Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine

Precision medicine to personalize medicine in allergic airway disease

PURPOSE OF REVIEW

The purpose of this study is to understand the approach to precision medicine and personalized medicine in the management of allergic airway disease.

RECENT FINDINGS

Identification of biomarkers as key tools used in precision medicine has led to the development of multiple biologic drugs being used as new treatments for allergic airway disease.

SUMMARY

In addition to these advances, there is still much needed effort to improve the feasibility and utility of integrating biologic omics data of precision medicine with physicochemical, behavioral, psychological, and social data to deliver optimized treatments that is personalized for each individual.

Role of Tunable Gold Nanostructures in Cancer Nanotheranostics: Implications on Synthesis, Toxicity, Clinical Applications and Their Associated Opportunities and Challenges

The existing diagnosis and treatment modalities have major limitations related to their precision and capability to understand several stages of disease development. A superior therapeutic system consists of a multifunctional approach in early diagnosis of the disease with a simultaneous progressive cure, using a precise medical approach towards complex treatment. These challenges can be addressed via nanotheranostics and explore suitable approaches to improve health care. Nanotechnology in combination with theranostics as an unconventional platform paved the way for developing novel strategies and modalities leading to diagnosis and therapy for complex disease conditions, ranging from acute to chronic levels. Among the metal nanoparticles, gold nanoparticles are being widely used for theranostics due to their inherent non-toxic nature and plasmonic properties. The unique optical and chemical properties of plasmonic metal nanoparticles along with theranostics have led to a promising era of plausible early detection of disease conditions, and they enable real-time monitoring with enhanced non-invasive or minimally invasive imaging of several ailments. This review aims to highlight the improvement and advancement brought to nanotheranostics by gold nanoparticles in the past decade. The clinical use of the metal nanoparticles in nanotheranostics is explained, along with the future perspectives on addressing the key applications related to diagnostics and therapeutics, respectively. The scope of gold nanoparticles and their realistic potential to design a sophisticated theranostic system is discussed in detail, along with their implications in clinical advancements which are the needs of the hour. The review concluded with the challenges, opportunities, and implications on translational potential of using gold nanoparticles in nanotheranostics.

Emerging theranostics to combat cancer: a perspective on metal-based nanomaterials

OBJECTIVE

Theranostics, encompassing diagnostics and therapeutics, has emerged as a critical component of cancer treatment. Metal-based theranostics is one such next-generation nanotechnology-based drug delivery system with a myriad of benefits in pre-clinical and clinical medication for the deadly diseases like cancer, where early detection can actually be life-saving.

SIGNIFICANCE

Metal theranostics have shown promising outcomes in terms of anticancer medication monitoring, targeted drug delivery, and simultaneous detection and treatment of early-stage cancer.

METHODS

For collection of literature data, different search engines including Google scholar, SciFinder, PubMed, ScienceDirect have been employed. With key words like, cancer, theranostics, metal nanoparticles relevant and appropriate data have been generated.

RESULTS

Noninvasive administration of the active drug is made possible by theranostics nanoparticulate systems' ability to aggregate at the tumor site and offer morphological and biochemical characteristics of the tumor site. The recent advancement of metal-based theranostics including metallic nanoparticles, metal oxides, metal sulfides, nanocomposites, etc. has been explored at length in this article.

CONCLUSION

The review highlights emerging applications in terms of molecular imaging, targeted therapy and different diagnostic approaches of metal theranostics. Possible challenges faced by nanotheranostics in terms of clinical immersion and toxicological aspects which need to be addressed at depth are also discussed at the end.

Network controllability solutions for computational drug repurposing using genetic algorithms

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdős-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.

Holistic Needs Assessment of Cancer Survivors-Supporting the Process Through Digital Monitoring of Circadian Physiology

The year 2022 could represent a significant juncture in the incorporation of mHealth solutions in routine cancer care. With the recent global COVID-19 pandemic leading a surge in both observation- and intervention-based studies predominantly aimed at remote monitoring there has been huge intellectual investment in developing platforms able to provide real time analytics that are readily usable. Another fallout from the pandemic has seen record waiting times and delayed access to cancer therapies leading to exhausting pressures on global healthcare providers. It seems an opportune time to utilize this boom in platforms to offer more efficient “at home” clinical assessments and less “in department” time for patients. Here, we will focus specifically on the role of digital tools around cancer survivorship, a relevant aspect of the cancer journey, particularly benefiting from integrative approaches. Within that context a further concept will be introduced and that is of the likely upsurge in circadian-based interpretation of continuous monitoring and the engendered therapeutic modifications. Chronobiology across the 24-hour span has long been understood to control key bodily aspects and circadian dysregulation plays a significant role in the risk of cancer and also the response to therapy and therefore progressive outcome. The rapid improvement in minimally invasive monitoring devices is, in the opinion of the authors, likely to advance introducing chronobiological amendments to routine clinical practices with positive impact on cancer survivors.

