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This paper delves into the intricate relationship between the evolving field of genomic medicine and healthcare providers' legal and ethical responsibilities, with a particular focus on the duty to warn in the context of genetic risks. Through a critical examination of landmark cases such as Pate v. Threlkel and Safer v. Estate of Pack, this study underscores the expanding scope of healthcare providers' duties to include not just the patient but potentially at-risk family members as well. It highlights the legal, ethical, and practical challenges that arise when balancing patient confidentiality with the need to prevent harm through the disclosure of genetic information. The descriptive portion of the paper outlines the current legal precedents and the ambiguity surrounding healthcare providers' responsibilities. The prescriptive portion proposes the establishment of comprehensive, clear guidelines to support healthcare providers, particularly genomic counselors, in navigating these complexities. This includes recommendations for policy changes and the development of protocols that respect patient autonomy while addressing the preventive potential of genomic medicine. Through an analysis of legal frameworks and ethical considerations, this paper argues for a nuanced approach that equitably balances individual rights with the collective good, ultimately advocating for a future where genomic medicine is integrated into patient care in an ethically responsible and legally sound manner.

Laboratory practitioners working in oncology are increasingly involved in implementing genomic medicine, operating at the intersection of the laboratory and the clinic. This includes molecular diagnostic work and molecular testing to direct entry into molecular-based clinical trials and treatment decision-making based on molecular profiling. In this article, we draw on qualitative interviews with laboratory practitioners in the United Kingdom to explore the role of laboratory work in genomic cancer medicine, focusing on the handling of patient tissue and making of potentiality to guide patients' present and future care. With an increase in molecular testing to inform standard care and clinical trial participation, we show how practitioners "potentialized" the tissue by carefully negotiating what to test, how to test, and when. This included maximizing and managing small amounts of tissue in anticipation of possible future patient care. Tissue archives also took on new meaning, and potentiality, which practitioners negotiated alongside patient care. Potentiality was key to generating the "big" future of genomic medicine and also involved care work where the tissue emerged as an extension of the patient, as a form of "material patienthood," to secure present and future care for patients through their involvement in genomic medicine. [ABSTRACT FROM AUTHOR]

A health workforce capable of implementing genomic medicine requires effective genomics education. Genomics education interventions developed for health professions over the last two decades, and their impact, are variably described in the literature. To inform an evaluation framework for genomics education, we undertook an exploratory scoping review of published needs assessments for, and/or evaluations of, genomics education interventions for health professionals from 2000 to 2023. We retrieved and screened 4,659 records across the two searches with 363 being selected for full-text review and consideration by an interdisciplinary working group. 104 articles were selected for inclusion in the review—60 needs assessments, 52 genomics education evaluations, and eight describing both. Included articles spanned all years and described education interventions in over 30 countries. Target audiences included medical specialists, nurses/midwives, and/or allied health professionals. Evaluation questions, outcomes, and measures were extracted, categorized, and tabulated to iteratively compare measures across stages of genomics education evaluation: planning (pre-implementation), development and delivery (implementation), and impact (immediate, intermediate, or long-term outcomes). They are presented here along with descriptions of study designs. We document the wide variability in evaluation approaches and terminology used to define measures and note that few articles considered downstream (long-term) outcomes of genomics education interventions. Alongside the evaluation framework for genomics education, results from this scoping review form part of a toolkit to help educators to undertake rigorous genomics evaluation that is fit for purpose and can contribute to the growing evidence base of the contribution of genomics education in implementation strategies for genomic medicine. A scoping review of needs assessments for, and/or evaluations of, genomics education interventions for health professionals showed variability in evaluation approaches and terminology and identified few articles considering long-term outcomes of interventions. The reviewed evaluation questions, outcomes, and measures form part of a toolkit to help educators undertake rigorous evaluation. [ABSTRACT FROM AUTHOR]

Implementation of genomic medicine into healthcare requires a workforce educated through effective educational approaches. However, ascertaining the impact of genomics education activities or resources is limited by a lack of evaluation and inconsistent descriptions in the literature. We aim to support those developing genomics education to consider how best to capture evaluation data that demonstrate program outcomes and effectiveness within scope. Here, we present an evaluation framework that is adaptable to multiple settings for use by genomics educators with or without education or evaluation backgrounds. The framework was developed as part of a broader program supporting genomic research translation coordinated by the Australian Genomics consortium. We detail our mixed-methods approach involving an expert workshop, literature review and iterative expert input to reach consensus and synthesis of a new evaluation framework for genomics education. The resulting theory-informed and evidence-based framework encompasses evaluation across all stages of education program development, implementation and reporting, and acknowledges the critical role of stakeholders and the effects of external influences. We developed an evaluation framework for genomics education adaptable to multiple settings. Mixed methods were used (literature reviews and expert input). The theory-informed and evidence-based framework encompasses evaluation across all stages of program development, implementation and reporting, and acknowledges the critical role of stakeholders and the effects of external influences. [ABSTRACT FROM AUTHOR]

Overgrowth syndromes (OGS) comprise a heterogeneous group of disorders whose main characteristic is that the weight, height or the head circumference are above the 97th centile or 2–3 standard deviations above the mean for age, gender, and ethnic group. Several copy‐number variants (CNVs) have been associated with the development of OGS, such as the 5q35 microdeletion or the duplication of the 15q26.1‐qter, among many others. In this study, we have applied 850K SNP‐arrays to 112 patients and relatives with OGS from the Spanish OverGrowth Registry Initiative. We have identified CNVs associated with the disorder in nine individuals (8%). Subsequently, whole genome sequencing (WGS) analysis was performed in these nine samples in order to better understand these genomic imbalances. All the CNVs were detected by both techniques, settling that WGS is a useful tool for CNV detection. We have found six patients with genomic abnormalities associated with previously well‐established disorders and three patients with CNVs of unknown significance, which may be related to OGS, based on scientific literature. In this report, we describe these findings and comment on genes associated with OGS that are located within the CNV regions. [ABSTRACT FROM AUTHOR]

Adeno-associated viruses (AAVs) have become the delivery medium of choice for a variety of genomic medicine applications i.e., gene therapy, gene editing/regulation, and ex-vivo cell therapy. AAVs are protein-DNA complexes which have unique stability characteristics that are susceptible to various stress exposure conditions commonly seen in the drug product (DP) life cycle. This review takes a comprehensive look at AAV DP formulation and process development considerations that could impact critical quality attributes (CQAs) during manufacturing, packaging, shipping, and clinical use. Additional aspects related to AAV development reviewed herein are: (1) Different AAV serotypes with unique protein sequences and charge characteristics potentially leading to discrete stability profiles; (2) Manufacturing process challenges and optimization efforts to improve yield, recovery and purity especially during early development activities; and (3) Defining and identifying CQAs with analytical methods which are constantly evolving and present unique characterization challenges for AAV-based products. [Display omitted] [ABSTRACT FROM AUTHOR]

