Gastric cancer is a major cause of global cancer mortality. We surveyed the spectrum of somatic alterations in gastric cancer by sequencing the exomes of 15 gastric adenocarcinomas and their matched normal DNAs. Frequently mutated genes in the adenocarcinomas included TP53 (11/15 tumors), PIK3CA (3/15) and ARID1A (3/15). Cell adhesion was the most enriched biological pathway among the frequently mutated genes. A prevalence screening confirmed mutations in FAT4, a cadherin family gene, in 5% of gastric cancers (6/110) and FAT4 genomic deletions in 4% (3/83) of gastric tumors. Frequent mutations in chromatin remodeling genes (ARID1A, MLL3 and MLL) also occurred in 47% of the gastric cancers. We detected ARID1A mutations in 8% of tumors (9/110), which were associated with concurrent PIK3CA mutations and microsatellite instability. In functional assays, we observed both FAT4 and ARID1A to exert tumor-suppressor activity. Somatic inactivation of FAT4 and ARID1A may thus be key tumorigenic events in a subset of gastric cancers.

Background The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. Methods In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1•5, 2•0, and 3•0 at differing infection rate scenarios, including full suppression (0•001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. Findings We included 3 862 012 individuals (1 957 935 [50•7%] women and 1 904 077 [49•3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13•7% were older than 70 years and 6•3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4•46% (95% CI 4•41-4•51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1•5, four with an RR of 2•0, and seven with an RR of 3•0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1•5, 36 749 with an RR of 2•0, and 73 498 with an RR of 3•0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1•5, 293 991 with an RR of 2•0, and 587 982 with an RR of 3•0. Interpretation We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indir...

Many solid cancers are known to exhibit a high degree of heterogeneity in their deregulation of different oncogenic pathways. We sought to identify major oncogenic pathways in gastric cancer (GC) with significant relationships to patient survival. Using gene expression signatures, we devised an in silico strategy to map patterns of oncogenic pathway activation in 301 primary gastric cancers, the second highest cause of global cancer mortality. We identified three oncogenic pathways (proliferation/stem cell, NF-κB, and Wnt/β-catenin) deregulated in the majority (>70%) of gastric cancers. We functionally validated these pathway predictions in a panel of gastric cancer cell lines. Patient stratification by oncogenic pathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior. Individual GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups. Predicting pathway activity by expression signatures thus permits the study of multiple cancer-related pathways interacting simultaneously in primary cancers, at a scale not currently achievable by other platforms.

BACKGROUND & AIMS Gastric cancer (GC) is a heterogeneous disease comprising multiple subtypes that each have distinct biological properties and effects in patients. We sought to identify new, intrinsic subtypes of GC by gene expression analysis of a large panel of GC cell lines. We tested if these subtypes might be associated with differences patient survival times and responses to various standard-of-care cytotoxic drugs. METHODS We analyzed gene expression profiles for 37 GC cell lines to identify intrinsic GC subtypes. These subtypes were validated in primary tumors from 521 patients in 4 independent cohorts, where the subtypes were determined by either expression profiling or subtype-specific immunohistochemical markers (LGALS4, CDH17). In vitro sensitivity to 3 chemotherapy drugs (5-FU, cisplatin, oxaliplatin) was also assessed. RESULTS Unsupervised cell line analysis identified 2 major intrinsic genomic subtypes (G-INT and G-DIF), that had distinct patterns of gene expression. The intrinsic subtypes, but not subtypes based on Lauren’s histopathologic classification, were prognostic of survival, based on univariate and multivariate analysis in multiple patient cohorts. The G-INT cell lines were significantly more sensitive to 5-FU and oxaliplatin, but more resistant to cisplatin, than the G-DIF cell lines. In patients, intrinsic subtypes were associated with survival time following adjuvant, 5-FU based therapy. CONCLUSIONS Intrinsic subtypes of GC, based on distinct patterns of expression, are associated with patient survival and response to chemotherapy. Classification of GC based on intrinsic subtypes might be used to determine prognosis and customize therapy.

