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Single-cell technologies have elucidated mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are not amenable to a clinical diagnostic setting. In contrast, bulk RNA sequencing (RNA-seq) is now routine for research and clinical applications. Our workflow uses transcription factor (TF)-directed co-expression networks (regulons) inferred from single-cell RNA-seq data to deconvolute immune functional states from bulk RNA-seq data. Regulons preserve the phenotypic variation in CD45+ immune cells from metastatic melanoma samples (n=19, discovery dataset) treated with ICIs, despite reducing dimensionality by > 100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells were associated with therapy response; and were characterized by differentially active and cell-state specific regulons. Clustering of bulk RNA-seq melanoma samples from four independent studies (n=209, validation dataset) according to regulon-inferred scores identified four groups with significantly different response outcomes (p < 0.001). An intercellular link was established between exhausted T cells and monocyte lineage cells, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. The ligand–receptor expression analysis suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative co-stimulation. Together, our results demonstrate how regulon-based characterization of cell states provide robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

Purpose: KRAS wild-type status is an imperfect predictor of sensitivity to anti-EGF receptor (EGFR) monoclonal antibodies in colorectal cancer, motivating efforts to identify novel molecular aberrations driving RAS. This study aimed to build a quantitative readout of RAS pathway activity to (i) uncover molecular surrogates of RAS activity specific to colorectal cancer, (ii) improve the prediction of cetuximab response in patients, and (iii) suggest new treatment strategies.Experimental Design: A model of RAS pathway activity was trained in a large colorectal cancer dataset and validated in three independent colorectal cancer patient datasets. Novel molecular traits were inferred from The Cancer Genome Atlas colorectal cancer data. The ability of the RAS model to predict resistance to cetuximab was tested in mouse xenografts and three independent patient cohorts. Drug sensitivity correlations between our model and large cell line compendiums were performed.Results: The performance of the RAS model was remarkably robust across three validation datasets. (i) Our model confirmed the heterogeneity of the RAS phenotype in KRAS wild-type patients, and suggests novel molecular traits driving its phenotype (e.g., MED12 loss, FBXW7 mutation, MAP2K4 mutation). (ii) It improved the prediction of response and progression-free survival (HR, 2.0; P < 0.01) to cetuximab compared with KRAS mutation (xenograft and patient cohorts). (iii) Our model consistently predicted sensitivity to MAP–ERK kinase (MEK) inhibitors (P < 0.01) in two cell panel screens.Conclusions: Modeling the RAS phenotype in colorectal cancer allows for the robust interrogation of RAS pathway activity across cell lines, xenografts, and patient cohorts. It demonstrates clinical utility in predicting response to anti-EGFR agents and MEK inhibitors. Clin Cancer Res; 20(1); 265–72. ©2013 AACR.

PDF file - 35K, Characteristics of the 2 metastatic CRC datasets used for evaluation of prediction of PFS.

PDF file - 32K, PFS analysis within the French cohort.

PDF file - 33K, Univariate analysis.

PDF file - 219K, Supplemental Figure 1. A We evaluated 4 "RAS" signatures across 4 CRC data sets to assess their ability to discriminate KRAS mutant samples from KRAS wild-type. Supplemental Figure 2. The lower observed RIS of KRAS codon 13 mutated samples relative to KRAS codon 12 mutated samples may be a result of the imbalanced distribution of these mutations in the training data. Supplemental Figure 3. RIS distribution by mutation of KRAS, BRAF, NRAS mutations, and wild-type. Supplemental Figure 4. RAS index score (RIS) for PIK3CA mutant samples by exon in the TCGA CRC cohort. Supplemental Figure 5. Distribution of RIS for the subset of colorectal samples (n=19) within the Cancer Cell Line Encylopedia (CCLE), separated by KRAS codon, BRAF V600E, PIK3CA (KRAS/BRAF wild-type), or wild-type (no KRAS/BRAF/PIK3CA) mutation. Supplemental Figure 6. In the mouse early passage cohort (n=26), our RAS model outperforms the KRAS/BRAF mutation model (p=0.06 vs p > .1, respectively). Supplemental Figure 7. Association of RIS and cetuximab response for metastatic colorectal patients (Khambata-Ford, et al.) Patients annotated according to treatment response: Progressive Disease (PD), Stable Disease (SD) and Partial Response (PR) as well as KRAS mutation status.

