Refining Pathways: A Model Comparison Approach

Abstract

Cellular signalling pathways consolidate multiple molecular interactions into working models of signal propagation, amplification, and modulation. They are described and visualized as networks. Adjusting network topologies to experimental data is a key goal of systems biology. While network reconstruction algorithms like nested effects models are well established tools of computational biology, their data requirements can be prohibitive for their practical use. In this paper we suggest focussing on well defined aspects of a pathway and develop the computational tools to do so. We adapt the framework of nested effect models to focus on a specific aspect of activated Wnt signalling in HCT116 colon cancer cells: Does the activation of Wnt target genes depend on the secretion of Wnt ligands or do mutations in the signalling molecule β-catenin make this activation independent from them? We framed this question into two competing classes of models: Models that depend on Wnt ligands secretion versus those that do not. The model classes translate into restrictions of the pathways in the network topology. Wnt dependent models are more flexible than Wnt independent models. Bayes factors are the standard Bayesian tool to compare different models fairly on the data evidence. In our analysis, the Bayes factors depend on the number of potential Wnt signalling target genes included in the models. Stability analysis with respect to this number showed that the data strongly favours Wnt ligands dependent models for all realistic numbers of target genes.

β-catenin-independent regulation of Wnt target genes by RoR2 and ATF2/ATF4 in colon cancer cells

“…2 ). The expression pattern coincides with targets of canonical, β-catenin-dependent signaling, consistent with our previous data showing that Evi/Wls silencing led to downregulation of canonical Wnt target genes in HCT116 cells 49 , 51 .…”

“…In order to characterize the role of β-catenin-independent signaling in colon cancer, we devised perturbation experiments to identify gene sets that are dependent on auto-paracrine Wnt secretion, but unaffected by depletion of β-catenin or APC 49 . After siRNA silencing of APC, β-catenin/ CTNNB1 and Evi/Wls, changes in the transcriptome of HCT116 colon cancer cells were analyzed by RNA sequencing (RNAseq) (Supplementary Fig.…”

Depletion of Mediator Kinase Module Subunits Represses Superenhancer-Associated Genes in Colon Cancer Cells

“…We therefore asked if Wnt pathway activity underlies the selectivity of MED12/13/13L toward SE genes, such as MYC. For this purpose, we compared the RNA-seq profiles following depletion of MED12, MED13/13L, and BRD4 to the RNA-seq profile of HCT116 cells after siRNA-mediated depletion of ␤-catenin (25). Genes downregulated by MED12/ 4; n ϭ 1,552 and 1,945, respectively), SE genes downregulated (adjusted P Ͻ 0.05) by siMED12 and siMED13/13L or siBRD4 (rows 2 and 5; n ϭ 75 and 75, respectively), or SE genes unaffected by siMED12 and siMED13/13L (non-12/13/13L SE) or siBRD4 (non-BRD4 SE) (rows 3 and 6; n ϭ 211 and 211, respectively) to genes downregulated (left column; n ϭ 448) or upregulated (right column; n ϭ 632) by depletion of CTNNB1 in HCT116 cells.…”

Single-cell sequencing data reveal widespread recurrence and loss of mutational hits in the life histories of tumors

“…2) to test the infinite sites model (ISM), M I , that comprises all histories with a single event for every mutated site, against a model M F that allows multiple mutations at the same site, referred to as the finite sites model (FSM) (Methods). To compare the two alternative models, we compute the Bayes factor (BF) (Kass and Raftery 1995;Moffa et al 2016) based on single-cell sequencing data, D…”

“…We thank Giusi Moffa for very useful discussions about the Bayes Factor comparison (Moffa et al 2016); Mykola Lebid for running spatial simulations of tumor evolution (Waclaw et al 2015); and Jochen Singer for bioinformatics support with the leukemia data (Gawad et al 2014). J.K. was supported by the European Research Council Synergy Grant 609883 (http://erc.europa.eu/).…”