Mapping pediatric brain tumors to their origins in the developing cerebellum

A human fetal cerebellar map of the late second trimester reveals developmental molecular characteristics and abnormality in trisomy 21

Our understanding of human fetal cerebellum development during the late second trimester, a critical period for the generation of astrocytes, oligodendrocytes, and unipolar brush cells (UBCs), remains limited. Here, we performed single-cell RNA sequencing (scRNA-seq) in human fetal cerebellum samples from gestational weeks (GWs) 18-25. We find that proliferating UBC progenitors distribute in the subventricular zone of the rhombic lip (RLSVZ) near white matter (WM), forming a layer structure. We also delineate two trajectories from astrogenic radial glia (ARGs) to Bergmann glial progenitors (BGPs) and recognize oligodendrogenic radial glia (ORGs) as one source of primitive oligodendrocyte progenitor cells (PriOPCs). Additionally, our scRNA-seq analysis of the trisomy 21 fetal cerebellum at this stage reveals abnormal upregulated genes in pathways such as the cell adhesion pathway and focal adhesion pathway, which potentially promote neuronal differentiation. Overall, our research provides valuable insights into normal and abnormal development of the human fetal cerebellum.

Human Adapted Prosomeric Model: A Future for Brainstem Tumor Classification

This study reevaluates the conventional understanding of midbrain anatomy and neuroanatomical nomenclature in the context of recent genetic and anatomical discoveries. The authors assert that the midbrain should be viewed as an integral part of the forebrain due to shared genetic determinants and evolutionary lineage. The isthmo-mesencephalic boundary is recognized as a significant organizer for both the caudal midbrain and the isthmo-cerebellar area. The article adopts the prosomeric model, redefining the whole brain as neuromeres, offering a more precise depiction of brain development, including processes like proliferation, neurogenesis, cell migration, and differentiation. This shift in understanding challenges traditional definitions of the midbrain based on external brain morphology. The study also delves into the historical context of neuroanatomical models, including the columnar model proposed by Herrick in 1910, which has influenced our understanding of brain structure. Furthermore, the study has clinical implications, affecting neuroanatomy, neurodevelopmental studies, and the diagnosis and treatment of brain disorders. It emphasizes the need to integrate molecular research into human neuroanatomical studies and advocates for updating neuroanatomical terminology to reflect modern genetic and molecular insights. The authors propose two key revisions. First, we suggest reclassifying the isthmo-cerebellar prepontine region as part of the hindbrain, due to its role in cerebellar development and distinct location caudal to the genetically-defined midbrain. Second, we recommend redefining the anterior boundary of the genetically-defined midbrain to align with genetic markers. In conclusion, the authors highlight the importance of harmonizing neuroanatomical nomenclature with current scientific knowledge, promoting a more precise and informed understanding of brain structure, which is crucial for both research and clinical applications related to the human brain.

Mechanistic insights into the developmental origin of pediatric hematologic disorders

Embryonic and fetal hematopoietic stem and progenitor cells differ in some key properties from cells that are part of the adult hematopoietic system. These include higher proliferation and self-renewal capacity, different globin gene usage, and differing lineage biases. Although these evolved to cover specific requirements of embryonic development, they can have serious consequences for the pathogenesis of hematologic malignancies that initiate prebirth in fetal blood cells and may result in a particularly aggressive disease that does not respond well to treatments that have been designed for adult leukemias. This indicates that these infant/pediatric leukemias should be considered developmental diseases, where a thorough understanding of their unique biology is essential for designing more effective therapies. In this review, we will highlight some of these unique fetal properties and detail the underlying molecular drivers of these phenotypes. We specifically focus on those that are pertinent to disease pathogenesis and that may therefore reveal disease vulnerabilities. We have also included an extensive description of the origins, phenotypes, and key molecular drivers of the main infant and pediatric leukemias that have a known prenatal origin. Importantly, successes in recent years in generating faithful models of these malignancies in which cellular origins, key drivers, and potential vulnerabilities can be investigated have resulted in uncovering potential, new therapeutic avenues.

Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study

RATIONALE AND OBJECTIVES

Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set.

MATERIALS AND METHODS

Axial T2 weighted image (T2WI) and contrast-enhanced T1weighted image (CE-T1WI) MRI sequences of 135 patients from two centers were collected as train/test sets. Volume of interest (VOI) was manually delineated by an experienced neuroradiologist, supervised by a senior. Feature selection analysis and the least absolute shrinkage and selection operator (LASSO) algorithm identified valuable features, and Shapley additive explanations (SHAP) evaluated their significance. Five machine-learning classifiers-extreme gradient boosting (XGBoost), Bernoulli naive Bayes (Bernoulli NB), Logistic Regression (LR), support vector machine (SVM), linear support vector machine (Linear SVC) classifiers were built based on T2WI (T2 model), CE-T1WI (T1 model), and T1 + T2WI (T1 + T2 model). A human expert diagnosis was developed and corrected by senior radiologists. External validation was performed at Sun Yat-Sen University Cancer Center.

RESULTS

31 valuable features were extracted from T2WI and CE-T1WI. XGBoost demonstrated the highest performance with an area under the curve (AUC) of 0.92 on the test set and maintained an AUC of 0.80 during external validation. For the T1 model, XGBoost achieved the highest AUC of 0.85 on the test set and the highest accuracy of 0.71 on the external validation set. In the T2 model, XGBoost achieved the highest AUC of 0.86 on the test set and the highest accuracy of 0.82 on the external validation set. The human expert diagnosis had an AUC of 0.66 on the test set and 0.69 on the external validation set. The integrated T1 + T2 model achieved an AUC of 0.92 on the test set, 0.80 on the external validation set, achieved the best performance. Overall, XGBoost consistently outperformed in different classification models.

