Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models

Baur, Florentin; Nietzer, Sarah; Kunz, Meik; Saal, Fabian; Jeromin, Julian; Matschos, Stephanie; Linnebacher, Michael; Walles, Heike; Dandekar, Thomas; Dandekar, Gudrun

doi:10.3390/cancers12010028

Cited by 15 publications

(14 citation statements)

References 69 publications

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“…Small molecules are a group of molecules with a molecular weight below 900 Da that acts primarily inside the cell to inactivate selected enzymes, preventing tumor cell growth and triggering apoptosis. Cycle-independent kinases, proteasomes and poly(A) polymerases account for the majority of molecular targets for CRC [129].…”

Section: Standard-of-care Treatment Of Colorectal Cancermentioning

confidence: 99%

Therapeutic Potential of Mitotic Kinases’ Inhibitors in Cancers of the Gastrointestinal System

Javed

Malagraba

Yarmohammadi

et al. 2022

Future Pharmacology

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Mitosis entails mechanistic changes required for maintaining the genomic integrity in all dividing cells. The process is intricate and temporally and spatially regulated by the ordered series of activation and de-activation of protein kinases. The mitotic kinases ensure the stepwise progression of entry into mitosis after the G2 phase of the cell cycle, followed by prophase, pro-metaphase, metaphase, anaphase, telophase, and subsequently cytokinesis and birth of two daughter cells with equal segregation and distribution of the genome. The major mitotic kinases include cyclin-dependent kinase 1 (CDK1), Aurora A and B Kinases, and Polo-Like-Kinase 1 (PLK1), among others. Overexpression of some of these kinases has been reported in many cancers as the mitotic fidelity and genome integrity are interlinked and dependent on these regulators, the native irregularities in these factors can be targeted as therapeutic strategies for various cancers. Here, we report and summarize the recent updates on the literature describing the various mitotic inhibitors targeting kinases, which can be used as potential therapeutic interventions for gastrointestinal cancers including gastric cancer, liver cancer, pancreatic cancer and colorectal cancer.

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Section: Standard-of-care Treatment Of Colorectal Cancermentioning

confidence: 99%

Therapeutic Potential of Mitotic Kinases’ Inhibitors in Cancers of the Gastrointestinal System

Javed

Malagraba

Yarmohammadi

et al. 2022

Future Pharmacology

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“…A similar approach was utilized by Baur et al. for colorectal cancer application ( Baur et al., 2019 ).…”

Section: Mathematical Modeling Of Cancer At the Microenvironmental Anmentioning

confidence: 99%

“…Gö ttlich et al used in vitro data from immunofluorescence, ELISA, western blot, and phosphokinase arrays from a 3D lung model to generate cell specific in silico models for use in drug effect prediction and patient stratification in treatment of lung cancer (Gö ttlich et al, 2018). A similar approach was utilized by Baur et al for colorectal cancer application (Baur et al, 2019).…”

Section: Microenvironmental and Tissue Scale Data Appropriate For Incorporating Into Mathematical Modelsmentioning

confidence: 99%

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

et al. 2020

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Summary We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

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“…The purpose of the study we propose here is not to provide a comprehensive 60 molecular description of the response but to verify that the existence and functionality of the 61 suggested feedback loops around the signalling pathway in which BRAF is involved [21] may be a 62 first hint towards these differences. For a more thorough study of these cancers, we refer to other 63 works [4,24,25].…”

Section: Introductionmentioning

confidence: 99%

Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers

Béal

Pantolini

Noël

et al. 2020

Preprint

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The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signalling pathways, facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles.We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviours identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model's ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets.This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis. Author summaryWe constructed a logical model to study, from a dynamical perspective, the differences between melanomas and colorectal cancers that share the same BRAF mutations but exhibit different sensitivities to anti-BRAF treatments. The model was built from the literature and completed from May 30, 2020 1/24 existing pathway databases. The model encompasses the key proteins of the MAPK pathway and was made specific to each cancer cell line (100 melanoma and colorectal cell lines from public database) using available omics data, including mutations and RNAseq data. It can simulate the effect of drugs and show high correlation with experimental results. Moreover, the structure of the network confirms both the importance of the reactivation of the MAPK pathway through CRAF and the involvement of PI3K/AKT pathway in the mechanisms of resistance to BRAF inhibition. The study shows that, because of the low number of samples, the ...

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Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models

Cited by 15 publications

References 69 publications

Therapeutic Potential of Mitotic Kinases’ Inhibitors in Cancers of the Gastrointestinal System

Therapeutic Potential of Mitotic Kinases’ Inhibitors in Cancers of the Gastrointestinal System

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers

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