Orthanq: transparent and uncertainty-aware haplotype quantification with application in HLA-typing.

BACKGROUND: Identification of human leukocyte antigen (HLA) types from DNA-sequenced human samples is important in organ transplantation and cancer immunotherapy and remains a challenging task considering sequence homology and extreme polymorphism of HLA genes. RESULTS: We present Orthanq, a novel statistical model and corresponding application for transparent and uncertainty-aware quantification of haplotypes. We utilize our approach to perform HLA typing while, for the first time, reporting uncertainty of predictions and transparently observing mutations beyond reported HLA types. Using 99 gold standard samples from 1000 Genomes, Illumina Platinum Genomes and Genome In a Bottle projects, we show that Orthanq can provide overall superior accuracy and shorter runtimes than state-of-the-art HLA typers. CONCLUSIONS: Orthanq is the first approach that allows to directly utilize existing pangenome alignments and type all HLA loci. Moreover, it can be generalized for usages beyond HLA typing, e.g. for virus lineage quantification. Orthanq is available under https://orthanq.github.io .

Meta-analysis of genomic variants in power and endurance sports to decode the impact of genomics on athletic performance and success.

Association between genomic variants and athletic performance has seen a high degree of controversy, as there is often conflicting data as far as the association of genomic variants with endurance, speed and strength is concerned. Here, findings from a thorough meta-analysis from 4228 articles exploring the association of genomic variants with athletic performance in power and endurance sports are summarized, aiming to confirm or overrule the association of genetic variants with athletic performance of all types. From the 4228 articles, only 107 were eligible for further analysis, including 37 different genes. From these, there were 21 articles for the ACE gene, 29 articles for the ACTN3 gene and 8 articles for both the ACE and ACTN3 genes, including 54,382 subjects in total, from which 11,501 were endurance and power athletes and 42,881 control subjects. These data show that there is no statistically significant association between genomic variants and athletic performance either for endurance or power sports, underlying the fact that it is highly risky and even unethical to make such genetic testing services for athletic performance available to the general public. Overall, a strict regulatory monitoring should be exercised by health and other legislative authorities to protect the public from such services from an emerging discipline that still lacks the necessary scientific evidence and subsequent regulatory approval.

Novel Pathogenic Variants Leading to Sporadic Amyotrophic Lateral Sclerosis in Greek Patients.

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive disease that affects motor neurons, leading to paralysis and death usually 3-5 years after the onset of symptoms. The investigation of both sporadic and familial ALS highlighted four main genes that contribute to the pathogenesis of the disease: SOD1, FUS, TARDBP and C9orf72. This study aims to provide a comprehensive investigation of genetic variants found in SOD1, FUS and TARDBP genes in Greek sporadic ALS (sALS) cases. Our sequencing analysis of the coding regions of the abovementioned genes that include the majority of the variants that lead to ALS in 32 sALS patients and 3 healthy relatives revealed 6 variants in SOD1, 19 variants in FUS and 37 variants in TARDBP, of which the SOD1 p.D90A and the FUS c.*356G>A (rs886051940) variants have been previously associated with ALS, while two novel nonsense pathogenic variants were also identified, namely FUS p.R241* and TDP-43 p.Y214*. Our study contributes to the worldwide effort toward clarifying the genetic basis of sALS to better understand the disease's molecular pathology.

Systematic SARS-CoV-2 S-gene sequencing in wastewater samples enables early lineage detection and uncovers rare mutations in Portugal.

