Proteomics and biomarkers in clinical trials for drug development

Towards the Definition of the Molecular Hallmarks of Idiopathic Membranous Nephropathy in Serum Proteome: A DIA-PASEF Approach

Idiopathic membranous nephropathy (IMN) is a pathologically defined disorder of the glomerulus, primarily responsible for nephrotic syndromes (NS) in nondiabetic adults. The underlying molecular mechanisms are still not completely clarified. To explore possible molecular and functional signatures, an optimised mass spectrometry (MS) method based on next-generation data-independent acquisition combined with ion-mobility was applied to serum of patients affected by IMN (n = 15) or by other glomerulopathies (PN) (n = 15). The statistical comparison highlighted a panel of 57 de-regulated proteins with a significant increase in lipoprotein-related proteins (APOC1, APOB, APOA1, APOL1 and LCAT) and a substantial quantitative alteration of key serpins (including A4, D1, A7, A6, F2, F1 and 1) possibly associated with IMN or NS and podocyte stress. A critical dysregulation in metabolisms of lipids (e.g., VLDL assembly and clearance) likely to be related to known hyperlipidemia in IMN, along with involvement of non-classical complement pathways and a putative enrolment of ficolin-2 in sustaining the activation of the lectin-mediated complement system have been pinpointed. Moreover, mannose receptor CD206 (MRC1-down in IMN) and biotinidase (BTD-up in IMN) are able alone to accurately distinguish IMN vs. PN. To conclude, our work provides key proteomic insights into the IMN complexity, opening the way to an efficient stratification of MN patients.

Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging

This paper presents a combined optical imaging/artificial intelligence (OI/AI) technique for the real-time analysis of tissue morphology at the tip of the biopsy needle, prior to collecting a biopsy specimen. This is an important clinical problem as up to 40% of collected biopsy cores provide low diagnostic value due to high adipose or necrotic content. Micron-scale-resolution optical coherence tomography (OCT) images can be collected with a minimally invasive needle probe and automatically analyzed using a computer neural network (CNN)-based AI software. The results can be conveyed to the clinician in real time and used to select the biopsy location more adequately. This technology was evaluated on a rabbit model of cancer. OCT images were collected with a hand-held custom-made OCT probe. Annotated OCT images were used as ground truth for AI algorithm training. The overall performance of the AI model was very close to that of the humans performing the same classification tasks. Specifically, tissue segmentation was excellent (~99% accuracy) and provided segmentation that closely mimicked the ground truth provided by the human annotations, while over 84% correlation accuracy was obtained for tumor and non-tumor classification.

Protective effects of Qing-Re-Huo-Xue formula on bleomycin-induced pulmonary fibrosis through the p53/IGFBP3 pathway

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrosing lung disease with high mortality. Inflammation and epithelial mesenchymal transformation (EMT) may play an important role in the occurrence and development of IPF. Qing-Re-Huo-Xue formula (QRHXF) has been used clinically by our team for half a century and has obvious therapeutic effects on lung disease. Nevertheless, the role and mechanism of QRHXF in the treatment of IPF have never been studied.

Methods

A mouse pulmonary fibrosis model was established by intratracheal injection of BLM. The effects of QRHXF on the treatment of pulmonary fibrosis were studied by pulmonary function testing, imaging examination, pathological staining, transmission electron microscopy (TEM) observation and mRNA expression. Tandem mass tag (TMT)-based quantitative proteomics was carried out to analyse the lung protein expression profiles between the control (CTL), bleomycin (BLM) and QRHXF (BLM + QRHXF) groups. Immunohistochemistry and qRT-PCR were used to verify the possible existence of drug target proteins and signalling pathways.

Results

The results of pulmonary function, lung pathology and imaging examinations showed that QRHXF could significantly alleviate BLM-induced pulmonary fibrosis in vivo. Additionally, inflammatory cell infiltration and EMT were markedly reduced in BLM-induced PF mice administered QRHXF. Proteomics detected a total of 35 proteins, of which 17 were upregulated and 18 were downregulated. A total of 19 differentially expressed proteins (DEPs) overlapped between the BLM versus CTL groups and the BLM + QRHXF versus BLM groups. The expression of p53 and IGFBP3 was reversed in the QRHXF intervention group, which was verified by immunohistochemistry and qRT-PCR.

Conclusions

QRHXF attenuated BLM-induced pulmonary fibrosis, and regulation of the p53/IGFBP3 pathway might be associated with its efficacy, which holds promise as a novel treatment strategy for pulmonary fibrosis patients.

Graphical Abstract

Cost-Effectiveness of Therapeutic Drug Monitoring of Anti-TNF Therapy in Inflammatory Bowel Disease: A Systematic Review

Infliximab and adalimumab are monoclonal antibodies against tumor necrosis factor (anti-TNF) used to manage inflammatory bowel disease (IBD). Therapeutic Drug Monitoring (TDM) has been proven to prevent immunogenicity, to achieve better long-term clinical results and to save costs in IBD treatment. The aim of this study was to conduct a systematic review on cost-effectiveness analyses of studies that apply TDM of anti-TNF in IBD and to provide a critical analysis of the best scientific knowledge available in the literature. The quality of the included studies was assessed using Consolidated Health Economic Evaluation Reporting Standards (CHEERS). Cost-effectiveness of the TDM strategies was presented as total costs, cost savings, quality-adjusted life-years (QALY) and incremental cost-effectiveness ratio (ICER). Thirteen studies that examined the health economics of TDM of anti-TNF in IBD from 2013 to 2021 were included. Eight of them (61.5%) achieved a score between 17 and 23 on the CHEERS checklist. The comparison between the TDM strategy and an empirical strategy was cost saving. The ICER between reactive TDM and an empirical strategy was dominated (favorable) by reactive TDM, whereas the ICER value for proactive TDM compared to an empirical strategy ranged from EUR 56,845 to 3,901,554. This systematic review demonstrated that a TDM strategy is cost-effective or cost-saving in IBD.

