COLOSYS

Using computer models to predict drug resistance in colon cancer

In the subproject “Using computer models to predict drug resistance in colon cancer” we will develop a deeper understanding of colon cancer networks and convert them into computer models with which we will be better capable to predict response to treatment. The combination of computational, experimental and clinical testing will be explored to understand drug resistance mechanisms, further paving the way towards personalised treatment of colon cancer.

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Data management

About the project

Background COLOSYS project

In the COLOSYS project we set out to develop a deeper understanding of colon cancer networks and have converted them into computer models with we now use to predict response to treatment. In our approach we first gathered data on colon tumors that have been collected in international efforts and used these data to find genes that show frequent abnormal behavior in colon cancer. Such genes are believed to be ‘driving’ the disease, they play an important role in response to therapy and most importantly, these genes help us to focus on the most important parts of the colon molecular network. Next, have gathered published knowledge on molecular networks in colon cancer in a repository of data and knowledge, and we have combined this knowledge with the driver genes to construct computer models of the molecular networks in colon cancer. We have further refined these models with data collected on colon cancer cell lines (tumor cells grown in the laboratory), and are now using these models to predict response to therapy and test these predictions on cell lines and on cultures derived from patients (organoids).

The combination of computational, experimental and clinical testing will provide understanding of drug resistance mechanisms, and allow personalized treatment of colon cancer.

The state-of-the-art of our logical modelling platform Construction of logical models

NTNU has constructed a fully automated pipeline that builds cell-type or tumour-specific models from causal interaction statements (Atopo). The resulting general topology is configured to represent specific cell types using biomarkers generated from relevant omics data (Aomics). This configuration step uses a genetic algorithm to optimise the model’s logical rules such that its steady state maximally represents the activities of the biomarkers (see below).

Configuration of logical models

Conversion of general logical models (Topology, generated with Atopo) to specific models (Logical model, generated with Gitsbe) through the use of a genetic algorithm that iteratively mutates and tests model stable state fitness against biomarker baseline (steady state) observations (Steady states) until a final model is obtained that maximally represents observed biomarkers. These models are then ready to be used for testing the effects of drug perturbations.

Objectives

General

The central aim is to develop in silico therapy response predictors that allow individualised treatment based on patient-specific driver and resistance mechanisms, thus significantly increasing response and cure rates in Colon Cancer (CC).

Sub-objectives

To achieve this central aim, the specific objectives of COLOSYS are:

Identify known and novel CC driver genes by integration of multiple data types from public tumor data repositories;
Build CC-specific regulatory maps centered around the driver genes and use these to generate logic multi-scale models of mechanisms controlling cellular viability;
Identify treatments that diminish cell viability and test these in large panels of established cell lines and patient organoid cultures;
Identify cases of unexpected intrinsic resistance, construct in vitro models of acquired resistance, perform perturbation experiments and model these resistance mechanisms in silico;
Identify, based on the in silico resistance models, new targets leading to novel combination treatments that overcome resistance;
Validate these treatment strategies pre-clinically in patient-derived xenograft (PDX) models, organoid trials and in samples from patients included in clinical trials.

Thus, our overall aim is to develop an automated modelling pipeline that can produce logical models for any specific cancer type, be configured with baseline biomarker data of the tumor, and deliver candidate combinations that warrant further testing in pre-clinical and clinical applications.

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Deliverables

D1.1 Draft curation guidelines for causal statements

This deliverable is due at the end of the project. NTNU, together with members of the IMEx consortium (http://www.imexconsortium.org) is developing a checklist for the annotation of causal interactions, tentatively named MI2CAST (https://github.com/vtoure/MI2CAST) statements. This document outlines 4 rules for the metadata that ideally should be provided when annotating a causal relationship.
D1.2 Initial cancer omics data repository
D1.3 Causal cellular signalling statement repository