Building a Personalized Medicine Infrastructure for Gynecological Oncology Patients in a High-Volume Hospital

Gynecological cancers require complex intervention since patients have specific needs to be addressed. Centralization to high-volume centers improves the oncological outcomes of patients with gynecological cancers. Research in gynecological oncology is increasing thanks to modern technologies, from the comprehensive molecular characterization of tumors and individual pathophenotypes. Ongoing studies are focusing on personalizing therapies by integrating information across genomics, proteomics, and metabolomics with the genetic makeup and immune system of the patient. Hence, several challenges must be faced to provide holistic benefit to the patient. Personalized approaches should also recognize the unmet needs of each patient to successfully deliver the promise of personalized care, in a multidisciplinary effort. This may provide the greatest opportunity to improve patients' outcomes. Starting from a narrative review on gynecological oncology patients' needs, this article focuses on the experience of building a research and care infrastructure for personalized patient management.

From biobank and data silos into a data commons: convergence to support translational medicine

Background

To drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and effective use of data. In this article, we describe the journey of British Columbia’s Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcomes unit, and a collection of data silos, into an integrated data commons to support data standardization and resource sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients.

Results

Through several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: (1) biospecimen collections, (2) molecular and genomics data, and (3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community.

Conclusions

Proper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing frameworks, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03147-z.

Evaluation and Management of Sleep and Circadian Rhythm Disturbance in Cancer

OPINION STATEMENT

Sleep and circadian rhythm disturbance are among the most commonly experienced symptoms in patients with cancer. These disturbances occur throughout the spectrum of cancer care from diagnosis, treatment, and long into survivorship. The pathogenesis of these symptoms and disturbances is based on common inflammatory pathways related to cancer and its' treatments. The evaluation of sleep and circadian disorders requires an understanding of how these symptoms cluster with other cancer-related symptoms and potentiate each other. A thorough evaluation of these symptoms and disorders utilizing validated diagnostic tools, directed review of clinical information, and diagnostic testing is recommended. Treatment of sleep and circadian disturbance in cancer patients should be based on the findings of a detailed evaluation, including specific treatment of primary sleep and circadian disorders, and utilize integrative and personalised management of cancer-related symptoms through multiple pharmacologic and non-pharmacologic modalities. Recognition, evaluation, and treatment of sleep and circadian rhythm disturbance in cancer may lead to improved symptom management, quality of life, and outcomes.

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in "Personalized Medicine" and "Innovative Biomedical Technologies") could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.

Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics

Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.

Current State of "Omics" Biomarkers in Pancreatic Cancer

Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different "omics" levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.

Stochastic models for single-cell data: Current challenges and the way forward

Although the quantity and quality of single-cell data have progressed rapidly, making quantitative predictions with single-cell stochastic models remains challenging. The stochastic nature of cellular processes leads to at least three challenges in building models with single-cell data: (a) because variability in single-cell data can be attributed to multiple different sources, it is difficult to rule out conflicting mechanistic models that explain the same data equally well; (b) the distinction between interesting biological variability and experimental variability is sometimes ambiguous; (c) the nonstandard distributions of single-cell data can lead to violations of the assumption of symmetric errors in least-squares fitting. In this review, we first discuss recent studies that overcome some of the challenges or set up a promising direction and then introduce some powerful statistical approaches utilized in these studies. We conclude that applying and developing statistical approaches could lead to further progress in building stochastic models for single-cell data.

Decoding variants in drug-metabolizing enzymes and transporters in solid tumor patients by whole-exome sequencing

Background

Pharmacogenetics is involved in customizing therapy according to the genetic makeup of an individual, and is applicable for chemotherapy, radiotherapy as well as targeted therapy. Drug metabolizing enzymes (DMEs) involving both phase I, and phase II reactions are widely studied. Our study was involved in whole exome sequencing (WES) of cancer patients, followed by analysis for identifying key variations in DMEs, and associated transporters that have a potential impact on treatment outcome.