As the COVID-19 pandemic reached its peak, many countries implemented genomic surveillance systems to track the evolution and transmission of SARS-CoV-2. Transition from the pandemic to the endemic phase prioritized alternative testing strategies to maintain effective epidemic surveillance at the population level, with less intensive sequencing efforts. One such promising approach was Wastewater-Based Surveillance (WBS), which offers non-invasive, cost-effective means for analysing virus trends at the sewershed level. From 2020 onwards, wastewater has been recognized as an instrumental source of information for public health, with national and international authorities exploring options to implement national wastewater surveillance systems and increasingly relying on WBS as early warning of potential pathogen outbreaks. In Portugal, several pioneer projects joined the academia, water utilities and Public Administration around WBS. To validate WBS as an effective genomic surveillance strategy, it is crucial to collect long term performance data. In this work, we present one year of systematic SARS-CoV-2 wastewater surveillance in Portugal, representing 35 % of the mainland population. We employed two complementary methods for lineage determination - allelic discrimination by RT-PCR and S-gene sequencing. This combination allowed us to monitor variant evolution in near-real-time and identify low-frequency mutations. Over the course of this year-long study, spanning from May 2022 to April 2023, we successfully tracked the dominant Omicron sub-lineages, their progression and evolution, which aligned with concurrent clinical surveillance data. Our results underscore the effectiveness of WBS as a tracking system for virus variants, with the ability to unveil mutations undetected via massive sequencing of clinical samples from Portugal, demonstrating the ability of WBS to uncover new mutations and detect rare genetic variants. Our findings emphasize that knowledge of the genetic diversity of SARS-CoV-2 at the population level can be extended far beyond via the combination of routine clinical genomic surveillance with wastewater sequencing and genotyping.

A study of genetic variants associated with skin traits in the Vietnamese population.

BACKGROUND: Most skin-related traits have been studied in Caucasian genetic backgrounds. A comprehensive study on skin-associated genetic effects on underrepresented populations such as Vietnam is needed to fill the gaps in the field. OBJECTIVES: We aimed to develop a computational pipeline to predict the effect of genetic factors on skin traits using public data (GWAS catalogs and whole-genome sequencing (WGS) data from the 1000 Genomes Project-1KGP) and in-house Vietnamese data (WGS and genotyping by SNP array). Also, we compared the genetic predispositions of 25 skin-related traits of Vietnamese population to others to acquire population-specific insights regarding skin health. METHODS: Vietnamese cohorts of whole-genome sequencing (WGS) of 1008 healthy individuals for the reference and 96 genotyping samples (which do not have any skin cutaneous issues) by Infinium Asian Screening Array-24 v1.0 BeadChip were employed to predict skin-associated genetic variants of 25 skin-related and micronutrient requirement traits in population analysis and correlation analysis. Simultaneously, we compared the landscape of cutaneous issues of Vietnamese people with other populations by assessing their genetic profiles. RESULTS: The skin-related genetic profile of Vietnamese cohorts was similar at most to East Asian cohorts (JPT: Fst = 0.036, CHB: Fst = 0.031, CHS: Fst = 0.027, CDX: Fst = 0.025) in the population study. In addition, we identified pairs of skin traits at high risk of frequent co-occurrence (such as skin aging and wrinkles (r = 0.45, p = 1.50e-5) or collagen degradation and moisturizing (r = 0.35, p = 1.1e-3)). CONCLUSION: This is the first investigation in Vietnam to explore genetic variants of facial skin. These findings could improve inadequate skin-related genetic diversity in the currently published database.

Genomic insight into variations associated with flowering-time and early-maturity in pigeonpea mutant TAT-10 and its wild type parent T21.

Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important grain legume crop with a broad range of 90 to 300 days for maturity. To identify the genomic variations associated with the early maturity, we conducted whole-genome resequencing of an early-maturing pigeonpea mutant TAT-10 and its wild type parent T21. A total of 135.67 and 146.34 million sequencing reads were generated for T21 and TAT-10, respectively. From this resequencing data, 1,397,178 and 1,419,904 SNPs, 276,741 and 292,347 InDels, and 87,583 and 92,903 SVs were identified in T21 and TAT-10, respectively. We identified 203 genes in the pigeonpea genome that are homologs of flowering-related genes in Arabidopsis and found 791 genomic variations unique to TAT-10 linked to 94 flowering-related genes. We identified three candidate genes for early maturity in TAT-10; Suppressor of FRI 4 (SUF4), Early Flowering In Short Days (EFS), and Probable Lysine-Specific Demethylase ELF6. The variations in ELF6 were predicted to be possibly damaging and the expression profiles of EFS and ELF6 also supported their probable role during early flowering in TAT-10. The present study has generated information on genomic variations associated with candidate genes for early maturity, which can be further studied and exploited for developing the early-maturing pigeonpea cultivars.

