iBRIDGE: A Data Integration Method to Identify Inflamed Tumors from Single-cell RNA-Seq Data and Differentiate Cell Type-Specific Markers of Immune-Cell Infiltration.

The development of immune checkpoint-based immunotherapies has been a major advancement in the treatment of cancer, with a subset of patients exhibiting durable clinical responses. A predictive biomarker for immunotherapy response is the preexisting T-cell infiltration in the tumor immune microenvironment (TIME). Bulk transcriptomics-based approaches can quantify the degree of T-cell infiltration using deconvolution methods and identify additional markers of inflamed/cold cancers at the bulk level. However, bulk techniques are unable to identify biomarkers of individual cell types. Although single-cell RNA sequencing (scRNA-seq) assays are now being used to profile the TIME, to our knowledge there is no method of identifying patients with a T-cell inflamed TIME from scRNA-seq data. Here, we describe a method, iBRIDGE, which integrates reference bulk RNA-seq data with the malignant subset of scRNA-seq datasets to identify patients with a T-cell inflamed TIME. Using two datasets with matched bulk data, we show iBRIDGE results correlated highly with bulk assessments (0.85 and 0.9 correlation coefficients). Using iBRIDGE, we identified markers of inflamed phenotypes in malignant cells, myeloid cells, and fibroblasts, establishing type I and type II interferon pathways as dominant signals, especially in malignant and myeloid cells, and finding the TGFß-driven mesenchymal phenotype not only in fibroblasts but also in malignant cells. Besides relative classification, per-patient average iBRIDGE scores and independent RNAScope quantifications were used for threshold-based absolute classification. Moreover, iBRIDGE can be applied to in vitro grown cancer cell lines and can identify the cell lines that are adapted from inflamed/cold patient tumors.

Fast, accurate, and racially unbiased pan-cancer tumor-only variant calling with tabular machine learning.

Accurately identifying somatic mutations is essential for precision oncology and crucial for calculating tumor-mutational burden (TMB), an important predictor of response to immunotherapy. For tumor-only variant calling (i.e., when the cancer biopsy but not the patient's normal tissue sample is sequenced), accurately distinguishing somatic mutations from germline variants is a challenging problem that, when unaddressed, results in unreliable, biased, and inflated TMB estimates. Here, we apply machine learning to the task of somatic vs germline classification in tumor-only solid tumor samples using TabNet, XGBoost, and LightGBM, three machine-learning models for tabular data. We constructed a training set for supervised classification using features derived exclusively from tumor-only variant calling and drawing somatic and germline truth labels from an independent pipeline using the patient-matched normal samples. All three trained models achieved state-of-the-art performance on two holdout test datasets: a TCGA dataset including sarcoma, breast adenocarcinoma, and endometrial carcinoma samples (AUC > 94%), and a metastatic melanoma dataset (AUC > 85%). Concordance between matched-normal and tumor-only TMB improves from R2 = 0.006 to 0.71-0.76 with the addition of a machine-learning classifier, with LightGBM performing best. Notably, these machine-learning models generalize across cancer subtypes and capture kits with a call rate of 100%. We reproduce the recent finding that tumor-only TMB estimates for Black patients are extremely inflated relative to that of white patients due to the racial biases of germline databases. We show that our approach with XGBoost and LightGBM eliminates this significant racial bias in tumor-only variant calling.

The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases.

BACKGROUND: The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants. METHODS: To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles. RESULTS: We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33). CONCLUSIONS: This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas.

Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes.

Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results.

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing.

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing.

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.

Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank.

The UK Biobank Exome Sequencing Consortium (UKB-ESC) is a private-public partnership between the UK Biobank (UKB) and eight biopharmaceutical companies that will complete the sequencing of exomes for all ~500,000 UKB participants. Here, we describe the early results from ~200,000 UKB participants and the features of this project that enabled its success. The biopharmaceutical industry has increasingly used human genetics to improve success in drug discovery. Recognizing the need for large-scale human genetics data, as well as the unique value of the data access and contribution terms of the UKB, the UKB-ESC was formed. As a result, exome data from 200,643 UKB enrollees are now available. These data include ~10 million exonic variants-a rich resource of rare coding variation that is particularly valuable for drug discovery. The UKB-ESC precompetitive collaboration has further strengthened academic and industry ties and has provided teams with an opportunity to interact with and learn from the wider research community.

A balance score between immune stimulatory and suppressive microenvironments identifies mediators of tumour immunity and predicts pan-cancer survival.