Methodology

A total of 181 solid tumor patients at stage >/= III were subjected to WES by the SureSelect^XT Human All Exon V6 + UTR library preparation kit, and sequencing in the Illumina NextSeq 550 system. Bioinformatics analysis involved use of GATK pipeline, and the variants were further assessed for population frequency, functional impact with annovar insilico algorithms. Further variant information from significant DMEs, and transporters were extracted and analyzed with PharmGKB to assess level of evidence and infer their impact on the pathways involved in drug response.

Results

The total study cohort of 181 solid tumor patients included 60 males, and 121 females respectively. Among DMEs, deleterious mutation in dihydropyrimidine dehydrogenase (DPYD; rs67376798), solute carrier organic anion transporter family member 1B1 (SLCO1B1*5), and cytochrome P450 2D6 (CYP2D6*10) associated with metabolism of anticancer drugs was detected to be in high frequency of 26%, 21% and 25% respectively.

Conclusion

Our analysis detected variations in both phase I and phase II DMEs, as well as associated transporter genes which has been documented to reduce drug efficacy, as well as cause grade 3 and 4 toxicity. Our study reiterates the significance of pharmacogenomics in stratifying patients for appropriate therapy regimen focused at better treatment outcome and quality of life.

A Systems Bioinformatics Approach to Interconnect Biological Pathways

Signal transduction tasks as well as other complex biological processes involve many different changes in groups of genes, proteins, and metabolites linked together in chains or networks called pathways or networks of pathways. In a classical functional analysis, the biomolecules found to play a role in the biological status under investigation are members of a group of pathways that are not necessarily interconnected. However, interconnectivity is a critical factor for functionality. Thus, it is necessary to be able to construct "connected functional stories" to understand better the complex biological processes. PathwayConnector is a recently introduced web-tool that facilitates the construction of complementary pathway-to-pathway networks, bringing to our attention missing pathways that are crucial links towards the understanding of the molecular mechanisms related to complex diseases. Current version of the web-tool draws from an expanded pathway reference network and provides information deriving from 19 different organisms and 2 different pathway repositories: the KEGG and the REACTOME. Novel genes, proteins, and pathways derived from any experimental/computational method either in large-scale (omics) or even in smaller scale (specific laboratory experiments) can potentially be projected and analyzed through PathwayConnector. This chapter describes in details the pipeline and methodologies used for the latest updated version of PathwayConnector, providing an easy way for rapidly relating human or other organism's pathways together. Recent studies have shown that pathway networks and subnetworks, generated by PathwayConnector, are an integral part towards the individualization of disease, leading to a more precise and personalized management of the treatment.

The Outcomes of Scientific Debates Should Be Published: The Arivale Story

There is an ongoing scientific debate regarding the merits and shortcomings of P4 Medicine (predictive, preventive, personalized, and participatory) and O4 Medicine (overtesting, overdiagnosis, overtreatment, and overcharging). P4 Medicine promises to revolutionize scientific wellness through longitudinal big data collection, denoted as "dense phenotyping," which could uncover early, actionable signs of disease, thus allowing earlier interventions and possible disease reversal. On the other hand, O4 Medicine draws attention to the potential side effects of P4 Medicine: overtesting, overdiagnosis, overtreatment, and overcharging fees. Preliminary data from the P4 Medicine concept have been recently published. A novel biotechnology company, Arivale, provided customers with services based on P4 Medicine principles; however it could not sustain its operations and closed its doors in April 2019. In this report, we provide our own insights as to why Arivale failed. While we do not discount that in the future, improved testing strategies may provide a path to better health, we suggest that until the evidence is provided, selling of such products to the public, especially through the "direct to consumer" approach, should be discouraged. We hope that our analysis will provide useful information for the burgeoning fields of personalized medicine, preventive medicine, and direct to consumer health testing.

Precision/Genomic Medicine for Domestic Cats

The era of Precision / Genomic Medicine has arrived and can improve the veterinary healthcare of companion animals. The goal of Precision / Genomic Medicine is to use an individual's DNA signature / profile to tailor their treatments of their specific health problems. Whole genome sequencing is now a cost-effective diagnostic tool, leading to the discovery of DNA variants associated with health outcomes. These DNA variants become genetic tests and can readily be applied to future cases of individuals with similar symptoms. This article addresses the current state of Precision Medicine in domestic cats and the implications for veterinary care.

