Clinical implications of omics and systems medicine: focus on predictive and individualized treatment

The role of NOP58 in prostate cancer progression through SUMOylation regulation and drug response

Background

Prostate cancer is one of the leading causes of cancer-related deaths in men. Its molecular pathogenesis is closely linked to various genetic and epigenetic alterations, including posttranslational modifications like SUMOylation. Identifying biomarkers that predict outcomes and specific therapeutic targets depends on a comprehensive understanding of these processes. With growing interest in SUMOylation as a mechanism affecting prostate cancer-related genes, this study aimed to investigate the central role of SUMOylation in prostate cancer prognostics, focusing on the significance of NOP58.

Methods

We conducted a comprehensive bioinformatics analysis, integrating differential expression analysis, survival analysis, gene set enrichment analysis (GSEA), and single-cell transcriptomic analyses using data from The Cancer Genome Atlas (TCGA). Key genes were identified through intersections of Venn diagrams, Boralta algorithm signatures, and machine learning models. These signaling mechanisms were validated through experimental studies, including immunohistochemical staining and gene ontology analyses.

Results

The dual-gene molecular subtype analysis with SUMO1, SUMO2, and XPO1 genes revealed significant differences in survival outcomes across molecular subtypes, further emphasizing the potential impact of NOP58 on SUMOylation, a key post-translational modification, in prostate cancer. NOP58 overexpression was strongly associated with shorter overall survival (OS), progression-free interval (PFI), and disease-specific death in prostate cancer patients. Immunohistochemical analysis confirmed that NOP58 was significantly overexpressed in prostate cancer tissues compared to normal tissues. ROC curve analysis demonstrated that NOP58 could distinguish prostate cancer from control samples with high diagnostic accuracy. Gene Ontology analysis, along with GSVA and GSEA, suggested that NOP58 may be involved in cell cycle regulation and DNA repair pathways. Moreover, NOP58 knockdown led to increased BCL2 expression and decreased Ki67 levels, promoting apoptosis and inhibiting cell proliferation. Colony formation assays further showed that NOP58 knockdown inhibited, while its overexpression promoted, colony formation, highlighting the critical role of NOP58 in prostate cancer cell growth and survival. Additionally, NOP58 was linked to drug responses, including Methotrexate, Rapamycin, Sorafenib, and Vorinostat.

Conclusion

NOP58 is a key regulator of prostate cancer progression through its mediation of the SUMOylation pathway. Its expression level serves as a reliable prognostic biomarker and an actionable therapeutic target, advancing precision medicine for prostate cancer. Targeting NOP58 may enhance therapeutic efficacy and improve outcomes in oncology.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

[The promise of omics in the precision medicine era]

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

Public Views About Involvement in Decision-Making on Health Data Sharing, Access, Use and Reuse: The Importance of Trust in Science and Other Institutions

Background

Data-intensive and needs-driven research can deliver substantial health benefits. However, concerns with privacy loss, undisclosed surveillance, and discrimination are on the rise due to mounting data breaches. This can undermine the trustworthiness of data processing institutions and reduce people's willingness to share their data. Involving the public in health data governance can help to address this problem by imbuing data processing frameworks with societal values. This study assesses public views about involvement in individual-level decisions concerned with health data and their association with trust in science and other institutions.

Methods

Cross-sectional study with 162 patients and 489 informal carers followed at two reference centers for rare diseases in an academic hospital in Portugal (June 2019–March 2020). Participants rated the importance of involvement in decision-making concerning health data sharing, access, use, and reuse from “not important” to “very important”. Its association with sociodemographic characteristics, interpersonal trust, trust in national and international institutions, and the importance of trust in research teams and host institutions was tested.

Results

Most participants perceived involvement in decision-making about data sharing (85.1%), access (87.1%), use (85%) and reuse (79.9%) to be important or very important. Participants who ascribed a high degree of importance to trust in research host institutions were significantly more likely to value involvement in such decisions. A similar position was expressed by participants who valued trust in research teams for data sharing, access, and use. Participants with low levels of trust in national and international institutions and with lower levels of education attributed less importance to being involved in decisions about data use.

Conclusion

The high value attributed by participants to involvement in individual-level data governance stresses the need to broaden opportunities for public participation in health data decision-making, namely by introducing a meta consent approach. The important role played by trust in science and in other institutions in shaping participants' views about involvement highlights the relevance of pairing such a meta consent approach with the provision of transparent information about the implications of data sharing, the resources needed to make informed choices and the development of harm mitigation tools and redress.