There is remarkable genetic diversity throughout the human population, but variant interpretation lags far behind discovery. As a result, most genetic variation is not clinically actionable. Single-cell sequencing provides a high-throughput, disease- and pathway-agnostic means to profile variant effects with mechanistic detail across the entire regulatory cascade. Many single-cell genomics methods have been coupled to CRISPR-based gene disruption, but these techniques are rapidly expanding to accommodate increasingly diverse types of perturbations including missense variants, protein fusions, structural variants, genetic circuits, chemical perturbations, and increasingly their combinations. High-throughput, deep phenotyping of genetic variation will allow researchers to define causal mechanisms and nominate precision therapeutics for controlling cell states. Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine. [ABSTRACT FROM AUTHOR]

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Abstract Background Pharmacogenomics of hydroxyurea is an important aspect in the management of sickle cell disease (SCD), especially in the era of genomic medicine. Genetic variations in loci associated with HbF induction and drug metabolism are prime targets for hydroxyurea (HU) pharmacogenomics, as these can significantly impact the therapeutic efficacy and safety of HU in SCD patients. Methods This study involved designing of a custom panel targeting BCL11A, ARG2, HBB, HBG1, WAC, HBG2, HAO2, MYB, SAR1A, KLF10, CYP2C9, CYP2E1 and NOS1 as potential HU pharmacogenomics targets. These genes were selected based on their known roles in HbF induction and HU metabolism. The panel was designed using the Illumina Design Studio (Illumina, San Diego, CA, USA) and achieved a total coverage of 96% of all genomic targets over a span of 51.6 kilobases (kb). This custom panel was then sequenced using the Illumina MiSeq platform to ensure high coverage and accuracy. Results We are reporting a successfully designed Illumina (MiSeq) HU pharmacogenomics custom panel encompassing 51.6 kilobases. The designed panel achieved greater than 1000x amplicon coverage which is sufficient for genomic analysis. Conclusions This study provides a valuable tool for research in HU pharmacogenomics, especially in Africa where SCD is highly prevalent, and personalized medicine approaches are crucial for improving patient outcomes. The custom-designed Illumina (MiSeq) panel, with its extensive coverage and high sequencing depth, provides a robust platform for studying genetic variations associated with HU response. This panel can contribute to the development of tailored therapeutic strategies, ultimately enhancing the management of SCD through more effective and safer use of hydroxyurea.

Abstract Background Genetic variants that severely alter protein products (e.g. nonsense, frameshift) are often associated with disease. For some genes, these predicted loss-of-function variants (pLoFs) are observed throughout the gene, whilst in others, they occur only at specific locations. We hypothesised that, for genes linked with monogenic diseases that display incomplete penetrance, pLoF variants present in apparently unaffected individuals may be limited to regions where pLoFs are tolerated. To test this, we investigated whether pLoF location could explain instances of incomplete penetrance of variants expected to be pathogenic for Mendelian conditions. Methods We used exome sequence data in 454,773 individuals in the UK Biobank (UKB) to investigate the locations of pLoFs in a population cohort. We counted numbers of unique pLoF, missense, and synonymous variants in UKB in each quintile of the coding sequence (CDS) of all protein-coding genes and clustered the variants using Gaussian mixture models. We limited the analyses to genes with ≥ 5 variants of each type (16,473 genes). We compared the locations of pLoFs in UKB with all theoretically possible pLoFs in a transcript, and pathogenic pLoFs from ClinVar, and performed simulations to estimate the false-positive rate of non-uniformly distributed variants. Results For most genes, all variant classes fell into clusters representing broadly uniform variant distributions, but genes in which haploinsufficiency causes developmental disorders were less likely to have uniform pLoF distribution than other genes (P < 2.2 × 10−6). We identified a number of genes, including ARID1B and GATA6, where pLoF variants in the first quarter of the CDS were rescued by the presence of an alternative translation start site and should not be reported as pathogenic. For other genes, such as ODC1, pLoFs were located approximately uniformly across the gene, but pathogenic pLoFs were clustered only at the end, consistent with a gain-of-function disease mechanism. Conclusions Our results suggest the potential benefits of localised constraint metrics and that the location of pLoF variants should be considered when interpreting variants.

Primary mucinous ovarian carcinoma (PMOC) is a rare tumor, accounting for approximately 3% of all epithelial ovarian cancers (EOCs), with clinical risk factors and biologic features distinct from that of EOC. The prognosis for women with recurrent and high-grade PMOC remains poor, likely related to a poor response to conventional chemotherapy for EOC. A 27-year-old Chinese woman sought medical attention in January 2021 for abdominal distention from a large pelvic mass. After extensive investigations and workup, she was diagnosed with PMOC of the right ovary. Following multidisciplinary team (MDT) discussions, the patient underwent fertility-sparing surgery (FSS) (abdominal left adnexectomy, right partial oophorectomy, pelvic lymph node dissection, para-aortic lymph node dissection, omentectomy) as she yearned to preserve her fertility and the contralateral ovary appeared normal. Deep genetic analyses revealed ERBB2 co-amplification with CDK12 and chromosome 11q13.3 amplicon. Treatment with fertility-sparing surgery and adjuvant chemotherapy with trastuzumab results in complete remission. This novel strategy utilizing precise diagnostics and characterization of the histo-type of rare tumors allowed personalized targeting with optimum drug response for women who yearn fertility preservation and remission from the disease, especially when there is very limited clinical experience on management of such rare ovarian tumors. [ABSTRACT FROM AUTHOR]

Background Genomic kidney conditions often have a long lag between onset of symptoms and diagnosis. To design a real time genetic diagnosis process that meets the needs of nephrologists, we need to understand the current state, barriers, and facilitators nephrologists and other clinicians who treat kidney conditions experience, and identify areas of opportunity for improvement and innovation. Methods Qualitative in-depth interviews were conducted with nephrologists and internists from 7 health systems. Rapid analysis identified themes in the interviews. These were used to develop service blueprints and process maps depicting the current state of genetic diagnosis of kidney disease. Results Themes from the interviews included the importance of trustworthy resources, guidance on how to order tests, and clarity on what to do with results. Barriers included lack of knowledge, lack of access, and complexity surrounding the case and disease. Facilitators included good user experience, straightforward diagnoses, and support from colleagues. Discussion The current state of diagnosis of kidney diseases with genetic etiology is suboptimal, with information gaps, complexity of genetic testing processes, and heterogeneity of disease impeding efficiency and leading to poor outcomes. This study highlights opportunities for improvement and innovation to address these barriers and empower nephrologists and other clinicians who treat kidney conditions to access and use real time genetic information. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer genome analysis often reveals structural variants (SVs) involving fusion genes that are difficult to classify as drivers or passengers. Obtaining accurate AI predictions and explanations, which are crucial for a reliable diagnosis, is challenging. We developed an explainable AI (XAI) system that predicts the pathogenicity of SVs with gene fusions, providing reasons for its predictions. Our XAI achieved high accuracy, comparable to existing tools, and generated plausible explanations based on pathogenic mechanisms. This research represents a promising step towards AI-supported decision making in genomic medicine, enabling efficient and accurate diagnosis. When analyzing cancer sample genomes in clinical practice, many structural variants (SVs), other than single nucleotide variants (SNVs), have been identified. To identify driver variants, the leading candidates must be narrowed down. When fusion genes are involved, selection is particularly difficult, and highly accurate predictions from AI is important. Furthermore, we also wanted to determine how the prediction can make more reliable diagnoses. Here, we developed an explainable AI (XAI) suitable for SVs with gene fusions, based on the XAI technology we previously developed for the prediction of SNV pathogenicity. To cope with gene fusion variants, we added new data to the previous knowledge graph for SVs and we improved the algorithm. Its prediction accuracy was as high as that of existing tools. Moreover, our XAI could explain the reasons for these predictions. We used some variant examples to demonstrate that the reasons are plausible in terms of pathogenic basic mechanisms. These results can be seen as a hopeful step toward the future of genomic medicine, where efficient and correct decisions can be made with the support of AI. [ABSTRACT FROM AUTHOR]