Whole genome resequencing revealed genomic variants and functional pathways related to adaptation in Indian yak populations.

The present study aims to identify genomic variants through a whole genome sequencing (WGS) approach and uncover biological pathways associated with adaptation and fitness in Indian yak populations. A total of 30 samples (10 from each population) were included from Arunachali, Himachali and Ladakhi yak populations. WGS analysis revealed a total of 32171644, 27260825, and 32632460 SNPs and 4865254, 4429941, and 4847513 Indels in the Arunachali, Himachali, and Ladakhi yaks, respectively. Genes such as RYR2, SYNE2, BOLA, HF1, and the novel transcript ENSBGRG00000011079 were found to have the maximum number of high impact variants in all three yak populations, and might play a major role in local adaptation. Functional enrichment analysis of genes harboring high impact SNPs revealed overrepresented pathways related to response to stress, immune system regulation, and high-altitude adaptation. This study provides comprehensive information about genomic variants and their annotation in Indian yak populations, thus would serve as a data resource for researchers working on the yaks. Furthermore, it could be well exploited for better yak conservation strategies by estimating population genetics parameters viz., effective population size, inbreeding, and observed and expected heterozygosity.

Deep computational phenotyping of genomic variants impacting the SET domain of KMT2C reveal molecular mechanisms for their dysfunction.

Introduction: Kleefstra Syndrome type 2 (KLEFS-2) is a genetic, neurodevelopmental disorder characterized by intellectual disability, infantile hypotonia, severe expressive language delay, and characteristic facial appearance, with a spectrum of other distinct clinical manifestations. Pathogenic mutations in the epigenetic modifier type 2 lysine methyltransferase KMT2C have been identified to be causative in KLEFS-2 individuals. Methods: This work reports a translational genomic study that applies a multidimensional computational approach for deep variant phenotyping, combining conventional genomic analyses, advanced protein bioinformatics, computational biophysics, biochemistry, and biostatistics-based modeling. We use standard variant annotation, paralog annotation analyses, molecular mechanics, and molecular dynamics simulations to evaluate damaging scores and provide potential mechanisms underlying KMT2C variant dysfunction. Results: We integrated data derived from the structure and dynamics of KMT2C to classify variants into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). When compared with controls, these variants show values reflecting alterations in molecular fitness in both structure and dynamics. Discussion: We demonstrate that our 3D models for KMT2C variants suggest distinct mechanisms that lead to their imbalance and are not predictable from sequence alone. Thus, the missense variants studied here cause destabilizing effects on KMT2C function by different biophysical and biochemical mechanisms which we adeptly describe. This new knowledge extends our understanding of how variations in the KMT2C gene cause the dysfunction of its methyltransferase enzyme product, thereby bearing significant biomedical relevance for carriers of KLEFS2-associated genomic mutations.

The TiHoCL panel for canine lymphoma: a feasibility study integrating functional genomics and network biology approaches for comparative oncology targeted NGS panel design.

Targeted next-generation sequencing (NGS) enables the identification of genomic variants in cancer patients with high sensitivity at relatively low costs, and has thus opened the era to personalized human oncology. Veterinary medicine tends to adopt new technologies at a slower pace compared to human medicine due to lower funding, nonetheless it embraces technological advancements over time. Hence, it is reasonable to assume that targeted NGS will be incorporated into routine veterinary practice in the foreseeable future. Many animal diseases have well-researched human counterparts and hence, insights gained from the latter might, in principle, be harnessed to elucidate the former. Here, we present the TiHoCL targeted NGS panel as a proof of concept, exemplifying how functional genomics and network approaches can be effectively used to leverage the wealth of information available for human diseases in the development of targeted sequencing panels for veterinary medicine. Specifically, the TiHoCL targeted NGS panel is a molecular tool for characterizing and stratifying canine lymphoma (CL) patients designed based on human non-Hodgkin lymphoma (NHL) research outputs. While various single nucleotide polymorphisms (SNPs) have been associated with high risk of developing NHL, poor prognosis and resistance to treatment in NHL patients, little is known about the genetics of CL. Thus, the ~100 SNPs featured in the TiHoCL targeted NGS panel were selected using functional genomics and network approaches following a literature and database search that shielded ~500 SNPs associated with, in nearly all cases, human hematologic malignancies. The TiHoCL targeted NGS panel underwent technical validation and preliminary functional assessment by sequencing DNA samples isolated from blood of 29 lymphoma dogs using an Ion Torrent™ PGM System achieving good sequencing run metrics. Our design framework holds new possibilities for the design of similar molecular tools applied to other diseases for which limited knowledge is available and will improve drug target discovery and patient care.

