Patient-derived cells (PDC) mouse xenografts are increasingly important tools in glioblastoma (GBM) research, essential to investigate case-specific growth patterns and treatment responses. Despite the central role of xenograft models in the field, few good simulation models are available to probe the dynamics of tumor growth and to support therapy design. We therefore propose a new framework for the patient-specific simulation of GBM in the mouse brain. Unlike existing methods, our simulations leverage a high-resolution map of the mouse brain anatomy to yield patient-specific results that are in good agreement with experimental observations. To facilitate the fitting of our model to histological data, we use Approximate Bayesian Computation. Because our model uses few parameters, reflecting growth, invasion and niche dependencies, it is well suited for case comparisons and for probing treatment effects. We demonstrate how our model can be used to simulate different treatment by perturbing the different model parameters. We expect in silico replicates of mouse xenograft tumors can improve the assessment of therapeutic outcomes and boost the statistical power of preclinical GBM studies.

MOTIVATION: Inferring an accurate gene regulatory network (GRN) has long been a key goal in the field of systems biology. To do this, it is important to find a suitable balance between the maximum number of true positive and the minimum number of false-positive interactions. Another key feature is that the inference method can handle the large size of modern experimental data, meaning the method needs to be both fast and accurate. The Least Squares Cut-Off (LSCO) method can fulfill both these criteria, however as it is based on least squares it is vulnerable to known issues of amplifying extreme values, small or large. In GRN this manifests itself with genes that are erroneously hyper-connected to a large fraction of all genes due to extremely low value fold changes. RESULTS: We developed a GRN inference method called Least Squares Cut-Off with Normalization (LSCON) that tackles this problem. LSCON extends the LSCO algorithm by regularization to avoid hyper-connected genes and thereby reduce false positives. The regularization used is based on normalization, which removes effects of extreme values on the fit. We benchmarked LSCON and compared it to Genie3, LASSO, LSCO and Ridge regression, in terms of accuracy, speed and tendency to predict hyper-connected genes. The results show that LSCON achieves better or equal accuracy compared to LASSO, the best existing method, especially for data with extreme values. Thanks to the speed of least squares regression, LSCON does this an order of magnitude faster than LASSO. AVAILABILITY AND IMPLEMENTATION: Data: https://bitbucket.org/sonnhammergrni/lscon; Code: https://bitbucket.org/sonnhammergrni/genespider. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In many human cancers, the rate of cell growth depends crucially on the size of the tumor cell population. Low, zero, or negative growth at low population densities is known as the Allee effect; this effect has been studied extensively in ecology, but so far lacks a good explanation in the cancer setting. Here, we formulate and analyze an individual-based model of cancer, in which cell division rates are increased by the local concentration of an autocrine growth factor produced by the cancer cells themselves. We show, analytically and by simulation, that autocrine signaling suffices to cause both strong and weak Allee effects. Whether low cell densities lead to negative (strong effect) or reduced (weak effect) growth rate depends directly on the ratio of cell death to proliferation, and indirectly on cellular dispersal. Our model is consistent with experimental observations from three patient-derived brain tumor cell lines grown at different densities. We propose that further studying and quantifying population-wide feedback, impacting cell growth, will be central for advancing our understanding of cancer dynamics and treatment, potentially exploiting Allee effects for therapy.

MOTIVATION: Accurate inference of gene regulatory interactions is of importance for understanding the mechanisms of underlying biological processes. For gene expression data gathered from targeted perturbations, gene regulatory network (GRN) inference methods that use the perturbation design are the top performing methods. However, the connection between the perturbation design and gene expression can be obfuscated due to problems, such as experimental noise or off-target effects, limiting the methods' ability to reconstruct the true GRN. RESULTS: In this study, we propose an algorithm, IDEMAX, to infer the effective perturbation design from gene expression data in order to eliminate the potential risk of fitting a disconnected perturbation design to gene expression. We applied IDEMAX to synthetic data from two different data generation tools, GeneNetWeaver and GeneSPIDER, and assessed its effect on the experiment design matrix as well as the accuracy of the GRN inference, followed by application to a real dataset. The results show that our approach consistently improves the accuracy of GRN inference compared to using the intended perturbation design when much of the signal is hidden by noise, which is often the case for real data. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/sonnhammergrni/idemax. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential. RESULTS: We have integrated 23 transcription datasets, spanning 1350 MB and 291 normal brain samples. To remove batch effects, we combined the Removal of Unwanted Variation (RUV) method with a novel pipeline for determining empirical negative control genes and a panel of metrics to evaluate normalization performance. The documented approach enabled the removal of a majority of batch effects, producing a large-scale, integrative dataset of MB and cerebellar expression data. The proposed strategy will be broadly applicable for accurate integration of data and incorporation of normal reference samples for studies of various diseases. We hope that the integrated dataset will improve current research in the field of MB by allowing more large-scale gene expression analyses. AVAILABILITY AND IMPLEMENTATION: The RUV-normalized expression data is available through the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) and can be accessed via the GSE series number GSE124814. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The correct diagnosis of cancer patients conventionally depends on the pathologist's experience and ability to distinguish cancer tissue from normal tissue under a microscope. Advances in technology for measuring the abundance of, e.g., proteins and mRNAs in tissue samples make it interesting to search for an optimal subset of these for classification of samples as cancer or normal. We discuss issues of identification of biomarkers that provide distinct signatures for prediction of tissues as cancer or normal, exemplified by our recent study of cancer signalling signatures in human colon cancer characterised with regards to protein abundance using high sensitivity isoelectric focusing. We show that the optimal subset for separation of cancer tissues from normal tissues does not contain any of the proteins in the top quintile in terms of significant difference between the groups according to Mann-Whitney U-test or correlation to the diagnosis. Actually, one of the proteins belongs to the tertile with the lowest significance and correlation. This highlights the weakness of the practice of only looking for significant differences in the abundance of individual proteins and raises the question of how many lifesaving discoveries that have been missed due to it. We also demonstrate how Monte Carlo simulations of the separation with random class assignment can be used to calculate p-values for observing any specific separation by chance and selection of the optimal number of proteins in the subset based on these p-values. Both selection of the optimal number of biomarkers and calculation of p-values corrected for multiple hypothesis testing are essential to obtain a subset of biomarkers that yield robust predictions for clinical use.

MOTIVATION: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences. RESULTS: This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data.

The brain tumour glioblastoma is characterised by diffuse and infiltrative growth into surrounding brain tissue. At the macroscopic level, the progression speed of a glioblastoma tumour is determined by two key factors: the cell proliferation rate and the cell migration speed. At the microscopic level, however, proliferation and migration appear to be mutually exclusive phenotypes, as indicated by recent in vivo imaging data. Here, we develop a mathematical model to analyse how the phenotypic switching between proliferative and migratory states of individual cells affects the macroscopic growth of the tumour. For this, we propose an individual-based stochastic model in which glioblastoma cells are either in a proliferative state, where they are stationary and divide, or in motile state in which they are subject to random motion. From the model we derive a continuum approximation in the form of two coupled reaction-diffusion equations, which exhibit travelling wave solutions whose speed of invasion depends on the model parameters. We propose a simple analytical method to predict progression rate from the cell-specific parameters and demonstrate that optimal glioblastoma growth depends on a non-trivial trade-off between the phenotypic switching rates. By linking cellular properties to an in vivo outcome, the model should be applicable to designing relevant cell screens for glioblastoma and cytometry-based patient prognostics.