Socioeconomic and geographic determinants of survival of patients with digestive cancer in France

Using a multilevel Cox model, the association between socioeconomic and geographical aggregate variables and survival was investigated in 81 268 patients with digestive tract cancer diagnosed in the years 1980–1997 and registered in 12 registries in the French Network of Cancer Registries. This association differed according to cancer site: it was clear for colon (relative risk (RR)=1.10 (1.04–1.16), 1.10 (1.04–1.16) and 1.14 (1.05–1.23), respectively, for distances to nearest reference cancer care centre between 10 and 30, 30 and 50 and more than 90 km, in comparison with distance of less than 10 km; P-trend=0.003) and rectal cancer (RR=1.09 (1.03–1.15), RR=1.08 (1.02–1.14) and RR=1.12 (1.05–1.19), respectively, for distances between 10 and 30 km, 30 and 50 km and 50 and 70 km, P-trend=0.024) (n=28 010 and n=18 080, respectively) but was not significant for gall bladder and biliary tract cancer (n=2893) or small intestine cancer (n=1038). Even though the influence of socioeconomic status on prognosis is modest compared to clinical prognostic factors such as histology or stage at diagnosis, socioeconomic deprivation and distance to nearest cancer centre need to be considered as potential survival predictors in digestive tract cancer

Prognostic value of morphology and hormone receptor status in breast cancer - a population based study

We analysed the 5-year relative survival among 4473 breast cancer cases diagnosed in 1990-1992 from cancer registries in Estonia, France, Italy, Spain, the Netherlands and the UK. Among eight categories based on ICD-O codes (infiltrating ductal carcinoma, lobular plus mixed carcinoma, comedocarcinoma, 'special types', medullary carcinoma, not otherwise specified (NOS) carcinoma, other carcinoma and cancer without microscopic confirmation), the 5-year relative survival ranged from 66% (95% CI 61-71) for NOS carcinoma to 95% (95% CI 90-100) for special types (tubular, apocrine, cribriform, papillary, mucinous and signet ring cell); 27% (95% CI 18-36) for cases without microscopic confirmation. Differences in 5-year relative survival by tumor morphology and hormone receptor status were modelled using a multiple regression approach based on generalised linear models. Morphology and hormone receptor status were confirmed as significant survival predictors in this population-based study, even after adjusting for age and stage at diagnosis

A Federated Database for Obesity Research:An IMI-SOPHIA Study

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.</p

The Wolbachia endosymbiont as an anti-filarial nematode target

Human disease caused by parasitic filarial nematodes is a major cause of global morbidity. The parasites are transmitted by arthropod intermediate hosts and are responsible for lymphatic filariasis (elephantiasis) or onchocerciasis (river blindness). Within these filarial parasites are intracellular alpha-proteobacteria, Wolbachia, that were first observed almost 30 years ago. The obligate endosymbiont has been recognized as a target for anti-filarial nematode chemotherapy as evidenced by the loss of worm fertility and viability upon antibiotic treatment in an extensive series of human trials. While current treatments with doxycycline and rifampicin are not practical for widespread use due to the length of required treatments and contraindications, anti-Wolbachia targeting nevertheless appears a promising alternative for filariasis control in situations where current programmatic strategies fail or are unable to be delivered and it provides a superior efficacy for individual therapy. The mechanisms that underlie the symbiotic relationship between Wolbachia and its nematode hosts remain elusive. Comparative genomics, bioinfomatic and experimental analyses have identified a number of potential interactions, which may be drug targets. One candidate is de novo heme biosynthesis, due to its absence in the genome sequence of the host nematode, Brugia malayi, but presence in Wolbachia and its potential roles in worm biology. We describe this and several additional candidate targets, as well as our approaches for understanding the nature of the host-symbiont relationship

Predicting and elucidating the etiology of fatty liver disease : A machine learning modeling and validation study in the IMI DIRECT cohorts

Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n= 795) or at high risk of developing the disease (n= 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (= 5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86;p = 5%) rather than a continuous one. Conclusions In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see:) and made it available to the community.Peer reviewe

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. [Abstract copyright: © 2023. The Author(s).

New Insights into the Evolution of Wolbachia Infections in Filarial Nematodes Inferred from a Large Range of Screened Species

Wolbachia are intriguing symbiotic endobacteria with a peculiar host range that includes arthropods and a single nematode family, the Onchocercidae encompassing agents of filariases. This raises the question of the origin of infection in filariae. Wolbachia infect the female germline and the hypodermis. Some evidences lead to the theory that Wolbachia act as mutualist and coevolved with filariae from one infection event: their removal sterilizes female filariae; all the specimens of a positive species are infected; Wolbachia are vertically inherited; a few species lost the symbiont. However, most data on Wolbachia and filaria relationships derive from studies on few species of Onchocercinae and Dirofilariinae, from mammals.We investigated the Wolbachia distribution testing 35 filarial species, including 28 species and 7 genera and/or subgenera newly screened, using PCR, immunohistochemical staining, whole mount fluorescent analysis, and cocladogenesis analysis. (i) Among the newly screened Onchocercinae from mammals eight species harbour Wolbachia but for some of them, bacteria are absent in the hypodermis, or in variable density. (ii) Wolbachia are not detected in the pathological model Monanema martini and in 8, upon 9, species of Cercopithifilaria. (iii) Supergroup F Wolbachia is identified in two newly screened Mansonella species and in Cercopithifilaria japonica. (iv) Type F Wolbachia infect the intestinal cells and somatic female genital tract. (v) Among Oswaldofilariinae, Waltonellinae and Splendidofilariinae, from saurian, anuran and bird respectively, Wolbachia are not detected.The absence of Wolbachia in 63% of onchocercids, notably in the ancestral Oswaldofilariinae estimated 140 mya old, the diverse tissues or specimens distribution, and a recent lateral transfer in supergroup F Wolbachia, modify the current view on the role and evolution of the endosymbiont and their hosts. Further genomic analyses on some of the newly sampled species are welcomed to decipher the open questions