Human genomics projects and precision medicine

Pharmacometabolomics in Drug Disposition, Toxicity, and Precision Medicine

The precision medicine initiative has driven a substantial change in the way scientists and health care practitioners think about diagnosing and treating disease. While it has long been recognized that drug response is determined by the intersection of genetic, environmental, and disease factors, improvements in technology have afforded precision medicine guided dosing of drugs to improve efficacy and reduce toxicity. Pharmacometabolomics aims to evaluate small molecule metabolites in plasma and/or urine to help evaluate mechanisms that predict and/or reflect drug efficacy and toxicity. In this mini review, we provide an overview of pharmacometabolomic approaches and methodologies. Relevant examples where metabolomic techniques have been used to better understand drug efficacy and toxicity in major depressive disorder and cancer chemotherapy are discussed. In addition, the utility of metabolomics in drug development and understanding drug metabolism, transport, and pharmacokinetics is reviewed. Pharmacometabolomic approaches can help describe factors mediating drug disposition, efficacy, and toxicity. While important advancements in this area have been made, there remain several challenges that must be overcome before this approach can be fully implemented into clinical drug therapy. SIGNIFICANCE STATEMENT: Pharmacometabolomics has emerged as an approach to identify metabolites that allow for implementation of precision medicine approaches to pharmacotherapy. This review article provides an overview of pharmacometabolomics including highlights of important examples.

99mTc(I)-Labeled His-Tagged Proteins: Impact in the Development of Novel Imaging Probes and in Drug Discovery

Technetium-99 m (99mTc) remains the cornerstone of nuclear medicine for single photon emission computed tomography (SPECT) due to its widespread availability and chemical and physical features. Its multiple oxidation states allow for the design and production of radiopharmaceuticals with versatile properties, namely in terms of pharmacokinetic profile. 99mTc(V) is the most common oxidation state, but 99mTc(I) gained traction after the pioneering work of Alberto and colleagues, which resulted in the introduction of the organometallic core fac-[99mTc(CO)3(H2O)3]+. This core is readily available from [99mTcO4]- and displays three labile water molecules that can be easily swapped for ligands with different denticity and/or donor atoms in aqueous environment. This makes it possible to radiolabel small molecules as well as high molecular weight molecules, such as antibodies or other proteins, while assuring biological activity. Direct radiolabelling of those proteins with fac-[99mTc(CO)3]+ under mild conditions is accomplished through incorporation of a polyhistidine tag (His-tag), a commonly used tag for purification of recombinant proteins. This review aims to address the direct radiolabelling of His-tagged macromolecules with fac-[99mTc(CO)3]+ for development of molecular imaging agents and the impact of this technology in the discovery and development of imaging and/or therapeutic agents towards clinical application.

Principles of clinical genetics for rheumatologists: clinical indications and interpretation of broad-based genetic testing

Advances in DNA sequencing technologies, especially next-generation sequencing (NGS), which is the basis for whole-exome sequencing (WES) and whole-genome sequencing (WGS), have profoundly transformed immune-mediated rheumatic disease diagnosis. Recently, substantial cost reductions have facilitated access to these diagnostic tools, expanded the capacity of molecular diagnostics and enabled the pursuit of precision medicine in rheumatology. Understanding the fundamental principles of genetics and diversity in genetic variant classification is a crucial milestone in rheumatology. However, despite the growing availability of DNA sequencing platforms, a significant number of autoinflammatory diseases (AIDs), neuromuscular disorders, hereditary collagen diseases, and monogenic bone diseases remain unsolved, and variants of uncertain significance (VUS) pose a formidable challenge to addressing these unmet needs in the coming decades. This article aims to provide an overview of the clinical indications and interpretation of comprehensive genetic testing in the medical field, addressing the related complexities and implications.

Understanding genetic variability: exploring large-scale copy number variants through non-invasive prenatal testing in European populations

Large-scale copy number variants (CNVs) are structural alterations in the genome that involve the duplication or deletion of DNA segments, contributing to genetic diversity and playing a crucial role in the evolution and development of various diseases and disorders, as they can lead to the dosage imbalance of one or more genes. Massively parallel sequencing (MPS) has revolutionized the field of genetic analysis and contributed significantly to routine clinical diagnosis and screening. It offers a precise method for detecting CNVs with exceptional accuracy. In this context, a non-invasive prenatal test (NIPT) based on the sequencing of cell-free DNA (cfDNA) from pregnant women's plasma using a low-coverage whole genome MPS (WGS) approach represents a valuable source for population studies. Here, we analyzed genomic data of 12,732 pregnant women from the Slovak (9,230), Czech (1,583), and Hungarian (1,919) populations. We identified 5,062 CNVs ranging from 200 kbp and described their basic characteristics and differences between the subject populations. Our results suggest that re-analysis of sequencing data from routine WGS assays has the potential to obtain large-scale CNV population frequencies, which are not well known and may provide valuable information to support the classification and interpretation of this type of genetic variation. Furthermore, this could contribute to expanding knowledge about the central European genome without investing in additional laboratory work, as NIPTs are a relatively widely used screening method.

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

The Health History of First-Degree Relatives' Dyslipidemia Can Affect Preferences and Intentions following the Return of Genomic Results for Monogenic Familial Hypercholesterolemia

Genetic testing is key in modern healthcare, particularly for monogenic disorders such as familial hypercholesterolemia. This Tohoku Medical Megabank Project study explored the impact of first-degree relatives' dyslipidemia history on individual responses to familial hypercholesterolemia genomic results. Involving 214 participants and using Japan's 3.5KJPN genome reference panel, the study assessed preferences and intentions regarding familial hypercholesterolemia genetic testing results. The data revealed a significant inclination among participants with a family history of dyslipidemia to share their genetic test results, with more than 80% of participants intending to share positive results with their partners and children and 98.1% acknowledging the usefulness of positive results for personal health management. The study underscores the importance of family health history in genetic-testing perceptions, highlighting the need for family-centered approaches in genetic counseling and healthcare. Notable study limitations include the regional scope and reliance on questionnaire data. The study results emphasize the association between family health history and genetic-testing attitudes and decisions.

