An atlas of genetic scores to predict multi-omic traits.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

Cracking the type 1 diabetes code: Genes, microbes, immunity, and the early life environment.

Type 1 diabetes (T1D) results from a complex interplay of genetic predisposition, immunological dysregulation, and environmental triggers, that culminate in the destruction of insulin-secreting pancreatic ß cells. This review provides a comprehensive examination of the multiple factors underpinning T1D pathogenesis, to elucidate key mechanisms and potential therapeutic targets. Beginning with an exploration of genetic risk factors, we dissect the roles of human leukocyte antigen (HLA) haplotypes and non-HLA gene variants associated with T1D susceptibility. Mechanistic insights gleaned from the NOD mouse model provide valuable parallels to the human disease, particularly immunological intricacies underlying ß cell-directed autoimmunity. Immunological drivers of T1D pathogenesis are examined, highlighting the pivotal contributions of both effector and regulatory T cells and the multiple functions of B cells and autoantibodies in ß-cell destruction. Furthermore, the impact of environmental risk factors, notably modulation of host immune development by the intestinal microbiome, is examined. Lastly, the review probes human longitudinal studies, unveiling the dynamic interplay between mucosal immunity, systemic antimicrobial antibody responses, and the trajectories of T1D development. Insights garnered from these interconnected factors pave the way for targeted interventions and the identification of biomarkers to enhance T1D management and prevention strategies.

Bayesian inference for identifying tumour-specific cancer dependencies through integration of ex-vivo drug response assays and drug-protein profiling.

The identification of tumor-specific molecular dependencies is essential for the development of effective cancer therapies. Genetic and chemical perturbations are powerful tools for discovering these dependencies. Even though chemical perturbations can be applied to primary cancer samples at large scale, the interpretation of experiment outcomes is often complicated by the fact that one chemical compound can affect multiple proteins. To overcome this challenge, Batzilla et al. (PLoS Comput Biol 18(8): e1010438, 2022) proposed DepInfeR, a regularized multi-response regression model designed to identify and estimate specific molecular dependencies of individual cancers from their ex-vivo drug sensitivity profiles. Inspired by their work, we propose a Bayesian extension to DepInfeR. Our proposed approach offers several advantages over DepInfeR, including e.g. the ability to handle missing values in both protein-drug affinity and drug sensitivity profiles without the need for data pre-processing steps such as imputation. Moreover, our approach uses Gaussian Processes to capture more complex molecular dependency structures, and provides probabilistic statements about whether a protein in the protein-drug affinity profiles is informative to the drug sensitivity profiles. Simulation studies demonstrate that our proposed approach achieves better prediction accuracy, and is able to discover unreported dependency structures.

Association between pregnancy-related complications and development of type 2 diabetes and hypertension in women: an umbrella review.

