Background The UK 100,000 Genomes Project is in the process of investigating the role of genome sequencing of patients with undiagnosed rare disease following usual care, and the alignment of research with healthcare implementation in the UK’s national health service. (Other parts of this Project focus on patients with cancer and infection.) Methods We enrolled participants, collected clinical features with human phenotype ontology terms, undertook genome sequencing and applied automated variant prioritization based on virtual gene panels (PanelApp) and phenotypes (Exomiser), alongside identification of novel pathogenic variants through research analysis. We report results on a pilot study of 4660 participants from 2183 families with 161 disorders covering a broad spectrum of rare disease. Results Diagnostic yields varied by family structure and were highest in trios and larger pedigrees. Likely monogenic disorders had much higher diagnostic yields (35%) with intellectual disability, hearing and vision disorders, achieving yields between 40 and 55%. Those with more complex etiologies had an overall 25% yield. Combining research and automated approaches was critical to 14% of diagnoses in which we found etiologic non-coding, structural and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohort-wide burden testing across 57,000 genomes enabled discovery of 3 new disease genes and 19 novel associations. Of the genetic diagnoses that we made, 24% had immediate ramifications for the clinical decision-making for the patient or their relatives. Conclusion Our pilot study of genome sequencing in a national health care system demonstrates diagnostic uplift across a range of rare diseases. (Funded by National Institute for Health Research and others)

Background: Cancer and multiple non-cancer conditions are considered by the Centers for Disease Control and Prevention (CDC) as high risk conditions in the COVID-19 emergency. Professional societies have recommended changes in cancer service provision to minimize COVID-19 risks to cancer patients and health care workers. However, we do not know the extent to which cancer patients, in whom multi-morbidity is common, may be at higher overall risk of mortality as a net result of multiple factors including COVID-19 infection, changes in health services, and socioeconomic factors. Methods: We report multi-center, weekly cancer diagnostic referrals and chemotherapy treatments until April 2020 in England and Northern Ireland. We analyzed population-based health records from 3,862,012 adults in England to estimate 1-year mortality in 24 cancer sites and 15 non-cancer comorbidity clusters (40 conditions) recognized by CDC as high-risk. We estimated overall (direct and indirect) effects of COVID-19 emergency on mortality under different Relative Impact of the Emergency (RIE) and different Proportions of the population Affected by the Emergency (PAE). We applied the same model to the US, using Surveillance, Epidemiology, and End Results (SEER) program data. Results: Weekly data until April 2020 demonstrate significant falls in admissions for chemotherapy (45-66% reduction) and urgent referrals for early cancer diagnosis (70-89% reduction), compared to pre-emergency levels. Under conservative assumptions of the emergency affecting only people with newly diagnosed cancer (incident cases) at COVID-19 PAE of 40%, and an RIE of 1.5, the model estimated 6,270 excess deaths at 1 year in England and 33,890 excess deaths in the US. In England, the proportion of patients with incident cancer with ≥1 comorbidity was 65.2%. The number of comorbidities was strongly associated with cancer mortality risk. Across a range of model assumptions, and across incident and prevalent cancer cases, 78% of excess deaths occur in cancer patients with ≥1 comorbidity. Conclusion: We provide the first estimates of potential excess mortality among people with cancer and multimorbidity due to the COVID-19 emergency and demonstrate dramatic changes in cancer services. To better inform prioritization of cancer care and guide policy change, there is an urgent need for weekly data on cause-specific excess mortality, cancer diagnosis and treatment provision and better intelligence on the use of effective treatments for comorbidities.

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.

Environmental carcinogenic exposures are major contributors to global disease burden yet how they promote cancer is unclear. Over 70 years ago, the concept of tumour promoting agents driving latent clones to expand was rst proposed. In support of this model, recent evidence suggests that human tissue contains a patchwork of mutant clones, some of which harbour oncogenic mutations, and many environmental carcinogens lack a clear mutational signature. We hypothesised that the environmental carcinogen, <2.5μm particulate matter (PM2.5), might promote lung cancer promotion through nonmutagenic mechanisms by acting on pre-existing mutant clones within normal tissues in patients with lung cancer who have never smoked, a disease with a high frequency of EGFR activating mutations. We analysed PM2.5 levels and cancer incidence reported by UK Biobank, Public Health England, Taiwan Chang Gung Memorial Hospital (CGMH) and Korean Samsung Medical Centre (SMC) from a total of 463,679 individuals between 2006-2018. We report associations between PM2.5 levels and the incidence of several cancers, including EGFR mutant lung cancer. We nd that pollution on a background of EGFR mutant lung epithelium promotes a progenitor-like cell state and demonstrate that PM accelerates lung cancer progression in EGFR and Kras mutant mouse lung cancer models. Through parallel exposure studies in mouse and human participants, we nd evidence that in ammatory mediators, such as interleukin-1 , may act upon EGFR mutant clones to drive expansion of progenitor cells. Ultradeep mutational pro ling of histologically normal lung tissue from 247 individuals across 3 clinical cohorts revealed oncogenic EGFR and KRAS driver mutations in 18% and 33% of normal tissue samples, respectively. These results support a tumour-promoting role for PM acting on latent mutant clones in normal lung tissue and add to evidence providing an urgent mandate to address air pollution in urban areas.

Objectives To illustrate how Apert syndrome, a rare autosomal dominant genetic syndrome, can be detected in the second-trimester of pregnancy using 2D ultrasound, and how 3D ultrasound examination may provide parents with a better understanding of the structural defects affecting their baby. Methods Fetal Medicine Unit database searches to identify Apert syndrome cases.Results Five cases of Apert syndrome were suspected in the second-trimester when sonography showed abnormal extremities including syndactyly, and an abnormal skull shape. In 1 case there was increased nuchal translucency with a normal fetal karyotype in the first-trimester. In all cases, a mutation of the FGFR2 gene confirmed the diagnosis of Apert syndrome. 3D ultrasound was used to show parents the extent of the abnormalities of the skull, face and extremities. Parents were counseled by craniofacial surgeons and geneticists.Conclusion Apert syndrome can be accurately suspected in the second-trimester by careful ultrasound examination of the fetus including the extremities and skull shape. 3D ultrasound can be a useful adjunct to 2D examination for parental counseling.