[Blood metabolome analysis for creating a digital image of a healthy person]

In the frame of the work, data on the implementation of metabolomics tests in medicine have been systematized. Based on the obtained data, a set of protocols was proposed, the sequential realization of which makes it possible to conduct a blood metabolome analysis for medical purposes. Using this analysis and the number of blood samples from healthy volunteers, a prototype of a healthy person's metabolomic image has been developed; it allows visually and digitally to assess the compliance of the human blood metabolome with the norm. At the same time, 99% of the metabolic processes reflected in the blood plasma are estimated. If abnormalities are detected, the metabolomic image allows to get the value of these deviations of metabolic processes in digital terms.

A Review of Challenges and Opportunities in Machine Learning for Health

Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.

Challenges of Immune Response Diversity in the Human Population Concerning New Tuberculosis Diagnostics, Therapies, and Vaccines

Universal approaches to the prevention and treatment of human diseases fail to take into account profound immune diversity resulting from genetic variations across populations. Personalized or precision medicine takes into account individual lifestyle, environment, and biology (genetics and immune status) and is being adopted in several disease intervention strategies such as cancer and heart disease. However, its application in infectious diseases, particularly global diseases such as tuberculosis (TB), is far more complex and in a state of infancy. Here, we discuss the impact of human genetic variations on immune responses and how they relate to failures seen in current TB diagnostic, therapy, and vaccine approaches across populations. We offer our perspective on the challenges and potential for more refined approaches going forward.

Pharmacogenomics at the center of precision medicine: challenges and perspective in an era of Big Data

Pharmacogenomics (PGx) is one of the core elements of personalized medicine. PGx information reduces the likelihood of adverse drug reactions and optimizes therapeutic efficacy. St Catherine Specialty Hospital in Zagreb/Zabok, Croatia has implemented a personalized patient approach using the RightMed^® Comprehensive PGx panel of 25 pharmacogenes plus Facor V Leiden, Factor II and MTHFR genes, which is interpreted by a special counseling team to offer the best quality of care. With the advent of significant technological advances comes another challenge: how can we harness the data to inform clinically actionable measures and how can we use it to develop better predictive risk models? We propose to apply the principles artificial intelligence to develop a medication optimization platform to prevent, manage and treat different diseases.

A comparison of mechanistic signaling pathway activity analysis methods

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

Synthetic biology-based cellular biomedical tattoo for detection of hypercalcemia associated with cancer

Diagnosis marks the beginning of any successful therapy. Because many medical conditions progress asymptomatically over extended periods of time, their timely diagnosis remains difficult, and this adversely affects patient prognosis. Focusing on hypercalcemia associated with cancer, we aimed to develop a synthetic biology-inspired biomedical tattoo using engineered cells that would (i) monitor long-term blood calcium concentration, (ii) detect onset of mild hypercalcemia, and (iii) respond via subcutaneous accumulation of the black pigment melanin to form a visible tattoo. For this purpose, we designed cells containing an ectopically expressed calcium-sensing receptor rewired to a synthetic signaling cascade that activates expression of transgenic tyrosinase, which produces melanin in response to persistently increased blood Ca²⁺ We confirmed that the melanin-generated color change produced by this biomedical tattoo could be detected with the naked eye and optically quantified. The system was validated in wild-type mice bearing subcutaneously implanted encapsulated engineered cells. All animals inoculated with hypercalcemic breast and colon adenocarcinoma cells developed tattoos, whereas no tattoos were seen in animals inoculated with normocalcemic tumor cells. All tumor-bearing animals remained asymptomatic throughout the 38-day experimental period. Although hypercalcemia is also associated with other pathologies, our findings demonstrate that it is possible to detect hypercalcemia associated with cancer in murine models using this cell-based diagnostic strategy.

High risk endometrial cancer: Clues towards a revision of the therapeutic paradigm

INTRODUCTION

Endometrial cancer (EC) is a major cause of mortality worldwide with nearly 200 000 cases diagnosed annually. The recent ESMO-ESGO-ESTRO guidelines include a new classification defining a heterogeneous high-risk group of recurrence (HR) comprising: (i) endometrioid (type 1) FIGO stage IB grade 3 tumors (type 1/G3ECs), (ii) non-endometrioid tumors (type 2) and (iii) advanced stages whatever the histological type (Colombo et al., 2016).

