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Schmidt B, Sers C, Klein N. BannMI deciphers potential n-to-1 information transduction in signaling pathways to unravel message of intrinsic apoptosis. BIOINFORMATICS ADVANCES 2023; 4:vbad175. [PMID: 38187472 PMCID: PMC10769817 DOI: 10.1093/bioadv/vbad175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/28/2023] [Accepted: 11/28/2023] [Indexed: 01/09/2024]
Abstract
Motivation Cell fate decisions, such as apoptosis or proliferation, are communicated via signaling pathways. The pathways are heavily intertwined and often consist of sequential interaction of proteins (kinases). Information integration takes place on the protein level via n-to-1 interactions. A state-of-the-art procedure to quantify information flow (edges) between signaling proteins (nodes) is network inference. However, edge weight calculation typically refers to 1-to-1 interactions only and relies on mean protein phosphorylation levels instead of single cell distributions. Information theoretic measures such as the mutual information (MI) have the potential to overcome these shortcomings but are still rarely used. Results This work proposes a Bayesian nearest neighbor-based MI estimator (BannMI) to quantify n-to-1 kinase dependency in signaling pathways. BannMI outperforms the state-of-the-art MI estimator on protein-like data in terms of mean squared error and Pearson correlation. Using BannMI, we analyze apoptotic signaling in phosphoproteomic cancerous and noncancerous breast cell line data. Our work provides evidence for cooperative signaling of several kinases in programmed cell death and identifies a potential key role of the mitogen-activated protein kinase p38. Availability and implementation Source code and applications are available at: https://github.com/zuiop11/nn_info and can be downloaded via Pip as Python package: nn-info.
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Affiliation(s)
- Bettina Schmidt
- Research Center Trustworthy Data Science and Security, Universitätsallianz Ruhr, 44227 Dortmund, North Rhine-Westphalia, Germany
- Department of Computer Science, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
| | - Christine Sers
- Institute of Pathology, Charité–Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
- Department of Biology, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
| | - Nadja Klein
- Research Center Trustworthy Data Science and Security, Universitätsallianz Ruhr, 44227 Dortmund, North Rhine-Westphalia, Germany
- Department of Statistics, Technische Universität Dortmund, 44227 Dortmund, North Rhine-Westphalia, Germany
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2
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Sultana Z, Dorel M, Klinger B, Sieber A, Dunkel I, Blüthgen N, Schulz EG. Modeling unveils sex differences of signaling networks in mouse embryonic stem cells. Mol Syst Biol 2023; 19:e11510. [PMID: 37735975 PMCID: PMC10632733 DOI: 10.15252/msb.202211510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023] Open
Abstract
For a short period during early development of mammalian embryos, both X chromosomes in females are active, before dosage compensation is ensured through X-chromosome inactivation. In female mouse embryonic stem cells (mESCs), which carry two active X chromosomes, increased X-dosage affects cell signaling and impairs differentiation. The underlying mechanisms, however, remain poorly understood. To dissect X-dosage effects on the signaling network in mESCs, we combine systematic perturbation experiments with mathematical modeling. We quantify the response to a variety of inhibitors and growth factors for cells with one (XO) or two X chromosomes (XX). We then build models of the signaling networks in XX and XO cells through a semi-quantitative modeling approach based on modular response analysis. We identify a novel negative feedback in the PI3K/AKT pathway through GSK3. Moreover, the presence of a single active X makes mESCs more sensitive to the differentiation-promoting Activin A signal and leads to a stronger RAF1-mediated negative feedback in the FGF-triggered MAPK pathway. The differential response to these differentiation-promoting pathways can explain the impaired differentiation propensity of female mESCs.
