The Journal of Molecular Diagnostics

Background: Short-read whole-genome sequencing (sWGS) has limitations in characterizing tandem-repeat expansions and variants in highly homologous or pseudogene-associated regions. Long-read sequencing can address some of these challenges but often benefits from high-molecular-weight DNA. TruPath Genome incorporates spatial proximity information into a short-read sequencing framework. This study evaluated its performance in characterizing selected complex genomic variants using archived clinical DNA specimens. Methods: Ten archived peripheral blood DNA specimens were analyzed, comprising seven cases with disease-associated short tandem repeat (STR) expansions and three cases involving homologous or pseudogene-associated loci (STRC, SMN1/SMN2, and CYP21A2). TruPath results were compared with previously available orthogonal assay results, including triplet repeat-primed PCR (TP-PCR), multiplex ligation-dependent probe amplification (MLPA), quantitative PCR (qPCR), and sWGS-based ExpansionHunter analysis. Results: TruPath generated repeat-length estimates across the evaluated STR loci. For two FMR1 full-mutation alleles, TruPath estimated 284 and 295 CGG repeats, respectively, while TP-PCR reported expansions exceeding 200 repeats. In a DMPK case, TruPath estimated an expanded allele of 1,203 CTG repeats, compared with a TP-PCR result exceeding 100 repeats. At the STRC and SMN1/SMN2 loci, TruPath findings were consistent with available orthogonal results for deletion and copy-number status. At the CYP21A2 locus, TruPath reconstructed the RCCX gene arrangement and assigned the I2G variant and a chimeric allele to different haplotypes. Conclusions: In this small series of archived clinical DNA specimens, TruPath Genome enabled characterization of selected repeat expansions and homologous or pseudogene-associated variants. The findings support further evaluation of proximity-mapped sequencing as a complementary approach for complex genomic regions, particularly when conventional short-read sequencing is limited.

Complex rearrangements are one of the rarest types of structural variants (SVs) and can be divided into two categories: complex chromosomal rearrangements (CCRs) and complex genomic rearrangements (CGRs). CCRs include structural rearrangements that present at least three breakpoints and show exchange of genetic material between more than two chromosomes and CGRs are rearrangements that present more than one junction and/or more than one SV in cis. They are usually formed by one of the chromoanagenesis mechanisms, where a massive disruptive cellular event leads to multiple structural rearrangements. Classical cytogenomic techniques have been commonly applied for their characterization, but methodologies that involve longer DNA molecules, namely optical genome mapping (OGM) and long-read genome sequencing (lrGS), present a considerably higher SV detection resolution, revealing more details about the rearrangements, including precise breakpoint location. Here, we describe six patients with complex rearrangements investigated through a combination of different techniques: karyotyping, chromosomal microarray, and OGM were performed to characterize the rearrangements. Subsequently, lrGS was used to further resolve the alterations, refine their breakpoints' location, and sequence their junction points. Three patients presented CCRs involving three, four, and six chromosomes, while three exhibited CGRs involving one different chromosome each, providing a variety of complex SVs to show the importance of each technique and their combination in rearrangement resolution. In total, the complex rearrangements presented 127 breakpoints, 66 junction points and involved 14 of the 24 chromosomes. Higher-resolution techniques revealed additional complexity in all cases. Despite the advances provided by OGM and lrGS, conventional karyotyping remained indispensable for complete rearrangement resolution. In two patients, the findings supported a novel mechanism combining features of the different chromoanagenesis processes. Furthermore, evidence of inherited alterations was identified, and the comprehensive characterization of the rearrangements enabled more accurate genotype-phenotype correlations. Our findings indicate that an integrated approach combining karyotyping, OGM, and lrGS can completely resolve SVs, including complex rearrangements.

