Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases

Potential clinical applications of advanced genomic analysis in cerebral palsy

Cerebral palsy (CP) has historically been attributed to acquired insults, but emerging research suggests that genetic variations are also important causes of CP. While microarray and whole-exome sequencing based studies have been the primary methods for establishing new CP-gene relationships and providing a genetic etiology for individual patients, the cause of their condition remains unknown for many patients with CP. Recent advancements in genomic technologies offer additional opportunities to uncover variations in human genomes, transcriptomes, and epigenomes that have previously escaped detection. In this review, we outline the use of these state-of-the-art technologies to address the molecular diagnostic challenges experienced by individuals with CP. We also explore the importance of identifying a molecular etiology whenever possible, given the potential for genomic medicine to provide opportunities to treat patients with CP in new and more precise ways.

Molecular mechanisms of human overgrowth and use of omics in its diagnostics: chances and challenges

Overgrowth disorders comprise a group of entities with a variable phenotypic spectrum ranging from tall stature to isolated or lateralized overgrowth of body parts and or organs. Depending on the underlying physiological pathway affected by pathogenic genetic alterations, overgrowth syndromes are associated with a broad spectrum of neoplasia predisposition, (cardio) vascular and neurodevelopmental anomalies, and dysmorphisms. Pathologic overgrowth may be of prenatal or postnatal onset. It either results from an increased number of cells (intrinsic cellular hyperplasia), hypertrophy of the normal number of cells, an increase in interstitial spaces, or from a combination of all of these. The underlying molecular causes comprise a growing number of genetic alterations affecting skeletal growth and Growth-relevant signaling cascades as major effectors, and they can affect the whole body or parts of it (mosaicism). Furthermore, epigenetic modifications play a critical role in the manifestation of some overgrowth diseases. The diagnosis of overgrowth syndromes as the prerequisite of a personalized clinical management can be challenging, due to their clinical and molecular heterogeneity. Physicians should consider molecular genetic testing as a first diagnostic step in overgrowth syndromes. In particular, the urgent need for a precise diagnosis in tumor predisposition syndromes has to be taken into account as the basis for an early monitoring and therapy. With the (future) implementation of next-generation sequencing approaches and further omic technologies, clinical diagnoses can not only be verified, but they also confirm the clinical and molecular spectrum of overgrowth disorders, including unexpected findings and identification of atypical cases. However, the limitations of the applied assays have to be considered, for each of the disorders of interest, the spectrum of possible types of genomic variants has to be considered as they might require different methodological strategies. Additionally, the integration of artificial intelligence (AI) in diagnostic workflows significantly contribute to the phenotype-driven selection and interpretation of molecular and physiological data.

Discordance between a deep learning model and clinical-grade variant pathogenicity classification in a rare disease cohort

Genetic testing has become an essential component in the diagnosis and management of a wide range of clinical conditions, from cancer to developmental disorders, especially in rare Mendelian diseases. Efforts to identify rare phenotype-associated variants have predominantly focused on protein-truncating variants, while the interpretation of missense variants presents a considerable challenge. Deep learning algorithms excel in various applications across biomedical tasks1,2, yet accurately distinguishing between pathogenic and benign genetic variants remains an elusive goal3-5. Specifically, even the most sophisticated models encounter difficulties in accurately assessing the pathogenicity of missense variants of uncertain significance (VUS). Our investigation of AlphaMissense (AM)5, the latest iteration of deep learning methods for predicting the potential functional impact of missense variants and assessing gene essentiality, reveals important limitations in its ability to identify pathogenic missense variants within a rare disease cohort. Indeed, AM struggles to accurately assess the pathogenicity of variants in intrinsically disordered regions (IDRs), leading to unreliable gene-level essentiality scores for certain genes containing IDRs. This limitation highlights the challenges in applying AM faces in the context of clinical genetics6.