Understanding Individual Subject Differences through Large Behavioral Datasets: Analytical and Statistical Considerations

A core feature of behavior analysis is the single-subject design, in which each subject serves as its own control. This approach is powerful for identifying manipulations that are causal to behavioral changes but often fails to account for individual differences, particularly when coupled with a small sample size. It is more common for other subfields of psychology to use larger-N approaches; however, these designs also often fail to account for the individual by focusing on aggregate-level data only. Moving forward, it is important to study individual differences to identify subgroups of the population that may respond differently to interventions and to improve the generalizability and reproducibility of behavioral science. We propose that large-N datasets should be used in behavior analysis to better understand individual subject variability. First, we describe how individual differences have been historically treated and then outline practical reasons to study individual subject variability. Then, we describe various methods for analyzing large-N datasets while accounting for the individual, including correlational analyses, machine learning, mixed-effects models, clustering, and simulation. We provide relevant examples of these techniques from published behavioral literature and from a publicly available dataset compiled from five different rat experiments, which illustrates both group-level effects and heterogeneity across individual subjects. We encourage other behavior analysts to make use of the substantial advancements in online data sharing to compile large-N datasets and use statistical approaches to explore individual differences.

Public health genomics research in Italy: an overview of ongoing projects

Public health genomics (PHG) aims to integrate advances in genomic sciences into healthcare for the benefit of the general population. As in many countries, there are various research initiatives in this field in Italy, but a clear picture of the national research portfolio has never been sketched. Thus, we aimed to provide an overview of current PHG research projects at the national or international level by consultation with Italian institutional and academic experts. We included 68 PHG projects: the majority were international projects in which Italian researchers participated (n = 43), mainly funded by the European Commission, while the remainder were national initiatives (N = 25), mainly funded by central government. Funding varied considerably, from € 50,000 to € 80,803,177. Three main research themes were identified: governance (N = 20); precision medicine (PM; N = 46); and precision public health (N = 2). We found that research activities are preferentially aimed at the clinical application of PM, while other efforts deal with the governance of the complex translation of genomic innovation into clinical and public health practice. To align such activities with national and international priorities, the development of an updated research agenda for PHG is needed.

Assessment of the current status of real-world pharmacogenomic testing: informed consent, patient education, and related practices

Introduction: The practice of informed consent (IC) for pharmacogenomic testing in clinical settings varies, and there is currently no consensus on which elements of IC to provide to patients. This study aims to assess current IC practices for pharmacogenomic testing. Methods: An online survey was developed and sent to health providers at institutions that offer clinical germline pharmacogenomic testing to assess current IC practices. Results: Forty-six completed surveys representing 43 clinical institutions offering pharmacogenomic testing were received. Thirty-two (74%) respondents obtain IC from patients with variability in elements incorporated. Results revealed that twenty-nine (67%) institutions discuss the benefits, description, and purpose of pharmacogenomic testing with patients. Less commonly discussed elements included methodology and accuracy of testing, and laboratory storage of samples. Discussion: IC practices varied widely among survey respondents. Most respondents desire the establishment of consensus IC recommendations from a trusted pharmacogenomics organization to help address these disparities.

Biomarker-Guided Tailored Therapy in Major Depression

This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.

Letter to the editor: Aggressive variant prostate cancer: An exemplary case study and comprehensive literature survey

This article enthusiastically explores the study of highly aggressive variant prostate cancer (AVPC), acknowledging its relatively rare yet highly menacing presence within the realm of prostate cancer. The paper delves into the pathological characteristics of AVPC, diagnostic and therapeutic challenges, and the potential applications of precision medicine and molecular imaging in the future.

Cancer organoid-based diagnosis reactivity prediction (CODRP) index-based anticancer drug sensitivity test in ALK-rearrangement positive non-small cell lung cancer (NSCLC)

BACKGROUND

Recently, cancer organoid-based drug sensitivity tests have been studied to predict patient responses to anticancer drugs. The area under curve (AUC) or IC50 value of the dose-response curve (DRC) is used to differentiate between sensitive and resistant patient's groups. This study proposes a multi-parameter analysis method (cancer organoid-based diagnosis reactivity prediction, CODRP) that considers the cancer stage and cancer cell growth rate, which represent the severity of cancer patients, in the sensitivity test.

METHODS

On the CODRP platform, patient-derived organoids (PDOs) that recapitulate patients with lung cancer were implemented by applying a mechanical dissociation method capable of high yields and proliferation rates. A disposable nozzle-type cell spotter with efficient high-throughput screening (HTS) has also been developed to dispense a very small number of cells due to limited patient cells. A drug sensitivity test was performed using PDO from the patient tissue and the primary cancer characteristics of PDOs were confirmed by pathological comparision with tissue slides.

RESULTS

The conventional index of drug sensitivity is the AUC of the DRC. In this study, the CODRP index for drug sensitivity test was proposed through multi-parameter analyses considering cancer cell proliferation rate, the cancer diagnosis stage, and AUC values. We tested PDOs from eight patients with lung cancer to verify the CODRP index. According to the anaplastic lymphoma kinase (ALK) rearrangement status, the conventional AUC index for the three ALK-targeted drugs (crizotinib, alectinib, and brigatinib) did not classify into sensitive and resistant groups. The proposed CODRP index-based drug sensitivity test classified ALK-targeted drug responses according to ALK rearrangement status and was verified to be consistent with the clinical drug treatment response.

CONCLUSIONS

Therefore, the PDO-based HTS and CODRP index drug sensitivity tests described in this paper may be useful for predicting and analyzing promising anticancer drug efficacy for patients with lung cancer and can be applied to a precision medicine platform.

Opportunities and challenges of 5G network technology toward precision medicine

Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.