An individualized Bayesian method for estimating genomic variants of hypertension.

BACKGROUND: Genomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual's genome that provide a strong explanation of the phenotype observed in this individual. RESULTS: We applied the IBI algorithm to the data from the Framingham Heart Study to explore the genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, IBI discovered more individualized and diverse variants that explain hypertension patients better than GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that GWAS missed in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than GWAS, according to the area under the ROC curve. CONCLUSIONS: The results support IBI as a promising approach for complementing GWAS, especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine.

Identification of druggable genes for multiple myeloma based on genomic information.

Multiple myeloma (MM) is a hematological malignancy. It is widely believed that genetic factors play a significant role in the development of MM, as investigated in numerous studies. However, the application of genomic information for clinical purposes, including diagnostic and prognostic biomarkers, remains largely confined to research. In this study, we utilized genetic information from the Genomic-Driven Clinical Implementation for Multiple Myeloma database, which is dedicated to clinical trial studies on MM. This genetic information was sourced from the genome-wide association studies catalog database. We prioritized genes with the potential to cause MM based on established annotations, as well as biological risk genes for MM, as potential drug target candidates. The DrugBank database was employed to identify drug candidates targeting these genes. Our research led to the discovery of 14 MM biological risk genes and the identification of 10 drugs that target three of these genes. Notably, only one of these 10 drugs, panobinostat, has been approved for use in MM. The two most promising genes, calcium signal-modulating cyclophilin ligand (CAMLG) and histone deacetylase 2 (HDAC2), were targeted by four drugs (cyclosporine, belinostat, vorinostat, and romidepsin), all of which have clinical evidence supporting their use in the treatment of MM. Interestingly, five of the 10 drugs have been approved for other indications than MM, but they may also be effective in treating MM. Therefore, this study aimed to clarify the genomic variants involved in the pathogenesis of MM and highlight the potential benefits of these genomic variants in drug discovery.

Identifying pathogenic variants related to systemic lupus erythematosus by integrating genomic databases and a bioinformatic approach.

Systemic lupus erythematosus (SLE) is an inflammatory-autoimmune disease with a complex multi-organ pathogenesis, and it is known to be associated with significant morbidity and mortality. Various genetic, immunological, endocrine, and environmental factors contribute to SLE. Genomic variants have been identified as potential contributors to SLE susceptibility across multiple continents. However, the specific pathogenic variants that drive SLE remain largely undefined. In this study, we sought to identify these pathogenic variants across various continents using genomic and bioinformatic-based methodologies. We found that the variants rs35677470, rs34536443, rs17849502, and rs13306575 are likely damaging in SLE. Furthermore, these four variants appear to affect the gene expression of NCF2, TYK2, and DNASE1L3 in whole blood tissue. Our findings suggest that these genomic variants warrant further research for validation in functional studies and clinical trials involving SLE patients. We conclude that the integration of genomic and bioinformatic-based databases could enhance our understanding of disease susceptibility, including that of SLE.

Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis.