BackgroundThe number of proposed prognostic models for COVID-19 is growing rapidly, but it is unknown whether any are suitable for widespread clinical implementation.MethodsWe independently externally validated the performance candidate prognostic models, identified through a living systematic review, among consecutive adults admitted to hospital with a final diagnosis of COVID-19. We reconstructed candidate models as per original descriptions and evaluated performance for their original intended outcomes using predictors measured at admission. We assessed discrimination, calibration and net benefit, compared to the default strategies of treating all and no patients, and against the most discriminating predictor in univariable analyses.ResultsWe tested 22 candidate prognostic models among 411 participants with COVID-19, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. Highest areas under receiver operating characteristic (AUROC) curves were achieved by the NEWS2 score for prediction of deterioration over 24 h (0.78; 95% CI 0.73–0.83), and a novel model for prediction of deterioration <14 days from admission (0.78; 0.74–0.82). The most discriminating univariable predictors were admission oxygen saturation on room air for in-hospital deterioration (AUROC 0.76; 0.71–0.81), and age for in-hospital mortality (AUROC 0.76; 0.71–0.81). No prognostic model demonstrated consistently higher net benefit than these univariable predictors, across a range of threshold probabilities.ConclusionsAdmission oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated here offered incremental value for patient stratification to these univariable predictors.

KLF5/GATA4/GATA6 may promote GC development by engaging in mutual crosstalk, collaborating to maintain a pro-oncogenic transcriptional regulatory network in GC cells.

Regulatory enhancer elements in solid tumours remain poorly characterized. Here we apply micro-scale chromatin profiling to survey the distal enhancer landscape of primary gastric adenocarcinoma (GC), a leading cause of global cancer mortality. Integrating 110 epigenomic profiles from primary GCs, normal gastric tissues and cell lines, we highlight 36,973 predicted enhancers and 3,759 predicted super-enhancers respectively. Cell-line-defined super-enhancers can be subclassified by their somatic alteration status into somatic gain, loss and unaltered categories, each displaying distinct epigenetic, transcriptional and pathway enrichments. Somatic gain super-enhancers are associated with complex chromatin interaction profiles, expression patterns correlated with patient outcome and dense co-occupancy of the transcription factors CDX2 and HNF4α. Somatic super-enhancers are also enriched in genetic risk SNPs associated with cancer predisposition. Our results reveal a genome-wide reprogramming of the GC enhancer and super-enhancer landscape during tumorigenesis, contributing to dysregulated local and regional cancer gene expression.

Mutations of bone morphogenetic protein receptor type II (BMPR-II) have been associated with familial and idiopathic pulmonary arterial hypertension (PAH). BMPR-II is a member of the transforming growth factor-␤ receptor superfamily. It consists of extracellular, transmembrane, and kinase domains, and a unique C-terminus with mostly unknown function. However, a number of PAH-causing mutations are predicted to truncate the C-terminus, suggesting that this domain plays an important role in the homeostasis of pulmonary vessels. In this study, we sought to elucidate the functional role of this C-terminus by seeking its interacting partners. Using yeast two-hybrid screening, we identified c-Src tyrosine kinase as a binding partner of this C-terminus. In vitro co-immunoprecipitation confirmed their interaction. Mutations truncating the C-terminus disrupted their interaction, while missense mutation within kinase domain reduced their interaction. In addition, BMPR-II and c-Src tyrosine kinase colocalized within intracellular aggregates when overexpressed in HEK293 cells. Moreover, mutations truncating the C-terminus disrupted their colocalization, whereas missense mutation within kinase domain had no effect on their colocalization. Keywords: bone morphogenetic protein receptor type II; c-Src tyrosine kinase; pulmonary arterial hypertension Bone morphogenetic protein receptor type II (BMPR-II) is a member of the transforming growth factor-␤ (TGF-␤) receptor superfamily. Members of the TGF-␤ receptor superfamily include type I and type II receptor proteins, and play a complex and multifunctional role in the regulation of cell proliferation, differentiation, and apoptosis during embryogenesis and throughout adult life (1). Homozygous BMPR-II knockout mice die very early in development during gastrulation (2), while mice that express a mutant BMPR-II, lacking half of the ligandbinding domain, undergo normal gastrulation, but die at midgestation with cardiovascular and skeletal defects (3).