AREAS COVERED

The aim of this review is to summarize current evidence for therapeutic approaches in HR-EC according to the updated ESMO-ESGO-ESTRO classification by discussing the following issues: i) HR-EC heterogeneity, (ii) prognostic factors and current classification, and (iii) optimal staging strategies (site and extent) and the role of adjuvant treatment.

EXPERT COMMENTARY

HR-EC treatment is based on surgery, radiation therapy, brachytherapy, and chemotherapy, either alone or sequentially, in combination with other treatments depending on disease stage, histological grade and risk group. Specific trials are needed to establish the role of systematic pelvic and paraaortic lymphadenectomy, adjuvant therapies and targeted drugs. Although molecular characterization has been reported to customize therapeutic strategies and thereby improve therapeutic outcomes in EC, none of the targeted agents investigated (antiangiogenic and mTOR/PI3K pathway inhibitor agents) have resulted in a change in clinical practice in HR-EC.

The effect of intraparenchymal blood patching on the rate of pneumothorax in patients undergoing percutaneous CT-guided core biopsy of the lung

PURPOSE

To assess the effect of intraparenchymal blood patching (IBP) as well as tumor- and operator-related risk factors on the rate of pneumothoraxes after percutaneous CT-guided core needle biopsy of the lung.

MATERIALS AND METHODS

We performed a retrospective analysis of 868 CT-guided lung biopsies that were conducted at our institution between 2003 and 2018, of which 419 (48%) received an IBP. Outcome variable included the rates of pneumothorax and chest tube placement, as well as lesion size (<3 cm versus ≥3 cm long axis diameter), lesion depth (≤2 cm, >2-4 cm, >4-5 cm and >5 cm distance to the pleura), location within the lungs (upper lobe, lower lobe, middle lobe), needle caliber (13 G, 15 G, 17 G, 19 G), number of samples taken (1-3 versus ≥4 samples), and experience of the performing physician.

RESULTS

The rate of pneumothorax was significantly (p < 0.05) lower in the group with IBP (10.7%) compared to the group without IBP (15.4%). The number of post-interventional chest tube placements was also lower in the IBP group (3.1% vs. 5.8%) but not statistically significant. The lesion size correlated negatively with the rate of pneumothoraxes, whereas in both groups (±IBP) lesions ≥ 3 cm showed a significantly lower rate of pneumothorax (p < 0.05). With increasing lesion depth, the pneumothorax rate increased with (p < 0.01) and without (p < 0.001) IBP. The rate of pneumothorax was significantly lower (p < 0.05) for 17 G needles with IBP, but not for other calibers. For biopsies in the lower lobe, the pneumothorax rate reduced significantly (p < 0.001) with IBP. In case of ≥4 tissue samples, the pneumothorax rate was significantly lower with IBP (p < 0.01). For experienced operators, the overall pneumothorax rate was significantly lower compared to less experienced operators (p < 0001).

CONCLUSIONS

IBP significantly reduces the rate of pneumothorax following CT-guided lung biopsies in particular for lesions located deeper in the lungs, when ≥4 samples are taken, when samples are taken by less-experienced operators, and when sampling from the lower lobes.

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

Cytomegalovirus: Shape-Shifting the Immune System

Systems-based based approaches have begun to shed light on extrinsic factors that contribute to immune system variation. Among these, CMV (HHV-5, a β-herpesvirus) imposes a surprisingly profound impact. Most of the world's population is CMV⁺, and the virus goes through three distinct infection phases en route to establishing lifelong détente with its host. Immune control of CMV in each phase recruits unique arms of host defense, and in turn the virus employs multiple immune-modulatory strategies that help facilitate the establishment of lifelong persistence. In this review, we explain how CMV shapes immunity and discuss the impact it may have on overall health.