Developing Novel Anticancer Drugs for Targeted Populations: An Update

BACKGROUND

Due to higher failure rates, lengthy time and high cost of the traditional de novo drug discovery and development process, the rate of opportunity to get new, safe and efficacious drugs for the targeted population, including pediatric patients with cancer, becomes sluggish.

OBJECTIVES

This paper discusses the development of novel anticancer drugs focusing on the identification and selection of targeted anticancer drug development for the targeted population.

METHODS

Information presented in this review was obtained from different databases, including PUBMED, SCOPUS, Web of Science, and EMBASE. Various keywords were used as search terms.

RESULTS

The pharmaceutical companies currently are executing drug repurposing as an alternative means to accelerate the drug development process that reduces the risk of failure, time and cost, which take 3-12 years with almost 25% overall probability of success as compared to de novo drug discovery and development process (10- 17 years) which has less than 10% probability of success. An alternative strategy to the traditional de novo drug discovery and development process, called drug repurposing, is also presented.

CONCLUSION

Therefore, to continue with the progress of developing novel anticancer drugs for the targeted population, identification and selection of target to specific disease type is important. Considering the aspects of the age of the patient and the disease stages such as each cancer types are different when we study the disease at a molecular level. Drug repurposing technique becomes an influential alternative strategy to discover and develop novel anticancer drug candidates.

A proteomic glimpse into the effect of antimalarial drugs on Plasmodium falciparum proteome towards highlighting possible therapeutic targets

There is no effective vaccine against malaria; therefore, chemotherapy is to date the only choice to fight against this infectious disease. However, there is growing evidences of drug-resistance mechanisms in malaria treatments. Therefore, the identification of new drug targets is an urgent need for the clinical management of the disease. Proteomic approaches offer the chance of determining the effects of antimalarial drugs on the proteome of Plasmodium parasites. Accordingly, we reviewed the effects of antimalarial drugs on the Plasmodium falciparum proteome pointing out the relevance of several proteins as possible drug targets in malaria treatment. In addition, some of the P. falciparum stage-specific altered proteins and parasite-host interactions might play important roles in pathogenicity, survival, invasion and metabolic pathways and thus serve as potential sources of drug targets. In this review, we have identified several proteins, including thioredoxin reductase, helicases, peptidyl-prolyl cis-trans isomerase, endoplasmic reticulum-resident calcium-binding protein, choline/ethanolamine phosphotransferase, purine nucleoside phosphorylase, apical membrane antigen 1, glutamate dehydrogenase, hypoxanthine guanine phosphoribosyl transferase, heat shock protein 70x, knob-associated histidine-rich protein and erythrocyte membrane protein 1, as promising antimalarial drugs targets. Overall, proteomic approaches are able to partially facilitate finding possible drug targets. However, the integration of other 'omics' and specific pharmaceutical techniques with proteomics may increase the therapeutic properties of the critical proteins identified in the P. falciparum proteome.

Image-guided biopsy in the age of personalised medicine: strategies for success and safety

Oncology has progressed into an era of personalised medicine, whereby the therapeutic regimen is tailored to the molecular profile of the patient's cancer. Determining personalised therapeutic options is achieved by using tumour genomics and proteomics to identify the specific molecular targets against which candidate drugs can interact. Several dozen targeted drugs, many for multiple cancer types are already widely in clinical use. Molecular profiling of tumours is contingent on high-quality biopsy specimens and the most common method of tissue sampling is image-guided biopsy. Thus, for radiologists performing these biopsies, the paradigm has now shifted away from obtaining specimens simply for histopathological diagnosis to acquiring larger amounts of viable tumour cells for DNA, RNA, or protein analysis. These developments have highlighted the central role now played by radiologists in the delivery of personalised cancer care. This review describes the principles of molecular profiling assays and biopsy techniques for optimising yield, and describes a scoring system to assist in patient selection for percutaneous biopsy.

Next-Generation Sequencing and MALDI Mass Spectrometry in the Study of Multiresistant Processed Meat Vancomycin-Resistant Enterococci (VRE)

Vancomycin-resistant enterococci (VRE), due to their intrinsic resistance to various commonly used antibiotics and their malleable genome, make the treatment of infections caused by these bacteria less effective. The aims of this work were to characterize isolates of Enterococcus spp. that originated from processed meat, through phenotypic and genotypic techniques, as well as to detect putative antibiotic resistance biomarkers. The 19 VRE identified had high resistance to teicoplanin (89%), tetracycline (94%), and erythromycin (84%) and a low resistance to kanamycin (11%), gentamicin (11%), and streptomycin (5%). Based on a Next-Generation Sequencing NGS technique, most isolates were vanA-positive. The most prevalent resistance genes detected were erm(B) and aac(6')-Ii, conferring resistance to the classes of macrolides and aminoglycosides, respectively. MALDI-TOF mass spectrometry (MS) analysis detected an exclusive peak of the Enterococcus genus at m/z (mass-to-charge-ratio) 4428 ± 3, and a peak at m/z 6048 ± 1 allowed us to distinguish Enterococcus faecium from the other species. Several statistically significant protein masses associated with resistance were detected, such as peaks at m/z 6358.27 and m/z 13237.3 in ciprofloxacin resistance isolates. These results reinforce the relevance of the combined and complementary NGS and MALDI-TOF MS techniques for bacterial characterization.

Putative Protein Biomarkers of Escherichia coli Antibiotic Multiresistance Identified by MALDI Mass Spectrometry

The commensal bacteria Escherichia coli causes several intestinal and extra-intestinal diseases, since it has virulence factors that interfere in important cellular processes. These bacteria also have a great capacity to spread the resistance genes, sometimes to phylogenetically distant bacteria, which poses an additional threat to public health worldwide. Here, we aimed to use the analytical potential of MALDI-TOF mass spectrometry (MS) to characterize E. coli isolates and identify proteins associated closely with antibiotic resistance. Thirty strains of extended-spectrum beta-lactamase producing E. coli were sampled from various animals. The phenotypes of antibiotic resistance were determined according to Clinical and Laboratory Standards Institute (CLSI) methods, and they showed that all bacterial isolates were multi-resistant to trimethoprim-sulfamethoxazole, tetracycline, and ampicillin. To identify peptides characteristic of resistance to particular antibiotics, each strain was grown in the presence or absence of the different antibiotics, and then proteins were extracted from the cells. The protein fingerprints of the samples were determined by MALDI-TOF MS in linear mode over a mass range of 2 to 20 kDa. The spectra obtained were compared by using the ClinProTools bioinformatics software, using three machine learning classification algorithms. A putative species biomarker was also detected at a peak m/z of 4528.00.

