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.

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.

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.

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.

Purpose A significant proportion of cases with rare inherited retinal disease (IRD) remain genetically unresolved following short-read whole genome sequencing (WGS). This study aimed to increase the diagnostic yield of two cohorts in a research setting. One cohort was previously screened by short-read WGS (n=120 cases), while the second cohort (n=28 cases) was not included in any previous short-read WGS studies. In contrast to the larger cohort, these cases only underwent either exome sequencing (ES) or single-molecule molecular inversion probe (smMIPs) sequencing as pre-screening in earlier studies. For all cases, we performed a stepwise, case by case reanalysis in which short-read WGS was generated (n=28 cases) and (re)analyzed short-read WGS data using an optimized approach. For a subset of cases (n=20) that remained unresolved, this was followed by long read WGS. Together, these steps aimed to improve the diagnostic yield in these cohorts. Methods Short-read WGS data of 148 IRD-cases were (re)analyzed using updated allele frequency databases, new variant caller and variant predictor tools, as well as updated and extended gene-panels to identify causal single nucleotide variants (SNVs) and structural variants (SVs). For 20 genetically unresolved cases with sufficient high-quality DNA available, long-read WGS was performed. Results The (re)analysis of short-read WGS data in the 120 previously WGS-screened unresolved cases resulted in a diagnostic yield of 15% (18/120). For the 28 individuals without prior WGS, performing WGS analysis within this study provided an additional yield of 32% (9/28). Furthermore, long-read WGS contributed two genetic diagnoses among the 20 long-read WGS-sequenced probands. Amongst other reasons, new genetic diagnoses were attributed to pathogenic variants in genes newly associated with IRDs and in genes that were not included in the gene panels applied in previous WGS studies. The Mobile Element Locator Tool facilitated the identification of two pathogenic Alu insertions in short-read WGS. Long-read WGS identified two additional pathogenic variants. Conclusion These results support periodic reanalysis of existing short-read WGS data as a key component of IRD genetic diagnostics, with selective long-read WGS representing a valuable additional approach for unresolved cases. Together, these complementary strategies offer a practical framework for improving molecular diagnosis and narrowing the remaining diagnostic gap in IRDs.

Hybrid capture approaches can detect a broad range of pathogens directly from clinical samples with greater sensitivity than untargeted approaches. Clinical adoption has been limited by long turnaround times, complex workflows, and perceived high costs. We iteratively developed and validated a rapid hybrid capture workflow based on Illumina's RPIP protocol (standard workflow). Rapid-RPIP v1 reduced wet-lab time from ~11.4 to ~7.3 h, for a turnaround time (TAT) of <15 h. Rapid-RPIP v2 further reduced wet-lab time by ~0.7 h and used Illumina Turbo Sequencing shortening the sequencing time from ~5.5 to ~2.5 h thus achieving a TAT of <10 h without compromising sensitivity and yielding higher RPKMs for RNA viruses than the standard workflow. This approach could be readily adapted to a broad range of clinical and public health applications.

ABSTRACT BACKGROUND: Next-generation sequencing (NGS) and the development of targeted therapies have revolutionized cancer treatment. Molecular Tumour Boards (MTB) serve as collaborative platforms to integrate individual clinical and molecular information to make recommendations for personalized treatment. METHOD: This is a single-center, retrospective cohort study of patients presented at the Jewish General Hospital (JGH) MTB between July 2018 and Dec 2022. Clinical, molecular/pathologic data, MTB recommendations, and treatment details were collected. Treatment response was determined by radiological evaluation and physician clinical assessment. Fishers exact test and Log-rank tests were used to assess differences between groups. RESULTS: A total of 328 patients were presented to the MTB, including 23 with repeat presentations, resulting in 355 case reviews. Tumour-agnostic biomarkers were detected in 18.9% of cases, most commonly high tumour mutational burden (TMB, 9.5%) and HER2 amplification (4.3%). The most frequent actionable mutations were PIK3CA (19.8%) and PTEN (13.1%), often leading to recommendations for mTOR inhibitor therapy (26.0%). Overall, therapeutic recommendations were issued in 84.5% (300/355) of cases, and implemented in 43.3% (130/300). Patients who received MTB-recommended therapies achieved significantly higher objective response rates (ORR) (14.2% vs. 4.2%; P = 0.047), longer real-world progression-free survival (rwPFS) (5.6 vs. 3.5 months; HR 0.57; 95% CI 0.390 - 0.828; P = 0.003), and improved median overall survival (OS) (12.8 vs. 5.2 months; HR 0.60, 95% CI 0.452 - 0.793; P < 0.001) compared with those treated without MTB guidance. CONCLUSION: MTB is effective in identifying biomarker-matched therapeutic recommendations for advanced malignancies where limited standard-of-care treatment option is available.