PDF file - 35K, Data sets used for evaluation of the colorectal RAS model, and reported AUC in classification of KRAS mutant from wild-type samples.

This paper investigates statistical reproducibility of the [Formula: see text]-test. We formulate reproducibility as a predictive inference problem and apply the nonparametric predictive inference method. Within our research framework, statistical reproducibility provides inference on the probability that the same test outcome would be reached, if the test were repeated under identical conditions. We present an nonparametric predictive inference algorithm to calculate the reproducibility of the [Formula: see text]-test and then use simulations to explore the reproducibility both under the null and alternative hypotheses. We then apply nonparametric predictive inference reproducibility to a real-life scenario of a preclinical experiment, which involves multiple pairwise comparisons of test groups, where different groups are given a different concentration of a drug. The aim of the experiment is to decide the concentration of the drug which is most effective. In both simulations and the application scenario, we study the relationship between reproducibility and two test statistics, the Cohen’s [Formula: see text] and the [Formula: see text]-value. We also compare the reproducibility of the [Formula: see text]-test with the reproducibility of the Wilcoxon Mann–Whitney test. Finally, we examine reproducibility for the final decision of choosing a particular dose in the multiple pairwise comparisons scenario. This paper presents advances on the topic of test reproducibility with relevance for tests used in pharmaceutical research.

Kidney disease is a complex disease with several different etiologies and underlying associated pathophysiology. This is reflected by the lack of effective treatment therapies in chronic kidney disease (CKD) that stop disease progression. However, novel strategies, recent scientific breakthroughs, and technological advances have revealed new possibilities for finding novel disease drivers in CKD. This review describes some of the latest advances in the field and brings them together in a more holistic framework as applied to identification and validation of disease drivers in CKD. It uses high-resolution ‘patient-centric’ omics data sets, advanced in silico tools (systems biology, connectivity mapping, and machine learning) and ‘state-of-the-art‘ experimental systems (complex 3D systems in vitro, CRISPR gene editing, and various model biological systems in vivo). Application of such a framework is expected to increase the likelihood of successful identification of novel drug candidates based on strong human target validation and a better scientific understanding of underlying mechanisms.

The unravelling of the complexity of cellular metabolism is in its infancy. Cancer-associated genetic alterations may result in changes to cellular metabolism that aid in understanding phenotypic changes, reveal detectable metabolic signatures, or elucidate vulnerabilities to particular drugs. To understand cancer-associated metabolic transformation we performed untargeted metabolite analysis of 173 different cancer cell lines from 11 different tissues under constant conditions for 1099 different species using liquid chromatography-mass spectrometry (LC-MS). We correlate known cancer-associated mutations and gene expression programs with metabolic signatures, generating novel associations of known metabolic pathways with known cancer drivers. We show that metabolic activity correlates with drug sensitivity and use metabolic activity to predict drug response and synergy. Finally, we study the metabolic heterogeneity of cancer mutations across tissues, and find that genes exhibit a range of context specific, and more general metabolic control.

BackgroundSingle-cell sequencing studies have elucidated some of the underlying mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are difficult to implement as a general strategy or in a clinical diagnostic setting. In contrast, bulk RNAseq is now routine for both research and clinical applications. Therefore, our analysis extracts small transcription factor-directed co-expression networks (regulons) from single-cell RNA-seq data and uses them to deconvolute immune functional states from bulk RNA-seq data to characterize patient responses.MethodsRegulons were inferred in pre-treatment CD45+ cells from metastatic melanoma samples (n=19) treated with first-line ICI therapy (discovery dataset). A logistic regression-based classifier identified immune cell states associated with response, which were characterized according to differentially active, cell-state specific regulons. The complexity of these regulons was reduced and scored in bulk RNAseq melanoma samples from four independent studies (n=209, validation dataset). Patients were clustered according to their regulon scores, and the associations between cluster assignment, response, and survival were determined. Intercellular communication analysis of cell states was performed, and the resulting effector genes were analyzed by trajectory inference.ResultsRegulons preserved the information of gene expression data and accurately delineated immune cell phenotypes, despite reducing dimensionality by > 100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells, were associated with therapeutic responses in the discovery dataset. The cell states were characterized by seven differentially active and specific regulons that showed low specificity in non-immune cells. Four clusters with significantly different response outcomes (P ConclusionsRegulon-based characterization of cell states provides robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

