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Tumor microenvironment (TME) mapping: MRI of intratumoral heterogeneity of oxygen metabolism and neovascularization uncovers two survival relevant subgroups of IDH1 wild-type glioblastoma

Andreas Stadlbauer^1,2, Max Zimmermann¹, Arnd Dörfler³, Roland Coras⁴, Stefan Oberndorfer⁵, Michael Buchfelder¹, and Karl Rössler¹

¹Department of Neurosurgery, University of Erlangen-Nürnberg, Erlangen, Germany, ²Institute of Medical Radiology, University Clinic of St. Pölten, St. Pölten, Austria, ³Department of Neuroradiology, University of Erlangen-Nürnberg, Erlangen, Germany, ⁴Department of Neuropathology, University of Erlangen-Nürnberg, Erlangen, Germany, ⁵Department of Neurology, University Clinic of St. Pölten, St. Pölten, Austria

Synopsis

The dismal prognosis of glioblastoma is largely attributed to hypoxic and perivascular niches in the tumor microenvironment (TME) which are essential for elucidation of pathophysiological mechanisms behind therapy resistance and recurrence. Here, we combined MRI biomarkers for oxygen metabolism and neovascularization with an automatic classification strategy for localization of hypoxic and vascular niches within the heterogeneously structured TME. Correlation with the metabolic pathway for energy production uncovered two different phenotypes for glioblastoma IDH1wt: A glycolytic phenotype with stable functional neovasculature, and a necrotic/hypoxic phenotype with defective neovasculature and a more aggressive tumor behavior. The glycolytic phenotype showed longer progression-free survival.

Introduction

Knowledge of the hypoxic and perivascular niches in the tumor microenvironments (TMEs) is essential for understanding treatment failure. Reprogramming of energy metabolism additionally represent a considerable problem in therapeutic management of glioblastoma (1). In contrast to normal differentiated cells, which rely primarily on mitochondrial oxidative phosphorylation (OxPhos) for energy production, most cancer cells instead rely on aerobic glycolysis, a phenomenon termed “Warburg effect”. The interplay between oxygen metabolism, neovascularization and energy metabolism is of crucial importance in tumor biology. The purpose was to combine MRI biomarkers for oxygen metabolism and neovascularization with an automatic classification strategy including energy metabolic pathways for localization of hypoxic and vascular niches. We termed our approach TME mapping.

Methods

52 patients with newly-diagnosed, untreated glioblastoma (IDH1 wildtype) were examined with a physiological MRI protocol including multiparametric quantitative blood-oxygen-level-dependent (qBOLD) and vascular architecture mapping (VAM). For qBOLD, R₂*- and R₂-mapping (8 echoes each) was performed. For VAM we used spin-echo and gradient-echo EPI dynamic susceptibility contrast (DSC) sequences in combination with a dual contrast agent injections approach (Fig.1, phase1)(2–4). All data processing was performed with custom-made MatLab software. qBOLD data were used for calculation of MRI biomarker maps of oxygen metabolism including oxygen extraction fraction (OEF), cerebral metabolic rate of oxygen (CMRO₂)(5) and the average mitochondrial oxygen tension (mitoPO₂)(6,7). The VAM data were used for calculation of MRI biomarker maps of neovascularization including the microvessel type indicator (MTI) as well as the upper limit of microvessel radius (R_U) and microvessel density (N_U) (Fig.1, phases2 and 3)(8). Fusion of MRI biomarker information about oxygen metabolism (OEF, CMRO2, mitoPO₂) and neovascularization (MTI, R_U, N_U) as well as classification of TME (Fig.1, phase4) consisted of four steps. (i) classification of the oxidative status in mitochondria; (ii) classification of the tumor neovasculature integrity; (iii) fusion of this classified information in one imaging data set; and (iv) classification of TME within this data set considering the CMRO₂-OEF-scatterplot (Fig.1, bottom). This procedure was associated with the introduction of six different TMEs for oxygen metabolism and neovascularization: (i) TME with mitoPO₂ <10mmHg and dysfunctional neovasculature: “Hypoxia, no NV”; red voxels in the TME map; (ii) TME with mitoPO₂ <10mmHg with functional neovasculature: “Hypoxia+NV”; yellow; (iii) TME with mitoPO₂ = 10–60mmHg and no neovascularization: “Necrosis”; black; (iv) TME with mitoPO₂ = 10–60mmHg and neovascularization: “OxPhos+NV”; green; (v) TME with mitoPO₂>60mmHg and neovascularization: “Glycolysis+NV”; blue; and (vi) TME with mitoPO₂ >60mmHg and no neovascularization: “Glycolysis, no NV”; blue. As described above, the voxels of each TME were assigned with different colors which resulted in the so-called oxygen metabolism-neovascularization TME map. Association of the different TME volume fractions with progression-free survival (PFS) was assessed using Kaplan-Meier analysis and Cox proportional hazard models.

