OBJECTIVES To determine the diagnostic and clinical utility of trio-rapid genome sequencing in critically ill infants. DESIGN In this prospective study, samples from critically ill infants were analyzed using both proband-only clinical exome sequencing and trio-rapid genome sequencing (proband and biological parents). The study occurred between April 2019 and December 2019. SETTING Thirteen member hospitals of the China Neonatal Genomes Project spanning 10 provinces were involved. PARTICIPANTS Critically ill infants (n = 202), from birth up until 13 months of life were enrolled based on eligibility criteria (e.g., CNS anomaly, complex congenital heart disease, evidence of metabolic disease, recurrent severe infection, suspected immune deficiency, and multiple malformations). INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Of the 202 participants, neuromuscular (45%), respiratory (22%), and immunologic/infectious (18%) were the most commonly observed phenotypes. The diagnostic yield of trio-rapid genome sequencing was higher than that of proband-only clinical exome sequencing (36.6% [95% CI, 30.1-43.7%] vs 20.3% [95% CI, 15.1-26.6%], respectively; p = 0.0004), and the average turnaround time for trio-rapid genome sequencing (median: 7 d) was faster than that of proband-only clinical exome sequencing (median: 20 d) (p < 2.2 × 10-16). The metagenomic analysis identified pathogenic or likely pathogenic microbes in six infants with symptoms of sepsis, and these results guided the antibiotic treatment strategy. Sixteen infants (21.6%) experienced a change in clinical management following trio-rapid genome sequencing diagnosis, and 24 infants (32.4%) were referred to a new subspecialist. CONCLUSIONS Trio-rapid genome sequencing provided higher diagnostic yield in a shorter period of time in this cohort of critically ill infants compared with proband-only clinical exome sequencing. Precise and fast molecular diagnosis can alter medical management and positively impact patient outcomes.

Application of Full-Spectrum Rapid Clinical Genome Sequencing Improves Diagnostic Rate and Clinical Outcomes in Critically Ill Infants in the China Neonatal Genomes Project.

The routine assessment to determine the genetic etiology for fetal ultrasound anomalies follows a sequential approach, which usually takes about 6–8 weeks turnaround time (TAT). We evaluated the clinical utility of simultaneous detection of copy number variations (CNVs) and single nucleotide variants (SNVs)/small insertion-deletions (indels) in fetuses with a normal karyotype with ultrasound anomalies. We performed CNV detection by chromosomal microarray analysis (CMA) or low pass CNV-sequencing (CNV-seq), and in parallel SNVs/indels detection by trio-based clinical exome sequencing (CES) or whole exome sequencing (WES). Eight-three singleton pregnancies with a normal fetal karyotype were enrolled in this prospective observational study. Pathogenic or likely pathogenic variations were identified in 30 cases (CNVs in 3 cases, SNVs/indels in 27 cases), indicating an overall molecular diagnostic rate of 36.1% (30/83). Two cases had both a CNV of uncertain significance (VOUS) and likely pathogenic SNV, and one case carried both a VOUS CNV and an SNV. We demonstrated that simultaneous analysis of CNVs and SNVs/indels can improve the diagnostic yield of prenatal diagnosis with shortened reporting time, namely, 2–3 weeks. Due to the relatively long TAT for sequential procedure for prenatal genetic diagnosis, as well as recent sequencing technology advancements, it is clinically necessary to consider the simultaneous evaluation of CNVs and SNVs/indels to enhance the diagnostic yield and timely TAT, especially for cases in the late second trimester or third trimester.

https://www.mdpi.com/2073-4425/11/12/1397/pdf

Simultaneous Detection of CNVs and SNVs Improves the Diagnostic Yield of Fetuses with Ultrasound Anomalies and Normal Karyotypes.