Background & AimsWe previously developed multiple machine-learning and regression-based risk prediction models using retrospective electronic health records (EHR) to estimate near-term risk of pancreatic cancer (PC). This pilot study aimed to assess the feasibility of an early detection strategy combining initial EHR-based risk prediction with downstream imaging and blood-based testing. MethodsThis prospective study included adults aged 50-84 years with an estimated [&ge;]1% 18-month risk of PC, identified between January 2021 and March 2023 using four previously validated models. Patients with a history or active suspicion of PC were excluded. Participants completed cross-sectional imaging and CA19-9 labs at baseline and again at 18 months. Outcomes included enrollment rates, test completion rate, and burden of incidental findings. ResultsOf 1,251 eligible patients, 102 (8.2%) enrolled (median age 69 years [IQR 62-75]; 58% women; 48% Hispanic, 34% White, 11% Black, and 7% Asian). At baseline, 89 (87%) completed imaging and 98 (96%) completed CA19-9. At 18 months, 79% completed both imaging and CA19-9. Ten (10%) participants underwent additional clinical evaluations triggered by imaging findings. Evaluations included an endoscopic ultrasound, renal imaging, and FibroScan. CA19-9 results were normal in most participants, though 18% had elevated levels at baseline. ConclusionsReal-time EHR-based ML algorithms can be combined with blood- and imaging-based surveillance for early detection of PC with acceptable burden of incidental findings. However, improved patient engagement is a key aspect that will need to be addressed in order to successfully scale an AI-guided strategy for early detection.

Detecting somatic mutations is a cornerstone of cancer genomics and clinical genotyping, traditionally relying on whole exome (WES) or whole genome sequencing (WGS) data. While RNA sequencing (RNA-seq) is primarily used to assess gene expression, it can also be utilized for somatic variant detection. RNA-seq inherently captures expressed variants, thereby reducing the identification of passenger mutations and mitigating the annotation biases associated with WES. We performed somatic variant calling and cancer driver detection from tumor RNA-seq data from 1,359 RNA-seq samples with matched WGS from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Our approach using RNA-seq data, alone, identified at least one putative driver gene in 15 of 16 cancer types, consistent with driver predictions from WGS. We further observed that the variant allele frequency (VAF) of RNA-seq-derived variants reflects their predicted functional impact, providing information specific to transcriptomic data. Because RNA-seq data can be generated and analyzed at lower computational cost and time than WGS or WES, it represents a resource-efficient alternative for cancer driver detection. This study provides an extensive pan-cancer resource of RNA-seq-derived variants from the PCAWG consortium and supports the utility of RNA-seq as a complementary approach for characterizing expressed somatic variation in cancer.

ObjectiveTo evaluate usability and workflow implications of Pheno+, an electronic health record- (EHR) embedded natural language processing tool that extracts patient phenotypes and maps them to Human Phenotype Ontology (HPO) terms. Materials and MethodsThis study performed a mixed-methods formative evaluation of Pheno+ in the context of rapid genome sequencing in the neonatal intensive care unit. Two rounds of think-aloud interviews and surveys were conducted with neonatologists, advanced practice providers, medical geneticists, genetic counselors, non-genetics pediatric subspecialists and laboratory molecular geneticists. Usability was assessed using the System Usability Scale (SUS). Structured data were analyzed descriptively. Qualitative data underwent hybrid deductive-inductive thematic analysis. ResultsTwenty-eight interviewees participated in two rounds of data collection. Users retained an average of 24.2 HPO terms from an average of 45.6 terms per patient. Mean time curating terms was 8:59 per patient. Final SUS score was 76 (above scale mean). Qualitative themes included: burden of curating terms; interpretation of phenotypes; adding information to phenotypes; role of genetics expertise; and tool uses beyond the original core use case for Pheno+. DiscussionPheno+ demonstrated positive usability but required curation effort to reduce erroneous terms. Participant responses indicate that effective use may depend on knowledge of HPO structure and laboratory workflows. Automated phenotyping seems likely to reduce net clinician workload, while also reshaping clinical responsibilities, expertise requirements, and communication in genetic testing. ConclusionTools like Pheno+ can support scalable genomic medicine. Design and implementation may benefit from addressing curation burden, phenotype contextualization, expertise requirements, and other workflow needs.

