Alexandra Jauhiainen

Normalization of metabolomics data with applications to correlation maps

MOTIVATION In metabolomics, the goal is to identify and measure the concentrations of different metabolites (small molecules) in a cell or a biological system. The metabolites form an important layer in the complex metabolic network, and the interactions between different metabolites are often of interest. It is crucial to perform proper normalization of metabolomics data, but current methods may not be applicable when estimating interactions in the form of correlations between metabolites. We propose a normalization approach based on a mixed model, with simultaneous estimation of a correlation matrix. We also investigate how the common use of a calibration standard in nuclear magnetic resonance (NMR) experiments affects the estimation of correlations. RESULTS We show with both real and simulated data that our proposed normalization method is robust and has good performance when discovering true correlations between metabolites. The standardization of NMR data is shown in simulation studies to affect our ability to discover true correlations to a small extent. However, comparing standardized and non-standardized real data does not result in any large differences in correlation estimates. AVAILABILITY AND IMPLEMENTATION Source code is freely available at https://sourceforge.net/projects/metabnorm/ CONTACT alexandra.jauhiainen@ki.se SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Metabolomic changes during cellular transformation monitored by metabolite-metabolite correlation analysis and correlated with gene expression

To investigate metabolic changes during cellular transformation, we used a 1H NMR based metabolite–metabolite correlation analysis (MMCA) method, which permits analysis of homeostatic mechanisms in cells at the steady state, in an inducible cell transformation model. Transcriptomic data were used to further explain the results. Transformed cells showed many more metabolite–metabolite correlations than control cells. Some had intuitively plausible explanations: a shift from glycolysis to amino acid oxidation after transformation was accompanied by a strongly positive correlation between glucose and glutamine and a strongly negative one between lactate and glutamate; there were also many correlations between the branched chain amino acids and the aromatic amino acids. Others remain puzzling: after transformation strong positive correlations developed between choline and a group of five amino acids, whereas the same amino acids showed negative correlations with phosphocholine, a membrane phospholipid precursor. MMCA in conjunction with transcriptome analysis has opened a new window into the metabolome.

Dipeptidyl Peptidase 1 Inhibitor AZD7986 Induces a Sustained, Exposure‐Dependent Reduction in Neutrophil Elastase Activity in Healthy Subjects

Neutrophil serine proteases (NSPs), such as neutrophil elastase (NE), are activated by dipeptidyl peptidase 1 (DPP1) during neutrophil maturation. High NSP levels can be detrimental, particularly in lung tissue, and inhibition of NSPs is therefore an interesting therapeutic opportunity in multiple lung diseases, including chronic obstructive pulmonary disease (COPD) and bronchiectasis. We conducted a randomized, placebo‐controlled, first‐in‐human study to assess the safety, tolerability, pharmacokinetics, and pharmacodynamics of single and multiple oral doses of the DPP1 inhibitor AZD7986 in healthy subjects. Pharmacokinetic and pharmacodynamic data were analyzed using nonlinear mixed effects modeling and showed that AZD7986 inhibits whole blood NE activity in an exposure‐dependent, indirect manner—consistent with in vitro and preclinical predictions. Several dose‐dependent, possibly DPP1‐related, nonserious skin findings were observed, but these were not considered to prevent further clinical development. Overall, the study results provided confidence to progress AZD7986 to phase II and supported selection of a clinically relevant dose.

A novel endpoint for exacerbations in asthma to accelerate clinical development: A post-hoc analysis of randomised controlled trials

BACKGROUND Occurrence of severe asthma exacerbations are the cornerstone of the evaluation of asthma management, but severe asthma exacerbations are rare events. Therefore, trials that assess drug efficacy on exacerbations are done late in clinical development programmes. We aimed to establish an endpoint capturing clinically relevant deteriorations (diary events) that, when combined with severe exacerbations, create a composite outcome (CompEx). CompEx needs to strongly mirror results seen with the severe exacerbation-validated outcome, to allow the design of clinical trials of shorter duration and that include fewer patients than trials assessing severe exacerbations. METHODS Data from 12 asthma trials of 6 months or 12 months duration and, with standardised collection of exacerbations and diary card variables, were used to construct and test CompEx. The study populations had a mean age of 35-53 years, 59-69% were female, and had a mean FEV1 percentage of predicted normal of 63-84%. With data from five trials, we established a series of diary events based on peak expiratory flow (P), reliever use (R), symptoms (S), awakenings (A), and threshold values for change from baseline and slopes to assess trends. For the development phase, we evaluated different variable combinations and deterioration criteria to select the most robust algorithm to define a diary event for the composite outcome. We defined a composite outcome, CompEx, as first occurrence of a diary event or a severe exacerbation. We assessed the performance of CompEx in seven trials by comparing the event frequency, treatment effect (hazard ratio; HR), and the sample size needed for future trials for the CompEx versus episodes of severe exacerbations. FINDINGS CompEx (based on PRS) was the algorithm that best fulfilled our two-set criteria. When censored at 3 months, CompEx resulted in 2·8 times more events than severe exacerbations, and while preserving the treatment effect observed on severe exacerbations (CompEx over severe exacerbation average HR 1·01). The increased number of events, together with the sustained treatment effect, resulted in a large net gain in power, with a 67% mean reduction in the number of patients required in a drug trial for severe exacerbations. In six of seven comparisons tested, CompEx reduced the sample size needed by at least 50%. Validation of independent test populations confirmed the ability of CompEx to increase event frequencies, preserve treatment effect, and reduce the number of patients needed. INTERPRETATION CompEx is a composite outcome for evaluation of new asthma therapies. CompEx allows design of shorter trials that require fewer patients than studies of severe exacerbations, while preserving the ability to show a treatment effect compared with severe exacerbations. FUNDING AstraZeneca.