Background: Pharmacogenomic point-of-care testing (PGx POCT) enables genotype-informed prescribing at the bedside, but implementation is limited by the lack of suitable reference materials and standardised external quality assessment (EQA). Methods: Fixed lymphoblastoid cell lines were formulated as swab-based materials at defined cellular inputs for MTRNR1 POCT. Materials were evaluated using Genedrive MTRNR1 assays, quantitative PCR, acceptance quality limit batch release, workflow perturbation testing and stability studies, then deployed in an international EQA programme. Results: Materials were homogeneous, workflow-compatible and reproducible, with 100% correct genotype calls at the lowest intended manufacturing cell load. Seven EQA surveys yielded 253 participant results, with full-mark rates between 87.5 and 100%, and six test failures. Most failures occurred with low cell-input materials near the assay limit of detection. The materials supported genotype concordance, performance monitoring and identification of workflow and sample-related variability. Conclusions: Workflow-compatible cell-based reference materials combined with EQA provide a scalable framework for quality-assured PGx POCT beyond specialist laboratories. International MT-RNR1 PGx POCT EQA programme deployment demonstrated feasibility in decentralised settings. The framework may provide a model for the implementation, verification and ongoing quality assessment of PGx POCT. Further evaluation across platforms, analytes, settings and participant groups is required to establish broader applicability.

Next-generation sequencing (NGS) for the detection of somatic variants has become the method of choice in a variety of molecular oncology fields and in the clinic. Its use ranges from sequencing entire tumor genomes and transcriptomes to targeted clinical diagnostic gene panels. The NYU Langone Genome PACT (Profiling of Actionable Cancer Targets, LG-PACT) assay is a qualitative in vitro diagnostic test that uses targeted next generation sequencing (NGS) of formalin-fixed paraffin-embedded (FFPE) tumor tissue matched with normal specimens from patients to detect gene alterations in a targeted panel covering 606 genes and the TERT promoter. Indications for testing are cancer (solid tumors and hematological malignancies) where a mutational profile from multiple genes would be informative for disease stratification, prognosis, or treatment options including targeted therapies and eligibility for clinical trials. The test is intended to provide information on somatic mutations including point mutations, small insertions/deletions (indels), and copy number aberrations for diagnostic and treatment decisions. LG-PACT is a United States Food and Drug Administration (FDA) cleared diagnostic test (510K: K202304). The clinical interpretation of sequencing data of molecular tumor markers from NGS encompasses automated variant calling tools with human interpretation. This final mostly manual review of data step is intensive, involving highly trained scientists, encompassing literature review, interpretation and clinical tier classification by pathologists, who then provide a complete molecular diagnostic report to the treating oncologists. We provide analysis of 1339 clinical genomic profiles from 31 different cancers and their subtypes, comprising of central nervous system (CNS) 792 (59%) cases (incl. meningioma, glioma and glioblastoma), with 267 (20%) cases predominantly of lung, pancreatic and colorectal and 280 of others (21%). Here, we present the technical challenges of validating an NGS oncological diagnostic targeted assay for clinical grade accuracy and sensitivity for patient care. We show how copy number alterations provide a more comprehensive description of the tumors genomic profile. We then outline the utility of targeted panel sequencing based on certified pathologist selection of reportable variants for our current patient cohort. Where analysis of variant detection has led to 49.4% (661/1339) of our clinical tumor samples containing mutations in known therapy targeted genes, 35.6% (477/1339) with mutation detected in other genes, and 15% (201/1339) cases being negative.

Access to accurate cost-effective technologies for typing high-risk human papillomaviruses (hrHPV) is critical to expand cervical cancer screening and inform vaccination strategies. Compared with clinical-standard quantitative polymerase chain reaction (qPCR) assays, HPV genotyping by next-generation sequencing (NGS) provides greater flexibility, scalability, and genotype specificity. We have developed a method for HPV genotyping, HPV Phased Amplicon Multiplex Sequencing (PhAM-Seq), that uses combinatorial barcoding of amplicons with short, variable-length inline sequences to enable higher throughput and lower per-sample costs than conventional amplicon sequencing approaches. We evaluated HPV PhAM-Seq using degenerate and type-specific primers targeting the L1 and E6-E7 gene loci in a blinded cohort of 170 cervical samples previously typed by the Seegene Anyplex II HPV28 Detection qPCR assay. Across eight common hrHPV types (HPV16, 18, 31, 33, 35, 45, 52, and 58), HPV PhAM-Seq demonstrated >80% overall agreement with qPCR using degenerate L1-targeting primers, with the highest sensitivity for HPV16, 31, 33, and 58. Sensitivity for HPV35, 45, and 52 improved to 85% or greater with type-specific primers targeting genes E6/E7. Parallel processing and sequencing enables a single technician to assay hundreds of samples per week at a reagent cost of around $10 USD per sample, with laboratory automation and sequencing on higher-output platforms enabling further scaling and cost reduction to a scale amenable to population-level surveillance. We include a detailed SOP; tools for primer design, sequencing library construction, and sample tracking; and all scripts needed for data analysis to ensure HPV PhAM-Seq can be readily implemented for scalable, cost-effective hrHPV genotyping or extended to other similar applications.