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Affiliation(s)
- Zeba Sultana
- Systems Epigenetics, Otto‐Warburg‐LaboratoriesMax Planck Institute for Molecular GeneticsBerlinGermany
| | - Mathurin Dorel
- Computational Modelling in Medicine, Institute of PathologyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Bertram Klinger
- Computational Modelling in Medicine, Institute of PathologyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Anja Sieber
- Computational Modelling in Medicine, Institute of PathologyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Ilona Dunkel
- Systems Epigenetics, Otto‐Warburg‐LaboratoriesMax Planck Institute for Molecular GeneticsBerlinGermany
| | - Nils Blüthgen
- Computational Modelling in Medicine, Institute of PathologyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Edda G Schulz
- Systems Epigenetics, Otto‐Warburg‐LaboratoriesMax Planck Institute for Molecular GeneticsBerlinGermany
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3
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Borg JP, Colinge J, Ravel P. Modular response analysis reformulated as a multilinear regression problem. Bioinformatics 2023; 39:btad166. [PMID: 37021935 PMCID: PMC10097436 DOI: 10.1093/bioinformatics/btad166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/29/2023] [Accepted: 03/07/2023] [Indexed: 04/07/2023] Open
Abstract
MOTIVATION Modular response analysis (MRA) is a well-established method to infer biological networks from perturbation data. Classically, MRA requires the solution of a linear system, and results are sensitive to noise in the data and perturbation intensities. Due to noise propagation, applications to networks of 10 nodes or more are difficult. RESULTS We propose a new formulation of MRA as a multilinear regression problem. This enables to integrate all the replicates and potential additional perturbations in a larger, over-determined, and more stable system of equations. More relevant confidence intervals on network parameters can be obtained, and we show competitive performance for networks of size up to 1000. Prior knowledge integration in the form of known null edges further improves these results. AVAILABILITY AND IMPLEMENTATION The R code used to obtain the presented results is available from GitHub: https://github.com/J-P-Borg/BioInformatics.
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Affiliation(s)
- Jean-Pierre Borg
- Institut de Recherche en Cancérologie de Montpellier, Inserm U1194, Montpellier 34298, France
- Institut régional du Cancer Montpellier, Montpellier 34298, France
- Université de Montpellier, Montpellier 34090, France
| | - Jacques Colinge
- Institut de Recherche en Cancérologie de Montpellier, Inserm U1194, Montpellier 34298, France
- Institut régional du Cancer Montpellier, Montpellier 34298, France
- Université de Montpellier, Montpellier 34090, France
- Faculté de Médecine, Montpellier 34090, France
| | - Patrice Ravel
- Institut de Recherche en Cancérologie de Montpellier, Inserm U1194, Montpellier 34298, France
- Institut régional du Cancer Montpellier, Montpellier 34298, France
- Université de Montpellier, Montpellier 34090, France
- Faculté de Pharmacie, Montpellier 34090, France
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4
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Sarmah D, Smith GR, Bouhaddou M, Stern AD, Erskine J, Birtwistle MR. Network inference from perturbation time course data. NPJ Syst Biol Appl 2022; 8:42. [PMID: 36316338 PMCID: PMC9622863 DOI: 10.1038/s41540-022-00253-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 10/18/2022] [Indexed: 11/05/2022] Open
Abstract
Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible.
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Affiliation(s)
- Deepraj Sarmah
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Gregory R Smith
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mehdi Bouhaddou
- J. David Gladstone Institutes, San Francisco, CA, 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Alan D Stern
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - James Erskine
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Marc R Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.
- Department of Bioengineering, Clemson University, Clemson, SC, USA.