The emerging field of genomic medicine offers an opportunity for biology and anatomy teachers to bring the topics of DNA, genetics, molecular processes, and evolution together into one experience. Through the genomic medicine paradigm, students see the unbroken connection between small biological topics such as mutations and their potential connection to disease phenotypes. In this paper, we present as a main example cystic fibrosis, which is an often-studied genetic disease in general biology class, for examination through the genomic medicine lens. Concepts such as genes, the plasma membrane, variation, mutations, the nucleus, and chromosomes can be used in a narrative and visual approach to genetics through the genomic medicine standpoint to engage and connect students with next-generation genomics and with the fundamental unit of life—the cell. It is through the genomic medicine lens that the cell's context and relationship to the evolving world takes place. [ABSTRACT FROM AUTHOR]

Background: Genetic variants that severely alter protein products (e.g. nonsense, frameshift) are often associated with disease. For some genes, these predicted loss-of-function variants (pLoFs) are observed throughout the gene, whilst in others, they occur only at specific locations. We hypothesised that, for genes linked with monogenic diseases that display incomplete penetrance, pLoF variants present in apparently unaffected individuals may be limited to regions where pLoFs are tolerated. To test this, we investigated whether pLoF location could explain instances of incomplete penetrance of variants expected to be pathogenic for Mendelian conditions. Methods: We used exome sequence data in 454,773 individuals in the UK Biobank (UKB) to investigate the locations of pLoFs in a population cohort. We counted numbers of unique pLoF, missense, and synonymous variants in UKB in each quintile of the coding sequence (CDS) of all protein-coding genes and clustered the variants using Gaussian mixture models. We limited the analyses to genes with ≥ 5 variants of each type (16,473 genes). We compared the locations of pLoFs in UKB with all theoretically possible pLoFs in a transcript, and pathogenic pLoFs from ClinVar, and performed simulations to estimate the false-positive rate of non-uniformly distributed variants. Results: For most genes, all variant classes fell into clusters representing broadly uniform variant distributions, but genes in which haploinsufficiency causes developmental disorders were less likely to have uniform pLoF distribution than other genes (P < 2.2 × 10−6). We identified a number of genes, including ARID1B and GATA6, where pLoF variants in the first quarter of the CDS were rescued by the presence of an alternative translation start site and should not be reported as pathogenic. For other genes, such as ODC1, pLoFs were located approximately uniformly across the gene, but pathogenic pLoFs were clustered only at the end, consistent with a gain-of-function disease mechanism. Conclusions: Our results suggest the potential benefits of localised constraint metrics and that the location of pLoF variants should be considered when interpreting variants. [ABSTRACT FROM AUTHOR]

Engagement with genomic medicine and research has increased globally during the past few decades, including rapid developments in Sri Lanka. Genomic research is carried out in Sri Lanka on a variety of scales and with different aims and perspectives. However, there are concerns about participants' understanding of genomic research, including the validity of informed consent. This article reports a qualitative study aiming to explore the understanding, knowledge, and attitudes of the Sri Lankan public towards genomic medicine and to inform the development of an effective and appropriate process for informed consent in that setting. Purposive sampling was employed. Participants were recruited from a sub-group of the public in Colombo, Sri Lanka who had either consented or refused to donate genetic material for a biobank. Data were collected using face-to-face semi-structured interviews. Interview data were transcribed verbatim and translated into English. Conventional content analysis was used. The analysis developed three key themes: a) 'Scientific literacy' describes an apparent lack of scientific knowledge that seems to affect a participant's ability to understand the research, b) 'Motivation' describes narratives about why participants chose (not) to take part in the research, despite not understanding it, and c) 'Trust' describes how trust served to mitigate the apparent ethical deficit created by not being fully informed. In this article, we argue that informed trust is likely an acceptable basis for consent, particularly in settings where scientific literacy might be low. However, researchers must work to be worthy of that trust and ensure that misconceptions are actively addressed. [ABSTRACT FROM AUTHOR]

BACKGROUND: The utility of long-read genome sequencing platforms has been shown in many fields including whole genome assembly, metagenomics, and amplicon sequencing. Less clear is the applicability of long reads to reference-guided human genomics, which is the foundation of genomic medicine. Here, we benchmark available platform-agnostic alignment tools on datasets from nanopore and single-molecule real-time platforms to understand their suitability in producing a genome representation. RESULTS: For this study, we leveraged publicly-available data from sample NA12878 generated on Oxford Nanopore and sample NA24385 on Pacific Biosciences platforms. We employed state of the art sequence alignment tools including GraphMap2, long-read aligner (LRA), Minimap2, CoNvex Gap-cost alignMents for Long Reads (NGMLR), and Winnowmap2. Minimap2 and Winnowmap2 were computationally lightweight enough for use at scale, while GraphMap2 was not. NGMLR took a long time and required many resources, but produced alignments each time. LRA was fast, but only worked on Pacific Biosciences data. Each tool widely disagreed on which reads to leave unaligned, affecting the end genome coverage and the number of discoverable breakpoints. No alignment tool independently resolved all large structural variants (1,001-100,000 base pairs) present in the Database of Genome Variants (DGV) for sample NA12878 or the truthset for NA24385. CONCLUSIONS: These results suggest a combined approach is needed for LRS alignments for human genomics. Specifically, leveraging alignments from three tools will be more effective in generating a complete picture of genomic variability. It should be best practice to use an analysis pipeline that generates alignments with both Minimap2 and Winnowmap2 as they are lightweight and yield different views of the genome. Depending on the question at hand, the data available, and the time constraints, NGMLR and LRA are good options for a third tool. If computational resources and time are not a factor for a given case or experiment, NGMLR will provide another view, and another chance to resolve a case. LRA, while fast, did not work on the nanopore data for our cluster, but PacBio results were promising in that those computations completed faster than Minimap2. Due to its significant burden on computational resources and slow run time, Graphmap2 is not an ideal tool for exploration of a whole human genome generated on a long-read sequencing platform.

The genetic basis of many epilepsies is increasingly understood, giving rise to the possibility of precision treatments tailored to specific genetic etiologies. Despite this, current medical therapy for most epilepsies remains imprecise, aimed primarily at empirical seizure reduction rather than targeting specific disease processes. Intellectual and technological leaps in diagnosis over the past 10 years have not yet translated to routine changes in clinical practice. However, the epilepsy community is poised to make impressive gains in precision therapy, with continued innovation in gene discovery, diagnostic ability, and bioinformatics; increased access to genetic testing and counseling; fuller understanding of natural histories; agility and rigor in preclinical research, including strategic use of emerging model systems; and engagement of an evolving group of stakeholders (including patient advocates, governmental resources, and clinicians and scientists in academia and industry). In each of these areas, we highlight notable examples of recent progress, new or persistent challenges, and future directions. The future of precision medicine for genetic epilepsy looks bright if key opportunities on the horizon can be pursued with strategic and coordinated effort.