The endothelial response to vascular endothelial growth factor A (VEGF-A) regulates many aspects of animal physiology in health and disease. Such VEGF-A-regulated phenomena include vasculogenesis, angiogenesis, tumor growth and progression. VEGF-A binding to receptor tyrosine kinases such as vascular endothelial growth factor receptor 2 (VEGFR2 ) activates multiple signal transduction pathways and changes in homeostasis, metabolism, gene expression, cell proliferation, migration, and survival. One such VEGF-A-regulated response is a rapid rise in cytosolic calcium ion levels which modulates different biochemical events and impacts on endothelial-specific responses. Here, we present a series of detailed and robust protocols for evaluating ligand-stimulated cytosolic calcium ion flux in endothelial cells. By monitoring an endogenous endothelial transcription factor (NFATc2 ) which displays calcium-sensitive redistribution, we can assess the relevance of cytosolic calcium to protein function. This protocol can be easily applied to both adherent and non-adherent cultured cells to evaluate calcium ion flux in response to exogenous stimuli such as VEGF-A.

Excessive production of reactive oxygen species (ROS) is characteristic of numerous diseases, but most studies in this area have not considered the impact of endogenous antioxidative defenses. Here, utilizing multi-omics, we demonstrate that in cardiomyocytes mitochondrial isocitrate dehydrogenase (IDH2) constitutes a major antioxidant defense. In both male and female mice and humans the paradoxical reduction in expression of IDH2 associated with heart failure is compensated for by an increase in the enzyme’s activity. We describe extensive mutual regulation of the antioxidant activities of IDH2 and NRF2 by a network involving 2-oxoglutarate and L2-hydroxyglutarate and mediated in part through unconventional hydroxymethylation of cytosine residues present in introns. Conditional targeting of ROS in a murine model of heart failure improves cardiac function. Together, these insights may explain why previous attempts to treat heart failure with antioxidants have been unsuccessful and open new approaches to personalizing and, thereby, improving such treatment.Graphical abstractHighlightsParadoxical downregulation of mitochondrial isocitrate dehydrogenase (IDH2) in response to oxidative stress leads to the discovery of a robust antioxidative defense in the heart.An antioxidative loop involving IDH2 coordinates other antioxidative defenses, such as NRF2.This loop produces epigenetic modifications that link oxidative stress to mitochondrial function.The conclusion that enhancing antioxidative capacity improves cardiac function only when the endogenous capacity is insufficient opens new approaches to individualized treatment of patients with heart failure.

This paper investigates statistical reproducibility of the

A fundamental feature of cancer cells is genomic heterogeneity. It is a main driver of phenotypic differences, including the response to drugs, and therefore a key factor in therapy selection. Motivated by the increasing role attributed to metabolic reprogramming in tumor development, we wondered how genomic heterogeneity affects metabolic phenotype. To this end, we profiled the intracellular metabolome of 180 cancer cell lines grown in similar conditions to exclude environmental factors. For each cell line, we estimate activity for 49 pathways across the whole metabolic network. Upon clustering of activity data, we found a convergence into only two major metabolic types. These were further characterized by 13C-flux analysis, lipidomics, and analysis of sensitivity to perturbations. These experiments revealed differences in lipid, mitochondrial, and carbohydrate metabolism between the two major types. Finally, a thorough integration of our metabolic data with multiple omics data revealed a strong association with markers of epithelial-mesenchymal transition (EMT). Our analysis indicates that in absence of variations imposed by the microenvironment, the metabolism of cancer cell lines falls into only two major classes despite genetic heterogeneity.

The cellular thermal shift assay (CETSA) has been used extensively since its introduction to study drug-target engagement within both live cells and cellular lysate. This has proven to be a useful tool in early stage drug discovery and is used to study a wide range of protein classes. We describe the application of a single-cell CETSA workflow within a microfluidic affinity capture (MAC) chip. This has enabled us to quantitatively determine the active FOXO1 single-molecule count and observe FOXO1 stabilization and destabilization in the presence of three small molecule inhibitors, including demonstrating the determination of EC50. The successful use of the MAC chip for single-cell CETSA paves the way for the study of precious clinical samples owing to the low number of cells needed by the chip. It also provides a useful tool for studying any underlying population heterogeneity that exists within a cellular system, a feature that is usually masked when conducting ensemble measurements.