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5
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Bosdriesz E, Fernandes Neto JM, Sieber A, Bernards R, Blüthgen N, Wessels LF. Identifying mutant-specific multi-drug combinations using Comparative Network Reconstruction. iScience 2022; 25:104760. [PMID: 35992065 PMCID: PMC9385552 DOI: 10.1016/j.isci.2022.104760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 05/30/2022] [Accepted: 07/11/2022] [Indexed: 10/28/2022] Open
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6
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Berlak M, Tucker E, Dorel M, Winkler A, McGearey A, Rodriguez-Fos E, da Costa BM, Barker K, Fyle E, Calton E, Eising S, Ober K, Hughes D, Koutroumanidou E, Carter P, Stankunaite R, Proszek P, Jain N, Rosswog C, Dorado-Garcia H, Molenaar JJ, Hubank M, Barone G, Anderson J, Lang P, Deubzer HE, Künkele A, Fischer M, Eggert A, Kloft C, Henssen AG, Boettcher M, Hertwig F, Blüthgen N, Chesler L, Schulte JH. Mutations in ALK signaling pathways conferring resistance to ALK inhibitor treatment lead to collateral vulnerabilities in neuroblastoma cells. Mol Cancer 2022; 21:126. [PMID: 35689207 PMCID: PMC9185889 DOI: 10.1186/s12943-022-01583-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 04/22/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Development of resistance to targeted therapies has tempered initial optimism that precision oncology would improve poor outcomes for cancer patients. Resistance mechanisms, however, can also confer new resistance-specific vulnerabilities, termed collateral sensitivities. Here we investigated anaplastic lymphoma kinase (ALK) inhibitor resistance in neuroblastoma, a childhood cancer frequently affected by activating ALK alterations. METHODS Genome-wide forward genetic CRISPR-Cas9 based screens were performed to identify genes associated with ALK inhibitor resistance in neuroblastoma cell lines. Furthermore, the neuroblastoma cell line NBLW-R was rendered resistant by continuous exposure to ALK inhibitors. Genes identified to be associated with ALK inhibitor resistance were further investigated by generating suitable cell line models. In addition, tumor and liquid biopsy samples of four patients with ALK-mutated neuroblastomas before ALK inhibitor treatment and during tumor progression under treatment were genomically profiled. RESULTS Both genome-wide CRISPR-Cas9-based screens and preclinical spontaneous ALKi resistance models identified NF1 loss and activating NRASQ61K mutations to confer resistance to chemically diverse ALKi. Moreover, human neuroblastomas recurrently developed de novo loss of NF1 and activating RAS mutations after ALKi treatment, leading to therapy resistance. Pathway-specific perturbations confirmed that NF1 loss and activating RAS mutations lead to RAS-MAPK signaling even in the presence of ALKi. Intriguingly, NF1 loss rendered neuroblastoma cells hypersensitive to MEK inhibition. CONCLUSIONS Our results provide a clinically relevant mechanistic model of ALKi resistance in neuroblastoma and highlight new clinically actionable collateral sensitivities in resistant cells.
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Affiliation(s)
- Mareike Berlak
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
- Berlin School of Integrative Oncology (BSIO), Augustenburger Platz 1, 13353, Berlin, Germany
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Kelchstr.31, 12169, Berlin, Germany
| | - Elizabeth Tucker
- Paediatric Solid Tumour Biology and Therapeutics Team, Clinical Division and Cancer Therapeutics Division, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Mathurin Dorel
- Otto Warburg Laboratory Gene Regulation and Systems Biology of Cancer, Max Planck Institute for Molecular Genetics, Berlin, Germany
- Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany
- IRI Life Sciences, Humboldt University Berlin, 10115, Berlin, Germany
| | - Annika Winkler
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Aleixandria McGearey
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Elias Rodriguez-Fos
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
- Experimental and Clinical Research Center (ECRC) of the Charité and Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, 13125, Berlin, Germany
| | - Barbara Martins da Costa
- Paediatric Solid Tumour Biology and Therapeutics Team, Clinical Division and Cancer Therapeutics Division, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Karen Barker
- Paediatric Solid Tumour Biology and Therapeutics Team, Clinical Division and Cancer Therapeutics Division, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Elicia Fyle
- Paediatric Solid Tumour Biology and Therapeutics Team, Clinical Division and Cancer Therapeutics Division, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Elizabeth Calton
- Paediatric Solid Tumour Biology and Therapeutics Team, Clinical Division and Cancer Therapeutics Division, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Selma Eising
- Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Kim Ober
- Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Deborah Hughes
- Molecular Diagnostics Department, The Institute of Cancer Research and Clinical Genomics, The Royal Marsden NHS Foundation, London, UK
| | - Eleni Koutroumanidou
- Molecular Diagnostics Department, The Institute of Cancer Research and Clinical Genomics, The Royal Marsden NHS Foundation, London, UK
| | - Paul Carter
- Molecular Diagnostics Department, The Institute of Cancer Research and Clinical Genomics, The Royal Marsden NHS Foundation, London, UK
| | - Reda Stankunaite
- Molecular Diagnostics Department, The Institute of Cancer Research and Clinical Genomics, The Royal Marsden NHS Foundation, London, UK
| | - Paula Proszek
- Molecular Diagnostics Department, The Institute of Cancer Research and Clinical Genomics, The Royal Marsden NHS Foundation, London, UK
| | - Neha Jain
- Cancer Section, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Carolina Rosswog
- Department of Experimental Pediatric Oncology, Center for Molecular Medicine Cologne, 50931, Cologne, Germany
| | - Heathcliff Dorado-Garcia
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Jan Jasper Molenaar
- Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Department of pharmaceutical sciences, Utrecht University, Utrecht, The Netherlands
| | - Mike Hubank
- Molecular Diagnostics Department, The Institute of Cancer Research and Clinical Genomics, The Royal Marsden NHS Foundation, London, UK
| | - Giuseppe Barone
- Cancer Section, UCL Great Ormond Street Institute of Child Health, London, UK
| | - John Anderson
- Cancer Section, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Peter Lang
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
- Department of Pediatric Hematology and Oncology, University Hospital, Tübingen, Germany
| | - Hedwig Elisabeth Deubzer
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
- Experimental and Clinical Research Center (ECRC) of the Charité and Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, 13125, Berlin, Germany
- German Cancer Consortium (DKTK), Berlin, Germany
- German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Annette Künkele
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
- German Cancer Consortium (DKTK), Berlin, Germany
- German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Matthias Fischer
- Department of Experimental Pediatric Oncology, Center for Molecular Medicine Cologne, 50931, Cologne, Germany
| | - Angelika Eggert
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
- German Cancer Consortium (DKTK), Berlin, Germany
- German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Kelchstr.31, 12169, Berlin, Germany
| | - Anton George Henssen
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
- Experimental and Clinical Research Center (ECRC) of the Charité and Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, 13125, Berlin, Germany
- German Cancer Consortium (DKTK), Berlin, Germany
- German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Michael Boettcher
- Medical Faculty, Martin Luther University Halle-Wittenberg, Halle (Saale), 06120, Halle, Germany
| | - Falk Hertwig
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Nils Blüthgen
- Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany
- IRI Life Sciences, Humboldt University Berlin, 10115, Berlin, Germany
- German Cancer Consortium (DKTK), Berlin, Germany
- German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Louis Chesler
- Paediatric Solid Tumour Biology and Therapeutics Team, Clinical Division and Cancer Therapeutics Division, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Johannes Hubertus Schulte
- Department of Pediatric Oncology/Hematology, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
- German Cancer Consortium (DKTK), Berlin, Germany.
- German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.