Dermatomyositis (DM) is an autoimmune disease that is classified as a type of idiopathic inflammatory myopathy, which affects human skin and muscles. The most common clinical symptoms of DM are muscle weakness, rash, and scaly skin. There is currently no cure for DM. Genetic factors are known to play a pivotal role in DM progression, but few have utilized this information geared toward drug discovery for the disease. Here, we exploited genomic variation associated with DM and integrated this with genomic and bioinformatic analyses to discover new drug candidates. We first integrated genome-wide association study (GWAS) and phenome-wide association study (PheWAS) catalogs to identify disease-associated genomic variants. Biological risk genes for DM were prioritized using strict functional annotations, further identifying candidate drug targets based on druggable genes from databases. Overall, we analyzed 1239 variants associated with DM and obtained 43 drugs that overlapped with 13 target genes (JAK2, FCGR3B, CD4, CD3D, LCK, CD2, CD3E, FCGR3A, CD3G, IFNAR1, CD247, JAK1, IFNAR2). Six drugs clinically investigated for DM, as well as eight drugs under pre-clinical investigation, are candidate drugs that could be repositioned for DM. Further studies are necessary to validate potential biomarkers for novel DM therapeutics from our findings.

Evaluation of two Massive Open Online Courses (MOOCs) in genomic variant interpretation for the NHS workforce.

BACKGROUND: The implementation of the National Genomic Medicine Service in the UK has increased patient access to germline genomic testing. Increased testing leads to more genetic diagnoses but does result in the identification of genomic variants of uncertain significance (VUS). The rigorous process of interpreting these variants requires multi-disciplinary, highly trained healthcare professionals (HCPs). To meet this training need, we designed two Massive Open Online Courses (MOOCs) for HCPs involved in germline genomic testing pathways: Fundamental Principles (FP) and Inherited Cancer Susceptibility (ICS). METHODS: An evaluation cohort of HCPs involved in genomic testing were recruited, with additional data also available from anonymous self-registered learners to both MOOCs. Pre- and post-course surveys and in-course quizzes were used to assess learner satisfaction, confidence and knowledge gained in variant interpretation. In addition, granular feedback was collected on the complexity of the MOOCs to iteratively improve the resources. RESULTS: A cohort of 92 genomics HCPs, including clinical scientists, and non-genomics clinicians (clinicians working in specialties outside of genomics) participated in the evaluation cohort. Between baseline and follow-up, total confidence scores improved by 38% (15.2/40.0) (95% confidence interval [CI] 12.4-18.0) for the FP MOOC and 54% (18.9/34.9) (95%CI 15.5-22.5) for the ICS MOOC (p < 0.0001 for both). Of those who completed the knowledge assessment through six summative variant classification quizzes (V1-6), a mean of 79% of respondents classified the variants such that correct clinical management would be undertaken (FP: V1 (73/90) 81% Likely Pathogenic/Pathogenic [LP/P]; V2 (55/78) 70% VUS; V3 (59/75) 79% LP/P; V4 (62/72) 86% LP/LP. ICS: V5 (66/91) 73% VUS; V6 (76/88) 86% LP/P). A non-statistically significant higher attrition rate was seen amongst the non-genomics workforce when compared to genomics specialists for both courses. More participants from the non-genomics workforce rated the material as "Too Complex" (FP n = 2/7 [29%], ICS n = 1/5 [20%]) when compared to the specialist genomics workforce (FP n = 1/43 [2%], ICS n = 0/35 [0%]). CONCLUSIONS: After completing one or both MOOCs, self-reported confidence in genomic variant interpretation significantly increased, and most respondents could correctly classify variants such that appropriate clinical management would be instigated. Genomics HCPs reported higher satisfaction with the level of content than the non-genomics clinicians. The MOOCs provided foundational knowledge and improved learner confidence, but should be adapted for different workforces to maximise the benefit for clinicians working in specialties outside of genetics.

Current understanding of the genomic abnormities in premature ovarian failure: chance for early diagnosis and management.