Objective: Erythropoietic protoporphyria (EPP) is a rare photodermatosis marked by multi-year diagnostic delays. We developed and externally validated machine learning models to identify patients with EPP earlier from longitudinal electronic health record (EHR) data and estimate undiagnosed disease burden. Materials and Methods: In a retrospective case-control study at two San Francisco health systems, an academic referral center (UCSF) and a safety-net hospital (ZSFG) we identified 74 confirmed EPP cases using combined diagnostic coding, biochemical criteria, and specialty chart review. Symptom-enriched controls were sampled at a 40:1 ratio. Longitudinal diagnoses, laboratory results, medications, procedures, and encounters preceding the outcome date were modeled with a gradient-boosting classifier (CatBoost) and a state-space sequence model (MAMBA). The best model was deployed across the UCSF population and externally validated at ZSFG without retraining. Results: On the UCSF held-out test set (n=1,865; 43 cases), MAMBA outperformed CatBoost (AUC ROC 0.91 vs 0.89; average precision 0.42 vs 0.27; precision 65% vs 20%), flagging cases a median of 229 days before documented diagnosis. Deployed across 297,967 symptom-compatible patients, it identified 310 high-risk individuals, implying a prevalence approaching genetic estimates. External validation at ZSFG showed attenuated performance (AUC ROC 0.72; average precision 0.10) while preserving early detection (median 264 days). Discussion: A sequence model integrating temporal EHR signals detected EPP months before clinical recognition, corroborating genetic evidence of substantial underdiagnosis. Cross-site attenuation reflects population and documentation differences and underscores the need for local recalibration. Conclusion: Longitudinal EHR-based machine learning can shorten EPP diagnostic delay and prioritize patients for confirmatory testing, supporting proactive rare-disease case finding.

Genomic, transcriptomic and epigenomic characterization has accelerated the discovery of clinically-relevant alterations in cancer, predominantly using fresh frozen (FF) specimens. However, clinical molecular pathology laboratories prefer formalin-fixed paraffin-embedded (FFPE) methods, known to introduce artifacts at the nucleic acid level, over fresh frozen methods. Extending the multi-platform analysis to FFPE specimens for comprehensive clinical molecular diagnosis requires a thorough understanding of the consequence of formalin-fixation. We present a detailed multi-platform characterization of FFPE preservation using paired FF specimens as the 'gold standard'. DNA and RNA were obtained from 38 patients across 6 cancer types using a FFPE optimized co-isolation. The impact of FFPE on exome sequencing was dependent on filtering, where a minimum coverage or supporting read filter can mitigate FFPE-specific false positives. Copy number alterations, MSI assessment, mutational signatures, and DNA methylation were comparable between FFPE and FF. FFPE biases in RNA expression can be overcome when using biology-relevant genes and we describe a novel consequence of FFPE on miRNA species diversity. Collectively, this data provides a broad view of FFPE artifact and offers best practices for overcome these biases.

Antimicrobial resistance (AMR) represents a major global health threat, with plasmid-borne mcr genes driving colistin resistance and exposing critical gaps in One-Health surveillance across human, animal, and environmental reservoirs. The most prevalent variant, mcr-1, remains difficult to monitor in resource-limited settings due to the lack of rapid, affordable, and field-deployable molecular tools. Here, we developed C12amcr, an integrated molecular toolbox that combines pre-amplification PCR with a fluorescent CRISPR-Cas12a assay targeting a conserved region of mcr-1 and a custom low-cost, hand-held 3D-printed portable fluorometer. Under optimized conditions, the assay achieved a limit of detection of 630 cells/mL. In poultry feces spiked with mcr-1-positive E. coli, C12amcr detected as few as 1,800 cells/mL. When tested on 22 community-derived E. coli isolates, the assay showed 100% concordance with both next-generation sequencing for mcr-1 detection and phenotypic colistin susceptibility testing by broth microdilution. The accompanying portable fluorometer performed equivalently to a laboratory microplate reader while enabling fully decentralized workflows compatible with portable PCR platforms. By integrating locally produced molecular reagents, straightforward protocols, and an accessible field-ready fluorescence reader, C12amcr overcomes key barriers to decentralized AMR surveillance and provides a practical, scalable solution for One-Health monitoring in resource-limited settings.