Breast cancer in the era of integrating “Omics” approaches

Worldwide, breast cancer is the leading cause of cancer-related deaths in women. Breast cancer is a heterogeneous disease characterized by different clinical outcomes in terms of pathological features, response to therapies, and long-term patient survival. Thus, the heterogeneity found in this cancer led to the concept that breast cancer is not a single disease, being very heterogeneous both at the molecular and clinical level, and rather represents a group of distinct neoplastic diseases of the breast and its cells. Indubitably, in the past decades we witnessed a significant development of innovative therapeutic approaches, including targeted and immunotherapies, leading to impressive results in terms of increased survival for breast cancer patients. However, these multimodal treatments fail to prevent recurrence and metastasis. Therefore, it is urgent to improve our understanding of breast tumor and metastasis biology. Over the past few years, high-throughput “omics” technologies through the identification of novel biomarkers and molecular profiling have shown their great potential in generating new insights in the study of breast cancer, also improving diagnosis, prognosis and prediction of response to treatment. In this review, we discuss how the implementation of “omics” strategies and their integration may lead to a better comprehension of the mechanisms underlying breast cancer. In particular, with the aim to investigate the correlation between different “omics” datasets and to define the new important key pathway and upstream regulators in breast cancer, we applied a new integrative meta-analysis method to combine the results obtained from genomics, proteomics and metabolomics approaches in different revised studies.

A roadmap for multi-omics data integration using deep learning

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.

What are the applications of single-cell RNA sequencing in cancer research: a systematic review

Single-cell RNA sequencing (scRNA-seq) is a tool for studying gene expression at the single-cell level that has been widely used due to its unprecedented high resolution. In the present review, we outline the preparation process and sequencing platforms for the scRNA-seq analysis of solid tumor specimens and discuss the main steps and methods used during data analysis, including quality control, batch-effect correction, normalization, cell cycle phase assignment, clustering, cell trajectory and pseudo-time reconstruction, differential expression analysis and gene set enrichment analysis, as well as gene regulatory network inference. Traditional bulk RNA sequencing does not address the heterogeneity within and between tumors, and since the development of the first scRNA-seq technique, this approach has been widely used in cancer research to better understand cancer cell biology and pathogenetic mechanisms. ScRNA-seq has been of great significance for the development of targeted therapy and immunotherapy. In the second part of this review, we focus on the application of scRNA-seq in solid tumors, and summarize the findings and achievements in tumor research afforded by its use. ScRNA-seq holds promise for improving our understanding of the molecular characteristics of cancer, and potentially contributing to improved diagnosis, prognosis, and therapeutics.

High Throughput Multi-Omics Approaches for Clinical Trial Evaluation and Drug Discovery

High throughput single cell multi-omics platforms, such as mass cytometry (cytometry by time-of-flight; CyTOF), high dimensional imaging (>6 marker; Hyperion, MIBIscope, CODEX, MACSima) and the recently evolved genomic cytometry (Citeseq or REAPseq) have enabled unprecedented insights into many biological and clinical questions, such as hematopoiesis, transplantation, cancer, and autoimmunity. In synergy with constantly adapting new single-cell analysis approaches and subsequent accumulating big data collections from these platforms, whole atlases of cell types and cellular and sub-cellular interaction networks are created. These atlases build an ideal scientific discovery environment for reference and data mining approaches, which often times reveals new cellular disease networks. In this review we will discuss how combinations and fusions of different -omic workflows on a single cell level can be used to examine cellular phenotypes, immune effector functions, and even dynamic changes, such as metabolomic state of different cells in a sample or even in a defined tissue location. We will touch on how pre-print platforms help in optimization and reproducibility of workflows, as well as community outreach. We will also shortly discuss how leveraging single cell multi-omic approaches can be used to accelerate cellular biomarker discovery during clinical trials to predict response to therapy, follow responsive cell types, and define novel druggable target pathways. Single cell proteome approaches already have changed how we explore cellular mechanism in disease and during therapy. Current challenges in the field are how we share these disruptive technologies to the scientific communities while still including new approaches, such as genomic cytometry and single cell metabolomics.