NTNU has assembled a base set of causal interaction statements of the type A –> B (A up-regulates B) and C –| D (C down-regulates D) for the automatic building of logical model topologies that represent regulatory networks of specific cell types including relevant drug targets. This resource is being extended further with causal statements extracted from Reactome and other pathway databases. NTNU is working with the IntAct group of EMBL-EBI to make causal statements widely available through an upgrade of the PSICQUIC webservice.
D2.1 Comprehensive map of molecular interactions depicting the known mechanisms of driver gene action, available as NaviCell environment.
D2.2 Minimal logic models descriptive of CC cell viability control mechanisms
The AGS Model
The Cascade 1.0 model

Graphical representation of the initial CASCADE model. The model contains 144 nodes and 366 edges. Edges are directed and signed. Activating edges have a green color, while inhibiting edges are colored red. Rectangular dark blue nodes represent the two output nodes. Nodes organized in a cycle represent the preliminary modules. Barbara Niederdorfer et al. Manuscript in preparation. Contact: barbara.niederdorfer@ntnu.no

The CASCADE 1.0 model was constructed using a bottom-up approach, extending on the AGS model and adding additional nodes and pathways that were known actors in various types of cancer, and included actionable targets that could be used in drug simulations. $\"\"$
The Cascade 2.0 model

Graph of the CASCADE 2.0 model. There is no distinction between the representation of genes and proteins in the model, with both being represented as cyclical, light blue colored nodes. Rectangular dark blue nodes represent the three output nodes (Proliferation/Growth, Apoptosis and Metastasis). Edges are directed and signed. Activating edges have a green color, while inhibiting edges are colored red. Nodes organized in a cycle represent the 25 modules of the model. Note the additional blue output node, indicating metastasis. Eirini Tsirvouli et al. Manuscript in preparation. Contact: eirinit@stud.ntnu.no

The CASCADE 2.0 model was constructed using a middle-out approach, starting with an integrated omics data analysis produced on tumor material from patients of the TCGA-COAD cohort (Consensus Molecular Subtypes (CMS) of colorectal cancer; Guinney et al. 2015, Nat Med 21:1350–1356), and using the CMS relevant genes as seed genes to extend the CASCADE 1.0 model.

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D2.3 Characterisation of all genetic interactions of the logical model with respect to all model phenotypes (CO)
D2.4 Target-drug and synergistic drug pair predictions validated on cell lines and organoids
D3.1 Experimental models that are i) characterised for intrinsic resistance and ii) generated and characterised for acquired resistance (CO)
D3.2 Data set for dynamic and logic modelling in cell lines with specific acquired resistance
D3.3 Boolean models that capture resistance mechanisms and allow design of (combination) treatments that overcome resistance
D4.1 Database of available cell lines, 3D cultures, PDX and retrospective samples from all sites and associated molecular data
D4.2 Validated models for treatment prediction to be tested in clinical trials

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Data management plan

The COLOSYS consortium makes data available through the FAIRDOMhub for a persistent storage and sharing of the produced results. All data is findable and accessible at https://fairdomhub.org/projects/81. Software tools are publicly provided in software development platforms such as GitHub. In the FAIRDOMhub, a link to these platforms is provided to enable users to access the code source and implementations. Datasets are stored, or linked, in the FAIRDOMhub, from experimental datasets to computational models. It should be noted that the computational models are provided in different formats which allows to use them in diverse tools (Cytoscape, Navicell, GINsim, COLOGICS).

Overview of shareable results produced by NTNU

The shareable results include descriptions of new standards, software tools, experimental datasets and executable models:

Plan for release of additional data and software

This involves the Drug Synergy prediction platform COLOGICS and the CRISPRi data. Both will be made available through GitHub and Figshare, once the manuscripts describing these have been published.

Guidelines for the overview of all COLOSYS consortium data

The COLOSYS consortium has described its WP activities and various deliverables/datasets in the FAIDOMhub platform, following the ISA format. The structure is as follows: Each workpackages, defined as investigations, contains deliverables that are defined as studies in FAIRDOMhub. Meaning that for each deliverable, specific datasets or software tools are linked as being assays or models of a particular study.