Results

A common spatial structure of TMEs was detected: central necrosis surrounded by tumor hypoxia (with defective and functional neovasculature) and different TMEs with a predominance of OxPhos and glycolysis for energy production, respectively (Fig.2 and 3). The percentage of the different TMEs on the total tumor volume uncovered two clearly different subtypes of glioblastoma IDH1wt: a glycolytic dominated phenotype with predominantly functional neovasculature and a necrotic/hypoxic dominated phenotype with approximately 50% of defective neovasculature (Fig.4). Patients with a necrotic/hypoxic dominated phenotype showed significantly shorter PFS (P=0.035; Fig.5).

Discussion

The finding of a common spatial structure of TMEs is conclusively explainable due to the known sequence of events during the development of glioblastoma obtained from preclinical studies and histopathology (9,10). The widely accepted model of glioblastoma progression includes an explanation of the relationship between pseudopalisades, angiogenesis, and aggressive clinical behavior (11,12). The glycolytic phenotype with high percentage of stable functional neovasculature is dominated by the vascular niche that provides a protective microenvironment in which glioblastoma stem cells are able to freely proliferate and remain undifferentiated and are unaffected by any external influences (13,14). In the necrotic/hypoxic phenotype with a high percentage of defective neovasculature, however, the hypoxic niche plays a major role which is associated with tumor progression and resistance to both radiotherapy and chemotherapy (10). Hypoxia promotes a more malignant phenotype of cancer cells and supports the survival of glioma stem cells which possess greater drug resistance, self-renewal potential and tumorigenicity (15,16).

Conclusions

We performed fusion of imaging parameters about energy production and neovascularization (two hallmarks of cancer) and thereby gained a more precise insight into the intratumoral heterogeneity of human glioblastoma's pathophysiology. Our non-invasive mapping approach allows for the user-independent classification of TMEs and the detection of hypoxic and vascular niches in glioblastoma. This enabled us to identify patient subgroups with significant different PFS.

Acknowledgements

No acknowledgement found.

References

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Figures

Figure 1. The TME mapping approach. After acquisition (phase 1) and pre-processing (phase 2) of the MRI raw data, calculation of maps for OEF, CMRO₂ and mitoPO₂ was performed (phase 3). Fusion and classification of MRI biomarker information about oxygen metabolism and neovascularization (phase 4) completed TME mapping in four steps: (i) classification of mitochondrial oxidative status using mitoPO2; (ii) classification of tumor neovasculature using MTI; (iii) voxelwise fusion of classified information; (iv) each voxel was attributed to one of the six introduced TMEs and assigned with the appropriate color. This resulted in the so-called oxygen metabolism-neovascularization TME map.

Figure 2. Conventional anatomic MRI (cMRI; grey box), oxygen metabolism (OEF, CMRO₂, mitoPO₂; blue box), neovascularization (MTI; red box), and TME mapping (downright) in a 61-year-old male patient with glioblastoma IDH1wt. Oxygen metabolism and neovascularization demonstrated spatial heterogeneity within the lesion. Information fusion of oxygen metabolism and neovascularization using our TME approach resulted in the CMRO2-OEF-scatterplot and finally in the TME map. This glioblastoma was dominated by glycolysis (50.6% of the tumor volume) and functional, stable neovasculature (80.8% of the tumor volume showed neovascularization).

Figure 3. Conventional anatomic MRI (cMRI; grey box), oxygen metabolism (OEF, CMRO₂, mitoPO₂; blue box), neovascularization (MTI; red box), and TME mapping (downright) in a 71-year-old female glioblastoma IDH1wt patient. Spatial heterogeneity of oxygen metabolism and neovascularization in this glioblastoma was similar to the patient in Fig. 2. The CMRO₂-OEF-scatterplot, however, demonstrated a substantially lower percentage of glycolysis and higher percentage of necrosis (21.5%), hypoxia (total 54.9%), and defective tumor vasculature (50.4%).

Figure 4. The two metabolic phenotypes of glioblastoma IDH1wt. Upper part: Pie charts illustrating the percentage distribution of the TMEs for the glycolytic dominated phenotype with predominantly functional neovasculature (left) and the necrotic/hypoxic dominated phenotype with relatively high percentage of defective tumor neovasculature (right). Lower part: Box and whisker plots show the percentage distribution of the TMEs. Boxes are mean values ± standard deviation, and whiskers are minimum and maximum values, respectively. Boxes marked with an asterisk (two asterisks) in the glycolytic phenotype were significantly different to their counterpart in the necrotic/hypoxic phenotype (*P < 0.001; **P = 0.005).

Figure 5. Differences between the two metabolic phenotypes of glioblastoma and survival analysis. Box-whisker-plots show (A) the mean CMRO₂ averaged over the whole tumor volume and (B) the percentage of tumor volume with defective and functional neovasculature, respectively. Boxes are mean values ± standard deviation; whiskers are minima and maxima. Asterisks* mark significant differences between the two phenotypes (P<0.001). C) Scatterplot showing the correlation between mean CMRO₂ and percentage of tumor volume with functional neovasculature for the glycolytic (blue) and the necrotic/hypoxic phenotype (red). D) Kaplan-Meier curves for progression-free survival (PFS) in the glycolytic (blue) and the necrotic/hypoxic phenotype (red).

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)

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