CONCLUSION

The combination of radiomics and machine learning on multiparametric MRI effectively distinguishes between MB and EM in childhood, surpassing human expert diagnosis in training and testing sets.

Clinically unfavorable transcriptome subtypes of non-WNT/non-SHH medulloblastomas are associated with a predominance in proliferating and progenitor-like cell subpopulations

The non-WNT/non-SHH (Grp3/Grp4) medulloblastomas (MBs) include eight second-generation subgroups (SGS; I-VIII) each with distinct molecular and clinical characteristics. Recently, we also identified two prognostically relevant transcriptome subtypes within each SGS MB, which are associated with unique gene expression signatures and signaling pathways. These prognostic subsets may be in connection to the intra-tumoral cell landscape that underlies SGS MB clinical-molecular diversity. Here, we performed a deconvolution analysis of the Grp3/Grp4 MB bulk RNA profiles using the previously identified single-cell RNA-seq reference dataset and focusing on variability in the cellular composition of SGS MB. RNA deconvolution analysis of the Grp3/Grp4 MB disclosed the subgroup-specific neoplastic cell subpopulations. Neuronally differentiated axodendritic GP3-C1 and glutamatergic GP4-C1 subpopulations were distributed within Grp3- and Grp4-associated SGS MB, respectively. Progenitor GP3-B2 subpopulation was prominent in aggressive SGS II MB, whereas photoreceptor/visual perception GP3/4-C2 cell content was typical for SGS III/IV MB. The current study also revealed significant variability in the proportions of cell subpopulations between clinically relevant SGS MB transcriptome subtypes, where unfavorable cohorts were enriched with cell cycle and progenitor-like cell subpopulations and, vice versa, favorable subtypes were composed of neuronally differentiated cell fractions predominantly. A higher than median proportion of proliferating and progenitor cell subpopulations conferred the shortest survival of the Grp3 and Grp 4 MB, and similar survival associations were identified for all SGS MB except SGS IV MB. In summary, the recently identified clinically relevant Grp3/Grp4 MB transcriptome subtypes are composed of different cell populations. Future studies should aim to validate the prognostic and therapeutic role of the identified Grp3/Grp4 MB inter-tumoral cellular heterogeneity. The application of the single-cell techniques on each SGS MB separately could help to clarify the clinical significance of subgroup-specific variability in tumor cell content and its relation with prognostic transcriptome signatures identified before.

Heterogeneity and tumoral origin of medulloblastoma in the single-cell era

Medulloblastoma is one of the most common malignant pediatric brain tumors derived from posterior fossa. The current treatment includes maximal safe surgical resection, radiotherapy, whole cranio-spinal radiation and adjuvant with chemotherapy. However, it can only limitedly prolong the survival time with severe side effects and relapse. Defining the intratumoral heterogeneity, cellular origin and identifying the interaction network within tumor microenvironment are helpful for understanding the mechanisms of medulloblastoma tumorigenesis and relapse. Due to technological limitations, the mechanisms of cellular heterogeneity and tumor origin have not been fully understood. Recently, the emergence of single-cell technology has provided a powerful tool for achieving the goal of understanding the mechanisms of tumorigenesis. Several studies have demonstrated the intratumoral heterogeneity and tumor origin for each subtype of medulloblastoma utilizing the single-cell RNA-seq, which has not been uncovered before using conventional technologies. In this review, we present an overview of the current progress in understanding of cellular heterogeneity and tumor origin of medulloblastoma and discuss novel findings in the age of single-cell technologies.

Developmental basis of SHH medulloblastoma heterogeneity

Many genes that drive normal cellular development also contribute to oncogenesis. Medulloblastoma (MB) tumors likely arise from neuronal progenitors in the cerebellum, and we hypothesized that the heterogeneity observed in MBs with sonic hedgehog (SHH) activation could be due to differences in developmental pathways. To investigate this question, here we perform single-nucleus RNA sequencing on highly differentiated SHH MBs with extensively nodular histology and observed malignant cells resembling each stage of canonical granule neuron development. Through innovative computational approaches, we connect these results to published datasets and find that some established molecular subtypes of SHH MB appear arrested at different developmental stages. Additionally, using multiplexed proteomic imaging and MALDI imaging mass spectrometry, we identify distinct histological and metabolic profiles for highly differentiated tumors. Our approaches are applicable to understanding the interplay between heterogeneity and differentiation in other cancers and can provide important insights for the design of targeted therapies.

Cellular development and evolution of the mammalian cerebellum

The expansion of the neocortex, a hallmark of mammalian evolution1,2, was accompanied by an increase in cerebellar neuron numbers3. However, little is known about the evolution of the cellular programmes underlying the development of the cerebellum in mammals. In this study we generated single-nucleus RNA-sequencing data for around 400,000 cells to trace the development of the cerebellum from early neurogenesis to adulthood in human, mouse and the marsupial opossum. We established a consensus classification of the cellular diversity in the developing mammalian cerebellum and validated it by spatial mapping in the fetal human cerebellum. Our cross-species analyses revealed largely conserved developmental dynamics of cell-type generation, except for Purkinje cells, for which we observed an expansion of early-born subtypes in the human lineage. Global transcriptome profiles, conserved cell-state markers and gene-expression trajectories across neuronal differentiation show that cerebellar cell-type-defining programmes have been overall preserved for at least 160 million years. However, we also identified many orthologous genes that gained or lost expression in cerebellar neural cell types in one of the species or evolved new expression trajectories during neuronal differentiation, indicating widespread gene repurposing at the cell-type level. In sum, our study unveils shared and lineage-specific gene-expression programmes governing the development of cerebellar cells and expands our understanding of mammalian brain evolution.