Validation and clinical application of transactivation assays for RUNX1 variant classification

Familial platelet disorder with associated myeloid malignancies (RUNX1-FPD) is caused by heterozygous pathogenic germline variants of RUNX1. In the present study, we evaluate the applicability of transactivation assays to investigate RUNX1 variants in different regions of the protein. We studied 11 variants to independently validate transactivation assays supporting variant classification following the ClinGen Myeloid Malignancies variant curation expert panel guidelines. Variant classification is key for the translation of genetic findings. We showed that new assays need to be developed to assess C-terminal RUNX1 variants. Two variants of uncertain significance (VUS) were reclassified to likely pathogenic. Additionally, our analyses supported the (likely) pathogenic classification of two other variants. We demonstrated functionality of four VUS, but reclassification to (likely) benign was challenging and suggested the need to reevaluate current classification guidelines. Finally, clinical utility of our assays was illustrated in the context of seven families. Our data confirmed RUNX1-FPD suspicion in three families with RUNX1-FPD-specific family history. Whereas for three variants identified in non RUNX1-FPD-typical families, no functional defect was detected. Applying functional assays to support RUNX1 variant classification can be essential for adequate care of index patients and their relatives at risk. It facilitates translation of genetic data into personalized medicine.

Multiplexed CRISPR-based microfluidic platform for clinical testing of respiratory viruses and SARS-CoV-2 variants

The COVID-19 pandemic has demonstrated a clear need for high-throughput, multiplexed, and sensitive assays for detecting SARS-CoV-2 and other respiratory viruses as well as their emerging variants. Here, we present microfluidic CARMEN (mCARMEN), a cost-effective virus and variant detection platform that combines CRISPR-based diagnostics and microfluidics with a streamlined workflow for clinical use. We developed the mCARMEN respiratory virus panel (RVP) and demonstrated its diagnostic-grade performance on 533 patient specimens in an academic setting and then 166 specimens in a clinical setting. We further developed a panel to distinguish 6 SARS-CoV-2 variant lineages, including Delta and Omicron, and evaluated it on 106 patient specimens, with near-perfect concordance to sequencing-based variant classification. Lastly, we implemented a combined Cas13 and Cas12 approach that enables quantitative measurement of viral copies in samples. mCARMEN enables high-throughput surveillance of multiple viruses and variants simultaneously.

A calibrated functional patch clamp assay to enhance clinical variant interpretation in KCNH2-related long QT syndrome

Purpose: Modern sequencing technologies have revolutionised our detection of gene variants. In most genes, including KCNH2, the majority of missense variants are currently classified as variants of uncertain significance (VUS). The aim of this study is to investigate the utility of an automated patch-clamp assay for aiding clinical variant classification in the KCNH2 gene. Methods: The assay was designed according to recommendations of the ClinGen sequence variant interpretation framework. Thirty-one control variants of known clinical significance (17 pathogenic/likely pathogenic, 14 benign/likely benign) were heterozygously expressed in Flp-In HEK293 cells. Variants were analysed for effects on current density and channel gating. A panel of 44 VUS was then assessed for reclassification. Results: All 17 pathogenic variant controls had reduced current density and 13/14 benign variant controls had normal current density, which enabled determination of normal and abnormal ranges for applying moderate or supporting evidence strength for variant classification. Inclusion of KCNH2 functional assay evidence enabled us to reclassify 6 out of 44 VUS as likely pathogenic. Conclusion: The high-throughput patch clamp assay can provide moderate strength evidence for clinical interpretation of clinical KCNH2 variants and demonstrates the value proposition for developing automated patch clamp assays for other ion channel genes.

Variant Selection and Interpretation: An Example of Modified VarSome Classifier of ACMG Guidelines in the Diagnostic Setting

Variant interpretation is challenging as it involves combining different levels of evidence in order to evaluate the role of a specific variant in the context of a patient’s disease. Many in-depth refinements followed the original 2015 American College of Medical Genetics (ACMG) guidelines to overcome subjective interpretation of criteria and classification inconsistencies. Here, we developed an ACMG-based classifier that retrieves information for variant interpretation from the VarSome Stable-API environment and allows molecular geneticists involved in clinical reporting to introduce the necessary changes to criterion strength and to add or exclude criteria assigned automatically, ultimately leading to the final variant classification. We also developed a modified ACMG checklist to assist molecular geneticists in adjusting criterion strength and in adding literature-retrieved or patient-specific information, when available. The proposed classifier is an example of integration of automation and human expertise in variant curation, while maintaining the laboratory analytical workflow and the established bioinformatics pipeline.

A multi-class gene classifier for SARS-CoV-2 variants based on convolutional neural network

Surveillance of circulating variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of great importance in controlling the coronavirus disease 2019 (COVID-19) pandemic. We propose an alignment-free in silico approach for classifying SARS-CoV-2 variants based on their genomic sequences. A deep learning model was constructed utilizing a stacked 1-D convolutional neural network and multilayer perceptron (MLP). The pre-processed genomic sequencing data of the four SARS-CoV-2 variants were first fed to three stacked convolution-pooling nets to extract local linkage patterns in the sequences. Then a 2-layer MLP was used to compute the correlations between the input and output. Finally, a logistic regression model transformed the output and returned the probability values. Learning curves and stratified 10-fold cross-validation showed that the proposed classifier enables robust variant classification. External validation of the classifier showed an accuracy of 0.9962, precision of 0.9963, recall of 0.9963 and F1 score of 0.9962, outperforming other machine learning methods, including logistic regression, K-nearest neighbor, support vector machine, and random forest. By comparing our model with an MLP model without the convolution-pooling network, we demonstrate the essential role of convolution in extracting viral variant features. Thus, our results indicate that the proposed convolution-based multi-class gene classifier is efficient for the variant classification of SARS-CoV-2.

ClinGen CDH1 specifications for the ACMG/AMP guidelines: improvement of germline variant clinical assertions and updated curation guidelines

Purpose: The Clinical Genome Resource (ClinGen) CDH1 Variant Curation Expert Panel (VCEP) developed specifications for CDH1 variant curation with a goal to resolve variants of uncertain significance (VUS) and with ClinVar conflicting interpretations for effective medical care. In addition, the CDH1 VCEP continues to update these specifications in keeping with evolving clinical practice and variant interpretation guidelines. Methods: CDH1 variant classification specifications were modified based on updated genetic testing clinical criteria, new recommendations from ClinGen, and expert knowledge from ongoing CDH1 variant curations. Trained biocurators curated 273 variants using updated CDH1 interpretation guidelines and incorporated published and unpublished data provided by diagnostic laboratories. All variants were reviewed by the ClinGen VCEP and classifications submitted to ClinVar. Results: Updated CDH1-specific variant interpretation guidelines include eleven major modifications since the initial specifications from 2018. Using the refined guidelines, 97% (36/37) of variants with ClinVar conflicting interpretations were resolved into benign, likely benign, likely pathogenic, or pathogenic, and 35% (15/43) of VUS were resolved into benign or likely benign. Overall, 88% (239/273) of curated variants had non-VUS classifications. Conclusion: The development and evolution of CDH1-specific criteria by the expert panel results in decreased uncertain and conflicting interpretations of variants in this clinically actionable gene.