Functional dissection of complex and molecular trait variants at single nucleotide resolution.

Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.

Gastroesophageal Adenocarcinomas With Defective Mismatch Repair: Current Knowledge and Clinical Management.

Esophageal, gastroesophageal junction, and gastric adenocarcinomas, referred to collectively as gastroesophageal adenocarcinomas (GEAs), are a major cause of global cancer-related mortality. Our increasing molecular understanding has led to the addition of biomarker-directed approaches to defined subgroups and has improved survival in selected patients, such as those with HER2 and Claudin18.2 overexpression. Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of cancer, including GEA, but biomarkers beyond PD-L1 expression are lacking. Mismatch repair deficiency and/or high microsatellite instability (dMMR/MSI-H) is observed in 8% to 22% of nonmetastatic GEA, and 3% to 5% of patients with metastatic disease. dMMR/MSI-H tumors are associated with more favorable prognosis and significant benefit from ICIs, although some heterogeneity exists. The activity of ICIs in advanced dMMR/MSI-H cancer is seen across lines of therapy and should be recommended in the frontline setting. In patients with nonmetastatic dMMR/MSI-H cancer, increasing evidence suggests that perioperative and adjuvant chemotherapy may not provide benefit to the dMMR/MSI-H subgroup. The activity of perioperative chemotherapy-free immune checkpoint regimens in patients with nonmetastatic dMMR/MSI-H cancer is highly promising and underscores the need to identify this unique subgroup. We recommend MMR/MSI testing for all patients with GEA at diagnosis, and review the key rationale and clinical management implications for patient with dMMR/MSI-H tumors across disease stages.

Genome-wide association study identifies human genetic variants associated with fatal outcome from Lassa fever.

Infection with Lassa virus (LASV) can cause Lassa fever, a haemorrhagic illness with an estimated fatality rate of 29.7%, but causes no or mild symptoms in many individuals. Here, to investigate whether human genetic variation underlies the heterogeneity of LASV infection, we carried out genome-wide association studies (GWAS) as well as seroprevalence surveys, human leukocyte antigen typing and high-throughput variant functional characterization assays. We analysed Lassa fever susceptibility and fatal outcomes in 533 cases of Lassa fever and 1,986 population controls recruited over a 7 year period in Nigeria and Sierra Leone. We detected genome-wide significant variant associations with Lassa fever fatal outcomes near GRM7 and LIF in the Nigerian cohort. We also show that a haplotype bearing signatures of positive selection and overlapping LARGE1, a required LASV entry factor, is associated with decreased risk of Lassa fever in the Nigerian cohort but not in the Sierra Leone cohort. Overall, we identified variants and genes that may impact the risk of severe Lassa fever, demonstrating how GWAS can provide insight into viral pathogenesis.

Convergence of coronary artery disease genes onto endothelial cell programs.

Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.

Identification and Characterization of Avoidable Hospital Admissions in Patients With Lung Cancer.

BACKGROUND: More than 50% of patients with lung cancer are admitted to the hospital while receiving treatment, which is a burden to patients and the healthcare system. This study characterizes the risk factors and outcomes of patients with lung cancer who were admitted to the hospital. METHODS: A multidisciplinary oncology care team conducted a retrospective medical record review of patients with lung cancer admitted in 2018. Demographics, disease and admission characteristics, and end-of-life care utilization were recorded. Following a multidisciplinary consensus review process, admissions were determined to be either "avoidable" or "unavoidable." Generalized estimating equation logistic regression models assessed risks and outcomes associated with avoidable admissions. RESULTS: In all, 319 admissions for 188 patients with a median age of 66 years (IQR, 59-74 years) were included. Cancer-related symptoms accounted for 65% of hospitalizations. Common causes of unavoidable hospitalizations were unexpected disease progression causing symptoms, chronic obstructive pulmonary disease exacerbation, and infection. Of the 47 hospitalizations identified as avoidable (15%), the median overall survival was 1.6 months compared with 9.7 months (hazard ratio, 2.07; 95% CI, 1.34-3.19; P<.001) for unavoidable hospitalizations. Significant reasons for avoidable admissions included cancer-related pain (P=.02), hypervolemia (P=.03), patient desire to initiate hospice services (P=.01), and errors in medication reconciliation or distribution (P<.001). Errors in medication management caused 26% of the avoidable hospitalizations. Of admissions in patients receiving immunotherapy (n=102) or targeted therapy (n=44), 9% were due to adverse effects of treatment. Patients receiving immunotherapy and targeted therapy were at similar risk of avoidable hospitalizations compared with patients not receiving treatment (P=.3 and P=.1, respectively). CONCLUSIONS: We found that 15% of hospitalizations among patients with lung cancer were potentially avoidable. Uncontrolled symptoms, delayed implementation of end-of-life care, and errors in medication reconciliation were associated with avoidable inpatient admissions. Symptom management tools, palliative care integration, and medication reconciliations may mitigate hospitalization risk.