Tandem repeat expansions cause more than 60 Mendelian disorders but are difficult to detect and size accurately with short-read sequencing because of their length and repetitive structure. Long-read sequencing (LRS) resolves these loci directly, yet analytic tooling for nanopore sequencing data has lagged behind that available for other long-read platforms. Here we present Mahi-mahi, a tandem repeat genotyper designed for nanopore reads that reports allele-level repeat length, motif composition, interruptions, and read-level flags for systematic basecalling artifacts. Across six Genome in a Bottle samples, whole-genome and adaptive sampling (real-time targeted) data returned calls within one repeat unit of each other for 829 of 832 alleles, and Mahi-mahi calls were within one repeat unit of orthogonal HiFi data at 95.6% of compared alleles after excluding three loci at which the two tools define the repeat differently. In 245 samples from the 1000 Genomes Project, Mahi-mahi calls were within one repeat unit of assembly-derived calls for 98.6% of 10,927 alleles at 57 loci, and Mahi-mahi distinguished pathogenic from nonpathogenic RFC1 alleles of overlapping size by motif. Across 14 Coriell reference lines, Mahi-mahi identified the expected expansion in every line and agreed with orthogonal assays to within two repeat units at HTT, ATXN1, DMPK, and PABPN1. Finally, in 103 samples from three laboratories, Mahi-mahi detected an expanded allele in all samples in which prior testing had reported a pathogenic expansion, assigned the same category as the referring laboratory for 98% of 180 genotypes, and identified known ATXN1 interruption structure. Our results demonstrate that Mahi-mahi accurately identifies repeat expansions at disease-associated loci and could serve as a single assay to evaluate repeat expansions.

DNA methylation-based classification is increasingly a cornerstone of CNS tumor diagnostics. Recently,nanopore sequencing enables very rapid methylation profiling to support surgical decision making. Time-constrained sequencing yields sparse methylation profiles, necessitating specialized classifiers for accurate predictions. All publicly available sparse-profile classifiers rely on a single reference set of only 2,801 samples published in 2018 and its accompanying classification system. Here we present Sturgeon V2, trained on nearly 10,000 methylation profiles assembled from a range of public repositories and collaborations. To harmonize differences in tumor class labels, we developed an iterative semi-supervised relabeling strategy with a two-tiered concordance filter. We demonstrate that this filtering is essential for optimal performance. Compared to Sturgeon V1, we further redesigned the classifier architecture to provide more stable, and consistent confidence estimates while accumulating sequencing data and we introduce an updated class system including new classes, such as meningioma subtypes, PATZ- fusion neuroepithelial tumors and PLAG1/2-amplified embryonal tumors. In a retrospective intraoperative cohort of 94 pediatric cases, Sturgeon V2 was confidently correct in 85 cases (Sturgeon V1: 78) with one confident misclassification, and in simulated sequencing runs (n=1880) reduced the error rate among confident calls from 7.1% to 2.8%, outperforming publicly available alternatives. Sturgeon V2 is the first openly available, locally executable classifier for sparse methylation profiles trained beyond the 2018 reference era.