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7
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Dorel M, Klinger B, Mari T, Toedling J, Blanc E, Messerschmidt C, Nadler-Holly M, Ziehm M, Sieber A, Hertwig F, Beule D, Eggert A, Schulte JH, Selbach M, Blüthgen N. Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance. PLoS Comput Biol 2021; 17:e1009515. [PMID: 34735429 PMCID: PMC8604339 DOI: 10.1371/journal.pcbi.1009515] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 11/19/2021] [Accepted: 10/01/2021] [Indexed: 12/20/2022] Open
Abstract
Very high risk neuroblastoma is characterised by increased MAPK signalling, and targeting MAPK signalling is a promising therapeutic strategy. We used a deeply characterised panel of neuroblastoma cell lines and found that the sensitivity to MEK inhibitors varied drastically between these cell lines. By generating quantitative perturbation data and mathematical modelling, we determined potential resistance mechanisms. We found that negative feedbacks within MAPK signalling and via the IGF receptor mediate re-activation of MAPK signalling upon treatment in resistant cell lines. By using cell-line specific models, we predict that combinations of MEK inhibitors with RAF or IGFR inhibitors can overcome resistance, and tested these predictions experimentally. In addition, phospho-proteomic profiling confirmed the cell-specific feedback effects and synergy of MEK and IGFR targeted treatment. Our study shows that a quantitative understanding of signalling and feedback mechanisms facilitated by models can help to develop and optimise therapeutic strategies. Our findings should be considered for the planning of future clinical trials introducing MEKi in the treatment of neuroblastoma.
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Affiliation(s)
- Mathurin Dorel
- Institute of Pathology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Integrative Research Institute for the Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bertram Klinger
- Institute of Pathology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Integrative Research Institute for the Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), partner site Berlin, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tommaso Mari
- Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Joern Toedling
- Department of Pediatric, Division of Oncology and Haematology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Eric Blanc
- Berlin Institute of Health, Berlin, Germany
| | | | | | - Matthias Ziehm
- Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Anja Sieber
- Institute of Pathology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Integrative Research Institute for the Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), partner site Berlin, Germany
| | - Falk Hertwig
- Department of Pediatric, Division of Oncology and Haematology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Angelika Eggert
- Department of Pediatric, Division of Oncology and Haematology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), partner site Berlin, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Johannes H. Schulte
- Department of Pediatric, Division of Oncology and Haematology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), partner site Berlin, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | | | - Nils Blüthgen
- Institute of Pathology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Integrative Research Institute for the Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
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8
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Jimenez-Dominguez G, Ravel P, Jalaguier S, Cavaillès V, Colinge J. An R package for generic modular response analysis and its application to estrogen and retinoic acid receptor crosstalk. Sci Rep 2021; 11:7272. [PMID: 33790340 PMCID: PMC8012374 DOI: 10.1038/s41598-021-86544-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 03/09/2021] [Indexed: 11/23/2022] Open
Abstract
Modular response analysis (MRA) is a widely used inference technique developed to uncover directions and strengths of connections in molecular networks under a steady-state condition by means of perturbation experiments. We devised several extensions of this methodology to search genomic data for new associations with a biological network inferred by MRA, to improve the predictive accuracy of MRA-inferred networks, and to estimate confidence intervals of MRA parameters from datasets with low numbers of replicates. The classical MRA computations and their extensions were implemented in a freely available R package called aiMeRA (https://github.com/bioinfo-ircm/aiMeRA/). We illustrated the application of our package by assessing the crosstalk between estrogen and retinoic acid receptors, two nuclear receptors implicated in several hormone-driven cancers, such as breast cancer. Based on new data generated for this study, our analysis revealed potential cross-inhibition mediated by the shared corepressors NRIP1 and LCoR. We designed aiMeRA for non-specialists and to allow biologists to perform their own analyses.
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Affiliation(s)
- Gabriel Jimenez-Dominguez
- Inserm U1194, Institut de Recherche en Cancérologie de Montpellier, Montpellier, France.,University of Montpellier, Montpellier, France.,ICM, Institut régional du Cancer de Montpellier, 208 avenue des Apothicaires, 34298, Montpellier cedex 5, France
| | - Patrice Ravel
- Inserm U1194, Institut de Recherche en Cancérologie de Montpellier, Montpellier, France.,University of Montpellier, Montpellier, France.,ICM, Institut régional du Cancer de Montpellier, 208 avenue des Apothicaires, 34298, Montpellier cedex 5, France
| | - Stéphan Jalaguier
- Inserm U1194, Institut de Recherche en Cancérologie de Montpellier, Montpellier, France.,University of Montpellier, Montpellier, France.,ICM, Institut régional du Cancer de Montpellier, 208 avenue des Apothicaires, 34298, Montpellier cedex 5, France
| | - Vincent Cavaillès
- Inserm U1194, Institut de Recherche en Cancérologie de Montpellier, Montpellier, France. .,University of Montpellier, Montpellier, France. .,ICM, Institut régional du Cancer de Montpellier, 208 avenue des Apothicaires, 34298, Montpellier cedex 5, France.
