Stratification of patient subgroups using high-dimensional and time-series observations

Precision medicine and patient stratification are expanding as a result of innovations in high-throughput technologies applied to clinical medicine. Stratification can explain differences in disease trajectories and outcomes in heterogeneous cohorts. Thus, approaches employed for patient treatment can be tailored by taking into account individual variabilities and specificities. This thesis focuses on clustering approaches and how they can be applied to both single time points and time-series high-dimensional data for the identification of disease subtypes defined by distinct mechanisms, also called endotypes, in complex and/or heterogeneous diseases. Multiple carefully selected clustering strategies were compared to highlight which would produce the most relevant stratification in terms of mathematical robustness and biological meaning, both of which quantified using standardised methods. More specifically, this strategy was applied to time-series multi-omics data from a cohort of patients with acute pancreatitis, an inflammatory disease of the pancreas. Using this high-dimensional multi-omics data as well as routine lab and clinical measurements, the cohort was stratified into four subgroups. Findings from the analysis of acute pancreatitis data showed that two of the four subgroups could be detected in another syndrome, acute respiratory distress syndrome, suggesting that inflammatory signatures are comparable between diseases. With the aim of applying these principles to other diseases and using preliminary results from other studies suggesting that relevant subgroups might be highlighted, data from inflammatory bowel disease and Parkinson's disease cohorts was analysed. Results from our analyses confirmed that disease knowledge could be gained using this approach. Work from this thesis provides novel approaches for the application and evaluation of stratification methods. Furthermore, results may constitute a basis for the development of tailored treatment approaches for acute pancreatitis, acute respiratory distress syndrome, inflammatory bowel disease and Parkinson’s disease. Also, the observation of commonalities between distinct inflammatory diseases will broaden the perspectives when analysing disease data and more specifically, in biomarker discovery and drug development processes

Characterizing preclinical sub-phenotypic models of acute respiratory distress syndrome:An experimental ovine study

Abstract The acute respiratory distress syndrome (ARDS) describes a heterogenous population of patients with acute severe respiratory failure. However, contemporary advances have begun to identify distinct sub‐phenotypes that exist within its broader envelope. These sub‐phenotypes have varied outcomes and respond differently to several previously studied interventions. A more precise understanding of their pathobiology and an ability to prospectively identify them, may allow for the development of precision therapies in ARDS. Historically, animal models have played a key role in translational research, although few studies have so far assessed either the ability of animal models to replicate these sub‐phenotypes or investigated the presence of sub‐phenotypes within animal models. Here, in three ovine models of ARDS, using combinations of oleic acid and intravenous, or intratracheal lipopolysaccharide, we investigated the presence of sub‐phenotypes which qualitatively resemble those found in clinical cohorts. Principal Component Analysis and partitional clustering identified two clusters, differentiated by markers of shock, inflammation, and lung injury. This study provides a first exploration of ARDS phenotypes in preclinical models and suggests a methodology for investigating this phenomenon in future studies

Quantitative magnetic resonance imaging predicts individual future liver performance after liver resection for cancer

The risk of poor post-operative outcome and the benefits of surgical resection as a curative therapy require careful assessment by the clinical care team for patients with primary and secondary liver cancer. Advances in surgical techniques have improved patient outcomes but identifying which individual patients are at greatest risk of poor post-operative liver performance remains a challenge. Here we report results from a multicentre observational clinical trial (ClinicalTrials.gov NCT03213314) which aimed to inform personalised pre-operative risk assessment in liver cancer surgery by evaluating liver health using quantitative multiparametric magnetic resonance imaging (MRI). We combined estimation of future liver remnant (FLR) volume with corrected T1 (cT1) of the liver parenchyma as a representation of liver health in 143 patients prior to treatment. Patients with an elevated preoperative liver cT1, indicative of fibroinflammation, had a longer post-operative hospital stay compared to those with a cT1 within the normal range (6.5 vs 5 days; p = 0.0053). A composite score combining FLR and cT1 predicted poor liver performance in the 5 days immediately following surgery (AUROC = 0.78). Furthermore, this composite score correlated with the regenerative performance of the liver in the 3 months following resection. This study highlights the utility of quantitative MRI for identifying patients at increased risk of poor post-operative liver performance and a longer stay in hospital. This approach has the potential to inform the assessment of individualised patient risk as part of the clinical decision-making process for liver cancer surgery

hMSH5 is a nucleocytoplasmic shuttling protein whose stability depends on its subcellular localization

MSH5 is a MutS-homologous protein required for meiotic DNA recombination. In addition, recent studies suggest that the human MSH5 protein (hMSH5) participates to mitotic recombination and to the cellular response to DNA damage and thus raise the possibility that a tight control of hMSH5 function(s) may be important for genomic stability. With the aim to characterize mechanisms potentially involved in the regulation of hMSH5 activity, we investigated its intracellular trafficking properties. We demonstrate that hMSH5 possesses a CRM1-dependent nuclear export signal (NES) and a nuclear localization signal that participates to its nuclear targeting. Localization analysis of various mutated forms of hMSH5 by confocal microscopy indicates that hMSH5 shuttles between the nucleus and the cytoplasm. We also provide evidence suggesting that hMSH5 stability depends on its subcellular compartmentalization, hMSH5 being much less stable in the nucleus than in the cytoplasm. Together, these data suggest that hMSH5 activity may be regulated by nucleocytoplasmic shuttling and nuclear proteasomal degradation, both of these mechanisms contributing to the control of nuclear hMSH5 content. Moreover, data herein also support that in tissues where both hMSH5 and hMSH4 proteins are expressed, hMSH5 might be retained in the nucleus through masking of its NES by binding of hMSH4

Distinct clinical symptom patterns in patients hospitalised with COVID-19 in an analysis of 59,011 patients in the ISARIC-4C study.

COVID-19 is clinically characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. However, it is the clinical associations of different patterns of symptoms which influence diagnostic and therapeutic decision-making. In this study, we applied clustering techniques to a large prospective cohort of hospitalised patients with COVID-19 to identify clinically meaningful sub-phenotypes. We obtained structured clinical data on 59,011 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 25,477 cases according to symptoms reported at recruitment. We validated our findings in a second group of 33,534 cases recruited to ISARIC-4C, and in 4,445 cases recruited to a separate study of community cases. Unsupervised clustering identified distinct sub-phenotypes. First, a core symptom set of fever, cough, and dyspnoea, which co-occurred with additional symptoms in three further patterns: fatigue and confusion, diarrhoea and vomiting, or productive cough. Presentations with a single reported symptom of dyspnoea or confusion were also identified, alongside a sub-phenotype of patients reporting few or no symptoms. Patients presenting with gastrointestinal symptoms were more commonly female, had a longer duration of symptoms before presentation, and had lower 30-day mortality. Patients presenting with confusion, with or without core symptoms, were older and had a higher unadjusted mortality. Symptom sub-phenotypes were highly consistent in replication analysis within the ISARIC-4C study. Similar patterns were externally verified in patients from a study of self-reported symptoms of mild disease. The large scale of the ISARIC-4C study enabled robust, granular discovery and replication. Clinical interpretation is necessary to determine which of these observations have practical utility. We propose that four sub-phenotypes are usefully distinct from the core symptom group: gastro-intestinal disease, productive cough, confusion, and pauci-symptomatic presentations. Importantly, each is associated with an in-hospital mortality which differs from that of patients with core symptoms

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

<div><p>Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn’s disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.</p></div

Metagenomic Sequencing in the ICU for Precision Diagnosis of Critical Infectious Illnesses.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901