Gliosarcoma: Neuroimaging and Immunohistochemical Findings

Clinicopathological and Neuroimaging Features of Primary Gliosarcoma: A Case Series and Review of Literature

BACKGROUND

Gliosarcoma (GS) is a rare primary high-grade brain neoplasm with a poor prognosis and challenging surgical resection. Although it is now considered a morphologic variant of IDH-wildtype glioblastoma (World Health Organization Classification of Tumours 2021), GS may display peculiarities that hamper both surgical and oncological management.

METHODS

In this retrospective study, we searched our registry for histologically confirmed GS patients between 2006 and 2020. Cases were reviewed for clinical information, pathologic characteristics, imaging findings, management, and outcome.

RESULTS

21 patients with histologically confirmed GS were identified with a median age of 62 years. Twelve were men and 9 women. The temporal lobe was the most common location (9 patients, 42.9%). Nineteen patients underwent surgical resection, and only 4 (19%) demonstrated gross total resection on postsurgical MRI, with an overall median survival of 7 months (range, 0.5-37). Diagnostic MRI demonstrated heterogenous lesions with necrotic-cystic areas and a ring-enhancement pattern. Only 1 case of extracranial extension was seen in our sample, and no patient showed distant metastases.

CONCLUSIONS

The rarity of primary GS and the absence of specific therapeutic guidelines represent a significant clinical challenge. Our study provides a comprehensive analysis of clinical and neuroimaging characteristics in a real-world patient cohort and compares our findings with the available literature.

Chasing a rarity: a retrospective single-center evaluation of prognostic factors in primary gliosarcoma

Background and purpose

Primary gliosarcoma (GS) is a rare variant of IDH-wildtype glioblastoma multiforme. We performed a single-center analysis to identify prognostic factors.

Patients and methods

We analyzed the records of 26 patients newly diagnosed with primary WHO grade IV GS. Factors of interest were clinical and treatment data, as well as molecular markers, time to recurrence, and time to death.

Results

Median follow-up was 9 months (range 5–21 months). Gross total resection did not lead to improved survival, most likely due to the relatively small sample size. Low symptom burden at the time of diagnosis was associated with longer PFS (P = 0.023) and OS (P = 0.018). Median OS in the entire cohort was 12 months. Neither MGMT promoter hypermethylation nor adjuvant temozolomide therapy influenced survival, consistent with some previous reports.

Conclusion

In this retrospective study, patients exhibiting low symptom burden at diagnosis showed improved survival. None of the other factors analyzed were associated with an altered outcome.

Genomic landscape of gliosarcoma: distinguishing features and targetable alterations

Gliosarcoma is an aggressive brain tumor with histologic features of glioblastoma (GBM) and soft tissue sarcoma. Despite its poor prognosis, its rarity has precluded analysis of its underlying biology. We used a multi-center database to characterize the genomic landscape of gliosarcoma. Sequencing data was obtained from 35 gliosarcoma patients from Genomics Evidence Neoplasia Information Exchange (GENIE) 5.0, a database curated by the American Association of Cancer Research (AACR). We analyzed genomic alterations in gliosarcomas and compared them to GBM (n = 1,449) and soft tissue sarcoma (n = 1,042). 30 samples were included (37% female, median age 59 [IQR: 49–64]). Nineteen common genes were identified in gliosarcoma, defined as those altered in > 5% of samples, including TERT Promoter (92%), PTEN (66%), and TP53 (60%). Of the 19 common genes in gliosarcoma, 6 were also common in both GBM and soft tissue sarcoma, 4 in GBM alone, 0 in soft tissue sarcoma alone, and 9 were more distinct to gliosarcoma. Of these, BRAF harbored an OncoKB level 1 designation, indicating its status as a predictive biomarker of response to an FDA-approved drug in certain cancers. EGFR, CDKN2A, NF1, and PTEN harbored level 4 designations in solid tumors, indicating biological evidence of these biomarkers predicting a drug-response. Gliosarcoma contains molecular features that overlap GBM and soft tissue sarcoma, as well as its own distinct genomic signatures. This may play a role in disease classification and inclusion criteria for clinical trials. Gliosarcoma mutations with potential therapeutic indications include BRAF, EGFR, CDKN2A, NF1, and PTEN.

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.

Optic pathway gliosarcoma: A very rare location for a rare disease

Gliosarcoma, a variant of glioblastoma, is a rare and aggressive tumor of the central nervous system (CNS) composed of glial and sarcomatous tissues. Up to now, there are only 2 reported cases of gliosarcoma of the optical pathway. We report a case from March 2018 of a 53-year-old male patient presented with 6 months’ of right fronto-orbital pulsatile headache, behavior changes, and visual loss. The MRI study showed an expansile optic pathway lesion involving the chiasm and right optic nerve. The diagnosis of gliosarcoma was obtained by open brain biopsy and immunohistochemical analysis. Although gliosarcoma is rare, it should be considered a differential diagnosis even in optic pathway tumors in older patients. The experience of the neuropathologist with a trained eye can be the differential in the accurate diagnostic process.

Optic pathway, Gliosarcoma, Glioblastoma, Magnetic resonance imaging

Do gliosarcomas have distinct imaging features on routine MRI?

PURPOSE

The aim of this study was the development and external validation of a logistic regression model to differentiate gliosarcoma (GSC) and glioblastoma multiforme (GBM) on standard MR imaging.

METHODS

A univariate and multivariate analysis was carried out of a logistic regression model to discriminate patients histologically diagnosed with primary GSC and an age and sex-matched group of patients with primary GBM on presurgical MRI with external validation.