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined. [ABSTRACT FROM AUTHOR]

Genomics has been forecasted to revolutionise human health by improving medical treatment through a better understanding of the molecular mechanisms of human diseases. Despite great successes of the last decade's genome-wide association studies (GWAS), the results have been translated to genomic medicine to a limited extent. One route to get closer to improved medical treatment could be by understanding the genetics of medication use. Current medication profiles from 335,744 individuals from the UK Biobank were obtained, and a GWAS was conducted to identify common genetic variants associated with current medication use. In total, 59 independent loci were identified for medication use, and approximately 18% of the total variation was attributable to common genetic variation. The largest fraction of genetic variance for current medication use was captured by variants with low-to-medium minor allele frequency, with coding, conserved genomic regions and transcription start sites being enriched for associated variants. The average correlation (R) between medication use and the polygenic score was 0.14. The results further demonstrated that individuals with higher polygenic burden for medication use were, on average, sicker and had a higher risk for adverse drug reactions. These results provide an insight into the genetic contribution of medication use and pave the way for developments of novel multiple trait polygenic scores, which include the genetically informed medication use. [ABSTRACT FROM AUTHOR]

The technological advances of sequencing methods during the past 20 years have fuelled the generation of large amounts of sequencing data that comprise common variations, as well as millions of rare and personal variants that would not be identified by conventional genotyping. While comprehensive sequencing is technically feasible, its clinical utility for guiding personalized treatment decisions remains controversial. We discuss the opportunities and challenges of comprehensive sequencing compared to targeted genotyping for pharmacogenomic applications. Current pharmacogenomic sequencing panels are heterogeneous and clinical actionability of the included genes is not a major focus. We provide a current overview and critical discussion of how current studies utilize sequencing data either retrospectively from biobanks, databases or repurposed diagnostic sequencing, or prospectively using pharmacogenomic sequencing. While sequencing-based pharmacogenomics has provided important insights into genetic variations underlying the safety and efficacy of a multitude pharmacological treatments, important hurdles for the clinical implementation of pharmacogenomic sequencing remain. We identify gaps in the interpretation of pharmacogenetic variants, technical challenges pertaining to complex loci and variant phasing, as well as unclear cost-effectiveness and incomplete reimbursement. It is critical to address these challenges in order to realize the promising prospects of pharmacogenomic sequencing. [ABSTRACT FROM AUTHOR]

Introduction: Advancements in precision medicine and genomics have led to prospects in a wide range of clinical fields, including oncology. In particular, developments in next-generation sequencing multigene panel tests have led to the possibility of tailoring treatment to the specific genomic markers of a patient's cancer. However, findings from current literature suggest that the path to implementation and uptake of genomic medicine is not without uncertainties and challenges. Methods: To better understand the current challenges to the implementation of genomic medicine services, we investigated the current state of patient access to genomic medicine in Japan and Switzerland. In this investigation, we focused on equal access, patient autonomy, and healthcare affordability. Results: Results have shown that although multigene panel testing is in principle covered by health insurance in both countries, barriers exist in terms of where the tests are available, comprehensive information for patients, and the affordability of not only the test itself but the overall process from diagnosis to treatment. Discussion: These results suggest a need to continue examining a more diverse range of clinical landscapes for genomic medicine to reveal more nuanced understandings of barriers to implementation and thus better identify best practices for overcoming them. [ABSTRACT FROM AUTHOR]

Background: Precision medicine has transformed cancer treatment by focusing on personalized approaches based on genomic abnormalities. However, comprehensive genomic profiling (CGP) and access to targeted therapies are limited in Japan. This study investigates the BELIEVE trial, which aims to improve drug accessibility for patients with actionable genetic abnormalities through off-label drug administration. Methods: The BELIEVE trial is a platform trial with a single master protocol, conducted under the Clinical Trials Act and the patient-proposed health services (PPHS) scheme. Eligible patients with solid tumors exhibiting actionable alterations were enrolled, and CGP tests covered by national health insurance were employed. Treatment selection, study drugs from collaborating pharmaceutical companies, and treatment schedules adhered to predefined protocols. Primary and secondary endpoints were evaluated, and statistical analysis was conducted based on patient response rates. Results: The BELIEVE trial offered treatment opportunities for patients with relapse/refractory disease who lacked standard therapies or clinical trial options. This study addresses unmet medical needs and contributes to the establishment of precision medicine systems. Similar trials like NCI-MATCH and TAPUR are being conducted globally. The BELIEVE trial provides a platform for off-label drug administration, collects essential clinical data, and contributes to drug approval applications. Conclusion: The BELIEVE trial provides hope for patients with actionable genetic abnormalities by facilitating access to targeted therapies through off-label drug administration. It establishes a regulatory framework and promotes collaboration between industry and academia by expanding organ-specific and cross-organ biomarker-based treatments. [ABSTRACT FROM AUTHOR]

Personalized medicine, a cornerstone of modern healthcare, aims to tailor medical treatments to individual characteristics, including genetics, lifestyle, and environment. This article explores emerging trends in personalized medicine, highlighting advancements in genomic medicine, precision diagnostics, targeted therapies, immunotherapy, digital health, wearable technologies, and integrative approaches. Key topics include the use of genomic sequencing technologies for personalized treatment plans, innovative diagnostic techniques enabling early detection and accurate disease monitoring, and the development of targeted therapies and companion diagnostics. Additionally, the role of immunotherapy in revolutionizing cancer treatment and the integration of digital health tools and wearable devices in personalized care are discussed. By considering holistic health and lifestyle factors, personalized medicine offers promising avenues to improve patient outcomes and reduce healthcare costs. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a chronic metabolic disease characterized by hyperglycemia resulting from inadequate insulin signaling. Current management relies on biomarkers such as hemoglobin A1c (HbA1c) to guide therapy, but emerging tools offer opportunities to transform care through more personalized approaches. Molecular biomarkers, including microRNAs, metabolites, and proteins, may enable better prediction of disease course and risk of complications in individuals. Genomic medicine leverages knowledge of genetic architecture to guide tailored prevention and treatment based on an individual’s genomic profile. Stem cell research differentiates functional insulin-secreting cells for transplantation into patients as an alternative to exogenous insulin. Gene silencing techniques such as RNA interference can restore defective insulin production and secretion pathways by inhibiting dysregulated gene expression. Artificial intelligence applications automate glucose monitoring, insulin delivery, diagnostic screening for complications, and digital health coaching. Despite barriers to translation, these technologies have disruptive potential for predictive, preventive, precise, and participatory care paradigms in diabetes management. Continued research on molecular biomarkers, pharmacogenomics, stem cell therapies, gene editing, and artificial intelligence (AI) aims to improve patient outcomes through more personalized approaches tailored to the specific biological vulnerabilities underlying each individual’s diabetes.

