A pilot radiogenomic study of DIPG reveals distinct subgroups with unique clinical trajectories and therapeutic targets

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study

Background

Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS).

Methods

We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, T2 FLAIR) and manual segmentations from two centers of 53 (internal cohort) and 16 (external cohort) DMG patients. We pretrained a deep learning model on a public adult brain tumor dataset, and finetuned it to automatically segment tumor core (TC) and whole tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 1-year survival from diagnosis. One model used only diagnostic tumor features and the other used both diagnostic and post-RT features.

Results

For segmentation, Dice score (mean [median]±SD) was 0.91 (0.94)±0.12 and 0.74 (0.83)±0.32 for TC, and 0.88 (0.91)±0.07 and 0.86 (0.89)±0.06 for WT for internal and external cohorts, respectively. For OS prediction, accuracy was 77% and 81% at time of diagnosis, and 85% and 78% post-RT for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS.

Conclusions

Machine learning analysis of MRI radiomics has potential to accurately and non-invasively predict which pediatric patients with DMG will survive less than one year from the time of diagnosis to provide patient stratification and guide therapy.

Immune Microenvironment and Immunotherapies for Diffuse Intrinsic Pontine Glioma

Diffuse intrinsic pontine glioma (DIPG) is a primary glial glioma that occurs in all age groups but predominates in children and is the main cause of solid tumor-related childhood mortality. Due to its rapid progression, the inability to operate and insensitivity to most chemotherapies, there is a lack of effective treatment methods in clinical practice for DIPG patients. The prognosis of DIPG patients is extremely poor, with a median survival time of no more than 12 months. In recent years, there have been continuous breakthroughs for immunotherapies in various hematological tumors and malignant solid tumors with extremely poor prognoses, which provides new insights into tumors without effective treatment strategies. Meanwhile, with the gradual development of stereotactic biopsy techniques, it is gradually becoming easier and safer to obtain live DIPG tissue, and the understanding of the immune properties of DIPG has also increased. On this basis, a series of immunotherapy studies of DIPG are under way, some of which have shown encouraging results. Herein, we review the current understanding of the immune characteristics of DIPG and critically reveal the limitations of current immune research, as well as the opportunities and challenges for immunological therapies in DIPG, hoping to clarify the development of novel immunotherapies for DIPG treatment.

A druggable addiction to de novo pyrimidine biosynthesis in diffuse midline glioma

Diffuse midline glioma (DMG) is a uniformly fatal pediatric cancer driven by oncohistones that do not readily lend themselves to drug development. To identify druggable targets for DMG, we conducted a genome-wide CRISPR screen that reveals a DMG selective dependency on the de novo pathway for pyrimidine biosynthesis. This metabolic vulnerability reflects an elevated rate of uridine/uracil degradation that depletes DMG cells of substrates for the alternate salvage pyrimidine biosynthesis pathway. A clinical stage inhibitor of DHODH (rate-limiting enzyme in the de novo pathway) diminishes uridine-5'-phosphate (UMP) pools, generates DNA damage, and induces apoptosis through suppression of replication forks-an "on-target" effect, as shown by uridine rescue. Matrix-assisted laser desorption/ionization (MALDI) mass spectroscopy imaging demonstrates that this DHODH inhibitor (BAY2402234) accumulates in the brain at therapeutically relevant concentrations, suppresses de novo pyrimidine biosynthesis in vivo, and prolongs survival of mice bearing intracranial DMG xenografts, highlighting BAY2402234 as a promising therapy against DMGs.

Immunogenic Cell Death Enhances Immunotherapy of Diffuse Intrinsic Pontine Glioma: From Preclinical to Clinical Studies

Diffuse intrinsic pontine glioma (DIPG) is the most lethal tumor involving the pediatric central nervous system. The median survival of children that are diagnosed with DIPG is only 9 to 11 months. More than 200 clinical trials have failed to increase the survival outcomes using conventional cytotoxic or myeloablative chemotherapy. Immunotherapy presents exciting therapeutic opportunities against DIPG that is characterized by unique and heterogeneous features. However, the non-inflammatory DIPG microenvironment greatly limits the role of immunotherapy in DIPG. Encouragingly, the induction of immunogenic cell death, accompanied by the release of damage-associated molecular patterns (DAMPs) shows satisfactory efficacy of immune stimulation and antitumor strategies. This review dwells on the dilemma and advances in immunotherapy for DIPG, and the potential efficacy of immunogenic cell death (ICD) in the immunotherapy of DIPG.

