Peripheral blood immune cell profiling of acute corneal transplant rejection.

Acute rejection (AR) of corneal transplants (CT) has a profound effect on subsequent graft survival but detailed immunological studies in human CT recipients are lacking. In this multi-site, cross-sectional study, clinical details and blood samples were collected from adults with clinically diagnosed AR of full-thickness (FT)-CT (n = 35) and posterior lamellar (PL)-CT (n = 21) along with Stable CT recipients (n = 177) and adults with non-transplanted corneal disease (n = 40). For those with AR, additional samples were collected 3 months later. Immune cell analysis was performed by whole-genome microarrays (whole blood) and high-dimensional multi-color flow cytometry (peripheral blood mononuclear cells). For both, no activation signature was identified within the B cell and T cell repertoire at the time of AR diagnosis. Nonetheless, in FT- but not PL-CT recipients, AR was associated with differences in B cell maturity and regulatory CD4+ T cell frequency compared to stable allografts. These data suggest that circulating B cell and T cell subpopulations may provide insights into the regulation of anti-donor immune response in human CT recipients with differing AR risk. Our results suggest that, in contrast to solid organ transplants, genetic or cellular assays of peripheral blood are unlikely to be clinically exploitable for prediction or diagnosis of AR.

Tumor Fusion Burden as a Hallmark of Immune Infiltration in Prostate Cancer.

Prostate cancer is the second leading cause of cancer-related death in men. Despite having a relatively lower tumor mutational burden than most tumor types, multiple gene fusions such as TMPRSS2:ERG have been characterized and linked to more aggressive disease. Individual tumor samples have been found to contain multiple fusions, and it remains unknown whether these fusions increase tumor immunogenicity. Here, we investigated the role of fusion burden on the prevalence and expression of key molecular and immune effectors in prostate cancer tissue specimens that represented the different stages of disease progression and androgen sensitivity, including hormone-sensitive and castration-resistant prostate cancer. We found that tumor fusion burden was inversely correlated with tumor mutational burden and not associated with disease stage. High fusion burden correlated with high immune infiltration, PD-L1 expression on immune cells, and immune signatures, representing activation of T cells and M1 macrophages. High fusion burden inversely correlated with immune-suppressive signatures. Our findings suggest that high tumor fusion burden may be a more appropriate biomarker than tumor mutational burden in prostate cancer, as it more closely associates with immunogenicity, and suggests that tumors with high fusion burden could be potential candidates for immunotherapeutic agents.

Application of pharmacogenomics and bioinformatics to exemplify the utility of human ex vivo organoculture models in the field of precision medicine.

Here we describe a collaboration between industry, the National Health Service (NHS) and academia that sought to demonstrate how early understanding of both pharmacology and genomics can improve strategies for the development of precision medicines. Diseased tissue ethically acquired from patients suffering from chronic obstructive pulmonary disease (COPD), was used to investigate inter-patient variability in drug efficacy using ex vivo organocultures of fresh lung tissue as the test system. The reduction in inflammatory cytokines in the presence of various test drugs was used as the measure of drug efficacy and the individual patient responses were then matched against genotype and microRNA profiles in an attempt to identify unique predictors of drug responsiveness. Our findings suggest that genetic variation in CYP2E1 and SMAD3 genes may partly explain the observed variation in drug response.

Microbe-host interplay in atopic dermatitis and psoriasis.

Despite recent advances in understanding microbial diversity in skin homeostasis, the relevance of microbial dysbiosis in inflammatory disease is poorly understood. Here we perform a comparative analysis of skin microbial communities coupled to global patterns of cutaneous gene expression in patients with atopic dermatitis or psoriasis. The skin microbiota is analysed by 16S amplicon or whole genome sequencing and the skin transcriptome by microarrays, followed by integration of the data layers. We find that atopic dermatitis and psoriasis can be classified by distinct microbes, which differ from healthy volunteers microbiome composition. Atopic dermatitis is dominated by a single microbe (Staphylococcus aureus), and associated with a disease relevant host transcriptomic signature enriched for skin barrier function, tryptophan metabolism and immune activation. In contrast, psoriasis is characterized by co-occurring communities of microbes with weak associations with disease related gene expression. Our work provides a basis for biomarker discovery and targeted therapies in skin dysbiosis.

Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors.