Digital entrepreneurship from cellular data: How omics afford the emergence of a new wave of digital ventures in health

Data has become an indispensable input, throughput, and output for the healthcare industry. In recent years, omics technologies such as genomics and proteomics have generated vast amounts of new data at the cellular level including molecular, structural, and functional levels. Cellular data holds the potential to innovate therapeutics, vaccines, diagnostics, consumer products, or even ancestry services. However, data at the cellular level is generated with rapidly evolving omics technologies. These technologies use scientific knowledge from resource-rich environments. This raises the question of how new ventures can use cellular-level data from omics technologies to create new products and scale their business. We report on a series of interviews and a focus group discussion with entrepreneurs, investors, and data providers. By conceptualizing omics technologies as external enablers, we show how characteristics of cellular-level data negatively affect the combination mechanisms that drive venture creation and growth. We illustrate how data characteristics set boundary conditions for innovation and entrepreneurship and highlight how ventures seek to mitigate their impact.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12525-023-00669-w.

Use of Big Data and Ethical Issues for Populations With Substance Use Disorder

With expanding data availability and computing power, health research is increasingly relying on big data from a variety of sources. We describe a state-level effort to address aspects of the opioid epidemic through public health research, which has resulted in an expansive data resource combining dozens of administrative data sources in Massachusetts. The Massachusetts Public Health Data Warehouse is a public health innovation that serves as an example of how to address the complexities of balancing data privacy and access to data for public health and health services research. We discuss issues of data protection and data access, and provide recommendations for ethical data governance. Keeping these issues in mind, the use of this data resource has the potential to allow for transformative research on critical public health issues.

A display and analysis platform for gut microbiomes of minority people and phenotypic data in China

The minority people panmicrobial community database (MPPCD website: http://mppmcdb.cloudna.cn/ ) is the first microbe-disease association database of Chinese ethnic minorities. To research the relationships between intestinal microbes and diseases/health in the ethnic minorities, we collected the microbes of the Han people for comparison. Based on the data, such as age, among the different ethnic groups of the different regions of Sichuan Province, MPPCD not only provided the gut microbial composition but also presented the relative abundance value at the phylum, class, order, family and genus levels in different groups. In addition, differential analysis was performed in different microbes in the two different groups, which contributed to exploring the difference in intestinal microbe structures between the two groups. Meanwhile, a series of related factors, including age, sex, body mass index, ethnicity, physical condition, and living altitude, were included in the MPPCD, with special focus on living altitude. To date, this is the first intestinal microbe database to introduce altitude features. In conclusion, we hope that MPPCD will serve as a fundamental research support for the relationship between human gut microbes and host health and disease, especially in ethnic minorities.

Antioxidant evaluation and computational prediction of prospective drug-like compounds from polyphenolic-rich extract of Hibiscus cannabinus L. seed as antidiabetic and neuroprotective targets: assessment through in vitro and in silico studies

BACKGROUND

Reports have implicated diabetes mellitus (DM) and Alzheimer's disease (AD) as some of the global persistent health challenges with no lasting solutions, despite of significant inputs of modern-day pharmaceutical firms. This study therefore, aimed to appraise the in vitro antioxidant potential, enzymes inhibitory activities, and as well carry out in silico study on bioactive compounds from polyphenolic-rich extract of Hibiscus cannabinus seed (PEHc).

METHODS

In vitro antioxidant assays were performed on PEHc using standard methods while the identification of phytoconstituents was carried out with high performance liquid chromatography (HPLC). For the in silico molecular docking using Schrodinger's Grid-based ligand docking with energetics software, seven target proteins were retrieved from the database ( https://www.rcsb.org/ ).

RESULTS

HPLC technique identified twelve chemical compounds in PEHc, while antioxidant quantification revealed higher total phenolic contents (243.5 ± 0.71 mg GAE/g) than total flavonoid contents (54.06 ± 0.09 mg QE/g) with a significant (p < 0.05) inhibition of ABTS (IC₅₀ = 218.30 ± 0.87 µg/ml) and 1, 1-diphenyl-2-picrylhydrazyl free radicals (IC₅₀ = 227.79 ± 0.74 µg/ml). In a similar manner, the extract demonstrated a significant (p < 0.05) inhibitory activity against α-amylase (IC₅₀ = 256.88 ± 6.15 µg/ml) and α-glucosidase (IC₅₀ = 183.19 ± 0.23 µg/ml) as well as acetylcholinesterase (IC₅₀ = 262.95 ± 1.47 µg/ml) and butyrylcholinesterase (IC₅₀ = 189.97 ± 0.82 µg/ml), respectively. Furthermore, In silico study showed that hibiscetin (a lead) revealed a very strong binding affinity energies for DPP-4, (PDB ID: 1RWQ) and α-amylase (PDB ID: 1SMD), gamma-tocopherol ( for peptide-1 receptor; PDB ID: 3C59, AChE; PDB ID: 4EY7 and BChE; PDB ID: 7B04), cianidanol for α-glucosidase; PDB ID: 7KBJ and kaempferol for Poly [ADP-ribose] polymerase 1 (PARP-1); PDB ID: 6BHV, respectively. More so, ADMET scores revealed drug-like potentials of the lead compounds identified in PEHc.

CONCLUSION

As a result, the findings of this study point to potential drug-able compounds in PEHc that could be useful for the management of DM and AD.

Digital health in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) can be prevented and treated through effective care, reducing exacerbations and hospitalizations. Early identification of individuals at high risk of COPD exacerbation is an opportunity for preventive measures. However, many patients struggle to follow their treatment plans because of a lack of knowledge about the disease, limited access to resources, and insufficient clinical support. The growth of digital health-which encompasses advancements in health information technology, artificial intelligence, telehealth, the Internet of Things, mobile health, wearable technology, and digital therapeutics-offers opportunities for improving the early diagnosis and management of COPD. This study reviewed the field of digital health in terms of COPD. The findings showed that despite significant advances in digital health, there are still obstacles impeding its effectiveness. Finally, we highlighted some of the major challenges and possibilities for developing and integrating digital health in COPD management.