Abstract Background The relationship between molecular characteristics and the prognosis of colorectal cancer (CRC) patients has not been fully understood. This study explored the impact of targeted therapy on the prognosis of CRC patients with different TP53 mutations, in the context of comprehensive treatment. Methods This study included patients with stage III/IV primary CRC from the electronic medical record system. TP53 mutations were detected via next‐generation sequencing (NGS) using formalin‐fixed paraffin‐embedded (FFPE) tissues. Applying two methods, we classified TP53 mutations as gain of function (GOF)/non‐GOF mutations or known/likely loss of function (LOF) mutations. Kaplan–Meier plot and parametric survival analysis were performed to evaluate the prognosis of CRC patients and identify potential predictors. Results There were 286 patients included, of which 166 (58.04%) patients received targeted therapy and 120 (41.96%) did not. There were 286 patients in the TP53 GOF classification set and 247 in the TP53 LOF classification set. Parametric survival analysis, adjusted for sex, onset, KRAS mutation, sidedness, stage, and surgery, showed that receiving targeted therapy predicted better overall survival (OS) among patients who harbored TP53 GOF mutations (HR 0.40, 95% confidence interval (CI) [0.21, 0.76], p = 0.005) or known LOF mutations (HR 0.21, 95% CI [0.07, 0.60], p = 0.002). However, there was no significant impact of receiving targeted therapy on OS among patients harboring TP53 non‐GOF mutations (HR 1.68, 95% CI [0.50, 5.63], p = 0.403) or likely LOF mutations (HR 0.90, 95% CI [0.34, 2.39], p = 0.837). Conclusions Receiving targeted therapy had a heterogeneous impact on the prognosis of CRC patients harboring different TP53 mutations. These results provide promising value for future personalized treatment and precision medicine.

Abstract Background Classification of rare missense variants remains an ongoing challenge in genomic medicine. Evidence of pathogenicity is often sparse, and decisions about how to weigh different evidence classes may be subjective. We used a Bayesian variant classification framework to investigate the performance of variant co-localisation, missense constraint, and aggregating data across paralogous protein domains (“meta-domains”). Methods We constructed a database of all possible coding single nucleotide variants in the human genome and used PFam predictions to annotate structurally-equivalent positions across protein domains. We counted the number of pathogenic and benign missense variants at these equivalent positions in the ClinVar database, calculated a regional constraint score for each meta-domain, and assessed this approach versus existing missense constraint metrics for classifying variant pathogenicity and benignity. Results Alternative pathogenic missense variants at the same amino acid position in the same protein provide strong evidence of pathogenicity (positive likelihood ratio, LR+ = 85). Additionally, clinically annotated pathogenic or benign missense variants at equivalent positions in different proteins can provide moderate evidence of pathogenicity (LR+ = 7) or benignity (LR+ = 5), respectively. Applying these approaches sequentially (through PM5) increases sensitivity for classifying pathogenic missense variants from 27 to 41%. Missense constraint can also provide strong evidence of pathogenicity for some variants, but its absence provides no evidence of benignity. Conclusions We propose using structurally equivalent positions across related protein domains from different genes to augment evidence for variant co-localisation when classifying novel missense variants. Additionally, we advocate adopting a numerical evidence-based approach to integrating diverse data in variant interpretation.

PurposeThere is a critical need for genomic medicine research that reflects and benefits socioeconomically and ancestrally diverse populations. However, disparities in research populations persist, highlighting that traditional study designs and materials may be insufficient or inaccessible to all groups. New approaches can be gained through collaborations with patient/community stakeholders. Although some benefits of stakeholder engagement are recognized, routine incorporation into the design and implementation of genomics research has yet to be realized.MethodsThe National Institutes of Health-funded Clinical Sequencing Evidence-Generating Research (CSER) consortium required stakeholder engagement as a dedicated project component. Each CSER project planned and carried out stakeholder engagement activities with differing goals and expected outcomes. Examples were curated from each project to highlight engagement strategies and outcomes throughout the research lifecycle from development through dissemination.ResultsProjects tailored strategies to individual study needs, logistical constraints, and other challenges. Lessons learned include starting early with engagement efforts across project stakeholder groups and planned flexibility to enable adaptations throughout the project lifecycle.ConclusionEach CSER project used more than 1 approach to engage with relevant stakeholders, resulting in numerous adaptations and tremendous value added throughout the full research lifecycle. Incorporation of community stakeholder insight improves the outcomes and relevance of genomic medicine research.

Background: The relationship between molecular characteristics and the prognosis of colorectal cancer (CRC) patients has not been fully understood. This study explored the impact of targeted therapy on the prognosis of CRC patients with different TP53 mutations, in the context of comprehensive treatment. Methods: This study included patients with stage III/IV primary CRC from the electronic medical record system. TP53 mutations were detected via next‐generation sequencing (NGS) using formalin‐fixed paraffin‐embedded (FFPE) tissues. Applying two methods, we classified TP53 mutations as gain of function (GOF)/non‐GOF mutations or known/likely loss of function (LOF) mutations. Kaplan–Meier plot and parametric survival analysis were performed to evaluate the prognosis of CRC patients and identify potential predictors. Results: There were 286 patients included, of which 166 (58.04%) patients received targeted therapy and 120 (41.96%) did not. There were 286 patients in the TP53 GOF classification set and 247 in the TP53 LOF classification set. Parametric survival analysis, adjusted for sex, onset, KRAS mutation, sidedness, stage, and surgery, showed that receiving targeted therapy predicted better overall survival (OS) among patients who harbored TP53 GOF mutations (HR 0.40, 95% confidence interval (CI) [0.21, 0.76], p = 0.005) or known LOF mutations (HR 0.21, 95% CI [0.07, 0.60], p = 0.002). However, there was no significant impact of receiving targeted therapy on OS among patients harboring TP53 non‐GOF mutations (HR 1.68, 95% CI [0.50, 5.63], p = 0.403) or likely LOF mutations (HR 0.90, 95% CI [0.34, 2.39], p = 0.837). Conclusions: Receiving targeted therapy had a heterogeneous impact on the prognosis of CRC patients harboring different TP53 mutations. These results provide promising value for future personalized treatment and precision medicine. [ABSTRACT FROM AUTHOR]

Background: Classification of rare missense variants remains an ongoing challenge in genomic medicine. Evidence of pathogenicity is often sparse, and decisions about how to weigh different evidence classes may be subjective. We used a Bayesian variant classification framework to investigate the performance of variant co-localisation, missense constraint, and aggregating data across paralogous protein domains ("meta-domains"). Methods: We constructed a database of all possible coding single nucleotide variants in the human genome and used PFam predictions to annotate structurally-equivalent positions across protein domains. We counted the number of pathogenic and benign missense variants at these equivalent positions in the ClinVar database, calculated a regional constraint score for each meta-domain, and assessed this approach versus existing missense constraint metrics for classifying variant pathogenicity and benignity. Results: Alternative pathogenic missense variants at the same amino acid position in the same protein provide strong evidence of pathogenicity (positive likelihood ratio, LR+ = 85). Additionally, clinically annotated pathogenic or benign missense variants at equivalent positions in different proteins can provide moderate evidence of pathogenicity (LR+ = 7) or benignity (LR+ = 5), respectively. Applying these approaches sequentially (through PM5) increases sensitivity for classifying pathogenic missense variants from 27 to 41%. Missense constraint can also provide strong evidence of pathogenicity for some variants, but its absence provides no evidence of benignity. Conclusions: We propose using structurally equivalent positions across related protein domains from different genes to augment evidence for variant co-localisation when classifying novel missense variants. Additionally, we advocate adopting a numerical evidence-based approach to integrating diverse data in variant interpretation. [ABSTRACT FROM AUTHOR]