BACKGROUND: Despite many systematic reviews and meta-analyses examining the associations of pregnancy complications with risk of type 2 diabetes mellitus (T2DM) and hypertension, previous umbrella reviews have only examined a single pregnancy complication. Here we have synthesised evidence from systematic reviews and meta-analyses on the associations of a wide range of pregnancy-related complications with risk of developing T2DM and hypertension. METHODS: Medline, Embase and Cochrane Database of Systematic Reviews were searched from inception until 26 September 2022 for systematic reviews and meta-analysis examining the association between pregnancy complications and risk of T2DM and hypertension. Screening of articles, data extraction and quality appraisal (AMSTAR2) were conducted independently by two reviewers using Covidence software. Data were extracted for studies that examined the risk of T2DM and hypertension in pregnant women with the pregnancy complication compared to pregnant women without the pregnancy complication. Summary estimates of each review were presented using tables, forest plots and narrative synthesis and reported following Preferred Reporting Items for Overviews of Reviews (PRIOR) guidelines. RESULTS: Ten systematic reviews were included. Two pregnancy complications were identified. Gestational diabetes mellitus (GDM): One review showed GDM was associated with a 10-fold higher risk of T2DM at least 1 year after pregnancy (relative risk (RR) 9.51 (95% confidence interval (CI) 7.14 to 12.67) and although the association differed by ethnicity (white: RR 16.28 (95% CI 15.01 to 17.66), non-white: RR 10.38 (95% CI 4.61 to 23.39), mixed: RR 8.31 (95% CI 5.44 to 12.69)), the between subgroups difference were not statistically significant at 5% significance level. Another review showed GDM was associated with higher mean blood pressure at least 3 months postpartum (mean difference in systolic blood pressure: 2.57 (95% CI 1.74 to 3.40) mmHg and mean difference in diastolic blood pressure: 1.89 (95% CI 1.32 to 2.46) mmHg). Hypertensive disorders of pregnancy (HDP): Three reviews showed women with a history of HDP were 3 to 6 times more likely to develop hypertension at least 6 weeks after pregnancy compared to women without HDP (meta-analysis with largest number of studies: odds ratio (OR) 4.33 (3.51 to 5.33)) and one review reported a higher rate of T2DM after HDP (hazard ratio (HR) 2.24 (1.95 to 2.58)) at least a year after pregnancy. One of the three reviews and five other reviews reported women with a history of preeclampsia were 3 to 7 times more likely to develop hypertension at least 6 weeks postpartum (meta-analysis with the largest number of studies: OR 3.90 (3.16 to 4.82) with one of these reviews reporting the association was greatest in women from Asia (Asia: OR 7.54 (95% CI 2.49 to 22.81), Europe: OR 2.19 (95% CI 0.30 to 16.02), North and South America: OR 3.32 (95% CI 1.26 to 8.74)). CONCLUSIONS: GDM and HDP are associated with a greater risk of developing T2DM and hypertension. Common confounders adjusted for across the included studies in the reviews were maternal age, body mass index (BMI), socioeconomic status, smoking status, pre-pregnancy and current BMI, parity, family history of T2DM or cardiovascular disease, ethnicity, and time of delivery. Further research is needed to evaluate the value of embedding these pregnancy complications as part of assessment for future risk of T2DM and chronic hypertension.

Rarity: discovering rare cell populations from single-cell imaging data.

MOTIVATION: Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori, while unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that is magnified when they are defined by differentially expressing a small number of genes. RESULTS: Typical unsupervised approaches fail to identify such rare subpopulations, and these cells tend to be absorbed into more prevalent cell types. In order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent, and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries. We systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC datasets. AVAILABILITY AND IMPLEMENTATION: Implementation of Rarity together with examples is available from the Github repository (https://github.com/kasparmartens/rarity).

Completing a genomic characterisation of microscopic tumour samples with copy number.

BACKGROUND: Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges. We previously developed the DigiPico sequencing platform to accurately identify somatic mutations from such samples. RESULTS: Here, we complete this genomic characterisation with copy number. We present a novel protocol, PicoCNV, to call allele-specific somatic copy number alterations from picogram quantities of tumour DNA. We find that PicoCNV provides exactly accurate copy number in 84% of the genome for even the smallest samples, and demonstrate its clinical potential in maintenance therapy. CONCLUSIONS: PicoCNV complements our existing platform, allowing for accurate and comprehensive genomic characterisations of cancers in settings where only microscopic samples are available.

Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England.

BACKGROUND: Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions. METHODS AND FINDINGS: We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results. CONCLUSIONS: Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.

Polypharmacy during pregnancy and associated risk factors: a retrospective analysis of 577 medication exposures among 1.5 million pregnancies in the UK, 2000-2019.

