Lazaridis, Konstantinos N.

Abstract Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease, and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline, and artifact. In whole-exome sequencing data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome sequencing data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes and predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had < 5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies. The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.

Abstract The advent of high-throughput sequencing has led to an exponential increase in genomic data, highlighting the need for efficient and accurate models to analyze and interpret this information. Here, we introduce a novel, exomic foundational model that leverages a combination of human reference genome and multispecies data to improve variant detection and interpretation. Our model utilizes a short- range transformer architecture and is trained on a large dataset of human exomic sequences derived from the Tapestry study. Through a series of ablation studies and scaling experiments, we demonstrate the effectiveness of our model in pre- dicting next token accuracy and identifying clinically pathogenic variants. We also show that our model outperforms existing models in a range of downstream tasks, including variant effect prediction and disease state identification. In fact, our largest STRAND variant (1B parameters) surpassed previous benchmarks, demonstrating a mean accuarcy of 0.880 (8.2% improvement over the original NT and 7% improvement over NT-v2). Furthermore, we construct a unique exomic ClinVar dataset to evaluate the model’s performance on pathogenicity and disease states. Our results highlight the potential of this model to improve our understanding of the human exome and its role in disease. The model and its applications have significant implications for genomic based diagnosis and personalized medicine including tailored therapeutic development.

Background and Aims: Among those with primary sclerosing cholangitis (PSC), perihilar cholangiocarcinoma (pCCA) is often diagnosed at a late stage and is a leading source of mortality. Detection of pCCA in PSC when curative action can be taken is challenging. Our aim was to create a deep learning model that analyzed MRI to detect early-stage pCCA and compare its diagnostic performance with expert radiologists. Approach and Results: We conducted a multicenter, international, retrospective cohort study involving adults with large duct PSC who underwent contrast-enhanced MRI. Senior abdominal radiologists reviewed the images. All patients with pCCA had early-stage cancer and were registered for liver transplantation. We trained a 3D DenseNet-121 model, a form of deep learning, using MRI images and assessed its performance in a separate test cohort. The study included 398 patients (training cohort n=150; test cohort n=248). pCCA was present in 230 individuals (training cohort n=64; test cohort n=166). In the test cohort, the respective performances of the model compared to the radiologists were: sensitivity 87.9% versus 50.0%, p <0.001; specificity 84.1% versus 100.0%, p <0.001; area under receiving operating curve 86.0% versus 75.0%, p <0.001. Even when a mass was absent, the model had a higher sensitivity for pCCA than radiologists (91.6% vs. 50.6%, p <0.001) and maintained good specificity (84.1%). Conclusions: The 3D DenseNet-121 MRI model effectively detects early-stage pCCA in PSC patients. Compared to expert radiologists, the model missed fewer cases of cancer.

Introduction: The lack of understanding about the genetic and molecular basis of young stroke (<55 years of age) has hindered development of diagnostic, management, and therapeutic approaches. Whole exome sequencing (WES) can be leveraged as a tool for studying etiological processes underlying stroke. At the time of study inception, this is the single largest WES study in this unique young stroke population to uncover elusive stroke etiologies. Methods: The Mayo Clinic TAPESTRY database is a research program conducting whole exome sequencing of 100,000 participants. Of these, 406 participants met the criteria for young stroke (18-54 years of age) based on ICD-9 and ICD-10 diagnostic codes. Controls were TAPESTRY participants without a stroke-related ICD diagnosis. Cases were matched for age, gender, and race for a 1:4 case-control ratio. Ischemic stroke etiology was classified using TOAST classification. To uncover genetic mechanisms, a gene centric approach was used to identify variants in our cohort. A list of 66 genes known to be associated with young stroke was curated based on existing genetic databases. Various filtration strategies were used to identify potentially damaging variants (graphic 1). Results: There were 406 cases (363 ischemic and 43 hemorrhagic; 291 female; age at stroke onset 39.8±10.4; 368 were White) and 3,857 controls. TOAST classification yielded 20 patients with large artery atherosclerosis, 58 cardioembolic, 10 small vessel disease, 148 other determined (58 patent foramen ovale, 50 dissection, 48 hypercoagulable state, 11 Moyamoya, 11 vasculitis, 10 fibromuscular dysplasia), and 165 undetermined etiologies. Several individuals had multiple competing etiologies. Of cerebral hemorrhages, there were 6 aneurysmal, 6 arteriovenous malformation, 5 hypertensive, 2 substance abuse, 3 trauma, and 21 other etiologies. Across the entire cohort, 35,054 variants were found within the 66 genes identified from the literature. Filtration identified 48 potentially damaging variants across 30 genes in 44 patients (20 cryptogenic and 21 other: 12 patent foramen ovale, 5 vasculitis, 4 dissection, 4 cardioembolic) that were absent in controls. Conclusion: WES is a powerful tool that can be used to uncover potential genetic mechanisms in the young stroke population. An improved understanding of etiologies would improve diagnostics and pave the way for new preventive and therapeutic approaches, thus improving the overall care of young stroke patients.