Immune checkpoint inhibitors (ICIs) targeting cytotoxic T lymphocyte-associated protein-4 (CTLA-4) and programmed cell death protein-1 (PD-1) have been hailed as major advances in cancer therapeutics; however, in many cancers response rates remain low. Extensive research efforts are underway to improve the efficacy of ICIs. The signaling pathways regulated by immune checkpoints (ICs) may be an important lever as they interfere with T-cell activation when activated by ICIs. Here, we review the current understanding of T-cell receptor signaling and their intersection with IC signaling pathways. As these signaling processes are highly dynamic and controlled by intricate spatiotemporal mechanisms, we focus on aspects of kinetic regulation that are modulated by ICs. Recent advances in computational modeling and experimental methods that can resolve spatiotemporal dynamics provide insights that reveal molecular mechanisms and new potential approaches for improving the design and application of ICIs.

An important part the absorption, distribution, metabolism and excretion of an oral therapeutic is the flux rate of drug compound crossing the mucus lining of the gut. To understand this part of the absorption process, we develop a mathematical model of advection, diffusion and binding of drug compounds within the mucus layer of the intestines. Analysis of this model yields simple, measurable criteria for the successful mucin layer traversal of drug compound.

In an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In particular, the use of discrete modelling allows generation of signalling networks in the absence of full quantitative descriptions of systems, which are necessary for ordinary differential equation (ODE) models. In order to make such techniques more accessible to mainstream researchers, tools such as the BioModelAnalyzer (BMA) have been developed to provide a user-friendly graphical interface for discrete modelling of biological systems. Here we use the BMA to build a library of discrete target functions of known canonical molecular interactions, translated from ordinary differential equations (ODEs). We then show that these BMA target functions can be used to reconstruct complex networks, which can correctly predict many known genetic perturbations. This new library supports the accessibility ethos behind the creation of BMA, providing a toolbox for the construction of complex cell signalling models without the need for extensive experience in computer programming or mathematical modelling, and allows for construction and simulation of complex biological systems with only small amounts of quantitative data.

We present two applications of conformal prediction relevant to drug discovery. The first application is around interpretation of predictions and the second one around the selection of compounds to progress in a drug discovery project setting.

The high cost for new medicines is hindering their development and machine learning is therefore being used to avoid carrying out physical experiments. Here, we present a comparison between three different machine learning approaches in a classification setting where learning and prediction follow a teaching schedule to mimic the drug discovery process. The approaches are standard SVM classification, SVM based multi-kernel classification and SVM classification based on learning using privileged information. Our two main conclusions are derived using experimental in-vitro data and compound structure descriptors. The in-vitro data is assumed to i) be completely absent in the standard SVM setting, ii) be available at all times when applying multi-kernel learning, or iii) be available as privileged information during training only. The structure descriptors are always available. One conclusion is that multi-kernel learning has higher odds than standard SVM in producing higher accuracy. The second is that learning using privileged information does not have higher odds than the standard SVM, although it may improve accuracy when the training sets are small.

While deep learning has seen many recent applications to drug discovery, most have focused on predicting activity or toxicity directly from chemical structure. Phenotypic changes exhibited in cellular images are also indications of the mechanism of action (MoA) of chemical compounds. In this paper, we show how pre-trained convolutional image features can be used to assist scientists in discovering interesting chemical clusters for further investigation. Our method reduces the dimensionality of raw fluorescent stained images from a high throughput imaging (HTI) screen, producing an embedding space that groups together images with similar cellular phenotypes. Running standard unsupervised clustering on this embedding space yields a set of distinct phenotypic clusters. This allows scientists to further select and focus on interesting clusters for downstream analyses. We validate the consistency of our embedding space qualitatively with t-sne visualizations, and quantitatively by measuring embedding variance among images that are known to be similar. Results suggested the usefulness of our proposed workflow using deep learning and clustering and it can lead to robust HTI screening and compound triage.