Premature ovarian failure (POF) is an insidious cause of female infertility and a devastating condition for women. POF also has a strong familial and heterogeneous genetic background. Management of POF is complicated by the variable etiology and presentation, which are generally characterized by abnormal hormone levels, gene instability and ovarian dysgenesis. To date, abnormal regulation associated with POF has been found in a small number of genes, including autosomal and sex chromosomal genes in folliculogenesis, granulosa cells, and oocytes. Due to the complex genomic contributions, ascertaining the exact causative mechanisms has been challenging in POF, and many pathogenic genomic characteristics have yet to be elucidated. However, emerging research has provided new insights into genomic variation in POF as well as novel etiological factors, pathogenic mechanisms and therapeutic intervention approaches. Meanwhile, scattered studies of transcriptional regulation revealed that ovarian cell function also depends on specific biomarker gene expression, which can influence protein activities, thus causing POF. In this review, we summarized the latest research and issues related to the genomic basis for POF and focused on insights gained from their biological effects and pathogenic mechanisms in POF. The present integrated studies of genomic variants, gene expression and related protein abnormalities were structured to establish the role of etiological genes associated with POF. In addition, we describe the design of some ongoing clinical trials that may suggest safe, feasible and effective approaches to improve the diagnosis and therapy of POF, such as Filgrastim, goserelin, resveratrol, natural plant antitoxin, Kuntai capsule et al. Understanding the candidate genomic characteristics in POF is beneficial for the early diagnosis of POF and provides appropriate methods for prevention and drug treatment. Additional efforts to clarify the POF genetic background are necessary and are beneficial for researchers and clinicians regarding genetic counseling and clinical practice. Taken together, recent genomic explorations have shown great potential to elucidate POF management in women and are stepping from the bench to the bedside.

CoVigator-A Knowledge Base for Navigating SARS-CoV-2 Genomic Variants.

BACKGROUND: The outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in the global COVID-19 pandemic. The urgency for an effective SARS-CoV-2 vaccine has led to the development of the first series of vaccines at unprecedented speed. The discovery of SARS-CoV-2 spike-glycoprotein mutants, however, and consequentially the potential to escape vaccine-induced protection and increased infectivity, demonstrates the persisting importance of monitoring SARS-CoV-2 mutations to enable early detection and tracking of genomic variants of concern. RESULTS: We developed the CoVigator tool with three components: (1) a knowledge base that collects new SARS-CoV-2 genomic data, processes it and stores its results; (2) a comprehensive variant calling pipeline; (3) an interactive dashboard highlighting the most relevant findings. The knowledge base routinely downloads and processes virus genome assemblies or raw sequencing data from the COVID-19 Data Portal (C19DP) and the European Nucleotide Archive (ENA), respectively. The results of variant calling are visualized through the dashboard in the form of tables and customizable graphs, making it a versatile tool for tracking SARS-CoV-2 variants. We put a special emphasis on the identification of intrahost mutations and make available to the community what is, to the best of our knowledge, the largest dataset on SARS-CoV-2 intrahost mutations. In the spirit of open data, all CoVigator results are available for download. The CoVigator dashboard is accessible via covigator.tron-mainz.de. CONCLUSIONS: With increasing demand worldwide in genome surveillance for tracking the spread of SARS-CoV-2, CoVigator will be a valuable resource of an up-to-date list of mutations, which can be incorporated into global efforts.

Ex vivo to in vivo model of malignant peripheral nerve sheath tumors for precision oncology.

BACKGROUND: Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft tissue sarcomas that often develop in patients with neurofibromatosis type 1 (NF1). To address the critical need for novel therapeutics in MPNST, we aimed to establish an ex vivo 3D platform that accurately captured the genomic diversity of MPNST and could be utilized in a medium-throughput manner for drug screening studies to be validated in vivo using patient-derived xenografts (PDX). METHODS: Genomic analysis was performed on all PDX-tumor pairs. Selected PDX were harvested for assembly into 3D microtissues. Based on prior work in our labs, we evaluated drugs (trabectedin, olaparib, and mirdametinib) ex vivo and in vivo. For 3D microtissue studies, cell viability was the endpoint as assessed by Zeiss Axio Observer. For PDX drug studies, tumor volume was measured twice weekly. Bulk RNA sequencing was performed to identify pathways enriched in cells. RESULTS: We developed 13 NF1-associated MPNST-PDX and identified mutations or structural abnormalities in NF1 (100%), SUZ12 (85%), EED (15%), TP53 (15%), CDKN2A (85%), and chromosome 8 gain (77%). We successfully assembled PDX into 3D microtissues, categorized as robust (>90% viability at 48 h), good (>50%), or unusable (<50%). We evaluated drug response to "robust" or "good" microtissues, namely MN-2, JH-2-002, JH-2-079-c, and WU-225. Drug response ex vivo predicted drug response in vivo, and enhanced drug effects were observed in select models. CONCLUSIONS: These data support the successful establishment of a novel 3D platform for drug discovery and MPNST biology exploration in a system representative of the human condition.

