Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine

Decision support systems for personalized and participative radiation oncology

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

Prediction models for the risk of gestational diabetes: a systematic review

BACKGROUND

Numerous prediction models for gestational diabetes mellitus (GDM) have been developed, but their methodological quality is unknown. The objective is to systematically review all studies describing first-trimester prediction models for GDM and to assess their methodological quality.

METHODS

MEDLINE and EMBASE were searched until December 2014. Key words for GDM, first trimester of pregnancy, and prediction modeling studies were combined. Prediction models for GDM performed up to 14 weeks of gestation that only include routinely measured predictors were eligible.Data was extracted by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Data on risk predictors and performance measures were also extracted. Each study was scored for risk of bias.

RESULTS

Our search yielded 7761 articles, of which 17 were eligible for review (14 development studies and 3 external validation studies). The definition and prevalence of GDM varied widely across studies. Maternal age and body mass index were the most common predictors. Discrimination was acceptable for all studies. Calibration was reported for four studies. Risk of bias for participant selection, predictor assessment, and outcome assessment was low in general. Moderate to high risk of bias was seen for the number of events, attrition, and analysis.

CONCLUSIONS

Most studies showed moderate to low methodological quality, and few prediction models for GDM have been externally validated. External validation is recommended to enhance generalizability and assess their true value in clinical practice.

Systems biology approaches to understand the effects of nutrition and promote health

Within the last years the implementation of systems biology in nutritional research has emerged as a powerful tool to understand the mechanisms by which dietary components promote health and prevent disease as well as to identify the biologically active molecules involved in such effects. Systems biology, by combining several '-omics' disciplines (mainly genomics/transcriptomics, proteomics and metabolomics), creates large data sets that upon computational integration provide in silico predictive networks that allow a more extensive analysis of the individual response to a nutritional intervention and provide a more global comprehensive understanding of how diet may influence health and disease. Numerous studies have demonstrated that diet and particularly bioactive food components play a pivotal role in helping to counteract environmental-related oxidative damage. Oxidative stress is considered to be strongly implicated in ageing and the pathophysiology of numerous diseases including neurodegenerative disease, cancers, metabolic disorders and cardiovascular diseases. In the following review we will provide insights into the role of systems biology in nutritional research and focus on transcriptomic, proteomic and metabolomics studies that have demonstrated the ability of functional foods and their bioactive components to fight against oxidative damage and contribute to health benefits.

Prediction models for exacerbations in patients with COPD

Personalised medicine aims to tailor medical decisions to the individual patient. A possible approach is to stratify patients according to the risk of adverse outcomes such as exacerbations in chronic obstructive pulmonary disease (COPD). Risk-stratified approaches are particularly attractive for drugs like inhaled corticosteroids or phosphodiesterase-4 inhibitors that reduce exacerbations but are associated with harms. However, it is currently not clear which models are best to predict exacerbations in patients with COPD. Therefore, our aim was to identify and critically appraise studies on models that predict exacerbations in COPD patients. Out of 1382 studies, 25 studies with 27 prediction models were included. The prediction models showed great heterogeneity in terms of number and type of predictors, time horizon, statistical methods and measures of prediction model performance. Only two out of 25 studies validated the developed model, and only one out of 27 models provided estimates of individual exacerbation risk, only three out of 27 prediction models used high-quality statistical approaches for model development and evaluation. Overall, none of the existing models fulfilled the requirements for risk-stratified treatment to personalise COPD care. A more harmonised approach to develop and validate high- quality prediction models is needed to move personalised COPD medicine forward.

Virus-host interactome: Putting the accent on how it changes

Viral infections are extremely complex processes that could only be well understood by precisely characterizing the interaction networks between the virus and the host components. In recent years, much effort has gone in this direction with the aim of unveiling the molecular basis of viral pathology. These networks are mostly formed by viral and host proteins, and are expected to be dynamic both with time and space (i.e., with the progression of infection, as well as with the virus and host genotypes; what we call plastodynamic). This largely overlooked spatio-temporal evolution urgently calls for a change both in the conceptual paradigms and experimental techniques used so far to characterize virus-host interactions. More generally, molecular plasticity and temporal dynamics are unavoidable components of the mechanisms that underlie any complex disease; components whose understanding will eventually enhance our ability to modulate those networks with the aim of improving disease treatments.