Tumor core biopsies adequately represent immune microenvironment of high-grade serous carcinoma

The prognostic and therapeutic value of the tumor microenvironment (TME) in various cancer types is of major interest. Characterization of the TME often relies on a small representative tissue sample. However, the adequacy of such a sample for assessing components of the TME is not yet known. Here, we used immunohistochemical (IHC) staining and 7-color multiplex staining to evaluate CD8 (cluster of differentiation 8), CD68, PD-L1 (programmed death-ligand 1), CD34, FAP (fibroblast activation protein), and cytokeratin in 220 tissue cores from 26 high-grade serous ovarian cancer samples. Comparisons were drawn between a larger tumor specimen and smaller core biopsies based on number and location (central tumor vs. peripheral tumor) of biopsies. Our analysis found that the correlation between marker-specific cell subsets in larger tumor versus smaller core was stronger with two core biopsies and was not further strengthened with additional biopsies. Moreover, this correlation was consistently strong regardless of whether the biopsy was taken at the center or at the periphery of the original tumor sample. These findings could have a substantial impact on longitudinal assessment for detection of biomarkers in clinical trials.

Proteomics for cancer drug design

Introduction: Signal transduction cascades drive cellular proliferation, apoptosis, immune, and survival pathways. Proteins have emerged as actionable drug targets because they are often dysregulated in cancer, due to underlying genetic mutations, or dysregulated signaling pathways. Cancer drug development relies on proteomic technologies to identify potential biomarkers, mechanisms-of-action, and to identify protein binding hot spots. Areas covered: Brief summaries of proteomic technologies for drug discovery include mass spectrometry, reverse phase protein arrays, chemoproteomics, and fragment based screening. Protein-protein interface mapping is presented as a promising method for peptide therapeutic development. The topic of biosimilar therapeutics is presented as an opportunity to apply proteomic technologies to this new class of cancer drug. Expert opinion: Proteomic technologies are indispensable for drug discovery. A suite of technologies including mass spectrometry, reverse phase protein arrays, and protein-protein interaction mapping provide complimentary information for drug development. These assays have matured into well controlled, robust technologies. Recent regulatory approval of biosimilar therapeutics provides another opportunity to decipher the molecular nuances of their unique mechanisms of action. The ability to identify previously hidden protein hot spots is expanding the gamut of potential drug targets. Proteomic profiling permits lead compound evaluation beyond the one drug, one target paradigm.

Pharmaceutical Advances and Proteomics Researches

Proteomics enables understanding the composition, structure, function and interactions of the entire protein complement of a cell, a tissue, or an organism under exactly defined conditions. Some factors such as stress or drug effects will change the protein pattern and cause the present or absence of a protein or gradual variation in abundances. The aim of this study is to explore relationship between proteomics application and drug discovery. "proteomics", "Application", and "pharmacology were the main keywords that were searched in PubMed (PubMed Central), Web of Science, and Google Scholar. The titles that were stablished by 2019, were studied and after study of the appreciated abstracts, the full texts of the 118 favor documents were extracted. Changes in the proteome provide a snapshot of the cell activities and physiological processes. Proteomics shows the observed protein changes to the causal effects and generate a complete three-dimensional map of the cell indicating their exact location. Proteomics is used in different biological fields and is applied in medicine, agriculture, food microbiology, industry, and pharmacy and drug discovery. Biomarker discovery, follow up of drug effect on the patients, and in vitro and in vivo proteomic investigation about the drug treated subjects implies close relationship between proteomics advances and application and drug discovery and development. This review overviews and summarizes the applications of proteomics especially in pharmacology and drug discovery.

Clinical Proteomics: Closing the Gap from Discovery to Implementation

Clinical proteomics, the application of proteome analysis to serve a clinical purpose, represents a major field in the area of proteome research. Over 1000 manuscripts on this topic are published each year, with numbers continuously increasing. However, the anticipated outcome, the transformation of the reported findings into improvements in patient management, is not immediately evident. In this article, the value and validity of selected clinical proteomics findings are investigated, and it is assessed how far implementation has progressed. A main conclusion from this assessment is that to achieve implementation, well-powered clinical studies are required in the appropriate population, addressing a specific clinical need and with a clear context-of-use. Efforts toward implementation, to be feasible, must be supported by the key players in science: publishers and funders. The authors propose a change on objectives, from additional discovery studies toward studies aiming at validation of the plethora of potential biomarkers that have been described, to demonstrate practical value of clinical proteomics. All elements required, potential biomarkers, technologies, and bio-banked samples are available (based on today's literature), hence a change in focus from discovery toward validation and application is not only urgently necessary, but also possible based on resources available today.

Advances in the design of nanomaterial-based electrochemical affinity and enzymatic biosensors for metabolic biomarkers: A review

This review (with 340 refs) focuses on methods for specific and sensitive detection of metabolites for diagnostic purposes, with particular emphasis on electrochemical nanomaterial-based sensors. It also covers novel candidate metabolites as potential biomarkers for diseases such as neurodegenerative diseases, autism spectrum disorder and hepatitis. Following an introduction into the field of metabolic biomarkers, a first major section classifies electrochemical biosensors according to the bioreceptor type (enzymatic, immuno, apta and peptide based sensors). A next section covers applications of nanomaterials in electrochemical biosensing (with subsections on the classification of nanomaterials, electrochemical approaches for signal generation and amplification using nanomaterials, and on nanomaterials as tags). A next large sections treats candidate metabolic biomarkers for diagnosis of diseases (in the context with metabolomics), with subsections on biomarkers for neurodegenerative diseases, autism spectrum disorder and hepatitis. The Conclusion addresses current challenges and future perspectives. Graphical abstract This review focuses on the recent developments in electrochemical biosensors based on the use of nanomaterials for the detection of metabolic biomarkers. It covers the critical metabolites for some diseases such as neurodegenerative diseases, autism spectrum disorder and hepatitis.

