scholarly journals TBIO-05. INTEGRATION OF GENOMIC DATA FROM THE ONCOKIDSSM NEXT GENERATION SEQUENCING PANEL AND CHROMOSOMAL MICROARRAY ANALYSIS FOR DIAGNOSIS AND PROGNOSIS OF PEDIATRIC CNS TUMORS

2018 ◽  
Vol 20 (suppl_2) ◽  
pp. i181-i181
Author(s):  
Kristiyana Kaneva ◽  
Matthew Hiemenz ◽  
Jianling Ji ◽  
Nathan Robison ◽  
Ashley Margol ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Microarray Analysis ◽  
Genomic Data ◽  
Cns Tumors ◽  
Chromosomal Microarray ◽  
Chromosomal Microarray Analysis ◽  
Next Generation ◽  
Diagnosis And Prognosis ◽  
Generation Sequencing ◽  
Pediatric Cns Tumors

scholarly journals Diagnosis of Duchenne Muscular Dystrophy in a Presymptomatic Infant Using Next-Generation Sequencing and Chromosomal Microarray Analysis: A Case Report

Children ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 377
Author(s):  
Eun-Woo Park ◽  
Ye-Jee Shim ◽  
Jung-Sook Ha ◽  
Jin-Hong Shin ◽  
Soyoung Lee ◽  
...  
Keyword(s):  
Creatine Kinase ◽  
Next Generation Sequencing ◽  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Early Diagnosis ◽  
Microarray Analysis ◽  
Chromosomal Microarray ◽  
Chromosomal Microarray Analysis ◽  
Next Generation ◽  
Generation Sequencing

Duchenne muscular dystrophy is a progressive and lethal X-linked recessive neuromuscular disease caused by mutations in the dystrophin gene. It has a high rate of diagnostic delay; early diagnosis and treatment are often not possible due to delayed recognition of muscle weakness and lack of effective treatments. Current treatments based on genetic therapy can improve clinical results, but treatment must begin as early as possible before significant muscle damage. Therefore, early diagnosis and rehabilitation of Duchenne muscular dystrophy are needed before symptom aggravation. Creatine kinase is a diagnostic marker of neuromuscular disorders. Herein, the authors report a case of an infant patient with Duchenne muscular dystrophy with a highly elevated creatine kinase level but no obvious symptoms of muscle weakness. The patient was diagnosed with Duchenne muscular dystrophy via next-generation sequencing and chromosomal microarray analysis to identify possible inherited metabolic and neuromuscular diseases related to profound hyperCKemia. The patient is enrolled in a rehabilitation program and awaits the approval of the genetic treatment in Korea. This is the first report of an infantile presymptomatic Duchenne muscular dystrophy diagnosis using next-generation sequencing and chromosomal microarray analysis.

scholarly journals Discrepancy of Karyotype and CMA/NGS in a PGD Patient With Cryptical Complex Chromosomal Rearrangement

2021 ◽  
Author(s):  
Ting Wang ◽  
Yinhuan Zhong ◽  
Xianzheng Li ◽  
Hanbiao Chen ◽  
Jian Lu ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Chromosomal Abnormalities ◽  
Chromosome Rearrangement ◽  
Genetic Diagnosis ◽  
Chromosomal Microarray ◽  
Chromosomal Microarray Analysis ◽  
Next Generation ◽  
Chromosome 10 ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Abstract Introductions: Complex chromosome rearrangement (CCR) is a structural rearrangement involving more than two breakpoints. CCR carriers are at high risk for phenotypic abnormalities or reproductive failure, such as chromosomal abnormalities in fetuses and infertility. In this study, we presented a carriers with chromosome (3,18) balanced translocation, whose fetus had duplications in chromosome 3 and deletions in chromosome 10 demonstrated by chromosomal microarray analysis (CMA).By revealing the cryptical translocation, we aimed to provide CCR carriers with more accurate risk assessment of abnormal pregnancy and better assisted reproduction with CMA and next generation sequencing(NGS).Results: By using the high resolution of GTG-banding technology, a cryptical translocation in chromosome 10 was found and the karyotype of the carrier was revised as 46,XY,t(3;10;18) (p26.3;q26.1;q21.1).In the cycle of preimplantation genetic diagnosis (PGD),21 oocytes were retrieved, and 15 were fertilized. At last 7 embryos were biospied and sent to diagnosis by next generation sequencing(NGS).Unfortunately, none of the NGS results from the 7 biopsy embryos were normal. Combining previous literature and our results, we assessed the odds of a balanced embryo in a CCR carrier to be about 9.3%(28/302).The transferable embryo rate was approximately 71.4%(20/28) and healthy live born delivery rate was 55%(11/20).Conclusions: NGS and CMA featured high automation, relatively low cost, high throughput, and high repeatability, which made them commonly used during prenatal diagnosis and PGD. The multiple technology combination can provide more accurate diagnosis and better fertility services for CCR patients.

scholarly journals Next-Generation Sequencing Gene Panels and “Solo” Clinical Exome Sequencing Applied in Structurally Abnormal Fetuses

2021 ◽  
pp. 1-11
Author(s):  
Montse Pauta ◽  
Berta Campos ◽  
Maria Segura-Puimedon ◽  
Gemma Arca ◽  
Alfons Nadal ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Exome Sequencing ◽  
Diagnostic Yield ◽  
Monogenic Disease ◽  
Chromosomal Microarray Analysis ◽  
Next Generation ◽  
Clinical Exome Sequencing ◽  
Gene Panels ◽  
Generation Sequencing ◽  
Structural Anomalies

<b><i>Objective:</i></b> The aim of the study was to assess the diagnostic yield of 2 different next-generation sequencing (NGS) approaches: gene panel and “solo” clinical exome sequencing (solo-CES), in fetuses with structural anomalies and normal chromosomal microarray analysis (CMA), in the absence of a known familial mutation. <b><i>Methodology:</i></b> Gene panels encompassing from 2 to 140 genes, were applied mainly in persistent nuchal fold/fetal hydrops and in large hyperechogenic kidneys. Solo-CES, which entails sequencing the fetus alone and only interpreting the Online Mendelian Inheritance in Man genes, was performed in multisystem or recurrent structural anomalies. <b><i>Results:</i></b> During the study period (2015–2020), 153 NGS studies were performed in 148 structurally abnormal fetuses with a normal CMA. The overall diagnostic yield accounted for 35% (53/153) of samples and 36% (53/148) of the fetuses. Diagnostic yield with the gene panels was 31% (15/49), similar to 37% (38/104) in solo-CES. <b><i>Conclusions:</i></b> A monogenic disease was established as the underlying cause in 35% of selected fetal structural anomalies by gene panels and solo-CES.

scholarly journals Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

2019 ◽  
Author(s):  
Kate Chkhaidze ◽  
Timon Heide ◽  
Benjamin Werner ◽  
Marc J. Williams ◽  
Weini Huang ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Tumour Growth ◽  
Evolutionary Dynamics ◽  
Clonal Selection ◽  
Genomic Data ◽  
Confounding Factors ◽  
Data Generation ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

AbstractQuantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constrains, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data.SummarySequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected, impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mathematical models and computer simulations that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial simulation of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to overcome these biases and more robustly measure aspects of the biology of tumours from the data.

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