Public and patient involvement in health data governance (DATAGov): protocol of a people-centred, mixed-methods study on data use and sharing for rare diseases care and research

INTRODUCTION

International policy imperatives for the public and patient involvement in the governance of health data coexist with conflicting cross-border policies on data sharing. This can challenge the planning and implementation of participatory data governance in healthcare services locally. Engaging with local stakeholders and understanding how their needs, values and preferences for governing health data can be articulated with policies made at the supranational level is crucial. This paper describes a protocol for a project that aims to coproduce a people-centred model for involving patients and the public in decision-making processes about the use and sharing of health data for rare diseases care and research.

METHODS AND ANALYSIS

This multidisciplinary project draws on an explanatory sequential mixed-methods study. A hospital-based survey with patients, informal carers, health professionals and technical staff recruited at two reference centres for rare diseases in Portugal will be conducted first. The qualitative study will follow consisting of semi-structured interviews and scenario-based workshops with a subsample of the participant groups recruited at baseline. Quantitative data will be analysed using descriptive and inferential statistics. Inductive and deductive approaches will be combined to analyse the qualitative interviews. Data from scenario-based workshops will be iteratively compared using the constant comparison method to identify cross-cutting themes and categories.

ETHICS AND DISSEMINATION

The Ethics Committee for Health from the University Hospital Centre São João/Faculty of Medicine of University of Porto approved the study protocol (Ref. 99/19). Research findings will be disseminated at academic conferences and science promotion events, and through public meetings involving patient representatives, practitioners, policy-makers and students, a project website and peer-reviewed journal publications.

The overexpression of DNA repair genes in invasive ductal and lobular breast carcinomas: Insights on individual variations and precision medicine

In the era of precision medicine, analyzing the transcriptomic profile of patients is essential to tailor the appropriate therapy. In this study, we explored transcriptional differences between two invasive breast cancer subtypes; infiltrating ductal carcinoma (IDC) and lobular carcinoma (LC) using RNA-Seq data deposited in the TCGA-BRCA project. We revealed 3854 differentially expressed genes between normal ductal tissues and IDC. In addition, IDC to LC comparison resulted in 663 differentially expressed genes. We then focused on DNA repair genes because of their known effects on patients' response to therapy and resistance. We here report that 36 DNA repair genes are overexpressed in a significant number of both IDC and LC patients' samples. Despite the upregulation in a significant number of samples, we observed a noticeable variation in the expression levels of the repair genes across patients of the same cancer subtype. The same trend is valid for the expression of miRNAs, where remarkable variations between patients' samples of the same cancer subtype are also observed. These individual variations could lie behind the differential response of patients to treatment. The future of cancer diagnostics and therapy will inevitably depend on high-throughput genomic and transcriptomic data analysis. However, we propose that performing analysis on individual patients rather than a big set of patients' samples will be necessary to ensure that the best treatment is determined, and therapy resistance is reduced.

Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.

Systems Cytogenomics: Are We Ready Yet?

With the introduction of systems theory to genetics, numerous opportunities for genomic research have been identified. Consequences of DNA sequence variations are systematically evaluated using the network- or pathway-based analysis, a technological basis of systems biology or, more precisely, systems genomics. Despite comprehensive descriptions of advantages offered by systems genomic approaches, pathway-based analysis is uncommon in cytogenetic (cytogenomic) studies, i.e. genome analysis at the chromosomal level. Here, we would like to express our opinion that current cytogenomics benefits from the application of systems biology methodology. Accordingly, systems cytogenomics appears to be a biomedical area requiring more attention than it actually receives.

Male infertility

It is estimated that infertility affects 8-12% of couples globally, with a male factor being a primary or contributing cause in approximately 50% of couples. Causes of male subfertility vary highly, but can be related to congenital, acquired, or idiopathic factors that impair spermatogenesis. Many health conditions can affect male fertility, which underscores the need for a thorough evaluation of patients to identify treatable or reversible lifestyle factors or medical conditions. Although semen analysis remains the cornerstone for evaluating male infertility, advanced diagnostic tests to investigate sperm quality and function have been developed to improve diagnosis and management. The use of assisted reproductive techniques has also substantially improved the ability of couples with infertility to have biological children. This Seminar aims to provide a comprehensive overview of the assessment and management of men with infertility, along with current controversies and future endeavours.

LAMP: disease classification derived from layered assessment on modules and pathways in the human gene network

Background

Classification of diseases based on genetic information is of great significance as the basis for precision medicine, increasing the understanding of disease etiology and revolutionizing personalized medicine. Much effort has been directed at understanding disease associations by constructing disease networks, and classifying patient samples according to gene expression data. Integrating human gene networks overcomes limited coverage of genes. Incorporating pathway information into disease classification procedure addresses the challenge of cellular heterogeneity across patients.