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.

Nanomaterials engineering for drug delivery: a hybridization approach

The last twenty years have witnessed great advances in biology, medicine, and materials science, leading to the development of various nanoparticle (NP)-mediated drug delivery systems. Innovation in materials science has led the generation of biodegradable, biocompatible, stimuli-responsive, and targeted delivery systems. However, currently available nanotherapeutic technologies are not efficient, which has culminated in the failure of their clinical trials. Despite huge efforts devoted to drug delivery nanotherapeutics, only a small amount of the injected material could reach the desired target. One promising strategy to enhance the efficiency of NP drug delivery is to hybridize multiple materials, where each component could play a critical role in an efficient multipurpose delivery system. This review aims to comprehensively cover different techniques, materials, advantages, and drawbacks of various systems to develop hybrid nano-vesicles for drug delivery. Attention is finally given to the hybridization benefits in overcoming the biological barriers for drug delivery. It is believed that the advent of modern nano-formulations for multifunctional hybrid carriers paves the way for future advances to achieve more efficient drug delivery systems.

Proteomic profiling predicts drug response to novel targeted anticancer therapeutics

Most recently approved anti-cancer drugs by the US FDA are targeted therapeutic agents and this represents an important trend for future anticancer therapy. Unlike conventional chemotherapy that rarely considers individual differences, it is crucial for targeted therapies to identify the beneficial subgroup of patients for the treatment. Currently, genomics and transcriptomics are the major 'omic' analytics used in studies of drug response prediction. However, proteomic profiling excels both in its advantages of directly detecting an instantaneous dynamic of the whole proteome, which contains most current diagnostic markers and therapeutic targets. Moreover, proteomic profiling improves understanding of the mechanism for drug resistance and helps finding optimal combination therapy. This article reviews the recent success of applications of proteomic analytics in predicting the response to targeted anticancer therapeutics, and discusses the potential avenues and pitfalls of proteomic platforms and techniques used most in the field.

A custom grow-cut based scheme for 2D-gel image segmentation

This work introduces a novel method for the detection and segmentation of protein spots in 2D-gel images. A multi-thresholding approach is utilized for the detection of protein spots, while a custom grow-cult algorithm combined with region growing and morphological operators is used for the segmentation process. The experimental evaluation against four state-of-the-art 2D-gel image segmentation algorithms demonstrates the superiority of the proposed approach and indicates that it constitutes an advantageous and reliable solution for 2D-gel image analysis.

Clinical proteomics in cancer: Where we are

Proteomics has emerged as a promising field in the post-genomic era. Notwithstanding the great advances provided by gene expression analysis in cancer, the lack of a correlation between gene expression and protein levels has highlighted the need for a proteomic focus on cancer. Although the increasing knowledge regarding cancer biology, a reliable marker to improve diagnosis, prognosis and treatment for cancer patients is not a reality at present. In this review, we address the main considerations regarding proteomics-based studies and their clinical applications on cancer research, highlighting some considerations related to strengths and limitations of proteomics-based studies and its application to clinical practice.

Confronting the Care Delivery Challenges Arising from Precision Medicine

Understanding the biology of cancer at the cellular and molecular levels, and the application of such knowledge to the patient, has opened new opportunities and uncovered new obstacles to quality cancer care delivery. Benefits include our ability to now understand that many, if not most, cancers are not one-size-fits-all. Cancers are a variety of diseases for which intervention may be very different. This approach is beginning to bear fruit in gynecologic cancers where we are investigating therapeutic optimization at a more focused level, that while not yet precision care, is perhaps much improved. Obstacles to quality care for patients come from many directions. These include incomplete understanding of the role of the mutant proteins in the cancers, the narrow spectrum of agents, broader mutational profiles in solid tumors, and sometimes overzealous application of the findings of genetic testing. This has been further compromised by the unbridled use of social media by all stakeholders in cancer care often without scientific qualification, where anecdote sometimes masquerades as a fact. The only current remedy is to wave the flag of caution, encourage all patients who undergo genetic testing, either germline or somatic, to do so with the oversight of genetic counselors and physician scientists knowledgeable in the pathways involved. This aspiration is accomplished with well-designed clinical trials that inform next steps in this complex and ever evolving process.

2D-gel spot detection and segmentation based on modified image-aware grow-cut and regional intensity information

BACKGROUND

Proteomics, the study of proteomes, has been increasingly utilized in a wide variety of biological problems. The Two-Dimensional Gel Electrophoresis (2D-PAGE) technique is a powerful proteomics technique aiming at separation of the complex protein mixtures. Spot detection and segmentation are fundamental components of 2D-gel image analysis but remain arduous and difficult tasks. Several software packages and academic approaches are available for 2D-gel image spot detection and segmentation. Each one has its respective advantages and disadvantages and achieves a different level of success in dealing with the challenges of 2D-gel image analysis. A common characteristic of the available methods is their dependency on user intervention in order to achieve optimal results, a process that can lead to subjective and non-reproducible results. In this work, the authors propose a novel spot detection and segmentation methodology for 2D-gel images.

METHODS

This work introduces a novel spot detection and spot segmentation methodology that is based on a multi-thresholding scheme applied on overlapping regions of the image, a custom grow-cut algorithm, a region growing scheme and morphological operators. The performance of the proposed methodology is evaluated on real as well as synthetic 2D-gel images using well established statistical measures, including precision, sensitivity, and their weighted measure, F-measure, as well as volumetric overlap, volumetric error and volumetric overlap error.

RESULTS

Experimental results show that the proposed methodology outperforms state-of-the-art software packages and methods proposed in the literature and results in more plausible spot boundaries and more accurate segmentation. The proposed method achieved the highest F-measure (94.8%) for spot detection and the lowest volumetric overlap error (8.3%) for the segmentation process.

