Dataset Information

Identification and monitoring of Copy Number Variants (CNV) in monoclonal gammopathy.

ABSTRACT: Monoclonal gammopathy of undetermined significance (MGUS) represents the pre-clinical stage of Multiple Myeloma (MM) with the 5% of MGUS progresses to MM. Although the progression from MGUS to MM has not been completely characterized, it is possible to monitor the DNA modifications of patients diagnosed with MGUS to detect early specific genomic abnormalities, including copy number variations (CNV). The CNVs of chromosome 1q and chromosome 13q are associated with a worse prognosis in MM.In the present study, we showed that it is possible to monitor the 1q21 gain and 13q deletion frequencies in gDNA using digital PCR. The CNV analysis of three cell lines with a well-characterized cytogenetic profile were compared with measures performed by a real-time PCR approach and with a digital PCR approach. Then, we analyzed CNVs in CD138⁺ plasma cells isolated from bone marrow of MGUS and MM patients.Our results show that digital PCR and targeted DNA monitoring represent a specific and accurate technique for the early detection of specific genomic abnormalities both in MM and in MGUS patients.Our results could represent a remarkable advancement in MM and MGUS diagnosis and in CNV analysis for the evaluation of the risk of progression from MGUS to MM.

SUBMITTER: Sallustio F

PROVIDER: S-EPMC8386727 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and monitoring of Copy Number Variants (CNV) in monoclonal gammopathy.

Sallustio Fabio F Curci Claudia C Solimando Antonio Giovanni AG Leone Patrizia P Pontrelli Paola P Gesualdo Loreto L Vacca Angelo A Racanelli Vito V Gallone Anna A

Cancer biology & therapy 20210601 5-6

Monoclonal gammopathy of undetermined significance (MGUS) represents the pre-clinical stage of Multiple Myeloma (MM) with the 5% of MGUS progresses to MM. Although the progression from MGUS to MM has not been completely characterized, it is possible to monitor the DNA modifications of patients diagnosed with MGUS to detect early specific genomic abnormalities, including copy number variations (CNV). The CNVs of chromosome 1q and chromosome 13q are associated with a worse prognosis in MM.In the p ...[more]

PMID: 34288806

Similar Datasets

Project description:Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is of significant importance in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates the efficient prioritization of samples for subsequent, costly analyses such as short-read and long-read whole genome sequencing. We focus on five genes-Parkin (PRKN), Leucine Rich Repeat And Ig Domain Containing 2 (LINGO2), Microtubule Associated Protein Tau (MAPT), alpha-Synuclein (SNCA), and Amyloid Beta Precursor Protein (APP)-which may be relevant to neurological diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinson's Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finder's interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using both short-read and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied sparsity, noise, and size. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub (https://github.com/GP2code/CNV-Finder; DOI 10.5281/zenodo.14182563). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.

Dataset Information

Identification and monitoring of Copy Number Variants (CNV) in monoclonal gammopathy.

Publications

Identification and monitoring of Copy Number Variants (CNV) in monoclonal gammopathy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets