Ambulatory human chorionic gonadotrophin (hCG) testing: a verification of two hCG point of care devices.

OBJECTIVES: Quantitative human chorionic gonadotropin (hCG) measurements are used to manage women classified with a pregnancy of unknown location (PUL). Two point of care testing (POCT) devices that quantify hCG are commercially available. We verified the i-STAT 1 (Abbott) and the AQT 90 FLEX (Radiometer) prior to use in PUL triage. METHODS: Tests for precision, external quality assurance (EQA), correlation, hook effect and recovery were undertaken alongside a POCT usability assessment during this prospective multi-center verification. RESULTS: Coefficients of variation ranged between 4.0 and 5.1 % for the three i-STAT 1 internal quality control (IQC) solutions and between 6.8 and 7.3 % for the two AQT IQC solutions. Symmetric differences in POCT EQA results when compared with laboratory and EQA stock values ranged between 3.2 and 24.5 % for the i-STAT 1 and between 3.3 and 36.9 % for the AQT. Correlation coefficients (i-STAT 1: 0.96, AQT: 0.99) and goodness of fit curves (i-STAT 1: 0.92, AQT: 0.99) were excellent when using suitable whole blood samples. An hCG hook effect was noted with the i-STAT 1 between 572,194 and 799,089 IU/L, lower than the hook effect noted with the AQT, which was between 799,089 and 1,619,309 IU/L. When hematocrit concentration was considered in sample types validated for use with each device, hCG recovery was 108 % with the i-STAT 1 and 98 % with the AQT. The i-STAT 1 scored lower on usability overall (90/130) than the AQT (121/130, p<0.001, Mann-Whitney). CONCLUSIONS: Both hCG POCT devices were verified for use in clinical practice. Practical factors must also be considered when choosing which device to use in each unit.

veRification: an R Shiny application for laboratory method verification and validation.

OBJECTIVES: According to international standards, clinical laboratories are required to verify the performance of assays prior to their implementation in routine practice. This typically involves the assessment of the assay's imprecision and trueness vs. appropriate targets. The analysis of these data is typically performed using frequentist statistical methods and often requires the use of closed source, proprietary software. The motivation for this paper was therefore to develop an open-source, freely available software capable of performing Bayesian analysis of verification data. METHODS: The veRification application presented here was developed with the freely available R statistical computing environment, using the Shiny application framework. The codebase is fully open-source and is available as an R package on GitHub. RESULTS: The developed application allows the user to analyze imprecision, trueness against external quality assurance, trueness against reference material, method comparison, and diagnostic performance data within a fully Bayesian framework (with frequentist methods also being available for some analyses). CONCLUSIONS: Bayesian methods can have a steep learning curve and thus the work presented here aims to make Bayesian analyses of clinical laboratory data more accessible. Moreover, the development of the application and seeks to encourage the dissemination of open-source software within the community and provides a framework through which Shiny applications can be developed, shared, and iterated upon.

HDAC Inhibition Restores Response to HER2-Targeted Therapy in Breast Cancer via PHLDA1 Induction.

The downregulation of Pleckstrin Homology-Like Domain family A member 1 (PHLDA1) expression mediates resistance to targeted therapies in receptor tyrosine kinase-driven cancers. The restoration and maintenance of PHLDA1 levels in cancer cells thus constitutes a potential strategy to circumvent resistance to inhibitors of receptor tyrosine kinases. Through a pharmacological approach, we identify the inhibition of MAPK signalling as a crucial step in PHLDA1 downregulation. Further ChIP-qPCR analysis revealed that MEK1/2 inhibition produces significant epigenetic changes at the PHLDA1 locus, specifically a decrease in the activatory marks H3Kme3 and H3K27ac. In line with this, we show that treatment with the clinically relevant class I histone deacetylase (HDAC) inhibitor 4SC-202 restores PHLDA1 expression in lapatinib-resistant human epidermal growth factor receptor-2 (HER2)+ breast cancer cells. Critically, we show that when given in combination, 4SC-202 and lapatinib exert synergistic effects on 2D cell proliferation and colony formation capacity. We therefore propose that co-treatment with 4SC-202 may prolong the clinical efficacy of lapatinib in HER2+ breast cancer patients.

A Practical Guide to Bayesian Statistics in Laboratory Medicine.

Statistical analyses form a fundamental part of causal inference in the experimental sciences. The statistical paradigm most commonly taught to science students around the world is that of frequentism, with a particular emphasis on the null hypothesis significance testing borne by the work of Neyman and Pearson in the early 20th century. This paradigm is often lauded as being the most objective of methods and remains commonplace in scientific journals. Despite its widespread use-and, indeed, requirement for publication in some journals-this paradigm has received substantial criticism in recent decades, and its impact on scientific publishing has been subjected to deeper scrutiny in response to the replication crisis in the psychological and medical sciences. It has been posited that the increasing use of the Bayesian statistical paradigm, made more accessible through technological advances in the last few decades, may have an important role to play in rendering research and statistical inference more robust, transparent, and reproducible. These methods can have a steep learning curve, and thus this paper seeks to introduce those working within clinical laboratories to the Bayesian paradigm of statistical analysis and provides worked examples of the Bayesian analysis of data commonly encountered in laboratory medicine using freely available, open source tools.

A Machine Learning Approach for the Automated Interpretation of Plasma Amino Acid Profiles.

BACKGROUND: Plasma amino acid (PAA) profiles are used in routine clinical practice for the diagnosis and monitoring of inherited disorders of amino acid metabolism, organic acidemias, and urea cycle defects. Interpretation of PAA profiles is complex and requires substantial training and expertise to perform. Given previous demonstrations of the ability of machine learning (ML) algorithms to interpret complex clinical biochemistry data, we sought to determine if ML-derived classifiers could interpret PAA profiles with high predictive performance. METHODS: We collected PAA profiling data routinely performed within a clinical biochemistry laboratory (2084 profiles) and developed decision support classifiers with several ML algorithms. We tested the generalization performance of each classifier using a nested cross-validation (CV) procedure and examined the effect of various subsampling, feature selection, and ensemble learning strategies. RESULTS: The classifiers demonstrated excellent predictive performance, with the 3 ML algorithms tested producing comparable results. The best-performing ensemble binary classifier achieved a mean precision-recall (PR) AUC of 0.957 (95% CI 0.952, 0.962) and the best-performing ensemble multiclass classifier achieved a mean F4 score of 0.788 (0.773, 0.803). CONCLUSIONS: This work builds upon previous demonstrations of the utility of ML-derived decision support tools in clinical biochemistry laboratories. Our findings suggest that, pending additional validation studies, such tools could potentially be used in routine clinical practice to streamline and aid the interpretation of PAA profiles. This would be particularly useful in laboratories with limited resources and large workloads. We provide the necessary code for other laboratories to develop their own decision support tools.

Kinase activity ranking using phosphoproteomics data (KARP) quantifies the contribution of protein kinases to the regulation of cell viability.

Cell survival is regulated by a signaling network driven by the activity of protein kinases; however, determining the contribution that each kinase in the network makes to such regulation remains challenging. Here, we report a computational approach that uses mass spectrometry-based phosphoproteomics data to rank protein kinases based on their contribution to cell regulation. We found that the scores returned by this algorithm, which we have termed kinase activity ranking using phosphoproteomics data (KARP), were a quantitative measure of the contribution that individual kinases make to the signaling output. Application of KARP to the analysis of eight hematological cell lines revealed that cyclin-dependent kinase (CDK) 1/2, casein kinase (CK) 2, extracellular signal-related kinase (ERK), and p21-activated kinase (PAK) were the most frequently highly ranked kinases in these cell models. The patterns of kinase activation were cell-line specific yet showed a significant association with cell viability as a function of kinase inhibitor treatment. Thus, our study exemplifies KARP as an untargeted approach to empirically and systematically identify regulatory kinases within signaling networks.

Using Machine Learning to Aid the Interpretation of Urine Steroid Profiles.

BACKGROUND: Urine steroid profiles are used in clinical practice for the diagnosis and monitoring of disorders of steroidogenesis and adrenal pathologies. Machine learning (ML) algorithms are powerful computational tools used extensively for the recognition of patterns in large data sets. Here, we investigated the utility of various ML algorithms for the automated biochemical interpretation of urine steroid profiles to support current clinical practices. METHODS: Data from 4619 urine steroid profiles processed between June 2012 and October 2016 were retrospectively collected. Of these, 1314 profiles were used to train and test various ML classifiers' abilities to differentiate between "No significant abnormality" and "?Abnormal" profiles. Further classifiers were trained and tested for their ability to predict the specific biochemical interpretation of the profiles. RESULTS: The best performing binary classifier could predict the interpretation of No significant abnormality and ?Abnormal profiles with a mean area under the ROC curve of 0.955 (95% CI, 0.949-0.961). In addition, the best performing multiclass classifier could predict the individual abnormal profile interpretation with a mean balanced accuracy of 0.873 (0.865-0.880). CONCLUSIONS: Here we have described the application of ML algorithms to the automated interpretation of urine steroid profiles. This provides a proof-of-concept application of ML algorithms to complex clinical laboratory data that has the potential to improve laboratory efficiency in a setting of limited staff resources.