CONCLUSIONS

Evaluation against state-of-the-art 2D-gel image analysis software packages and techniques proposed in the literature, including Melanie 7, Delta2D, PDQuest and Scimo, demonstrates that the proposed approach outperforms the other methods evaluated in this work and constitutes an advantageous and reliable solution for 2D-gel image analysis.

Enrichment of phosphorylated peptides and proteins by selective precipitation methods

Protein phosphorylation is one of the most prominent post-translational modifications involved in the regulation of cellular processes. Fundamental understanding of biological processes requires appropriate bioanalytical methods for selectively enriching phosphorylated peptides and proteins. Most of the commonly applied enrichment approaches include chromatographic materials including Fe(3+)-immobilized metal-ion affinity chromatography or metal oxides. In the last years, the introduction of several non-chromatographic isolation technologies has increasingly attracted the interest of many scientists. Such approaches are based on the selective precipitation of phosphorylated peptides and proteins by applying various metal cations. The excellent performance of precipitation-based enrichment methods can be explained by the absence of any stationary phase, resin or sorbent, which usually leads to unspecific binding. This review provides an overview of recently published methods for the selective precipitation of phosphorylated peptides and proteins.

A Review of Biomarkers in Mood and Psychotic Disorders: A Dissection of Clinical vs. Preclinical Correlates

Despite significant research efforts aimed at understanding the neurobiological underpinnings of mood (depression, bipolar disorder) and psychotic disorders, the diagnosis and evaluation of treatment of these disorders are still based solely on relatively subjective assessment of symptoms as well as psychometric evaluations. Therefore, biological markers aimed at improving the current classification of psychotic and mood-related disorders, and that will enable patients to be stratified on a biological basis into more homogeneous clinically distinct subgroups, are urgently needed. The attainment of this goal can be facilitated by identifying biomarkers that accurately reflect pathophysiologic processes in these disorders. This review postulates that the field of psychotic and mood disorder research has advanced sufficiently to develop biochemical hypotheses of the etiopathology of the particular illness and to target the same for more effective disease modifying therapy. This implies that a "one-size fits all" paradigm in the treatment of psychotic and mood disorders is not a viable approach, but that a customized regime based on individual biological abnormalities would pave the way forward to more effective treatment. In reviewing the clinical and preclinical literature, this paper discusses the most highly regarded pathophysiologic processes in mood and psychotic disorders, thereby providing a scaffold for the selection of suitable biomarkers for future studies in this field, to develope biomarker panels, as well as to improve diagnosis and to customize treatment regimens for better therapeutic outcomes.

Safety and diagnostic accuracy of tumor biopsies in children with cancer

BACKGROUND

Tumor biopsies are central to the diagnosis and management of cancer and are critical to efforts in personalized medicine and targeted therapeutics. In the current study, the authors sought to evaluate the safety and accuracy of biopsies in children with cancer.

METHODS

All biopsies performed in children at the study institution with a suspected or established diagnosis of cancer from 2003 through 2012 were reviewed retrospectively. Patient characteristics and disease-related and procedure-related factors were correlated with procedure-related complications and diagnostic accuracy using logistic regression analysis.

RESULTS

A total of 1073 biopsies were performed in 808 patients. Of 1025 biopsies with adequate follow-up, 79 (7.7%) were associated with an adverse event, 35 (3.4%) of which were minor (grade 1-2) and 32 (3.1%) of which were major (grade 3-4) (grading was performed according to the National Cancer Institute Common Terminology Criteria for Adverse Events [version 4.0]). The most common major adverse events were blood transfusion (>10 mL/kg; 24 cases) and infection requiring intravenous antibiotics (6 cases). Eleven deaths (1.4%) occurred within 30 days after the procedure, but the procedure may have contributed to the outcome in only 2 cases. A total of 926 biopsies (90.3%) provided definitive histologic diagnoses. Using multivariable analysis, biopsy site, preprocedure hematocrit level, and body mass index were found to be associated with the risk of postprocedural complications (P<.0001, P<.0001, and P =.0029, respectively). Excisional biopsy and biopsy site were found to be independently associated with obtaining a diagnostic result (P =.0002 and P =.0008, respectively).

CONCLUSIONS

Tumor biopsies in children with cancer are associated with a low incidence of complications and a high rate of diagnostic accuracy. The predictive factors identified for adverse outcomes may aid in risk assessment and preprocedural counseling.

An osmolyte-based micro-volume ultrafiltration technique

This paper discusses a novel, simple, and inexpensive micro-volume ultrafiltration technique for protein concentration, desalting, buffer exchange, and size-based protein purification. The technique is suitable for processing protein samples in a high-throughput mode. It utilizes a combination of capillary action, and osmosis for drawing water and other permeable species from a micro-volume sample droplet applied on the surface of an ultrafiltration membrane. A macromolecule coated on the permeate side of the membrane functions as the osmolyte. The action of the osmolyte could, if required, be augmented by adding a supersorbent polymer layer over the osmolyte. The mildly hydrophobic surface of the polymeric ultrafiltration membrane used in this study minimized sample droplet spreading, thus making it easy to recover the retained material after separation, without sample interference and cross-contamination. High protein recoveries were observed in the micro-volume ultrafiltration experiments described in the paper.

Signaling pathway profiling by reverse-phase protein array for personalized cancer medicine

Deregulation of intracellular signaling through accumulation of genetic alterations is a hallmark of cancer. In the past few decades, concerted and systematic efforts have been made to identify key genetic alterations and to develop therapeutic agents targeting active signaling molecules. However, the efficacy of molecular therapeutics often varies among individuals, and precise mapping of active molecules in individual patients is now considered an essential for therapy optimization. Reverse-phase protein array or microarray (RPPA or RPPM) is an emerging antibody-based highly quantitative proteomic technology, especially suitable for profiling of expression and modification of signaling proteins in low abundance. Because the supply of clinical materials is often limited, RPPA technology is highly advantageous for clinical proteomics in view of its high sensitivity as well as accurate quantification. RPPA has now begun to be incorporated into various clinical trials employing molecular-targeted therapeutics. In this article we review and discuss the application of RPPA technology in the fields of basic, preclinical, and clinical research. The RPPA Global Workshop was recently launched to accelerate the exchange of rapidly expanding knowledge of this fascinating technology among academic laboratories and industries worldwide. This article is part of a Special Issue entitled: Medical Proteomics.