Personalized Nutrition in Disrupting Cancer - Proceedings From the 2017 American College of Nutrition Annual Meeting

Cancer is a major public health problem and is the second leading cause of death in the United States and worldwide; nearly one in six deaths are attributable to cancer. Approximately 20% of all cancers diagnosed in the United States are attributable to unhealthy diet, excessive alcohol consumption, physical inactivity, and body fatness. Individual cancers are distinct disease states that are multifactorial in their causation, making them exceedingly cumbersome to study from a nutrition standpoint. Genetic influences are a major piece of the puzzle and personalized nutrition is likely to be most effective in disrupting cancer during all stages. Increasing evidence shows that after a cancer diagnosis, continuing standard dietary recommendations may not be appropriate. This is because powerful dietary interventions such as short-term fasting and carbohydrate restriction can disrupt tumor metabolism, synergizing with standard therapies such as radiation and drug therapy to improve efficacy and ultimately, cancer survival. The importance of identifying dietary interventions cannot be overstated, and the American College of Nutrition's commitment to advancing knowledge and research is evidenced by dedication of the 2017 ACN Annual Meeting to "Disrupting Cancer: The Role of Personalized Nutrition" and this resulting proceedings manuscript, which summarizes the meeting's findings.

User Perspectives of a Web-Based Data-Sharing Platform (Open Humans) on Ethical Oversight in Participant-Led Research: Protocol for a Quantitative Study

BACKGROUND

Advances in medicine rely to a great extent on people's willingness to share their data with researchers. With increasingly widespread use of digital technologies, several Web-based communities have emerged aiming to enable their users to share large amounts of data, some of which can possibly be employed for research purposes by scientists, or to conduct participant-led research (PLR). Scholarship has recently addressed the necessity of interrogating how existing ethical standards can and should be applied and adapted in view of the specificities of such Web-based activities. So far, no study has explored participants' beliefs about and attitudes toward ethical oversight when it comes to platforms that involve medical data sharing.

OBJECTIVE

This paper presents the protocol for a survey study aimed at understanding users' beliefs about Web-based data-sharing platforms regarding how research ethics principles should be applied in such a setting. Furthermore, the study aims at quantitatively assessing the relationship between participants' perspectives on ethical oversight and other variables such as previous participation in research, beliefs about data sharing, and attitudes toward self-experimentation.

METHODS

We are conducting a Web-based survey with users of a popular Web-based data-sharing platform, Open Humans. The survey has been sent to approximately 4640 users registered for the Open Humans newsletter. To fill out the survey, participants need to have an account on Open Humans. We expect a 5%-10% response rate (between 200 and 400 completed surveys out of approximately 4000 survey invitations sent). Independent variables include past data-sharing behavior and intention, beliefs about data sharing, past participation in research, attitudes toward self-experimentation, perceived knowledge of the platform's guidelines and terms, perceived importance of having transparent guidelines, and governance-related beliefs. The main dependent variable is participants' expectations regarding who should ensure that ethical requirements are met within research projects conducted on open data-sharing platforms, based on Emanuel et al's ethical framework. We will use chi-square tests to assess the relationship between participants' expectations regarding ethical oversight and their past behavior, future intentions, beliefs, attitudes, and knowledge.

RESULTS

Data collection started on June 13, 2018. A reminder to fill out the survey was sent to participants in mid-July. We expect to gain insights on users' perspectives on the ethical oversight of Web-based data-sharing platforms and on the associated experiences, beliefs, and sociodemographic characteristics.

CONCLUSIONS

When digital tools allow people to engage in PLR including medical data, understanding how people interpret and envision the ethical oversight of their data-sharing practices is crucial. This will be the first study to explore users' perspectives on ethical oversight of Web-based data-sharing platforms. The results will help inform the development of a framework that can be employed for platforms hosting various kinds of research projects to accommodate participants' ethical oversight needs.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/10939.

Consumer and clinician perspectives on personalising breast cancer prevention information

BACKGROUND

Personalised prevention of breast cancer has focused on women at very high risk, yet most breast cancers occur in women at average, or moderately increased risk (≤moderate risk).

OBJECTIVES

To determine; 1) interest of women at ≤ moderate risk (consumers) in personalised information about breast cancer risk; 2) familial cancer clinicians' (FCCs) perspective on managing women at ≤ moderate risk, and; 3) both consumers' and FCCs reactions to iPrevent, a personalised breast cancer risk assessment and risk management decision support tool.

METHODS

Seven focus groups on breast cancer risk were conducted with 49 participants; 27 consumers and 22 FCCs. Data were analysed thematically.

RESULTS

Consumers reported some misconceptions, low trust in primary care practitioners for breast cancer prevention advice and frustration that they often lacked tailored advice about breast cancer risk. They expressed interest in receiving personalised risk information using iPrevent. FCCs reported an inadequate workforce to advise women at ≤ moderate risk and reacted positively to the potential of iPrevent to assist.