Empirical inference of circuitry and plasticity in a kinase signaling network.

Our understanding of physiology and disease is hampered by the difficulty of measuring the circuitry and plasticity of signaling networks that regulate cell biology, and how these relate to phenotypes. Here, using mass spectrometry-based phosphoproteomics, we systematically characterized the topology of a network comprising the PI3K/Akt/mTOR and MEK/ERK signaling axes and confirmed its biological relevance by assessing its dynamics upon EGF and IGF1 stimulation. Measuring the activity of this network in models of acquired drug resistance revealed that cells chronically treated with PI3K or mTORC1/2 inhibitors differed in the way their networks were remodeled. Unexpectedly, we also observed a degree of heterogeneity in the network state between cells resistant to the same inhibitor, indicating that even identical and carefully controlled experimental conditions can give rise to the evolution of distinct kinase network statuses. These data suggest that the initial conditions of the system do not necessarily determine the mechanism by which cancer cells become resistant to PI3K/mTOR targeted therapies. The patterns of signaling network activity observed in the resistant cells mirrored the patterns of response to several drug combination treatments, suggesting that the activity of the defined signaling network truly reflected the evolved phenotypic diversity.

Cross-species proteomics reveals specific modulation of signaling in cancer and stromal cells by phosphoinositide 3-kinase (PI3K) inhibitors.

The tumor microenvironment plays key roles in cancer biology, but its impact on the regulation of signaling pathway activity in cancer cells has not been systemically investigated. We designed an analytical strategy that allows differential analysis of signaling between cancer and stromal cells present in tumor xenografts. We used this approach to investigate how in vivo growth conditions and PI3K inhibitors regulate pathway activities in both cancer and stromal cell populations. We found that, despite inducing more modest changes in protein expression, in vivo growing conditions extensively rewired protein kinase networks in cancer cells. As a result, different sets of phosphorylation sites were modulated by PI3K inhibitors in cancer cells growing in tumors relative to when these cells were in culture. The p110δ PI3K-selective compound CAL-101 (Idelalisib) did not inhibit markers of PI3K activity in cancer or stromal cells; however, unexpectedly, it induced phosphorylation on SQ motifs in both subpopulations of tumor cells in vivo but not in vitro. Thus, the interaction between cancer cells and the stroma modulated the ability of PI3K inhibitors to induce the activation of apoptosis in solid tumors. Our study provides proof-of-principle of a proteomics workflow for measuring signaling specifically in cancer and stromal cells and for investigating how cancer biochemistry is modulated in vivo.

Approaches for measuring signalling plasticity in the context of resistance to targeted cancer therapies.

The ability of cells in multicellular organisms to respond to signals in their environment is critical for their survival, development and differentiation. Once differentiated and occupying their functional niche, cells need to maintain phenotypic stability while responding to diverse extracellular perturbations and environmental signals (such as nutrients, temperature, cytokines and hormones) in a co-ordinated manner. To achieve these requirements, cells have evolved numerous intracellular signalling mechanisms that confer on them the ability to resist, respond and adapt to external changes. Although fundamental to normal biological processes, as is evident from their evolutionary conservation, such mechanisms also allow cancer cells to evade targeted therapies, a problem of immediate clinical importance. In the present article, we discuss the role of signalling plasticity in the context of the mechanisms underlying both intrinsic and acquired resistance to targeted cancer therapies. We then examine the emerging analytical techniques and theoretical paradigms that are contributing to a greater understanding of signalling on a global and untargeted scale. We conclude with a discussion on how integrative approaches to the study of cell signalling have been used, and could be used in the future, to advance our understanding of resistance mechanisms to therapies that target the kinase signalling network.

Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence.

OBJECTIVES: The purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation. BACKGROUND: Coronary artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests. METHODS: An automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study. An ensemble machine learning classifier was trained, using the extracted features, to identify patients with severe coronary artery disease on invasive coronary angiography. The model was tested in an independent U.S. STUDY: How availability of an AI classification might impact clinical interpretation of stress echocardiograms was evaluated in a randomized crossover reader study. RESULTS: Acceptable classification accuracy for identification of patients with severe coronary artery disease in the training data set was achieved on cross-fold validation based on 31 unique geometric and kinematic features, with a specificity of 92.7% and a sensitivity of 84.4%. This accuracy was maintained in the independent validation data set. The use of the AI classification tool by clinicians increased inter-reader agreement and confidence as well as sensitivity for detection of disease by 10% to achieve an area under the receiver-operating characteristic curve of 0.93. CONCLUSIONS: Automated analysis of stress echocardiograms is possible using AI and provision of automated classifications to clinicians when reading stress echocardiograms could improve accuracy, inter-reader agreement, and reader confidence.