UPO Biobank: The Challenge of Integrating Biobanking into the Academic Environment to Support Translational Research

Biobanks are driving motors of precision and personalized medicine by providing high-quality biological material/data through the standardization and harmonization of their collection, preservation, and distribution. UPO Biobank was established in 2020 as an institutional, disease, and population biobank within the University of Piemonte Orientale (UPO) for the promotion and support of high-quality, multidisciplinary studies. UPO Biobank collaborates with UPO researchers, sustaining academic translational research, and supports the Novara Cohort Study, a longitudinal cohort study involving the population in the Novara area that will collect data and biological specimens that will be available for epidemiological, public health, and biological studies on aging. UPO Biobank has been developed by implementing the quality standards for the field and the ethical and legal issues and normative about privacy protection, data collection, and sharing. As a member of the "Biobanking and Biomolecular Resources Research Infrastructure" (BBMRI) network, UPO Biobank aims to expand its activity worldwide and launch cooperation with new national and international partners and researchers. The objective of this manuscript is to report an institutional and operational experience through the description of the technical and procedural solutions and ethical and scientific implications associated with the establishment of this university research biobank.

Identification of novel missense mutation related with non-syndromic sensorineural deafness, DFNA11 in korean family by NGS

BACKGOUND

Hereditary hearing loss is one of the most common genetically heterogeneous defects in human. About 70% of hereditary hearing loss is defined as non-syndromic hearing loss showing loss of hearing ability without any other symptoms. Up to date, the identified genes associated with non-syndromic hearing loss are 128, including 52 genes for DFNA and 76 genes for DFNB. Because of high levels of heterogeneity, it is difficult to identify the causative factors for hearing loss using Sanger sequencing.

OBJECTIVE

Our aim was to detect causative factors and investigate pathogenic mutations, which co-segregates within the candidate family.

METHODS

We used Next Generation Sequencing technique to investigate whole-exome sequences of a Korean family with non-syndromic hereditary hearing loss. The family showed autosomal dominant inheritance pattern.

RESULTS

We identified a novel missense variation, c.1978G > A in MYO7A gene, in the family with the autosomal dominant inheritance pattern. c.1978G > A produced Gly660Arg in the motor head domain of Myosin VIIA disrupt the ATP- and actin-binding motif function.

CONCLUSION

This study is the first to report pathogenic mutations within MYO7A gene in Korean family and our data would facilitate diagnosing the primary cause of hereditary hearing loss in Korean.

Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach

Background

The increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process.

Purpose

To design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders.

Methods

Design science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system.

Results

The analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations.

Impact

Our work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients.

Conclusion

Augmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.

Biomarkers discovery for endometrial cancer: A graph convolutional sample network method

BACKGROUND

Endometrial carcinoma is the sixth most common cancer in women worldwide. Importantly, endometrial cancer is among the few types of cancers with patient mortality that is still increasing, which indicates that the improvement in its diagnosis and treatment is still urgent. Moreover, biomarker discovery is essential for precise classification and prognostic prediction of endometrial cancer.

METHODS

A novel graph convolutional sample network method was used to identify and validate biomarkers for the classification of endometrial cancer. The sample networks were first constructed for each sample, and the gene pairs with high frequencies were identified to construct a subtype-specific network. Putative biomarkers were then screened using the highest degrees in the subtype-specific network. Finally, simplified sample networks are constructed using the biomarkers for the graph convolutional network (GCN) training and prediction.

RESULTS

Putative biomarkers (23) were identified using the novel bioinformatics model. These biomarkers were then rationalised with functional analyses and were found to be correlated to disease survival with network entropy characterisation. These biomarkers will be helpful in future investigations of the molecular mechanisms and therapeutic targets of endometrial cancers.

CONCLUSIONS

A novel bioinformatics model combining sample network construction with GCN modelling is proposed and validated for biomarker discovery in endometrial cancer. The model can be generalized and applied to biomarker discovery in other complex diseases.

Digital Technologies: Advancing Individualized Treatments through Gene and Cell Therapies, Pharmacogenetics, and Disease Detection and Diagnostics

Digital technologies are shifting the paradigm of medicine in a way that will transform the healthcare industry. Conventional medical approaches focus on treating symptoms and ailments for large groups of people. These approaches can elicit differences in treatment responses and adverse reactions based on population variations, and are often incapable of treating the inherent pathophysiology of the medical conditions. Advances in genetics and engineering are improving healthcare via individualized treatments that include gene and cell therapies, pharmacogenetics, disease detection, and diagnostics. This paper highlights ways that artificial intelligence can help usher in an age of personalized medicine.

Individual Genetic Heterogeneity

Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbeing of an individual. Mutation rates vary widely for variation types, e.g., due to the sequence context. Genetic information guides numerous aspects in organisms. Types of inheritance, whether Mendelian or non-Mendelian, zygosity, sexual reproduction, and sex determination are covered. Functions of DNA and functional effects of variations are introduced, along with mechanism that reduce and modulate functional effects, including TARAR countermeasures and intraindividual genetic conflict. TARAR countermeasures for tolerance, avoidance, repair, attenuation, and resistance are essential for life, integrity of genetic information, and gene expression. The genetic composition, effects of variations, and their expression are considered also in diseases and personalized medicine. The text synthesizes knowledge and insight on individual genetic heterogeneity and organizes and systematizes the central concepts.

Digital Disruption and Big Data in Healthcare - Opportunities and Challenges

Background

As the amount of medical data in the electronic medical records system (EMR) is increasing tremendously, the required time to read it by health providers is growing by the exact proportionality. This means that physicians must increase the time spared for each patient again by the precise proportionality. This may lead to exposing the accuracy and quality of the course of action to be taken for the patients. Increasing the physician’s required time for one patient means that the physician can see fewer patients. This will create an issue with the medical management authority as more physicians are needed, and higher expenses will be required.

