Latest classification of ependymoma in the molecular era and advances in its treatment: a review

Advances in molecular prognostication and treatments in ependymoma

Ependymoma is the third most common brain tumour of childhood and historically has posed a major challenge to both pediatric and adult neuro-oncologists. Ependymoma can occur anywhere in the central nervous system throughout the entire age spectrum. Treatment options have been limited to surgery and radiation, and outcomes have been widely disparate across studies. Indeed, these disparate outcomes have rendered it extraordinarily difficult to compare studies and to truly understand which patients are low and high-risk. Over the past two decades there have been tremendous advances in our understanding of the biology of ependymoma, which have changed risk stratification dramatically. Indeed, it is now well accepted that ependymoma comprises multiple distinct entities, whereby each compartment (supratentorial, posterior fossa, spinal) are distinct, and within each compartment there exist unique groups. The driver events, demographics and response to treatment vary widely across these groups and allow for a better classification of thee disease. Herein, we review the advances in the molecular stratification of ependymoma including how an improved classification and risk stratification allows for more precise therapies.

Prognostic Factors and Nomogram for Malignant Brainstem Ependymoma: A Population-Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis

PURPOSE

This study aimed to identify prognostic factors and develop a nomogram for survival in patients with brainstem ependymoma.

METHODS

Data of 652 patients diagnosed with brainstem ependymoma extracted from the Surveillance, Epidemiology, and End Results (SEER) registry from 2000 to 2020 were analyzed. Univariate and multivariable Cox regression analyses were performed to examine factors influencing overall survival (OS). Receiver operating characteristic curve (ROC) and calibration curves were used to verify the nomogram. The Kaplan-Meier method was used to analyze OS based on treatment methods stratification or different age patterns.

RESULTS

Six independent prognostic factors of patients with brainstem ependymoma were identified, including age, race, marital status, radiation, gross total resection (GTR), and histology. A comprehensive nomogram model was developed utilizing these predictors identified through multivariable Cox regression analysis. Furthermore, we found that patients with GTR have improved overall survival than patient with no surgery and biopsy only or with partial resection (GTR vs. no: p = 0.0004, GTR vs. partial resection: p = 0.022). Patients with radiation have improved overall survival than patient without radiation (p = 0.00013). Patients with GTR combined radiation therapy have improved overall survival than patient without or with GTR or radiation therapy only (p < 0.0001). Different treatment methods have no significant difference in the overall survival probability of the elderly group.

CONCLUSIONS

Individuals who are Black and anaplastic ependymomas were negative risk factors for brainstem ependymoma associated with an increased risk of mortality. Patients aged < 50 years with GTR and radiation always had better survival.

Toward a Unified Theory of Why Young People Develop Cancer

Epidemiologic and genetic studies have now defined specific patterns of incidence and distinct molecular features of cancers in young versus aging people. Here, I review a general framework for the causes of cancer in children and young adults by relating somatic genetic mosaicism and developmental tissue mutagenesis. This framework suggests how aging-associated cancers such as carcinomas, glioblastomas, and myelodysplastic leukemias are causally distinct from cancers that predominantly affect children and young adults, including lymphoblastic and myeloid leukemias, sarcomas, neuroblastomas, medulloblastomas, and other developmental cancers. I discuss the oncogenic activities of known developmental mutators RAG1/2, AID, and PGBD5, and describe strategies needed to define missing developmental causes of young-onset cancers. Thus, a precise understanding of the mechanisms of tissue-specific somatic mosaicism, developmental mutators, and their control by human genetic variation and environmental exposures is needed for improved strategies for cancer screening, prevention, and treatment.

Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us?

Machine learning (ML) models have become capable of making critical decisions on our behalf. Nevertheless, due to complexity of these models, interpreting their decisions can be challenging, and humans cannot always control them. This paper provides explanations of decisions made by ML models in diagnosing four types of posterior fossa tumors: medulloblastoma, ependymoma, pilocytic astrocytoma, and brainstem glioma. The proposed methodology involves data analysis using kernel density estimations with Gaussian distributions to examine individual MRI features, conducting an analysis on the relationships between these features, and performing a comprehensive analysis of ML model behavior. This approach offers a simple yet informative and reliable means of identifying and validating distinguishable MRI features for the diagnosis of pediatric brain tumors. By presenting a comprehensive analysis of the responses of the four pediatric tumor types to each other and to ML models in a single source, this study aims to bridge the knowledge gap in the existing literature concerning the relationship between ML and medical outcomes. The results highlight that employing a simplistic approach in the absence of very large datasets leads to significantly more pronounced and explainable outcomes, as expected. Additionally, the study also demonstrates that the pre-analysis results consistently align with the outputs of the ML models and the clinical findings reported in the existing literature.