RESULTS

In total, 56 patients with GSC and 56 patients with GBM were included. Evidence of haemorrhage suggested the diagnosis of GSC, whereas cystic components and pial as well as ependymal invasion were more commonly observed in GBM patients. The logistic regression model yielded a mean area under the curve (AUC) of 0.919 on the training dataset and of 0.746 on the validation dataset. The accuracy in the validation dataset was 0.67 with a sensitivity of 0.85 and a specificity of 0.5.

CONCLUSIONS

Although some imaging criteria suggest the diagnosis of GSC or GBM, differentiation between these two tumour entities on standard MRI alone is not feasible.

IDH1-mutant primary intraventricular gliosarcoma: Case report and systematic review of a rare location and molecular profile

Background:

Gliosarcoma (GS) is classified as an IDH-wild-type variant of glioblastoma (GBM). While GS is already an unusual presentation of GBM, IDH1-mutant cases are especially rare. We present an IDH1-mutant primary intraventricular GS case report and a systematic review of the molecular profile in GS correlating to the prognostic and pathogenesis of IDH1/2 mutations.

Case Description:

A 44-years-old man presented with ongoing fatigue symptoms and a new-onset intense occipital headache. The patient complained of memory loss, dyscalculia, and concentration difficulties. An MRI revealed a bihemispheric intraventricular mass crossing the midline through the corpus callosum and infiltrating the trigone of the lateral ventricles, hypointense, and hyperintense on the T1- and T2-weighted image. We performed a microsurgical resection with a transparietal transsulcal approach; however, the contralateral mass was attached to vascular structures and we decided to reoperate the patient in another moment. The histopathological study showed a Grade IV tumor and the immunohistochemistry confirmed the diagnosis of GS. The patient presented progressive neurologic decline and died 45 days after the surgical approach.

Conclusion:

We did two systematic reviews studies from PubMed, EMBASE, MEDLINE, Cochrane, and SCOPUS databases, and included molecular and intraventricular studies of GS. We performed further meta-analysis using OpenMetaAnalyst™ software. We conducted a forest plot with the molecular profile of GS. When correlated IDH1 mutation versus tp53 mutation, we found an odds ratio (OR) of 0.018 (0.005–0.064) and P < 0.001. Moreover, we compared IDH1 mutation versus MGMT methylation (P = 0.006; OR = 0.138 [0.034–0.562]). The studies evaluating the molecular profile in GS prognostics are often extended from all GBMs despite specifics GBM variants (i.e., GS). We found a correlation between IDH1 mutation expression with tp53 and MGMT expression in GS, and future studies exploring this molecular profile in GS are strongly encouraged.

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.

Clinical Characteristics of Gliosarcoma and Outcomes From Standardized Treatment Relative to Conventional Glioblastoma

Background: Gliosarcoma (GS) is a rare histopathologic variant of glioblastoma (GBM) characterized by a biphasic growth pattern consisting of both glial and sarcomatous components. Reports regarding its relative prognosis compared to conventional GBM are conflicting and although GS is treated as conventional GBM, supporting evidence is lacking. The aim of this study was to characterize demographic trends, clinical outcomes and prognostic variables of GS patients receiving standardized therapy and compare these to conventional GBM. Methods: Six hundred and eighty GBM patients, treated with maximal safe resection followed by radiotherapy with concomitant and adjuvant temozolomide at a single institution, were retrospectively reevaluated by reviewing histopathological records and tumor tissue for identification of GS patients. Clinico-pathological- and tumor growth characteristics were obtained via assessment of medical records and imaging analysis. Kaplan-Meier survival estimates were compared with log-rank testing, while Cox-regression modeling was tested for prognostic factors in GS patients. Results: The cohort included 26 primary gliosarcoma (PGS) patients (3.8%) and 7 secondary gliosarcoma (SGS) patients (1.0%). Compared to conventional GBM tumors, PGS tumors were significantly more often MGMT-unmethylated (73.9%) and located in the temporal lobe (57.7%). GS tumors often presented dural contact, while extracranial metastasis was only found in 1 patient. No significant differences were found between PGS and conventional GBM in progression-free-survival (6.8 and 7.6 months, respectively, p = 0.105) and in overall survival (13.4 and 15.7 months, respectively, p = 0.201). Survival following recurrence was not significantly different between PGS, SGS, and GBM. Temporal tumor location and MGMT status were found associated with PGS survival (p = 0.036 and p = 0.022, respectively). Conclusion: Despite histopathological and location difference between GS and GBM tumors, the patients present similar survival outcome from standardized treatment. These findings support continued practice of radiation and temozolomide for GS patients.

Glioblastoma heterogeneity and the tumour microenvironment: implications for preclinical research and development of new treatments

Glioblastoma is the deadliest form of brain cancer. Aside from inadequate treatment options, one of the main reasons glioblastoma is so lethal is the rapid growth of tumour cells coupled with continuous cell invasion into surrounding healthy brain tissue. Significant intra- and inter-tumour heterogeneity associated with differences in the corresponding tumour microenvironments contributes greatly to glioblastoma progression. Within this tumour microenvironment, the extracellular matrix profoundly influences the way cancer cells become invasive, and changes to extracellular (pH and oxygen levels) and metabolic (glucose and lactate) components support glioblastoma growth. Furthermore, studies on clinical samples have revealed that the tumour microenvironment is highly immunosuppressive which contributes to failure in immunotherapy treatments. Although technically possible, many components of the tumour microenvironment have not yet been the focus of glioblastoma therapies, despite growing evidence of its importance to glioblastoma malignancy. Here, we review recent progress in the characterisation of the glioblastoma tumour microenvironment and the sources of tumour heterogeneity in human clinical material. We also discuss the latest advances in technologies for personalised and in vitro preclinical studies using brain organoid models to better model glioblastoma and its interactions with the surrounding healthy brain tissue, which may play an essential role in developing new and more personalised treatments for this aggressive type of cancer.