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. 2023 May 12;80(6):147.
doi: 10.1007/s00018-023-04772-1.

Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma

Affiliations

Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma

Dena Panovska et al. Cell Mol Life Sci. .

Abstract

Background: Functional profiling of freshly isolated glioblastoma (GBM) cells is being evaluated as a next-generation method for precision oncology. While promising, its success largely depends on the method to evaluate treatment activity which requires sufficient resolution and specificity.

Methods: Here, we describe the 'precision oncology by single-cell profiling using ex vivo readouts of functionality' (PROSPERO) assay to evaluate the intrinsic susceptibility of high-grade brain tumor cells to respond to therapy. Different from other assays, PROSPERO extends beyond life/death screening by rapidly evaluating acute molecular drug responses at single-cell resolution.

Results: The PROSPERO assay was developed by correlating short-term single-cell molecular signatures using mass cytometry by time-of-flight (CyTOF) to long-term cytotoxicity readouts in representative patient-derived glioblastoma cell cultures (n = 14) that were exposed to radiotherapy and the small-molecule p53/MDM2 inhibitor AMG232. The predictive model was subsequently projected to evaluate drug activity in freshly resected GBM samples from patients (n = 34). Here, PROSPERO revealed an overall limited capacity of tumor cells to respond to therapy, as reflected by the inability to induce key molecular markers upon ex vivo treatment exposure, while retaining proliferative capacity, insights that were validated in patient-derived xenograft (PDX) models. This approach also allowed the investigation of cellular plasticity, which in PDCLs highlighted therapy-induced proneural-to-mesenchymal (PMT) transitions, while in patients' samples this was more heterogeneous.

Conclusion: PROSPERO provides a precise way to evaluate therapy efficacy by measuring molecular drug responses using specific biomarker changes in freshly resected brain tumor samples, in addition to providing key functional insights in cellular behavior, which may ultimately complement standard, clinical biomarker evaluations.

Keywords: Ex vivo treatment; Functional diagnostics; Glioblastoma; Precision medicine; Single-cell.

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Conflict of interest statement

Authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Setup of PROSPERO assay focusing on functional analysis
Fig. 2
Fig. 2
Mapping of bulk cytotoxicity profiles and single-cell drug heterogeneity in PDCLs. A PROSPERO workflow in PDCLs. B, C Dose–response curves representing the cell viability after AMG232 and RT, respectively. D, E Bar charts of PLIs after AMG232 (0-10 μM for 72 h) and RT (0–10 Gy for 6 days), respectively. The values represent mean ± SD of three (AMG232) or four (RT) replicates and were normalized to 100% assigned to the vehicle control for each assay. Overall responses of the models stratified by TP53 status and assigned colored circles (TP53MUT—red; TP53WT-blue and MDM2AMP—white/blue stripes). Wilcoxon rank sum test was used to calculate statistical significance between the TP53WT and TP53MUT groups (ns not significant (P > 0.05); *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; ****P ≤ 0.0001). F AMG232 targeting MDM2/p53 complex and downstream cellular effects. G Stacked barplots representation of drug-induced signatures across the PDCLs (n = 14) ordered by response to AMG232 (from most to least responsive). H Molecular effects induced by RT. I Stacked barplots representation of drug-induced signatures across the PDCLs (n = 14) ordered by response to RT (from most to least responsive)
Fig. 3
Fig. 3
Unbiased probability modeling of AMG232 and RT responses. AC AMG232 treated samples. A Pareto front of signature optimization correlated to AUC/PLI (R). The preferred signature with highest correlation (R) to PLI is highlighted in red (p21). B Pseudotime of the correlation between percentage of cells with p21 induction in each sample and PLI, highlighting MDM2AMP cell lines. C Correlation between PLI and predicted PLI. DF RT treated samples. D Pareto front of RT panel optimization correlated to AUC/PLI (R). The preferred signature with highest correlation (R) to PLI is highlighted in red (p21/pATM/pH2AX). E Correlation of the pseudotime to PLI. (f) Correlation between PLI and Predicted PLI. G, H Testing the prognostic capacity of each signature set (functional, cell cycle or combination of both) in drug- (left panels) and vehicle-treated cells (right panels)
Fig. 4
Fig. 4
Applying PROSPERO on freshly resected GBM biopsies. A Schematic of PROSPERO’s workflow. B Barplot ranking based on MDM2 levels. The cutoff level was assessed in the PDCLs (vertical dashed line) and every sample exceeding the cutoff value is considered as an MDM2AMP. C A pseudotime correlation between p21 evels and predicted PLI. D Ranking of patients’ samples based on predicted PLI ranging from least (top) to most sensitive (bottom). EG Ranking of patients’ samples based on proportions of responsive cells and colored by: E tumor region (core vs invasion); F diagnosis; G surgery- newly vs recurrent tumor
Fig. 5
Fig. 5
Baseline heterogeneity and therapy-induced responses upon AMG232 and RT treatment in tumor cell subclasses. UMAP visualization of tumor cells in A PDCLs and B biopsies (colored by phenotype). Cells responding to AMG232 (p21 upregulation; red color) in C PDCLs and D biopsies. Responsive cells to RT (upregulation of pATM, pH2AX and p21; red color) are presented in E PDCLs and F biopsies
Fig. 6
Fig. 6
Heatmap representations of therapy-induced plasticity by AMG232 and RT as recorded in PDCLs and high-grade glioma biopsies. Relative distribution of the tumor subtypes in control samples (baseline distribution) in A PDCLs and B biopsies. Therapy-induced plasticity represented by phenotypic shifts upon therapy are presented for AMG232 treatment in C PDCLs and D biopsies (normalized and z-scored). E RT-driven enrichment/depletion of tumor phenotypic subclusters in PDCLs (normalized and z-scored). F RT-driven enrichment/depletion of tumor phenotypic subclusters in biopsies (normalized and z-scored). Samples in each heatmap are hierarchically clustered based on TP53 and (inferred) MDM2 mutational status
Fig. 7
Fig. 7
Validation of PROSPERO in PDX model. A Schematic diagram of experimental setup. B Percentage of cells showing drug-related marker induction (mean ± SD) in BT112 (PDCL), ex vivo treated cells with PROSPERO and cells isolated from in vivo treated mice

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