoma recurrence is that cytotoxic therapy fails to target effectively glioma stem cells. Recent advances indicate that therapy-driven molecular evolution is a fundamental trait associated with glioblastoma recurrence. There is a growing body of evidence indicating that intratumor heterogeneity, longitudinal changes in molecular biomarkers and specific impacts of glioma stem cells need to be taken into consideration in order to increase the accuracy of molecular diagnostics still relying on readouts obtained from a single tumor specimen. Methods: This study integrates a multisampling strategy, longitudinal approach and complementary transcriptomic investigations in order to identify transcriptomic traits of recurrent glioblastoma in whole-tissue specimens of glioblastoma or glioblastoma stem cells. In this study, 128 tissue samples of 44 tumors including 23 first diagnosed, 19 recurrent and 2 secondary recurrent glioblastomas were analyzed along with 27 primary cultures of glioblastoma stem cells by RNA sequencing. A novel algorithm was used to quantify longitudinal changes in pathway activities and model efficacy of anti-cancer drugs based on gene expression data. Results: Our study reveals that intratumor heterogeneity of gene expression patterns is a fundamental characteristic of not only newly diagnosed but also recurrent glioblastomas. Evidence is provided that glioblastoma stem cells recapitulate intratumor heterogeneity, longitudinal transcriptomic changes and drug sensitivity patterns associated with the state of recurrence. Conclusions: Our results provide a transcriptional rationale for the lack of significant therapeutic benefit from temozolomide in patients with recurrent glioblastoma. Our findings imply that the spectrum of potentially effective drugs is likely to differ between newly diagnosed and recurrent glioblastomas and underscore the merits of glioblastoma stem cells as prognostic models for identifying alternative drugs and predicting drug response in recurrent glioblastoma. With the majority of recurrent glioblastomas being inoperable, glioblastoma stem cell models provide the means of compensating for the limited availability of recurrent glioblastoma specimens.

Keywords: gene expression; glioblastoma; glioblastoma stem cells; molecular pathways; recurrent glioblastoma; target anti-cancer therapy; transcriptomics.

Conflict of interest statement

M.S. (Maxim Sorokin), A.G. (Andrew Garazha) and A.B. have a financial relationship with Omicsway Corp.

Figures

Figure 1
Figure 1
Study design. Tissue blocks were resected from newly diagnosed glioblastomas (ndGBs) or recurrent GBs (recGBs) using a multisampling approach. Each block was portioned and processed either for a direct sequencing of tissue RNA or glioma stem cell (GSC) isolation. RNA-seq of tissue samples or primary cultures of GSCs was followed by bioinformatic analysis to compare gene expression along the following lines: (1) ndGB vs. recGB tissues; (2) between different regions from the same tumor; (3) tissue biopsies vs. GSCs; (4) GSCs from ndGBs vs. GSCs from recGBs.
Figure 2
Figure 2
Hierarchical clustering dendrogram of RNA-seq profiles for GB tissue samples. Normalized gene expression (A) and pathway activation level (PAL, B) data were used to calculate Euclidian distance between the samples. Principal component (PC) analysis of RNA-seq profiles for GB tissue samples using normalized gene expression (C) or PAL (D) data. Color marker in PCA plots indicates tissue type. Values aligned with axis show proportion of variance in percent for principal components 1 and 2, respectively. Color marker indicates tissue type (ndGB or recGB).
Figure 3
Figure 3
Principal component analysis of multisampled GB tissues using normalized gene expression data. Panels represent, respectively, colored data for individual tumors. Number of profiled samples is indicated. Color marker indicates tissue type (ndGB or recGB). Values aligned with axis show proportion of variance in percent for principal components 1 and 2, respectively.
Figure 4
Figure 4
Assessments of stemness attributes and proliferation in GSCs. (A) Expression of the neural stem cell/neural progenitor marker nestin (green) and astrocytic marker GFAP (red) in GSCs isolated from ndGBs (samples 295-ndGB and 139-ndGB) or recGB (samples 295-recGB and 700-recGB). Samples 295-ndGB and 295-recGB represent isogenic GSCs derived from the same patient at the time of initial diagnosis or at recurrence, respectively. (B,C) Quantitative analyses of the self-renewal activity by ELDA (B, representative image) and proliferation rate by assessing Ki67 expressing cells using immunofluorescence staining (C, representative image). The table summarizes quantification results of ELDA and Ki67. “SCF”, stem cell frequency; “lower” and “upper”, confidence intervals for 1/(stem cell frequency). 295-ndGB and 295-recGB correspond to isogenic GSCs derived from the same patient at the time of initial diagnosis or at recurrence, respectively.
Figure 5
Figure 5
Varying degrees of stemness and diversity of gene expression patterns among isogenic GSCs. (A,B) GSCs isolated from different regions (designated as R2 or R3) of the same tumor (ndGB #726) differ significantly (p = 5.08 × 10−12) in the degree of self-renewal (A, graphical presentation of ELDA results) and cellular morphology (B, phase contrast microscopy using objective lens magnifications 20× and 1.6×, scale bars indicated); “log fraction nonresponding” indicates frequency of cells uncapable of forming clonal spheres, “dose (number of cells)” indicates number of cells per mL. The data values with zero negative responses are represented by down-pointing triangles. The dotted lines give 95% confidence interval. (C) Variable expression of TGFβ (top), CD133 (middle), PDGFRA and MGMT (bottom) in GSCs derived from different regions of the same tumor (designated as R1, R2 or R3). Grey bars beneath the “CD133” panel correspond to the varying amounts of cell lysate loaded per well. Arrows indicate major isoforms of the proteins analyzed. “*” indicates an abnormally migrating MGMT isoform expressed in GSCs. 726-ndGSC and 226-recGSC correspond to isogenic GSCs obtained from ndGB or recGB, respectively.
Figure 6
Figure 6
(A) Venn diagram illustrating differential molecular pathways in recGB vs. ndGB stages for GSCs and GB tissues. (B) Pathway activation profile of the co-stimulatory T-cell signaling pathway in recGB vs. ndGB stages for tissue samples and GSCs. Genes encoding molecular targets for immunotherapeutics (Ipilimumab, Durvalumab) are circled. (C) Pathway activation profile of the DNA damage response pathway in recGB vs. ndGB stages for tissue samples and GSCs. Gene MGMT is circled.
Figure 7
Figure 7
Oncobox BES for TMZ is higher in ndGB vs. recGB sets for both (A) GB tissues, Wilcoxon test p-value = 1.06 × 10−7; and (B) GSCs, Wilcoxon p = 0.042. (C) Dependence of GB patient’s progression-free survival on Oncobox BES for Temozolomide, n = 16 patients with ndGB. Hazard ratio = 0.29 (95% CI, 0.088–0.96, p = 0.043) for patient level analysis. Hazard ratio = 0.18 (95% CI, 0.078–0.4, p < 0.0001) for individual sample level analysis. The analysis was performed using R ggsurvplot package.

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