Clinical Utility Of Biomarkers Research Articles

An NMR based panorama of the heterogeneous biology of acute respiratory distress syndrome (ARDS) from the standpoint of metabolic biomarkers.

Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. 1 H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. In the present review, the physiological derangements that jeopardize the whole metabolic functioning in ARDS are exploited and the biomarkers involved in progression are addressed and substantiated. The following sections of the review also outline the clinical spectrum of ARDS from the standpoint of NMR based metabolomics which is an emerging element of systems biology. ARDS is the main premise of intensivists textbook, which has been thoroughly reviewed along with its incidence, progressive stages of severity, new proposed diagnostic definition, and the preventive measures and the current pitfalls of clinical management. The advent of new therapies, the need for biomarkers, the methodology and the contemporary promising approaches needed to improve survival and address heterogeneity have also been evaluated. The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.

Proteomic research in sarcomas - current status and future opportunities.

Sarcomas are a rare group of mesenchymal cancers comprising over 70 different histological subtypes. For the majority of these diseases, the molecular understanding of the basis of their initiation and progression remains unclear. As such, limited clinical progress in prognosis or therapeutic regimens have been made over the past few decades. Proteomics techniques are being increasingly utilised in the field of sarcoma research. Proteomic research efforts have thus far focused on histological subtype characterisation for the improvement of biological understanding, as well as for the identification of candidate diagnostic, predictive, and prognostic biomarkers for use in clinic. However, the field itself is in its infancy, and none of these proteomic research findings have been translated into the clinic. In this review, we provide a brief overview of the proteomic strategies that have been employed in sarcoma research. We evaluate key proteomic studies concerning several rare and ultra-rare sarcoma subtypes including, gastrointestinal stromal tumours, osteosarcoma, liposarcoma, leiomyosarcoma, malignant rhabdoid tumours, Ewing sarcoma, myxofibrosarcoma, and alveolar soft part sarcoma. Consequently, we illustrate how routine implementation of proteomics within sarcoma research, integration of proteomics with other molecular profiling data, and incorporation of proteomics into clinical trial studies has the potential to propel the biological and clinical understanding of this group of complex rare cancers moving forward.

Early detection of acute kidney injury in the perioperative period of liver transplant with neutrophil gelatinase-associated lipocalin

BackgroundAcute kidney injury (AKI) is a common complication in patients undergoing liver transplant (LT) and is associated with high morbidity and mortality. We aim to evaluate the pattern of urine and plasma neutrophil gelatinase-associated lipocalin (NGAL) elevation during the perioperative period of LT and to assess it as a prognostic marker for AKI progression, need for dialysis and mortality.MethodsWe assessed NGAL levels before induction of anesthesia, after portal reperfusion and at 6, 18, 24, and 48 h after surgery. Patients were monitored daily during the first week after LT.ResultsOf 100 enrolled patients undergoing liver transplant, 59 developed severe AKI based on the KDIGO serum creatinine (sCr) criterion; 34 were dialysed, and 21 died within 60 days after LT. Applying a cut-off value of 136 ng/ml, UNGAL values 6 h after surgery was a good predictor of AKI development within 7 days after surgery, having a positive predictive value (PPV) of 80% with an AUC of 0.76 (95% CI 0.67–0.86). PNGAL at 18 h after LT was also a good predictor of AKI in the first week, having a PPV of 81% and AUC of 0.74 (95% CI 0.60–0.88). Based on PNGAL and UNGAL cut-off criteria levels, time to AKI diagnosis was 28 and 23 h earlier than by sCr, respectively. The best times to assess the need for dialysis were 18 h after LT by PNGAL and 06 h after LT by UNGAL.ConclusionIn conclusion, the plasma and urine NGAL elevation pattern in the perioperative period of the liver transplant can predict AKI diagnosis earlier. UNGAL was an early independent predictor of AKI development and need for dialysis. Further studies are needed to assess whether the clinical use of biomarkers can improve patient outcomes.Trial registrationRegistered at Clinical Trials (clinicaltrials.gov) in March 24th, 2014 by title “Acute Kidney Injury Biomarkers: Diagnosis and Application in Pre-operative Period of Liver Transplantation (AKIB)” and identifier NCT02095431, retrospectively registered.

Clinical Utility of Biomarkers in Diagnosis and Management of High Grade Cervical Intraepithelial Neoplasia (HSIL)

Objective of the study: The increasing inter- and intra-observer variability in the diagnosis of CIN by histopathology has led to the advent of different biomarkers. The aim of this study is to evaluate Ki-67 and p16 INK4a marker in differentiating CIN lesions and the clinical utility of it in the management of high grade squamous intraepithelial lesions (HSIL).
 Materials and methods: This cross-sectional study has been carried out among the biopsy proved cases of CIN2 and CIN3 in which P16 and Ki-67 immunostain was performed in the Colposcopy clinic, Bangabandhu Sheikh Mujib Medical University (BSMMU) and Department of Pathology, BSMMU respectively from 14-3-17 to 30-05-18. Thirty-two cases were incorporated in the study by purposive sampling. Data was analyzed using SPSS version 17.
 Results: Among 32 cases of HSIL, 25 were diagnosed as CIN 2 (78.13 %) and 7 cases as CIN 3 (21.87 %) by histopathology. All the cases were subjected to immunohistochemistry with P16 and Ki 67. Regarding the status of P16, it was found positive only in 32% cases of CIN 2 but in 100% cases of CIN 3. Similarly, Ki67 was found positive in 36 % of CIN2 cases but in all cases of CIN 3 lesions. Thus immunostain confirmed one third histopathologically diagnosed CIN 2 cases. On the other hand, all cases of CIN 3 were confirmed as HSIL with the immunostain. All the cases of CIN 2 were reviewed for consensus opinion. Only 8 (32%) cases of CIN 2 were both immunostain positive , the review diagnosis by consensus opinion was also CIN 2 in those cases. One case (4%) of CIN 2 was P16 negative but Ki 67 positive, which ultimately revealed as immature squamous metaplasia. Sixteen cases of CIN 2 were negative for both P16 and Ki 67. Among these, majority cases (52%) , consensus opinion revealed as CIN 1 and 12% cases as immature squamous metaplasia. Therefore, an HSIL diagnosed by histopathology could be false positive in a significant number of cases and overtreatment of a false positive CIN 2 lesion can affect future reproductive status, also have some psychological consequences and economic burden in the health sector.
 Conclusion: By using the biomarkers, overtreatment and undertreatment of CIN can be avoided. All cases of CIN 2 should be carefully dealt with either review of the slides by consensus opinion or by doing immunohistochemistry if facility is available.
 Bangladesh J Obstet Gynaecol, 2019; Vol. 34(2): 79-86

The Emerging Role of Biomarkers in Adaptive Modulation of Clinical Brain Stimulation.

Therapeutic brain stimulation has proven efficacious for treatment of nervous system diseases, exerting widespread influence via disease-specific neural networks. Activation or suppression of neural networks could theoretically be assessed by either clinical symptom modification (ie, tremor, rigidity, seizures) or development of specific biomarkers linked to treatment of symptomatic disease states. For example, biomarkers indicative of disease state could aid improved intraoperative localization of electrode position, optimize device efficacy or efficiency through dynamic control, and eventually serve to guide automatic adjustment of stimulation settings. Biomarkers to control either extracranial or intracranial stimulation span from continuous physiological brain activity, intermittent pathological activity, and triggered local phenomena or potentials, to wearable devices, blood flow, biochemical or cardiac signals, temperature perturbations, optical or magnetic resonance imaging changes, or optogenetic signals. The goal of this review is to update new approaches to implement control of stimulation through relevant biomarkers. Critical questions include whether adaptive systems adjusted through biomarkers can optimize efficiency and eventually efficacy, serve as inputs for stimulation adjustment, and consequently broaden our fundamental understanding of abnormal neural networks in pathologic states. Neurosurgeons are at the forefront of translating and developing biomarkers embedded within improved brain stimulation systems. Thus, criteria for developing and validating biomarkers for clinical use are important for the adaptation of device approaches into clinical practice.

PTEN expression and mutations in TSC1, TSC2 and MTOR are associated with response to rapalogs in patients with renal cell carcinoma.