Rapid protein stability prediction using deep learning representations.

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 230 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available-including via a Web interface-and enables large-scale analyses of stability in experimental and predicted protein structures.

The outcome of ibrutinib-based regimens in relapsed/refractory central nervous system lymphoma and the potential impact of genomic variants.

BACKGROUND: Relapsed/refractory (r/r) central nervous system lymphoma (CNSL) exhibits aggressive behavior and poor outcomes. As an effective bruton tyrosine kinase (BTK) inhibitor, ibrutinib yields benefits in B-cell malignancies. OBJECTIVES: We aimed to explore the efficacy of ibrutinib in treating r/r CNSL patients, and whether genomic variants impact treatment outcomes. MATERIAL AND METHODS: The ibrutinib-based regimens in 12 r/r primary CNSL (PCNSL) and 2 secondary CNSL (SCNSL) patients were analyzed retrospectively. The impact of genetic variants on the effects of treatments was examined using whole-exome sequencing (WES) technology. RESULTS: In PCNSL, the overall response rate was 75%, with median overall survival (OS) not reached (NR) and progression-free survival (PFS) of 4 months. Both SCNSL patients responded to ibrutinib, with median OS NR and PFS of 0.5-1.5 months. Infections were common during ibrutinib therapy (42.86%). The PCNSL patients harboring gene mutations in PIM1, MYD88 and CD79B, and the proximal BCR and nuclear factor kappa B (NF-κB) pathways responded to ibrutinib. Patients who harbored simple genetic variants and those with a low tumor mutation burden (TMB; 2.39-5.56/Mb) responded swiftly and maintained remission for more than 10 months. A patient with a TMB of 11/Mb responded to ibrutinib but continued to experience disease progression. In contrast, patients with complex genomic features, especially extremely high TMB (58.39/Mb), responded poorly to ibrutinib. CONCLUSIONS: Our study demonstrates that ibrutinib-based therapy is effective and relatively safe for the treatment of r/r CNSL. Patients with less genomic complexity, especially with regard to TMB, might benefit more from ibrutinib regimens.

Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics.

Insulin is amongst the human genome's most well-studied genes/proteins due to its connection to metabolic health. Within this article, we review literature and data to build a knowledge base of Insulin (INS) genetics that influence transcription, transcript processing, translation, hormone maturation, secretion, receptor binding, and metabolism while highlighting the future needs of insulin research. The INS gene region has 2076 unique variants from population genetics. Several variants are found near the transcriptional start site, enhancers, and following the INS transcripts that might influence the readthrough fusion transcript INS-IGF2. This INS-IGF2 transcript splice site was confirmed within hundreds of pancreatic RNAseq samples, lacks drift based on human genome sequencing, and has possible elevated expression due to viral regulation within the liver. Moreover, a rare, poorly characterized African population-enriched variant of INS-IGF2 results in a loss of the stop codon. INS transcript UTR variants rs689 and rs3842753, associated with type 1 diabetes, are found in many pancreatic RNAseq datasets with an elevation of the 3'UTR alternatively spliced INS transcript. Finally, by combining literature, evolutionary profiling, and structural biology, we map rare missense variants that influence preproinsulin translation, proinsulin processing, dimer/hexamer secretory storage, receptor activation, and C-peptide detection for quasi-insulin blood measurements.