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      Patient‐specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

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          Abstract

          Mechanistic modeling of signaling pathways mediating patient‐specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large‐scale perturbation data. Here, we present an approach that couples ex vivo high‐throughput screenings of cancer biopsies using microfluidics with logic‐based modeling to generate patient‐specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K‐Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

          Abstract

          Patient‐specific signaling models are built from microfludic‐based perturbation screenings on cells from tumour biopsies and pathway knowledge. Combination therapies predicted by the models are validated experimentally.

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          Most cited references36

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          Mutant p53 drives metastasis and overcomes growth arrest/senescence in pancreatic cancer.

          TP53 mutation occurs in 50-75% of human pancreatic ductal adenocarcinomas (PDAC) following an initiating activating mutation in the KRAS gene. These p53 mutations frequently result in expression of a stable protein, p53(R175H), rather than complete loss of protein expression. In this study we elucidate the functions of mutant p53 (Trp53(R172H)), compared to knockout p53 (Trp53(fl)), in a mouse model of PDAC. First we find that although Kras(G12D) is one of the major oncogenic drivers of PDAC, most Kras(G12D)-expressing pancreatic cells are selectively lost from the tissue, and those that remain form premalignant lesions. Loss, or mutation, of Trp53 allows retention of the Kras(G12D)-expressing cells and drives rapid progression of these premalignant lesions to PDAC. This progression is consistent with failed growth arrest and/or senescence of premalignant lesions, since a mutant of p53, p53(R172P), which can still induce p21 and cell cycle arrest, is resistant to PDAC formation. Second, we find that despite similar kinetics of primary tumor formation, mutant p53(R172H), as compared with genetic loss of p53, specifically promotes metastasis. Moreover, only mutant p53(R172H)-expressing tumor cells exhibit invasive activity in an in vitro assay. Importantly, in human PDAC, p53 accumulation significantly correlates with lymph node metastasis. In summary, by using 'knock-in' mutations of Trp53 we have identified two critical acquired functions of a stably expressed mutant form of p53 that drive PDAC; first, an escape from Kras(G12D)-induced senescence/growth arrest and second, the promotion of metastasis.
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            Cross-talk between mitogenic Ras/MAPK and survival PI3K/Akt pathways: a fine balance.

            In the present paper, we describe multiple levels of cross-talk between the PI3K (phosphoinositide 3-kinase)/Akt and Ras/MAPK (mitogen-activated protein kinase) signalling pathways. Experimental data and computer simulations demonstrate that cross-talk is context-dependent and that both pathways can activate or inhibit each other. Positive influence of the PI3K pathway on the MAPK pathway is most effective at sufficiently low doses of growth factors, whereas negative influence of the MAPK pathway on the PI3K pathway is mostly pronounced at high doses of growth factors. Pathway cross-talk endows a cell with emerging capabilities for processing and decoding signals from multiple receptors activated by different combinations of extracellular cues.
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              Tumour cell survival signalling by the ERK1/2 pathway.

              Several advances in recent years have focused increasing attention on the role of the RAF-MEK-ERK1/2 pathway in promoting cell survival. The demonstration that BRAF is a human oncogene mutated at high frequency in melanoma, thyroid and colon cancer has provided a pathophysiological context, whilst the description of potent and highly selective inhibitors of BRAF or MEK has allowed a more informed and rational intervention in both normal and tumour cells. In addition, separate studies have uncovered new mechanisms by which the ERK1/2 pathway can control the activity or abundance of members of the BCL-2 protein family to promote cell survival. It is now apparent that various oncogenes co-opt ERK1/2 signalling to de-regulate these BCL-2 proteins and this contributes to, and even underpins, survival signalling in some tumours. New oncogene-targeted therapies allow direct or indirect inhibition of ERK1/2 signalling and can cause quite striking tumour cell death. In other cases, inhibition of the ERK1/2 pathway may be more effective in combination with other conventional and novel therapeutics. Here, we review recent advances in our understanding of how the ERK1/2 pathway regulates BCL-2 proteins to promote survival, how this is de-regulated in tumour cells and the opportunities this might afford with the use of new targeted therapies.
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                Author and article information

                Contributors
                julio.saez@bioquant.uni-heidelberg.de
                Journal
                Mol Syst Biol
                Mol. Syst. Biol
                10.1002/(ISSN)1744-4292
                MSB
                msb
                Molecular Systems Biology
                John Wiley and Sons Inc. (Hoboken )
                1744-4292
                19 February 2020
                February 2020
                : 16
                : 2 ( doiID: 10.1002/msb.v16.2 )
                : e8664
                Affiliations
                [ 1 ] European Molecular Biology Laboratory (EMBL) Genome Biology Unit Heidelberg Germany
                [ 2 ] European Molecular Biology Laboratory European Bioinformatics Institute (EMBL‐EBI) Hinxton UK
                [ 3 ] Joint Research Centre for Computational Biomedicine (JRC‐COMBINE) Faculty of Medicine RWTH Aachen University Aachen Germany
                [ 4 ] Department of Biomedical Engineering Eindhoven University of Technology Eindhoven The Netherlands
                [ 5 ] Wellcome Trust Sanger Institute Hinxton UK
                [ 6 ] Department Surgery Molecular Tumor Biology RWTH University Hospital Aachen Germany
                [ 7 ] ESCAM – European Surgery Center Aachen Maastricht Aachen Germany
                [ 8 ] ESCAM – European Surgery Center Aachen Maastricht Maastricht The Netherlands
                [ 9 ] Institute for Computational Biomedicine Faculty of Medicine BIOQUANT‐Center Heidelberg University Heidelberg Germany
                Author notes
                [*] [* ]Corresponding author. Tel: +49 6221 5451334; E‐mail: julio.saez@ 123456bioquant.uni-heidelberg.de
                Author information
                https://orcid.org/0000-0002-7822-3867
                https://orcid.org/0000-0002-6462-239X
                https://orcid.org/0000-0002-8552-8976
                Article
                MSB188664
                10.15252/msb.20188664
                7029724
                32073727
                cd51fccf-4909-48e3-8d80-566f251947a0
                © 2020 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 September 2018
                : 27 January 2020
                : 28 January 2020
                Page count
                Figures: 4, Tables: 0, Pages: 13, Words: 9515
                Funding
                Funded by: European Molecular Biology Laboratory Interdisciplinary postdoc (EMBL EIPOD) and Marie Curie Action (COFUND)
                Funded by: JRC for Computational Biomedicine was partially funded by Bayer AG , open-funder-registry 10.13039/100004326;
                Categories
                Article
                Articles
                Custom metadata
                2.0
                February 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.5 mode:remove_FC converted:19.02.2020

                Quantitative & Systems biology
                drug combinations,logic modeling,patient‐specific models,precision oncology,signaling pathways,cancer,computational biology,signal transduction

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