Esophageal Cancer: Overview, Risk Factors, and Reasons for the Rise.

PURPOSE OF REVIEW: Esophageal cancer (EC) is a common cancer affecting many regions of the world and carries significant morbidity and mortality. In this article, we review the key risk factors and their associated impact on the changing incidence and prevalence of EC subtypes within different global regions. We also highlight potential reasons for the ever-changing epidemiology of this prevalent cancer type. RECENT FINDINGS: There has been a shift in incidence of Esophageal Adenocarcinoma (AC) and Squamous Cell Carcinoma (SCC) within certain populations primarily due to an increase prevalence of primary risk factors. In Western nations, more often the United States, there has been a shift from SCC predominance to the majority of new cases of EC being adenocarcinoma. This shift within the United States has largely correlated with a rise in obesity. The prevalence of AC in Asia is also starting to rise as more countries adopt a western diet. The pathophysiology, associated risk factors, and presentation of ESCC and AC are different. This difference is seen in varying lifestyles, population health, and certain genetic risks. With further development closer analysis of primary risk factors and implementation of policies and programs that promote public health literacy, there is a potential to decrease esophageal cancer's global disease burden.

Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies.

Linkage disequilibrium (LD) is the correlation among nearby genetic variants. In genetic association studies, LD is often modeled using large correlation matrices, but this approach is inefficient, especially in ancestrally diverse studies. In the present study, we introduce LD graphical models (LDGMs), which are an extremely sparse and efficient representation of LD. LDGMs are derived from genome-wide genealogies; statistical relationships among alleles in the LDGM correspond to genealogical relationships among haplotypes. We published LDGMs and ancestry-specific LDGM precision matrices for 18 million common variants (minor allele frequency >1%) in five ancestry groups, validated their accuracy and demonstrated order-of-magnitude improvements in runtime for commonly used LD matrix computations. We implemented an extremely fast multiancestry polygenic prediction method, BLUPx-ldgm, which performs better than a similar method based on the reference LD correlation matrix. LDGMs will enable sophisticated methods that scale to ancestrally diverse genetic association data across millions of variants and individuals.

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.

Massively parallel base editing to map variant effects in human hematopoiesis.

Systematic evaluation of the impact of genetic variants is critical for the study and treatment of human physiology and disease. While specific mutations can be introduced by genome engineering, we still lack scalable approaches that are applicable to the important setting of primary cells, such as blood and immune cells. Here, we describe the development of massively parallel base-editing screens in human hematopoietic stem and progenitor cells. Such approaches enable functional screens for variant effects across any hematopoietic differentiation state. Moreover, they allow for rich phenotyping through single-cell RNA sequencing readouts and separately for characterization of editing outcomes through pooled single-cell genotyping. We efficiently design improved leukemia immunotherapy approaches, comprehensively identify non-coding variants modulating fetal hemoglobin expression, define mechanisms regulating hematopoietic differentiation, and probe the pathogenicity of uncharacterized disease-associated variants. These strategies will advance effective and high-throughput variant-to-function mapping in human hematopoiesis to identify the causes of diverse diseases.

Using the HER2/CEP17 FISH Ratio to Predict Pathologic Complete Response Following Neoadjuvant Anti-HER2 Doublet Therapy in HER2+ Breast Cancer.

