Invited Review: DNA methylation‐based classification of paediatric brain tumours

DNA methylation-based machine learning algorithms represent powerful diagnostic tools that are currently emerging for several fields of tumour classification. For various reasons, paediatric brain tumours have been the main driving forces behind this rapid development and brain tumour classification tools are likely further advanced than in any other field of cancer diagnostics. In this review, we will discuss the main characteristics that were important for this rapid advance, namely the high clinical need for improvement of paediatric brain tumour diagnostics, the robustness of methylated DNA and the consequential possibility to generate high-quality molecular data from archival formalin-fixed paraffin-embedded pathology specimens, the implementation of a single array platform by most laboratories allowing data exchange and data pooling to an unprecedented extent, as well as the high suitability of the data format for machine learning. We will further discuss the four most central output qualities of DNA methylation profiling in a diagnostic setting (tumour classification, tumour sub-classification, copy number analysis and guidance for additional molecular testing) individually for the most frequent types of paediatric brain tumours. Lastly, we will discuss DNA methylation profiling as a tool for the detection of new paediatric brain tumour classes and will give an overview of the rapidly growing family of new tumours identified with the aid of this technique

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Pitfalls in the assessment of MGMT expression and in its correlation with survival in diffuse astrocytomas: proposal of a feasible immunohistochemical approach

Immunohistochemical studies showed that O6-methylguanine-DNA methyltransferase (MGMT) protein expression is negatively associated with survival in glioblastomas treated with alkylating agents in accordance with previous results of methylation-specific PCR. Implementation of this data in routine clinical diagnostics is limited due to often inappropriate study designs, e.g. pooling of tumor entities, WHO grades or primary and secondary glioblastomas, disregard concerning the infiltration zone or various epidemiological factors. The aim of our study was to evaluate MGMT expression and its prognostic value taking into consideration the aforementioned deficiencies. For this, 162 astrocytic tumors WHO II-IV (36 diffuse astrocytomas WHO II, 51 anaplastic astrocytomas, 75 primary glioblastomas) as well as 25 glioblastoma infiltration zones and 19 glioblastoma relapses were analyzed for immunohistochemical MGMT protein expression using tissue microarray technique. Expression of MGMT significantly decreased from WHO grade II (25.6%) to glioblastoma (16.8%, p=0.01) with lowest levels in grade III tumors (10.2%, II/III p<0.0001). Significant negative associations of MGMT and survival were detected for WHO grade II and IV (p=0.003 and 0.013). The optimal cut-off value of MGMT positive nuclei in primary glioblastomas discriminating patients with significantly different survival rates was at 15% (Log-Rank p=0.0002). Individual relapse tumors showed changes of MGMT expression to a varying degree. The infiltration zone demonstrated a significant increase of MGMT (p<0.0001). We conclude that immunohistochemical MGMT assessment has potential as a powerful diagnostic tool but analysis should only be performed in a grade dependent manner, before radio-/chemotherapy and with special attention to the infiltration zone of diffuse astrocytoma

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Sarcoma classification by DNA methylation profiling in clinical everyday life: the Charité experience

Background: Sarcomas are a heterogeneous group of rare malignant tumors with more than 100 subtypes. Accurate diagnosis remains challenging due to a lack of characteristic molecular or histomorphological hallmarks. A DNA methylation-based tumor profiling classifier for sarcomas (known as sarcoma classifier) from the German Cancer Research Center (Deutsches Krebsforschungszentrum) is now employed in selected cases to guide tumor classification and treatment decisions at our institution. Data on the usage of the classifier in daily clinical routine are lacking. Methods: In this single-center experience, we describe the clinical course of five sarcoma cases undergoing thorough pathological and reference pathological examination as well as DNA methylation-based profiling and their impact on subsequent treatment decisions. We collected data on the clinical course, DNA methylation analysis, histopathology, radiological imaging, and next-generation sequencing. Results: Five clinical cases involving DNA methylation-based profiling in 2021 at our institution were included. All patients' DNA methylation profiles were successfully matched to a methylation profile cluster of the sarcoma classifier's dataset. In three patients, the classifier reassured diagnosis or aided in finding the correct diagnosis in light of contradictory data and differential diagnoses. In two patients with intracranial tumors, the classifier changed the diagnosis to a novel diagnostic tumor group. Conclusions: The sarcoma classifier is a valuable diagnostic tool that should be used after comprehensive clinical and histopathological evaluation. It may help to reassure the histopathological diagnosis or indicate the need for thorough reassessment in cases where it contradicts previous findings. However, certain limitations (non-classifiable cases, misclassifications, unclear degree of sample purity for analysis and others) currently preclude wide clinical application. The current sarcoma classifier is therefore not yet ready for a broad clinical routine. With further refinements, this promising tool may be implemented in daily clinical practice in selected cases