Background: Early and accurate evaluation of the risk of bronchopulmonary dysplasia (BPD) in premature infants is pivotal in implementing preventive strategies. Risk prediction models for BPD risk that included only clinical factors but without genetic factors nowadays, are either too complex without practicability or provide poor-to-moderate discrimination. We aim to identify the role of genetic factors in BPD risk prediction early and accurately. Methods: Exome sequencing was performed in a cohort of 245 premature infants (gestational age <32 weeks), with 131 BPD infants and 114 infants without BPD as controls. A gene burden test was performed to find risk genes with loss-of-function (LOF) mutations or missense (MIS) mutations over-represented in BPD and severe BPD (sBPD) patients, with risk gene sets (RGS) defined as BPD-RGS and sBPD-RGS, respectively. We then developed two predictive models for the risk of BPD and sBPD by integrating patient clinical and genetic features. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: Thirty and 21 genes were included in BPD-RGS and severe BPD-RGS, respectively. The predictive model for BPD, which combined the BPD-RGS and basic clinical risk factors, showed better discrimination than the model that was only based on basic clinical features (AUC, 0.915 vs. AUC, 0.814, P=0.013, respectively) in the independent testing dataset. The same was observed in the predictive model for severe BPD (AUC, 0.907 vs. AUC, 0.826; P=0.016). Conclusions: This study suggests that genetic information contributes to susceptibility to BPD. The predictive model in this study, which combined BPD-RGS with basic clinical risk factors, can thus accurately stratify BPD risk in premature infants.

/pdf/bronchopulmonary-dysplasia-predicted-by-developing-a-machine-387sashdey.pdf

Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information.

Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.

https://www.science.org/doi/pdf/10.1126/sciadv.abj1624?download=true

CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.

Neurodevelopmental disorders (NDDs) are a genetically heterogeneous group of diseases, affecting 1%-3% of children. Whole-exome sequencing (WES) has been widely used as a first-tier tool for identifying genetic causes of rare diseases. Trio-WES was performed in a cohort of 74 pedigrees with NDDs. Exome-based copy number variant (CNV) calling was incorporated into the traditional single-nucleotide variant (SNV) and small insertion/deletion (Indel) analysis pipeline for WES data. An overall positive diagnostic yield of 54.05% (40/74) was obtained in the pipeline of combinational SNV/Indel and CNV analysis, including 35.13% (26/74) from SNV/Indel analysis and 18.92% (14/74) from exome-based CNV analysis, respectively. In total, SNV/Indel analysis identified 38 variants in 28 different genes, of which 24 variants were novel; exome-based CNV analysis identified 14 CNVs, including 2 duplications and 12 deletions, which ranged from 440 bp (single exon) to 16.86 Mb (large fragment) in size. In particular, a hemizygous deletion of exon 1 in the SLC16A2 gene was detected. Based on the diagnostic results, two families underwent prenatal diagnosis and had unaffected babies. The incorporation of exome-based CNV detection into conventional SNV/Indel analysis for a single trio-WES test significantly improved the diagnostic rate, making WES a more powerful, practical, and cost-effective tool in the clinical diagnosis of NDDs.

Incorporation of exome-based CNV analysis makes trio-WES a more powerful tool for clinical diagnosis in neurodevelopmental disorders: A retrospective study.

Background Developmental disorders (DDs) are early onset disorders affecting 5%–10% of children worldwide. Chromosomal microarray analysis detecting CNVs is currently recommended as the first-tier test for DD diagnosis. However, this analysis omits a high percentage of disease-causing single nucleotide variations (SNVs) that warrant further sequencing. Currently, next-generation sequencing can be used in clinical scenarios detecting CNVs, and the use of exome sequencing in the DD cohort ahead of the microarray test has not been evaluated. Methods Clinical exome sequencing (CES) was performed on 1090 unrelated Chinese DD patients who were classified into five phenotype subgroups. CNVs and SNVs were both detected and analysed based on sequencing data. Results An overall diagnostic rate of 41.38% was achieved with the combinational analysis of CNV and SNV. Over 12.02% of patients were diagnosed based on CNV, which was comparable with the published CMA diagnostic rate, while 0.74% were traditionally elusive cases who had dual diagnosis or apparently homozygous mutations that were clarified. The diagnostic rates among subgroups ranged from 21.82% to 50.32%. The top three recurrent cytobands with diagnostic CNVs were 15q11.2-q13.1, 22q11.21 and 7q11.23. The top three genes with diagnostic SNVs were: MECP2, SCN1A and SCN2A. Both the diagnostic rate and spectrums of CNVs and SNVs showed differences among the phenotype subgroups. Conclusion With a higher diagnostic rate, more comprehensive observation of variations and lower cost compared with conventional strategies, simultaneous analysis of CNVs and SNVs based on CES showed potential as a new first-tier choice to diagnose DD.