The mammalian target of rapamycin (mTOR) pathway inhibitors are key drugs for the treatment of many tumor types, however, there are no predictive biomarkers in clinical use. Here, we performed a molecular and immunohistochemical characterization of key mTOR pathway components in a series of 105 renal cell carcinoma patients treated with rapalogs, aimed at identifying markers of treatment response. Mutational analysis in MTOR, TSC1 and TSC2 was performed through targeted next-generation sequencing (NGS), and immunohistochemistry (IHC) was performed for PTEN, pAKT, pS6K1, pS6 and p21. Among patients with NGS data, 11 of 87 (13%) had mTOR pathway mutations (8 in MTOR, 1 in TSC1 and 2 in TSC2). When comparing the molecular data to the response of the patients, we found that partial response was more frequent in cases with mTOR pathway mutations than in those without mutations (odds ratio [OR] = 0.08, 95% confidence interval [CI] = 0.008-0.79, p = 0.030 univariate; p = 0.038 multivariable). Regarding IHC, negative PTEN staining was detected in 58% of the tumors, and it was more frequent in rapalog responder patients (OR = 0.24, 95% CI = 0.065-0.86, p = 0.029 univariate; p = 0.029 multivariable). Mutations and PTEN IHC were not mutually exclusive events and its combination improved response prediction (OR = 0.16, 95% CI = 0.04-0.62, p = 0.008 univariate; p = 0.013 multivariable). The staining of other proteins did not show and association with response and no association with PFS was observed in unselected patients. In conclusion, our findings suggest that mTOR pathway mutations, negative PTEN IHC and their combination are potential markers of rapalog response.

Evolving Concepts in Chronic Obstructive Pulmonary Disease Blood-Based Biomarkers.

In recent years, there has been a great deal of interest in the identification and validation of blood-based biomarkers for clinical use in chronic obstructive pulmonary disease (COPD). We now have panels of blood biomarkers that potentially hold great promise as they show statistically significant associations with COPD, but biomarkers for the diagnosis of COPD remain elusive. In fact, they are yet to demonstrate sufficient accuracy to be accepted in clinical use, and many are not specific to COPDbut more related to inflammation (e.g. interleukin-6) or associated with other chronic diseases such as diabetes (e.g.soluble receptor for advanced glycation endproducts [sRAGE]). Although no single blood-based biomarker has demonstrated clinical utility for either the diagnosis or progression of COPD, it has been suggested that combinations of individual markers may provide important diagnostic or prognostic information; however, the interpretation of COPD biomarker results still requires thought and many questions remain unanswered.

Diagnostic Accuracy of Clinical Biomarkers for Preoperative Prediction of Lymph Node Metastasis in Endometrial Carcinoma: A Systematic Review and Meta-Analysis.

In endometrial carcinoma (EC), preoperative classification is based on histopathological criteria, with only moderate diagnostic performance for the risk of lymph node metastasis (LNM). So far, existing molecular classification systems have not been evaluated for prediction of LNM. Optimized use of clinical biomarkers as recommended by international guidelines might be a first step to improve tailored treatment, awaiting future molecular biomarkers. To determine the diagnostic accuracy of preoperative clinical biomarkers for the prediction of LNM in endometrial cancer. A systematic review was performed according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Studies identified in MEDLINE and EMBASE were selected by two independent reviewers. Included biomarkers were based on recommended guidelines (cancer antigen 125 [Ca-125], lymphadenopathy on magnetic resonance imaging, computed tomography, and 18F-fluorodeoxyglucose positron emission tomography/computed tomography [18FDG PET-CT]) or obtained by physical examination (body mass index, cervical cytology, blood cell counts). Pooled sensitivity, specificity, area under the curve (AUC), and likelihood ratios were calculated with bivariate random-effects meta-analysis. Likelihood ratios were classified into small (0.5-1.0 or 1-2.0), moderate (0.2-0.5 or 2.0-5.0) or large (0.1-0.2 or ≥ 5.0) impact. Eighty-three studies, comprising 18,205 patients, were included. Elevated Ca-125 and thrombocytosis were associated with a moderate increase in risk of LNM; lymphadenopathy on imaging with a large increase. Normal Ca-125, cytology, and no lymphadenopathy on 18FDG PET-CT were associated with a moderate decrease. AUCs were above 0.75 for these biomarkers. Other biomarkers had an AUC <0.75 and incurred only small impact. Ca-125, thrombocytosis, and imaging had a large and moderate impact on risk of LNM and could improve preoperative risk stratification. Routine lymphadenectomy in clinical early-stage endometrial carcinoma does not improve outcome and is associated with 15%-20% surgery-related morbidity, underlining the need for improved preoperative risk stratification. New molecular classification systems are emerging but have not yet been evaluated for the prediction of lymph node metastasis. This article provides a robust overview of diagnostic performance of all clinical biomarkers recommended by international guidelines. Based on these, at least measurement of cancer antigen 125 serum level, assessment of thrombocytosis, and imaging focused on lymphadenopathy should complement current preoperative risk stratification in order to better stratify these patients by risk.