BACKGROUND: Clinical trials of HER2-directed therapy that omit neoadjuvant conventional chemotherapy for HER+ breast cancer demonstrate that a subset of patients still obtains a pCR. Identifying tumor characteristics which predict pCR may help select patients for de-escalated neoadjuvant dual HER2-targeted treatment without chemotherapy. This is the first study evaluating the HER2/CEP17 ratio by FISH as a biomarker to predict pCR among patients who received neoadjuvant anti-HER2 regimens without chemotherapy. PATIENTS AND METHODS: Data from patients with locally advanced HER2+ breast cancer who received neoadjuvant dual HER2-targeted therapy without conventional chemotherapy from a single center was retrospectively reviewed. All patients were enrolled in one of 3 clinical trials evaluating chemotherapy de-escalation. Logistic regression modeling assessed for a relationship between the HER2/CEP17 FISH ratio obtained from baseline tissue biopsy and pCR based on pathology at the time of definitive breast surgery following neoadjuvant treatment. RESULTS: Following neoadjuvant treatment with dual HER2-targeted therapies in 56 patients, the probability of pCR was 73% among patients with a HER2 ratio of 13.1 compared to a probability of 38% among patients with HER2 ratio of 5.5 (OR 4.14, 95% CI 1.44-11.89; P = .012). This positive association persisted after controlling for different treatment regimens administered (OR 2.87, 95% CI 0.9-9.18, P = .020). CONCLUSIONS: These data found a positive association between the HER2/CEP17 FISH ratio and pCR following neoadjuvant dual HER2-targeted therapy without chemotherapy. Larger prospective studies are needed to validate the HER2 ratio as a biomarker to select patients for neoadjuvant dual anti-HER2 therapy without chemotherapy.

Rewriting regulatory DNA to dissect and reprogram gene expression.

Regulatory DNA sequences within enhancers and promoters bind transcription factors to encode cell type-specific patterns of gene expression. However, the regulatory effects and programmability of such DNA sequences remain difficult to map or predict because we have lacked scalable methods to precisely edit regulatory DNA and quantify the effects in an endogenous genomic context. Here we present an approach to measure the quantitative effects of hundreds of designed DNA sequence variants on gene expression, by combining pooled CRISPR prime editing with RNA fluorescence in situ hybridization and cell sorting (Variant-FlowFISH). We apply this method to mutagenize and rewrite regulatory DNA sequences in an enhancer and the promoter of PPIF in two immune cell lines. Of 672 variant-cell type pairs, we identify 497 that affect PPIF expression. These variants appear to act through a variety of mechanisms including disruption or optimization of existing transcription factor binding sites, as well as creation of de novo sites. Disrupting a single endogenous transcription factor binding site often led to large changes in expression (up to -40% in the enhancer, and -50% in the promoter). The same variant often had different effects across cell types and states, demonstrating a highly tunable regulatory landscape. We use these data to benchmark performance of sequence-based predictive models of gene regulation, and find that certain types of variants are not accurately predicted by existing models. Finally, we computationally design 185 small sequence variants (≤10 bp) and optimize them for specific effects on expression in silico. 84% of these rationally designed edits showed the intended direction of effect, and some had dramatic effects on expression (-100% to +202%). Variant-FlowFISH thus provides a powerful tool to map the effects of variants and transcription factor binding sites on gene expression, test and improve computational models of gene regulation, and reprogram regulatory DNA.

Gene Sequencing Identifies Perturbation in Nitric Oxide Signaling as a Nonlipid Molecular Subtype of Coronary Artery Disease.

BACKGROUND: A key goal of precision medicine is to disaggregate common, complex diseases into discrete molecular subtypes. Rare coding variants in the low-density lipoprotein receptor gene (LDLR) are identified in 1% to 2% of coronary artery disease (CAD) patients, defining a molecular subtype with risk driven by hypercholesterolemia. METHODS: To search for additional subtypes, we compared the frequency of rare, predicted loss-of-function and damaging missense variants aggregated within a given gene in 41 081 CAD cases versus 217 115 controls. RESULTS: Rare variants in LDLR were most strongly associated with CAD, present in 1% of cases and associated with 4.4-fold increased CAD risk. A second subtype was characterized by variants in endothelial nitric oxide synthase gene (NOS3), a key enzyme regulating vascular tone, endothelial function, and platelet aggregation. A rare predicted loss-of-function or damaging missense variants in NOS3 was present in 0.6% of cases and associated with 2.42-fold increased risk of CAD (95% CI, 1.80-3.26; P=5.50×10-9). These variants were associated with higher systolic blood pressure (+3.25 mm Hg; [95% CI, 1.86-4.65]; P=5.00×10-6) and increased risk of hypertension (adjusted odds ratio 1.31; [95% CI, 1.14-1.51]; P=2.00×10-4) but not circulating cholesterol concentrations, suggesting that, beyond lipid pathways, nitric oxide synthesis is a key nonlipid driver of CAD risk. CONCLUSIONS: Beyond LDLR, we identified an additional nonlipid molecular subtype of CAD characterized by rare variants in the NOS3 gene.

A patient-driven clinicogenomic partnership for metastatic prostate cancer.