Multiplex Immunosensor Arrays for Electrochemical Detection of Cancer Biomarker Proteins

Measuring panels of protein biomarkers offer a new personalized approach to early cancer detection, disease monitoring and patients' response to therapy. Multiplex electrochemical methods are uniquely positioned to provide faster, more sensitive, point of care (POC) devices to detect protein biomarkers for clinical diagnosis. Nanomaterials-based electrochemical methods offer sensitivity needed for early cancer detection. This review discusses recent advances in multiplex electrochemical immunosensors for cancer diagnostics and disease monitoring. Different electrochemical strategies including enzyme-based immunoarrays, nanoparticle-based immunoarrays and electrochemiluminescence methods are discussed. Many of these methods have been integrated into microfluidic systems, but measurement of more than 2-4 protein markers in a small single serum sample is still a challenge. For POC applications, a simple, low cost method is required. Major challenges in multiplexed microfluidic immunoassays are reagent additions and washing steps that require creative engineering solutions. 3-D printed microfluidics and paper-based microfluidic devices are also explored.

Serum Antibody Profile during Colonization of the Mouse Gut by Candida albicans: Relevance for Protection during Systemic Infection

Candida albicans is a commensal microorganism in the oral cavity and gastrointestinal and urogenital tracts of most individuals that acts as an opportunistic pathogen when the host immune response is reduced. Here, we established different immunocompetent murine models to analyze the antibody responses to the C. albicans proteome during commensalism, commensalism followed by infection, and infection (C, C+I, and I models, respectively). Serum anti-C. albicans IgG antibody levels were higher in colonized mice than in infected mice. The antibody responses during gut commensalism (up to 55 days of colonization) mainly focused on C. albicans proteins involved in stress response and metabolism and differed in both models of commensalism. Different serum IgG antibody-reactivity profiles were also found over time among the three murine models. C. albicans gut colonization protected mice from an intravenous lethal fungal challenge, emphasizing the benefits of fungal gut colonization. This work highlights the importance of fungal gut colonization for future immune prophylactic therapies.

Synthesis of HIV-1 capsid protein assembly inhibitor (CAP-1) and its analogues based on a biomass approach

A biomass-derived platform chemical was utilized to access a demanded pharmaceutical substance with anti-HIV activity (HIV, human immunodeficiency virus) and a variety of structural analogues. Step economy in the synthesis of the drug core (single stage from cellulose) is studied including flexible variability of four structural units. The first synthesis and X-ray structure of the inhibitor of HIV-1 capsid protein assembly (CAP-1) is described.

Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era

Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era.

Metabolomics: Bridging the Gap between Pharmaceutical Development and Population Health

Metabolomics has emerged as an essential tool for studying metabolic processes, stratification of patients, as well as illuminating the fundamental metabolic alterations in disease onset, progression, or response to therapeutic intervention. Metabolomics materialized within the pharmaceutical industry as a standalone assay in toxicology and disease pathology and eventually evolved towards aiding in drug discovery and pre-clinical studies via supporting pharmacokinetic and pharmacodynamic characterization of a drug or a candidate. Recent progress in the field is illustrated by coining of the new term—Pharmacometabolomics. Integration of data from metabolomics with large-scale omics along with clinical, molecular, environmental and behavioral analysis has demonstrated the enhanced utility of deconstructing the complexity of health, disease, and pharmaceutical intervention(s), which further highlight it as an essential component of systems medicine. This review presents the current state and trend of metabolomics applications in pharmaceutical development, and highlights the importance and potential of clinical metabolomics as an essential part of multi-omics protocols that are directed towards shaping precision medicine and population health.

The impact of personalized medicine of Type 2 diabetes mellitus in the global health context

Advances in the fields of genomic sciences have given rise to personalized medicine. This new paradigm draws upon a patient's genetic and metabolic makeup in order to tailor diagnostics and treatment. Personalized medicine holds remarkable promises to improve prevention and management of chronic diseases of global relevance, such as Type 2 diabetes mellitus (T2DM). This review article aims at summarizing the evidence from genome-based sciences on T2DM risk and management in different populations and in the Global Health context. Opinions from leading experts in the field were also included. Based on these findings, strengths and weaknesses of personalized approach to T2DM in a global context are delineated. Implications for future research and implementation on that subject are discussed.

Big Data Analytics for Prostate Radiotherapy

Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose-volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the "RadoncSpace") in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches.

Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective

In this era of tremendous technological capabilities and increased focus on improving clinical outcomes, decreasing costs, and increasing precision, there is a need for a more quantitative approach to the field of surgery. Multiscale computational modeling has the potential to bridge the gap to the emerging paradigms of Precision Medicine and Translational Systems Biology, in which quantitative metrics and data guide patient care through improved stratification, diagnosis, and therapy. Achievements by multiple groups have demonstrated the potential for (1) multiscale computational modeling, at a biological level, of diseases treated with surgery and the surgical procedure process at the level of the individual and the population; along with (2) patient-specific, computationally-enabled surgical planning, delivery, and guidance and robotically-augmented manipulation. In this perspective article, we discuss these concepts, and cite emerging examples from the fields of trauma, wound healing, and cardiac surgery.

Comprehension and Data-Sharing Behavior of Direct-To-Consumer Genetic Test Customers

AIM

The aim of this study was to evaluate current direct-to-consumer (DTC) genetic customers' ability to interpret and comprehend test results and to determine if honest brokers are needed.

METHOD

One hundred and twenty-two customers of the DTC genetic testing company 23andMe were polled in an online survey. The subjects were asked about their personal test results and to interpret the results of two mock test cases (type 2 diabetes and multiple sclerosis), where results were translated into disease probability for an individual compared to the public.

RESULTS

When asked to evaluate the risk, 72.1% correctly assessed the first case and 77% were correct on the second case. Only 23.8% of those surveyed were able to interpret both cases correctly. x03C7;2 and logistic regression were used to interpret the results. Participants who took the time to read the DTC test-provided supplemental material were 3.93 times (p = 0.040) more likely to correctly interpret the test results than those who did not. The odds for correctly interpreting the test cases were 3.289 times (p = 0.011) higher for those who made more than USD 50,000 than those who made less. Survey results were compared to the Health Information National Trends Survey (HINTS) phase 4 cycle 3 data to evaluate national trends.

CONCLUSIONS

Most of the subjects were able to correctly interpret the test cases, yet a majority did not share their results with a health-care professional. As the market for DTC genetic testing grows, test comprehension will become more critical. Involving more health professionals in this process may be necessary to ensure proper interpretations.

Applications and limitations in translating genomics to clinical practice

Recent efforts to broadly apply genetics to clinical practice have been driven by the rapid advancement of genomic technologies and the discovery of genes associated with disease risk, progression, and treatment response. Yet there remain valid concerns about the complexities and limitations that confront the popular notion of clinical utility of genetics in personalized medicine. Research is still very much in the mode of discovery. The excitement of discovery and applications to diagnostics are well described in each of the articles in this issue. Yet, each article appropriately acknowledges the limitations that need to be overcome to apply new knowledge to clinical practice.

Translating the genetics of cystic fibrosis to personalized medicine

Cystic fibrosis (CF) is the most common life-threatening recessive genetic disease in the Caucasian population. This multiorgan disease is caused by mutations in the gene encoding the CF transmembrane conductance regulator (CFTR) protein, a chloride channel recognized as regulating several apical ion channels. The gene mutations result either in the lack of the protein at the apical surface or in an improperly functioning protein. Morbidity and mortality because of the mutation of CFTR are mainly attributable to lung disease resulting from chronic infection and inflammation. Since its discovery as the causative gene in 1989, much progress has been achieved not only in clinical genetics but also in basic science studies. Recently, combinations of these efforts have been successfully translated into development and availability for patients of new therapies targeting specific CFTR mutations to correct the CFTR at the protein level. Current technologies such as next gene sequencing and novel genomic editing tools may offer new strategies to identify new CFTR variants and modifier genes, and to correct CFTR to pursue personalized medicine, which is already developed in some patient subsets. Personalized medicine or P4 medicine ("personalized," "predictive," "preventive," and "participatory") is currently booming for CF. The various current and future challenges of personalized medicine as they apply to the issues faced in CF are discussed in this review.

Aging and computational systems biology

Aging research is undergoing a paradigm shift, which has led to new and innovative methods of exploring this complex phenomenon. The systems biology approach endeavors to understand biological systems in a holistic manner, by taking account of intrinsic interactions, while also attempting to account for the impact of external inputs, such as diet. A key technique employed in systems biology is computational modeling, which involves mathematically describing and simulating the dynamics of biological systems. Although a large number of computational models have been developed in recent years, these models have focused on various discrete components of the aging process, and to date no model has succeeded in completely representing the full scope of aging. Combining existing models or developing new models may help to address this need and in so doing could help achieve an improved understanding of the intrinsic mechanisms which underpin aging.