A natural history model for planning prostate cancer testing: calibration and validation using Swedish registry data

Recent prostate cancer screening trials have given conflicting results and it is unclear how to reduce prostate cancer mortality while minimising overdiagnosis and overtreatment. Prostate cancer testing is a partially observable process, and planning for testing requires either extrapolation from randomised controlled trials or, more flexibly, modelling of the cancer natural history. An existing US prostate cancer natural history model (Gulati et al, Biostatistics 2010;11:707-719) did not model for differences in survival between Gleason 6 and 7 cancers and predicted too few Gleason 7 cancers for contemporary Sweden. We re-implemented and re-calibrated the US model to Sweden. We extended the model to more finely describe the disease states, their time to biopsy-detectable cancer and prostate cancer survival. We first calibrated the model to the incidence rate ratio observed in the European Randomised Study of Screening for Prostate Cancer (ERSPC) together with age-specific cancer staging observed in the Stockholm PSA (prostate-specific antigen) and Biopsy Register; we then calibrated age-specific survival by disease states under contemporary testing and treatment using the Swedish National Prostate Cancer Register. After calibration, we were able to closely match observed prostate cancer incidence trends in Sweden. Assuming that patients detected at an earlier stage by screening receive a commensurate survival improvement, we find that the calibrated model replicates the observed mortality reduction in a simulation of ERSPC. Using the resulting model, we predicted incidence and mortality following the introduction of regular testing. Compared with a model of the current testing pattern, organised 8 yearly testing for men aged 55–69 years was predicted to reduce prostate cancer incidence by 0.11% with no increase in the mortality rate. The model is open source and suitable for planning for effective prostate cancer screening into the future. Author summary A naïve perspective is that cancer screening is simple: people are screened, some cancers are detected early, and cancer mortality rates decline. However, the mathematics for screening becomes difficult quickly, it is hard to infer causation from observational data, and even large randomised screening studies provide limited evidence. Simulations are therefore important for planning cancer screening. We found an older US prostate cancer natural history model to be poorly suited for contemporary Sweden. We therefore re-implemented and re-calibrated the US model using data from Swedish registries. Our revised model, the Stockholm “Prostata” model, provides predictions similar to those observed in the detailed Swedish registers on prostate cancer incidence and mortality. By modelling the mechanisms of the screening effect, we can predict the benefits and harms under a range of screening interventions.

Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models

Motivation: Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over‐parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis (GSA) in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as to give guidance on parameters that are essential in distinguishing different qualitative output behaviours. Results: We used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a GSA was performed on the predictions using the posterior distribution as the possible values for the parameters. This methodology was applied on a relatively large model relevant for synaptic plasticity, using experimental data from several sources. We could hereby point out those parameters that by themselves have the largest contribution to the uncertainty of the prediction as well as identify parameters important to separate between qualitatively different predictions. This approach is useful both for experimental design as well as model building. Availability and implementation: Source code is freely available at https://github.com/alexjau/uqsa. Supplementary information: Supplementary data are available at Bioinformatics online.

Exploration of human brain tumour metabolism using pairwise metabolite-metabolite correlation analysis (MMCA) of HR-MAS 1H NMR spectra

Methods We quantified 378 HRMAS 1H NMR spectra of human brain tumours (132 glioblastomas, 101 astrocytomas, 75 meningiomas, 37 oligodendrogliomas and 33 metastases) from the eTumour database and looked for metabolic interactions by metabolite-metabolite correlation analysis (MMCA). Results All tumour types showed remarkably similar metabolic correlations. Lactate correlated positively with alanine, glutamate with glutamine; creatine + phosphocreatine (tCr) correlated positively with lactate, alanine and choline + phosphocholine + glycerophosphocholine (tCho), and tCho correlated positively with lactate; fatty acids correlated negatively with lactate, glutamate + glutamine (tGlut), tCr and tCho. Oligodendrogliomas had fewer correlations but they still fitted that pattern. Conclusions Possible explanations include (i) glycolytic tumour cells (the Warburg effect) generating pyruvate which is converted to lactate, alanine, glutamate and then glutamine; (ii) an association between elevated glycolysis and increased choline turnover in membranes; (iii) an increase in the tCr pool to facilitate phosphocreatine-driven glutamate uptake; (iv) lipid signals come from cytosolic lipid droplets in necrotic or pre-necrotic tumour tissue that has lower concentrations of anabolic and catabolic metabolites. Additional metabolite exchanges with host cells may also be involved. If tumours co-opt a standard set of biochemical mechanisms to grow in the brain, then drugs might be developed to disrupt those mechanisms.

E-Science technologies in a workflow for personalized medicine using cancer screening as a case study

Abstract Objective:We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings. Materials and Methods:We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences. Results:The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform. Discussion and Conclusion:E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.