Digital solutions are needed to support rapid increases in the application of genetic/genomic tests (GTs) in diverse clinical settings and patient populations. We developed GUÍA, a bilingual digital application that facilitates disclosure of GT results. The NYCKidSeq randomized controlled trial enrolled diverse children with neurologic, cardiac, and immunologic conditions who underwent GTs. The trial evaluated GUÍA's impact on understanding the GT results by randomizing families to results disclosure genetic counseling with GUÍA (intervention) or standard of care (SOC). Parents/legal guardians (participants) completed surveys at baseline, post-results disclosure, and 6 months later. Survey measures assessed the primary study outcomes of participants' perceived understanding of and confidence in explaining their child's GT results and the secondary outcome of objective understanding. The analysis included 551 diverse participants, 270 in the GUÍA arm and 281 in SOC. Participants in the GUÍA arm had significantly higher perceived understanding post-results (OR = 2.8, CI[1.004, 7.617], p = 0.049) and maintained higher objective understanding over time (OR = 1.1, CI[1.004, 1.127], p = 0.038) compared to SOC. There was no impact on perceived confidence. Hispanic/Latino(a) individuals in the GUÍA arm maintained higher perceived understanding (OR = 3.9, CI[1.603, 9.254], p = 0.003), confidence (OR = 2.7, CI[1.021, 7.277], p = 0.046), and objective understanding (OR = 1.1, CI[1.009, 1.212], p = 0.032) compared to SOC. This trial demonstrates that GUÍA positively impacts understanding of GT results in diverse parents of children with suspected genetic conditions and builds a case for utilizing GUÍA to deliver complex results. Continued development and evaluation of digital applications in diverse populations are critical for equitably scaling GT offerings in specialty clinics. [Display omitted] GUÍA, a digital application designed to enhance the delivery of genetic/genomic test results, was evaluated through the NYCKidSeq randomized controlled trial. Findings from the trial demonstrate that GUÍA positively impacts perceived and objective understanding of genetic results in diverse, multilingual parents/guardians of children with suspected genetic conditions who underwent testing. [ABSTRACT FROM AUTHOR]

Aim: Technological advances and increased access have led to genomics expanding beyond the genetics clinic. Consequently, nephrologists can now order genomic testing for their patients. Consistent decision‐making around patient and test selection is required to ensure equitable access while maximising the utility of genomic testing. However, there are currently no frameworks to guide decision‐making for testing in this context. We aimed to develop an ethical decision‐making framework for genomic testing in paediatric nephrology. Methods: A three‐stage approach was used: (i) review of the literature on decision‐making for genomic testing in nephrology and other disciplines; (ii) ethnographic observation of approaches to genomic testing in the general nephrology and renal genetics clinics at an Australian paediatric hospital; (iii) review and revision of the framework with key stakeholders, including clinical geneticists, genetic counsellors, paediatric nephrologists and families from the renal genetics service. The initial framework was modified until consensus from key stakeholders was reached. Results: A decision‐making framework was created with questions designed to explore the impact of genomic testing on patient management, clinical validity, patient characteristics, alternatives to genomic testing, genetic counselling, resource availability, implications for family members, psychosocial considerations, patient autonomy, research, support services and insurance. Case studies were developed to demonstrate the framework's application. Conclusions: This framework was designed to guide decisions around patient selection for genomic testing in nephrology in the Australian health‐care setting, with potential utility in other institutions and medical disciplines. It may help facilitate consistent approaches to genomic testing, to maximise equity and utility. [ABSTRACT FROM AUTHOR]

Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses 'solved' or 'possibly solved' ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% 'solved' and ∼13% 'possibly solved' outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally. [ABSTRACT FROM AUTHOR]

With increased technological sophistication and rapidly reducing costs, currently, a huge amount of personal and population-level human genomic data and information is generated globally. There is an urgent need for an adequately curated and annotated human genome variant database for successful and large-scale application and translation in biomedical research, medical (healthcare) applications, socio-economic benefits and many other applications. The bulk of the available genomic data is generated from peoples of European descent. The genome data, particularly the human genome variant data is skewed with minimal content from other populations, particularly the minority or diverse populations. It has further contributed to global health inequality, which is visible in inefficiency, lack of effectiveness and disparity in clinical diagnosis, and precision-personalized medicine and preventive healthcare. Inevitably, this gap is widened with ensuing socio-economic implications. This problem is now faced by medical practitioners and healthcare providers in India, South Asia and other low and middle-income countries (LMICs). The current review provides views and critical appraisal of the current status of genomic research, clinical utility and genome variant databases in India and South Asia. A few observations and recommendations are made to ensure harmonization that requires further structured audit and appraisal by the indigenous populations’ consortium. Emphasis is made on the urgent need for statutory regulation of genome data generation, storage, and retrieval systems in research and diagnostic genomic laboratories. [ABSTRACT FROM AUTHOR]

Great advances have been made in the field of heritable skin disorders using next-generation sequencing (NGS) technologies (ie, whole-genome sequencing, whole-exome sequencing, whole-transcriptome sequencing, and disease-targeted multigene panels). When NGS first became available, the cost and lack of access to these technologies were limiting factors; however, with decreasing sequencing costs and the expanding knowledge base of genetic skin diseases, fundamental awareness of NGS has become prudent. The heritable ichthyoses comprise a genotypically and phenotypically heterogeneous group of monogenic keratinization disorders characterized by persistent scaling, with at least 55 distinct genes currently implicated in causing nonsyndromic and syndromic forms of the disease. By providing a simplified overview of available NGS techniques and applying them in the context of ichthyosis, one of the most common genodermatoses, we hope to encourage dermatologists to offer, when appropriate, genetic testing earlier in patients with unsolved presentations. With the aid of NGS, dermatologists can provide diagnostic certainty in cases of suspected genodermatoses and offer potentially life-changing genome-guided and targeted therapies as they become available. [ABSTRACT FROM AUTHOR]

Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/. [ABSTRACT FROM AUTHOR]

Background: Germline genome sequencing in childhood cancer precision medicine trials may reveal pathogenic or likely pathogenic variants in cancer predisposition genes in more than 10% of children. These findings can have implications for diagnosis, treatment, and the child's and family's future cancer risk. Understanding parents' perspectives of germline genome sequencing is critical to successful clinical implementation. Methods: A total of 182 parents of 144 children (<18 years of age) with poor‐prognosis cancers enrolled in the Precision Medicine for Children with Cancer trial completed a questionnaire at enrollment and after the return of their child's results, including clinically relevant germline findings (received by 13% of parents). Parents' expectations of germline genome sequencing, return of results preferences, and recall of results received were assessed. Forty‐five parents (of 43 children) were interviewed in depth. Results: At trial enrollment, most parents (63%) believed it was at least "somewhat likely" that their child would receive a clinically relevant germline finding. Almost all expressed a preference to receive a broad range of germline genomic findings, including variants of uncertain significance (88%). Some (29%) inaccurately recalled receiving a clinically relevant germline finding. Qualitatively, parents expressed confusion and uncertainty after the return of their child's genome sequencing results by their child's clinician. Conclusions: Many parents of children with poor‐prognosis childhood cancer enrolled in a precision medicine trial expect their child may have an underlying cancer predisposition syndrome. They wish to receive a wide scope of information from germline genome sequencing but may feel confused by the reporting of trial results. Parents of children with poor‐prognosis cancer enrolled in a precision medicine trial may overestimate the likelihood of their child receiving a clinically relevant germline finding and may feel confused by the reporting of trial results. We discuss the implications of these data for informed‐consent and return of results practices in precision medicine trials. [ABSTRACT FROM AUTHOR]

