Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient

Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

Differences in the diagnostic value between fiberoptic and high definition laryngoscopy for the characterisation of pharyngeal and laryngeal lesions:A multi-observer paired analysis of videos

Objectives High definition laryngoscopy (HDL) could lead to better interpretation of the pharyngeal and laryngeal mucosa than regularly used fiberoptic laryngoscopy (FOL). The primary aim of this study is to quantify the diagnostic advantage of HDL over FOL in detecting mucosal anomalies in general, in differentiating malignant from benign lesions and in predicting specific histological entities. The secondary aim is to analyse image quality of both laryngoscopes. Design Retrospective paired analysis with multiple observers evaluating endoscopic videos simulating daily clinical practice. Setting A tertiary referral hospital. Participants In 36 patients, both FOL and HDL videos were obtained. Six observers were provided with additional clinical information, and 36 FOL and HDL videos were evaluated in a randomised order. Main outcome measures Sensitivity, specificity, positive and negative predictive value and diagnostic accuracy of observers using both flexible laryngoscopes were calculated for detection of mucosal lesions in general and uncovering malignant lesions. Sensitivities were calculated for prediction of specific histological entities. Image quality (scale 1-10) was assessed for both flexible laryngoscopes. Results HDL reached higher sensitivity compared to FOL for detection of mucosal abnormalities in general (96.0% vs 90.4%; P = .03), differentiating malignant from benign lesions (91.7% vs 79.8%; P = .03) and prediction of specific histological entities (59.7% vs 47.2%; P <.01). Image quality was judged better with HDL in comparison with FOL (mean: 8.4 vs 5.4, P <.01). Conclusions HDL is superior to FOL in detecting mucosal anomalies in general, malignancies and specific histological entities. Image quality is considered as superior using HDL compared to FOL

Risk and temporal order of disease diagnosis of comorbidities in patients with COPD: a population health perspective

Introduction: Comorbidities in patients with chronic obstructive pulmonary disease (COPD) generate a major burden on ealthcare. Identification of costeffective strategies aiming at preventing and enhancing management of comorbid conditions in patients with COPD requires deeper knowledge on epidemiological patterns and on shared biological pathways xplaining cooccurrence of diseases. Methods: The study assesses the co-occurrence of several chronic conditions in patients with COPD using two different datasets: Catalan Healthcare Surveillance System (CHSS) (ES, 1.4 million registries) and Medicare (USA, 13 million registries). Temporal order of disease diagnosis was analysed in the CHSS dataset. Results The results demonstrate higher prevalence of most of the diseases, as comorbid conditions, in elderly (>65) patients with COPD compared with non-COPD subjects, an effect observed in both CHSS and Medicare datasets. Analysis of temporal order of disease diagnosis showed that comorbid conditions in elderly patients with COPD tend to appear after the diagnosis of the obstructive disease, rather than before it. Conclusion: The results provide a population health perspective of the comorbidity challenge in patients with COPD, indicating the increased risk of developing comorbid conditions in these patients. The research reinforces the need for novel approaches in the prevention and management of comorbidities in patients with COPD to effectively reduce the overall burden of the disease on these patients

Patient-generated data in the management of HIV: a scoping review

Objectives Patient-generated data (PGData) are an emergent research area and may improve HIV care. The objectives of this scoping review were to synthesise, evaluate and make recommendations based on the available literature regarding PGData use in HIV care. Design Scoping review. Data sources Embase, Medline, CINAHL Plus, Web of Science, Scopus, PsycINFO and Emcare databases. Eligibility criteria Studies involving PGData use within HIV care for people living with HIV and/or healthcare professionals (HCPs) published before February 2021. Data extraction and synthesis Data were extracted using a table and the Mixed Methods Appraisal Tool was used to assess empirical rigour. We used thematic analysis to evaluate content. Results 11 articles met the eligibility criteria. Studies were observational, predominantly concerned hypothetical or novel digital platforms, mainly conducted in high-income settings, and had small sample sizes (range=10–160). There were multiple definitions of PGData. In the majority of studies (n=9), participants were people living with HIV, with a few studies including HCPs, informatics specialists or mixed participant groups. Participants living with HIV were aged 23–78 years, mostly men, of diverse ethnicities, and had low educational, health literacy and income levels. We identified four key themes: (1) Perceptions of PGData and associated digital platforms; (2) Opportunities; (3) Anticipated barriers and (4) Potential impact on patient–HCP relationships. Conclusions Use of PGData within HIV care warrants further study, especially with regard to digital inequalities, data privacy and security. There is a need for longitudinal data on use within HIV in a variety of settings with a broad range of users, including impact on clinical outcomes. This will allow greater understanding of the role of PGData use in improving the health and well-being of people living with HIV, which is increasingly pertinent as digital healthcare becomes more widespread as a result of COVID-19

Steps to Improve Precision Medicine in Epilepsy

Precision medicine is an old concept, but it is not widely applied across human health conditions as yet. Numerous attempts have been made to apply precision medicine in epilepsy, this has been based on a better understanding of aetiological mechanisms and deconstructing disease into multiple biological subsets. The scope of precision medicine is to provide effective strategies for treating individual patients with specific agent(s) that are likely to work best based on the causal biological make-up. We provide an overview of the main applications of precision medicine in epilepsy, including the current limitations and pitfalls, and propose potential strategies for implementation and to achieve a higher rate of success in patient care. Such strategies include establishing a definition of precision medicine and its outcomes; learning from past experiences, from failures and from other fields (e.g. oncology); using appropriate precision medicine strategies (e.g. drug repurposing versus traditional drug discovery process); and using adequate methods to assess efficacy (e.g. randomised controlled trials versus alternative trial designs). Although the progress of diagnostic techniques now allows comprehensive characterisation of each individual epilepsy condition from a molecular, biological, structural and clinical perspective, there remain challenges in the integration of individual data in clinical practice to achieve effective applications of precision medicine in this domain

Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning

PURPOSE: To apply a deep learning algorithm for automated, objective, and comprehensive quantification of optical coherence tomography (OCT) scans to a large real-world dataset of eyes with neovascular age-related macular degeneration (AMD), and make the raw segmentation output data openly available for further research. DESIGN: Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database. PARTICIPANTS: 2473 first-treated eyes and another 493 second-treated eyes that commenced therapy for neovascular AMD between June 2012 and June 2017. METHODS: A deep learning algorithm was used to segment all baseline OCT scans. Volumes were calculated for segmented features such as neurosensory retina (NSR), drusen, intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperreflective material (SHRM), retinal pigment epithelium (RPE), hyperreflective foci (HRF), fibrovascular pigment epithelium detachment (fvPED), and serous PED (sPED). Analyses included comparisons between first and second eyes, by visual acuity (VA) and by race/ethnicity, and correlations between volumes. MAIN OUTCOME MEASURES: Volumes of segmented features (mm3), central subfield thickness (CST) (μm). RESULTS: In first-treated eyes, the majority had both IRF and SRF (54.7%). First-treated eyes had greater volumes for all segmented tissues, with the exception of drusen, which was greater in second-treated eyes. In first-treated eyes, older age was associated with lower volumes for RPE, SRF, NSR and sPED; in second-treated eyes, older age was associated with lower volumes of NSR, RPE, sPED, fvPED and SRF. Eyes from black individuals had higher SRF, RPE and serous PED volumes, compared with other ethnic groups. Greater volumes of the vast majority of features were associated with worse VA. CONCLUSION: We report the results of large scale automated quantification of a novel range of baseline features in neovascular AMD. Major differences between first and second-treated eyes, with increasing age, and between ethnicities are highlighted. In the coming years, enhanced, automated OCT segmentation may assist personalization of real-world care, and the detection of novel structure-function correlations. These data will be made publicly available for replication and future investigation by the AMD research community

The potential of current polygenic risk scores to predict high myopia and myopic macular degeneration in multi-ethnic Singapore adults

Purpose: To evaluate the trans-ancestry portability of current myopia polygenic risk scores (PRS) to predict high myopia (HM) and myopic macular degeneration (MMD) in an Asian population. Design: Population-based study. Subjects: A total of 5,894 (2,141 Chinese, 1,913 Indians, and 1,840 Malays) adults from the Singapore Epidemiology of Eye Diseases (SEED) study were included in the analysis. The mean age was 57.0 (standard deviation, SD = 9.31) years. A total of 361 adults had HM (spherical equivalent, SE -0.5D). Methods: The PRS, derived from 687,289 HapMap3 SNPs from the largest genome-wide association study of myopia in Europeans to date (n = 260,974), was assessed on its ability to predict HM and MMD versus controls. Main outcome measures: The primary outcomes were the area under the receiver operating characteristic curve (AUROC) to predict HM and MMD. Results: The PRS had an AUROC of 0.73 (95% CI: 0.70, 0.75) for HM and 0.66 (95% CI: 0.63, 0.70) for MMD versus no myopia controls. The inclusion of the PRS with other predictors (age, sex, educational attainment (EA), and ancestry; age-by-ancestry; sex-by-ancestry and EA-by-ancestry interactions; and 20 genotypic principal components) increased the AUROC to 0.84 (95% CI: 0.82, 0.86) for HM and 0.79 (95% CI: 0.76, 0.82) for MMD. Individuals with a PRS in the top 5% had 4.66 (95% CI: 3.34, 6.42) times higher risk for HM and 3.43 (95% CI: 2.27, 5.05) times higher risk for MMD compared to the remaining 95% of individuals. Conclusion: The PRS is a good predictor for HM and will facilitate the identification of high-risk children to prevent myopia progression to HM. In addition, the PRS also predicts MMD and will help to identify high-risk myopic adults who require closer monitoring for myopia-related complications.info:eu-repo/semantics/publishedVersio

Omics derived biomarkers and novel drug targets for improved intervention in advanced prostate cancer

Prostate cancer (PCa) is one of the most frequently diagnosed malignancies, and the fifth leading cause of cancer related mortality in men. For advanced PCa, radical prostatectomy, radiotherapy, and/or long-term androgen deprivation therapy are the recommended treatment options. However, subsequent progression to metastatic disease after initial therapy results in low 5-year survival rates (29%). Omics technologies enable the acquisition of high-resolution large datasets that can provide insights into molecular mechanisms underlying PCa pathology. For the purpose of this article, a systematic literature search was conducted through the Web of Science Database to critically evaluate recent omics-driven studies that were performed towards: (a) Biomarker development and (b) characterization of novel molecular-based therapeutic targets. The results indicate that multiple omics-based biomarkers with prognostic and predictive value have been validated in the context of PCa, with several of those being also available for commercial use. At the same time, omics-driven potential drug targets have been investigated in pre-clinical settings and even in clinical trials, holding the promise for improved clinical management of advanced PCa, as part of personalized medicine pipelines