Results

In this work, we propose a disease classification model LAMP, which concentrates on the layered assessment on modules and pathways. Directed human gene interactions are the foundation of constructing the human gene network, where the significant roles of disease and pathway genes are recognized. The fast unfolding algorithm identifies 11 modules in the largest connected component. Then layered networks are introduced to distinguish positions of genes in propagating information from sources to targets. After gene screening, hierarchical clustering and refined process, 1726 diseases from KEGG are classified into 18 categories. Also, it is expounded that diseases with overlapping genes may not belong to the same category in LAMP. Within each category, entropy is applied to measure the compositional complexity, and to evaluate the prospects for combination diagnosis and gene-targeted therapy for diseases.

Conclusion

In this work, by collecting data from BioGRID and KEGG, we develop a disease classification model LAMP, to support people to view diseases from the perspective of commonalities in etiology and pathology. Comprehensive research on existing diseases can help meet the challenges of unknown diseases. The results provide suggestions for combination diagnosis and gene-targeted therapy, which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies.

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor-host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Resolving Clinical Phenotypes into Endotypes in Allergy: Molecular and Omics Approaches

Allergic diseases are highly complex with respect to pathogenesis, inflammation, and response to treatment. Current efforts for allergic disease diagnosis have focused on clinical evidence as a binary outcome. Although outcome status based on clinical phenotypes (observable characteristics) is convenient and inexpensive to measure in large studies, it does not adequately provide insight into the complex molecular determinants of allergic disease. Individuals with similar clinical diagnoses do not necessarily have similar disease etiologies, natural histories, or responses to treatment. This heterogeneity contributes to the ineffective response to treatment leading to an annual estimated cost of $350 billion in the USA alone. There has been a recent focus to deconvolute the clinical heterogeneity of allergic diseases into specific endotypes using molecular and omics approaches. Endotypes are a means to classify patients based on the underlying pathophysiological mechanisms involving distinct functions or treatment response. The advent of high-throughput molecular omics, immunophenotyping, and bioinformatics methods including machine learning algorithms is facilitating the development of endotype-based diagnosis. As we move to the next decade, we should truly start treating clinical endotypes not clinical phenotype. This review highlights current efforts taking place to improve allergic disease endotyping via molecular omics profiling, immunophenotyping, and machine learning approaches in the context of precision diagnostics in allergic diseases. Graphical Abstract.

Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

The current state of omics technologies in the clinical management of asthma and allergic diseases

OBJECTIVE

To review the state of omics science specific to asthma and allergic diseases and discuss the current and potential applicability of omics in clinical disease prediction, treatment, and management.

DATA SOURCES

Studies and reviews focused on the use of omics technologies in asthma and allergic disease research and clinical management were identified using PubMed.

STUDY SELECTIONS

Publications were included based on relevance, with emphasis placed on the most recent findings.

RESULTS

Omics-based research is increasingly being used to differentiate asthma and allergic disease subtypes, identify biomarkers and pathological mediators, predict patient responsiveness to specific therapies, and monitor disease control. Although most studies have focused on genomics and transcriptomics approaches, increasing attention is being placed on omics technologies that assess the effect of environmental exposures on disease initiation and progression. Studies using omics data to identify biological targets and pathways involved in asthma and allergic disease pathogenesis have primarily focused on a specific omics subtype, providing only a partial view of the disease process.

CONCLUSION

Although omics technologies have advanced our understanding of the molecular mechanisms underlying asthma and allergic disease pathology, omics testing for these diseases are not standard of care at this point. Several important factors need to be addressed before these technologies can be used effectively in clinical practice. Use of clinical decision support systems and integration of these systems within electronic medical records will become increasingly important as omics technologies become more widely used in the clinical setting.

Asthma from immune pathogenesis to precision medicine

Asthma is characterized by multiple immunological mechanisms (endotypes) determining variable clinical presentations (phenotypes). The identification of endotypic mechanisms is crucial to better characterize patients and to identify tailored therapeutic approaches with novel biological agents targeting specific immunological pathways. This review focused on summarizing the major immunological mechanisms involved in the pathogenesis of asthma, as well as on discussing the emergence of phenotypic features of the disease. Novel biological agents and other drugs targeting specific endotypes are discussed, as their use represent a precision medicine approach to the disease that is nowadays mandatory particularly for treating more severe patients.