Trauma in silico: Individual-specific mathematical models and virtual clinical populations

Trauma-induced critical illness is driven by acute inflammation, and elevated systemic interleukin-6 (IL-6) after trauma is a biomarker of adverse outcomes. We constructed a multicompartment, ordinary differential equation model that represents a virtual trauma patient. Individual-specific variants of this model reproduced both systemic inflammation and outcomes of 33 blunt trauma survivors, from which a cohort of 10,000 virtual trauma patients was generated. Model-predicted length of stay in the intensive care unit, degree of multiple organ dysfunction, and IL-6 area under the curve as a function of injury severity were in concordance with the results from a validation cohort of 147 blunt trauma patients. In a subcohort of 98 trauma patients, those with high-IL-6 single-nucleotide polymorphisms (SNPs) exhibited higher plasma IL-6 levels than those with low IL-6 SNPs, matching model predictions. Although IL-6 could drive mortality in individual virtual patients, simulated outcomes in the overall cohort were independent of the propensity to produce IL-6, a prediction verified in the 98-patient subcohort. In silico randomized clinical trials suggested a small survival benefit of IL-6 inhibition, little benefit of IL-1β inhibition, and worse survival after tumor necrosis factor-α inhibition. This study demonstrates the limitations of extrapolating from reductionist mechanisms to outcomes in individuals and populations and demonstrates the use of mechanistic simulation in complex diseases.

High-Resolution Metabolomics: Review of the Field and Implications for Nursing Science and the Study of Preterm Birth

Most complex health conditions do not have a single etiology but rather develop from exposure to multiple risk factors that interact to influence individual susceptibility. In this review, we discuss the emerging field of metabolomics as a means by which metabolic pathways underlying a disease etiology can be exposed and specific metabolites can be identified and linked, ultimately providing biomarkers for early detection of disease onset and new strategies for intervention. We present the theoretical foundation of metabolomics research, the current methods employed in its conduct, and the overlap of metabolomics research with other "omic" approaches. As an exemplar, we discuss the potential of metabolomics research in the context of deciphering the complex interactions of the maternal-fetal exposures that underlie the risk of preterm birth, a condition that accounts for substantial portions of infant morbidity and mortality and whose etiology and pathophysiology remain incompletely defined. We conclude by providing strategies for including metabolomics research in future nursing studies for the advancement of nursing science.

Role of Systems Biology in Brain Injury Biomarker Discovery: Neuroproteomics Application

Years of research in the field of neurotrauma have led to the concept of applying systems biology as a tool for biomarker discovery in traumatic brain injury (TBI). Biomarkers may lead to understanding mechanisms of injury and recovery in TBI and can be potential targets for wound healing, recovery, and increased survival with enhanced quality of life. The literature available on neurotrauma studies from both animal and clinical studies has provided rich insight on the molecular pathways and complex networks of TBI, elucidating the proteomics of this disease for the discovery of biomarkers. With such a plethora of information available, the data from the studies require databases with tools to analyze and infer new patterns and associations. The role of different systems biology tools and their use in biomarker discovery in TBI are discussed in this chapter.

Endogenous carbon monoxide downregulates hepatic cystathionine-γ-lyase in rats with liver cirrhosis

The aim of the present study was to investigate the effect of endogenous carbon monoxide (CO) on the hydrogen sulfide/cystathionine-γ-lyase (H₂S/CSE) pathway in cirrhotic rat livers. The rats were allocated at random into four groups: Sham, cirrhosis, cobalt protoporphyrin (CoPP) and zinc protoporphyrin IX (ZnPP). The expression of hepatic CSE mRNA was evaluated using a quantitative polymerase chain reaction, while CSE protein expression was determined using immunohistochemical analysis. Hematoxylin and eosin staining was performed for the histological evaluation of liver fibrosis. The levels of H₂S, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL) and carboxyhemoglobin (COHb) in the arterial blood were determined, in addition to the portal vein pressure. The mRNA and protein expression levels of hepatic CSE and the serum levels of H₂S were significantly decreased in the cirrhosis group compared with those in the sham group (P<0.05). Compared with the cirrhosis group, rats in the ZnPP group had significantly lower levels of serum ALT, AST and TBIL, arterial COHb and hepatic fibrosis, while hepatic CSE expression and the production of H₂S were significantly increased (P<0.05). The CoPP group exhibited decreased hepatic CSE expression and H₂S production, but aggravated hepatic function and fibrosis (P<0.05). In conclusion, the H₂S/CSE pathway is involved in the formation of liver cirrhosis and serves a crucial function in protecting liver cells against the progression of liver fibrosis. Endogenous CO downregulates hepatic CSE mRNA and protein expression and the production of H₂S in rats with liver cirrhosis.