BACKGROUND: The number of medications prescribed during pregnancy has increased over the past few decades. Few studies have described the prevalence of multiple medication use among pregnant women. This study aims to describe the overall prevalence over the last two decades among all pregnant women and those with multimorbidity and to identify risk factors for polypharmacy in pregnancy. METHODS: A retrospective cohort study was conducted between 2000 and 2019 using the Clinical Practice Research Datalink (CPRD) pregnancy register. Prescription records for 577 medication categories were obtained. Prevalence estimates for polypharmacy (ranging from 2+ to 11+ medications) were presented along with the medications commonly prescribed individually and in pairs during the first trimester and the entire pregnancy period. Logistic regression models were performed to identify risk factors for polypharmacy. RESULTS: During the first trimester (812,354 pregnancies), the prevalence of polypharmacy ranged from 24.6% (2+ medications) to 0.1% (11+ medications). During the entire pregnancy period (774,247 pregnancies), the prevalence ranged from 58.7 to 1.4%. Broad-spectrum penicillin (6.6%), compound analgesics (4.5%) and treatment of candidiasis (4.3%) were commonly prescribed. Pairs of medication prescribed to manage different long-term conditions commonly included selective beta 2 agonists or selective serotonin re-uptake inhibitors (SSRIs). Risk factors for being prescribed 2+ medications during the first trimester of pregnancy include being overweight or obese [aOR: 1.16 (1.14-1.18) and 1.55 (1.53-1.57)], belonging to an ethnic minority group [aOR: 2.40 (2.33-2.47), 1.71 (1.65-1.76), 1.41 (1.35-1.47) and 1.39 (1.30-1.49) among women from South Asian, Black, other and mixed ethnicities compared to white women] and smoking or previously smoking [aOR: 1.19 (1.18-1.20) and 1.05 (1.03-1.06)]. Higher and lower age, higher gravidity, increasing number of comorbidities and increasing level of deprivation were also associated with increased odds of polypharmacy. CONCLUSIONS: The prevalence of polypharmacy during pregnancy has increased over the past two decades and is particularly high in younger and older women; women with high BMI, smokers and ex-smokers; and women with multimorbidity, higher gravidity and higher levels of deprivation. Well-conducted pharmaco-epidemiological research is needed to understand the effects of multiple medication use on the developing foetus.

The development of a core outcome set for studies of pregnant women with multimorbidity.

BACKGROUND: Heterogeneity in reported outcomes can limit the synthesis of research evidence. A core outcome set informs what outcomes are important and should be measured as a minimum in all future studies. We report the development of a core outcome set applicable to observational and interventional studies of pregnant women with multimorbidity. METHODS: We developed the core outcome set in four stages: (i) a systematic literature search, (ii) three focus groups with UK stakeholders, (iii) two rounds of Delphi surveys with international stakeholders and (iv) two international virtual consensus meetings. Stakeholders included women with multimorbidity and experience of pregnancy in the last 5 years, or are planning a pregnancy, their partners, health or social care professionals and researchers. Study adverts were shared through stakeholder charities and organisations. RESULTS: Twenty-six studies were included in the systematic literature search (2017 to 2021) reporting 185 outcomes. Thematic analysis of the focus groups added a further 28 outcomes. Two hundred and nine stakeholders completed the first Delphi survey. One hundred and sixteen stakeholders completed the second Delphi survey where 45 outcomes reached Consensus In (≥70% of all participants rating an outcome as Critically Important). Thirteen stakeholders reviewed 15 Borderline outcomes in the first consensus meeting and included seven additional outcomes. Seventeen stakeholders reviewed these 52 outcomes in a second consensus meeting, the threshold was ≥80% of all participants voting for inclusion. The final core outcome set included 11 outcomes. The five maternal outcomes were as follows: maternal death, severe maternal morbidity, change in existing long-term conditions (physical and mental), quality and experience of care and development of new mental health conditions. The six child outcomes were as follows: survival of baby, gestational age at birth, neurodevelopmental conditions/impairment, quality of life, birth weight and separation of baby from mother for health care needs. CONCLUSIONS: Multimorbidity in pregnancy is a new and complex clinical research area. Following a rigorous process, this complexity was meaningfully reduced to a core outcome set that balances the views of a diverse stakeholder group.

A case-control study of the interaction of fetal heart rate abnormalities, fetal growth restriction, meconium in the amniotic fluid and tachysystole, in relation to the outcome of labour.

OBJECTIVE: To quantify the incidence of intrapartum risk factors in labours with an adverse outcome, and compare them with the incidence of the same indicators in a series of consecutive labours without adverse outcome. DESIGN: Case-control study. SETTING: Twenty-six maternity units in the UK. POPULATION OR SAMPLE: Sixty-nine labours with an adverse outcome and 198 labours without adverse outcome. METHODS: Observational study. MAIN OUTCOME MEASURES: Incidence of risk factors in hourly assessments for 7 hours before birth in the two groups. RESULTS: A risk score combining suspected fetal growth restriction, tachysystole, meconium in the amniotic fluid and fetal heart rate abnormalities (baseline rate and variability, presence of decelerations) gave the best indication of likely outcome group. CONCLUSIONS: Accurate risk assessment in labour requires fetal heart rate abnormalities to be considered in context with additional intrapartum risk factors.

Epidemiology of pre-existing multimorbidity in pregnant women in the UK in 2018: a population-based cross-sectional study.