Liver Activation of Hepatocellular Nuclear Factor-4α by Small Activating RNA Rescues Dyslipidemia and Improves Metabolic Profile.

Non-alcoholic fatty liver disease (NAFLD) culminates in insulin resistance and metabolic syndrome. Because there are no approved pharmacological treatment agents for non-alcoholic steatohepatitis (NASH) and NAFLD, different signaling pathways are under investigation for drug development with the focus on metabolic pathways. Hepatocyte nuclear factor 4-alpha (HNF4A) is at the center of a complex transcriptional network where its disruption is directly linked to glucose and lipid metabolism. Resetting HNF4A expression in NAFLD is therefore crucial for re-establishing normal liver function. Here, small activating RNA (saRNA) specific for upregulating HNF4A was injected into rats fed a high-fat diet for 16 weeks. Intravenous delivery was carried out using 5-(G5)-triethanolamine-core polyamidoamine (PAMAM) dendrimers. We observed a significant reduction in liver triglyceride, increased high-density lipoprotein/low-density lipoprotein (HDL/LDL) ratio, and decreased white adipose tissue/body weight ratio, all parameters to suggest that HNF4A-saRNA treatment induced a favorable metabolic profile. Proteomic analysis showed significant regulation of genes involved in sphingolipid metabolism, fatty acid ß-oxidation, ketogenesis, detoxification of reactive oxygen species, and lipid transport. We demonstrate that HNF4A activation by oligonucleotide therapy may represent a novel single agent for the treatment of NAFLD and insulin resistance.

PHLDA1 Mediates Drug Resistance in Receptor Tyrosine Kinase-Driven Cancer.

Development of resistance causes failure of drugs targeting receptor tyrosine kinase (RTK) networks and represents a critical challenge for precision medicine. Here, we show that PHLDA1 downregulation is critical to acquisition and maintenance of drug resistance in RTK-driven cancer. Using fibroblast growth factor receptor (FGFR) inhibition in endometrial cancer cells, we identify an Akt-driven compensatory mechanism underpinned by downregulation of PHLDA1. We demonstrate broad clinical relevance of our findings, showing that PHLDA1 downregulation also occurs in response to RTK-targeted therapy in breast and renal cancer patients, as well as following trastuzumab treatment in HER2+ breast cancer cells. Crucially, knockdown of PHLDA1 alone was sufficient to confer de novo resistance to RTK inhibitors and induction of PHLDA1 expression re-sensitized drug-resistant cancer cells to targeted therapies, identifying PHLDA1 as a biomarker for drug response and highlighting the potential of PHLDA1 reactivation as a means of circumventing drug resistance.

Label-Free Phosphoproteomic Approach for Kinase Signaling Analysis.

Phosphoproteomics is a powerful platform for the unbiased profiling of kinase-driven signaling pathways. Quantitation of phosphorylation can be performed by means of either labeling or label-free mass spectrometry (MS) methods. Because of their simplicity and universality, label-free methodology is gaining acceptance and popularity in molecular biology research. Analytical workflows for label-free quantification of phosphorylation, however, need to overcome several hurdles for the technique to be accurate and precise. These include the use of biochemical extraction procedures that efficiently and reproducibly isolate phosphopeptides from complex peptide matrices and an analytical strategy that can cope with missing MS/MS phosphopeptide spectra in a subset of the samples being compared. Testing the accuracy of the developed workflows is an essential prerequisite in the analysis of small molecules by MS, and this is achieved by constructing calibration curves to demonstrate linearity of quantification for each analyte. This level of analytical rigor is rarely shown in large-scale quantification of proteins using either label-based or label-free techniques. In this chapter we show an approach to test linearity of quantification of each phosphopeptide quantified by liquid chromatography (LC)-MS without the need to synthesize standards or label proteins. We further describe the appropriate sample handling techniques required for the reproducible recovery of phosphopeptides and explore the essential algorithmic features that enable the handling of missing MS/MS spectra and thus make label-free data suitable for such analyses. The combined technology described in this chapter expands the applicability of phosphoproteomics to questions not previously tractable with other methodologies.

