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Towards accurate and reliable resolution of structural variants for clinical diagnosis.

ABSTRACT: Structural variants (SVs) are a major source of human genetic diversity and have been associated with different diseases and phenotypes. The detection of SVs is difficult, and a diverse range of detection methods and data analysis protocols has been developed. This difficulty and diversity make the detection of SVs for clinical applications challenging and requires a framework to ensure accuracy and reproducibility. Here, we discuss current developments in the diagnosis of SVs and propose a roadmap for the accurate and reproducible detection of SVs that includes case studies provided from the FDA-led SEquencing Quality Control Phase II (SEQC-II) and other consortium efforts.

SUBMITTER: Liu Z

PROVIDER: S-EPMC8892125 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Towards accurate and reliable resolution of structural variants for clinical diagnosis.

Liu Zhichao Z Roberts Ruth R Mercer Timothy R TR Xu Joshua J Sedlazeck Fritz J FJ Tong Weida W

Genome biology 20220303 1

Structural variants (SVs) are a major source of human genetic diversity and have been associated with different diseases and phenotypes. The detection of SVs is difficult, and a diverse range of detection methods and data analysis protocols has been developed. This difficulty and diversity make the detection of SVs for clinical applications challenging and requires a framework to ensure accuracy and reproducibility. Here, we discuss current developments in the diagnosis of SVs and propose a road ...[more]

PMID: 35241127

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Project description:ObjectiveTo assess the accuracy of deep learning reconstruction (DLR) technique on synthetic MRI (SyMRI) including T2 measurements and diagnostic performance of DLR synthetic MRI (SyMRIDL) in patients with knee osteoarthritis (KOA) using conventional MRI as standard reference.Materials and methodsThis prospective study recruited 36 volunteers and 70 patients with suspected KOA from May to October 2023. DLR and non-DLR synthetic T2 measurements (T2-SyMRIDL, T2-SyMRI) for phantom and in vivo knee cartilage were compared with multi-echo fast-spin-echo (MESE) sequence acquired standard T2 values (T2MESE). The inter-reader agreement on qualitative evaluation of SyMRIDL and the positive percent agreement (PPA) and negative percentage agreement (NPA) were analyzed using routine images as standard diagnosis.ResultsDLR significantly narrowed the quantitative differences between T2-SyMRIDL and T2MESE for 0.8 ms with 95% LOA [-5.5, 7.1]. The subjective assessment between DLR synthetic MR images and conventional MRI was comparable (all p > 0.05); Inter-reader agreement for SyMRIDL and conventional MRI was substantial to almost perfect with values between 0.62 and 0.88. SyMRIDL MOAKS had substantial inter-reader agreement and high PPA/NPA values (95%/99%) using conventional MRI as standard reference. Moreover, T2-SyMRIDL measurements instead of non-DLR ones significantly differentiated normal-appearing from injury-visible cartilages.ConclusionDLR synthetic knee MRI provided both weighted images for clinical diagnosis and accurate T2 measurements for more confidently identifying early cartilage degeneration from normal-appearing cartilages.Critical relevance statementOne-acquisition synthetic MRI based on deep learning reconstruction provided an accurate quantitative T2 map and morphologic images in relatively short scan time for more confidently identifying early cartilage degeneration from normal-appearing cartilages compared to the conventional morphologic knee sequences.Key pointsDeep learning reconstruction (DLR) synthetic knee cartilage T2 values showed no difference from conventional ones. DLR synthetic T1-, proton density-, STIR-weighted images had high positive percent agreement and negative percentage agreement using MRI OA Knee Score features. DLR synthetic T2 measurements could identify early cartilage degeneration from normal-appearing ones.

Project description:BackgroundStudies have recently revealed that almost every type of cells including tumor cells abundantly release small vesicles known as extracellular vesicles (EVs) into the extracellular milieu. EVs carry a repertoire of biological molecules including nucleic acids, proteins, lipids, and carbohydrates and transport their cargo between cells in the vicinity as well as distantly located cells and hence act as messengers of intercellular communication. In this review, we aimed to discuss the tumor-derived exosome biology and the pivotal roles of exosomes in cancer diagnosis and treatment.MethodsIn the present review study, the authors studied several articles over the past two decades published on the kinetics of EVs in tumor environment as well as on the application of these vesicles in cancer diagnosis and therapy.ResultsA growing body of evidence indicates that nucleic acids such as microRNAs (miRNAs) transferring by EVs participate to create a conducive tumor environment. As EV-associated miRNAs are tissue-specific and present in most biological fluids, they hold great potential for clinical application in cancer early diagnosis, prognosis, and treatment response. Furthermore, exosomes can serve as drug delivery vehicles transferring miRNAs as well as therapeutic agents to target cells. These nano-vesicles exhibit ideal properties in comparison with the synthetic carriers that attracted scientist's attention in the field of nanotechnology medicine. Scientists have employed different strategies to build exosomes-based drug delivery system. In general, two methods (direct engineering and indirect engineering) are being utilized to produce artificial exosomes. Para-clinical data have confirmed the beneficial effects of engineering exosomes in cancer therapy.ConclusionExosomal miRNAs hold great promise for clinical application in early diagnosis and treatment of cancers. In addition, in spite of enthusiastic results obtained by engineered exosomes, however, there is an increasing concern over the use of optimal methods for engineering exosomes and the safety of engineered exosomes in clinical trials is still unclear.

Dataset Information

Towards accurate and reliable resolution of structural variants for clinical diagnosis.

Publications

Towards accurate and reliable resolution of structural variants for clinical diagnosis.

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