Image-guided interventional therapy for cancer with radiotherapeutic nanoparticles

One of the major limitations of current cancer therapy is the inability to deliver tumoricidal agents throughout the entire tumor mass using traditional intravenous administration. Nanoparticles carrying beta-emitting therapeutic radionuclides that are delivered using advanced image-guidance have significant potential to improve solid tumor therapy. The use of image-guidance in combination with nanoparticle carriers can improve the delivery of localized radiation to tumors. Nanoparticles labeled with certain beta-emitting radionuclides are intrinsically theranostic agents that can provide information regarding distribution and regional dosimetry within the tumor and the body. Image-guided thermal therapy results in increased uptake of intravenous nanoparticles within tumors, improving therapy. In addition, nanoparticles are ideal carriers for direct intratumoral infusion of beta-emitting radionuclides by convection enhanced delivery, permitting the delivery of localized therapeutic radiation without the requirement of the radionuclide exiting from the nanoparticle. With this approach, very high doses of radiation can be delivered to solid tumors while sparing normal organs. Recent technological developments in image-guidance, convection enhanced delivery and newly developed nanoparticles carrying beta-emitting radionuclides will be reviewed. Examples will be shown describing how this new approach has promise for the treatment of brain, head and neck, and other types of solid tumors.

PeptideManager: a peptide selection tool for targeted proteomic studies involving mixed samples from different species

The search for clinically useful protein biomarkers using advanced mass spectrometry approaches represents a major focus in cancer research. However, the direct analysis of human samples may be challenging due to limited availability, the absence of appropriate control samples, or the large background variability observed in patient material. As an alternative approach, human tumors orthotopically implanted into a different species (xenografts) are clinically relevant models that have proven their utility in pre-clinical research. Patient derived xenografts for glioblastoma have been extensively characterized in our laboratory and have been shown to retain the characteristics of the parental tumor at the phenotypic and genetic level. Such models were also found to adequately mimic the behavior and treatment response of human tumors. The reproducibility of such xenograft models, the possibility to identify their host background and perform tumor-host interaction studies, are major advantages over the direct analysis of human samples. At the proteome level, the analysis of xenograft samples is challenged by the presence of proteins from two different species which, depending on tumor size, type or location, often appear at variable ratios. Any proteomics approach aimed at quantifying proteins within such samples must consider the identification of species specific peptides in order to avoid biases introduced by the host proteome. Here, we present an in-house methodology and tool developed to select peptides used as surrogates for protein candidates from a defined proteome (e.g., human) in a host proteome background (e.g., mouse, rat) suited for a mass spectrometry analysis. The tools presented here are applicable to any species specific proteome, provided a protein database is available. By linking the information from both proteomes, PeptideManager significantly facilitates and expedites the selection of peptides used as surrogates to analyze proteins of interest.

Personalized oncology in interventional radiology

As personalized medicine becomes more applicable to oncologic practice, image-guided biopsies will be integral for enabling predictive and pharmacodynamic molecular pathology. Interventional radiology has a key role in defining patient-specific management. Advances in diagnostic techniques, genomics, and proteomics enable a window into subcellular mechanisms driving hyperproliferation, metastatic capabilities, and tumor angiogenesis. A new era of personalized medicine has evolved whereby clinical decisions are adjusted according to a patient's molecular profile. Several mutations and key markers already have been introduced into standard oncologic practice. A broader understanding of personalized oncology will help interventionalists play a greater role in therapy selection and discovery.

Protein Quantification by MRM for Biomarker Validation

In this chapter we describe how mass spectrometry-based quantitative protein measurements by multiple reaction monitoring (MRM) have opened up the opportunity for the assembly of large panels of candidate protein biomarkers that can be simultaneously validated in large clinical cohorts to identify diagnostic protein biomarker signatures. We outline a workflow in which candidate protein biomarker panels are initially assembled from multiple diverse sources of discovery data, including proteomics and transcriptomics experiments, as well as from candidates found in the literature. Subsequently, the individual candidates in these large panels may be prioritised by application of a range of bioinformatics tools to generate a refined panel for which MRM assays may be developed. We describe a process for MRM assay design and implementation, and illustrate how the data generated from these multiplexed MRM measurements of prioritised candidates may be subjected to a range of statistical tools to create robust biomarker signatures for further clinical validation in large patient sample cohorts. Through this overall approach MRM has the potential to not only support individual biomarker validation but also facilitate the development of clinically useful protein biomarker signatures.

An effective approach for detection and segmentation of protein spots on 2-D gel images

Two-dimensional gel image analysis is widely recognized as a particularly challenging and arduous process in proteomics field. The detection and segmentation of protein spots are two significant stages of this process as they can considerably affect the final biological conclusions of a proteomic experiment. The available techniques and commercial software packages deal with the existing challenges of 2-D gel images in a different degree of success. Furthermore, they require extensive human intervention which not only limits the throughput but unavoidably questions the objectivity and reproducibility of results. This paper introduces a novel approach for the detection and segmentation of protein spots on 2-D gel images. The proposed approach is based on 2-D image histograms as well as on 3-D spots morphology. It is automatic and capable to deal with the most common deficiencies of existing software programs and techniques in an effective manner. Experimental evaluation includes tests on several real and synthetic 2-D gel images produced by different technology setups, containing a total of ∼ 21,400 spots. Furthermore, the proposed approach has been compared with two commercial software packages as well as with two state-of-the-art techniques. Results have demonstrated the effectiveness of the proposed approach and its superiority against compared software packages and techniques.