INTRODUCTION: Accurate prognostication is difficult in malignant pleural mesothelioma (MPM). We developed a set of robust computational models to quantify the prognostic value of routinely available clinical data, which form the basis of published MPM prognostic models. METHODS: Data regarding 269 patients with MPM were allocated to balanced training (n=169) and validation sets (n=100). Prognostic signatures (minimal length best performing multivariate trained models) were generated by least absolute shrinkage and selection operator regression for overall survival (OS), OS <6 months and OS <12 months. OS prediction was quantified using Somers DXY statistic, which varies from 0 to 1, with increasing concordance between observed and predicted outcomes. 6-month survival and 12-month survival were described by area under the curve (AUC) scores. RESULTS: Median OS was 270 (IQR 140-450) days. The primary OS model assigned high weights to four predictors: age, performance status, white cell count and serum albumin, and after cross-validation performed significantly better than would be expected by chance (mean DXY0.332 (±0.019)). However, validation set DXY was only 0.221 (0.0935-0.346), equating to a 22% improvement in survival prediction than would be expected by chance. The 6-month and 12-month OS signatures included the same four predictors, in addition to epithelioid histology plus platelets and epithelioid histology plus C-reactive protein (mean AUC 0.758 (±0.022) and 0.737 (±0.012), respectively). The <6-month OS model demonstrated 74% sensitivity and 68% specificity. The <12-month OS model demonstrated 63% sensitivity and 79% specificity. Model content and performance were generally comparable with previous studies. CONCLUSIONS: The prognostic value of the basic clinical information contained in these, and previously published models, is fundamentally of limited value in accurately predicting MPM prognosis. The methods described are suitable for expansion using emerging predictors, including tumour genomics and volumetric staging.

Characterization of the human T cell response to in vitro CD27 costimulation with varlilumab.

BACKGROUND: Clinical targeting of TNFR family of receptors (CD40, CD134 and CD137) with immunostimulatory monoclonal antibodies has been successful in cancer immunotherapy. However, targeting of CD27 with a mAb is a relatively new approach to provide costimulation of immune cells undergoing activation. Thus, activation of human CD27 (TNFRSF7) with a monoclonal antibody (varlilumab) has previously been demonstrated to result in T cell activation and anti-tumor activity in preclinical models, and is currently in early phase clinical trials in patients with advanced malignancies. In this study we used an in vitro system using human peripheral blood T cells to characterize the varlilumab-mediated costimulatory effects in combination with TCR stimulation in terms of phenotypic, transcriptional and functionality changes. METHODS: T cells were isolated from normal volunteer PBMCs using magnetic bead isolation kits and stimulated in vitro with plate bound anti-CD3 Ab (OKT3) and varlilumab or control Ab for 72 h. Activation profiles were monitored by ELISA or Luminex-based testing cytokine/chemokine releases, cell surface phenotyping for costimulatory and coinhibitory markers and CFSE dye dilution by proliferating T cells and Tregs. Changes in gene expression and transcriptome analysis of varlilumab-stimulated T cells was carried on Agilent Human whole genome microarray datasets using a suite of statistical and bioinformatic software tools. RESULTS: Costimulation of T cells with varlilumab required continuous TCR signaling as pre-activated T cells were unable to produce cytokines with CD27 signaling alone. Analysis of T cell subsets further revealed that memory CD4+ and CD8+ T cells were specifically activated with a bias toward CD8+ T lymphocyte proliferation. Activation was accompanied by upregulated cell surface expression of costimulatory [4-1BB, OX40, GITR and ICOS] and coinhibitory [PD-1] molecules. Importantly, varlilumab costimulation did not activate purified Tregs as measured by cytokine production, proliferation and suppression of dividing non-Treg T cells. Analysis of changes in gene expression during varlilumab stimulation of T cells revealed modulation of pro-inflammatory signatures consistent with cellular activation and proliferation, with the IL-2 pathway showing the highest frequency of gene modulation. CONCLUSIONS: Altogether, the data reveal the requirements and T cell subtype-specific effects of CD27 costimulation, and helps select relevant biomarkers for studying the effects of varlilumab in patients.

Extracting meaningful information from metabonomic data using multivariate statistics.

Metabonomics aims to identify and quantify all small-molecule metabolites in biologically relevant samples using high-throughput techniques such as NMR and chromatography/mass spectrometry. This generates high-dimensional data sets with properties that require specialized approaches to data analysis. This chapter describes multivariate statistics and analysis tools to extract meaningful information from metabonomic data sets. The focus is on the use and interpretation of latent variable methods such as principal component analysis (PCA), partial least squares/projections to latent structures (PLS), and orthogonal PLS (OPLS). Descriptions of the key steps of the multivariate data analyses are provided with demonstrations from example data.