What's in a word? The person of personalized (nano)medicine

Personalized medicine has recently become a main goal for healthcare policy. It is often defined as the tailoring of diagnosis and therapies to the genetic profile of each patient, and as such it is supposed to overcome the major thorny issues at stake in biomedicine today. This challenging program is primarily carried out by new approaches in biomedical imaging, molecular analysis, drug delivery and follow-up, taking more and more advantage of nanotechnology. However, in current literature and debates, the term ‘personalized medicine’ appears to be polysemous. The paper examines this polysemy. It links it to rival epistemic and technological choices in research programs, and it finally argues that this techno-epistemic plurality echoes conflicting expectations and values among today's biomedicine actors.

From Molecules to Patients: The Clinical Applications of Translational Bioinformatics

OBJECTIVE

In order to realize the promise of personalized medicine, Translational Bioinformatics (TBI) research will need to continue to address implementation issues across the clinical spectrum. In this review, we aim to evaluate the expanding field of TBI towards clinical applications, and define common themes and current gaps in order to motivate future research.

METHODS

Here we present the state-of-the-art of clinical implementation of TBI-based tools and resources. Our thematic analyses of a targeted literature search of recent TBI-related articles ranged across topics in genomics, data management, hypothesis generation, molecular epidemiology, diagnostics, therapeutics and personalized medicine.

RESULTS

Open areas of clinically-relevant TBI research identified in this review include developing data standards and best practices, publicly available resources, integrative systemslevel approaches, user-friendly tools for clinical support, cloud computing solutions, emerging technologies and means to address pressing legal, ethical and social issues.

CONCLUSIONS

There is a need for further research bridging the gap from foundational TBI-based theories and methodologies to clinical implementation. We have organized the topic themes presented in this review into four conceptual foci - domain analyses, knowledge engineering, computational architectures and computation methods alongside three stages of knowledge development in order to orient future TBI efforts to accelerate the goals of personalized medicine.

Sharing and Reuse of Sensitive Data and Samples: Supporting Researchers in Identifying Ethical and Legal Requirements

Availability of and access to data and biosamples are essential in medical and translational research, where their reuse and repurposing by the wider research community can maximize their value and accelerate discovery. However, sharing human-related data or samples is complicated by ethical, legal, and social sensitivities. The specific ethical and legal requirements linked to sensitive data are often unfamiliar to life science researchers who, faced with vast amounts of complex, fragmented, and sometimes even contradictory information, may not feel competent to navigate through it. In this case, the impulse may be not to share the data in order to safeguard against unintentional misuse. Consequently, helping data providers to identify relevant ethical and legal requirements and how they might address them is an essential and frequently neglected step in removing possible hurdles to data and sample sharing in the life sciences. Here, we describe the complex regulatory context and discuss relevant online tools-one which the authors co-developed-targeted at assisting providers of sensitive data or biosamples with ethical and legal questions. The main results are (1) that the different approaches of the tools assume different user needs and prior knowledge of ethical and legal requirements, affecting how a service is designed and its usefulness, (2) that there is much potential for collaboration between tool providers, and (3) that enriched annotations of services (e.g., update status, completeness of information, and disclaimers) would increase their value and facilitate quick assessment by users. Further, there is still work to do with respect to providing researchers using sensitive data or samples with truly 'useful' tools that do not require pre-existing, in-depth knowledge of legal and ethical requirements or time to delve into the details. Ultimately, separate resources, maintained by experts familiar with the respective fields of research, may be needed while-in the longer term-harmonization and increase in ease of use will be very desirable.

