Epidemiology and prognostic factors of pediatric brain tumor survival in the US: Evidence from four decades of population data

2021 ◽  
Vol 72 ◽  
pp. 101942
Author(s):  
Md. Jobayer Hossain ◽  
Wendi Xiao ◽  
Maliha Tayeb ◽  
Saira Khan
Keyword(s):  
Brain Tumor ◽  
Prognostic Factors ◽  
Pediatric Brain Tumor ◽  
Population Data ◽  
The Us ◽  
Tumor Survival ◽  
Pediatric Brain

scholarly journals QOL-55. INTEGRATED MULTI-SCALE MODEL FOR PEDIATRIC BRAIN TUMOR SURVIVAL PREDICTION

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii440-iii441
Author(s):  
Yeping Lina Qiu ◽  
Amaury Sabran ◽  
Hong Zheng ◽  
Olivier Gevaert
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Genomic Data ◽  
Pediatric Brain Tumor ◽  
Adult Brain ◽  
Scale Model ◽  
Survival Prediction ◽  
Imaging Data ◽  
Tumor Survival ◽  
Pediatric Brain

Abstract Brain tumors are the most common solid tumors affecting children, and its prognosis has been a great challenge for physicians and researchers. With the advances in high-throughput sequencing technology and digital pathology, more quantitative data is now becoming available and more information may potentially be discovered in whole slide images (WSIs) and molecular tumor characteristics to determine survival and treatment. Imaging and genomic data, though very different in nature, both may contain different aspects of disease characteristics that are important for survival prediction. Hence our work aims to build a framework to integrate two data modules, whole-slide histopathology image data, and RNA sequencing data, for a unified model to improve pediatric brain tumor survival outcome prediction. The imaging data and genomic data are both of high dimensions and on different scales. We use two independent modules, each of which consists of a deep neural network, to extract lower dimensional features from imaging and genomic data respectively. We concatenate the extracted features and use a third neural network to train a Cox regression model using the merged feature as input. Each module is first pre-trained with TCGA adult brain tumor data, and subsequently fine-tuned with pediatric brain tumor data. The entire pipeline is tested on the holdout pediatric brain tumor dataset. Preliminary results suggest that the integrated framework achieves improved prediction performance than using each single data module alone. The concordance index (C-index) of integrated model is 0.68, compared to 0.62 with imaging data only, and 0.66 with genomic data only.

The Pediatric Brain Tumor Preclinical Testing Program: an update on the current status

2012 ◽  
Vol 224 (06) ◽  
Author(s):  
T Milde ◽  
M Zucknick ◽  
M Kool ◽  
A Korshunov ◽  
H Witt ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Pediatric Brain Tumor ◽  
Current Status ◽  
Preclinical Testing ◽  
Testing Program ◽  
Pediatric Brain

scholarly journals A pediatric brain tumor atlas of genes deregulated by somatic genomic rearrangement

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yiqun Zhang ◽  
Fengju Chen ◽  
Lawrence A. Donehower ◽  
Michael E. Scheurer ◽  
Chad J. Creighton
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Tyrosine Kinases ◽  
Pediatric Brain Tumor ◽  
Pediatric Brain Tumors ◽  
Altered Expression ◽  
Critical Pathways ◽  
Cis Regulation ◽  
Or Gene ◽  
Pediatric Brain

AbstractThe global impact of somatic structural variants (SSVs) on gene expression in pediatric brain tumors has not been thoroughly characterised. Here, using whole-genome and RNA sequencing from 854 tumors of more than 30 different types from the Children’s Brain Tumor Tissue Consortium, we report the altered expression of hundreds of genes in association with the presence of nearby SSV breakpoints. SSV-mediated expression changes involve gene fusions, altered cis-regulation, or gene disruption. SSVs considerably extend the numbers of patients with tumors somatically altered for critical pathways, including receptor tyrosine kinases (KRAS, MET, EGFR, NF1), Rb pathway (CDK4), TERT, MYC family (MYC, MYCN, MYB), and HIPPO (NF2). Compared to initial tumors, progressive or recurrent tumors involve a distinct set of SSV-gene associations. High overall SSV burden associates with TP53 mutations, histone H3.3 gene H3F3C mutations, and the transcription of DNA damage response genes. Compared to adult cancers, pediatric brain tumors would involve a different set of genes with SSV-altered cis-regulation. Our comprehensive and pan-histology genomic analyses reveal SSVs to play a major role in shaping the transcriptome of pediatric brain tumors.

Piecing together the Pediatric Brain Tumor Puzzle

2021 ◽  
Vol 37 (3) ◽  
pp. 204-206
Author(s):  
Carolina Nör ◽  
Vijay Ramaswamy
Keyword(s):  
Brain Tumor ◽  
Pediatric Brain Tumor ◽  
Pediatric Brain
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