Characteristics of children ≤36 months of age with DIPG: A report from the international DIPG registry

BACKGROUND

Children ≤36 months with diffuse intrinsic pontine glioma (DIPG) have increased long-term survival (LTS, overall survival (OS) ≥24 months). Understanding distinguishing characteristics in this population is critical to improving outcomes.

METHODS

Patients ≤36 months at diagnosis enrolled on the International DIPG Registry (IDIPGR) with central imaging confirmation were included. Presentation, clinical course, imaging, pathology and molecular findings were analyzed.

RESULTS

Among 1183 patients in IDIPGR, 40 were eligible (median age: 29 months). Median OS was 15 months. Twelve patients (30%) were LTS, 3 (7.5%) very long-term survivors ≥5 years. Among 8 untreated patients, median OS was 2 months. Patients enrolled in the registry but excluded from our study by central radiology review or tissue diagnosis had median OS of 7 months. All but 1 LTS received radiation. Among 32 treated patients, 1-, 2-, 3-, and 5-year OS rates were 68.8%, 31.2%, 15.6% and 12.5%, respectively. LTS had longer duration of presenting symptoms (P = .018). No imaging features were predictive of outcome. Tissue and genomic data were available in 18 (45%) and 10 patients, respectively. Among 9 with known H3K27M status, 6 had a mutation.

CONCLUSIONS

Children ≤36 months demonstrated significantly more LTS, with an improved median OS of 15 months; 92% of LTS received radiation. Median OS in untreated children was 2 months, compared to 17 months for treated children. LTS had longer duration of symptoms. Excluded patients demonstrated a lower OS, contradicting the hypothesis that children ≤36 months with DIPG show improved outcomes due to misdiagnosis.

The intrinsic and microenvironmental features of diffuse midline glioma; implications for the development of effective immunotherapeutic treatment strategies

Diffuse midline glioma (DMG), including those of the brainstem (diffuse intrinsic pontine glioma), are pediatric tumors of the central nervous system (CNS). Recognized as the most lethal of all childhood cancers, palliative radiotherapy remains the only proven treatment option, however, even for those that respond, survival is only temporarily extended. DMG harbor an immunologically “cold” tumor microenvironment (TME) with few infiltrating immune cells. The mechanisms underpinning the cold TME are not well understood. Low expression levels of immune checkpoint proteins, including PD-1, PD-L1, and CTLA-4, are recurring features of DMG and likely contribute to the lack of response to immune checkpoint inhibitors (ICIs). The unique epigenetic signatures (including stem cell-like methylation patterns), a low tumor mutational burden, and recurring somatic mutations (H3K27M, TP53, ACVR1, MYC, and PIK3CA), possibly play a role in the reduced efficacy of traditional immunotherapies. Therefore, to circumvent the lack of efficacy thus far seen for the use of ICIs, adoptive cell transfer (including chimeric antigen receptor T cells) and the use of oncolytic viruses, are currently being evaluated for the treatment of DMG. It remains an absolute imperative that we improve our understanding of DMG’s intrinsic and TME features if patients are to realize the potential benefits offered by these sophisticated treatments. Herein, we summarize the limitations of immunotherapeutic approaches, highlight the emerging safety and clinical efficacy shown for sophisticated cell-based therapies, as well as the evolving knowledge underpinning the DMG-immune axis, to guide the development of immunotherapies that we hope will improve outcomes.