Background A growing body of work has highlighted advantages of long-read genome sequencing (LR-GS) over short-read genome sequencing (SR-GS) with regards to the detection and interpretation of pathogenic variants. However, the incremental diagnostic yield of LR-GS over SR-GS for patients with suspected Mendelian conditions has not been systematically characterized. Methods We performed LR-GS on 144 cases that previously had undergone SR-GS through the Pediatric Mendelian Genomics Research Center at the University of California, Irvine (PMGRC-UCI, a founding center of the GREGoR Consortium), of which 107 remained undiagnosed after SR-GS. Results We identified new molecular diagnoses in 13 cases, five of which could not have been detected with SR-GS, corresponding to an incremental diagnostic yield of 4.7%. These five diagnoses leveraged distinct capabilities of LR-GS: the detection of variants in short-read dark regions, the detection of structural variants and tandem repeat expansions, and the identification of de novo variants using only one biological parent. In two of these five cases, confirmation of the diagnosis also relied critically on identifying aberrant transcript production via RNA-seq, while in one other case LR-GS-based CpG methylation profiling further supported the diagnosis through detection of abnormal methylation at episignature-associated CpGs. Conclusions In this paired comparison of LR-GS and SR-GS, LR-GS provided an incremental diagnostic yield of 4.7% among cases that remained undiagnosed after SR-GS. Together, our findings demonstrate that LR-GS can resolve a meaningful subset of cases beyond the reach of SR-GS while simultaneously expanding the range of genomic and epigenomic mechanisms accessible to a single sequencing assay.

Background Histological diagnosis of early-stage mycosis fungoides (MF) is hindered by profound overlap with benign inflammatory dermatoses (BIDs), leading to diagnostic delays and extensive ancillary testing. We developed MIMIC (Multiple Instance-learning for Identification of Mycosis fungoides In Cutaneous biopsies), a weakly supervised deep learning model designed as a triage tool at initial H&E whole slide image (WSI) review to distinguish classic patch and plaque stage MF from BIDs. We externally validated the model and evaluated its clinical utility. Methods In this retrospective multicentre study, we trained a base model using weakly supervised attention based multiple instance learning on 3,339 WSIs from two Dutch centres. Crucially, all MF training labels were derived from a deeply phenotyped national cohort featuring strict multidisciplinary expert panel consensus diagnoses (the clinical gold standard). Transportability was evaluated on 371 WSIs from four independent European centres. A blinded reader study on 171 WSIs compared morphology only performance of MIMIC with 11 (dermato-)pathologists. We then retrained an updated model on all retrospective multicentre data and assessed clinical utility in a strictly held out, consecutive Utrecht cohort (2022-2023; 486 accessions, 863 WSIs). Primary analysis focused on classic MF versus BIDs (453 accessions). Decision curve analysis, using Platt scaled probabilities to correct for spectrum bias, evaluated net benefit at a prespecified, safety oriented threshold of 0.04. Findings The base model showed good multicentre transportability (mean centre specific AUROC 0.91; pooled AUROC 0.84). In the reader study, MIMIC achieved an AUROC of 0.87, exceeding the mean pathologist AUROC (0.79) and the best individual reader (0.83). In the consecutive MF versus BID cohort, the updated model achieved an AUROC of 0.87 (95% CI 0.81-0.92). At the 0.04 threshold, sensitivity was 97.8% (44/45 MF cases) and specificity 50.2%, reducing unnecessary ancillary workups by 39.9 per 100 screening cases versus a test all strategy. Interpretation By identifying nearly half of BIDs as low risk while preserving near complete sensitivity for classic early stage MF in a European digital pathology workflow, this unimodal H&E approach offers a scalable digital solution to reduce defensive ancillary testing and accelerate the diagnostic journey for patients with MF. Further validation is needed in non European centres and in populations with darker skin phototypes.

