A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines

Abstract

Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model's predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model

Synergistic effects of complex drug combinations in colorectal cancer cells predicted by logical modelling

“…Computational models hold great promise as supportive tools within many fields of medicine. Models presented in the medical literature span a wide range of application areas, from studies of disease development (Baratchart et al, 2015) to biomarker discovery (Ding et al, 2019) and treatment prediction (Eduati et al, 2020;Folkesson et al, 2020;Kuenzi, 2020;Niederdorfer et al, 2020;Tsirvouli et al, 2020). Prediction of treatment response is of special importance within the field of cancer, where the time spent on ineffective therapy will have severe consequences as it may allow disease progression beyond treatment.…”

Data Integration in Logic-based Models of Biological Mechanisms

“…In the same line, researchers developed a methodology that integrates several -omics datasets to identify candidate genes, serving as seeds for network modelling. They analysed multi-omics data from the Consensus Molecular Subtypes [15,16] study of colorectal cancer to expand a previously built generic cell-fate decision network [17].…”

Data Integration in Logic-based Models of Biological Mechanisms

“…In the same line, researchers developed a methodology that integrates several -omics datasets to identify candidate genes, serving as seeds for network modelling. They analysed multi-omics data from the Consensus Molecular Subtypes (CMSs) [15,16] study of colorectal cancer (CRC) to expand a previously built generic cell-fate decision network [17].…”