Clinical chemistry最新文献

Background: Persistent latent reservoirs of intact HIV-1 proviruses, capable of rebounding despite suppressive antiretroviral therapy (ART), hinder efforts towards an HIV-1 cure. Hence, assays specifically quantifying intact proviruses are crucial to assess the impact of curative interventions. Two recent assays have been utilized in clinical trials: intact proviral DNA assay (IPDA) and quadruplex quantitative PCR (Q4PCR). While IPDA is more sensitive due to amplifying short fragments, it may overestimate intact fractions by relying only on quantification of 2 proviral regions. Q4PCR samples 4 proviral regions, yet is sequencing-based, favoring amplification of shorter, hence non-intact, proviral sequences.

Methods: Leveraging digital PCR (dPCR) advancements, we developed the "Rainbow" 5-plex proviral HIV-1 DNA assay. This first-in-its-kind assay was evaluated using standard materials and samples from 83 people living with HIV-1, enabling simultaneous quantification of both total and intact HIV-1 DNA levels. HIV proviral unique molecular identifier (UMI)-mediated long-read sequencing (HIV-PULSE) was used to validate the specificity of the Rainbow HIV-1 DNA assay.

Results: The Rainbow assay proved equally sensitive but more specific than IPDA and is not subjected to bias against full-length proviruses, enabling high-throughput quantification of total and intact reservoir size. The near full-length sequences allowed validation of the Rainbow specificity and the design of personalized Rainbow primer/probe sets, which enabled the detection of intact HIV-1 DNA.

Conclusions: This innovation offers potential for targeted evaluation and monitoring of potential rebound-competent reservoirs, contributing to HIV-1 management and cure strategies. ClinicalTrials.gov Registration Numbers: NCT04553081, NCT04305665.

Background: Structural variation (SV), defined as balanced and unbalanced chromosomal rearrangements >1 kb, is a major contributor to germline and neoplastic disease. Large variants have historically been evaluated by chromosome analysis and now are commonly recognized by chromosomal microarray analysis (CMA). The increasing application of genome sequencing (GS) in the clinic and the relatively high incidence of chromosomal abnormalities in sick newborns and children highlights the need for accurate SV interpretation and reporting. In this review, we describe SV patterns of common cytogenetic abnormalities for laboratorians who review GS data.

Content: GS has the potential to detect diverse chromosomal abnormalities and sequence breakpoint junctions to clarify variant structure. No single GS analysis pipeline can detect all SV, and visualization of sequence data is crucial to recognize specific patterns. Here we describe genomic signatures of translocations, inverted duplications adjacent to terminal deletions, recombinant chromosomes, marker chromosomes, ring chromosomes, isodicentric and isochromosomes, and mosaic aneuploidy. Distinguishing these more complex abnormalities from simple deletions and duplications is critical for phenotypic interpretation and recurrence risk recommendations.

Summary: Unlike single-nucleotide variant calling, identification of chromosome rearrangements by GS requires further processing and multiple callers. SV databases have caveats and limitations depending on the platform (CMA vs sequencing) and resolution (exome vs genome). In the rapidly evolving era of clinical genomics, where a single test can identify both sequence and structural variants, optimal patient care stems from the integration of molecular and cytogenetic expertise.

Background: Newborn screening is a public health system designed to identify infants at risk for conditions early in life to facilitate timely intervention and treatment to prevent or mitigate adverse health outcomes. Newborn screening programs use tandem mass spectrometry as a platform to detect several treatable inborn errors of metabolism, and the T-cell receptor excision circle assay to detect some inborn errors of the immune system. Recent advancements in DNA sequencing have decreased the cost of sequencing and allow us to consider DNA sequencing as an additional platform to complement other newborn screening methods.

Content: This review provides an overview of DNA-based newborn screening, including its applications, opportunities, challenges, and future directions. We discuss the potential benefits of expanded DNA sequencing in newborn screening, such as expanding conditions screened and improved specificity and sensitivity of currently screened conditions. Additionally, we examine the ethical, legal, and social implications of implementing genomic sequencing in newborn screening programs, including issues related to consent, privacy, equity, data interpretation, scalability, and psychosocial impact on families. Additionally, we explore emerging strategies for addressing current limitations and advancing the field of newborn screening.

Summary: DNA sequencing in newborn screening has the potential to improve the diagnosis and management of rare diseases but also presents significant challenges that need to be addressed before implementation at the population level.

Background: Many viruses can cause persistent infection and/or viral shedding in immunocompromised hosts. This is a well-described occurrence not only with SARS-CoV-2 but for many other viruses as well. Understanding how viruses evolve and mutate in these patients and the global impact of this phenomenon is critical as the immunocompromised population expands.