A substantial challenge in phenotypic drug discovery is the identification of the molecular targets that govern a phenotypic response of interest. Several experimental strategies are available for this, the so-called target deconvolution process. Most of these approaches exploit the affinity between a small-molecule compound and its putative targets or use large-scale genetic manipulations and profiling. Each of these methods has strengths but also limitations such as bias toward high-affinity interactions or risks from genetic compensation. The use of computational methods for target and mechanism of action identification is a complementary approach that can influence each step of a phenotypic screening campaign. Here, we describe how cheminformatics and bioinformatics are embedded in the process from initial selection of a focused compound library from a large set of historical small-molecule screens through the analysis of screening results. We present a deconvolution method based on enrichment analysis and using known bioactivity data of screened compounds to infer putative targets, pathways, and biological processes that are consistent with the observed phenotypic response. As an example, the approach is applied to a cellular screen aiming at identifying inhibitors of tumor necrosis factor-α production in lipopolysaccharide-stimulated THP-1 cells. In summary, we find that the approach can contribute to solving the often very complex target deconvolution task.

Purpose: KRAS wild-type status is an imperfect predictor of sensitivity to anti-EGF receptor (EGFR) monoclonal antibodies in colorectal cancer, motivating efforts to identify novel molecular aberrations driving RAS. This study aimed to build a quantitative readout of RAS pathway activity to (i) uncover molecular surrogates of RAS activity specific to colorectal cancer, (ii) improve the prediction of cetuximab response in patients, and (iii) suggest new treatment strategies. Experimental Design: A model of RAS pathway activity was trained in a large colorectal cancer dataset and validated in three independent colorectal cancer patient datasets. Novel molecular traits were inferred from The Cancer Genome Atlas colorectal cancer data. The ability of the RAS model to predict resistance to cetuximab was tested in mouse xenografts and three independent patient cohorts. Drug sensitivity correlations between our model and large cell line compendiums were performed. Results: The performance of the RAS model was remarkably robust across three validation datasets. (i) Our model confirmed the heterogeneity of the RAS phenotype in KRAS wild-type patients, and suggests novel molecular traits driving its phenotype (e.g., MED12 loss, FBXW7 mutation, MAP2K4 mutation). (ii) It improved the prediction of response and progression-free survival (HR, 2.0; P < 0.01) to cetuximab compared with KRAS mutation (xenograft and patient cohorts). (iii) Our model consistently predicted sensitivity to MAP–ERK kinase (MEK) inhibitors (P < 0.01) in two cell panel screens. Conclusions: Modeling the RAS phenotype in colorectal cancer allows for the robust interrogation of RAS pathway activity across cell lines, xenografts, and patient cohorts. It demonstrates clinical utility in predicting response to anti-EGFR agents and MEK inhibitors. Clin Cancer Res; 20(1); 265–72. ©2013 AACR.

A single click ensemble segmentation (SCES) approach based on an existing "Click & Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76%, respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.

Many antimicrobial and anti-tumour drugs elicit hormetic responses characterised by low-dose stimulation and high-dose inhibition. While this can have profound consequences for human health, with low drug concentrations actually stimulating pathogen or tumour growth, the mechanistic understanding behind such responses is still lacking. We propose a novel, simple but general mechanism that could give rise to hormesis in systems where an inhibitor acts on an enzyme. At its core is one of the basic building blocks in intracellular signalling, the dual phosphorylation-dephosphorylation motif, found in diverse regulatory processes including control of cell proliferation and programmed cell death. Our analytically-derived conditions for observing hormesis provide clues as to why this mechanism has not been previously identified. Current mathematical models regularly make simplifying assumptions that lack empirical support but inadvertently preclude the observation of hormesis. In addition, due to the inherent population heterogeneities, the presence of hormesis is likely to be masked in empirical population-level studies. Therefore, examining hormetic responses at single-cell level coupled with improved mathematical models could substantially enhance detection and mechanistic understanding of hormesis., Author Summary Hormesis is a highly controversial and poorly understood phenomenon. It describes the idea that an inhibitor molecule, like an anti-cancer or anti-microbial drug, can inadvertently stimulate cell growth instead of suppressing it. This can have a profound effect on human health leading to failures in clinical treatments. Therefore, getting at the mechanistic basis of hormesis is critical for drug development and clinical practice, however molecular mechanisms underpinning hormesis remain poorly understood. In this paper we use a mathematical model to propose a simple and yet general mechanism that could explain why we find hormesis so widely in living systems. In particular, we discover that hormesis is present within a fundamental structure that forms a basic building block of many intracellular signalling pathways found in diverse processes including control of cell reproduction and programmed cell death. The benefits of our study are two-fold. Having simple molecular understanding of the causes of hormetic responses can greatly improve the design of new drug compounds that avoid such responses. Moreover, due to the fundamental nature of the newly proposed mechanism, our findings have a potential broad applicability to both anti-cancer and anti-microbial drugs.