Identification and validation of an anthracycline/cyclophosphamide-based chemotherapy response assay in breast cancer.

BACKGROUND: There is no method routinely used to predict response to anthracycline and cyclophosphamide-based chemotherapy in the clinic; therefore patients often receive treatment for breast cancer with no benefit. Loss of the Fanconi anemia/BRCA (FA/BRCA) DNA damage response (DDR) pathway occurs in approximately 25% of breast cancer patients through several mechanisms and results in sensitization to DNA-damaging agents. The aim of this study was to develop an assay to detect DDR-deficient tumors associated with loss of the FA/BRCA pathway, for the purpose of treatment selection. METHODS: DNA microarray data from 21 FA patients and 11 control subjects were analyzed to identify genetic processes associated with a deficiency in DDR. Unsupervised hierarchical clustering was then performed using 60 BRCA1/2 mutant and 47 sporadic tumor samples, and a molecular subgroup was identified that was defined by the molecular processes represented within FA patients. A 44-gene microarray-based assay (the DDR deficiency assay) was developed to prospectively identify this subgroup from formalin-fixed, paraffin-embedded samples. All statistical tests were two-sided. RESULTS: In a publicly available independent cohort of 203 patients, the assay predicted complete pathologic response vs residual disease after neoadjuvant DNA-damaging chemotherapy (5-fluorouracil, anthracycline, and cyclophosphamide) with an odds ratio of 3.96 (95% confidence interval [Cl] =1.67 to 9.41; P = .002). In a new independent cohort of 191 breast cancer patients treated with adjuvant 5-fluorouracil, epirubicin, and cyclophosphamide, a positive assay result predicted 5-year relapse-free survival with a hazard ratio of 0.37 (95% Cl = 0.15 to 0.88; P = .03) compared with the assay negative population. CONCLUSIONS: A formalin-fixed, paraffin-embedded tissue-based assay has been developed and independently validated as a predictor of response and prognosis after anthracycline/cyclophosphamide-based chemotherapy in the neoadjuvant and adjuvant settings. These findings warrant further validation in a prospective clinical study.

Development and independent validation of a prognostic assay for stage II colon cancer using formalin-fixed paraffin-embedded tissue.

PURPOSE: Current prognostic factors are poor at identifying patients at risk of disease recurrence after surgery for stage II colon cancer. Here we describe a DNA microarray-based prognostic assay using clinically relevant formalin-fixed paraffin-embedded (FFPE) samples. PATIENTS AND METHODS: A gene signature was developed from a balanced set of 73 patients with recurrent disease (high risk) and 142 patients with no recurrence (low risk) within 5 years of surgery. RESULTS: The 634-probe set signature identified high-risk patients with a hazard ratio (HR) of 2.62 (P < .001) during cross validation of the training set. In an independent validation set of 144 samples, the signature identified high-risk patients with an HR of 2.53 (P < .001) for recurrence and an HR of 2.21 (P = .0084) for cancer-related death. Additionally, the signature was shown to perform independently from known prognostic factors (P < .001). CONCLUSION: This gene signature represents a novel prognostic biomarker for patients with stage II colon cancer that can be applied to FFPE tumor samples.

Non-linear modeling of 1H NMR metabonomic data using kernel-based orthogonal projections to latent structures optimized by simulated annealing.

Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and a study of Trypanosoma brucei brucei infection. Automated and user-friendly procedures for the kernel-optimization have been incorporated into version 1.1.1 of the freely available K-OPLS software package for both R and Matlab to enable easy application of K-OPLS for non-linear prediction modeling.

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Changes in diurnal patterns within the Populus transcriptome and metabolome in response to photoperiod variation.

Changes in seasonal photoperiod provides an important environmental signal that affects the timing of winter dormancy in perennial, deciduous, temperate tree species, such as hybrid aspen (Populus tremula x Populus tremuloides). In this species, growth cessation, cold acclimation and dormancy are induced in the autumn by the detection of day-length shortening that occurs at a given critical day length. Important components in the detection of such day-length changes are photoreceptors and the circadian clock, and many plant responses at both the gene regulation and metabolite levels are expected to be diurnal. To directly examine this expectation and study components in these events, here we report transcriptomic and metabolomic responses to a change in photoperiod from long to short days in hybrid aspen. We found about 16% of genes represented on the arrays to be diurnally regulated, as assessed by our pre-defined criteria. Furthermore, several of these genes were involved in circadian-associated processes, including photosynthesis and primary and secondary metabolism. Metabolites affected by the change in photoperiod were mostly involved in carbon metabolism. Taken together, we have thus established a molecular catalog of events that precede a response to winter.

