Causes of death in pediatric neuro-oncology: the sickkids experience from 2000 to 2017

Renzi, Samuele; Michaeli, Orli; Ramaswamy, Vijay; Huang, Annie; Stephens, Derek; Maguire, Bryan; Tabori, Uri; Bouffet, Éric; Bartels, Ute

doi:10.1007/s11060-020-03590-w

Cited by 14 publications

(10 citation statements)

References 34 publications

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“…Other studies reported a higher mortality rate in children diagnosed under 1 year of age when compared to older children [20,26,27]. Despite a trend in literature towards worse outcome in children diagnosed in the first year of life, we could not confirm this finding in our cohort, noting however a trend towards worse OS in children diagnosed in the first 6 months of life (Fig.…”

Section: Discussioncontrasting

confidence: 76%

Central nervous system tumors in children under 5 years of age: a report on treatment burden, survival and long-term outcomes

et al. 2022

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Purpose The challenges of treating central nervous system (CNS) tumors in young children are many. These include age-specific tumor characteristics, limited treatment options, and susceptibility of the developing CNS to cytotoxic therapy. The aim of this study was to analyze the long-term survival, health-related, and educational/occupational outcomes of this vulnerable patient population. Methods Retrospective study of 128 children diagnosed with a CNS tumor under 5 years of age at a single center in Switzerland between 1990 and 2019. Results Median age at diagnosis was 1.81 years [IQR, 0.98–3.17]. Median follow-up time of surviving patients was 8.39 years [range, 0.74–23.65]. The main tumor subtypes were pediatric low-grade glioma (36%), pediatric high-grade glioma (11%), ependymoma (16%), medulloblastoma (11%), other embryonal tumors (7%), germ cell tumors (3%), choroid plexus tumors (6%), and others (9%). The 5-year overall survival (OS) was 78.8% (95% CI, 71.8–86.4%) for the whole cohort. Eighty-seven percent of survivors > 5 years had any tumor- or treatment-related sequelae with 61% neurological complications, 30% endocrine sequelae, 17% hearing impairment, and 56% visual impairment at last follow-up. Most patients (72%) attended regular school or worked in a skilled job at last follow-up. Conclusion Young children diagnosed with a CNS tumor experience a range of complications after treatment, many of which are long-lasting and potentially debilitating. Our findings highlight the vulnerabilities of this population, the need for long-term support and strategies for rehabilitation, specifically tailored for young children.

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Section: Discussioncontrasting

confidence: 76%

Central nervous system tumors in children under 5 years of age: a report on treatment burden, survival and long-term outcomes

et al. 2022

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“…The results suggested that SOX B1 gene expression was higher in GBM and low‐grade glioma (LGG) patients than in normal tissue samples. Because LGG patients have always been characterized by a relatively good prognosis, 24 , 25 we focused on the expression levels of SOX B1 genes in GBM. The results showed that GBM patients had higher SOX2 expression levels than healthy patients ( p < 0.01) (Figure 2A–2E ).…”

Section: Resultsmentioning

confidence: 99%

Expression and significance of SOX B1 genes in glioblastoma multiforme patients

Pan

Liang

Wang³

et al. 2021

J Cellular Molecular Medi

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Glioblastoma multiforme (GBM) is the most common, malignant and high-grade brain tumour. 1,2 The WHO classification system divides glioma into 4 subtypes and GBM as grade 4 glial tumour has the worst prognosis. 3 The intra-and intertumoral genetic and epigenetic heterogeneity observed in GBM highlights the complexity of cancer. 4 The median survival of GBM patients is around 12-15 months, even with

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“…LGGs grow relatively slowly, and due to their characteristic diffuse and in ltrative growth, they are prone to recurrence after surgical resection, occasionally accompanied by an increase in malignancy grade, and have a high clinical disability and mortality rate 5 . In contrast, glioblastoma multiforme (GBM, grade 4) displays a much more unfavorable course.…”

Section: Introductionmentioning

confidence: 99%

Clinical Significance and Molecular Annotation of Cellular Morphometric Subtypes in Lower Grade Gliomas discovered by Machine Learning: a retrospective multicentric study

Jin

Ahmadian

et al. 2021

Preprint

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Lower grade gliomas (LGGs) are heterogenous diseases by clinical, histological and molecular criteria. Here, we developed a machine learning pipeline to extract cellular morphometric biomarkers from whole slide images of tissue histology; and identified and externally validated robust cellular morphometric subtypes of LGGs in multi-center cohorts. The subtypes have significantly independent predictive power for overall survival across all three independent cohorts. In the TCGA-LGG cohort, we found that patients within the poor-prognosis subtype responded poorly to primary therapy and follow-up treatment. Furthermore, LGGs within the poor-prognosis subtype were characterized by higher mutational burden, higher frequencies of copy number alterations, and higher level of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher level of PD-1/PD-L1/CTLA-4 was confirmed by immunohistochemical staining. In addition, the subtypes learned from LGG demonstrates translational impact on Glioblastoma (GBM). Overall, we developed and validated a framework for the cellular morphometric subtype discovery in LGGs associated with specific molecular alterations, immune micro-environment, prognosis and treatment response.

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Causes of death in pediatric neuro-oncology: the sickkids experience from 2000 to 2017

Cited by 14 publications

References 34 publications

Central nervous system tumors in children under 5 years of age: a report on treatment burden, survival and long-term outcomes

Central nervous system tumors in children under 5 years of age: a report on treatment burden, survival and long-term outcomes

Expression and significance of SOX B1 genes in glioblastoma multiforme patients

Clinical Significance and Molecular Annotation of Cellular Morphometric Subtypes in Lower Grade Gliomas discovered by Machine Learning: a retrospective multicentric study

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