Understanding the therapeutic effect of drug dose and scheduling is critical to inform the design and implementation of clinical trials. The increasing complexity of both mono, and particularly combination therapies presents a substantial challenge in the clinical stages of drug development for oncology. Using a systems pharmacology approach, we have extended an existing PK-PD model of tumor growth with a mechanistic model of the cell cycle, enabling simulation of mono and combination treatment with the ATR inhibitor AZD6738 and ionizing radiation. Using AZD6738, we have developed multi-parametric cell based assays measuring DNA damage and cell cycle transition, providing quantitative data suitable for model calibration. Our in vitro calibrated cell cycle model is predictive of tumor growth observed in in vivo mouse xenograft studies. The model is being used for phase I clinical trial designs for AZD6738, with the aim of improving patient care through quantitative dose and scheduling prediction.

Successful development of drugs against novel targets crucially depends on reliable identification of the activity of the target gene product in vivo and a clear demonstration of its specific functional role for disease development. Here, we describe an immunological knockdown (IKD) method, a novel approach for the in vivo validation and functional study of endogenous gene products. This method relies on the ability to elicit a transient humoral response against the selected endogenous target protein. Anti-target antibodies specifically bind to the target protein and a fraction of them effectively neutralize its activity. We applied the IKD method to the in vivo validation of plasma PCSK9 as a potential target for the treatment of elevated levels of plasma LDL-cholesterol. We show that immunization with human-PCSK9 in mice is able to raise antibodies that cross-react and neutralize circulating mouse-PCSK9 protein thus resulting in increased liver LDL receptor levels and plasma cholesterol uptake. These findings closely resemble those described in PCSK9 knockout mice or in mice treated with antibodies that inhibit PCSK9 by preventing the PCSK9/LDLR interaction. Our data support the IKD approach as an effective method to the rapid validation of new target proteins.

Age-related sarcopenia results in frailty and decreased mobility, which are associated with increased falls and long-term disability in the elderly. Given the global increase in lifespan, sarcopenia is a growing, unmet medical need. This report aims to systematically characterize muscle aging in preclinical models, which may facilitate the development of sarcopenia therapies. Naïve rats and mice were subjected to noninvasive micro X-ray computed tomography (micro-CT) imaging, terminal in situ muscle function characterizations, and ATPase-based myofiber analysis. We developed a Definiens (Parsippany, NJ)-based algorithm to automate micro-CT image analysis, which facilitates longitudinal in vivo muscle mass analysis. We report development and characterization of translational in situ skeletal muscle performance assay systems in rat and mouse. The systems incorporate a custom-designed animal assay stage, resulting in enhanced force measurement precision, and LabVIEW (National Instruments, Austin, TX)-based algorithms to support automated data acquisition and data analysis. We used ATPase-staining techniques for myofibers to characterize fiber subtypes and distribution. Major parameters contributing to muscle performance were identified using data mining and integration, enabled by Labmatrix (BioFortis, Columbia, MD). These technologies enabled the systemic and accurate monitoring of muscle aging from a large number of animals. The data indicated that longitudinal muscle cross-sectional area measurement effectively monitors change of muscle mass and function during aging. Furthermore, the data showed that muscle performance during aging is also modulated by myofiber remodeling factors, such as changes in myofiber distribution patterns and changes in fiber shape, which affect myofiber interaction. This in vivo muscle assay platform has been applied to support identification and validation of novel targets for the treatment of sarcopenia.

OBJECTIVE: This study was designed to characterize the reproducibility of measurement for tumor volumes and their longest tumor diameters (LDs) and estimate the potential impact of using changes in tumor volumes instead of LDs as the basis for response assessments. METHODS: We studied patients with advanced lung cancer who have been observed longitudinally with x-ray computed tomography in a multinational trial. A total of 71 time points from 10 patients with 13 morphologically complex target lesions were analyzed. A total of 6461 volume measurements and their corresponding LDs were made by seven independent teams using their own work flows and image analysis tools. Interteam agreement and overall interrater concurrence were characterized. RESULTS: Interteam agreement between volume measurements was better than between LD measurements (i = 0.945 vs 0.734, P = .005). The variability in determining the nadir was lower for volumes than for LDs ( P = .005). Use of standard thresholds for the RECIST-based method and use of experimentally determined cutoffs for categorizing responses showed that volume measurements had a significantly greater sensitivity for detecting partial responses and disease progression. Earlier detection of progression would have led to earlier changes in patient management in most cases. CONCLUSIONS: Our findings indicate that measurement of changes in tumor volumes is adequately reproducible. Using tumor volumes as the basis for response assessments could have a positive impact on both patient management and clinical trials. More authoritative work to qualify or discard changes in volume as the basis for response assessments should proceed.

