Alterations in the RB Pathway With Inactivation of RB1 Characterize Glioblastomas With a Primitive Neuronal Component

Glioblastoma: An Update in Pathology, Molecular Mechanisms and Biomarkers

Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumor in adults. Despite important advances in understanding the molecular pathogenesis and biology of this tumor in the past decade, the prognosis for GBM patients remains poor. GBM is characterized by aggressive biological behavior and high degrees of inter-tumor and intra-tumor heterogeneity. Increased understanding of the molecular and cellular heterogeneity of GBM may not only help more accurately define specific subgroups for precise diagnosis but also lay the groundwork for the successful implementation of targeted therapy. Herein, we systematically review the key achievements in the understanding of GBM molecular pathogenesis, mechanisms, and biomarkers in the past decade. We discuss the advances in the molecular pathology of GBM, including genetics, epigenetics, transcriptomics, and signaling pathways. We also review the molecular biomarkers that have potential clinical roles. Finally, new strategies, current challenges, and future directions for discovering new biomarkers and therapeutic targets for GBM will be discussed.

CDKN2A mutations have equivalent prognostic significance to homozygous deletion in IDH-mutant astrocytoma

Homozygous deletion of CDKN2A/B is currently considered a molecular signature for grade 4 in IDH-mutant astrocytomas, irrespective of tumor histomorphology. The 2021 WHO Classification of CNS Tumors does not currently include grading recommendations for histologically lower-grade (grade 2-3) IDH-mutant astrocytoma with CDKN2A mutation or other CDKN2A alterations, and little is currently known about the prognostic implications of these alternative CDKN2A inactivating mechanisms. To address this, we evaluated a cohort of institutional and publicly available IDH-mutant astrocytomas, 15 with pathogenic mutations in CDKN2A, 47 with homozygous CDKN2A deletion, and 401 with retained/wildtype CDKN2A. The IDH-mutant astrocytomas with mutant and deleted CDKN2A had significantly higher overall copy number variation compared to those with retained/wildtype CDKN2A, consistent with more aggressive behavior. Astrocytoma patients with CDKN2A mutation had significantly worse progression-free (p = 0.0025) and overall survival (p < 0.0001) compared to grade-matched patients with wildtype CDKN2A, but statistically equivalent progression-free survival and overall survival outcomes to patients with CDKN2A deletion. No significant survival difference was identified between CDKN2A mutant cases with or without loss of the second allele. These findings suggest that CDKN2A mutation has a detrimental effect on survival in otherwise lower-grade IDH-mutant astrocytomas, similar to homozygous CDKN2A deletion, and should be considered for future grading schemes.

Coexisting cancer stem cells with heterogeneous gene amplifications, transcriptional profiles, and malignancy are isolated from single glioblastomas

Glioblastoma (GBM) is known as an intractable, highly heterogeneous tumor encompassing multiple subclones, each supported by a distinct glioblastoma stem cell (GSC). The contribution of GSC genetic and transcriptional heterogeneity to tumor subclonal properties is debated. In this study, we describe the systematic derivation, propagation, and characterization of multiple distinct GSCs from single, treatment-naive GBMs (GSC families). The tumorigenic potential of each GSC better correlates with its transcriptional profile than its genetic make-up, with classical GSCs being inherently more aggressive and mesenchymal more dependent on exogenous growth factors across multiple GBMs. These GSCs can segregate and recapitulate different histopathological aspects of the same GBM, as shown in a paradigmatic tumor with two histopathologically distinct components, including a conventional GBM and a more aggressive primitive neuronal component. This study provides a resource for investigating how GSCs with distinct genetic and/or phenotypic features contribute to individual GBM heterogeneity and malignant escalation.

Mismatch repair protein mutations in isocitrate dehydrogenase (IDH)-mutant astrocytoma and IDH-wild-type glioblastoma

BACKGROUND

Mutations in mismatch repair (MMR) genes (MSH2, MSH6, MLH1, and PMS2) are associated with microsatellite instability and a hypermutator phenotype in numerous systemic cancers, and germline MMR mutations have been implicated in multi-organ tumor syndromes. In gliomas, MMR mutations can function as an adaptive response to alkylating chemotherapy, although there are well-documented cases of germline and sporadic mutations, with detrimental effects on patient survival.

METHODS

The clinical, pathologic, and molecular features of 18 IDH-mutant astrocytomas and 20 IDH-wild-type glioblastomas with MMR mutations in the primary tumor were analyzed in comparison to 361 IDH-mutant and 906 IDH-wild-type tumors without MMR mutations. In addition, 12 IDH-mutant astrocytomas and 18 IDH-wild-type glioblastomas that developed MMR mutations between initial presentation and tumor recurrence were analyzed in comparison to 50 IDH-mutant and 104 IDH-wild-type cases that remained MMR-wild-type at recurrence.

RESULTS

In both IDH-mutant astrocytoma and IDH-wild-type glioblastoma cohorts, the presence of MMR mutation in primary tumors was associated with significantly higher tumor mutation burden (TMB) (P < .0001); however, MMR mutations only resulted in worse overall survival in the IDH-mutant astrocytomas (P = .0069). In addition, gain of MMR mutation between the primary and recurrent surgical specimen occurred more frequently with temozolomide therapy (P = .0073), and resulted in a substantial increase in TMB (P < .0001), higher grade (P = .0119), and worse post-recurrence survival (P = .0022) in the IDH-mutant astrocytoma cohort.

CONCLUSIONS

These results suggest that whether present initially or in response to therapy, MMR mutations significantly affect TMB but appear to only influence the clinical outcome in IDH-mutant astrocytoma subsets.