Purpose

The two questions that arise here are 1. Identify the potential opportunities and challenges for extensive data analysis in the healthcare sector. 2. Evaluate different ways in which big medical data can be analyzed?

Methods

The authors identified the four concerned parties representing the four potential solutions dimensions to answer these two questions. These parties are 1. physicians, 2. health information systems management (HISM) departments, mainly the EMR system, and 3. Health management departments 4. Relevant Health Information Systems (HIS) parties. A literature review and 25 interviews were conducted. The interviews covered 1: Two global organizations: John Hopkins and Joint Commission International (JCI), 2: Three United Arab Emirates-based health organizations: Department of health in Abu Dhabi, SEHA in Abu Dhabi, Dubai health Authority (DHA) in Dubai, 3: 10 Physicians from different specialties, 4: Five EMR managers and 5: Five IT (Information Technology) professionals representing the HIS parties. Qualitative analysis is used as the approach for data analysis.

Results

Identifying the managerial and the technical recommendations to be utilized mainly based on digital disruption technologies, tools, and processes.

Conclusion

Healthcare has been slow in embracing digital disruption and transformation. In most areas, it is still in the initial stages. Recommendations are based on the UAE cases, highlighting the specific technologies and their features.

SNPMap—An integrated visual SNP interpretation tool

New technologies, such as next-generation sequencing, have advanced the ability to diagnose diseases and improve prognosis but require the identification of thousands of variants in each report based on several databases scattered across places. Curating an integrated interpretation database is time-consuming, costly, and needs regular update. On the other hand, the automatic curation of knowledge sources always results in overloaded information. In this study, an automated pipeline was proposed to create an integrated visual single-nucleotide polymorphism (SNP) interpretation tool called SNPMap. SNPMap pipelines periodically obtained SNP-related information from LitVar, PubTator, and GWAS Catalog API tools and presented it to the user after extraction, integration, and visualization. Keywords and their semantic relations to each SNP are rendered into two graphs, with their significance represented by the size/width of circles/lines. Moreover, the most related SNPs for each keyword that appeared in SNPMap were calculated and sorted. SNPMap retains the advantage of an automatic process while assisting users in accessing more lucid and detailed information through visualization and integration with other materials.

Managing the introduction of genomic applications into the National Health Service: A special challenge for health technology assessment in Italy

In recent years, the rapid proliferation of genomic tests for use in clinical practice has prompted healthcare systems to use a health technology assessment (HTA) approach to distinguish valuable from unwarranted applications. In this study, we narratively review the Italian HTA mechanisms for medical devices (MDs), both at the national and regional levels, and discuss the opportunity and benefits of extending them to genomic technologies, for which a dedicated assessment path was advocated by the National Plan for Public Health Genomics in 2017. We found that the National Health Technology Assessment Program for MDs, completed in 2019, had developed a structured pathway for the HTA of MDs; it established a hub-and-spoke structure, run by a governmental institution, and put in place transparent methodological procedures to cover all four HTA phases (i.e., proposal and prioritization, assessment, appraisal, and dissemination). However, several factors have hindered its adoption, and the regions are at different stages of its implementation. For these reasons, efforts should be made to ensure its effective deployment, both at national and regional levels. In addition, we argue that to harmonize the institutional roles and methodological procedures adopted, the time has come to concentrate resources on a single pathway for the assessment of all technologies that include both MDs and genomic applications.

Zebrafish Is a Powerful Tool for Precision Medicine Approaches to Neurological Disorders

Personalized medicine is currently one of the most promising tools which give hope to patients with no suitable or no available treatment. Patient-specific approaches are particularly needed for common diseases with a broad phenotypic spectrum as well as for rare and yet-undiagnosed disorders. In both cases, there is a need to understand the underlying mechanisms and how to counteract them. Even though, during recent years, we have been observing the blossom of novel therapeutic techniques, there is still a gap to fill between bench and bedside in a patient-specific fashion. In particular, the complexity of genotype-to-phenotype correlations in the context of neurological disorders has dampened the development of successful disease-modifying therapeutics. Animal modeling of human diseases is instrumental in the development of therapies. Currently, zebrafish has emerged as a powerful and convenient model organism for modeling and investigating various neurological disorders. This model has been broadly described as a valuable tool for understanding developmental processes and disease mechanisms, behavioral studies, toxicity, and drug screening. The translatability of findings obtained from zebrafish studies and the broad prospect of human disease modeling paves the way for developing tailored therapeutic strategies. In this review, we will discuss the predictive power of zebrafish in the discovery of novel, precise therapeutic approaches in neurosciences. We will shed light on the advantages and abilities of this in vivo model to develop tailored medicinal strategies. We will also investigate the newest accomplishments and current challenges in the field and future perspectives.

Sema3d Restrained Hepatocellular Carcinoma Progression Through Inactivating Pi3k/Akt Signaling via Interaction With FLNA

Hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors worldwide due to the high incidence rate of metastasis and recurrence. Semaphorin 3d (Sema3d) has been shown to play a critical role in vascular development during early embryogenesis and several forms of cancer progression via regulating cell migration. However, the function of Sema3d in hepatocellular carcinoma (HCC) remains elusive. This study aimed to explore the function and mechanisms of Sema3d in HCC. In our study, Sema3d expression was significantly downregulated in HCC tissues and cell lines. Downregulated Sema3d was closely correlated with aggressive clinicopathological features and poor clinical outcomes in HCC patients. Moreover, overexpression of Sema3d in HCCLM3 cells was significantly inhibited and knockdown of Sema3d in PLC/PRF/5 cells promoted proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT) of HCC cells in vitro and tumor growth, EMT, and metastasis in vivo. Furthermore, the RNA sequencing and gene set enrichment analysis (GSEA) indicated that these phenotypic and functional changes in Sema3d-interfered HCC cells were mediated by the Pi3k/Akt signaling pathway, and co-IP–combined mass spectrometry indicated Sema3d might interact with FLNA. Finally, we proved that Sema3d exerted its tumor-restraining effect by interacting with FLNA to inactivate the Pi3k/Akt signaling pathway and remodel the cytoskeleton. Our data showed that Sema3d restrained hepatocellular carcinoma proliferation, invasion, and metastasis through inactivating Pi3k/Akt via interaction with FLNA, which may serve as a novel prognostic predictor and a potential therapeutic target for HCC patients.