Reverse phase protein arrays in signaling pathways: a data integration perspective

The reverse phase protein array (RPPA) data platform provides expression data for a prespecified set of proteins, across a set of tissue or cell line samples. Being able to measure either total proteins or posttranslationally modified proteins, even ones present at lower abundances, RPPA represents an excellent way to capture the state of key signaling transduction pathways in normal or diseased cells. RPPA data can be combined with those of other molecular profiling platforms, in order to obtain a more complete molecular picture of the cell. This review offers perspective on the use of RPPA as a component of integrative molecular analysis, using recent case examples from The Cancer Genome Altas consortium, showing how RPPA may provide additional insight into cancer besides what other data platforms may provide. There also exists a clear need for effective visualization approaches to RPPA-based proteomic results; this was highlighted by the recent challenge, put forth by the HPN-DREAM consortium, to develop visualization methods for a highly complex RPPA dataset involving many cancer cell lines, stimuli, and inhibitors applied over time course. In this review, we put forth a number of general guidelines for effective visualization of complex molecular datasets, namely, showing the data, ordering data elements deliberately, enabling generalization, focusing on relevant specifics, and putting things into context. We give examples of how these principles can be utilized in visualizing the intrinsic subtypes of breast cancer and in meaningfully displaying the entire HPN-DREAM RPPA dataset within a single page.

Microchip with an open tubular immobilized ph gradient for UV whole column imaging detection

This study reports a new method for establishing an open tubular IPG in a microchip coupled with a whole column image detection (WCID) system for protein separation applications. This method allows a wider range of immobilized pH (2.6-9.5) to be established in a PDMS/quartz channel by controlling the diffusion of acidic and basic polymer solutions into the channel through well-designed channel dimensions. The developed pH gradient was experimentally validated by performing the separation of a mixture of standard pI markers. It was further validated by the separation of the hemoglobin control AFSC sample. This method is advantageous over existing IPG methods because it has a wider range of pH and maintains the open tubular feature that matches the UV WCID to improve the sensitivity.

Continuous Flow Microfluidic Bioparticle Concentrator

Innovative microfluidic technology has enabled massively parallelized and extremely efficient biological and clinical assays. Many biological applications developed and executed with traditional bulk processing techniques have been translated and streamlined through microfluidic processing with the notable exception of sample volume reduction or centrifugation, one of the most widely utilized processes in the biological sciences. We utilize the high-speed phenomenon known as inertial focusing combined with hydraulic resistance controlled multiplexed micro-siphoning allowing for the continuous concentration of suspended cells into pre-determined volumes up to more than 400 times smaller than the input with a yield routinely above 95% at a throughput of 240 ml/hour. Highlighted applications are presented for how the technology can be successfully used for live animal imaging studies, in a system to increase the efficient use of small clinical samples, and finally, as a means of macro-to-micro interfacing allowing large samples to be directly coupled to a variety of powerful microfluidic technologies.

Cumulative impact of health deficits, social vulnerabilities, and protective factors on cognitive dynamics in late life: a multistate modeling approach

Introduction

Many factors influence late-life cognitive changes, and evaluating their joint impact is challenging. Typical approaches focus on average decline and a small number of factors. We used multistate transition models and index variables to look at changes in cognition in relation to frailty (accumulation of health deficits), social vulnerability, and protective factors in the Honolulu-Asia Aging Study (HAAS).

Methods

The HAAS is a prospective cohort study of 3,845 men of Japanese descent, aged 71 to 93 years at baseline. Cognitive function was measured using the Cognitive Abilities Screening Instrument (CASI). Baseline index variables were constructed of health deficits (frailty), social vulnerabilities, and protective factors. The chances of improvement/stability/decline in cognitive function and death were simultaneously estimated using multistate transition modeling for 3- and 6-year transitions from baseline.

Results

On average, CASI scores declined by 5.3 points (standard deviation (SD) = 10.0) over 3 years and 9.5 points (SD = 13.9) over 6 years. After adjusting for education and age, baseline frailty was associated with an increased risk of cognitive decline at 3 years (β = 0.18, 95% confidence interval (CI), 0.08 to 0.29) and 6 years (β = 0.40, 95% CI, 0.27 to 0.54). The social vulnerability index was associated with 3-year changes (β = 0.16, 95% CI, 0.09 to 0.23) and 6-year changes (β = 0.14, 95% CI, 0.05 to 0.24) in CASI scores. The protective index was associated with reductions in cognitive decline over the two intervals (3-year: β = −0.16, 95% CI, −0.24 to −0.09; 6-year: β = −0.21, 95% CI, −0.31 to –0.11,).