Food allergy and omics

Food allergy (FA) prevalence has been increasing over the last few decades and is now a global health concern. Current diagnostic methods for FA result in a high number of false-positive results, and the standard of care is either allergen avoidance or use of epinephrine on accidental exposure, although currently with no other approved treatments. The increasing prevalence of FA, lack of robust biomarkers, and inadequate treatments warrants further research into the mechanism underlying food allergies. Recent technological advances have made it possible to move beyond traditional biological techniques to more sophisticated high-throughput approaches. These technologies have created the burgeoning field of omics sciences, which permit a more systematic investigation of biological problems. Omics sciences, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, have enabled the construction of regulatory networks and biological pathway models. Parallel advances in bioinformatics and computational techniques have enabled the integration, analysis, and interpretation of these exponentially growing data sets and opens the possibility of personalized or precision medicine for FA.

Disease classification via gene network integrating modules and pathways

Disease classification based on gene information has been of significance as the foundation for achieving precision medicine. Previous works focus on classifying diseases according to the gene expression data of patient samples, and constructing disease network based on the overlap of disease genes, as many genes have been confirmed to be associated with diseases. In this work, the effects of diseases on human biological functions are assessed from the perspective of gene network modules and pathways, and the distances between diseases are defined to carry out the classification models. In total, 1728 diseases are divided into 12 and 14 categories by the intensity and scope of effects on pathways, respectively. Each category is a mix of several types of diseases identified based on congenital and acquired factors as well as diseased tissues and organs. The disease classification models on the basis of gene network are parallel with traditional pathology classification based on anatomic and clinical manifestations, and enable us to look at diseases in the viewpoint of commonalities in etiology and pathology. Our models provide a foundation for exploring combination therapy of diseases, which in turn may inform strategies for future gene-targeted therapy.

A new optimization strategy for MALDI FTICR MS tissue analysis for untargeted metabolomics using experimental design and data modeling

Ultra-high-resolution imaging mass spectrometry using matrix-assisted laser desorption ionization (MALDI) MS coupled to a Fourier transform ion cyclotron resonance (FTICR) mass analyzer is a powerful technique for the visualization of small molecule distribution within biological tissues. The FTICR MS provides ultra-high resolving power and mass accuracy that allows large molecular coverage and molecular formula assignments, both essential for untargeted metabolomics analysis. These performances require fine optimizations of the MALDI FTICR parameters. In this context, this study proposes a new strategy, using experimental design, for the optimization of ion transmission voltages and MALDI parameters, for tissue untargeted metabolomics analysis, in both positive and negative ionization modes. These experiments were conducted by assessing the effects of nine factors for ion transmission voltages and four factors for MALDI on the number of peaks, the weighted resolution, and the mean error within m/z 150-1000 mass range. For this purpose, fractional factorial designs were used with multiple linear regression (MLR) to evaluate factor effects and to optimize parameter values. The optimized values of ion transmission voltages (RF amplitude TOF, RF amplitude octopole, frequency transfer optic, RF frequency octopole, deflector plate, funnel 1, skimmer, funnel RF amplitude, time-of-flight, capillary exit), MALDI parameters (laser fluence, number of laser shots), and detection parameters (data size, number of scans) led to an increase of 32% and 18% of the number of peaks, an increase of 8% and 39% of the resolution, and a decrease of 56% and 34% of the mean error in positive and negative ionization modes, respectively. Graphical abstract.

Patienthood and participation in the digital era

The 'digital era' of informatics and knowledge integration has changed the roles and experiences of patients, research participants and health consumers. No longer figured (merely) as passive recipients of healthcare services or as beneficiaries of top-down biomedical information, individuals are increasingly seen as active contributors in healthcare and research. They are positioned into multiple roles that are experienced simultaneously by those who access and co-produce digital content that can easily be transformed into data. This is contextualised by 'big data' technologies that have altered biomedicine, enabling collation and analysis of myriad data from digitised records to personal mobile data. Social media facilitate new formations of communities and knowledge enacted online, while novel kinds of commercial value emerge from digital networks that enable health data commodification. In this paper, we draw from exemplary digital era shifts towards participatory medicine to cast light on the rapprochements between patienthood, participation and consumption, and we explore how these rapprochements are mediated by, and materialise through, the use of participatory digital technologies and big data. We argue that there is a need to use new conceptual tools that account for the multiple roles and experiences of patient-participant-consumers that co-emerge through digital technologies. We must also ethically re-assess the rights and responsibilities of individuals in the digital era, and the implications of digital era changes for the future of biomedicine and healthcare.