| | - Jacques Colinge
- Inserm U1194, Institut de Recherche en Cancérologie de Montpellier, Montpellier, France. .,University of Montpellier, Montpellier, France. .,ICM, Institut régional du Cancer de Montpellier, 208 avenue des Apothicaires, 34298, Montpellier cedex 5, France.
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9
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Abstract
MOTIVATION A common strategy to infer and quantify interactions between components of a biological system is to deduce them from the network's response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimizing the experimental design is essential to achieve a meaningful characterization of biological networks. However, it remains difficult to predict which combination of perturbations allows to infer specific interaction strengths in a given network topology. Yet, such a description of identifiability is necessary to select perturbations that maximize the number of inferable parameters. RESULTS We show analytically that the identifiability of network parameters can be determined by an intuitive maximum-flow problem. Furthermore, we used the theory of matroids to describe identifiability relationships between sets of parameters in order to build identifiable effective network models. Collectively, these results allowed to device strategies for an optimal design of the perturbation experiments. We benchmarked these strategies on a database of human pathways. Remarkably, full network identifiability was achieved, on average, with less than a third of the perturbations that are needed in a random experimental design. Moreover, we determined perturbation combinations that additionally decreased experimental effort compared to single-target perturbations. In summary, we provide a framework that allows to infer a maximal number of interaction strengths with a minimal number of perturbation experiments. AVAILABILITY AND IMPLEMENTATION IdentiFlow is available at github.com/GrossTor/IdentiFlow. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Torsten Gross
- Institut für Pathologie, Charité-Universitätsmedizin Berlin, Berlin, Germany
- IRI Life Sciences, Humboldt University, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Nils Blüthgen
- Institut für Pathologie, Charité-Universitätsmedizin Berlin, Berlin, Germany
- IRI Life Sciences, Humboldt University, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
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10
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Abstract
Motivation A major challenge in molecular and cellular biology is to map out the regulatory networks of cells. As regulatory interactions can typically not be directly observed experimentally, various computational methods have been proposed to disentangling direct and indirect effects. Most of these rely on assumptions that are rarely met or cannot be adapted to a given context. Results We present a network inference method that is based on a simple response logic with minimal presumptions. It requires that we can experimentally observe whether or not some of the system’s components respond to perturbations of some other components, and then identifies the directed networks that most accurately account for the observed propagation of the signal. To cope with the intractable number of possible networks, we developed a logic programming approach that can infer networks of hundreds of nodes, while being robust to noisy, heterogeneous or missing data. This allows to directly integrate prior network knowledge and additional constraints such as sparsity. We systematically benchmark our method on KEGG pathways, and show that it outperforms existing approaches in DREAM3 and DREAM4 challenges. Applied to a novel perturbation dataset on PI3K and MAPK pathways in isogenic models of a colon cancer cell line, it generates plausible network hypotheses that explain distinct sensitivities toward various targeted inhibitors due to different PI3K mutants. Availability and implementation A Python/Answer Set Programming implementation can be accessed at github.com/GrossTor/response-logic. Data and analysis scripts are available at github.com/GrossTor/response-logic-projects. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Torsten Gross
- Institut für Pathologie, Charité-Universitätsmedizin, Berlin.,IRI Life Sciences, Humboldt Universität zu Berlin, Berlin.,Berlin Institute of Health, Berlin, Germany
| | | | - Yibing Yan
- Oncology Biomarker Development, Genentech Inc., South San Francisco, CA, USA
| | - Nils Blüthgen
- Institut für Pathologie, Charité-Universitätsmedizin, Berlin.,IRI Life Sciences, Humboldt Universität zu Berlin, Berlin.,Berlin Institute of Health, Berlin, Germany
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11
