De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens

Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline which collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo which enable us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations, and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies which were highly varied across patients demonstrating the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.

Which older Brazilians will accept a COVID-19 vaccine? Cross-sectional evidence from the Brazilian Longitudinal Study of Aging (ELSI-Brazil)

ObjectivesVaccine hesitancy may represent a barrier to effective COVID-19 immunisation campaigns. This study assesses individual, disease-specific and contextual factors associated with COVID-19 vaccine acceptance among a nationally representative sample of older Brazilian adults.DesignCross-sectional analysis of data from household interviews and a supplementary telephone survey.SettingBrazil and its five geographic regions.ParticipantsData are derived from 6584 individuals aged 50 years and over who participated in the second wave of the Brazilian Longitudinal Study of Aging.Primary and secondary outcome measuresSurvey-weighted multinomial logistic regression assesses factors associated with intending, not intending or being uncertain about one’s intention to vaccinate against COVID-19.FindingsSeventy-one per cent of study participants intend to receive a COVID-19 vaccine once available, while 17% (representative of nearly 9 million people) have no intention to vaccinate, and 12% are still undecided. Besides age, demographic and health-related factors related to COVID-19 severity and complications were not associated with intention to vaccinate. Those who most trusted social media or friends and family for COVID-19 information and those who did not trust any information source were 68% and 78% more likely to refuse vaccination, respectively, as compared with those who trusted official information sources. People who inconsistently used face masks when outside were 3.4 times more likely than consistent face mask users to intend to refuse vaccination. Higher municipal COVID-19 fatality rates were negatively associated with vaccine refusal.ConclusionsMost national COVID-19 immunisation strategies identify older individuals as among those prioritised for early vaccination, given their increased risk of more severe symptoms and complications of the disease. Because individual, disease-specific, and contextual factors were associated with vaccine acceptance, there is a clear need for multilevel and multichannel information and outreach campaigns to increase COVID-19 vaccine acceptance among vulnerable older populations.

Consensus Recommendations of the German Consortium for Hereditary Breast and Ovarian Cancer

<b><i>Background:</i></b> The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) has established a multigene panel (TruRisk®) for the analysis of risk genes for familial breast and ovarian cancer. <b><i>Summary:</i></b> An interdisciplinary team of experts from the GC-HBOC has evaluated the available data on risk modification in the presence of pathogenic mutations in these genes based on a structured literature search and through a formal consensus process. <b><i>Key Messages:</i></b> The goal of this work is to better assess individual disease risk and, on this basis, to derive clinical recommendations for patient counseling and care at the centers of the GC-HBOC from the initial consultation prior to genetic testing to the use of individual risk-adapted preventive/therapeutic measures.

Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network

Background Deep neural networks learn from former experiences on a large scale and can be used to predict future disease activity as potential clinical decision support. AdaptiveNet is a novel adaptive recurrent neural network optimized to deal with heterogeneous and missing clinical data. Objective We investigate AdaptiveNet for the prediction of individual disease activity in patients from a rheumatoid arthritis (RA) registry. Methods Demographic and disease characteristics from over 9500 patients and 65.000 visits from the Swiss Quality Management (SCQM) database were used to train and evaluate the network. Patient characteristics, clinical and patient reported outcomes, laboratory values and medication were used as input features. DAS28-BSR served as a target to predict active RA and future numeric individual disease activity by classification and regression. Results AdaptiveNet predicted active disease defined as DAS28-BSR >2.6 at the next visit with an overall accuracy of 75.6% (SD +- 0.7%) and a sensitivity and specificity of 84.2% (SD +- 1.6%) and 61.5% (SD +- 3.6%), respectively. Prediction performance was significantly higher in patients with a disease duration >3 years and positive rheumatoid factor. Regression allowed forecasting individual DAS28-BSR values with a mean squared error (MSE) of 0.9 (SD +- 0.05). This corresponds to a 8% deviation between estimated and real DAS28-BSR values. Compared to linear regression, random forest and support vector machines, AdaptiveNet showed an increased performance of over 7% in MSE. Medication played a minor role in the prediction of RA disease activity. Conclusion AdaptiveNet has a superior capacity to predict numeric RA disease activity compared to classical machine learning architectures. All investigated models had limitations in low specificity.