Background: General-purpose large language models (LLMs) are increasingly evaluated in diagnostic pathology, but prior studies have largely emphasized diagnostic accuracy rather than how models fail. We evaluated four LLMs for diagnostic performance, pathology-relevant errors and hallucinations, their burden, and potential clinical impact across multi-organ pathology cases. Design: In this retrospective multicenter study, 153 pathology cases from two institutions spanning 20 organs were evaluated using ChatGPT-5.3 (LLM1), Gemini 3 (LLM2), Grok 4.20 (LLM3), and Claude Opus 4.6 (LLM4). Each LLM received multi-magnification histologic images with clinical context and generated a microscopic description and diagnosis. No data splitting, training, or fine-tuning was performed. Twenty-one pathologists assessed 612 outputs for diagnostic correctness, error and hallucination type and burden, clinical impact (0-4), and overall performance (1-5). Results: Strict diagnostic accuracy was 48.9% overall (60.1% including partially correct diagnoses) and ranged from 36.6% to 58.2% across LLMs. Errors occurred in 82.0% of outputs and hallucinations in 76.6%; 90.2% contained at least one error or hallucination. Misinterpretation was the most frequent error (72.1%), while fabricated histologic features were the dominant hallucination type (74.5%). LLM2 had significantly lower misinterpretation rates than the other three LLMs, while LLM3 had significantly higher fabricated-feature hallucination rates than LLM1 and LLM2. Errors were more frequent in incorrect than correct diagnoses (97.5% vs 66.2%), as were hallucinations (95.1% vs 59.9%; both p < 0.001). Every strictly incorrect diagnosis contained an error and/or hallucination, while 79.9% of strictly correct diagnoses also contained at least one. In multivariable analysis, LLM3 was independently associated with higher odds of an incorrect diagnosis. Diagnostic correctness was the strongest determinant of high clinical impact; each one-point increase in hallucination burden increased the odds by 63% (adjusted OR, 1.63). Conclusion: Diagnostic accuracy alone substantially underestimates the safety limitations of general-purpose LLMs in histopathology. Errors and hallucinations were common even when the final diagnosis was correct, and their burden was independently associated with clinical impact. Evaluation frameworks should therefore assess both diagnostic correctness and the reliability and potential consequences of accompanying generated content.

Background: Current Lynch syndrome (LS) prediction models do not account for detailed family structure and tumour mismatch repair (MMR) status. We developed LynchRisk, a Bayesian model that estimates individualized LS probability from pedigree, cancer history and tumour MMR test results. Methods: LynchRisk combines Mendelian inheritance, age- and sex-specific cancer hazards, affected and unaffected relatives, and detailed MMR status. Parameters were derived from the literature. Results: In two simulated cases of probands with LS-associated cancers, family structure and MMR status markedly changed LS probability estimates, with implications for germline testing decisions. LynchRisk also identified situations in which a negative germline result might be considered sufficient to rule out LS or, conversely, prompt further investigations such as somatic MMR or long-read sequencing. Discussion: LynchRisk gives individualized LS probability by combining clinical, family, and tumour data at different stages of the diagnostic process. Further work will focus on recalibrating cancer hazard functions, validating model performance through simulations, and developing an R package for use by interested research groups.

Targeted gene panels (TGPs) dominate clinical sequencing, yet most copy-number (CN) signature studies rely on genome-wide assays. Whether signatures can be recovered from TGPs and retain clinical relevance remains unclear. We analyzed two real-world TGP cohorts comprising 1,726 patients across 62 tumor types, including 825 with clinical annotations, and made the underlying data publicly available. TGP-derived signatures recapitulated established biological associations, including links to homologous recombination deficiency and TP53 alterations, concordant with published genome-wide assay-derived CN signatures. Using our detailed clinical data, we found TGP-derived CN signatures associated with overall survival under standard therapies in ovarian, pancreatic, and colorectal cancer. In ovarian cancer, CN2 was associated with CCNE1 amplification and shorter survival under paclitaxel/carboplatin, with the survival association validated in an independent SNP-array cohort. These findings demonstrate that routine TGPs yield biologically and clinically relevant CN signatures with potential as biomarkers for therapy stratification.