Conclusions

Research on cognition in late life needs to consider overall health, the accumulation of protective factors, and the dynamics of cognitive change. Index variables and multistate transition models can enhance understanding of the multifactorial nature of late-life changes in cognition.

Electronic supplementary material

The online version of this article (doi:10.1186/s13195-015-0120-7) contains supplementary material, which is available to authorized users.

Lumican is overexpressed in lung adenocarcinoma pleural effusions

Adenocarcinoma (AdC) is the most common lung cancer subtype and is often associated with pleural effusion (PE). Its poor prognosis is attributable to diagnostic delay and lack of effective treatments and there is a pressing need in discovering new biomarkers for early diagnosis or targeted therapies. To date, little is known about lung AdC proteome. We investigated protein expression of lung AdC in PE using the isobaric Tags for Relative and Absolute Quantification (iTRAQ) approach to identify possible novel diagnostic/prognostic biomarkers. This provided the identification of 109 of lung AdC-related proteins. We further analyzed lumican, one of the overexpressed proteins, in 88 resected lung AdCs and in 23 malignant PE cell-blocks (13 lung AdCs and 10 non-lung cancers) using immunohistochemistry. In AdC surgical samples, lumican expression was low in cancer cells, whereas it was strong and diffuse in the stroma surrounding the tumor. However, lumican expression was not associated with tumor grade, stage, and vascular/pleural invasion. None of the lung cancer cell-blocks showed lumican immunoreaction, whereas those of all the other tumors were strongly positive. Finally, immunoblotting analysis showed lumican expression in both cell lysate and conditioned medium of a fibroblast culture but not in those of A549 lung cancer cell line. PE is a valid source of information for proteomic analysis without many of the restrictions of plasma. The high lumican levels characterizing AdC PEs are probably due to its release by the fibroblasts surrounding the tumor. Despite the role of lumican in lung AdC is still elusive, it could be of diagnostic value.

PEIMAN 1.0: Post-translational modification Enrichment, Integration and Matching ANalysis

Conventional proteomics has discovered a wide gap between protein sequences and biological functions. The third generation of proteomics was provoked to bridge this gap. Targeted and untargeted post-translational modification (PTM) studies are the most important parts of today’s proteomics. Considering the expensive and time-consuming nature of experimental methods, computational methods are developed to study, analyze, predict, count and compute the PTM annotations on proteins. The enrichment analysis softwares are among the common computational biology and bioinformatic software packages. The focus of such softwares is to find the probability of occurrence of the desired biological features in any arbitrary list of genes/proteins. We introduce Post-translational modification Enrichment Integration and Matching Analysis (PEIMAN) software to explore more probable and enriched PTMs on proteins. Here, we also represent the statistics of detected PTM terms used in enrichment analysis in PEIMAN software based on the latest released version of UniProtKB/Swiss-Prot. These results, in addition to giving insight to any given list of proteins, could be useful to design targeted PTM studies for identification and characterization of special chemical groups.

Database URL:http://bs.ipm.ir/softwares/PEIMAN/

Translating next-generation sequencing from clinical trials to clinical practice for the treatment of advanced cancers

Next-generation sequencing (NGS) is being applied in oncology care to identify specific molecular aberrations of patient's tumors. The use of NGS now allows for sequencing entire human genomes within a reasonable cost and practical time frames for treatment decision making. Further delineation of epigenetics, transcriptomics, metagenomics and NGS at the level of circulating tumor DNA reveal ever increasing complexity to understand these interactions and the roles they play in cancer. With the improvement in understanding the study of proteomics, it has become clear that NGS has room for innovation to someday include sequencing of proteins. Early embarkation of NGS incorporated into clinical trials has begun. Here, we review the feasibility and practicality of translating NGS from clinical trials to clinical practice.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.

Comparison of two methods to extract DNA from formalin-fixed, paraffin-embedded tissues and their impact on EGFR mutation detection in non-small cell lung carcinoma

OBJECTIVE

Molecular pathology tests are often carried for clinicopathological diagnosis and pathologists have established large collections of formalin-fixed, paraffin-embedded tissue (FFPE) banks. However, extraction of DNA from FFPE is a laborious and challenging for researchers in clinical laboratories. The aim of this study was to compare two widely used DNA extraction methods: using a QIAamp DNA FFPE kit from Qiagen and a Cobas Sample Preparation Kit from Roche, and evaluated the effect of the DNA quality on molecular diagnostics.