Molecular profiling studies have enabled discoveries for metastatic prostate cancer (MPC) but have predominantly occurred in academic medical institutions and involved non-representative patient populations. We established the Metastatic Prostate Cancer Project (MPCproject, mpcproject.org), a patient-partnered initiative to involve patients with MPC living anywhere in the US and Canada in molecular research. Here, we present results from our partnership with the first 706 MPCproject participants. While 41% of patient partners live in rural, physician-shortage, or medically underserved areas, the MPCproject has not yet achieved racial diversity, a disparity that demands new initiatives detailed herein. Among molecular data from 333 patient partners (572 samples), exome sequencing of 63 tumor and 19 cell-free DNA (cfDNA) samples recapitulated known findings in MPC, while inexpensive ultra-low-coverage sequencing of 318 cfDNA samples revealed clinically relevant AR amplifications. This study illustrates the power of a growing, longitudinal partnership with patients to generate a more representative understanding of MPC.

Neurocognitive trajectory and proteomic signature of inherited risk for Alzheimer's disease.

For Alzheimer's disease-a leading cause of dementia and global morbidity-improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer's disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer's disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer's disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer's disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer's disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution.

Inferring gene regulation from stochastic transcriptional variation across single cells at steady state.

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

Compatibility rules of human enhancer and promoter sequences.

Gene regulation in the human genome is controlled by distal enhancers that activate specific nearby promoters1. A proposed model for this specificity is that promoters have sequence-encoded preferences for certain enhancers, for example, mediated by interacting sets of transcription factors or cofactors2. This 'biochemical compatibility' model has been supported by observations at individual human promoters and by genome-wide measurements in Drosophila3-9. However, the degree to which human enhancers and promoters are intrinsically compatible has not yet been systematically measured, and how their activities combine to control RNA expression remains unclear. Here we design a high-throughput reporter assay called enhancer × promoter self-transcribing active regulatory region sequencing (ExP STARR-seq) and applied it to examine the combinatorial compatibilities of 1,000 enhancer and 1,000 promoter sequences in human K562 cells. We identify simple rules for enhancer-promoter compatibility, whereby most enhancers activate all promoters by similar amounts, and intrinsic enhancer and promoter activities multiplicatively combine to determine RNA output (R2 = 0.82). In addition, two classes of enhancers and promoters show subtle preferential effects. Promoters of housekeeping genes contain built-in activating motifs for factors such as GABPA and YY1, which decrease the responsiveness of promoters to distal enhancers. Promoters of variably expressed genes lack these motifs and show stronger responsiveness to enhancers. Together, this systematic assessment of enhancer-promoter compatibility suggests a multiplicative model tuned by enhancer and promoter class to control gene transcription in the human genome.

Mapping transcriptomic vector fields of single cells.

Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo's power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Cross-Sectional Assessment of SARS-CoV-2 Viral Load by Symptom Status in Massachusetts Congregate Living Facilities.

Transmission of coronavirus disease 2019 (COVID-19) from people without symptoms confounds societal mitigation strategies. From April to June 2020, we tested nasopharyngeal swabs by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) from 15 514 staff and 16 966 residents of nursing homes and assisted living facilities in Massachusetts. Cycle threshold (Ct) distributions were very similar between populations with (n = 739) and without (n = 2179) symptoms at the time of sampling (mean Ct, 25.7 vs 26.4; ranges 12-38). However, as local cases waned, those without symptoms shifted towards higher Ct. With such similar viral load distributions, existing testing modalities should perform comparably regardless of symptoms, contingent upon time since infection.

Compressed sensing for highly efficient imaging transcriptomics.

Recent methods for spatial imaging of tissue samples can identify up to ~100 individual proteins1-3 or RNAs4-10 at single-cell resolution. However, the number of proteins or genes that can be studied in these approaches is limited by long imaging times. Here we introduce Composite In Situ Imaging (CISI), a method that leverages structure in gene expression across both cells and tissues to limit the number of imaging cycles needed to obtain spatially resolved gene expression maps. CISI defines gene modules that can be detected using composite measurements from imaging probes for subsets of genes. The data are then decompressed to recover expression values for individual genes. CISI further reduces imaging time by not relying on spot-level resolution, enabling lower magnification acquisition, and is overall about 500-fold more efficient than current methods. Applying CISI to 12 mouse brain sections, we accurately recovered the spatial abundance of 37 individual genes from 11 composite measurements covering 180 mm2 and 476,276 cells.