CONCLUSIONS

While highlighting a potential role for iPrevent, several outstanding issues remain. For personalised prevention of breast cancer to extend beyond women at high risk, we must harness women's interest in receiving tailored information about breast cancer prevention and identify a workforce willing to advise women.

Key Enabling Technologies for Point-of-Care Diagnostics

A major trend in biomedical engineering is the development of reliable, self-contained point-of-care (POC) devices for diagnostics and in-field assays. The new generation of such platforms increasingly addresses the clinical and environmental needs. Moreover, they are becoming more and more integrated with everyday objects, such as smartphones, and their spread among unskilled common people, has the power to improve the quality of life, both in the developed world and in low-resource settings. The future success of these tools will depend on the integration of the relevant key enabling technologies on an industrial scale (microfluidics with microelectronics, highly sensitive detection methods and low-cost materials for easy-to-use tools). Here, recent advances and perspectives will be reviewed across the large spectrum of their applications.

A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition

Personalized or precision medicine is a new paradigm that holds great promise for individualized patient diagnosis, treatment, and care. However, personalized medicine has only been described on an informal level rather than through rigorous practical guidelines and statistical protocols that would allow its robust practical realization for implementation in day-to-day clinical practice. In this paper, we discuss three key factors, which we consider dimensions that effect the experimental design for personalized medicine: (I) phenotype categories; (II) population size; and (III) statistical analysis. This formalization allows us to define personalized medicine from a machine learning perspective, as an automized, comprehensive knowledge base with an ontology that performs pattern recognition of patient profiles.

HiGHmed - An Open Platform Approach to Enhance Care and Research across Institutional Boundaries

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. HiGHmed brings together 24 partners from academia and industry, aiming at improvements in care provision, biomedical research and epidemiology. By establishing a shared information governance framework, data integration centers and an open platform architecture in cooperation with independent healthcare providers, the meaningful reuse of data will be facilitated. Complementary, HiGHmed integrates a total of seven Medical Informatics curricula to develop collaborative structures and processes to train medical informatics professionals, physicians and researchers in new forms of data analytics.

Governance and Policies: We describe governance structures and policies that have proven effective during the conceptual phase. These were further adapted to take into account the specific needs of the development and networking phase, such as roll-out, carerelated aspects and our focus on curricula development in Medical Inform atics.

Architectural Framework and Methodology: To address the challenges of organizational, technical and semantic interoperability, a concept for a scalable platform architecture, the HiGHmed Platform, was developed. We outline the basic principles and design goals of the open platform approach as well as the roles of standards and specifications such as IHE XDS, openEHR, SNOMED CT and HL7 FHIR. A shared governance framework provides the semantic artifacts which are needed to establish semantic interoperability.

Use Cases: Three use cases in the fields of oncology, cardiology and infection control will demonstrate the capabilities of the HiGHmed approach. Each of the use cases entails diverse challenges in terms of data protection, privacy and security, including clinical use of genome sequencing data (oncology), continuous longitudinal monitoring of physical activity (cardiology) and cross-site analysis of patient movement data (infection control).

Discussion: Besides the need for a shared governance framework and a technical infrastructure, backing from clinical leaders is a crucial factor. Moreover, firm and sustainable commitment by participating organizations to collaborate in further development of their information system architectures is needed. Other challenges including topics such as data quality, privacy regulations, and patient consent will be addressed throughout the project.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

Models and Data Sources Used in Systems Medicine

Background: Systems medicine is a new approach for the development and selection of treatment strategies for patients with complex diseases. It is often referred to as the application of systems biology methods for decision making in patient care. For systems medicine computer applications, many different data sources have to be integrated and included into models. This is a challenging task for Medical Informatics since the approach exceeds traditional systems like Electronic Health Records. To prioritize research activities for systems medicine applications, it is necessary to get an overview over modelling methods and data sources already used in this field.

Objectives: We performed a systematic literature review with the objective to capture current use of 1) modelling methods and 2) data sources in systems medicine related research projects.

Methods: We queried the MEDLINE and ScienceDirect databases for papers associated with the search term systems medicine and related terms. Papers were screened and assessed in full text in a two-step process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines.

Results: The queries returned 698 articles of which 34 papers were finally included into the study. A multitude of modelling approaches such as machine learning and network analysis was identified and classified. Since these approaches are also used in other domains, no methods specific for systems medicine could be identified. Omics data are the most widely used data types followed by clinical data. Most studies only include a rather limited number of data sources.

