Background: Digital polymerase chain reaction (dPCR) is widely recognized for its high sensitivity in detecting low-frequency variants; however, conventional 2-color systems have limited multiplex capacity. Expanding this capability is essential for simultaneous detection of multiple driver mutations in cancer-related genes. KRAS and GNAS are key driver genes in the early development of pancreatic cancer and its precursor lesions, and mutations in these genes are often present at low abundance in clinical samples.
Methods: Two 6-color dPCR assays were developed using a droplet-based platform. PlexScreen-dPCR is a multicolored drop-off assay designed to screen for mutations in KRAS codons 12/13 and 61 and GNAS codon 201, without specifying individual variants. PlexID-dPCR employs variant-specific probes to distinguish among 14 predefined KRAS and GNAS mutations in a single reaction. The assays were validated using synthetic DNA, cell lines, 23 tissue samples, and 12 duodenal fluid samples. Customized primer/probe sets with 6 fluorophores were employed in a 6-color droplet dPCR system, and the limits of detection (LOD) were determined.
Results: PlexScreen-dPCR, applied in contrived samples, demonstrated LODs as low as 0.03% to 0.06%, enabling high-sensitivity detection of low-abundance mutations. PlexID-dPCR accurately identified all 14 variants in a single well. Both assays showed complete concordance with conventional methods, exhibiting a strong correlation for variant allele frequency quantification.
Conclusions: These 6-color dPCR assays offer scalable solutions for improved throughput detection of KRAS and GNAS mutations. Their compatibility with commercially available platforms and streamlined workflow support their integration into clinical practice. Further optimization can enhance cluster interpretation in high-plex settings and facilitate expansion toward broader genomic targets.
Background: Urinalysis is a standard clinical test that includes the microscopic examination of urinary sediment to identify formed elements. Manual evaluation by laboratory technicians is time-intensive and subject to human error. Automated analysis using digital microscopy images presents a potential alternative. This study evaluates the integration of a deep learning approach to automatically classify urinary sediment images in the clinical laboratory, including independent prospective validation of its performance.
Methods: An annotated data set comprising 13 classes of urinary sediment elements was created from a database of Sysmex UD-10 digital microscope images. An EfficientNet-based model was trained and tested across three experimental scenarios to evaluate the effects of data collection strategies on performance. Uncertainty calibration was examined. The model's robustness and interpretability were examined using gradient-weighted class activation mapping (Grad-CAM) to visualize influential image regions and t-distributed stochastic neighbor embedding (t-SNE) to analyze learned feature embeddings. Lastly, a graphical user interface was developed for a prospective evaluation in the laboratory.
Results: The model achieved approximately 97% overall accuracy on the test set. Experiments revealed sensitivity to data set variability, suggesting that performance may improve by integrating additional training examples. Confidence scores aligned with accuracy, and interpretability analyses showed that the model focused on relevant image regions and learned embeddings demonstrated clear class separation. In the prospective evaluation, top 1 and top 3 accuracies decreased to approximately 78% and 92%, respectively.
Conclusions: Our results indicate that a lightweight deep learning model can achieve high performance in classifying urine particles. Analysis of discrepancies between retrospective and prospective evaluations provides important insights toward reliable clinical application.
Background: Neurofilament light chain (Nf-L) is a key early biomarker for axonal damage and neurodegeneration, increasingly used in clinical practice for diagnosis, prognosis, and treatment monitoring. To ensure reliable clinical implementation, standardized measurement procedures and calibrators traceable to the International System of Units (SI) are needed. Although a few mass-spectrometry methods for Nf-L quantification in cerebrospinal fluid CSF) and plasma have been described, none currently use properly defined SI-traceable calibrators and existing harmonization efforts rely solely on immunoassays. This study presents a validated immunoprecipitation (IP)-LC-MS/MS assay using an SI-traceable calibrator and compares it with Lumipulse and Simoa platforms to assess agreement and bias.
Methods: A new IP-LC-MS/MS method was developed based on SI-traceable calibrator quantification and analytically validated using International Council for Harmonisation (ICH) guidelines. Analysis of 69 CSF samples by MS assay and Lumipulse (Fujirebio®) was performed as well as head-to-head comparisons between MS, Simoa (Quanterix®) and Lumipulse assays on 12 CSF pools.
Results: The method relying on 3 peptides was validated analytically. Significant results (P < 0.05) were obtained between amyloid positive to negative group when using the LC-MS/MS assay. MS and Lumipulse results were correlated. Head-to-head comparison of the 3 methods showed great correlation (r2 > 0.98) but systematic bias was identified between all techniques.
Conclusion: A new IP-LC-MS/MS method using a SI-traceable calibrator was developed, and analytically and clinically validated. Comparison between available immunoassays resulted in great correlation but biases were identified reinforcing the need of standardization for Nf-L measurement.
Background: Germline loss-of-function variants in BRCA1 and BRCA2 are established drivers of hereditary breast and ovarian cancer, often acting through aberrant splicing. However, not all spliceogenic changes are pathogenic, and many variants remain classified as uncertain due to insufficient experimental evidence and challenges in applying the ACMG/AMP variant interpretation framework to splicing alterations.