Antimicrobial resistance (AMR) is a growing public health threat, where drug-resistant Candida infections are associated with increased hospital costs, lengths of stay, and high mortality rates. Current diagnostic methods are slow and labor-intensive, causing delays in diagnosis and patient treatment. In this research, we propose a rapid CRISPR-based diagnostic for identifying Candida pathogens and antifungal resistance biomarkers in clinical applications. One-pot CRISPR assay designs are optimized for three clinically relevant Candida species, achieving attomolar sensitivity with synthetic DNA and validating performance on cultured isolates. Strategies are developed to engineer CRISPR gRNA to control one-pot reaction kinetics, enhancing discrimination of single nucleotide polymorphism (SNP) resistance mutations associated with azole and echinocandin antifungals. This research expands our understanding of the design of one-pot CRISPR reactions for sensitive detection of DNA, while establishing a foundation for faster, more precise AMR diagnostics to improve surveillance and outbreak management of fungal infections.

Background Given the augmented sampling and ancestry stratification of the Genome Aggregation Database (gnomAD) v4.1 compared to v2.1.1, population evidence for globin gene variants was assessed for changes in the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) criteria fulfillment, which can alter downstream variant interpretation. Methods Variants and constraint metrics for -globin locus genes HBA1, HBA2, and HBZ, and {beta}-globin locus genes HBB, HBD, HBG1, HBG2, and HBE1, were extracted from gnomAD v2.1.1 and v4.1 callsets, harmonized, matched to ClinVar annotations, and evaluated against BA1, BS1, BS2, BS2_Supporting, and PM2_Supporting ACMG/AMP criteria using Clinical Genome Resource Hemoglobinopathy Variant Curation Expert Panel thresholds. Results Globin gene variants increased fourfold across gnomAD releases, with HBB contributing the largest share. In v4.1, 68.7% of quality-passing variants were ancestry-specific, 14.4% overlapped ClinVar and PM2_Supporting fulfillment rose from 81.5% to 93.5%. Among variants present in both releases, ACMG/AMP population-criterion assessments were largely concordant, with conflicting combinations in <1%. Conclusions Leveraging augmented sampling and improved ancestry representation, gnomAD v4.1 increased globin gene variant discovery, particularly for rare variants, enabling more precise allele frequency estimates and robust assessment of BA1, BS1, and PM2_Supporting, although criterion fulfillment remains release- and callset-specific.

Context: Cytological specimens allow minimally invasive, repeated sampling for diagnosis and molecular testing. However, cytology biobanking protocols have primarily been optimized for morphology, and the effects of preanalytical handling and storage on intracellular proteins remain unclear. Objective: To assess how fixation, staining, storage temperature, and duration affect intracellular protein preservation in cytological specimens and to identify conditions suitable for in situ proximity ligation assay (isPLA). Design: Human cancer cell lines were processed under different preanalytical conditions. Intracellular proteins and protein protein interactions were assessed by isPLA, fluorescence microscopy, and quantitative image analysis. Slides were stored at room temperature or minus 20 Celsius, with or without MayGrunwaldGiemsa (MGG) staining. Results: Storage temperature had the greatest effect on protein signal. Unstained slides stored at minus 20 Celsius retained strong signals for up to 24 months, whereas room temperature storage reduced detectable protein interactions by approximately 95%. MGG staining further reduced signal intensity during prolonged storage. Nevertheless, stained slides remained suitable for isPLA when staining, microscopic evaluation, destaining, and antibody labeling were completed on the same day. Prolonged room temperature storage of stained slides caused loss of antigenicity, increased autofluorescence, and false negative findings. Conclusions: Low temperature storage and standardized preanalytical handling preserve intracellular protein signals in cytological specimens. These conditions support reliable single cell protein profiling of archived samples and their use in biomarker research and translational pathology.