BACKGROUND: Although maternal death is rare in the United Kingdom, 90% of these women had multiple health/social problems. This study aims to estimate the prevalence of pre-existing multimorbidity (two or more long-term physical or mental health conditions) in pregnant women in the United Kingdom (England, Northern Ireland, Wales and Scotland). STUDY DESIGN: Pregnant women aged 15-49 years with a conception date 1/1/2018 to 31/12/2018 were included in this population-based cross-sectional study, using routine healthcare datasets from primary care: Clinical Practice Research Datalink (CPRD, United Kingdom, n = 37,641) and Secure Anonymized Information Linkage databank (SAIL, Wales, n = 27,782), and secondary care: Scottish Morbidity Records with linked community prescribing data (SMR, Tayside and Fife, n = 6099). Pre-existing multimorbidity preconception was defined from 79 long-term health conditions prioritised through a workshop with patient representatives and clinicians. RESULTS: The prevalence of multimorbidity was 44.2% (95% CI 43.7-44.7%), 46.2% (45.6-46.8%) and 19.8% (18.8-20.8%) in CPRD, SAIL and SMR respectively. When limited to health conditions that were active in the year before pregnancy, the prevalence of multimorbidity was still high (24.2% [23.8-24.6%], 23.5% [23.0-24.0%] and 17.0% [16.0 to 17.9%] in the respective datasets). Mental health conditions were highly prevalent and involved 70% of multimorbidity CPRD: multimorbidity with ≥one mental health condition/s 31.3% [30.8-31.8%]). After adjusting for age, ethnicity, gravidity, index of multiple deprivation, body mass index and smoking, logistic regression showed that pregnant women with multimorbidity were more likely to be older (CPRD England, adjusted OR 1.81 [95% CI 1.04-3.17] 45-49 years vs 15-19 years), multigravid (1.68 [1.50-1.89] gravidity ≥ five vs one), have raised body mass index (1.59 [1.44-1.76], body mass index 30+ vs body mass index 18.5-24.9) and smoked preconception (1.61 [1.46-1.77) vs non-smoker). CONCLUSION: Multimorbidity is prevalent in pregnant women in the United Kingdom, they are more likely to be older, multigravid, have raised body mass index and smoked preconception. Secondary care and community prescribing dataset may only capture the severe spectrum of health conditions. Research is needed urgently to quantify the consequences of maternal multimorbidity for both mothers and children.

Promises and challenges of adoptive T-cell therapies for solid tumours.

Cancer is a leading cause of death worldwide and, despite new targeted therapies and immunotherapies, many patients with advanced-stage- or high-risk cancers still die, owing to metastatic disease. Adoptive T-cell therapy, involving the autologous or allogeneic transplant of tumour-infiltrating lymphocytes or genetically modified T cells expressing novel T-cell receptors or chimeric antigen receptors, has shown promise in the treatment of cancer patients, leading to durable responses and, in some cases, cure. Technological advances in genomics, computational biology, immunology and cell manufacturing have brought the aspiration of individualised therapies for cancer patients closer to reality. This new era of cell-based individualised therapeutics challenges the traditional standards of therapeutic interventions and provides opportunities for a paradigm shift in our approach to cancer therapy. Invited speakers at a 2020 symposium discussed three areas-cancer genomics, cancer immunology and cell-therapy manufacturing-that are essential to the effective translation of T-cell therapies in the treatment of solid malignancies. Key advances have been made in understanding genetic intratumour heterogeneity, and strategies to accurately identify neoantigens, overcome T-cell exhaustion and circumvent tumour immunosuppression after cell-therapy infusion are being developed. Advances are being made in cell-manufacturing approaches that have the potential to establish cell-therapies as credible therapeutic options. T-cell therapies face many challenges but hold great promise for improving clinical outcomes for patients with solid tumours.

HIPEC after neoadjuvant chemotherapy and interval debulking is associated with development of platinum-refractory or -resistant disease.