A Strong B-cell Response Is Part of the Immune Landscape in Human High-Grade Serous Ovarian Metastases.

PURPOSE: In high-grade serous ovarian cancer (HGSOC), higher densities of both B cells and the CD8+ T-cell infiltrate were associated with a better prognosis. However, the precise role of B cells in the antitumor response remains unknown. As peritoneal metastases are often responsible for relapse, our aim was to characterize the role of B cells in the antitumor immune response in HGSOC metastases. EXPERIMENTAL DESIGN: Unmatched pre and post-chemotherapy HGSOC metastases were studied. B-cell localization was assessed by immunostaining. Their cytokines and chemokines were measured by a multiplex assay, and their phenotype was assessed by flow cytometry. Further in vitro and in vivo assays highlighted the role of B cells and plasma cell IgGs in the development of cytotoxic responses and dendritic cell activation. RESULTS: B cells mainly infiltrated lymphoid structures in the stroma of HGSOC metastases. There was a strong B-cell memory response directed at a restricted repertoire of antigens and production of tumor-specific IgGs by plasma cells. These responses were enhanced by chemotherapy. Interestingly, transcript levels of CD20 correlated with markers of immune cytolytic responses and immune complexes with tumor-derived IgGs stimulated the expression of the costimulatory molecule CD86 on antigen-presenting cells. A positive role for B cells in the antitumor response was also supported by B-cell depletion in a syngeneic mouse model of peritoneal metastasis. CONCLUSIONS: Our data showed that B cells infiltrating HGSOC omental metastases support the development of an antitumor response. Clin Cancer Res; 23(1); 250-62. ©2016 AACR.

NCK Associated Protein 1 Modulated by miRNA-214 Determines Vascular Smooth Muscle Cell Migration, Proliferation, and Neointima Hyperplasia.

BACKGROUND: MicroRNA miR-214 has been implicated in many biological cellular functions, but the impact of miR-214 and its target genes on vascular smooth muscle cell (VSMC) proliferation, migration, and neointima smooth muscle cell hyperplasia is unknown. METHODS AND RESULTS: Expression of miR-214 was closely regulated by different pathogenic stimuli in VSMCs through a transcriptional mechanism and decreased in response to vascular injury. Overexpression of miR-214 in serum-starved VSMCs significantly decreased VSMC proliferation and migration, whereas knockdown of miR-214 dramatically increased VSMC proliferation and migration. Gene and protein biochemical assays, including proteomic analyses, showed that NCK associated protein 1 (NCKAP1)-a major component of the WAVE complex that regulates lamellipodia formation and cell motility-was negatively regulated by miR-214 in VSMCs. Luciferase assays showed that miR-214 substantially repressed wild-type but not the miR-214 binding site mutated version of NCKAP1 3' untranslated region luciferase activity in VSMCs. This result confirmed that NCKAP1 is the functional target of miR-214 in VSMCs. NCKAP1 knockdown in VSMCs recapitulates the inhibitory effects of miR-214 overexpression on actin polymerization, cell migration, and proliferation. Data from cotransfection experiments also revealed that inhibition of NCKAP1 is required for miR-214-mediated lamellipodia formation, cell motility, and growth. Importantly, locally enforced expression of miR-214 in the injured vessels significantly reduced NCKAP1 expression levels, inhibited VSMC proliferation, and prevented neointima smooth muscle cell hyperplasia after injury. CONCLUSIONS: We uncovered an important role of miR-214 and its target gene NCKAP1 in modulating VSMC functions and neointima hyperplasia. Our findings suggest that miR-214 represents a potential therapeutic target for vascular diseases.

Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data.

Mass spectrometry is widely used to probe the proteome and its modifications in an untargeted manner, with unrivalled coverage. Applied to phosphoproteomics, it has tremendous potential to interrogate phospho-signalling and its therapeutic implications. However, this task is complicated by issues of undersampling of the phosphoproteome and challenges stemming from its high-content but low-sample-throughput nature. Hence, methods using such data to reconstruct signalling networks have been limited to restricted data sets and insights (for example, groups of kinases likely to be active in a sample). We propose a new method to handle high-content discovery phosphoproteomics data on perturbation by putting it in the context of kinase/phosphatase-substrate knowledge, from which we derive and train logic models. We show, on a data set obtained through perturbations of cancer cells with small-molecule inhibitors, that this method can study the targets and effects of kinase inhibitors, and reconcile insights obtained from multiple data sets, a common issue with these data.