Targeted precision therapies are increasingly used in the treatment of individuals with vascular anomalies (VAs). This increases the need for rapid, accurate and inexpensive genetic diagnosis. Droplet digital polymerase chain reaction (ddPCR) is an alternative to next-generation sequencing (NGS), permitting rapid, highly sensitive interrogation of recurrent pathogenic mosaic variants. We examined the feasibility of ddPCR as a primary diagnostic tool in a large cohort of individuals with VAs. Lesional tissue was collected for ddPCR of up to 46 recurrent pathogenic variants across 16 genes associated with VAs. Specimens were assessed on a subset of assays for each individual based on clinical phenotype. Most individuals who had negative ddPCR results went on to high-depth gene panel or deep exome NGS, or Sanger sequencing. Here we report the phenotypic and molecular findings for 78 newly recruited and tested individuals in addition to the 60 individuals already reported from our cohort. The overall diagnostic yield for our cohort when combined with individuals previously reported was 104/138 (75%). Of 138 individuals tested, recurrent pathogenic variants were detected in 71 (51%) on ddPCR. Variants were most frequently identified in PIK3CA (n=28), TEK (n=18), GNAQ (n=12), or MAP2K1 (n=7). In a further 33 individuals, pathogenic variants were identified on NGS or Sanger sequencing. Our findings indicate that ddPCR is an efficient method achieving a high diagnostic yield in our cohort when used prior to sequencing.

Background Cell free DNA (cfDNA) methylation profiling is promising for minimally invasive cancer detection, but its translation is limited by high dimensional data, modest cfDNA cohort sizes, and the difficulty of defining biologically grounded feature sets. CpGPT, a transformer based DNA methylation foundation model pretrained on large scale tissue methylation array datasets, may enable transfer of learned methylation representations to data limited cfDNA applications. We evaluated a differentially methylated region (DMR) informed framework for fine tuning CpGPT for gastrointestinal cancer classification using plasma cfDNA methylation data. Methods Plasma cfDNA methylation data were obtained from the EpiPanGI Dx cohort, including 254 gastrointestinal cancer samples and 46 non cancer controls. Tumor and matched normal tissue methylation data included 967 tumor normal pairs across 18 cancer types from The Cancer Genome Atlas (TCGA). Tissue derived and cfDNA derived DMRs were identified independently and intersected to define a candidate set of 20,499 CpG sites. CpGPT was evaluated in a technical sensitivity analysis using the previously published 896 CpG panGI panel and in DMR informed fine tuning using the 20,499 CpG candidate set. Performance was compared with radial kernel support vector machine (SVM radial), elastic net logistic regression, gradient boosting machine (GBM), and Random Forest models using identical data partitions. Results In the technical sensitivity analysis using the previously published panGI panel, the CpGPT base configuration achieved a mean test area under the receiver operating characteristic curve (AUROC) of 0.9799 (95% CI, 0.9654, 0.9944) across four fixed random seeds. Individual test AUROCs ranged from 0.9597 to 0.9927, while neighboring configurations achieved mean test AUROCs of 0.9661 to 0.9716. Using the DMR informed candidate set, CpGPT achieved a mean test AUROC of 0.9904 (95% CI, 0.9715, 1.0000) across three split runs. Mean test AUROCs were 0.9573 for SVM radial, 0.9420 for elastic net, 0.8460 for GBM, and 0.8408 for Random Forest. Cancer type specific mean test AUROCs ranged from 0.9600 for pancreatic adenocarcinoma to 1.0000 for esophageal squamous cell carcinoma, colorectal cancer, and esophageal adenocarcinoma. Conclusions DMR informed CpGPT finetuning achieved high internal discrimination and a higher mean test AUROC than the evaluated classical machine learning models. Integrating tissue and plasma cfDNA methylation evidence provides a biologically constrained feature space for adapting a tissue array pretrained foundation model to cfDNA classification. Independent external validation, clinically representative control populations, and further feature set reduction are needed before translation into a targeted cfDNA assay.

