ATRX-Deficient High-Grade Glioma Cells Exhibit Increased Sensitivity to RTK and PDGFR Inhibitors

Therapeutically targeting the unique disease landscape of pediatric high-grade gliomas

Pediatric high-grade gliomas (pHGG) are a rare yet devastating malignancy of the central nervous system's glial support cells, affecting children, adolescents, and young adults. Tumors of the central nervous system account for the leading cause of pediatric mortality of which high-grade gliomas present a significantly grim prognosis. While the past few decades have seen many pediatric cancers experiencing significant improvements in overall survival, the prospect of survival for patients diagnosed with pHGGs has conversely remained unchanged. This can be attributed in part to tumor heterogeneity and the existence of the blood-brain barrier. Advances in discovery research have substantiated the existence of unique subgroups of pHGGs displaying alternate responses to different therapeutics and varying degrees of overall survival. This highlights a necessity to approach discovery research and clinical management of the disease in an alternative subtype-dependent manner. This review covers traditional approaches to the therapeutic management of pHGGs, limitations of such methods and emerging alternatives. Novel mutations which predominate the pHGG landscape are highlighted and the therapeutic potential of targeting them in a subtype specific manner discussed. Collectively, this provides an insight into issues in need of transformative progress which arise during the management of pHGGs.

PICH deficiency limits the progression of MYC-induced B-cell lymphoma

Plk1-interacting checkpoint helicase (PICH) is a DNA translocase involved in resolving ultrafine anaphase DNA bridges and, therefore, is important to safeguard chromosome segregation and stability. PICH is overexpressed in various human cancers, particularly in lymphomas such as Burkitt lymphoma, which is caused by MYC translocations. To investigate the relevance of PICH in cancer development and progression, we have combined novel PICH-deficient mouse models with the Eμ-Myc transgenic mouse model, which recapitulates B-cell lymphoma development. We have observed that PICH deficiency delays the onset of MYC-induced lymphomas in Pich heterozygous females. Moreover, using a Pich conditional knockout mouse model, we have found that Pich deletion in adult mice improves the survival of Eμ-Myc transgenic mice. Notably, we show that Pich deletion in healthy adult mice is well tolerated, supporting PICH as a suitable target for anticancer therapies. Finally, we have corroborated these findings in two human Burkitt lymphoma cell lines and we have found that the death of cancer cells was accompanied by chromosomal instability. Based on these findings, we propose PICH as a potential therapeutic target for Burkitt lymphoma and for other cancers where PICH is overexpressed.

A fusion model integrating magnetic resonance imaging radiomics and deep learning features for predicting alpha-thalassemia X-linked intellectual disability mutation status in isocitrate dehydrogenase-mutant high-grade astrocytoma: a multicenter study

Background

The mutational status of alpha-thalassemia X-linked intellectual disability (ATRX) is an important indicator for the treatment and prognosis of high-grade gliomas, but reliable ATRX testing currently requires invasive procedures. The objective of this study was to develop a clinical trait-imaging fusion model that combines preoperative magnetic resonance imaging (MRI) radiomics and deep learning (DL) features with clinical variables to predict ATRX status in isocitrate dehydrogenase (IDH)-mutant high-grade astrocytoma.

Methods

A total of 234 patients with IDH-mutant high-grade astrocytoma (120 ATRX mutant type, 114 ATRX wild type) from 3 centers were retrospectively analyzed. Radiomics and DL features from different regions (edema, tumor, and the overall lesion) were extracted to construct multiple imaging models by combining different features in different regions for predicting ATRX status. An optimal imaging model was then selected, and its features and linear coefficients were used to calculate an imaging score. Finally, a fusion model was developed by combining the imaging score and clinical variables. The performance and application value of the fusion model were evaluated through the comparison of receiver operating characteristic curves, the construction of a nomogram, calibration curves, decision curves, and clinical application curves.

Results

The overall hybrid model constructed with radiomics and DL features from the overall lesion was identified as the optimal imaging model. The fusion model showed the best prediction performance with an area under curve of 0.969 in the training set, 0.956 in the validation set, and 0.949 in the test set as compared to the optimal imaging model (0.966, 0.916, and 0.936, respectively) and clinical model (0.677, 0.641, 0.772, respectively).

Conclusions

The clinical trait-imaging fusion model based on preoperative MRI could effectively predict the ATRX mutation status of individuals with IDH-mutant high-grade astrocytoma and has the potential to help patients through the development of a more effective treatment strategy before treatment.

Comparison of MRI Sequences to Predict ATRX Status Using Radiomics-Based Machine Learning

ATRX is an important molecular marker according to the 2021 WHO classification of adult-type diffuse glioma. We aim to predict the ATRX mutation status non-invasively using radiomics-based machine learning models on MRI and to determine which MRI sequence is best suited for this purpose. In this retrospective study, we used MRI images of patients with histologically confirmed glioma, including the sequences T1w without and with the administration of contrast agent, T2w, and the FLAIR. Radiomics features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects. Feature preselection and subsequent model development were performed using Lasso regression. The T2w sequence was found to be the most suitable and the FLAIR sequence the least suitable for predicting ATRX mutations using radiomics-based machine learning models. For the T2w sequence, our seven-feature model developed with Lasso regression achieved a mean AUC of 0.831, a mean accuracy of 0.746, a mean sensitivity of 0.772, and a mean specificity of 0.697. In conclusion, for the prediction of ATRX mutation using radiomics-based machine learning models, the T2w sequence is the most suitable among the commonly used MRI sequences.

Foreword Special Issue Genomic Instability in Tumor Evolution and Therapy Response

From an evolutionary perspective, mutations in the DNA molecule act as a source of genetic variation and thus, are beneficial to the adaptation and survival of the species [...].

ATRX, a guardian of chromatin

ATRX (alpha-thalassemia mental retardation X-linked) is one of the most frequently mutated tumor suppressor genes in human cancers, especially in glioma, and recent findings indicate roles for ATRX in key molecular pathways, such as the regulation of chromatin state, gene expression, and DNA damage repair, placing ATRX as a central player in the maintenance of genome stability and function. This has led to new perspectives about the functional role of ATRX and its relationship with cancer. Here, we provide an overview of ATRX interactions and molecular functions and discuss the consequences of its impairment, including alternative lengthening of telomeres and therapeutic vulnerabilities that may be exploited in cancer cells.