-Omics biomarker identification pipeline for translational medicine

BackgroundTranslational medicine (TM) is an emerging domain that aims to facilitate medical or biological advances efficiently from the scientist to the clinician. Central to the TM vision is to narrow the gap between basic science and applied science in terms of time, cost and early diagnosis of the disease state. Biomarker identification is one of the main challenges within TM. The identification of disease biomarkers from -omics data will not only help the stratification of diverse patient cohorts but will also provide early diagnostic information which could improve patient management and potentially prevent adverse outcomes. However, biomarker identification needs to be robust and reproducible. Hence a robust unbiased computational framework that can help clinicians identify those biomarkers is necessary.MethodsWe developed a pipeline (workflow) that includes two different supervised classification techniques based on regularization methods to identify biomarkers from -omics or other high dimension clinical datasets. The pipeline includes several important steps such as quality control and stability of selected biomarkers. The process takes input files (outcome and independent variables or -omics data) and pre-processes (normalization, missing values) them. After a random division of samples into training and test sets, Least Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods are applied to identify the most important features representing potential biomarker candidates. The penalization parameters are optimised using 10-fold cross validation and the process undergoes 100 iterations and a combinatorial analysis to select the best performing multivariate model. An empirical unbiased assessment of their quality as biomarkers for clinical use is performed through a Receiver Operating Characteristic curve and its Area Under the Curve analysis on both permuted and real data for 1000 different randomized training and test sets. We validated this pipeline against previously published biomarkers.ResultsWe applied this pipeline to three different datasets with previously published biomarkers: lipidomics data by Acharjee et al. (Metabolomics 13:25, 2017) and transcriptomics data by Rajamani and Bhasin (Genome Med 8:38, 2016) and Mills et al. (Blood 114:1063–1072, 2009). Our results demonstrate that our method was able to identify both previously published biomarkers as well as new variables that add value to the published results.ConclusionsWe developed a robust pipeline to identify clinically relevant biomarkers that can be applied to different -omics datasets. Such identification reveals potentially novel drug targets and can be used as a part of a machine-learning based patient stratification framework in the translational medicine settings.

Evaluation of Biomarkers p16/Ki-67 in Cervical Cytology for Diagnosis of Cervical Intraepithelial Neoplasia

To test the clinical utility of biomarkers p16/Ki-67 expression in cervical cytology smears as a marker for transforming HPV infection. Experimental study at a tertiary care hospital. Women who were screened positive on Pap and visual inspection tests (n = 280) underwent colposcopy and biopsy. p16/Ki-67 immunostaining was performed in abnormal Pap smears (n = 86), and HPV DNA testing was also performed in the same women. Sensitivity, specificity, negative predictive value, positive predictive value and likelihood ratios were calculated for each biomarker separately and in combination. McNemar test and receiver operating characteristic (ROC) curves were used to compare sensitivity and specificity of biomarkers with HPV DNA. Areas under the ROC curve (AUC) were compared using the Chi-square test. Eighty-six women with abnormal cytology were evaluated with p16/Ki-67 immunocytochemistry; 20.9% (n = 18) and 18.6% (n = 16) were positive for each biomarker, while dual marker was positive in 15% (n = 13). In all smears, the sensitivity of p16INK4a/Ki-67 in detecting CIN 2+ lesion was 76.9% and specificity was 95.8%. For ASCUS (n = 42) and LSIL (n = 23) smears, specificity and negative predictive value of p16/Ki-67 for CIN 2+ were 100% with a likelihood ratio (LR+) of 27 and 25, respectively, suggesting good diagnostic accuracy. In comparison with HPV DNA testing, combined marker p16/Ki-67 was significantly more specific (p = 0.003); AUC was 0.734 and 0.635, respectively. p16/Ki-67 evaluation in cervical cytology is a valuable biomarker in triaging for CIN 2+ disease in ASCUS and LSIL smears.