New Drug Development and Clinical Trial Design by Applying Genomic Information Management

Depending on the patients’ genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Gene-environment interactions related to maternal exposure to environmental and lifestyle-related chemicals during pregnancy and the resulting adverse fetal growth: a review

Background

There are only limited numbers of reviews on the association of maternal-child genetic polymorphisms and environmental and lifestyle-related chemical exposure during pregnancy with adverse fetal growth. Thus, this article aims to review: (1) the effect of associations between the above highlighted factors on adverse fetal growth and (2) recent birth cohort studies regarding environmental health risks.

Methods

Based on a search of the PubMed database through August 2021, 68 epidemiological studies on gene-environment interactions, focusing on the association between environmental and lifestyle-related chemical exposure and adverse fetal growth was identified. Moreover, we also reviewed recent worldwide birth cohort studies regarding environmental health risks.

Results

Thirty studies examined gene-smoking associations with adverse fetal growth. Sixteen maternal genes significantly modified the association between maternal smoking and adverse fetal growth. Two genes significantly related with this association were detected in infants. Moreover, the maternal genes that significantly interacted with maternal smoking during pregnancy were cytochrome P450 1A1 (CYP1A1), X-ray repair cross-complementing protein 3 (XRCC3), interleukin 6 (IL6), interleukin 1 beta (IL1B), human leukocyte antigen (HLA) DQ alpha 1 (HLA-DQA1), HLA DQ beta 1 (HLA-DQB1), and nicotinic acetylcholine receptor. Fetal genes that had significant interactions with maternal smoking during pregnancy were glutathione S-transferase theta 1 (GSTT1) and fat mass and obesity-associated protein (FTO). Thirty-eight studies examined the association between chemical exposures and adverse fetal growth. In 62 of the 68 epidemiological studies (91.2%), a significant association was found with adverse fetal growth. Across the studies, there was a wide variation in the analytical methods used, especially with respect to the genetic polymorphisms of interest, environmental and lifestyle-related chemicals examined, and the study design used to estimate the gene-environment interactions. It was also found that a consistently increasing number of European and worldwide large-scale birth cohort studies on environmental health risks have been conducted since approximately 1996.

Conclusion

There is some evidence to suggest the importance of gene-environment interactions on adverse fetal growth. The current knowledge on gene-environment interactions will help guide future studies on the combined effects of maternal-child genetic polymorphisms and exposure to environmental and lifestyle-related chemicals during pregnancy.

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1265/ehpm.21-00033.

Discovery of genes positively modulating treatment effect using potential outcome framework and Bayesian update

BACKGROUND

The recent explosion of cancer genomics provides extensive information about mutations and gene expression changes in cancer. However, most of the identified gene mutations are not clinically utilized. It remains uncertain whether the presence of a certain genetic alteration will affect treatment response. Conventional statistics have limitations for causal inferences and are hard to gain sufficient power in genomic datasets. Here, we developed and evaluated a C-search algorithm for searching the causal genes that maximize the effect of the treatment.

METHODS

The algorithm was developed based on the potential outcome framework and Bayesian posterior update. The precision of the algorithm was validated using a simulation dataset. The algorithm was implemented to a cBioPortal dataset. The genes discovered by the algorithm were externally validated within CancerSCAN screening data from Samsung Medical Center.

RESULTS

Simulation data analysis showed that the C-search algorithm was able to identify nine causal genes out of ten. The C-search algorithm shows the discovery rate rapidly increasing until the 1500 data instances. Meanwhile, the log-rank test shows a slower increase in performance. The C-search algorithm was able to suggest nine causal genes from the cBioPortal Metabric dataset. Treating the patients with the causal genes is associated with better survival outcome in both the cBioPortal dataset and the CancerSCAN dataset which is used for external validation.

CONCLUSIONS

Our C-search algorithm demonstrated better performance to identify causal effects of the genes than multiple log-rank test analysis especially within a limited number of data. The result suggests that the C-search can discover the causal genes from various genetic datasets, where the number of samples is limited compared to the number of variables.

The angiotensin-converting enzyme gene insertion/deletion polymorphism interacts with fear of falling in relation to stepping speed in community-dwelling older adults

BACKGROUND

Despite the association of genetic factors with falls, balance, and lower extremity functioning, interaction of the angiotensin-converting enzyme (ACE) gene insertion/deletion (I/D) polymorphism with fear of falling (FOF) in relation to stepping performance has, to the best of our knowledge, not been investigated in older adults.

OBJECTIVE

The purpose of this study was to examine the interaction effects of the ACE I/D polymorphism with FOF in relation to stepping performance in older adults.

METHODS

Eighty-eight community-dwelling adults 60 years or older participated in a cross-sectional observational study. Participants completed tests of rapid and distance stepping, and self-reported FOF (yes/no). Participants provided saliva for ACE genotyping. General linear models evaluated ACE genotype × FOF interaction effects in relation to stepping performance. The α level was set at 0.05.

RESULTS

The ACE I/D polymorphism exhibited significant interaction effects (p for interactions 0.002 ≤ p ≤ .04) with FOF in relation to stepping speed. Relationships between FOF and stepping speed varied among ACE genotypes. The insertion/insertion (II) genotype was significantly associated (p = .01) with slow stepping in individuals with, but not without FOF (p > .05).

CONCLUSION

Variation in relationships between FOF and stepping speed among ACE genotypes suggests a role for the ACE I/D polymorphism in modifying relationships between FOF and stepping speed in older adults. The association of the ACE II genotype with slow stepping performance in individuals with, but not without FOF, suggests that older adults with the ACE II genotype and FOF may be at increased risk for poor stepping performance and associated functional declines.