BACKGROUND: The balance between immune-stimulatory and immune-suppressive mechanisms in the tumour microenvironment is associated with tumour rejection and can predict the efficacy of immune checkpoint-inhibition therapies. METHODS: We consider the observed differences between the transcriptional programmes associated with cancer types where the levels of immune infiltration predict a favourable prognosis versus those in which the immune infiltration predicts an unfavourable prognosis and defined a score named Mediators of Immune Response Against Cancer in soLid microEnvironments (MIRACLE). MIRACLE deconvolves T cell infiltration, from inhibitory mechanisms, such as TGFß, EMT and PI3Kγ signatures. RESULTS: Our score outperforms current state-of-the-art immune signatures as a predictive marker of survival in TCGA (n = 9305, HR: 0.043, p value: 6.7 × 10-36). In a validation cohort (n = 7623), MIRACLE predicts better survival compared to other immune metrics (HR: 0.1985, p value: 2.73 × 10-38). MIRACLE also predicts response to checkpoint-inhibitor therapies (n = 333). The tumour-intrinsic factors inversely associated with the reported score such as EGFR, PRKAR1A and MAP3K1 are frequently associated with immune-suppressive phenotypes. CONCLUSIONS: The association of cancer outcome with the level of infiltrating immune cells is mediated by the balance of activatory and suppressive factors. MIRACLE accounts for this balance and predicts favourable cancer outcomes.

A Mutation in γ-Adducin Impairs Autoregulation of Renal Blood Flow and Promotes the Development of Kidney Disease.

BACKGROUND: The genes and mechanisms involved in the association between diabetes or hypertension and CKD risk are unclear. Previous studies have implicated a role for γ-adducin (ADD3), a cytoskeletal protein encoded by Add3. METHODS: We investigated renal vascular function in vitro and in vivo and the susceptibility to CKD in rats with wild-type or mutated Add3 and in genetically modified rats with overexpression or knockout of ADD3. We also studied glomeruli and primary renal vascular smooth muscle cells isolated from these rats. RESULTS: This study identified a K572Q mutation in ADD3 in fawn-hooded hypertensive (FHH) rats-a mutation previously reported in Milan normotensive (MNS) rats that also develop kidney disease. Using molecular dynamic simulations, we found that this mutation destabilizes a critical ADD3-ACTIN binding site. A reduction of ADD3 expression in membrane fractions prepared from the kidney and renal vascular smooth muscle cells of FHH rats was associated with the disruption of the F-actin cytoskeleton. Compared with renal vascular smooth muscle cells from Add3 transgenic rats, those from FHH rats had elevated membrane expression of BKα and BK channel current. FHH and Add3 knockout rats exhibited impairments in the myogenic response of afferent arterioles and in renal blood flow autoregulation, which were rescued in Add3 transgenic rats. We confirmed these findings in a genetic complementation study that involved crossing FHH and MNS rats that share the ADD3 mutation. Add3 transgenic rats showed attenuation of proteinuria, glomerular injury, and kidney fibrosis with aging and mineralocorticoid-induced hypertension. CONCLUSIONS: This is the first report that a mutation in ADD3 that alters ACTIN binding causes renal vascular dysfunction and promotes the susceptibility to kidney disease.

Identification of molecular signatures of cystic fibrosis disease status with plasma-based functional genomics.

Although cystic fibrosis (CF) is attributed to dysfunction of a single gene, the relationships between the abnormal gene product and the development of inflammation and progression of lung disease are not fully understood, which limits our ability to predict an individual patient's clinical course and treatment response. To better understand CF progression, we characterized the molecular signatures of CF disease status with plasma-based functional genomics. Peripheral blood mononuclear cells (PBMCs) from healthy donors were cultured with plasma samples from CF patients ( n = 103) and unrelated, healthy controls ( n = 31). Gene expression levels were measured with an Affymetrix microarray (GeneChip Human Genome U133 Plus 2.0). Peripheral blood samples from a subset of the CF patients ( n = 40) were immunophenotyped by flow cytometry, and the data were compared with historical data for age-matched healthy controls ( n = 351). Plasma samples from another subset of CF patients ( n = 56) and healthy controls ( n = 16) were analyzed by multiplex enzyme-linked immunosorbent assay (ELISA) for numerous cytokines and chemokines. Principal component analysis and hierarchical clustering of induced transcriptional data revealed disease-specific plasma-induced PBMC profiles. Among 1,094 differentially expressed probe sets, 51 genes were associated with pancreatic sufficient status, and 224 genes were associated with infection with Pseudomonas aeruginosa. The flow cytometry and ELISA data confirmed that various immune modulators are relevant contributors to the CF molecular signature. This study provides strong evidence for distinct molecular signatures among CF patients. An understanding of these molecular signatures may lead to unique molecular markers that will enable more personalized prognoses, individualized treatment plans, and rapid monitoring of treatment response.

Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease.