Integrated analysis of transcript, protein and metabolite data to study lignin biosynthesis in hybrid aspen.

Tree biotechnology will soon reach a mature state where it will influence the overall supply of fiber, energy and wood products. We are now ready to make the transition from identifying candidate genes, controlling important biological processes, to discovering the detailed molecular function of these genes on a broader, more holistic, systems biology level. In this paper, a strategy is outlined for informative data generation and integrated modeling of systematic changes in transcript, protein and metabolite profiles measured from hybrid aspen samples. The aim is to study characteristics of common changes in relation to genotype-specific perturbations affecting the lignin biosynthesis and growth. We show that a considerable part of the systematic effects in the system can be tracked across all platforms and that the approach has a high potential value in functional characterization of candidate genes.

A cross-species transcriptomics approach to identify genes involved in leaf development.

BACKGROUND: We have made use of publicly available gene expression data to identify transcription factors and transcriptional modules (regulons) associated with leaf development in Populus. Different tissue types were compared to identify genes informative in the discrimination of leaf and non-leaf tissues. Transcriptional modules within this set of genes were identified in a much wider set of microarray data collected from leaves in a number of developmental, biotic, abiotic and transgenic experiments. RESULTS: Transcription factors that were over represented in leaf EST libraries and that were useful for discriminating leaves from other tissues were identified, revealing that the C2C2-YABBY, CCAAT-HAP3 and 5, MYB, and ZF-HD families are particularly important in leaves. The expression of transcriptional modules and transcription factors was examined across a number of experiments to select those that were particularly active during the early stages of leaf development. Two transcription factors were found to collocate to previously published Quantitative Trait Loci (QTL) for leaf length. We also found that miRNA family 396 may be important in the control of leaf development, with three members of the family collocating with clusters of leaf development QTL. CONCLUSION: This work provides a set of candidate genes involved in the control and processes of leaf development. This resource can be used for a wide variety of purposes such as informing the selection of candidate genes for association mapping or for the selection of targets for reverse genetics studies to further understanding of the genetic control of leaf size and shape.

LAMINA: a tool for rapid quantification of leaf size and shape parameters.

BACKGROUND: An increased understanding of leaf area development is important in a number of fields: in food and non-food crops, for example short rotation forestry as a biofuels feedstock, leaf area is intricately linked to biomass productivity; in paleontology leaf shape characteristics are used to reconstruct paleoclimate history. Such fields require measurement of large collections of leaves, with resulting conclusions being highly influenced by the accuracy of the phenotypic measurement process. RESULTS: We have developed LAMINA (Leaf shApe deterMINAtion), a new tool for the automated analysis of images of leaves. LAMINA has been designed to provide classical indicators of leaf shape (blade dimensions) and size (area), which are typically required for correlation analysis to biomass productivity, as well as measures that indicate asymmetry in leaf shape, leaf serration traits, and measures of herbivory damage (missing leaf area). In order to allow Principal Component Analysis (PCA) to be performed, the location of a chosen number of equally spaced boundary coordinates can optionally be returned. CONCLUSION: We demonstrate the use of the software on a set of 500 scanned images, each containing multiple leaves, collected from a common garden experiment containing 116 clones of Populus tremula (European trembling aspen) that are being used for association mapping, as well as examples of leaves from other species. We show that the software provides an efficient and accurate means of analysing leaf area in large datasets in an automated or semi-automated work flow.

Global expression profiling in leaves of free-growing aspen.

BACKGROUND: Genomic studies are routinely performed on young plants in controlled environments which is very different from natural conditions. In reality plants in temperate countries are exposed to large fluctuations in environmental conditions, in the case of perennials over several years. We have studied gene expression in leaves of a free-growing aspen (Populus tremula) throughout multiple growing seasons RESULTS: We show that gene expression during the first month of leaf development was largely determined by a developmental program although leaf expansion, chlorophyll accumulation and the speed of progression through this program was regulated by the temperature. We were also able to define "transcriptional signatures" for four different substages of leaf development. In mature leaves, weather factors were important for gene regulation. CONCLUSION: This study shows that multivariate methods together with high throughput transcriptional methods in the field can provide additional, novel information as to plant status under changing environmental conditions that is impossible to mimic in laboratory conditions. We have generated a dataset that could be used to e.g. identify marker genes for certain developmental stages or treatments, as well as to assess natural variation in gene expression.