HKG: an open genetic variant database of 205 Hong Kong cantonese exomes

HKG is the first fully accessible variant database for Hong Kong Cantonese, constructed from 205 novel whole-exome sequencing data. There has long been a research gap in the understanding of the genetic architecture of southern Chinese subgroups, including Hong Kong Cantonese. HKG detected 196 325 high-quality variants with 5.93% being novel, and 25 472 variants were found to be unique in HKG compared to three Chinese populations sampled from 1000 Genomes (CHN). PCA illustrates the uniqueness of HKG in CHN, and the admixture study estimated the ancestral composition of HKG and CHN, with a gradient change from north to south, consistent with their geological distribution. ClinVar, CIViC and PharmGKB annotated 599 clinically significant variants and 360 putative loss-of-function variants, substantiating our understanding of population characteristics for future medical development. Among the novel variants, 96.57% were singleton and 6.85% were of high impact. With a good representation of Hong Kong Cantonese, we demonstrated better variant imputation using reference with the addition of HKG data, thus successfully filling the data gap in southern Chinese to facilitate the regional and global development of population genetics.

Locational effects on oral microbiota among long-term care patients

Background

Dysbiosis of oral microbiota is the cause of many diseases related to oral and general health. However, few Asia-based studies have evaluated the role of oral microbiota in patients receiving long-term care. Thus, new indications are needed for early prevention and risk management based on information derived from the oral microbiota.

Methods

We used next-generation sequencing (NGS) to identify the oral bacterial composition and abundance in patients receiving long-term care: 20 from the outpatient department (OPD) and 20 home-care patients. Their microbial compositions, taxonomy, and alpha/beta diversity were characterized.

Results

Microbiota from the two groups showed different diversity and homogeneity, as well as distinct bacterial species. A more diverse and stable microbial population was observed among OPD patients. Our findings indicated that home-care patients had a higher risk of oral diseases due to the existence of dominant species and a less stable microbial community.

Conclusion

This work was the first in Taiwan to use NGS to investigate the oral microbiota of long-term care patients. Our study demonstrated the potential use of dominant bacterial species as biomarkers for the risk management of posttreatment complications.

Targeting Oncogenic Pathways in the Era of Personalized Oncology: A Systemic Analysis Reveals Highly Mutated Signaling Pathways in Cancer Patients and Potential Therapeutic Targets

Cancer is the second leading cause of death globally. One of the main hallmarks in cancer is the functional deregulation of crucial molecular pathways via driver genetic events that lead to abnormal gene expression, giving cells a selective growth advantage. Driver events are defined as mutations, fusions and copy number alterations that are causally implicated in oncogenesis. Molecular analysis on tissues that have originated from a wide range of anatomical areas has shown that mutations in different members of several pathways are implicated in different cancer types. In recent decades, significant efforts have been made to incorporate this knowledge into daily medical practice, providing substantial insight towards clinical diagnosis and personalized therapies. However, since there is still a strong need for more effective drug development, a deep understanding of the involved signaling mechanisms and the interconnections between these pathways is highly anticipated. Here, we perform a systemic analysis on cancer patients included in the Pan-Cancer Atlas project, with the aim to select the ten most highly mutated signaling pathways (p53, RTK-RAS, lipids metabolism, PI-3-Kinase/Akt, ubiquitination, b-catenin/Wnt, Notch, cell cycle, homology directed repair (HDR) and splicing) and to provide a detailed description of each pathway, along with the corresponding therapeutic applications currently being developed or applied. The ultimate scope is to review the current knowledge on highly mutated pathways and to address the attractive perspectives arising from ongoing experimental studies for the clinical implementation of personalized medicine.

Data Integration Challenges for Machine Learning in Precision Medicine

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

Liquid Biopsy and Its Emerging Role in Rheumatology

Liquid biopsy is a rapidly evolving diagnostic technique used to analyze tissue-derived information found in the blood or other bodily fluids. It represents a new way to guide therapeutic decisions, mainly in cancer, but its application in other fields of medicine is still growing. Here, we discuss how liquid biopsy has been used in autoimmune rheumatic diseases such as rheumatoid arthritis, systemic lupus erythematosus, or primary Sjögren's syndrome. Additionally, in aspect of liquid biopsy, we analyze the molecular biomarkers utilized in the field of rheumatology, including circulating cell-free DNA, microRNA, and proteomic content.

Whole-exome sequencing in eccrine porocarcinoma indicates promising therapeutic strategies

Malignant sweat gland tumours are rare, with the most common form being Eccrine porocarcinoma (EP). To investigate the mutational landscape of EP, we performed whole-exome sequencing (WES) on 14 formalin-fixed paraffin-embedded samples of matched primary EP and healthy surrounding tissue. Mutational profiling revealed a high overall median mutation rate. This was attributed to signatures of mutational processes related to ultraviolet (UV) exposure, APOBEC enzyme dysregulation, and defective homologous double-strand break repair. All of these processes cause genomic instability and are implicated in carcinogenesis. Recurrent driving somatic alterations were detected in the EP candidate drivers TP53, FAT2, CACNA1S, and KMT2D. The analyses also identified copy number alterations and recurrent gains and losses in several chromosomal regions including that containing BRCA2, as well as deleterious alterations in multiple HRR components. In accordance with this reduced or even a complete loss of BRCA2 protein expression was detected in 50% of the investigated EP tumours. Our results implicate crucial oncogenic driver pathways and suggest that defective homologous double-strand break repair and the p53 pathway are involved in EP aetiology. Targeting of the p53 axis and PARP inhibition, and/or immunotherapy may represent promising treatment strategies.