OBJECTIVE: To describe our single-institution oncologic outcomes of patients who received neoadjuvant chemotherapy (NACT) and interval debulking surgery (IDS) with or without hyperthermic intraperitoneal chemotherapy (HIPEC). METHODS: We compared clinicopathologic information and outcomes for all patients with advanced stage, high-grade serous ovarian cancer who received NACT and IDS with (N = 20) or without (N = 48) HIPEC at our institution from 2010 to 2019 RESULTS: Mean age (62 years with HIPEC and 60 years without HIPEC) and proportion of stage 4 disease (40% for both) did not differ between cohorts. HIPEC patients had higher rates of complete cytoreduction (95% vs 50%), longer mean duration of surgery (530 vs. 216 min), more grade 3 or 4 postoperative complications (65% vs. 4%), and longer mean length of hospital stay (8 vs. 5 days). HIPEC patients had significantly higher risk for platinum-refractory progression or platinum-resistance recurrence (50% vs 23%; RR = 2.18; 95% CI 1.11, 4.30, p = 0.024). Median progression free survival (11.5 vs. 12 months) and all-cause mortality (19.1 vs. 30.5 months) in the HIPEC and non-HIPEC cohorts, respectively, did not differ CONCLUSIONS: HIPEC was associated with increased risk for platinum refractory or resistant disease. Higher surgical complexity may contribute to higher complication rates without improving oncologic outcomes in our patients. Further investigations and long-term follow-up are needed to assess the utility of HIPEC in primary treatment of advanced stage ovarian cancer.

Associations between baseline opioid use disorder severity, mental health and biopsychosocial functioning, with clinical responses to computer-assisted therapy treatment.

Background: Increasing rates of opioid-related overdose have been identified globally. Treatment for opioid use disorders (OUD) includes medications for opioid use disorder (MOUD) alongside behavioral support. Novel approaches to behavioral support should be explored, including computer-assisted therapy (CAT) programs.Objectives: Examine differences between baseline and post-treatment measures of opioid use and biopsychosocial functioning for individuals with OUD engaging with the CAT program 'Breaking Free Online,' and the extent to which participant characteristics may be associated with post-treatment measures.Methods: 1107 individuals engaged with CAT and provided baseline and post-treatment data - 724 (65.4%) were male, 383 (34.6%) were female.Results: Significant differences between baseline and post-treatment measures were identified (all p <.0001, effect sizes range:15 -.50). Participant characteristics were associated with post-treatment measures of opioid use, opioid dependence, mental health issues, quality of life, and biopsychosocial impairment (all p <.0001). An aggregated consensus measure of clinical impairment was found to be associated with changes in opioid use and post-treatment biopsychosocial functioning measures, with those participants with greater baseline clinical impairment demonstrating a greater magnitude of improvement from baseline to post-treatment than those with lower clinical impairment.Conclusion: CAT may reduce opioid use and improve biopsychosocial functioning in individuals with OUD. CAT could therefore provide a solution to the global opioid crisis if delivered as combination behavioral support alongside MOUD. Findings also indicate that it may be important for treatment systems to identify individuals with psychosocial complexity who might require behavioral support and MOUD.

A descriptive marker gene approach to single-cell pseudotime inference.

Motivation: Pseudotime estimation from single-cell gene expression data allows the recovery of temporal information from otherwise static profiles of individual cells. Conventional pseudotime inference methods emphasize an unsupervised transcriptome-wide approach and use retrospective analysis to evaluate the behaviour of individual genes. However, the resulting trajectories can only be understood in terms of abstract geometric structures and not in terms of interpretable models of gene behaviour. Results: Here we introduce an orthogonal Bayesian approach termed 'Ouija' that learns pseudotimes from a small set of marker genes that might ordinarily be used to retrospectively confirm the accuracy of unsupervised pseudotime algorithms. Crucially, we model these genes in terms of switch-like or transient behaviour along the trajectory, allowing us to understand why the pseudotimes have been inferred and learn informative parameters about the behaviour of each gene. Since each gene is associated with a switch or peak time the genes are effectively ordered along with the cells, allowing each part of the trajectory to be understood in terms of the behaviour of certain genes. We demonstrate that this small panel of marker genes can recover pseudotimes that are consistent with those obtained using the entire transcriptome. Furthermore, we show that our method can detect differences in the regulation timings between two genes and identify 'metastable' states-discrete cell types along the continuous trajectories-that recapitulate known cell types. Availability and implementation: An open source implementation is available as an R package at http://www.github.com/kieranrcampbell/ouija and as a Python/TensorFlow package at http://www.github.com/kieranrcampbell/ouijaflow. Supplementary information: Supplementary data are available at Bioinformatics online.