Development of immunoassays using interferometric real-time registration of their kinetics

A method for effective development of solid-phase immunoassays on a glass surface and for optimization of related protocols by highly sensitive quantitative monitoring of each assay step has been proposed and experimentally implemented. The method is based on the spectral correlation interferometry (SCI) that allows real-time measuring of the thickness of a biomolecular layer bound to the recognition molecular receptors on the sensor chip surface. The method is realized with compact 3-channel SCI-biosensors that employ as the sensor chips standard cover glass slips without deposition of any additional films. Different schemes for antibody immobilization on a glass surface have been experimentally compared and optimized toward a higher sorption capacity of the sensor chips. Comparative characterization of the kinetics of each immunoassay stage has been implemented with the optimized protocols: i) covalent immobilization of antibody on an epoxylated surface and ii) biotinylated antibody sorption on a biotinylated surface via a high-affinity biotin-streptavidin bond. We have shown that magnetic nanoparticles employed as labels with model detection of cardiac troponin I further amplify the SCI signal, resulting in 100-fold improvement of the detection limit. The developed protocols can also be used with the alternative immunoassay platforms, including the label methods based on registration of only the final assay result, which is the quantity of bound labels.

Targeted liquid chromatography quadrupole ion trap mass spectrometry analysis of tachykinin related peptides reveals significant expression differences in a rat model of neuropathic pain

Animal models are widely used to perform basic scientific research in pain. The rodent chronic constriction injury (CCI) model is widely used to study neuropathic pain. Animals were tested prior and after CCI surgery using behavioral tests (von Frey filaments and Hargreaves test) to evaluate pain. The brain and the lumbar enlargement of the spinal cord were collected from neuropathic and normal animals. Tachykinin related peptides were analyzed by high performance liquid chromatography quadrupole ion trap mass spectrometry. Our results reveal that the β-tachykinin₅₈₋₇₁, SP and SP₃₋₁₁ up-regulation are closely related to pain behavior. The spinal β-tachykinin₅₈₋₇₁, SP and SP₃₋₁₁ concentrations were significantly up-regulated in neuropathic animals compared with normal animals (p<0.001; p<0.001 and p<0.05, respectively). In contrast, the spinal SP5₅₋₁₁ concentration in neuropathic animals revealed a significant down-regulation compared with normal animals (p<0.05). The brain β-tachykinin₅₈₋₇₁ and SP concentrations were significantly up-regulated (p<0.05 and p<0.001, respectively). Interestingly, no significant concentration differences were observed in the spinal cord and brain for NKA, β-tachykinin₅₈₋₇₁, SP₁₋₇ and SP₆₋₁₁ (p>0.05). The β-tachykinin₅₈₋₇₁, SP and C-terminal SP metabolites could potentially serve as biomarkers in early drug discovery.

Meeting the challenge of ascites in ovarian cancer: new avenues for therapy and research

Malignant ascites presents a considerable clinical challenge to the management of ovarian cancer, but also provides a wealth of opportunities for translational research. The accessibility of ascitic fluid and its cellular components make it an excellent source of tumour tissue for the investigation of prognostic and predictive biomarkers, pharmacodynamic markers and for molecular profiling analysis. In this Opinion article, we discuss recent advances in our understanding of its pathophysiology, the development of new methods to characterize its molecular features and how these findings can be used to improve the treatment of malignant ascites, particularly in the context of ovarian cancer.

Feasibility and safety of sequential research-related tumor core biopsies in clinical trials

BACKGROUND

There has been increasing interest in serial research biopsies in studies of targeted therapies. Definition of patient characteristics and optimal target tissue for safe research tumor biopsy in the era of antiangiogenic and targeted agents is needed.

METHODS

This institutional review board-approved, retrospective study included chart and interventional radiology case review from 6 phase 1/2 studies at the National Cancer Institute.

RESULTS

One hundred forty-two of 150 protocol patients who were approached gave consent for research biopsies. Patients' median age was 56 years (range, 27-78 years), their median body mass index was 25.8 kg/m(2) (range, 14.4-46.2 kg/m(2) ), they had an Eastern Cooperative Oncology Group performance status of 0 or 1, and they had normal end-organ function. Baseline biopsies were collected from 138 of 142 patients (97%), and paired specimens were collected from 96 (70%). Most patients had metastatic gynecologic cancers (85%), and 78% had target disease below the diaphragm with a median size of 2.7 cm (range, 1-14.5 cm). Protocol therapies included kinase inhibitors (35%), angiogenesis inhibitors (54%), and olaparib/carboplatin (11%); therapy was not interrupted for biopsies. All adverse events were uncomplicated and were observed in 4 patients (liver subcapsular hematoma in 1 patient, vasovagal syncope in 2 patients, and pneumothorax in 1 patient). The complication rate in obese patients was similar to that in nonobese patients (3 of 108 patients vs 1 of 34 patients, respectively). Sixty-seven patients (48%) were receiving bevacizumab at the time of subsequent biopsies. The complication rate was not different between patients who were and were not receiving bevacizumab (3 of 67 patients vs 1 of 71 patients, respectively). Ninety-five percent of biopsies yielded useable material.

CONCLUSIONS

Serial percutaneous core-needle biopsies can be obtained safely and yield material applicable for multiple translational applications. Obesity and/or concomitant antiangiogenic therapy and depth of disease did not increase the risk or preclude the successful acquisition of useful tissue.

Mass spectrometry-based proteomics in molecular diagnostics: discovery of cancer biomarkers using tissue culture

Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomics-based approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA) were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches.

Translational predictive biomarker analysis of the phase 1b sorafenib and bevacizumab study expansion cohort