Pilot study in pharmacogenomic management of empagliflozin in type 2 diabetes mellitus patients

Background

Type 2 diabetes mellitus (T2DM) is a metabolic disorder in which the patients with high blood sugar develop insufficient insulin secretion or insulin resistance. The solute carrier family, 5 member 2 (SLC5A2) gene is a member of sodium/glucose transporter family which can reduce heart and kidney problems. The current study aims to look into any association between rs11646054 variant in SLC5A2 gene and the anti-diabetic efficacy and safety of empagliflozin.

Methods

14 T2DM who failed to respond to previous treatments, empagliflozin 10 mg was added for 6 months. Genotyping of the rs11646054 variant of SLC5A2 gene was performed by polymerase chain reaction (PCR) followed by Sanger sequencing.

Results

Although hemoglobin A1c (HbA1c) and low-density lipoprotein (LDL) were not significantly different, but the mean fasting blood sugar (FBS), 2-h post prandial (2hpp), albumin-to-creatinine ratio (ACR), and total cholesterol (TC) were significantly decreased after 6 months empagliflozin treatment. There was a significant difference in the mean final reductions in FBS level among genotypes. It's important to mention that those who were GG homozygotes had a tendency to have more decrements.

Conclusions

The study results indicate that effects of variation in SLC5A2 (rs11646054) on the clinical efficacy of empagliflozin were negligible.

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 that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables 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 the 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 that were highly varied across patients, showing 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.

Exosome microRNAs in Metabolic Syndrome as Tools for the Early Monitoring of Diabetes and Possible Therapeutic Options

Exosomes are nano-sized extracellular vesicles produced and released by almost all cell types. They play an essential role in cell-cell communications by delivering cellular bioactive compounds such as functional proteins, metabolites, and nucleic acids, including microRNA, to recipient cells. Thus, they are involved in various physio-pathological conditions. Exosome-miRNAs are associated with numerous diseases, including type 2 diabetes, a complex multifactorial metabolic disorder linked to obesity. In addition, exosome-miRNAs are emerging as essential regulators in the progression of diabetes, principally for pancreatic β-cell injury and insulin resistance. Here, we have clustered the recent findings concerning exosome-miRNAs associated with β-cell dysfunction to provide a novel approach for the early diagnosis and therapy of diabetes.

Genomics of Obsessive-Compulsive Disorder-Toward Personalized Medicine in the Era of Big Data

Pathogenesis of obsessive-compulsive disorder (OCD) mainly involves dysregulation of serotonergic neurotransmission, but a number of other factors are involved. Genetic underprints of OCD fall under the category of "common disease common variant hypothesis," that suggests that if a disease that is heritable is common in the population (a prevalence >1-5%), then the genetic contributors-specific variations in the genetic code-will also be common in the population. Therefore, the genetic contribution in OCD is believed to come from multiple genes simultaneously and it is considered a polygenic disorder. Genomics offers a number of advanced tools to determine causal relationship between the exposure and the outcome of interest. Particularly, methods such as polygenic risk score (PRS) or Mendelian Randomization (MR) enable investigation of new pathways involved in OCD pathogenesis. This premise is also facilitated by the existence of publicly available databases that include vast study samples. Examples include population-based studies such as UK Biobank, China Kadoorie Biobank, Qatar Biobank, All of US Program sponsored by National Institute of Health or Generations launched by Yale University, as well as disease-specific databases, that include patients with OCD and co-existing pathologies, with the following examples: Psychiatric Genomics Consortium (PGC), ENIGMA OCD, The International OCD Foundation Genetics Collaborative (IOCDF-GC) or OCD Collaborative Genetic Association Study. The aim of this review is to present a comprehensive overview of the available Big Data resources for the study of OCD pathogenesis in the context of genomics and demonstrate that OCD should be considered a disorder which requires the approaches offered by personalized medicine.

Review: Influence of the CYP450 Genetic Variation on the Treatment of Psychotic Disorders

Second-generation antipsychotic metabolism is mainly carried out by the CYP450 superfamily, which is highly polymorphic. Therefore, knowing the influence of the different known CYP450 polymorphisms on antipsychotic plasmatic levels and, consequently, the biological effect could contribute to a deeper knowledge of interindividual antipsychotic treatment variability, prompting possible solutions. Considering this, this state of the art review aimed to summarize the current knowledge about the influence of the diverse characterized phenotypes on the metabolism of the most used second-generation antipsychotics. Forty studies describing different single nucleotide polymorphisms (SNPs) associated with the genes CYP1A2, CYP2D6, CYP3A4, CYP3A5, and ABCB1 and their influence on pharmacokinetics of olanzapine, clozapine, aripiprazole, risperidone, and quetiapine. Most of the authors concluded that although significant differences in the pharmacokinetic parameters between the different phenotypes could be observed, more thorough studies describing pharmacokinetic interactions and environmental conditions, among other variables, are needed to fully comprehend these pharmacogenetic interactions.

Python for gene expression

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.

Python for gene expression

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.

Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience

PURPOSE

The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research.

METHODS

Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing.

RESULTS

The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case-focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse.

CONCLUSION

The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.

Diabetes Mellitus and Cardiovascular Diseases: Nutraceutical Interventions Related to Caloric Restriction

Type 2 diabetes (T2DM) and cardiovascular disease (CVD) are closely associated and represent a key public health problem worldwide. An excess of adipose tissue, NAFLD, and gut dysbiosis establish a vicious circle that leads to chronic inflammation and oxidative stress. Caloric restriction (CR) is the most promising nutritional approach capable of improving cardiometabolic health. However, adherence to CR represents a barrier to patients and is the primary cause of therapeutic failure. To overcome this problem, many different nutraceutical strategies have been designed. Based on several data that have shown that CR action is mediated by AMPK/SIRT1 activation, several nutraceutical compounds capable of activating AMPK/SIRT1 signaling have been identified. In this review, we summarize recent data on the possible role of berberine, resveratrol, quercetin, and L-carnitine as CR-related nutrients. Additionally, we discuss the limitations related to the use of these nutrients in the management of T2DM and CVD.