Genetic alterations in gliosarcoma and giant cell glioblastoma

The majority of glioblastomas develop rapidly with a short clinical history (primary glioblastoma IDH wild-type), whereas secondary glioblastomas progress from diffuse astrocytoma or anaplastic astrocytoma. IDH mutations are the genetic hallmark of secondary glioblastomas. Gliosarcomas and giant cell glioblastomas are rare histological glioblastoma variants, which usually develop rapidly. We determined the genetic patterns of 36 gliosarcomas and 19 giant cell glioblastomas. IDH1 and IDH2 mutations were absent in all 36 gliosarcomas and in 18 of 19 giant cell glioblastomas analyzed, indicating that they are histological variants of primary glioblastoma. Furthermore, LOH 10q (88%) and TERT promoter mutations (83%) were frequent in gliosarcomas. Copy number profiling using the 450k methylome array in 5 gliosarcomas revealed CDKN2A homozygous deletion (3 cases), trisomy chromosome 7 (2 cases), and monosomy chromosome 10 (2 cases). Giant cell glioblastomas had LOH 10q in 50% and LOH 19q in 42% of cases. ATRX loss was detected immunohistochemically in 19% of giant cell glioblastomas, but absent in 17 gliosarcomas. These and previous results suggest that gliosarcomas are a variant of, and genetically similar to, primary glioblastomas, except for a lack of EGFR amplification, while giant cell glioblastoma occupies a hybrid position between primary and secondary glioblastomas. This article is protected by copyright. All rights reserved

High-grade astrocytoma with piloid features (HGAP): the Charité experience with a new central nervous system tumor entity

Purpose: High-grade astrocytoma with piloid features (HGAP) is a recently described brain tumor entity defined by a specific DNA methylation profile. HGAP has been proposed to be integrated in the upcoming World Health Organization classification of central nervous system tumors expected in 2021. In this series, we present the first single-center experience with this new entity. Methods: During 2017 and 2020, six HGAP were identified. Clinical course, surgical procedure, histopathology, genome-wide DNA methylation analysis, imaging, and adjuvant therapy were collected. Results: Tumors were localized in the brain stem (n = 1), cerebellar peduncle (n = 1), diencephalon (n = 1), mesencephalon (n = 1), cerebrum (n = 1) and the thoracic spinal cord (n = 2). The lesions typically presented as T1w hypo- to isointense and T2w hyperintense with inhomogeneous contrast enhancement on MRI. All patients underwent initial surgical intervention. Three patients received adjuvant radiochemotherapy, and one patient adjuvant radiotherapy alone. Four patients died of disease, with an overall survival of 1.8, 9.1, 14.8 and 18.1 months. One patient was alive at the time of last follow-up, 14.6 months after surgery, and one patient was lost to follow-up. Apart from one tumor, the lesions did not present with high grade histology, however patients showed poor clinical outcomes. Conclusions: Here, we provide detailed clinical, neuroradiological, histological, and molecular pathological information which might aid in clinical decision making until larger case series are published. With the exception of one case, the tumors did not present with high-grade histology but patients still showed short intervals between diagnosis and tumor progression or death even after extensive multimodal therapy

Biomarker and Histopathology Evaluation of Patients with Recurrent Glioblastoma Treated with Galunisertib, Lomustine, or the Combination of Galunisertib and Lomustine

Galunisertib, a Transforming growth factor-βRI (TGF-βRI) kinase inhibitor, blocks TGF-β-mediated tumor growth in glioblastoma. In a three-arm study of galunisertib (300 mg/day) monotherapy (intermittent dosing; each cycle =14 days on/14 days off), lomustine monotherapy, and galunisertib plus lomustine therapy, baseline tumor tissue was evaluated to identify markers associated with tumor stage (e.g., histopathology, Ki67, glial fibrillary acidic protein) and TGF-β-related signaling (e.g., pSMAD2). Other pharmacodynamic assessments included chemokine, cytokine, and T cell subsets alterations. 158 patients were randomized to galunisertib plus lomustine (n = 79), galunisertib (n = 39) and placebo+lomustine (n = 40). In 127 of these patients, tissue was adequate for central pathology review and biomarker work. Isocitrate dehydrogenase (IDH1) negative glioblastoma patients with baseline pSMAD2+ in cytoplasm had median overall survival (OS) 9.5 months vs. 6.9 months for patients with no tumor pSMAD2 expression (p = 0.4574). Eight patients were IDH1 R132H+ and had a median OS of 10.4 months compared to 6.9 months for patients with negative IDH1 R132H (p = 0.5452). IDH1 status was associated with numerically higher plasma macrophage-derived chemokine (MDC/CCL22), higher whole blood FOXP3, and reduced tumor CD3+ T cell counts. Compared to the baseline, treatment with galunisertib monotherapy preserved CD4+ T cell counts, eosinophils, lymphocytes, and the CD4/CD8 ratio. The T-regulatory cell compartment was associated with better OS with MDC/CCL22 as a prominent prognostic marker. View Full-Tex