Conclusions: Currently, many different modelling approaches are used in systems medicine. Thus, highly flexible modular solutions are necessary for systems medicine clinical applications. However, the number of data sources included into the models is limited and most projects currently focus on prognosis. To leverage the potential of systems medicine further, it will be necessary to focus on treatment strategies for patients and consider a broader range of data.

Cystic fibrosis in the era of precision medicine

The treatment of people with cystic fibrosis (CF) has been transformed by the availability of drugs that target the basic chloride defect in the disease. The use of drugs that target specific molecular defects embodies the goals of precision medicine, which incorporate preventive and therapeutic strategies and takes into account differences among individuals. However, the entirety of CF care, from diagnosis to understanding the clinical phenotype and developing a therapeutic strategy, depends on taking into account individual characteristics to achieve optimal outcomes. Future therapies are likely to be even more individualized ushering in a new era of precision medicine.

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits

In Ayurveda system of medicine individuals are classified into seven constitution types, “Prakriti”, for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting Prakriti classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme Prakriti types, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme Prakriti classes. The supervised modelling approaches could classify individuals, with distinct Prakriti types, in the training and validation sets. This study is the first to demonstrate that Prakriti types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting Prakriti classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.

Systems Chronotherapeutics

Chronotherapeutics aim at treating illnesses according to the endogenous biologic rhythms, which moderate xenobiotic metabolism and cellular drug response. The molecular clocks present in individual cells involve approximately fifteen clock genes interconnected in regulatory feedback loops. They are coordinated by the suprachiasmatic nuclei, a hypothalamic pacemaker, which also adjusts the circadian rhythms to environmental cycles. As a result, many mechanisms of diseases and drug effects are controlled by the circadian timing system. Thus, the tolerability of nearly 500 medications varies by up to fivefold according to circadian scheduling, both in experimental models and/or patients. Moreover, treatment itself disrupted, maintained, or improved the circadian timing system as a function of drug timing. Improved patient outcomes on circadian-based treatments (chronotherapy) have been demonstrated in randomized clinical trials, especially for cancer and inflammatory diseases. However, recent technological advances have highlighted large interpatient differences in circadian functions resulting in significant variability in chronotherapy response. Such findings advocate for the advancement of personalized chronotherapeutics through interdisciplinary systems approaches. Thus, the combination of mathematical, statistical, technological, experimental, and clinical expertise is now shaping the development of dedicated devices and diagnostic and delivery algorithms enabling treatment individualization. In particular, multiscale systems chronopharmacology approaches currently combine mathematical modeling based on cellular and whole-body physiology to preclinical and clinical investigations toward the design of patient-tailored chronotherapies. We review recent systems research works aiming to the individualization of disease treatment, with emphasis on both cancer management and circadian timing system–resetting strategies for improving chronic disease control and patient outcomes.

Construction and Validation of a Multi-Institutional Tissue Microarray of Invasive Ductal Carcinoma From Racially and Ethnically Diverse Populations

BACKGROUND

The scarcity of tissues from racial and ethnic minorities at biobanks poses a scientific constraint to research addressing health disparities in minority populations.

METHODS

To address this gap, the Minority Biospecimen/Biobanking Geographic Management Program for region 3 (BMaP-3) established a working infrastructure for a "biobanking" hub in the southeastern United States and Puerto Rico. Herein we describe the steps taken to build this infrastructure, evaluate the feasibility of collecting formalin-fixed, paraffin-embedded tissue blocks and associated data from a single cancer type (breast), and create a web-based database and tissue microarrays (TMAs).

RESULTS

Cancer registry data from 6 partner institutions were collected, representing 12,408 entries from 8,279 unique patients with breast cancer (years 2001-2011). Data were harmonized and merged, and deidentified information was made available online. A TMA was constructed from formalin-fixed, paraffin-embedded samples of invasive ductal carcinoma (IDC) representing 427 patients with breast cancer (147 African Americans, 168 Hispanics, and 112 non-Hispanic whites) and was annotated according to biomarker status and race/ethnicity. Biomarker analysis of the TMA was consistent with the literature.

CONCLUSIONS

Contributions from participating institutions have facilitated a robust research tool. TMAs of IDC have now been released for 5 projects at 5 different institutions.