Methods: In this study, we examined the splicing outcomes of 17 variants-10 in BRCA1 [c.135-2A>G; c.135-5T>C; c.5074+1G>C; c.5332+2_5332+4del; c.5333-8C>T; c.5335C>G p.(Gln1779Glu); c.302-24_302-22del; c.302-23A>G; c.547+57T>C; c.4096+34C>G] and 7 in BRCA2 [c.-39-5delT; c.67+3A>G; c.425G>A p.(Ser142Asn); c.425G>T p.(Ser142Ile); c.517-13_517-9del; c.681+5G>C; c.67+84_67+85del]-identified in families with suspected hereditary breast and/or ovarian cancer. Depending on sample availability, we assessed splicing either on carrier-derived mRNA or via splicing-reporter minigene assay.
Results: Eight variants triggered aberrant splicing, while 9 showed no spliceogenic effect. Our findings, combined in some cases with previously published data, allowed us to apply the PVS1_(RNA) criterion at full strength to some variants. For others, residual full-length transcripts or in-frame mis-spliced isoforms precluded full application of PVS1_(RNA).
Conclusions: Following ClinGen ENIGMA BRCA1 and BRCA2 Variant Curation Expert Panel specifications based on ACMG/AMP guidelines, we classified 4 variants as pathogenic or likely pathogenic, 10 as benign or likely benign, and 3 as uncertain significance. This comprehensive analysis of splicing defects refines the clinical classification of BRCA1 and BRCA2 variants and highlights the value of combining experimental and computational evidence to enhance genetic risk assessment in hereditary cancer.
Background: Prenatal cell-free DNA (cfDNA) screening is primarily designed to detect fetal chromosomal abnormalities, but can also identify aneuploidies derived from other tissues, including cancer. The identification of aneuploidies of unknown origin during prenatal cfDNA screening can lead to time-consuming multistage investigations and anxiety for the expecting mother.
Methods: To expedite the identification of the origin of copy-number aberrations and guide clinical management of such profiles suggestive of maternal malignancy, we developed a methylation and aneuploidy-aware prenatal screening pipeline. Plasma cfDNA is enzymatically converted to identify the methylated cytosines during sequencing. The tissue of origin is predicted by leveraging a cell-type-specific methylome atlas into our methylation-based deconvolution algorithm, MetDecode.
Results: We demonstrate that aneuploidy profiling on enzymatically converted cfDNA enables the identification of placental and cancer-derived aneuploidies with similar accuracy compared with conventional prenatal cfDNA screening. The methylation-based deconvolution pinpointed the tumor origin correctly in 91.67% of the pregnant women with a tumor fraction >3%.
Conclusion: Methylome and aneuploidy-aware cfDNA screening could substantially improve the diagnostic processes, pinpoint the origins of aneuploidy and improve cancer management during pregnancy.
Background: Diagnosis of tuberculosis (TB) and multidrug-resistant tuberculosis (MDR-TB) is increasingly performed using molecular tools that detect Mycobacterium tuberculosis DNA. To ensure accurate and reliable results from the molecular tests, appropriate quality assessment is required. This involves implementing reference measurement procedures (RMPs) to characterize material standards that are representative of the clinical specimen. These material standards should address drug resistance and mixtures of drug-resistant and -susceptible bacteria. However, currently these RMPs and materials standards do not exist, which can hamper the accuracy and precision of routine clinical testing. To address this, we applied digital PCR (dPCR) as a RMP to MDR-TB material standards.
Methods: Four standards were prepared and characterized using dPCR to quantify drug-resistant and -susceptible genotypes. We investigated the performance of existing molecular tests via an interlaboratory study including 9 laboratories from Africa and Europe, assessing 3 methods for MDR-TB detection and 2 methods for TB-only detection.
Results: All tests correctly identified M. tuberculosis, and 2 out of 3 tests identified the associated drug resistance (one test failed to identify drug resistance in one of the materials). Generally, discrepancies occurred with the more challenging samples bearing lower concentrations and mixed genotypes.
Conclusions: The approaches used in this study will enhance the quality assessment of MDR-TB and can be applied to afford test manufacturers and clinical laboratories more accurate results to guide test development, selection, and regulation. Such an approach can improve confidence in MDR-TB testing, enabling physicians to guide treatment, potentially leading to better patient outcomes.
Background: Pregnancy is characterized by dynamic physiological changes that alter the concentrations of many maternal blood biomarkers. Reporting results against nonpregnant reference values can lead to misinterpretation, diagnostic error, and inappropriate clinical management. The use and reporting of pregnancy-specific reference intervals (RIs) by laboratories is not yet routine practice.
Content: This review underscores the critical need for pregnancy RIs to support accurate diagnosis, effective patient care, and optimal clinical decision-making in pregnancy and highlights unique considerations and challenges specific to pregnancy RI studies. Aspects such as defining inclusion/exclusion criteria and participant engagement are more complex in pregnant cohorts. Logistical and resource constraints must be anticipated when undertaking these studies. The current landscape of pregnancy RIs is summarized, drawing upon the literature, which shows substantial heterogeneity in study designs, populations, analytical methods, and partitioning strategies, with important details often missing or insufficient. These issues limit the comparability of findings between studies and the application of published RIs to other pregnant populations. Indirect RI approaches combined with clinical databases provide promising alternatives to traditional direct studies, which help overcome some of the barriers, particularly around recruitment. Experience and lessons learned from the authors' own involvement in prospective and retrospective studies for chemistry and hematology biomarkers are shared.
Summary: The challenges associated with developing pregnancy RIs require coordinated and uniform efforts. The discussion herein will help guide future work and knowledge translation to ensure high-quality, standardized studies generate pregnancy RIs that are widely applicable and support maternity care providers and patients alike.
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