Art is increasingly being used by researchers as a medium to engage the public, yet evaluating and capturing impact remains challenging. We report an evaluation of a four-year public engagement project, I:DNA, designed to engage the public with research that explores the views and experiences of people with genetic conditions. An immersive art installation was exhibited at six scientific/cultural venues (2019-22), alongside several supplementary engagement activities, including talks, a game, 'invisible theatre', poetry workshops/performance and children's art workshops. I:DNA reached over 26,500 people (online and in-person), and 268 people left some form of evaluation via postcards, online forms or emails. Through thematic analysis of this evaluation data, as well as the artistic outputs of supplementary activities, evidence of impact was identified in three key areas: changing views, inspiring behaviour change and supporting capacity for future public engagement. Implementation and evaluation of I:DNA highlights the challenges of evaluating the impact of complex arts-based public engagement projects, and the urgent need for methodological development to evaluate the processes by which impact occurs (not just the consequences of that impact), and the significance of venue and context, as well as the short-, medium- and long-term impacts of arts-based public engagement for both public and stakeholder groups. [ABSTRACT FROM AUTHOR]

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease—both with regards to symptoms and underlying causal mechanisms—and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic‐based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases—including psychiatric disorders and allergies—available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high‐throughput molecular technologies, together with large collections of health data and novel data‐driven approaches, offers promise toward improved individual health through precision medicine. [ABSTRACT FROM AUTHOR]

In the last decade, the utility of whole-exome sequencing in uncovering genetic aetiologies of a variety of liver diseases has been demonstrated. These new diagnoses have guided the management, treatment, and prognostication of previously undiagnosed patients, largely thanks to improved insight into the underlying pathogenesis of their conditions. Despite its clear benefits, the uptake of genetic testing by hepatologists has been limited, in part due to limited prior genetic training and/or opportunities for continuing education. Herein, we show that Hepatology Genome Rounds, an interdisciplinary forum highlighting hepatology cases of clinical interest and educational value, are an important venue for integrating genotypic and phenotypic information to enable accurate diagnosis and appropriate management, dissemination of genomic knowledge within the field of hepatology, and ongoing education to providers and trainees in genomic medicine. We describe our single-centre experience and discuss practical considerations for clinicians interested in launching such a series. We foresee that this format will be adopted at other institutions and by additional specialties, with the aim of further incorporating genomic information into clinical medicine. [ABSTRACT FROM AUTHOR]

Pulmonary arterial hypertension (PAH) is an infrequent disorder characterized by high blood pressure in the pulmonary arteries. It may lead to premature death or the requirement for lung and/or heart transplantation. Genetics plays an important and increasing role in the diagnosis of PAH. Here, we report seven additional patients with variants in SOX17 and a review of sixty previously described patients in the literature. Patients described in this study suffered with additional conditions including large septal defects, as described by other groups. Collectively, sixty-seven PAH patients have been reported so far with variants in SOX17, including missense and loss-of-function (LoF) variants. The majority of the loss-of-function variants found in SOX17 were detected in the last exon of the gene. Meanwhile, most missense variants were located within exon one, suggesting a probable tolerated change at the amino terminal part of the protein. In addition, we reported two idiopathic PAH patients presenting with the same variant previously detected in five patients by other studies, suggesting a possible hot spot. Research conducted on PAH associated with congenital heart disease (CHD) indicated that variants in SOX17 might be particularly prevalent in this subgroup, as two out of our seven additional patients presented with CHD. Further research is still necessary to clarify the precise association between the biological pathway of SOX17 and the development of PAH. [ABSTRACT FROM AUTHOR]

At the beginning of the 21st century, it became possible to analyze life science fields such as molecular genetics and molecular biochemistry at the molecular level. In addition, molecular genetics made progress in elucidating the human gene sequence. Therefore, the purpose of this mini-review was to describe and discuss the role of pharmacists in the medical field regarding the impact of genetic testing results in the medical, pharmaceutical, dental and life science field. Therefore, the importance and elucidation of gene sequences in this field is desired. This paper describes the following. 1) Development of genomic medicalization 2) Classification of gene-related tests, 3) Clinical application of genetic testing and genetic testing, 4) Role of pharmacists in genetic testing, 5) The Importance of Genetic Testing in Team Medicine and the Role of Pharmacists. Thus, in conclusion, the present manuscript suggested that genetic testing in genomic medicine plays an important role in patient drug treatment and pharmacist compliance treatment. [ABSTRACT FROM AUTHOR]

Genomic sequencing has been increasingly utilized for prenatal diagnosis in recent years and this trend is likely to continue. However, decision-making for parents in the prenatal period is particularly fraught, and prenatal sequencing would significantly expand the complexity of managing health risk information, reproductive options, and healthcare access. This qualitative study investigates decision-making processes amongst parents who enrolled or declined to enroll in the prenatal arm of the California-based Program in Prenatal and Pediatric Genome Sequencing (P3EGS), a study in the Clinical Sequencing Evidence-Generating Research (CSER) consortium that offered whole exome sequencing for fetal anomalies with a focus on underrepresented groups in genomic research. Drawing on the views of 18 prenatal families who agreed to be interviewed after enrolling (n = 15) or declining to enroll (n = 3) in P3EGS, we observed that the timing of sequencing, coupled with unique considerations around experiences of time during pregnancy and prenatal testing, intersect with structural supports beyond the clinic to produce preferences for and against prenatal sequencing and to contain the threat of unwelcome, uncertain knowledge. Particularly for those without structural supports, finding out consequential information may be more palatable after the birth, when the first stage of the uncertain future has been revealed. Future research should examine the role of temporality in decision-making around prenatal genomic sequencing across diverse population cohorts, in order to observe more precisely the role that structural barriers play in patient preferences.

Abstract Introduction Technological improvements alone have not led to the integration of genomic medicine across a broad range of diseases and populations. For genomic medicine to be successfully implemented across specialties and conditions, the challenges patients and caregivers experience need to be identified using a multi‐faceted understanding of the context in which these obstacles occur and how they are experienced. Individuals affected by rare conditions, like Ehlers–Danlos syndromes (EDS) and hypermobility spectrum disorders (HSD), express numerous challenges with accessing genomic medicine. Many patients living with rare diseases seek information and find comfort in online health communities. Methods Social media conversations facilitated through online health communities are windows into patients' and caregivers' authentic experiences. To date, no other study has examined genomic medicine barriers by analysing the content of social media posts, yet the novel methodological approach of social media listening permits the analysis of virtual, organic conversations about lived experiences. Results/Conclusions Using a modified social–ecological model, this study found that social–structural and interpersonal barriers most frequently impede access to genomic medicine for patients and caregivers living with EDS and HSD. Patient or Public Contribution Data were retrieved through social media conversations facilitated through publicly accessible health communities through Inspire, an online health community. Social media listening permits the analysis of virtual, organic conversations about lived experiences.