Background. High-throughput drug screening (HTS) is increasingly used in precision oncology across cell lines, patient-derived models such as organoids (PDO) and xenograft organoids (PDxO), and ex vivo patient samples, with multiple active clinical trials incorporating HTS for individualizing treatment. Yet HTS efforts predominantly rely on ad hoc, spreadsheet-based workflows, with no standardized infrastructure for storing, analyzing, and comparing screens across patients, cohorts, or institutions, limiting reproducibility and slowing clinical data exchange. Existing tools address isolated pieces of the workflow, handling either data management or analysis and visualization, but none integrate standardized storage, reproducible dose-response analysis, individualized drug prioritization, and clinician and molecular tumor board friendly reporting in a single open-source framework. Results. We present DS5, an open-source Python framework that operates on an HDF5-based DS5 file format that unifies standardized storage, quality control, dose-response analysis, cohort-level drug prioritization, and automated reporting in a single lightweight package. DS5 enforces raw-data immutability, integrates RxNorm-based drug name standardization, and scales from small clinical cohorts to large pharmacogenomics datasets on a standard laptop. We validated DS5 against two public benchmarks spanning over 428,000 drug, cell line pairs, recovering published LN IC50 values from GDSC2 (Pearson r = 0.973) and Emax values from CTRP (r = 0.961). Using a published breast cancer PDxO dataset (45 compounds by 16 models), we reproduced GR-based profiles via a custom-metric extension and used DS5's cohort-level prioritization to predict in vivo tumor response with ROC AUC = 0.907 across 30 drug PDX pairs. Finally, we demonstrate the utilities of DS5 in an ongoing pediatric brain tumor precision oncology initiative, where its cohort-normalized drug rankings and automated reports directly support molecular tumor board discussions. Conclusion. DS5 provides the cancer basic and translational research community with a standardized file format and reproducible computational analysis methods to support functional drug screening experiments; and its reporting features facilitate bridging raw drug screening measurements to translational decisions. DS5 is released open-source under the permissible MIT license.

The WHO recently endorsed two commercial culture-free targeted next-generation sequencing (tNGS) assays for detecting drug resistant Mycobacterium tuberculosis. These proprietary tNGS platforms present a black box. We developed an in-house tNGS assay using Oxford Nanopore Technology sequencing to detect an expanded range of resistance mutations directly from clinical specimens. The assay targeted 44 genomic loci; hsp65 for Mycobacterium tuberculosis complex (MTBC) identification; 12 loci for MTBC lineage classification; and 31 drug resistance loci predicted resistance to 16 anti-TB drugs. In total, 98 amplicons were distributed across three PCR pools. Analytical sensitivity was assessed using M. tuberculosis H37Rv, and proof-of-concept performance was evaluated in 63 archived clinical specimen extracts and one real-life sputum specimen in comparison with Illumina whole genome sequencing (WGS). All amplicons were successfully amplified and sequenced from M. tuberculosis H37Rv genomic DNA. At [&ge;]100 genome copies/L, 71.4% of amplicons met [&ge;]20x; mean depth across six replicates, including all loci for rifampicin, ethambutol, pyrazinamide, fluoroquinolone and bedaquiline resistance. In 63 clinical specimens, [&ge;]90% of amplicons achieved a mean depth of [&ge;]20x in specimens with IS6110 PCR cycle threshold (Ct) values [&le;]24. With Ct value [&le;]27 lineage classification was concordant with WGS in 94.7% (18/19) of specimens and drug-susceptibility prediction showed 94.7% (18/19) agreement with both phenotypic and genotypic (WGS) testing. In a single clinical case (Ct 26.5), lineage and resistance-associated mutations were accurately identified directly from sputum. The findings provide new insights into rapid culture-independent tNGS approaches for rapid M. tuberculosis drug resistance detection with lineage assignment.