Lack of association between CYB5A gene rs1790834 polymorphism and the response to leflunomide in women with rheumatoid arthritis

Aim Leflunomide is a disease-modifying antirheumatic drug used in therapy for rheumatoid arthritis (RA). Previous studies indicated that oestrogens and androgens may affect the response to leflunomide in RA patients. The synthesis of androgens is regulated by cytochrome CYB5A. The aim of this study was to examine the association between the CYB5A gene rs1790834 polymorphism and the response to leflunomide in women with RA. Methods The study included 111 women diagnosed with RA. Leflunomide was administered in monotherapy at a dose of 20 mg/day. All patients underwent a monthly evaluation for 12 months after the initiation of treatment with leflunomide. Results After 12 months of therapy, the changes in individual disease activity parameters, such as: DAS28, ESR, CRP and VAS, were not statistically significantly different between rs1790834 genotypes in the Kruskal–Wallis test. Conclusions The results of our study suggest lack of statistically significant association between the CYB5A gene rs1790834 polymorphism and the response to leflunomide in women with RA.

Prevalence and Temporal Dynamics of White Line Disease in Sheep: An Exploratory Investigation into Disease Distribution and Associated Risk Factors

Lameness in sheep is a global health, welfare and economic concern. White line disease (WLD), also known as shelly hoof, is a prevalent, non-infectious cause of lameness, characterised by the breakdown of the white line. Little is known about the predisposing factors, nor the individual disease dynamics over time. Our exploratory study aimed to investigate the prevalence and temporal dynamics of WLD, and the associated risk factors. Feet of 400 ewes from four UK commercial sheep farms were inspected for WLD at four time points across 12 months. The change in WLD state at foot-level (develop or recover) was calculated for three transition periods. We present WLD to be widespread, affecting 46.8% of foot-level and 76.6% of sheep-level observations. States in WLD changed over time, with feet readily developing and recovering from WLD within the study period. The presence of WLD at foot-level, the number of feet affected at sheep-level and dynamics in development and recovery were driven by a variety of foot-, sheep- and farm-level factors. We provide key insight into the multifaceted aetiology of WLD and corroborate previous studies demonstrating its multifactorial nature. Our study highlights an opportunity to reduce WLD prevalence and informs hypotheses for future prospective studies.

Genetics and Epigenetics of One-Carbon Metabolism Pathway in Autism Spectrum Disorder: A Sex-Specific Brain Epigenome?

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition affecting behavior and communication, presenting with extremely different clinical phenotypes and features. ASD etiology is composite and multifaceted with several causes and risk factors responsible for different individual disease pathophysiological processes and clinical phenotypes. From a genetic and epigenetic side, several candidate genes have been reported as potentially linked to ASD, which can be detected in about 10–25% of patients. Folate gene polymorphisms have been previously associated with other psychiatric and neurodegenerative diseases, mainly focused on gene variants in the DHFR gene (5q14.1; rs70991108, 19bp ins/del), MTHFR gene (1p36.22; rs1801133, C677T and rs1801131, A1298C), and CBS gene (21q22.3; rs876657421, 844ins68). Of note, their roles have been scarcely investigated from a sex/gender viewpoint, though ASD is characterized by a strong sex gap in onset-risk and progression. The aim of the present review is to point out the molecular mechanisms related to intracellular folate recycling affecting in turn remethylation and transsulfuration pathways having potential effects on ASD. Brain epigenome during fetal life necessarily reflects the sex-dependent different imprint of the genome-environment interactions which effects are difficult to decrypt. We here will focus on the DHFR, MTHFR and CBS gene-triad by dissecting their roles in a sex-oriented view, primarily to bring new perspectives in ASD epigenetics.