Content: In this review, we provide an overview of populations at risk for prolonged viral shedding, clinical manifestations of persistent viral infection, and methods of assessing viral evolution. We then review the literature on viral evolution in immunocompromised patients across an array of RNA viruses, including SARS-CoV-2, norovirus, influenza, and poliovirus, and discuss the global implications of persistent viral infections in these hosts.

Summary: There is significant evidence for accelerated viral evolution and accumulation of mutations in antigenic sites in immunocompromised hosts across many viral pathogens. However, the implications of this phenomenon are not clear; while there are rare reports of transmission of these variants, they have not clearly been shown to predict disease outbreaks or have significant global relevance. Emerging methods including wastewater monitoring may provide a more sophisticated understanding of the impact of variants that evolve in immunocompromised hosts on the wider host population.

Background: Polygenic risk scores (PRS) are measures of genetic susceptibility to human health traits. With the advent of large data repositories combining genetic data and phenotypic information, PRS are providing valuable insights into the genetic architecture of complex diseases and are transforming the landscape of precision medicine.

Content: PRS have emerged as tools with clinical utility in human disease. Herein, details on how to develop PRS are provided, followed by 5 areas in which they can be used to improve human health: (a) augmenting risk prediction, (b) refining diagnosis, (c) guiding treatment choices, (d) making clinical trials more efficient, and (e) improving public health. Finally, some of the ongoing challenges to the clinical implementation of PRS are noted.

Summary: PRS can offer valuable information for providers and patients, including identifying risk of disease earlier in life and before the onset of clinical risk factors, guiding treatment decisions, improving public health outcomes, and making clinical trials more efficient. The future of genomic-informed risk assessments of disease is through integrated risk models that combine genetic factors including PRS, monogenic, and somatic DNA information with nongenetic risk factors such as clinical risk estimators and multiomic data. However, adopting PRS in a clinical setting at scale faces some challenges, including cross-ancestry performance, standardization and calibration of risk models, downstream clinical decision-making from risk information, and seamless integration into existing health systems.

Background: Disease-causing copy-number variants (CNVs) often encompass contiguous genes and can be detected using chromosomal microarray analysis (CMA). Conversely, CNVs affecting single disease-causing genes have historically been challenging to detect due to their small sizes.

Methods: A custom comprehensive CMA (Baylor College of Medicine - BCM v11.2) containing 400k probes and featuring exonic coverage for >4200 known or candidate disease-causing genes was utilized for the detection of CNVs at single-exon resolution. CMA results across a consecutive clinical cohort of more than 13 000 patients referred for genetic investigation at Baylor Genetics were examined. The genomic characteristics of CNVs impacting single protein-coding genes were investigated.

Results: Pathogenic or likely pathogenic (P/LP) CNVs (n = 190) affecting single protein-coding genes were detected in 188 patients, accounting for 9.9% (188/1894) of patients with P/LP CMA findings. The P/LP monogenic CNVs accounted for 9.2% (190/2058) of all P/LP nuclear CNVs detected by CMA. A total of 57.9% (110/190) of P/LP monogenic CNVs were smaller than 50 kb in size. Single exons were affected by 26.3% (50/190) of P/LP monogenic CNVs while 13.2% (25/190) affected 2 exons. CNVs were detected across 107 unique genes associated with predominantly autosomal dominant (AD) and X-linked (XL) conditions but also contributed to autosomal recessive (AR) conditions.

Conclusions: CMA with exon-targeted coverage of disease-associated genes facilitated the detection of small CNVs affecting single protein-coding genes, adding substantial clinical sensitivity to comprehensive CNV investigation. This approach resolved monogenic CNVs associated with autosomal and X-linked monogenic etiologies and yielded multiple significant findings. Monogenic CNVs represent an underrecognized subset of disease-causing alleles for Mendelian disorders.

Pharmacogenomics (PGx) is focused on the relationship between an individual's genetic makeup and their response to medications, with the overarching aim of guiding prescribing decisions to improve drug efficacy and reduce adverse events. The PGx and genomic medicine communities have worked independently for over 2 decades, developing separate standards and terminology, making implementation of PGx across all areas of genomic medicine difficult. To address this issue, the Clinical Genome Resource (ClinGen) Pharmacogenomics Working Group (PGxWG) was established by the National Institutes of Health (NIH)-funded ClinGen to initially create frameworks for evaluating gene-drug response clinical validity and actionability aligned with the ClinGen frameworks for evaluating monogenic gene-disease relationships, and a framework for classifying germline PGx variants similar to the American College of Medical Genetics (ACMG) and Association of Molecular Pathology (AMP) system for interpretation of disease-causing variants. These frameworks will leverage decades of work from well-established PGx resources facilitating buy-in among PGx stakeholders. In this report, we describe the background and major activities of the ClinGen PGxWG, and how this initiative will facilitate the critical inclusion of PGx into the larger context of genomic medicine.