Background. Comprehensive genomic reports in oncology contain complex molecular information that must be translated into concise summaries for treating clinicians. Manual summarisation is time-intensive, and robust evidence supporting AI-assisted summarisation systems remains limited. We evaluated whether AI-generated summaries were non-inferior to manually produced summaries in faithfully representing information from tertiary paediatric cancer genomics reports. Methods. QNOMX-VHIR-CPSP-001 Phase 1 was a single-site, non-interventional clinical performance study conducted at Vall d'Hebron Institut de Recerca under ISO 20916:2019. De-identified tertiary paediatric cancer genomics reports were summarised using both the AI-assisted system under evaluation and the standard manual workflow. Three qualified raters assessed both summary types against the source reports using the six-domain, five-point Quality Summary Index (QSI) in a blinded, counterbalanced crossover design with a minimum fourteen-day washout. Co-primary endpoints were composite content and presentation scores. Non-inferiority was assessed using a pre-specified Bayesian hierarchical model with a margin of 0.5 points and confirmed using a frequentist linear mixed model. Results. Thirty-seven of 38 planned cases contributed 74 paired evaluations. Mean content scores were similar between AI-generated and manual summaries (4.01 vs 3.97), whereas AI-generated summaries achieved higher presentation scores (4.39 vs 4.12). Non-inferiority was demonstrated for both co-primary endpoints: content +0.05 (95% CrI -0.18 to +0.28), presentation +0.28 (+0.10 to +0.47). Frequentist analyses were concordant. Accuracy was the only QSI dimension for which Bayesian non-inferiority was not concluded. Inter-rater reliability was low across all QSI domains. Conclusions. AI-generated summaries were non-inferior to manually produced summaries for both content and presentation endpoints in tertiary paediatric oncology genomics reports. Presentation scores were higher for AI-generated summaries, whereas content scores were comparable between approaches. Low inter-rater reliability limits confidence in the dimension-level estimates and is the main constraint on future validation studies. Clinical validity and workflow effect were not assessed.

Background: Rapid genome sequencing (rGS) is a first-tier test in the neonatal intensive care unit (NICU). Implementation strategies to scale rGS for clinical care may change clinician roles. We analyzed the NICU rGS workflow to understand challenges rGS poses for clinical roles and responsibilities. Methods: The study took a qualitative approach to understanding the rGS workflow in Level III and Level IV NICUs. The five workflow phases studied included: i) identification of a patient with a suspected genetic disorder, ii) subspecialist consultation, iii) test selection and ordering, iv) return of results, and v) disclosure. Data were collected via job task journals, interviews, and workflow diagram construction with clinicians involved in rGS. Analysis included participant-guided workflow diagram refinement, qualitative thematic analysis, and identification of rGS workflow challenges. Results: We documented issues related to decision-making authority and scope of responsibility for clinicians, with the majority relating to complexities of subspecialist consultations. Issues included differences in: criteria for triggering consultations, expectations of consulted subspecialists' roles, test selection preferences, and responsibility for documenting clinical interpretations and disclosure. Conclusion: Subspecialist consultations in clinical rGS involved significant role ambiguity and communication challenges, requiring intentional implementation efforts to ensure clear clinical roles and responsibilities.

An important step toward clinical implementation of (epi-)genomic assays on liquid biopsies is their validation on identical samples within and across laboratories. For these validation studies, there is a need for cell-free DNA (cfDNA) samples with defined tumor fractions and (epi-)genomic aberrations. However, the amount of circulating cfDNA isolated from patient samples is often limited, especially in pediatric cases. Additionally, patient samples contain a high degree of variability in cfDNA yield and tumor fraction. Several commercial artificial cfDNA products are available for validation studies, however their use is restricted to specific assays, aberrations and/or tumor entities. Alternatively, artificial cfDNA samples can be produced by fragmenting genomic DNA to mimic highly fragmented cfDNA derived from both tumor and healthy blood, followed by mixing artificial tumoral and healthy cfDNA at defined fractions. In this study, we compared native cfDNA with artificial cfDNA generated by three different fragmentation methods, including sonication and two enzymatic digestions using micrococcal nuclease and double-stranded deoxyribonuclease (dsDNase). We assessed fragment length profiles, end motifs and nucleosome occupancy patterns from shallow whole-genome sequencing data, as well as coverage profiles from targeted panel sequencing, together with a small-scale mixing experiment of tumor and healthy cell derived artificial cfDNA. Although sonication remains a convenient high-throughput approach to generate artificial cfDNA for certain downstream applications, enzymatic fragmentation, particularly the dsDNase-based method, more faithfully reproduced native cfDNA characteristics.