Leveraging Multilayered "Omics" Data for Atopic Dermatitis: A Road Map to Precision Medicine

Atopic dermatitis (AD) is a complex multifactorial inflammatory skin disease that affects ~280 million people worldwide. About 85% of AD cases begin in childhood, a significant portion of which can persist into adulthood. Moreover, a typical progression of children with AD to food allergy, asthma or allergic rhinitis has been reported (“allergic march” or “atopic march”). AD comprises highly heterogeneous sub-phenotypes/endotypes resulting from complex interplay between intrinsic and extrinsic factors, such as environmental stimuli, and genetic factors regulating cutaneous functions (impaired barrier function, epidermal lipid, and protease abnormalities), immune functions and the microbiome. Though the roles of high-throughput “omics” integrations in defining endotypes are recognized, current analyses are primarily based on individual omics data and using binary clinical outcomes. Although individual omics analysis, such as genome-wide association studies (GWAS), can effectively map variants correlated with AD, the majority of the heritability and the functional relevance of discovered variants are not explained or known by the identified variants. The limited success of singular approaches underscores the need for holistic and integrated approaches to investigate complex phenotypes using trans-omics data integration strategies. Integrating omics layers (e.g., genome, epigenome, transcriptome, proteome, metabolome, lipidome, exposome, microbiome), which often have complementary and synergistic effects, might provide the opportunity to capture the flow of information underlying AD disease manifestation. Overlapping genes/candidates derived from multiple omics types include FLG, SPINK5, S100A8, and SERPINB3 in AD pathogenesis. Overlapping pathways include macrophage, endothelial cell and fibroblast activation pathways, in addition to well-known Th1/Th2 and NFkB activation pathways. Interestingly, there was more multi-omics overlap at the pathway level than gene level. Further analysis of multi-omics overlap at the tissue level showed that among 30 tissue types from the GTEx database, skin and esophagus were significantly enriched, indicating the biological interconnection between AD and food allergy. The present work explores multi-omics integration and provides new biological insights to better define the biological basis of AD etiology and confirm previously reported AD genes/pathways. In this context, we also discuss opportunities and challenges introduced by “big omics data” and their integration.

[Personalized medicine in allergology]

BACKGROUND

Personalized medicine offers new perspectives for diagnostic measurements and medical treatment, but also puts greater demands on the physician.

OBJECTIVES

Developments, potentials and potential pitfalls of personalized medicine in allergology.

METHODS

Overview, evaluation and discussion of the current state of science on the basis of selected examples.

RESULTS

Allergic diseases like allergic rhinitis, atopic eczema or anaphylaxis can be classified into various clinical phenotypes, which are based on different immunological endotypes. These can be captured and categorized by a wide variety of omics technologies. The identification of endotype specific biomarkers holds promising opportunities of more precise diagnostics, the implementation of novel targeted therapies or the development of optimized preventive strategies. However, individualized analysis and assessment of the significance of the measurements represent special challenges.

CONCLUSIONS

Findings of the complex omics technologies need to be evaluated by comprehensive prospective studies in order to validate their clinical relevance and suitability for personalized medicine in allergology.

Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.

Asthma: personalized and precision medicine

PURPOSE OF REVIEW

In this review, we herein describe the progress in management of severe asthma, evolving from a 'blockbuster approach' to a more personalized approach targeted to the utilization of endotype-driven therapies.

RECENT FINDINGS

Severe asthma characterization in phenotypes and endotypes, by means of specific biomarkers, have led to the dichotomization of the concepts of 'personalized medicine' and 'precision medicine', which are often used as synonyms, but actually have conceptual differences in meaning. The recent contribute of the omic sciences (i.e. proteomics, transcriptomics, metabolomics, genomics, …) has brought this initially theoretic evolution into a more concrete level.

SUMMARY

This step-by-step transition would bring to a better approach to severe asthmatic patients as the personalization of their therapeutic strategy would bring to a better patient selection, a more precise endotype-driven treatment, and hopefully to better results in terms of reduction of exacerbation rates, symptoms, pulmonary function and quality of life.

Single-cell analyses to tailor treatments

Single-cell RNA-seq could play a key role in personalized medicine by facilitating characterization of cells, pathways, and genes associated with human diseases such as cancer.

