Spectroscopic Characterization of Gliosarcomas—Do They Differ From Glioblastomas and Metastases?:

Primary and secondary gliosarcoma: differences in treatment and outcome

INTRODUCTION

There are only few studies comparing differences in the outcome of primary versus secondary gliosarcoma. This study aimed to review the outcome and survival of patients with primary or secondary gliosarcoma following surgical resection and adjuvant treatment. The data were also matched with data of patients with primary and secondary glioblastoma (GBM).

PATIENTS AND METHODS

Treatment histories of 10 patients with primary gliosarcoma and 10 patients with secondary gliosarcoma were analysed and compared. Additionally, data of 20 patients with primary and 20 patients with secondary GBM were analysed and compared. All patients underwent surgical resection of the tumour in our department. Follow-up data, progression-free survival (PFS), and median overall survival (mOS) were evaluated.

RESULTS

The median PFS in patients with primary gliosarcoma was significantly higher than in patients with secondary gliosarcoma (p = 0.037). The 6-month PFS rates were 80.0% in patients with primary and 30.0% in patients with secondary gliosarcoma. Upon recurrence, five patients with primary gliosarcoma and four patients with secondary gliosarcoma underwent repeat surgical resection. The mOS of patients with primary gliosarcoma was significantly higher than that of patients with secondary gliosarcoma (p = 0.031). The percentage of patients surviving at 1-year/2-year follow-up in primary gliosarcoma was 70%/20%, while it was only 10%/10% in secondary gliosarcoma. When PFS and mOS of primary gliosarcoma was compared to primary GBM, there were no statistically differences (p = 0.509; p = 0.435). The PFS and mOS of secondary gliosarcoma and secondary GBM were also comparable (p = 0.290 and p = 0.390).

CONCLUSION

Patients with primary gliosarcoma have a higher PFS and mOS compared to those with secondary gliosarcoma. In the case of tumour recurrence, patients with secondary gliosarcoma harbour an unfavourable prognosis with limited further options. The outcome of patients with primary or secondary gliosarcoma is comparable to that of patients with primary or secondary GBM.

TGF-β and BMP signaling are associated with the transformation of glioblastoma to gliosarcoma and then osteosarcoma

Background

Gliosarcoma, an isocitrate dehydrogenase wildtype (IDH-WT) variant of glioblastoma, is defined by clonal biphasic differentiation into gliomatous and sarcomatous components. While the transformation from a glioblastoma to gliosarcoma is uncommon, the subsequent transformation to osteosarcoma is rare but may provide additional insights into the biology of these typically distinct cancers. We observed a patient initially diagnosed with glioblastoma, that differentiated into gliosarcoma at recurrence, and further evolved to osteosarcoma at the second relapse. Our objective was to characterize the molecular mechanisms of tumor progression associated with this phenotypic transformation.

Methods

Tumor samples were collected at all 3 stages of disease and RNA sequencing was performed to capture their transcriptomic profiles. Sequential clonal evolution was confirmed by the maintenance of an identical PTEN mutation throughout the tumor differentiation using the TSO500 gene panel. Publicly available datasets and the Nanostring nCounter technology were used to validate the results.

Results

The glioblastoma tumor from this patient possessed mixed features of all 3 TCGA-defined transcriptomic subtypes of an IDH-WT glioblastoma and a proportion of osteosarcoma signatures were upregulated in the original tumor. Analysis showed that enhanced transforming growth factor-β (TGF-β) and bone morphogenic protein signaling was associated with tumor transformation. Regulatory network analysis revealed that TGF-β family signaling committed the lineage tumor to osteogenesis by stimulating the expression of runt-related transcription factor 2 (RUNX2), a master regulator of bone formation.

Conclusions

This unusual clinical case provided an opportunity to explore the modulators of longitudinal sarcomatous transformation, potentially uncovering markers indicating predisposition to this change and identification of novel therapeutic targets.

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma

Objective

To identify optimal machine-learning methods for the radiomics-based differentiation of gliosarcoma (GSM) from glioblastoma (GBM).

Materials and Methods

This retrospective study analyzed cerebral magnetic resonance imaging (MRI) data of 83 patients with pathologically diagnosed GSM (58 men, 25 women; mean age, 50.5 ± 12.9 years; range, 16-77 years) and 100 patients with GBM (58 men, 42 women; mean age, 53.4 ± 14.1 years; range, 12-77 years) and divided them into a training and validation set randomly. Radiomics features were extracted from the tumor mass and peritumoral edema. Three feature selection and classification methods were evaluated in terms of their performance in distinguishing GSM and GBM: the least absolute shrinkage and selection operator (LASSO), Relief, and Random Forest (RF); and adaboost classifier (Ada), support vector machine (SVM), and RF; respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of each method were analyzed.

Results

Based on tumor mass features, the selection method LASSO + classifier SVM was found to feature the highest AUC (0.85) and ACC (0.77) in the validation set, followed by Relief + RF (AUC = 0.84, ACC = 0.72) and LASSO + RF (AUC = 0.82, ACC = 0.75). Based on peritumoral edema features, Relief + SVM was found to have the highest AUC (0.78) and ACC (0.73) in the validation set. Regardless of the method, tumor mass features significantly outperformed peritumoral edema features in the differentiation of GSM from GBM (P < 0.05). Furthermore, the sensitivity, specificity, and accuracy of the best radiomics model were superior to those obtained by the neuroradiologists.

Conclusion

Our radiomics study identified the selection method LASSO combined with the classifier SVM as the optimal method for differentiating GSM from GBM based on tumor mass features.

Gliosarcomas: magnetic resonance imaging findings

BACKGROUND

Central nervous system (CNS) gliosarcoma (GSM) is a rare primary neoplasm characterized by the presence of glial and sarcomatous components.

OBJECTIVE

In this report, we describe the clinical and neuroimaging aspects of three cases of GSM and correlate these aspects with pathological findings. We also provide a brief review of relevant literature.

METHODS

Three patients were evaluated with magnetic resonance imaging (MRI), and biopsies confirmed the diagnosis of primary GSM, without previous radiotherapy.

RESULTS

The analysis of conventional sequences (T1, T1 after contrast injection, T2, Fluid attenuation inversion recovery, SWI and DWI/ADC map) and advanced (proton 1H MR spectroscopy and perfusion) revealed an irregular, necrotic aspect of the lesion, peritumoral edema/infiltration and isointensity of the solid component on a T2-weighted image. These features were associated with irregular and peripheral contrast enhancement, lipid and lactate peaks, increased choline and creatine levels in proton spectroscopy, increased relative cerebral blood volume (rCBV) in perfusion, multifocality and drop metastasis in one of the cases.

CONCLUSION

These findings are discussed in relation to the general characteristics of GSM reported in the literature.