Performance of plasminogen activator inhibitor-1 as a biomarker in patients undergoing coronary angiography: Analytical and biological considerations.

Target lesion failure continues to limit the efficacy of percutaneous coronary intervention despite advancements in stent design and medical therapy. Identification of biomarkers to risk stratify patients after percutaneous coronary intervention has the potential to focus therapies on cohorts with increased benefits. Plasminogen activator inhibitor-1 has been identified as a candidate biomarker. Herein, we evaluate biological variables which impact plasminogen activator inhibitor-1 levels and analytical characteristics which impact its utility as a biomarker in humans. Plasma plasminogen activator inhibitor-1 was measured in 689 patients undergoing coronary angiography. Plasminogen activator inhibitor-1 levels were measured. Clinical and procedural characteristics were collected in a prospective registry. Plasma plasminogen activator inhibitor-1 analytical (CVa = 4.1%), intra-individual (CVi = 44.0%) and inter-individual (CVg = 118.6%) variations with reference change value of 122.3% were calculated. Plasminogen activator inhibitor-1 levels were elevated in patients with cardiovascular risk factors, including type 2 diabetes, pre-diabetes, smokers, obesity, hypertension, and daytime variation in procedure and blood draw. Variation in plasma plasminogen activator inhibitor-1 levels is influenced by multiple biological and procedural characteristics. The performance of plasma plasminogen activator inhibitor-1 is consistent with biomarkers in clinical use (N-terminal pro-B-type natriuretic peptide and C-reactive protein) and its applicability is promising.

Setting clinical performance specifications to develop and evaluate biomarkers for clinical use.

Background Biomarker discovery studies often claim ‘promising’ findings, motivating further studies and marketing as medical tests. Unfortunately, the patient benefits promised are often inadequately explained to guide further evaluation, and few biomarkers have translated to improved patient care. We present a practical guide for setting minimum clinical performance specifications to strengthen clinical performance study design and interpretation. Methods We developed a step-by-step approach using test evaluation and decision-analytic frameworks and present with illustrative examples. Results We define clinical performance specifications as a set of criteria that quantify the clinical performance a new test must attain to allow better health outcomes than current practice. We classify the proposed patient benefits of a new test into three broad groups and describe how to set minimum clinical performance at the level where the potential harm of false-positive and false-negative results does not outweigh the benefits. (1) For add-on tests proposed to improve disease outcomes by improving detection, define an acceptable trade-off for false-positive versus true-positive results; (2) for triage tests proposed to reduce unnecessary tests and treatment by ruling out disease, define an acceptable risk of false-negatives as a safety threshold; (3) for replacement tests proposed to provide other benefits, or reduce costs, without compromising accuracy, use existing tests to benchmark minimum accuracy levels. Conclusions Researchers can follow these guidelines to focus their study objectives and to define statistical hypotheses and sample size requirements. This way, clinical performance studies will allow conclusions about whether test performance is sufficient for intended use.

Methodological consensus on clinical proton MRS of the brain: Review and recommendations.

Proton MRS (1 H MRS) provides noninvasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Although most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges toward obtaining reliable good-quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the PRESS localization sequence was found to be unacceptably high at 3 T, and use of the semi-adiabatic localization by adiabatic selective refocusing sequence is a recommended solution. Incorporation of simulated metabolite basis sets into analysis routines is recommended for reliably capturing the full spectral detail available from short TE acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B0 ) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. Implementation of these recommendations should strengthen current clinical applications and advance progress toward developing and validating new MRS biomarkers for clinical use.

Cell-Free DNA as a Biomarker in Autoimmune Rheumatic Diseases.