https://jmg.bmj.com/content/jmedgenet/57/8/558.full.pdf

Clinical exome sequencing as the first-tier test for diagnosing developmental disorders covering both CNV and SNV: a Chinese cohort

The explosion of next-generation sequencing (NGS) has enabled the widespread use of genomic data in precision medicine. Currently, several neonatal genome projects have emerged to explore the advantages of NGS to diagnose or screen for rare genetic disorders. These projects have made remarkable achievements, but still the genome data could be further explored with the assistance of phenotype collection. In contrast, longitudinal birth cohorts are great examples to record and apply phenotypic information in clinical studies starting at the neonatal period, especially the trajectory analyses for health development or disease progression. It is obvious that efficient integration of genotype and phenotype benefits not only the clinical management of rare genetic disorders but also the risk assessment of complex diseases. Here, we first summarize the recent neonatal genome projects as well as some longitudinal birth cohorts. Then, we propose two simplified strategies by integrating genotypic and phenotypic information in precision medicine based on current studies. Finally, research collaborations, sociological issues, and future perspectives are discussed. How to maximize neonatal genomic information to benefit the pediatric population remains an area in need of more research and effort. 

Precision Medicine via the Integration of Phenotype-Genotype Information in Neonatal Genome Project

Risk factors for death among children and young people hospitalized with COVID-19: a literature review

Objective This study sets out to explore if the relationship between the driving pressure and hospital mortality in ARDS patients is influenced by body mass index (BMI) level or the presence of abdominal obesity. Methods Data were extracted from an online database named “Multiparameter Intelligent Monitoring in Intensive Care III.” A total of 1556 patients were included and divided into four subgroups based on both BMI level (BMI ≥30 kg/m2 or BMI ＜30 kg/m2) and abdominal assessment. Driving pressure [i.e., the difference between plateau pressure and positive end-expiratory pressure (PEEP)] within 24 h of invasive mechanical ventilation was compared between survivors and nonsurvivors during hospitalization in each group. A logistic regression model was used for hospital mortality. Results There were 1556 patients with mild-to-severe ARDS, 666 (42.80%) nonobese patients with nonabdominal obesity, 259 (16.65%) nonobese patients with abdominal obesity, 97 (6.23%) obese patients with nonabdominal obesity, and 534 (34.32%) obese patients with abdominal obesity. Driving pressure in nonobese patients with nonabdominal obesity was significantly lower in survivors (12.77 ± 4.53 cm H2O) than in nonsurvivors (14.26 ± 5.52 cm H2O, p < 0.01). On the contrary, in the other three groups, driving pressure was not significantly different between survivors and nonsurvivors. After a logistic multivariable regression analysis, in nonobese (BMI<30 kg/m2) patients with nonabdominal obesity, the driving pressure was independently associated with increased hospital mortality (OR: 1.04, 95% CI 1.00–1.09, p < 0.05) but not in the other three subgroups. Conclusion Driving pressure is associated with increase in hospital mortality only in nonobese (BMI <30 kg/m2) patients with nonabdominal obesity.

/pdf/influence-of-the-driving-pressure-on-mortality-in-ards-c1s7sapr.pdf

Influence of the Driving Pressure on Mortality in ARDS Patients with or without Abdominal Obesity: A Retrospective Cohort Study

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Clinical exome sequencing as the first-tier test for diagnosing developmental disorders covering both CNV and SNV: a Chinese cohort

Precision Medicine via the Integration of Phenotype-Genotype Information in Neonatal Genome Project

Risk factors for death among children and young people hospitalized with COVID-19: a literature review

Influence of the Driving Pressure on Mortality in ARDS Patients with or without Abdominal Obesity: A Retrospective Cohort Study