K-OPLS package: kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space.

BACKGROUND: Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation. RESULTS: We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at http://www.sourceforge.net/projects/kopls/. The package includes essential functionality and documentation for model evaluation (using cross-validation), training and prediction of future samples. Incorporated is also a set of diagnostic tools and plot functions to simplify the visualisation of data, e.g. for detecting trends or for identification of outlying samples. The utility of the software package is demonstrated by means of a metabolic profiling data set from a biological study of hybrid aspen. CONCLUSION: The properties of the K-OPLS method are well suited for analysis of biological data, which in conjunction with the availability of the outlined open-source package provides a comprehensive solution for kernel-based analysis in bioinformatics applications.

Data integration in plant biology: the O2PLS method for combined modeling of transcript and metabolite data.

The technological advances in the instrumentation employed in life sciences have enabled the collection of a virtually unlimited quantity of data from multiple sources. By gathering data from several analytical platforms, with the aim of parallel monitoring of, e.g. transcriptomic, metabolomic or proteomic events, one hopes to answer and understand biological questions and observations. This 'systems biology' approach typically involves advanced statistics to facilitate the interpretation of the data. In the present study, we demonstrate that the O2PLS multivariate regression method can be used for combining 'omics' types of data. With this methodology, systematic variation that overlaps across analytical platforms can be separated from platform-specific systematic variation. A study of Populus tremula x Populus tremuloides, investigating short-day-induced effects at transcript and metabolite levels, is employed to demonstrate the benefits of the methodology. We show how the models can be validated and interpreted to identify biologically relevant events, and discuss the results in relation to a pairwise univariate correlation approach and principal component analysis.

Orthogonal projections to latent structures as a strategy for microarray data normalization.

BACKGROUND: During generation of microarray data, various forms of systematic biases are frequently introduced which limits accuracy and precision of the results. In order to properly estimate biological effects, these biases must be identified and discarded. RESULTS: We introduce a normalization strategy for multi-channel microarray data based on orthogonal projections to latent structures (OPLS); a multivariate regression method. The effect of applying the normalization methodology on single-channel Affymetrix data as well as dual-channel cDNA data is illustrated. We provide a parallel comparison to a wide range of commonly employed normalization methods with diverse properties and strengths based on sensitivity and specificity from external (spike-in) controls. On the illustrated data sets, the OPLS normalization strategy exhibits leading average true negative and true positive rates in comparison to other evaluated methods. CONCLUSION: The OPLS methodology identifies joint variation within biological samples to enable the removal of sources of variation that are non-correlated (orthogonal) to the within-sample variation. This ensures that structured variation related to the underlying biological samples is separated from the remaining, bias-related sources of systematic variation. As a consequence, the methodology does not require any explicit knowledge regarding the presence or characteristics of certain biases. Furthermore, there is no underlying assumption that the majority of elements should be non-differentially expressed, making it applicable to specialized boutique arrays.

A multivariate approach to investigate docking parameters' effects on docking performance.

Increasingly powerful docking programs for analyzing and estimating the strength of protein-ligand interactions have been developed in recent decades, and they are now valuable tools in drug discovery. Software used to perform dockings relies on a number of parameters that affect various steps in the docking procedure. However, identifying the best choices of the settings for these parameters is often challenging. Therefore, the settings of the parameters are quite often left at their default values, even though scientists with long experience with a specific docking tool know that modifying certain parameters can improve the results. In the study presented here, we have used statistical experimental design and subsequent regression based on root-mean-square deviation values using partial least-square projections to latent structures (PLS) to scrutinize the effects of different parameters on the docking performance of two software packages: FRED and GOLD. Protein-ligand complexes with a high level of ligand diversity were selected from the PDBbind database for the study, using principal component analysis based on 1D and 2D descriptors, and space-filling design. The PLS models showed quantitative relationships between the docking parameters and the ability of the programs to reproduce the ligand crystallographic conformation. The PLS models also revealed which of the parameters and what parameter settings were important for the docking performance of the two programs. Furthermore, the variation in docking results obtained with specific parameter settings for different protein-ligand complexes in the diverse set examined indicates that there is great potential for optimizing the parameter settings for selected sets of proteins.