Immunophenotypes of pancreatic ductal adenocarcinoma: Meta-analysis of transcriptional subtypes.

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of <5%. Recent studies of PDAC have provided several transcriptomic classifications based on separate analyses of individual patient cohorts. There is a need to provide a unified transcriptomic PDAC classification driven by therapeutically relevant biologic rationale to inform future treatment strategies. Here, we used an integrative meta-analysis of 353 patients from four different studies to derive a PDAC classification based on immunologic parameters. This consensus clustering approach indicated transcriptomic signatures based on immune infiltrate classified as adaptive, innate and immune-exclusion subtypes. This reveals the existence of microenvironmental interpatient heterogeneity within PDAC and could serve to drive novel therapeutic strategies in PDAC including immune modulation approaches to treating this disease.

The Influence of the Microbiome on Type 1 Diabetes.

Type 1 diabetes (T1D) is characterized by the autoimmune destruction of pancreatic ß cells. The rapid rise in T1D incidence during the past 50 y suggests environmental factors contribute to the disease. The trillion symbiotic microorganisms inhabiting the mammalian gastrointestinal tract (i.e., the microbiota) influence numerous aspects of host physiology. In this study we review the evidence linking perturbations of the gut microbiome to pancreatic autoimmunity. We discuss data from rodent models demonstrating the essential role of the gut microbiota on the development and function of the host's mucosal and systemic immune systems. Furthermore, we review findings from human longitudinal cohort studies examining the influence of environmental and lifestyle factors on microbiota composition and pancreatic autoimmunity. Taken together, these data underscore the requirement for mechanistic studies to identify bacterial components and metabolites interacting with the innate and adaptive immune system, which would set the basis for preventative or therapeutic strategies in T1D.

Colorectal Cancer Cell Line Proteomes Are Representative of Primary Tumors and Predict Drug Sensitivity.

BACKGROUND AND AIMS: Proteomics holds promise for individualizing cancer treatment. We analyzed to what extent the proteomic landscape of human colorectal cancer (CRC) is maintained in established CRC cell lines and the utility of proteomics for predicting therapeutic responses. METHODS: Proteomic and transcriptomic analyses were performed on 44 CRC cell lines, compared against primary CRCs (n=95) and normal tissues (n=60), and integrated with genomic and drug sensitivity data. RESULTS: Cell lines mirrored the proteomic aberrations of primary tumors, in particular for intrinsic programs. Tumor relationships of protein expression with DNA copy number aberrations and signatures of post-transcriptional regulation were recapitulated in cell lines. The 5 proteomic subtypes previously identified in tumors were represented among cell lines. Nonetheless, systematic differences between cell line and tumor proteomes were apparent, attributable to stroma, extrinsic signaling, and growth conditions. Contribution of tumor stroma obscured signatures of DNA mismatch repair identified in cell lines with a hypermutation phenotype. Global proteomic data showed improved utility for predicting both known drug-target relationships and overall drug sensitivity as compared with genomic or transcriptomic measurements. Inhibition of targetable proteins associated with drug responses further identified corresponding synergistic or antagonistic drug combinations. Our data provide evidence for CRC proteomic subtype-specific drug responses. CONCLUSIONS: Proteomes of established CRC cell line are representative of primary tumors. Proteomic data tend to exhibit improved prediction of drug sensitivity as compared with genomic and transcriptomic profiles. Our integrative proteogenomic analysis highlights the potential of proteome profiling to inform personalized cancer medicine.

switchde: inference of switch-like differential expression along single-cell trajectories.

Motivation: Pseudotime analyses of single-cell RNA-seq data have become increasingly common. Typically, a latent trajectory corresponding to a biological process of interest-such as differentiation or cell cycle-is discovered. However, relatively little attention has been paid to modelling the differential expression of genes along such trajectories. Results: We present switchde , a statistical framework and accompanying R package for identifying switch-like differential expression of genes along pseudotemporal trajectories. Our method includes fast model fitting that provides interpretable parameter estimates corresponding to how quickly a gene is up or down regulated as well as where in the trajectory such regulation occurs. It also reports a P -value in favour of rejecting a constant-expression model for switch-like differential expression and optionally models the zero-inflation prevalent in single-cell data. Availability and Implementation: The R package switchde is available through the Bioconductor project at https://bioconductor.org/packages/switchde . Contact: kieran.campbell@sjc.ox.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.