METHODS

DNA from FFPE non-small cell lung carcinoma tissues including biopsy and surgical specimens was extracted with both QIAamp DNA FFPE and Cobas Sample Preparation Kits and EGFR mutations of non-small cell lung carcinomas were detected by real-time quantitative PCR using the extracted DNA.

RESULTS AND CONCLUSION

Our results showed that DNA extracted by QIAamp and Cobas methods were both suitable to detect downstream EGFR mutation in surgical specimens. Howover, Cobas method could yield more DNA from biopsy specimens, and gain much better EGFR mutation results.

One Medicine One Science: a framework for exploring challenges at the intersection of animals, humans, and the environment

Characterizing the health consequences of interactions among animals, humans, and the environment in the face of climatic change, environmental disturbance, and expanding human populations is a critical global challenge in today's world. Exchange of interdisciplinary knowledge in basic and applied sciences and medicine that includes scientists, health professionals, key sponsors, and policy experts revealed that relevant case studies of monkeypox, influenza A, tuberculosis, and HIV can be used to guide strategies for anticipating and responding to new disease threats such as the Ebola and Chickungunya viruses, as well as to improve programs to control existing zoonotic diseases, including tuberculosis. The problem of safely feeding the world while preserving the environment and avoiding issues such as antibiotic resistance in animals and humans requires cooperative scientific problem solving. Food poisoning outbreaks resulting from Salmonella growing in vegetables have demonstrated the need for knowledge of pathogen evolution and adaptation in developing appropriate countermeasures for prevention and policy development. Similarly, pesticide use for efficient crop production must take into consideration bee population declines that threaten the availability of the two-thirds of human foods that are dependent on pollination. This report presents and weighs the objective merits of competing health priorities and identifies gaps in knowledge that threaten health security, to promote discussion of major public policy implications such that they may be decided with at least an underlying platform of facts.

Words matter: distinguishing "personalized medicine" and "biologically personalized therapeutics"

"Personalized medicine" has become a generic term referring to techniques that evaluate either the host or the disease to enhance the likelihood of beneficial patient outcomes from treatment interventions. There is, however, much more to personalization of care than just identifying the biotherapeutic strategy with the highest likelihood of benefit. In its new meaning, "personalized medicine" could overshadow the individually tailored, whole-person care that is at the bedrock of what people need and want when they are ill. Since names and definitional terms set the scope of the discourse, they have the power to define what personalized medicine includes or does not include, thus influencing the scope of the professional purview regarding the delivery of personalized care. Taxonomic accuracy is important in understanding the differences between therapeutic interventions that are distinguishable in their aims, indications, scope, benefits, and risks. In order to restore the due emphasis to the patient and his or her needs, we assert that it is necessary, albeit belated, to deconflate the contemporary term "personalized medicine" by taxonomizing this therapeutic strategy more accurately as "biologically personalized therapeutics" (BPT). The scope of truly personalized medicine and its relationship to biologically personalized therapeutics is described, emphasizing that the best of care must give due recognition and emphasis to both BPT and truly personalized medicine.

Biomarkers to assess the efficiency of treatment with platinum-based drugs: what can metallomics add?

Since the approval of cisplatin as an antineoplastic drug, the medical and the scientific communities have been concerned about the side effects of platinum-based drugs, and this has been the dose-limiting factor that leads to reduced treatment efficiency. Another important issue is the intrinsic or acquired resistance of some patients to treatment. Identifying proper biomarkers is crucial in evaluating the efficiency of a treatment, assisting physicians in determining, at early stages, whether or not the patient presents resistance to the drug, minimizing severe side effects, and allowing them to redirect the established course of chemotherapy. A great effort is being made to identify biomarkers that can be used to predict the outcome of the treatment of cancer patients with platinum-based drugs. In this context, the metallomic approach has not yet been used to its full potential. Since the basis of these drugs is platinum, the monitoring of biomarkers containing this metal should be the natural approach to evaluate treatment progress. This review intends to show where the research in this field stands and points out some gaps that can be filled by metallomics.