Parental Engagement in Identifying Information Needs After Newborn Screening for Families of Infants with Suspected Athymia

Congenital athymia is a rare T-lymphocytopaenic condition, which requires early corrective treatment with thymus transplantation (TT). Athymic patients are increasingly identified through newborn screening (NBS) for severe combined immunodeficiency (SCID). Lack of relatable information resources contributes to challenging patient and family journeys during the diagnostic period following abnormal NBS results. Patient and Public Involvement and Engagement (PPIE) activities, including parental involvement in paediatrics, are valuable initiatives to improve clinical communication and parental information strategies. Parents of infants with suspected athymia were therefore invited to discuss the information they received during the diagnostic period following NBS with the aim to identify parental information needs and targeted strategies to address these adequately. Parents reported that athymia was not considered with them as a possible differential diagnosis until weeks after initial NBS results. Whilst appropriate clinical information about athymia and TT was available upon referral to specialist immunology services, improved access to easy-to-understand information from reliable sources, including from clinical nurse specialists and peer support systems, remained desirable. A roadmap concept, with written or digital information, addressing parental needs in real time during a potentially complex diagnostic journey, was proposed and is transferrable to other inborn errors of immunity (IEI) and rare diseases. This PPIE activity provides insight into the information needs of parents of infants with suspected athymia who are identified through SCID NBS, and highlights the role for PPIE in promoting patient- and family-centred strategies to improve IEI care.

An Overview of Advances in Rare Cancer Diagnosis and Treatment

Cancer stands as the leading global cause of mortality, with rare cancer comprising 230 distinct subtypes characterized by infrequent incidence. Despite the inherent challenges in addressing the diagnosis and treatment of rare cancers due to their low occurrence rates, several biomedical breakthroughs have led to significant advancement in both areas. This review provides a comprehensive overview of state-of-the-art diagnostic techniques that encompass new-generation sequencing and multi-omics, coupled with the integration of artificial intelligence and machine learning, that have revolutionized rare cancer diagnosis. In addition, this review highlights the latest innovations in rare cancer therapeutic options, comprising immunotherapy, targeted therapy, transplantation, and drug combination therapy, that have undergone clinical trials and significantly contribute to the tumor remission and overall survival of rare cancer patients. In this review, we summarize recent breakthroughs and insights in the understanding of rare cancer pathophysiology, diagnosis, and therapeutic modalities, as well as the challenges faced in the development of rare cancer diagnosis data interpretation and drug development.

Expanding Genotype-Phenotype Correlation of CLCNKA and CLCNKB Variants Linked to Hearing Loss

The ClC-K channels CLCNKA and CLCNKB are crucial for the transepithelial transport processes required for sufficient urinary concentrations and sensory mechanoelectrical transduction in the cochlea. Loss-of-function alleles in these channels are associated with various clinical phenotypes, ranging from hypokalemic alkalosis to sensorineural hearing loss (SNHL) accompanied by severe renal conditions, i.e., Bartter's syndrome. Using a stepwise genetic approach encompassing whole-genome sequencing (WGS), we identified one family with compound heterozygous variants in the ClC-K channels, specifically a truncating variant in CLCNKA in trans with a contiguous deletion of CLCNKA and CLCNKB. Breakpoint PCR and Sanger sequencing elucidated the breakpoint junctions derived from WGS, and allele-specific droplet digital PCR confirmed one copy loss of the CLCNKA_CLCNKB contiguous deletion. The proband that harbors the CLCNKA_CLCNKB variants is characterized by SNHL without hypokalemic alkalosis and renal anomalies, suggesting a distinct phenotype in the ClC-K channels in whom SNHL predominantly occurs. These results expanded genotypes and phenotypes associated with ClC-K channels, including the disease entities associated with non-syndromic hearing loss. Repeated identification of deletions across various extents of CLCNKA_CLCNKB suggests a mutational hotspot allele, highlighting the need for an in-depth analysis of the CLCNKA_CLCNKB intergenic region, especially in undiagnosed SNHL patients with a single hit in CLCNKA.