Background and Aims: Metabolic dysfunction–associated steatotic liver disease affects about a third of adults worldwide and is projected soon to be the leading cause of liver cirrhosis. It occurs when fat accumulates in hepatocytes and can progress to metabolic dysfunction–associated steatohepatitis, liver cirrhosis, and HCC. Metabolic dysfunction–associated steatotic liver disease pathogenesis is believed to involve a combination of genetic and environmental risk factors. Single nucleotide polymorphisms have been implicated, but non-syndromic monogenic causes are lacking. Approach and Results: We identified a novel genetic variant in a familial case of metabolic dysfunction–associated steatohepatitis and performed deep variant functional analysis, including protein modeling, dynamics, and cell-based assays to assess molecular mechanisms of dysfunction and altered cellular signaling. We analyzed exome sequencing data of 3904 individuals with steatotic liver disease (SLD) to identify additional cases and establish the link between specific gene variants and SLD diagnosis. We discovered and functionally validated the NM_000245.4:c.3505A>T; p.(Ile1169Phe) variant in the MET (mesenchymal-epithelial transition) kinase domain as a monogenic cause of SLD. Subsequently, we detected additional ultra-rare, previously uninterpreted, and likely deleterious variants in MET from screening sequencing data. Among individuals with confirmed SLD based on electronic record review, 1.1% (45/3904) had rare predicted deleterious MET variants. Eight of 45 (17.7%) individuals had predicted deleterious variants in the MET kinase domain confirmed to be functionally like the familial case variant. Conclusions: We report the first germline nonmalignant rare MET -driven disease, a monogenic form of SLD.

Background: The epigenome, the set of modifications to DNA and associated molecules that control gene expression, cellular identity, and function, plays a major role in mediating cellular responses to outside factors. Thus, evaluation of the epigenetic state can provide insights into cellular adaptions occurring over the course of disease. Methods: We performed epigenome-wide association studies of primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) using the Illumina MethylationEPIC Bead Chip. Results: We found evidence of increased epigenetic age acceleration and differences in predicted immune cell composition in patients with PSC and PBC. Epigenetic profiles demonstrated differences in predicted protein levels including increased levels of tumor necrosis factor receptor superfamily member 1B in patients with cirrhotic compared to noncirrhotic PSC and PBC. Epigenome-wide association studies of PSC discovered strongly associated 5′-C-phosphate-G-3′ sites in genes including vacuole membrane protein 1 and SOCS3, and epigenome-wide association studies of PBC found strong 5′-C-phosphate-G-3′ associations in genes including NOD-like receptor family CARD domain containing 5, human leukocyte antigen-E, and PSMB8. Analyses identified disease-associated canonical pathways and upstream regulators involved with immune signaling and activation of macrophages and T-cells. A comparison of PSC and PBC data found relatively little overlap at the 5′-C-phosphate-G-3′ and gene levels with slightly more overlap at the level of pathways and upstream regulators. Conclusions: This study provides insights into methylation profiles of patients that support current concepts of disease mechanisms and provide novel data to inspire future research. Studies to corroborate our findings and expand into other -omics layers will be invaluable to further our understanding of these rare diseases with the goal to improve and individualize prognosis and treatment.

PURPOSE The autosomal dominant cancer predisposition disorders hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS) are genetic conditions for which early identification and intervention have a positive effect on the individual and public health. The goals of this study were to determine whether germline genetic screening using exome sequencing could be used to efficiently identify carriers of HBOC and LS. METHODS Participants were recruited from three geographically and racially diverse sites in the United States (Rochester, MN; Phoenix, AZ; Jacksonville, FL). Participants underwent Exome+ sequencing (Helix Inc, San Mateo, CA) and return of results for specific genetic findings: HBOC ( BRCA1 and BRCA1) and LS ( MLH1, MSH2, MSH6, PMS2, and EPCAM). Chart review was performed to collect demographics and personal and family cancer history. RESULTS To date, 44,306 participants have enrolled in Tapestry. Annotation and interpretation of all variants in genes for HBOC and LS resulted in the identification of 550 carriers (prevalence, 1.24%), which included 387 with HBOC (27.2% BRCA1, 42.8% BRCA2) and 163 with LS (12.3% MSH6, 8.8% PMS2, 4.5% MLH1, 3.8% MSH2, and 0.2% EPCAM). More than half of these participants (52.1%) were newly diagnosed carriers with HBOC and LS. In all, 39.2% of HBOC/LS carriers did not satisfy National Comprehensive Cancer Network (NCCN) criteria for genetic evaluation. NCCN criteria were less commonly met in underrepresented minority populations versus self-reported White race (51.5% v 37.5%, P = .028). CONCLUSION Our results emphasize the need for wider utilization of germline genetic sequencing for enhanced screening and detection of individuals who have LS and HBOC cancer predisposition syndromes.

Background: The pathogenesis of primary sclerosing cholangitis (PSC) is unclear, although studies implicate IL-17A as an inflammatory mediator in this disease. However, a direct assessment of IL-17 signaling in PSC cholangiocytes is lacking. In this study, we aimed to investigate and characterize the response of PSC extrahepatic cholangiocyte organoids (ECO) to IL-17A stimulation. Methods: Cholangiocytes obtained from patients with PSC and without PSC by endoscopic retrograde cholangiography were cultured as ECO. The ECO were treated with vehicle or IL-17A and assessed by transcriptomics, secretome analysis, and genome sequencing. Results: Unsupervised clustering of all integrated single-cell RNA sequencing data identified 8 cholangiocyte clusters that did not differ between PSC and non-PSC ECO. However, PSC ECO cells demonstrated a robust response to IL-17 treatment, as noted by an increased number of differentially expressed genes by transcriptomics and more abundant chemokine and cytokine expression and secretion. After rigorous filtering, genome sequencing identified candidate somatic variants shared among PSC ECO from unrelated individuals. However, no candidate rare variants in genes regulating the IL-17 pathway were identified, but rare variants regulating the MAPK signaling pathway were present in all PSC ECO. Conclusions: PSC and non-PSC patient-derived ECO respond differently to IL-17 stimulation, implicating this pathway in the pathogenesis of PSC.