Many of the fundamental concepts of signal transduction and kinase activity are attributed to the discovery and crystallization of cAMP-dependent protein kinase, or protein kinase A. PKA is one of the best-studied kinases in human biology, with emphasis in biochemistry and biophysics, all the way to metabolism, hormone action, and gene expression regulation. It is surprising, however, that our understanding of PKA's role in disease is largely underappreciated. Although genetic mutations in the PKA holoenzyme are known to cause diseases such as Carney complex, Cushing syndrome, and acrodysostosis, the story largely stops there. With the recent explosion of genomic medicine, we can finally appreciate the broader role of the Gαs-PKA pathway in disease, with contributions from aberrant functioning G proteins and G protein-coupled receptors, as well as multiple alterations in other pathway components and negative regulators. Together, these represent a broad family of diseases we term the Gαs-PKA pathway signalopathies. The Gαs-PKA pathway signalopathies encompass diseases caused by germline, postzygotic, and somatic mutations in the Gαs-PKA pathway, with largely endocrine and neoplastic phenotypes. Here, we present a signaling-centric review of Gαs-PKA-driven pathophysiology and integrate computational and structural analysis to identify mutational themes commonly exploited by the Gαs-PKA pathway signalopathies. Major mutational themes include hotspot activating mutations in Gαs, encoded by GNAS, and mutations that destabilize the PKA holoenzyme. With this review, we hope to incite further study and ultimately the development of new therapeutic strategies in the treatment of a wide range of human diseases. SIGNIFICANCE STATEMENT: Little recognition is given to the causative role of Gαs-PKA pathway dysregulation in disease, with effects ranging from infectious disease, endocrine syndromes, and many cancers, yet these disparate diseases can all be understood by common genetic themes and biochemical signaling connections. By highlighting these common pathogenic mechanisms and bridging multiple disciplines, important progress can be made toward therapeutic advances in treating Gαs-PKA pathway-driven disease.

Whereas the rising prevalence of nonalcoholic fatty liver disease (NAFLD) is closely related with the global obesity epidemic, up to 10–20% of individuals with NAFLD are lean as defined by a body mass index of < 25 kg/m2, or < 23 kg/m2 in Asians. This entity designated as “lean NAFLD” is estimated to affect 8 to 10 million individuals in the United States alone. Here, we review the emerging data on the epidemiology, natural history and prognosis of lean NAFLD and put forward a diagnostic approach that combines detailed clinical phenotyping with genomic analysis. We propose two subtypes of lean NAFLD referred to as type 1: individuals with visceral adiposity and insulin resistance but normal BMI; and type 2: lean individuals with hepatic steatosis secondary to a known or unknown monogenic disease. We envision that incorporation of genomic analysis in the diagnostic algorithm of lean patients with NAFLD will elucidate the contribution of common genetic variants through the calculation of NAFLD polygenic risk score and also characterize the diverse array of rare monogenic diseases that can lead to triglyceride accumulation in the cytoplasm of hepatocytes. Collectively, the integration of a molecular diagnosis in the clinical evaluation of patients with lean NAFLD will provide an accurate diagnosis, with possible targeted therapies and may uncover novel molecular mechanisms with potential broader therapeutic implications.

When analyzing cancer sample genomes in clinical practice, many structural variants (SVs), other than single nucleotide variants (SNVs), have been identified. To identify driver variants, the leading candidates must be narrowed down. When fusion genes are involved, selection is particularly difficult, and highly accurate predictions from AI is important. Furthermore, we also wanted to determine how the prediction can make more reliable diagnoses. Here, we developed an explainable AI (XAI) suitable for SVs with gene fusions, based on the XAI technology we previously developed for the prediction of SNV pathogenicity. To cope with gene fusion variants, we added new data to the previous knowledge graph for SVs and we improved the algorithm. Its prediction accuracy was as high as that of existing tools. Moreover, our XAI could explain the reasons for these predictions. We used some variant examples to demonstrate that the reasons are plausible in terms of pathogenic basic mechanisms. These results can be seen as a hopeful step toward the future of genomic medicine, where efficient and correct decisions can be made with the support of AI.

Background The utility of long-read genome sequencing platforms has been shown in many fields including whole genome assembly, metagenomics, and amplicon sequencing. Less clear is the applicability of long reads to reference-guided human genomics, which is the foundation of genomic medicine. Here, we benchmark available platform-agnostic alignment tools on datasets from nanopore and single-molecule real-time platforms to understand their suitability in producing a genome representation. Results For this study, we leveraged publicly-available data from sample NA12878 generated on Oxford Nanopore and sample NA24385 on Pacific Biosciences platforms. We employed state of the art sequence alignment tools including GraphMap2, long-read aligner (LRA), Minimap2, CoNvex Gap-cost alignMents for Long Reads (NGMLR), and Winnowmap2. Minimap2 and Winnowmap2 were computationally lightweight enough for use at scale, while GraphMap2 was not. NGMLR took a long time and required many resources, but produced alignments each time. LRA was fast, but only worked on Pacific Biosciences data. Each tool widely disagreed on which reads to leave unaligned, affecting the end genome coverage and the number of discoverable breakpoints. No alignment tool independently resolved all large structural variants (1,001–100,000 base pairs) present in the Database of Genome Variants (DGV) for sample NA12878 or the truthset for NA24385. Conclusions These results suggest a combined approach is needed for LRS alignments for human genomics. Specifically, leveraging alignments from three tools will be more effective in generating a complete picture of genomic variability. It should be best practice to use an analysis pipeline that generates alignments with both Minimap2 and Winnowmap2 as they are lightweight and yield different views of the genome. Depending on the question at hand, the data available, and the time constraints, NGMLR and LRA are good options for a third tool. If computational resources and time are not a factor for a given case or experiment, NGMLR will provide another view, and another chance to resolve a case. LRA, while fast, did not work on the nanopore data for our cluster, but PacBio results were promising in that those computations completed faster than Minimap2. Due to its significant burden on computational resources and slow run time, Graphmap2 is not an ideal tool for exploration of a whole human genome generated on a long-read sequencing platform.

Viral and non-viral vectors have revolutionised in the last 5 years the approaches to tackling pandemics, cancers and genetic diseases. The intrinsic properties of these vectors present new separation challenges to their manufacture in terms of both the process-related impurities to be removed and the complex labile nature of the target products. These characteristics make them susceptible to heterogeneity and the formation of product-related impurities.Conventional polyethersulfone membrane filters used for sterile filtration and ultrafiltration of viral vectors and lipid nanoparticles can display limited selectivity and cause product losses. To address these challenges, novel membrane materials and fabrication techniques to overcome the boundary of selectivity-permeability performance have become of interest. Isoporous membranes with well-defined pore size and pore dispersity at the nano-scale show promising separation performance but have only been demonstrated at small scales to date.This review summarises the decision process for the development of new membrane candidates for vector manufacturing in genomic medicine, including membranes fabricated by lithography, track-etched membranes, anodic aluminium oxide (AAO) membranes and self-assembled block copolymer membranes. By comparing these membranes to existing commercially available products, the possible advantages presented by novel materials and fabrication approaches are identified.