Endogenous DNA is primarily found intracellularly in nuclei and mitochondria. However, extracellular, cell-free (cf) DNA, has been observed in several pathological conditions, including autoimmune diseases, prompting the interest of developing cfDNA as a potential biomarker. There is an upsurge in studies considering cfDNA to stratify patients, monitor the treatment response and predict disease progression, thus evaluating the prognostic potential of cfDNA for autoimmune diseases. Since the discovery of elevated cfDNA levels in lupus patients in the 1960s, cfDNA research in autoimmune diseases has mainly focused on the overall quantification of cfDNA and the association with disease activity. However, with recent technological advancements, including genomic and methylomic sequencing, qualitative changes in cfDNA are being explored in autoimmune diseases, similar to the ones used in molecular profiling of cfDNA in cancer patients. Further, the intracellular origin, e.g., if derived from mitochondrial or nuclear source, as well as the complexing with carrier molecules, including LL-37 and HMGB1, has emerged as important factors to consider when analyzing the quality and inflammatory potential of cfDNA. The clinical relevance of cfDNA in autoimmune rheumatic diseases is strengthened by mechanistic insights into the biological processes that result in an enhanced release of DNA into the circulation during autoimmune and inflammatory conditions. Prior work have established an important role of accelerated apoptosis and impaired clearance in leakage of nucleic acids into the extracellular environment. Findings from more recent studies, including our own investigations, have demonstrated that NETosis, a neutrophil cell death process, can result in a selective extrusion of inflammatory mitochondrial DNA; a process which is enhanced in patients with lupus and rheumatoid arthritis. In this review, we will summarize the evolution of cfDNA, both nuclear and mitochondrial DNA, as biomarkers for autoimmune rheumatic diseases and discuss limitations, challenges and implications to establish cfDNA as a biomarker for clinical use. This review will also highlight recent advancements in mechanistic studies demonstrating mitochondrial DNA as a central component of cfDNA in autoimmune rheumatic diseases.

Semalytics: a semantic analytics platform for the exploration of distributed and heterogeneous cancer data in translational research.

Each cancer is a complex system with unique molecular features determining its dynamics, such as its prognosis and response to therapies. Understanding the role of these biological traits is fundamental in order to personalize cancer clinical care according to the characteristics of each patient’s disease. To achieve this, translational researchers propagate patients’ samples through in vivo and in vitro cultures to test different therapies on the same tumor and to compare their outcomes with the molecular profile of the disease. This in turn generates information that can be subsequently translated into the development of predictive biomarkers for clinical use. These large-scale experiments generate huge collections of hierarchical data (i.e. experimental trees) with relative annotations that are extremely difficult to analyze. To address such issues in data analyses, we came up with the Semalytics data framework, the core of an analytical platform that processes experimental information through Semantic Web technologies. Semalytics allows (i) the efficient exploration of experimental trees with irregular structures together with their annotations. Moreover, (ii) the platform links its data to a wider open knowledge base (i.e. Wikidata) to add an extended knowledge layer without the need to manage and curate those data locally. Altogether, Semalytics provides augmented perspectives on experimental data, allowing the generation of new hypotheses, which were not anticipated by the user a priori.In this work, we present the data core we created for Semalytics, focusing on its semantic nucleus and on how it exploits semantic reasoning and data integration to tackle issues of this kind of analyses. Finally, we describe a proof-of-concept study based on the examination of several dozen cases of metastatic colorectal cancer in order to illustrate how Semalytics can help researchers generate hypotheses about the role of genes alterations in causing resistance or sensitivity of cancer cells to specific drugs.

Metabolomics Biomarkers: A Strategy Toward Therapeutics Improvement in ALS.

Biomarkers research in amyotrophic lateral sclerosis (ALS) holds the promise of improving ALS diagnosis, follow-up of patients, and clinical trials outcomes. Metabolomics have a big impact on biomarkers identification. In this mini-review, we provide the main findings of metabolomics studies in ALS and discuss the most relevant therapeutics attempts that targeted some prominent alterations found in ALS, like glutamate excitotoxicity, oxidative stress, alterations in energetic metabolism, and creatinine levels. Metabolomics studies have reported putative diagnosis or prognosis biomarkers, but discrepancies among these studies did not allow validation of metabolic biomarkers for clinical use in ALS. In this context, we wonder whether metabolomics knowledge could improve ALS therapeutics. As metabolomics identify specific metabolic pathways modified by disease progression and/or treatment, we support that adjuvant or combined treatment should be used to rescue these pathways, creating a new perspective for ALS treatment. Some ongoing clinical trials are already trying to target these pathways. As clinical trials in ALS have been disappointing and considering the heterogeneity of the disease presentation, we support the application of a pharmacometabolomic approach to evaluate the individual response to drug treatments and their side effects, enabling the development of personalized treatments for ALS. We suggest that the best strategy to apply metabolomics for ALS therapeutics progress is to establish a metabolic signature for ALS patients in order to improve the knowledge of patient metabotypes, to choose the most adequate pharmacological treatment, and to follow the drug response and side effects, based on metabolomics biomarkers.

