Juha Kesseli

Stability of functions in Boolean models of gene regulatory networks

Boolean networks are used to model large nonlinear systems such as gene regulatory networks. We will present results that can be used to understand how the choice of functions affects the network dynamics. The so called bias-map and its fixed points depict much of the functions dynamical role in the network. We define the concept of stabilizing functions and show that many Post and canalizing functions are also stabilizing functions. Boolean networks constructed using the same type of stabilizing functions are always stable regardless of the average in-degree of network functions. We derive the number of all stabilizing functions and find it to be much larger than the number of Post and canalizing functions. We also discuss the implementation of functions and apply the presented results to biological data that give an approximation of the distribution of regulatory functions in eucaryotic cells. We find that the obtained theoretical results on the number of active genes are biologically plausible. Finally, based on the presented results, we discuss why canalizing and Post regulatory functions seem to be common in cells.

Estimation and inversion of the effects of cell population asynchrony in gene expression time-series

We introduce several approaches to improve the quality of gene expression data obtained from time-series measurements by applying signal processing tools. Performance of the proposed methods are examined using both simulated and real yeast gene expression data. In particular, we concentrate especially on a smoothing effect caused by the distribution of the cell population in time and introduce several methods for inverting this phenomenon. The proposed methods can be used to significantly improve the accuracy of the gene expression time-series measurements since the cell population asynchrony (wide distribution) is inevitably caused by the different operation pace of the cells. Some of the proposed methods rely on the partition of the genes, as well as the corresponding expression profiles, into the cell cycle regulated and noncell cycle regulated genes. For that purpose, we first study the cell cycle regulated genes and introduce a method that can be used to estimate the period length of those genes. We also estimate the spreading rate of the underlying distribution of the cell population based solely on the observed gene expression data. After the preliminary experiments, we introduce some methods for estimating the underlying distribution of the cell population instead of its spreading rate. These methods assume certain additional measurements, such as flow cytometry (e.g. fluorescent-activated cell sorter (FACS)) or bud counting measurements, to be available. We also apply the standard blind deconvolution method for estimating the true distribution of the cell population. The found estimates of the spreading rate of the cell distribution and the distributions of the cell population themself are used to invert the smoothing effect. To that end, we discuss some inversion approaches applicable to the problem in hand.

Information diversity in structure and dynamics of simulated neuronal networks.

Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.

Strong FGFR3 staining is a marker for FGFR3 fusions in diffuse gliomas

Abstract Background Inhibitors of fibroblast growth factor receptors (FGFRs) have recently arisen as a promising treatment option for patients with FGFR alterations. Gene fusions involving FGFR3 and transforming acidic coiled-coil protein 3 (TACC3) have been detected in diffuse gliomas and other malignancies, and fusion-positive cases have responded well to FGFR inhibition. As high FGFR3 expression has been detected in fusion-positive tumors, we sought to determine the clinical significance of FGFR3 protein expression level as well as its potential for indicating FGFR3 fusions. Methods We performed FGFR3 immunohistochemistry on tissue microarrays containing 676 grades II–IV astrocytomas and 116 grades II–III oligodendroglial tumor specimens. Fifty-one cases were further analyzed using targeted sequencing. Results Moderate to strong FGFR3 staining was detected in gliomas of all grades, was more common in females, and was associated with poor survival in diffuse astrocytomas. Targeted sequencing identified FGFR3-TACC3 fusions and an FGFR3-CAMK2A fusion in 10 of 15 strongly stained cases, whereas no fusions were found in 36 negatively to moderately stained cases. Fusion-positive cases were predominantly female and negative for IDH and EGFR/PDGFRA/MET alterations. These and moderately stained cases show lower MIB-1 proliferation index than negatively to weakly stained cases. Furthermore, stronger FGFR3 expression was commonly observed in malignant tissue regions of lower cellularity in fusion-negative cases. Importantly, subregional negative FGFR3 staining was also observed in a few fusion-positive cases. Conclusions Strong FGFR3 protein expression is indicative of FGFR3 fusions and may serve as a clinically applicable predictive marker for treatment regimens based on FGFR inhibitors.

Information-Theoretic Analysis of the Dynamics of an Executable Biological Model

To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.

Inference of Boolean models of genetic networks using monotonic time transformations

This paper considers the problem of inferring a Boolean network (BN) from gene expression data that is available as a frequently sampled time-series. The particular problems that arise are discussed and monotonic time transformations (MTT) are presented as a possible solution. Several different methods of clustering are used to form different transformations. The results with data generated by a simulation model show that the method presented can improve the inference performance in the described cases. The real-world measurements currently available are not yet suitable for testing the method because of the low sampling rates and the amount of noise present.

Balance between Noise and Information Flow Maximizes Set Complexity of Network Dynamics

Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity, that prior to reaching an attractor, random Boolean networks pass through a transient state characterized by high complexity. We justify this finding with a use of another measure of complexity, namely, the statistical complexity. We show that the networks can be tuned to the regime of maximal complexity by adding a suitable amount of noise to the deterministic Boolean dynamics. In fact, we show that for networks with Poisson degree distributions, all networks ranging from subcritical to slightly supercritical can be tuned with noise to reach maximal set complexity in their dynamics. For networks with a fixed number of inputs this is true for near-to-critical networks. This increase in complexity is obtained at the expense of disruption in information flow. For a large ensemble of networks showing maximal complexity, there exists a balance between noise and contracting dynamics in the state space. In networks that are close to critical the intrinsic noise required for the tuning is smaller and thus also has the smallest effect in terms of the information processing in the system. Our results suggest that the maximization of complexity near to the state transition might be a more general phenomenon in physical systems, and that noise present in a system may in fact be useful in retaining the system in a state with high information content.