BACKGROUND: Many patients remain without a diagnosis despite extensive medical evaluation. The Undiagnosed Diseases Network (UDN) was established to apply a multidisciplinary model in the evaluation of the most challenging cases and to identify the biologic characteristics of newly discovered diseases. The UDN, which is funded by the National Institutes of Health, was formed in 2014 as a network of seven clinical sites, two sequencing cores, and a coordinating center. Later, a central biorepository, a metabolomics core, and a model organisms screening center were added. METHODS: We evaluated patients who were referred to the UDN over a period of 20 months. The patients were required to have an undiagnosed condition despite thorough evaluation by a health care provider. We determined the rate of diagnosis among patients who subsequently had a complete evaluation, and we observed the effect of diagnosis on medical care. RESULTS: A total of 1519 patients (53% female) were referred to the UDN, of whom 601 (40%) were accepted for evaluation. Of the accepted patients, 192 (32%) had previously undergone exome sequencing. Symptoms were neurologic in 40% of the applicants, musculoskeletal in 10%, immunologic in 7%, gastrointestinal in 7%, and rheumatologic in 6%. Of the 382 patients who had a complete evaluation, 132 received a diagnosis, yielding a rate of diagnosis of 35%. A total of 15 diagnoses (11%) were made by clinical review alone, and 98 (74%) were made by exome or genome sequencing. Of the diagnoses, 21% led to recommendations regarding changes in therapy, 37% led to changes in diagnostic testing, and 36% led to variant-specific genetic counseling. We defined 31 new syndromes. CONCLUSIONS: The UDN established a diagnosis in 132 of the 382 patients who had a complete evaluation, yielding a rate of diagnosis of 35%. (Funded by the National Institutes of Health Common Fund.).

Characterization of Coding/Noncoding Variants for SHROOM3 in Patients with CKD.

Background Interpreting genetic variants is one of the greatest challenges impeding analysis of rapidly increasing volumes of genomic data from patients. For example, SHROOM3 is an associated risk gene for CKD, yet causative mechanism(s) of SHROOM3 allele(s) are unknown.Methods We used our analytic pipeline that integrates genetic, computational, biochemical, CRISPR/Cas9 editing, molecular, and physiologic data to characterize coding and noncoding variants to study the human SHROOM3 risk locus for CKD.Results We identified a novel SHROOM3 transcriptional start site, which results in a shorter isoform lacking the PDZ domain and is regulated by a common noncoding sequence variant associated with CKD (rs17319721, allele frequency: 0.35). This variant disrupted allele binding to the transcription factor TCF7L2 in podocyte cell nuclear extracts and altered transcription levels of SHROOM3 in cultured cells, potentially through the loss of repressive looping between rs17319721 and the novel start site. Although common variant mechanisms are of high utility, sequencing is beginning to identify rare variants involved in disease; therefore, we used our biophysical tools to analyze an average of 112,849 individual human genome sequences for rare SHROOM3 missense variants, revealing 35 high-effect variants. The high-effect alleles include a coding variant (P1244L) previously associated with CKD (P=0.01, odds ratio=7.95; 95% CI, 1.53 to 41.46) that we find to be present in East Asian individuals at an allele frequency of 0.0027. We determined that P1244L attenuates the interaction of SHROOM3 with 14-3-3, suggesting alterations to the Hippo pathway, a known mediator of CKD.Conclusions These data demonstrate multiple new SHROOM3-dependent genetic/molecular mechanisms that likely affect CKD.

Research Directions in the Clinical Implementation of Pharmacogenomics: An Overview of US Programs and Projects.

Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them.

Essential role of Kir5.1 channels in renal salt handling and blood pressure control.

Supplementing diets with high potassium helps reduce hypertension in humans. Inwardly rectifying K+ channels Kir4.1 (Kcnj10) and Kir5.1 (Kcnj16) are highly expressed in the basolateral membrane of distal renal tubules and contribute to Na+ reabsorption and K+ secretion through the direct control of transepithelial voltage. To define the importance of Kir5.1 in blood pressure control under conditions of salt-induced hypertension, we generated a Kcnj16 knockout in Dahl salt-sensitive (SS) rats (SSKcnj16-/-). SSKcnj16-/- rats exhibited hypokalemia and reduced blood pressure, and when fed a high-salt diet (4% NaCl), experienced 100% mortality within a few days triggered by salt wasting and severe hypokalemia. Electrophysiological recordings of basolateral K+ channels in the collecting ducts isolated from SSKcnj16-/- rats revealed activity of only homomeric Kir4.1 channels. Kir4.1 expression was upregulated in SSKcnj16-/- rats, but the protein was predominantly localized in the cytosol in SSKcnj16-/- rats. Benzamil, but not hydrochlorothiazide or furosemide, rescued this phenotype from mortality on a high-salt diet. Supplementation of high-salt diet with increased potassium (2% KCl) prevented mortality in SSKcnj16-/- rats and prevented or mitigated hypertension in SSKcnj16-/- or control SS rats, respectively. Our results demonstrate that Kir5.1 channels are key regulators of renal salt handling in SS hypertension.