The FORESEE trial (NCT04450706) was a prospective study to evaluate the feasibility of integrating organoid-based drug testing with comprehensive genomic characterization to inform treatment selection in patients with HER2-negative metastatic breast cancer. The primary objective of the study was to determine the feasibility of providing personalized therapy recommendations using a combination of genomic vulnerability information with drug testing in patient-derived organoids (PDOs) in a clinically relevant time frame. Of the 15 patients enrolled, 10 cases had biopsies with adequate tumor content, and PDOs were successfully established in 4 of 10 cases. Drug profiling and genomic testing was completed in all established organoids, yielding an overall success rate of 40% in cases of biopsies with adequate tumor content. Drug sensitivity and genomic results were returned within 12 weeks and informed subsequent treatment selection in all evaluable cases. The primary endpoint - demonstrating that PDO-based drug screening combined with genomic characterization would identify clinically actionable outcomes in at least 46% of patients based on prior data - was not met. In conclusion, successful PDO establishment represents a limiting step in the implementation of FPO for metastatic breast cancer; however when PDO were successful, drug responses were always obtained and physicians chose to use the information in 100% of cases. These findings highlight the need for improved strategies optimizing use of patient biopsy material for drug testing in order to fully realize the clinical potential of FPO.

Background: Recent work has brought large language models (LLMs) into clinical variant interpretation. In most existing approaches, the model is confined to text-level operations, retrieving, extracting, compacting, and summarizing heterogeneous evidence, while the genuine genetic evaluation is delegated to a separate deterministic engine. Other tools employ LLMs to mine functional evidence for a single ACMG criterion, to rerank a classifier's output, or to answer isolated variant questions. Integrative genetic reasoning, by contrast, demands multi-source evidence weighing for each variant, reconciliation of inheritance with observed zygosity and parental genotypes, joint interpretation of variants in the same gene, and the derivation of a case-level conclusion. Moreover, to protect patient privacy, this must be done on hospital premises. Methods: o build an end-to-end variant interpreter that runs on local infrastructure, we benchmarked 13 small language models (SLMs) on 150 multiple-choice genetics questions and selected Qwen3.5-9B. In IVA (Intelligent Variant Analyser), a four-stage pipeline, the model assigns ACMG/AMP criteria, applies de novo, dominant, recessive, and X-linked inheritance logic to trios, and writes a structured report, while deterministic code handles retrieval, phasing, point summation, citation checking, and formatting. We evaluated IVA on synthetic scenarios, 16 solved trios, and 327 solved singleton cases of visual impairment, on a single GPU. Results: On the benchmark, Qwen3.5-9B reached a mean accuracy of 0.81, compared with 0.89 for a 1.6-trillion-parameter cloud model. In the 16 trios, IVA reported every previously identified pathogenic, likely pathogenic, or uncertain-significance variant of interest (sensitivity 100%; precision 39.5%). In the 327 singleton cases, it recovered 93% of the expert-reported causative variants and classified 82% of them as pathogenic or likely pathogenic (precision 43%). Failures fell into four recurrent classes: omission, unverified mechanism, mismatch between reasoning and label, and arithmetic errors. Conclusions: A small language model running on local hardware can serve as the reasoning layer for case-level and trio-based variant interpretation when it is surrounded by deterministic safeguards. Because its outputs are not deterministic, analyst review and prospective multicenter validation remain necessary.

Background: Amplicon-based next-generation sequencing is a cornerstone of viral genomic surveillance, but it often produces datasets with highly uneven read coverage due to PCR amplification bias. This leads to excessive data generation and substantial computational overhead for downstream bioinformatic analysis. Results: We evaluated the efficacy of two data reduction strategies on 220 SARS-CoV-2 clinical specimens and 12 wastewater samples: random subsampling with SeqKit and read depth normalization with BBNorm. FASTQ files were reduced to target depths from 10X to 10,000X to compare genome coverage, lineage assignment concordance, and computational resource usage. Both strategies markedly reduced total processing time. For clinical isolates, read depth normalization corrected for amplification bias resulting in highly concordant lineage assignments at a target depth of just 200X. Random subsampling preserved inherent bias but produced reliable assignments at depths over 500X. Reducing target depths below 500X in complex wastewater mixtures severely degraded variant deconvolution accuracy and heavily skewed lineage abundance profiles for both methods. Conclusion: Both random subsampling and read depth normalization successfully reduce computational overhead while preserving key clinical sequencing metrics at appropriate reduction settings. The optimal method depends on the analytical goal, ranging from computationally lightweight random subsampling for rapid lineage assignment, to read depth normalization for generating high-quality consensus sequences. However, aggressive data reduction should be avoided in environmental surveillance, where maintaining a high baseline sequencing depth is critical for accurate mixed-variant deconvolution.