Making Sense of Connectivity.

In addition to the assessment of local alterations of specific brain regions, the investigation of entire networks with in vivo neuroimaging techniques has gained increasing attention. In general, connectivity analysis refers to the investigation of links between brain regions, with the aim to characterize their interactions and information transfer. These may represent or relate to different physiological characteristics (structural, functional, or metabolic information) and can be calculated across different levels of granularity (2 regions vs whole brain). In this article, we provide an overview of different connectivity analysis approaches with interpretations and limitations as well as examples in pharmacological imaging and clinical applications. Structural connectivity obtained from diffusion MRI enables the reconstruction of neuronal fiber tracts. These physical links represent major constraints of functional connections, which are in turn defined as correlations between signal time courses. In addition, molecular connectivity approaches based on PET imaging enable the assessment of interregional associations of metabolic demands and neurotransmitter systems. Application of these approaches in clinical investigations has demonstrated novel alterations in various neurological and psychiatric disorders on a network level. Future work should aim for the combined assessment of multiple imaging modalities and to establish robust biomarkers for clinical use. These advancements will further improve the biological interpretation of connectivity metrics and networks of the human brain.

Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards.

Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in the use of clinical biomarkers, cellular modeling, and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards that are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved mass spectrometry (MS) instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy-labeled peptides and flags those that fail quality-control metrics. Using four metrics, the tool detects problems with both individual SRM transitions and the collective group of transitions that monitor a single peptide. The program's speed and simplicity enable its use at the point of data acquisition and can be ideally run immediately upon the completion of a liquid chromatography-SRM-MS analysis.

The value of transcriptomics in advancing knowledge of the immune response and diagnosis in tuberculosis.

Blood transcriptomics analysis of tuberculosis has revealed an interferon-inducible gene signature that diminishes in expression after successful treatment; this promises improved diagnostics and treatment monitoring, which are essential for the eradication of tuberculosis. Sensitive radiography revealing lung abnormalities and blood transcriptomics have demonstrated heterogeneity in patients with active tuberculosis and exposed asymptomatic people with latent tuberculosis, suggestive of a continuum of infection and immune states. Here we describe the immune response to infection with Mycobacterium tuberculosis revealed through the use of transcriptomics, as well as differences among clinical phenotypes of infection that might provide information on temporal changes in host immunity associated with evolving infection. We also review the diverse blood transcriptional signatures, composed of small sets of genes, that have been proposed for the diagnosis of tuberculosis and the identification of at-risk asymptomatic people and suggest novel approaches for the development of such biomarkers for clinical use.

Analytical ultracentrifugation in saliva research: Impact of green tea astringency and its significance on the in-vivo aroma release

Current saliva testing methods rely on cutting edge yet expensive techniques for the detection and analysis of genetic material, proteins and biomarkers for clinical use. However, these techniques are limited in scope and often cannot be used with complex food materials. We propose an efficient ex-vivo tool for evaluating biologically relevant interactions between food components and human saliva using sedimentation velocity analytical ultracentrifugation (SV-AUC). We evaluated macromolecular content from “unstimulated” (US) and “stimulated” (SS) samples pooled from 5 healthy volunteers. Over 90% of total saliva protein consisted of α-amylase and mucin, and up to 10% was secretory immunoglobulin A (SIgA). It was shown that α-amylase concentration increased upon parafilm stimulation, which lead to a decrease in the viscosity of saliva. Then, we used a simple food system (green tea) to evaluate changes in the salivary protein content caused by green tea polyphenols. It was found that aroma release from green tea is highly influenced by interactions between α-amylase and polyphenol epigallocatechin 3-gallate (EGCG). This interaction was found to increase the viscosity of the salivary bulk, suggested to contribute to astringency, and increased the concentrations of β-ionone, benzaldehyde and isovaleraldehyde (P < 0.01), suggested to play a significant role in the characteristic flavour of green tea.