Differences in the MRI Signature and ADC Values of Diffuse Midline Gliomas with H3 K27M Mutation Compared to Midline Glioblastomas

We conducted a two-center retrospective survey on standard MRI features including apparent diffusion coefficient mapping (ADC) of diffuse midline gliomas H3 K27M-mutant (DMG) compared to midline glioblastomas H3 K27M-wildtype (midGBM-H3wt). We identified 39 intracranial DMG and 18 midGBM-H3wt tumors. Samples were microscopically re-evaluated for microvascular proliferations and necrosis. Image analysis focused on location, peritumoral edema, degree of contrast enhancement and DWI features. Within DMG, MRI features between tumors with or without histomorphological GBM features were compared. DMG occurred in 15/39 samples from the thalamus (38%), in 23/39 samples from the brainstem (59%) and in 1/39 tumors involving primarily the cerebellum (2%). Edema was present in 3/39 DMG cases (8%) versus 78% in the control (midGBM-H3wt) group (p < 0.001). Contrast enhancement at the tumor rim was detected in 17/39 DMG (44%) versus 67% in control (p = 0.155), and necrosis in 24/39 (62%) versus 89% in control (p = 0.060). Strong contrast enhancement was observed in 15/39 DMG (38%) versus 56% in control (p = 0.262). Apparent diffusion coefficient (ADC) histogram analysis showed significantly higher skewness and kurtosis values in the DMG group compared to the controls (p = 0.0016/p = 0.002). Minimum relative ADC (rADC) values, as well as the 10th and 25th rADC-percentiles, were lower in DMGs with GBM features within the DMG group (p < 0.001/p = 0.012/p = 0.027). In conclusion, DMG cases exhibited markedly less edema than midGBM-H3wt, even if histomorphological malignancy was present. Histologically malignant DMGs and midGBM-H3wt more often displayed strong enhancement, as well as rim enhancement, than DMGs without histomorphological malignancy. DMGs showed higher skewness and kurtosis values on ADC-histogram analysis compared to midGBM-H3wt. Lower minimum rADC values in DMGs indicated malignant histomorphological features, likely representing a more complex tissue microstructure.

Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.

Pharmaco-proteogenomic profiling of pediatric diffuse midline glioma to inform future treatment strategies

Diffuse midline glioma (DMG) is a deadly pediatric and adolescent central nervous system (CNS) tumor localized along the midline structures of the brain atop the spinal cord. With a median overall survival (OS) of just 9–11-months, DMG is characterized by global hypomethylation of histone H3 at lysine 27 (H3K27me3), driven by recurring somatic mutations in H3 genes including, HIST1H3B/C (H3.1K27M) or H3F3A (H3.3K27M), or through overexpression of EZHIP in patients harboring wildtype H3. The recent World Health Organization’s 5th Classification of CNS Tumors now designates DMG as, ‘H3 K27-altered’, suggesting that global H3K27me3 hypomethylation is a ubiquitous feature of DMG and drives devastating transcriptional programs for which there are no treatments. H3-alterations co-segregate with various other somatic driver mutations, highlighting the high-level of intertumoral heterogeneity of DMG. Furthermore, DMG is also characterized by very high-level intratumoral diversity with tumors harboring multiple subclones within each primary tumor. Each subclone contains their own combinations of driver and passenger lesions that continually evolve, making precision-based medicine challenging to successful execute. Whilst the intertumoral heterogeneity of DMG has been extensively investigated, this is yet to translate to an increase in patient survival. Conversely, our understanding of the non-genomic factors that drive the rapid growth and fatal nature of DMG, including endogenous and exogenous microenvironmental influences, neurological cues, and the posttranscriptional and posttranslational architecture of DMG remains enigmatic or at best, immature. However, these factors are likely to play a significant role in the complex biological sequelae that drives the disease. Here we summarize the heterogeneity of DMG and emphasize how analysis of the posttranslational architecture may improve treatment paradigms. We describe factors that contribute to treatment response and disease progression, as well as highlight the potential for pharmaco-proteogenomics (i.e., the integration of genomics, proteomics and pharmacology) in the management of this uniformly fatal cancer.