The role of the clinician in the multi-omics era: are you ready?

Since Garrod's first description of alkaptonuria in 1902, and newborn screening for phenylketonuria introduced in the 1960s, P4 medicine (preventive, predictive, personalized, and participatory) has been a reality for the clinician serving patients with inherited metabolic diseases. The era of high-throughput technologies promises to accelerate its scale dramatically. Genomics, transcriptomics, epigenomics, proteomics, glycomics, metabolomics, and lipidomics offer an amazing opportunity for holistic investigation and contextual pathophysiologic understanding of inherited metabolic diseases for precise diagnosis and tailored treatment. While each of the -omics technologies is important to systems biology, some are more mature than others. Exome sequencing is emerging as a reimbursed test in clinics around the world, and untargeted metabolomics has the potential to serve as a single biochemical testing platform. The challenge lies in the integration and cautious interpretation of these big data, with translation into clinically meaningful information and/or action for our patients. A daunting but exciting task for the clinician; we provide clinical cases to illustrate the importance of his/her role as the connector between physicians, laboratory experts and researchers in the basic, computer, and clinical sciences. Open collaborations, data sharing, functional assays, and model organisms play a key role in the validation of -omics discoveries. Having all the right expertise at the table when discussing the diagnostic approach and individualized management plan according to the information yielded by -omics investigations (e.g., actionable mutations, novel therapeutic interventions), is the stepping stone of P4 medicine. Patient participation and the adjustment of the medical team's plan to his/her and the family's wishes most certainly is the capstone. Are you ready?

Molecular Changes During Acute Myeloid Leukemia (AML) Evolution and Identification of Novel Treatment Strategies Through Molecular Stratification

Acute myeloid leukemia (AML) is a hematopoietic malignancy characterized by impaired differentiation and uncontrollable proliferation of myeloid progenitor cells. Due to high relapse rates, overall survival for this rapidly progressing disease is poor. The significant challenge in AML treatment is disease heterogeneity stemming from variability in maturation state of leukemic cells of origin, genetic aberrations among patients, and existence of multiple disease clones within a single patient. Disease heterogeneity and the lack of biomarkers for drug sensitivity lie at the root of treatment failure as well as selective efficacy of AML chemotherapies and the emergence of drug resistance. Furthermore, standard-of-care treatment is aggressive, presenting significant tolerability concerns to the commonly advanced-age AML patient. In this review, we examine the concept and potential of molecular stratification, particularly with biologically relevant drug responses, in identifying low-toxicity precision therapeutic combinations and clinically relevant biomarkers for AML patient care as a way to overcome these challenges in AML treatment.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

Pharmacogenomics of multifactorial diseases: a focus on psoriatic arthritis

This review will outline the current pharmacogenomics knowledge about psoriatic arthritis with a special attention to the perspectives and the challenges for its implementation in the clinical practice. To date, different drugs have been developed to contrast the symptoms and the progression of psoriatic arthritis. However, patients have shown high variability of drug response in relation to their genetic makeup. In this context, the advances made in the knowledge and the potentialities of genome-drugs associations paved the path for the development of a precision medicine. In fact, these associations may be successfully combined with the environment information to provide new strategies able to prevent and improve the disease management as well as to enhance the patients quality of life.

Depression in Diabetic Retinopathy: A Review and Recommendation for Psychiatric Management

BACKGROUND

Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and one of the main causes of irreversible vision impairment in the working-age population. Multiple studies have demonstrated the significantly high prevalence of depression in patients with DR in recent years. The progression of DR could lead to depression, whereas depressive symptoms often worsen the condition of DR. Therefore, DR is one of the causes of psychosomatic diseases, the treatment for which should combine traditional DR therapy with depression interventions.

METHODS

We reviewed existing articles that investigated the association between DR and depression in the context of prevalence, risk factors, biological mechanisms, and treatment indications.

RESULTS

The literature review in this article includes a brief introduction to current studies of depression and DR, followed by a focus on the epidemiology of depression in DR that help doctors better identify potential or existing depression patients upon first diagnosis. The underlying biologic mechanisms between the 2 diseases are briefly reviewed, and potential treatments are addressed.

CONCLUSIONS

Depression in patients with DR is not uncommon and has a negative effect on the condition of DR. To achieve optimal prognosis in patients with DR and depression, more attention to combined psychiatric therapies for depression is recommended.