POS0468 PREDICTION OF RHEUMATOID ARTHRITIS DISEASE ACTIVITY BY AN ADAPTIVE DEEP NEURAL NETWORK: BETTER RESULTS IN SEROPOSITIVE PATIENTS WITH LONGER DISEASE DURATION

Background:Deep neural networks learn from former experiences on a large scale and can be used to predict future disease activity as potential clinical decision support. AdaptiveNet is a novel adaptive recurrent neural network optimized to deal with missing clinical data. In rheumatoid arthritis (RA) it is unknown how disease characteristics influence the predictability by deep learning in terms of classification (e.g. active disease yes/no) or regression (numeric values such as DAS28).Objectives:To investigate in which clinical RA subtypes AdaptiveNet achieves the best results for the prediction of individual disease activityMethods:Demographic and disease characteristics from over 9500 patients and 65.000 visits from the Swiss Quality Management (SCQM) database were used to train and evaluate the network. Patient characteristics, clinical and patient reported outcomes, laboratory values and medication were used as input features. DAS28-BSR served as a target to predict active RA and future numeric individual disease activity by classification and regression. Feature importance was determined by a Random Forest to define the relative importance of variables for disease prediction.Results:AdaptiveNet predicted active disease defined as DAS28-BSR >2.6 at the next visit with an overall accuracy of 75.6% (SD +- 0.7%) and a sensitivity and specificity of 84.2% (SD +- 1.6%) and 61.5% (SD +- 3.6%), respectively. The performance of the prediction for correct disease status was significantly higher in patients with a disease duration >3 years and positive rheumatoid factor. Regression allowed forecasting individual DAS28-BSR values with a Mean Squared Error (MSE) of 0.9 (SD +- 0.05). Compared to Linear Regression, Random Forests and Support Vector Machines, AdaptiveNet showed an increased performance of 7% in MSE. MSE was significantly lower in patients with disease duration > 3 years and with positive anti-CCP antibodies. Feature importance identified number of painful joints, longer disease duration and age as most relevant factors for prediction of remission, whereas medication played a smaller role.Conclusion:Predictability of disease activity in RA by this deep neural network was stronger in patients with a longer disease history and a positive auto-antibody status, potentially due to a more stable disease course. Generally, AdaptiveNet had a superior capacity to predict numeric RA disease activity compared to classical machine learning architectures, however all investigated models had limitations in low specificity.References:[1]Hügle M, Kalweit G, Hügle T, Boedecker J. Dynamic Deep Neural Network For Multimodal Clinical Data Analysis. Stud Comput Intell: Springer Verl. 2020.Acknowledgements:We thank all rheumatologists and their patients for participation to SCQM.The entire SCQM staff was instrumental for data management and support.A list of rheumatology practices and hospitals that are contributing to the SCQM registries can be found on http://www.scqm.ch/institutions.Disclosure of Interests:None declared

Nasal Epithelial Cell-Based Models for Individualized Study in Cystic Fibrosis

The emergence of highly effective CFTR modulator therapy has led to significant improvements in health care for most patients with cystic fibrosis (CF). For some, however, these therapies remain inaccessible due to the rarity of their individual CFTR variants, or due to a lack of biologic activity of the available therapies for certain variants. One proposed method of addressing this gap is the use of primary human cell-based models, which allow preclinical therapeutic testing and physiologic assessment of relevant tissue at the individual level. Nasal cells represent one such tissue source and have emerged as a powerful model for individual disease study. The ex vivo culture of nasal cells has evolved over time, and modern nasal cell models are beginning to be utilized to predict patient outcomes. This review will discuss both historical and current state-of-the art use of nasal cells for study in CF, with a particular focus on the use of such models to inform personalized patient care.

Association between accepting the illness and effective insulin administration in patients with type 2 Diabetes Mellitus

Purpose To investigate the association between individual, disease-related and care-related properties of the individuals and their accepting the disease, and effective insulin administration. Methods 103 diabetic patients were included in the study. The Acceptance of Illness Scale (AIS) and the Diabetes Fear of Injecting and Self-Testing Questionnaire (D-FISQ) were used for the data collection. The data were analyzed using the descriptive statistics. Results The Cronbach alpha was 0.96 for AIS, 0.95 for fear of self-injecting (FSI), 0.80 for fear of self-testing (FST) and 0.85 for total D-FISQ. A negative association was determined between the AIS score, and FSI, FST, total D-FISQ; a positive statistically significant association was found between the FSI score and FST, total D-FISQ scores and between FST and the total D-FISQ scores. The FSI score was found to be higher among females; the AIS scores of the patients who were not measuring the plasma glucose before insulin injection were found to be higher; the FSI scores of the patients who received help for insulin injection were found to be higher. Conclusion Accepting the disease affects an effective insulin administration behavior. Hence, nursing care and education of the individuals should be planned and implemented so as to improve the acceptance level of diabetes.