Primer design is a fundamental component of molecular diagnostics in both quantitative polymerase chain reaction qPCR and loop-mediated isothermal amplification LAMP assays. However, assay design is often performed manually as nucleotide databases, sequence alignment tools and resources are found at different places on the Internet. In this study, an AI-orchestrated bioinformatics workflow was developed to automate the end-to-end qPCR and LAMP primers and probes. The workflow was implemented using LangGraph, LangChain and Biopython, where a series of specialized agents were coordinated to execute sequential bioinformatics tasks with minimal human intervention. Target sequences were then retrieved based on the user request from the National Center for Biotechnology Information nucleotide database and the requested sequence records were then subjected to multiple sequence alignment for the identification of conserved genomic regions. The multi-agentic primer design system can be used for assay development for applications in infectious disease diagnostics, outbreak surveillance and environmental monitoring. This study also demonstrates how multi-agentic systems can be combined with established bioinformatics methods to automate qPCR and LAMP assay design.

Background/Objectives: Many myeloid malignancies are defined by recurrent structural variants (SVs) and copy-number alterations (CNAs) under the current World Health Organization (WHO) and International Consensus Classification (ICC) frameworks. Detecting these abnormalities typically requires several standard-of-care (SOC) assays, each with its own blind spots, and then laboratories and clinicians are left to piece the results together. Genome mapping has emerged as a mainstream technology with the potential to serve as a high-resolution alternative to several standard-of-care cytogenetic assays such as karyotyping (KT), fluorescence in situ hybridization (FISH) or chromosomal microarray (CMA). Our laboratory at Augusta University has published numerous publications on the analytical and clinical utility of optical genome mapping (OGM) as a first-line assay for hematological malignancies. Electronic genome mapping (EGM) pursues the same goal with an electronic rather than optical readout. The objective of this study was to demonstrate the performance and utility of EGM as a valuable tool to detect clinically relevant SVs and CNAs found by SOC testing. Cytogenomic characterization of hematologic malignancies commonly requires multiple complementary assays, including karyotyping, FISH, and chromosomal microarray, each with distinct analytical limitations. Electronic genome mapping (EGM) provides genome-wide interrogation of structural variants and copy-number alterations using electronic single-molecule detection. We evaluated the feasibility and analytical performance of EGM in previously characterized myeloid malignancies. Methods: EGM was performed on the Nabsys OhmX platform using five previously characterized bone marrow aspirates from cases with acute myeloid leukemia (AML) or chronic myeloid leukemia (CML). EGM calls were compared with findings from karyotyping (KT), fluorescence in situ hybridization (FISH), and OGM. Results: All five samples passed quality control, with contig N50 values ranging from 1.8 to 3.8 Mb and more than 90% of reference labels represented in the assembled contigs. EGM detected every SOC-reported abnormality including two t(9;22)(q34.12;q11.23)/BCR::ABL1 rearrangements, trisomy 4, trisomy 21, and monosomy 7. In the most complex case, EGM resolved a del(20q) reported by KT and FISH into an intrachromosomal fusion, fus(20;20)(q11.21;q13.12), showing not only what was lost but where the chromosome rejoined. Like OGM, EGM identified an approximately 1.8 Mb loss at 7q22.1 involving 45 of genes of which only CUX1 is associated with cancer. This 7q loss was not detected by KT or FISH. Interestingly, EGM also detected the KMT2A-PTD variant previously detected by OGM. Collectively, EGM demonstrated 100% concordance (6/6 SOC-reported abnormalities; 9/9 OGM calls). Conclusions: In this pilot study on five hematological malignancy specimens, EGM matched SOC and OGM across translocations, aneuploidies, insertions and deletions, including BCR::ABL1 rearrangements in both AML and CML, and added high-resolution breakpoint detail beyond karyotype resolution. These results support larger studies across more subtypes to establish EGM as a first-line genome-wide cytogenomic tool for hematological malignancies. Keywords: Genome mapping, Electronic genome mapping, hematological malignancies, structural variants, copy number variants