The prognostic impact of subclonal IDH1 mutation in grade 2-4 astrocytomas

Background

Isocitrate dehydrogenase (IDH) mutations are thought to represent an early oncogenic event in glioma evolution, found with high penetrance across tumor cells; however, in rare cases, IDH mutation may exist only in a small subset of the total tumor cells (subclonal IDH mutation).

Methods

We present 2 institutional cases with subclonal IDH1 R132H mutation. In addition, 2 large publicly available cohorts of IDH-mutant astrocytomas were mined for cases harboring subclonal IDH mutations (defined as tumor cell fraction with IDH mutation ≤0.67) and the clinical and molecular features of these subclonal cases were compared to clonal IDH-mutant astrocytomas.

Results

Immunohistochemistry (IHC) performed on 2 institutional World Health Organization grade 4 IDH-mutant astrocytomas revealed only a minority of tumor cells in each case with IDH1 R132H mutant protein, and next-generation sequencing (NGS) revealed remarkably low IDH1 variant allele frequencies compared to other pathogenic mutations, including TP53 and/or ATRX. DNA methylation classified the first tumor as high-grade IDH-mutant astrocytoma with high confidence (0.98 scores). In the publicly available datasets, subclonal IDH mutation was present in 3.9% of IDH-mutant astrocytomas (18/466 tumors). Compared to clonal IDH-mutant astrocytomas (n = 156), subclonal cases demonstrated worse overall survival in grades 3 (P = .0106) and 4 (P = .0184).

Conclusions

While rare, subclonal IDH1 mutations are present in a subset of IDH-mutant astrocytomas of all grades, which may lead to a mismatch between IHC results and genetic/epigenetic classification. These findings suggest a possible prognostic role of IDH mutation subclonality, and highlight the potential clinical utility of quantitative IDH1 mutation evaluation by IHC and NGS.

Tyrosine Kinase Inhibitors for Glioblastoma Multiforme: Challenges and Opportunities for Drug Delivery

Glioblastoma multiforme (GBM) is an aggressive brain tumor with high mortality rates. Due to its invasiveness, heterogeneity, and incomplete resection, the treatment is very challenging. Targeted therapies such as tyrosine kinase inhibitors (TKIs) have great potential for GBM treatment, however, their efficacy is primarily limited by poor brain distribution due to the presence of the blood-brain barrier (BBB). This review focuses on the potential of TKIs in GBM therapy and provides an insight into the reasons behind unsuccessful clinical trials of TKIs in GBM despite the success in treating other cancer types. The main section is dedicated to the use of promising drug delivery strategies for targeted delivery to brain tumors. Use of brain targeted delivery strategies can help enhance the efficacy of TKIs in GBM. Among various drug delivery approaches used to bypass or cross BBB, utilizing nanocarriers is a promising strategy to augment the pharmacokinetic properties of TKIs and overcome their limitations. This is because of their advantages such as the ability to cross BBB, chemical stabilization of drug in circulation, passive or active targeting of tumor, modulation of drug release from the carrier, and the possibility to be delivered via non-invasive intranasal route.

Recurrence- and Malignant Progression-Associated Biomarkers in Low-Grade Gliomas and Their Roles in Immunotherapy

Despite a generally better prognosis than high-grade glioma (HGG), recurrence and malignant progression are the main causes for the poor prognosis and difficulties in the treatment of low-grade glioma (LGG). It is of great importance to learn about the risk factors and underlying mechanisms of LGG recurrence and progression. In this study, the transcriptome characteristics of four groups, namely, normal brain tissue and recurrent LGG (rLGG), normal brain tissue and secondary glioblastoma (sGBM), primary LGG (pLGG) and rLGG, and pLGG and sGBM, were compared using Chinese Glioma Genome Atlas (CGGA) and Genotype-Tissue Expression Project (GTEx) databases. In this study, 296 downregulated and 396 upregulated differentially expressed genes (DEGs) with high consensus were screened out. Univariate Cox regression analysis of data from The Cancer Genome Atlas (TCGA) yielded 86 prognostically relevant DEGs; a prognostic prediction model based on five key genes (HOXA1, KIF18A, FAM133A, HGF, and MN1) was established using the least absolute shrinkage and selection operator (LASSO) regression dimensionality reduction and multivariate Cox regression analysis. LGG was divided into high- and low-risk groups using this prediction model. Gene Set Enrichment Analysis (GSEA) revealed that signaling pathway differences in the high- and low-risk groups were mainly seen in tumor immune regulation and DNA damage-related cell cycle checkpoints. Furthermore, the infiltration of immune cells in the high- and low-risk groups was analyzed, which indicated a stronger infiltration of immune cells in the high-risk group than that in the low-risk group, suggesting that an immune microenvironment more conducive to tumor growth emerged due to the interaction between tumor and immune cells. The tumor mutational burden and tumor methylation burden in the high- and low-risk groups were also analyzed, which indicated higher gene mutation burden and lower DNA methylation level in the high-risk group, suggesting that with the accumulation of genomic mutations and epigenetic changes, tumor cells continued to evolve and led to the progression of LGG to HGG. Finally, the value of potential therapeutic targets for the five key genes was analyzed, and findings demonstrated that KIF18A was the gene most likely to be a potential therapeutic target. In conclusion, the prediction model based on these five key genes can better identify the high- and low-risk groups of LGG and lay a solid foundation for evaluating the risk of LGG recurrence and malignant progression.

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma

Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70-0.85)/0.75 (95% CI 0.64-0.79) and 0.75 (95% CI 0.65-0.84)/0.63 (95% CI 0.52-0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6-0.7) for clinical data improving to 0.75 (95% CI 0.72-0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.