Predictive biomarkers are needed to triage patients to the best therapy. We prospectively planned examination of sequential blood, biopsy, and functional imaging with which to confirm the mechanism and to identify potential predictive biomarkers in a phase Ib clinical trial expansion of patients with solid tumors receiving sorafenib/bevacizumab. The maximally tolerated doses of sorafenib at 200 mg twice daily with bevacizumab at 5 mg/kg every other week were given to biopsiable patients. Patients were randomized to receive either sorafenib or bevacizumab monotherapy for the first 28-day cycle with the second drug added with cycle 2. Biopsies, dynamic contrast-enhanced MRI, and fluorodeoxyglucose-proton emission tomography were done pre-therapy and at 2 and 6 weeks (2 weeks into combination therapy). Tumor and serum proteomics, Ras/Raf mutational analysis, and functional imaging results were examined individually and across the dataset to identify potential changes predictive of response to therapy and those that confirm the biochemical drug mechanism(s). Therapy with sorafenib/bevacizumab resulted in clinical benefit in 45% of this mixed solid tumor group. ERK activation and microvessel density were decreased with monotherapy treatment with sorafenib or bevacizumab, respectively; whereas a decreased signal over the group of total AKT, phospho(p)-VEGF receptor2, p-endothelial nitric-oxide synthase, b-RAF, and cleaved poly(ADP-ribose) polymerase was associated with earlier progression of disease. Tumor metabolic activity decreased in those patients with clinical benefits lasting longer than 4 months, and activity increased with progression of disease. Cleavage of caspase 3 and poly(ADP-ribose) polymerase was increased, and Ki67 expression decreased in patients with prolonged clinical benefits, consistent with decreased proliferation and increased apoptosis. The conglomerate analysis, incorporating pharmacodynamic and tumor biochemistry, demonstrated sorafenib/bevacizumab-targeted vascular activity in the tumor. Results suggest potential biomarkers for which changes, as a group, during early therapeutic exposure may predict clinical benefit.

Amphioxus CaVPT and creatine kinase are crucial immune-related molecules in response to bacterial infection and immunization

Although a great progress has been made, our understanding of innate immunity is incomplete. Here, we hypothesize that the innate immune response to pathogens is attributed into a group of functional proteins. The group contains information on host status post bacterial entry (infection or immunity) and bacterial species (Gram-positive or Gram-negative bacteria). Investigation of the group of proteins may result in disclosing of biomarkers identifying the status and species. For this regard, differential proteomics approach coupled with the pattern recognition methods are used to identify biomarkers from the proteins that being specifically regulated during the innate immune response of amphioxus to Gram-positive and Gram-negative bacteria with live or dead status. Four proteins, Calcium vector protein (CaVP), sarcoplasmic calcium-binding protein (SCP), CaVP-target protein (CaVPT) and creatine kinase (CK), are selected as the key biomarkers. Since immunoprotection of CaVP and SCP has been reported, the role of CaVPT and CK are further investigated. Gut CaVPT appears in dying amphioxus, whereas humoral fluid CK downregulates and gut CK keep no change in animals with immunity. The responses are stronger in Gram-negative than Gram-positive bacteria. These results indicate that CaVPT, CK, CaVP and SCP are the most important biomarkers to uncover amphioxus innate immunity to bacteria, and the approach is an efficient way to identify key biomarkers.

Microvesicles/exosomes as potential novel biomarkers of metabolic diseases

Biomarkers are of tremendous importance for the prediction, diagnosis, and observation of the therapeutic success of common complex multifactorial metabolic diseases, such as type II diabetes and obesity. However, the predictive power of the traditional biomarkers used (eg, plasma metabolites and cytokines, body parameters) is apparently not sufficient for reliable monitoring of stage-dependent pathogenesis starting with the healthy state via its initiation and development to the established disease and further progression to late clinical outcomes. Moreover, the elucidation of putative considerable differences in the underlying pathogenetic pathways (eg, related to cellular/tissue origin, epigenetic and environmental effects) within the patient population and, consequently, the differentiation between individual options for disease prevention and therapy - hallmarks of personalized medicine - plays only a minor role in the traditional biomarker concept of metabolic diseases. In contrast, multidimensional and interdependent patterns of genetic, epigenetic, and phenotypic markers presumably will add a novel quality to predictive values, provided they can be followed routinely along the complete individual disease pathway with sufficient precision. These requirements may be fulfilled by small membrane vesicles, which are so-called exosomes and microvesicles (EMVs) that are released via two distinct molecular mechanisms from a wide variety of tissue and blood cells into the circulation in response to normal and stress/pathogenic conditions and are equipped with a multitude of transmembrane, soluble and glycosylphosphatidylinositol-anchored proteins, mRNAs, and microRNAs. Based on the currently available data, EMVs seem to reflect the diverse functional and dysfunctional states of the releasing cells and tissues along the complete individual pathogenetic pathways underlying metabolic diseases. A critical step in further validation of EMVs as biomarkers will rely on the identification of unequivocal correlations between critical disease states and specific EMV signatures, which in future may be determined in rapid and convenient fashion using nanoparticle-driven biosensors.

Detection of protein complexes using a protein ranking algorithm

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. There is ample evidence that many disease mechanisms involve protein complexes, and being able to predict these complexes is important to the characterization of the relevant disease for diagnostic and treatment purposes. This article introduces a novel method for detecting protein complexes from PPI by using a protein ranking algorithm (ProRank). ProRank quantifies the importance of each protein based on the interaction structure and the evolutionarily relationships between proteins in the network. A novel way of identifying essential proteins which are known for their critical role in mediating cellular processes and constructing protein complexes is proposed and analyzed. We evaluate the performance of ProRank using two PPI networks on two reference sets of protein complexes created from Munich Information Center for Protein Sequence, containing 81 and 162 known complexes, respectively. We compare the performance of ProRank to some of the well known protein complex prediction methods (ClusterONE, CMC, CFinder, MCL, MCode and Core) in terms of precision and recall. We show that ProRank predicts more complexes correctly at a competitive level of precision and recall. The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of the proposed method.

Biobank resources for future patient care: developments, principles and concepts

The aim of the overview is to give a perspective of global biobank development is given in a view of positioning biobanking as a key resource for healthcare to identify new potential markers that can be used in patient diagnosis and complement the targeted personalized drug treatment. The fast progression of biobanks around the world is becoming an important resource for society where the patient benefit is in the focus, with a high degree of personal integrity and ethical standard. Biobanks are providing patient benefits by large scale screening studies, generating large database repositories. It is envisioned by all participating stakeholders that the biobank initiatives will become the future gateway to discover new frontiers within life science and patient care. There is a great importance of biobank establishment globally, as biobanks has been identified as a key area for development in order to speed up the discovery and development of new drugs and protein biomarker diagnostics. One of the major objectives in Europe is to establish concerted actions, where biobank networks are being developed in order to combine and have the opportunity to share and build new science and understanding from complex disease biology. These networks are currently building bridges to facilitate the establishments of best practice and standardizations.