Successful Application of Whole Genome Sequencing in a Medical Genetics Clinic.

A pilot program was initiated using whole genome sequencing (WGS) to diagnose suspected genetic disorders in the Genetics Clinic at Children's Hospital of Wisconsin. Twenty-two patients underwent WGS between 2010 and 2013. Initially, we obtained a 14% (3/22) diagnosis rate over 2 years; with subsequent reanalysis, this increased to 36% (8/22). Disease causing variants were identified in SKIV2L, CECR1, DGKE, PYCR2, RYR1, PDGFRB, EFTUD2, and BCS1L. In 75% (6/8) of diagnosed cases, the diagnosis affected treatment and/or medical surveillance. Additionally, one case demonstrated a homozygous A18V variant in VLDLR that appears to be associated with a previously undescribed phenotype.

Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research.

Genome sequencing has revolutionized the diagnosis of genetic diseases. Close collaborations between basic scientists and clinical genomicists are now needed to link genetic variants with disease causation. To facilitate such collaborations, we recommend prioritizing clinically relevant genes for functional studies, developing reference variant-phenotype databases, adopting phenotype description standards, and promoting data sharing.

Molecular modeling in the age of clinical genomics, the enterprise of the next generation.

Protein modeling and molecular dynamics hold a unique toolset to aide in the characterization of clinical variants that may result in disease. Not only do these techniques offer the ability to study under characterized proteins, but they do this with the speed that is needed for time-sensitive clinical cases. In this paper we retrospectively study a clinical variant in the XIAP protein, C203Y, while addressing additional variants seen in patients with similar gastrointestinal phenotypes as the C203Y mutation. In agreement with the clinical tests performed on the C203Y patient, protein modeling and molecular dynamics suggest that direct interactions with RIPK2 and Caspase3 are altered by the C203Y mutation and subsequent loss of Zn coordination in the second BIR domain of XIAP. Interestingly, the variant does not appear to alter interactions with SMAC, resulting in further damage to the caspase and NOD2 pathways. To expand the computational strategy designed when studying XIAP, we have applied the molecular modeling tools to a list of 140 variants seen in CFTR associated with cystic fibrosis, and a list of undiagnosed variants in 17 different genes. This paper shows the exciting applications of molecular modeling in the classification and characterization of genetic variants identified in next generation sequencing. Graphical abstract XIAP in Caspase 3 and NOD2 signaling pathways.

From rat pathophysiology to genomic medicine: an interview with Howard Jacob.

Howard Jacob is best known for pioneering genomic sequencing of a patient to solve a mysterious pediatric case in 2010. With roots in pharmacology and cardiovascular disease, however, his career has largely been dedicated to dissecting the physiology and genetics of the rat to help understand complex human diseases. Howard was Director of the Human and Molecular Genetics Center at the Medical College of Wisconsin for 16 years, during which time he applied a combination of approaches, including quantitative genetics, integrative physiology and next-generation sequencing, in rat models to shed light on cardiovascular, metabolic and renal disorders. He was a key contributor to the genomic toolbox for rat research, and generated the first targeted-knockout rat models using zinc-finger-nuclease technology. He also contributed to sequencing of the rat genome and establishment of the Rat Genome Database. In this interview, Howard provides his perspectives on the past, present and future of rat-based translational research and explains why, despite his many successes as the leader of a rat group, he recently made the transition to clinical genomics.

p66Shc regulates renal vascular tone in hypertension-induced nephropathy.

Renal preglomerular arterioles regulate vascular tone to ensure a large pressure gradient over short distances, a function that is extremely important for maintaining renal microcirculation. Regulation of renal microvascular tone is impaired in salt-sensitive (SS) hypertension-induced nephropathy, but the molecular mechanisms contributing to this impairment remain elusive. Here, we assessed the contribution of the SH2 adaptor protein p66Shc (encoded by Shc1) in regulating renal vascular tone and the development of renal vascular dysfunction associated with hypertension-induced nephropathy. We generated a panel of mutant rat strains in which specific modifications of Shc1 were introduced into the Dahl SS rats. In SS rats, overexpression of p66Shc was linked to increased renal damage. Conversely, deletion of p66Shc from these rats restored the myogenic responsiveness of renal preglomerular arterioles ex vivo and promoted cellular contraction in primary vascular smooth muscle cells (SMCs) that were isolated from renal vessels. In primary SMCs, p66Shc restricted the activation of transient receptor potential cation channels to attenuate cytosolic Ca2+ influx, implicating a mechanism by which overexpression of p66Shc impairs renal vascular reactivity. These results establish the adaptor protein p66Shc as a regulator of renal vascular tone and a driver of impaired renal vascular function in hypertension-induced nephropathy.