The aim of this study was to evaluate the efficacy of genetic vaccination with rat ErbB2 antigen in a therapeutic setting for the BALB-neuT mouse model of mammary carcinoma and to establish immunological correlates with vaccine efficacy. To define an early therapeutic setting we performed imaging studies of mouse mammary glands with a high-frequency ultrasound system that allowed the diagnosis of tumor lesions before they become palpable, starting from week 13 after mouse births. An intensive immunization protocol of vaccination was implemented at this stage, consisting of four weekly DNA injections with electroporation followed by two injections of adenovirus carrying the codon usage-optimized cDNA encoding the extracellular-transmembrane domain of rat ErbB2. Immunological parameters were monitored in each individual mouse by analyzing peripheral blood leukocytes. The appearance of the first palpable tumor in vaccinated mice was delayed and there was a statistically significant time gap before additional masses developed, indicating disease stabilization. As a result of the immunization, antibodies and CD8(+) T cells to rat ErbB2 were detected and the amplitude of elicited responses correlated with the efficacy of vaccination. Moreover, the vaccination regimen specifically halted the rise in circulating myeloid suppressor cells (MSCs). All three parameters, that is, CD8(+) T cells, antibodies to rat ErbB2, and circulating MSCs, measured at the end of vaccination could be used as predictive biomarkers for future tumor development. This study emphasizes the potential of genetic vaccines for the therapeutic treatment of malignancies and suggests possible predictive biomarkers to be further validated in the clinic for the follow-up of vaccinated cancer patients.

The quantum mechanical ground state of electrons is described by Density Functional Theory, which leads to large minimization problems. An efficient minimization method uses a selfconsistent field (SCF) solution of large eigenvalue problems. The iterative Davidson algorithm is often used, and we propose a new algorithm of this kind which is well suited for the SCF method, since the accuracy of the eigensolution is gradually improved along with the outer SCF-iterations. Best efficiency is obtained for small-block-size iterations, and the algorithm is highly memory efficient. The implementation works well on both serial and parallel computers, and good scalability of the algorithm is obtained., Comment: 20 pages, 4 figures. Accepted for Applied Numerical Mathematics (Elsevier)

A parallel, "across the method" implementation of a stiff ODE solver is presented. The construction of the methods is outlined and the main implementational issues are discussed. Performance results, using MPI on the IBM SP-2, are presented and they indicate that a speed-up between 3 and 5 typically can be obtained compared to state-of-the-art sequential codes.

The construction of stiffly accurate and B-stable multiimplicit Runge-Kutta methods for parallel implementation is discussed. A fifth and a seventh order method is constructed and a promising numerical comparison with the efficient Radau5 code of E. Hairer and G. Wanner is conducted.

We describe an implementation of QR up- and downdating on a massively parallel computer (the Connection Machine CM-200) and show that the algorithm maps well onto the computer. In particular, we show how the use of corrected semi-normal equations for downdating can be efficiently implemented. We also illustrate the use of our algorithms in a new LP algorithm.