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Koltai M, Noel V, Zinovyev A, Calzone L, Barillot E. Exact solving and sensitivity analysis of stochastic continuous time Boolean models. BMC Bioinformatics 2020; 21:241. [PMID: 32527218 PMCID: PMC7291460 DOI: 10.1186/s12859-020-03548-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 05/18/2020] [Indexed: 12/02/2022] Open
Abstract
Background Solutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all attractors. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways, raising the necessity of parameter sensitivity analysis. We address these two issues by an exact calculation method for this class of models. Results We show that the stationary probability values of the attractors of stochastic (asynchronous) continuous time Boolean models can be exactly calculated. The calculation does not require Monte Carlo simulations, instead it uses graph theoretical and matrix calculation methods previously applied in the context of chemical kinetics. In this version of the asynchronous updating framework the states of a logical model define a continuous time Markov chain and for a given initial condition the stationary solution is fully defined by the right and left nullspace of the master equation’s kinetic matrix. We use topological sorting of the state transition graph and the dependencies between the nullspaces and the kinetic matrix to derive the stationary solution without simulations. We apply this calculation to several published Boolean models to analyze the under-explored question of the effect of transition rates on the stationary solutions and show they can be sensitive to parameter changes. The analysis distinguishes processes robust or, alternatively, sensitive to parameter values, providing both methodological and biological insights. Conclusion Up to an intermediate size (the biggest model analyzed is 23 nodes) stochastic Boolean models can be efficiently solved by an exact matrix method, without using Monte Carlo simulations. Sensitivity analysis with respect to the model’s timescale parameters often reveals a small subset of all parameters that primarily determine the stationary probability of attractor states.
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Affiliation(s)
- Mihály Koltai
- Institut Curie, PSL Research University, Paris, F-75005, France. .,INSERM, U900, Paris, F-75005, France. .,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, F-75006, France.
| | - Vincent Noel
- Institut Curie, PSL Research University, Paris, F-75005, France.,INSERM, U900, Paris, F-75005, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, F-75006, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Paris, F-75005, France.,INSERM, U900, Paris, F-75005, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, F-75006, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, F-75005, France.,INSERM, U900, Paris, F-75005, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, F-75006, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Paris, F-75005, France.,INSERM, U900, Paris, F-75005, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, F-75006, France
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12
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Bosdriesz E, Prahallad A, Klinger B, Sieber A, Bosma A, Bernards R, Blüthgen N, Wessels LFA. Comparative Network Reconstruction using mixed integer programming. Bioinformatics 2019; 34:i997-i1004. [PMID: 30423075 PMCID: PMC6129277 DOI: 10.1093/bioinformatics/bty616] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Motivation Signal-transduction networks are often aberrated in cancer cells, and new anti-cancer drugs that specifically target oncogenes involved in signaling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. A quantitative understanding of these signaling networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells can help in addressing this problem. Results Here, we present Comparative Network Reconstruction (CNR), a computational method to reconstruct signaling networks based on possibly incomplete perturbation data, and to identify which edges differ quantitatively between two or more signaling networks. Prior knowledge about network topology is not required but can straightforwardly be incorporated. We extensively tested our approach using simulated data and applied it to perturbation data from a BRAF mutant, PTPN11 KO cell line that developed resistance to BRAF inhibition. Comparing the reconstructed networks of sensitive and resistant cells suggests that the resistance mechanism involves re-establishing wild-type MAPK signaling, possibly through an alternative RAF-isoform. Availability and implementation CNR is available as a python module at https://github.com/NKI-CCB/cnr. Additionally, code to reproduce all figures is available at https://github.com/NKI-CCB/CNR-analyses. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Evert Bosdriesz
- Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Anirudh Prahallad
- Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Bertram Klinger
- Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.,IRI Life Sciences, Humboldt University of Berlin, Berlin, Germany
| | - Anja Sieber
- Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.,IRI Life Sciences, Humboldt University of Berlin, Berlin, Germany
| | - Astrid Bosma
- Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - René Bernards
- Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Nils Blüthgen
- Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.,IRI Life Sciences, Humboldt University of Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands
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13
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Cell type-dependent differential activation of ERK by oncogenic KRAS in colon cancer and intestinal epithelium. Nat Commun 2019; 10:2919. [PMID: 31266962 PMCID: PMC6606648 DOI: 10.1038/s41467-019-10954-y] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 06/12/2019] [Indexed: 12/26/2022] Open
Abstract
Oncogenic mutations in KRAS or BRAF are frequent in colorectal cancer and activate the ERK kinase. Here, we find graded ERK phosphorylation correlating with cell differentiation in patient-derived colorectal cancer organoids with and without KRAS mutations. Using reporters, single cell transcriptomics and mass cytometry, we observe cell type-specific phosphorylation of ERK in response to transgenic KRASG12V in mouse intestinal organoids, while transgenic BRAFV600E activates ERK in all cells. Quantitative network modelling from perturbation data reveals that activation of ERK is shaped by cell type-specific MEK to ERK feed forward and negative feedback signalling. We identify dual-specificity phosphatases as candidate modulators of ERK in the intestine. Furthermore, we find that oncogenic KRAS, together with β-Catenin, favours expansion of crypt cells with high ERK activity. Our experiments highlight key differences between oncogenic BRAF and KRAS in colorectal cancer and find unexpected heterogeneity in a signalling pathway with fundamental relevance for cancer therapy. KRASG12V and BRAFV600E are oncogenic mutations that activate ERK signalling. Here, the authors use single cell analysis in intestinal organoids and show that BRAFV600E activates ERK in all intestinal cell types, while KRASG12V induces ERK activation in only a subset of cells, depending on cell differentiation state.
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14
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Lill D, Rukhlenko OS, Mc Elwee AJ, Kashdan E, Timmer J, Kholodenko BN. Mapping connections in signaling networks with ambiguous modularity. NPJ Syst Biol Appl 2019; 5:19. [PMID: 31149348 PMCID: PMC6533310 DOI: 10.1038/s41540-019-0096-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 04/24/2019] [Indexed: 12/16/2022] Open
Abstract
Modular Response Analysis (MRA) is a suite of methods that under certain assumptions permits the precise reconstruction of both the directions and strengths of connections between network modules from network responses to perturbations. Standard MRA assumes that modules are insulated, thereby neglecting the existence of inter-modular protein complexes. Such complexes sequester proteins from different modules and propagate perturbations to the protein abundance of a downstream module retroactively to an upstream module. MRA-based network reconstruction detects retroactive, sequestration-induced connections when an enzyme from one module is substantially sequestered by its substrate that belongs to a different module. Moreover, inferred networks may surprisingly depend on the choice of protein abundances that are experimentally perturbed, and also some inferred connections might be false. Here, we extend MRA by introducing a combined computational and experimental approach, which allows for a computational restoration of modular insulation, unmistakable network reconstruction and discrimination between solely regulatory and sequestration-induced connections for a range of signaling pathways. Although not universal, our approach extends MRA methods to signaling networks with retroactive interactions between modules arising from enzyme sequestration effects.
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Affiliation(s)
- Daniel Lill
- Institute of Physics, University of Freiburg, Freiburg, Germany
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | | | | | - Eugene Kashdan
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
| | - Jens Timmer
- Institute of Physics, University of Freiburg, Freiburg, Germany
- BIOSS Centre for Biological Signaling Studies, University of Freiburg, Freiburg, Germany
| | - Boris N. Kholodenko
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin, Ireland
- School of Medicine and Medical Science, University College Dublin, Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT USA
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