Feasibility of prostate PAXgene fixation for molecular research and diagnostic surgical pathology : comparison of matched fresh frozen, FFPE, and PFPE tissues

Advances in prostate cancer biology and diagnostics are dependent upon high-fidelity integration of clinical, histomorphologic, and molecular phenotypic findings. In this study, we compared fresh frozen, formalin-fixed paraffin-embedded (FFPE), and PAXgene-fixed paraffin-embedded (PFPE) tissue preparation methods in radical prostatectomy prostate tissue from 36 patients and performed a preliminary test of feasibility of using PFPE tissue in routine prostate surgical pathology diagnostic assessment. In addition to comparing histology, immunohistochemistry, and general measures of DNA and RNA integrity in each fixation method, we performed functional tests of DNA and RNA quality, including targeted Miseq RNA and DNA sequencing, and implemented methods to relate DNA and RNA yield and quality to quantified DNA and RNA picogram nuclear content in each tissue volume studied. Our results suggest that it is feasible to use PFPE tissue for routine robot-assisted laparoscopic prostatectomy surgical pathology diagnostics and immunohistochemistry, with the benefit of significantly improvedDNA and RNA quality and RNA picogram yield per nucleus as compared with FFPE tissue. For fresh frozen, FFPE, and PFPE tissues, respectively, the average Genomic Quality Numbers were 7.9, 3.2, and 6.2, average RNA Quality Numbers were 8.7, 2.6, and 6.3, average DNA picogram yields per nucleus were 0.41, 0.69, and 0.78, and average RNA picogram yields per nucleus were 1.40, 0.94, and 2.24. These findings suggest that where DNA and/or RNA analysis of tissue is required, and when tissue size is small, PFPE may provide important advantages over FFPE. The results also suggest several interesting nuances including potential avenues to improve RNA quality in FFPE tissues and confirm recent suggestions that some DNA sequence artifacts associated with FFPE can be avoided.

Clinical association analysis of ependymomas and pilocytic astrocytomas reveals elevated FGFR3 and FGFR1 expression in aggressive ependymomas

BackgroundFibroblast growth factor receptors (FGFRs) are well-known proto-oncogenes in several human malignancies and are currently therapeutically targeted in clinical trials. Among glioma subtypes, activating FGFR1 alterations have been observed in a subpopulation of pilocytic astrocytomas while FGFR3 fusions occur in IDH wild-type diffuse gliomas, resulting in high FGFR3 protein expression. The purpose of this study was to associate FGFR1 and FGFR3 protein levels with clinical features and genetic alterations in ependymoma and pilocytic astrocytoma.MethodsFGFR1 and FGFR3 expression levels were detected in ependymoma and pilocytic astrocytoma tissues using immunohistochemistry. Selected cases were further analyzed using targeted sequencing.ResultsExpression of both FGFR1 and FGFR3 varied within all tumor types. In ependymomas, increased FGFR3 or FGFR1 expression was associated with high tumor grade, cerebral location, young patient age, and poor prognosis. Moderate-to-strong expression of FGFR1 and/or FGFR3 was observed in 76% of cerebral ependymomas. Cases with moderate-to-strong expression of both proteins had poor clinical prognosis. In pilocytic astrocytomas, moderate-to-strong FGFR3 expression was detected predominantly in non-pediatric patients. Targeted sequencing of 12 tumors found no protein-altering mutations or fusions in FGFR1 or FGFR3.ConclusionsElevated FGFR3 and FGFR1 protein expression is common in aggressive ependymomas but likely not driven by genetic alterations. Further studies are warranted to evaluate whether ependymoma patients with high FGFR3 and/or FGFR1 expression could benefit from treatment with FGFR inhibitor based therapeutic approaches currently under evaluation in clinical trials.

Abstract 4148: Different immune cell responses are associated with glioblastoma subclassification and typical genetic alterations

Interactions between various components in the tumor microenvironment and dysregulated immune responses are thought to play important roles in cancer development. To better understand the role of immune cells in tumor pathogenesis and destruction, we computationally model the microenvironment of an aggressive brain tumor glioblastoma multiforme (GBM). We downloaded GBM patient RNA-seq data from the Cancer Genome Atlas (TCGA). Using cluster analysis, we identified 16 clusters, each containing 10 to 933 genes that show a statistical enrichment of immune response related gene ontology terms. Utilizing a panel of RNA-seq data from normal cell types, we constructed global and cluster specific regression models to characterize the expression profiles of GBM samples in the clusters of interest as linear combinations of normal cell and reference GBM expression profiles. Simulated data was used to validate that the regression model coefficients accurately reflect the contributions of normal cell types to the expression profiles of tumor samples. Based on the regression analysis, we were able to uncover high variability in the composition of microenvironment across the TCGA cohort, suggesting diverse immune responses in tumors. The results from global regression analysis were then associated with common genetic alterations in GBM and with GBM subclassification. Especially the estimated macrophage, granulocyte, and CD8+ T lymphocyte proportions show significant differences between different GBM subgroups. Accumulation of immune cells was increased in the mesenchymal and neural subtype compared to other subtypes. Furthermore, estimated immune cell proportions were associated with alterations in EGFR and NF1. Taken together, our analysis provided a characterization of the immunomicroenvironment in GBM and linked immune cell responses to typical GBM alterations and GBM subtypes. More detailed characterization of diverse immune responses will facilitate patient stratification and might provide tools for personalized immunotherapy in the future. Citation Format: Suvi Luoto, Juha Kesseli, Matti Nykter, Kirsi J. Granberg. Different immune cell responses are associated with glioblastoma subclassification and typical genetic alterations. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4148.