Background: Moderate concentrations of γ-secretase inhibitor increase Aβ production in different scenarios from cell lines to humans. Results: A mathematical model, including α-, β-, and γ-secretases, is proposed describing Aβ rise. Conclusion: The Aβ rise is decided by the interplay between the three secretases and not γ-secretase alone. Significance: This has important implications for the development of drugs targeting Aβ production in Alzheimer disease., Amyloid-β (Aβ) is produced by the consecutive cleavage of amyloid precursor protein (APP) first by β-secretase, generating C99, and then by γ-secretase. APP is also cleaved by α-secretase. It is hypothesized that reducing the production of Aβ in the brain may slow the progression of Alzheimer disease. Therefore, different γ-secretase inhibitors have been developed to reduce Aβ production. Paradoxically, it has been shown that low to moderate inhibitor concentrations cause a rise in Aβ production in different cell lines, in different animal models, and also in humans. A mechanistic understanding of the Aβ rise remains elusive. Here, a minimal mathematical model has been developed that quantitatively describes the Aβ dynamics in cell lines that exhibit the rise as well as in cell lines that do not. The model includes steps of APP processing through both the so-called amyloidogenic pathway and the so-called non-amyloidogenic pathway. It is shown that the cross-talk between these two pathways accounts for the increase in Aβ production in response to inhibitor, i.e. an increase in C99 will inhibit the non-amyloidogenic pathway, redirecting APP to be cleaved by β-secretase, leading to an additional increase in C99 that overcomes the loss in γ-secretase activity. With a minor extension, the model also describes plasma Aβ profiles observed in humans upon dosing with a γ-secretase inhibitor. In conclusion, this mechanistic model rationalizes a series of experimental results that spans from in vitro to in vivo and to humans. This has important implications for the development of drugs targeting Aβ production in Alzheimer disease.

Specific Aim To review the evidence indicating that volumetric image analysis of computed tomography scans meets specifications for qualification as a biomarker in clinical trials or the management of individual patients with lung cancer. Methods Claims of value were broken down into questions about technical feasibility, accuracy, the precision of measurement, sensitivity, the correlations with health outcomes, and the risks of producing misleading information. For each claim, the pertinent literature was reviewed. Results Technical feasibility has now been shown, but only in limited contexts. Accuracy has been demonstrated, but only for tumors with favorable anatomical features. Measurement error still makes the assessment of change in small nodules precarious in diagnostic settings unless rigorous image acquisition and analysis procedures are followed. Precision is sufficient in some larger masses to make volumetrics a sensitive biomarker. In a few trials, correlations with clinical outcomes have been higher for volumetric-based measures than for unidimensional or bidimensional diameters. Value in clinical practice settings and clinical trials has been suggested, but not proven. Conclusion The weight of the evidence indicates there are circumstances in which volumetric image analysis adds value to clinical trial science and the practice of medicine.

Insulin- and contraction-stimulated increases in glucose uptake into skeletal muscle occur in part as a result of the translocation of glucose transporter 4 (GLUT4) from intracellular stores to the plasma membrane (PM). This study aimed to use immunofluorescence microscopy in human skeletal muscle to quantify GLUT4 redistribution from intracellular stores to the PM in response to glucose feeding and exercise. Percutaneous muscle biopsy samples were taken from the m. vastus lateralis of ten insulin-sensitive men in the basal state and following 30 min of cycling exercise (65% VO2 max). Muscle biopsy samples were also taken from a second cohort of ten age-, BMI- and VO2 max-matched insulin-sensitive men in the basal state and 30 and 60 min following glucose feeding (75 g glucose). GLUT4 and dystrophin colocalization, measured using the Pearson's correlation coefficient, was increased following 30 min of cycling exercise (baseline r = 0.47 ± 0.01; post exercise r = 0.58 ± 0.02; P

The protection of our environment is one of the most important problems in the modern society. Reliable and robust control strategies for keeping the pollution caused by harmful chemical compounds under certain safe levels have to be developed and used in a routine way. Large mathematical models, in which all physical and chemical processes are adequately described, can successfully be used to solve this task. However, the use of large mathematical models in which all physical and chemical processes are adequately described leads, after the application of appropriate discretization and splitting procedures, to the treatment of huge computational tasks: in a typical simulation one has to perform several hundred runs, in each of these runs one has to carry out several thousand time-steps and at each time-step one has to solve numerically systems of ODE'S containing up to O(106) equations. Therefore, it is difficult to treat numerically such large mathematical models even when modern computers are available. Runs of an important module of a largescale air pollution model on parallel message passing machines will be discussed in this paper. Numerical results will be presented. Concluding remarks explaining how the performance could be further improved will be given.

This paper discusses the use of extrapolation methods for the parallel solution of differential algebraic equations. The DAEs investigated are implicit and have explicit constrains and the underlying methods used for the extrapolation are Runge-Kutta methods.

In the solution of stiff ODEs and especially DAEs it is desirable that the method used is stiffly accurate and B-stable. In this paper guidelines for the construction of Runge-Kutta methods with these properties are presented.