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Rukhlenko OS, Imoto H, Tambde A, McGillycuddy A, Junk P, Tuliakova A, Kolch W, Kholodenko BN. Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets. Cancers (Basel) 2024; 16:2354. [PMID: 39001416 PMCID: PMC11240448 DOI: 10.3390/cancers16132354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/17/2024] [Accepted: 06/23/2024] [Indexed: 07/16/2024] Open
Abstract
Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.
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Affiliation(s)
- Oleksii S Rukhlenko
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Hiroaki Imoto
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Ayush Tambde
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- Stratford College, D06 T9V3 Dublin, Ireland
| | - Amy McGillycuddy
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- School of Biological, Health and Sports Sciences, Technological University, D07 H6K8 Dublin, Ireland
| | - Philipp Junk
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Anna Tuliakova
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Walter Kolch
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Boris N Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
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Rukhlenko OS, Halasz M, Rauch N, Zhernovkov V, Prince T, Wynne K, Maher S, Kashdan E, MacLeod K, Carragher NO, Kolch W, Kholodenko BN. Control of cell state transitions. Nature 2022; 609:975-985. [PMID: 36104561 PMCID: PMC9644236 DOI: 10.1038/s41586-022-05194-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 08/04/2022] [Indexed: 11/09/2022]
Abstract
Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology. Here we present cell state transition assessment and regulation (cSTAR), an approach for mapping cell states, modelling transitions between them and predicting targeted interventions to convert cell fate decisions. cSTAR uses omics data as input, classifies cell states, and develops a workflow that transforms the input data into mechanistic models that identify a core signalling network, which controls cell fate transitions by influencing whole-cell networks. By integrating signalling and phenotypic data, cSTAR models how cells manoeuvre in Waddington's landscape1 and make decisions about which cell fate to adopt. Notably, cSTAR devises interventions to control the movement of cells in Waddington's landscape. Testing cSTAR in a cellular model of differentiation and proliferation shows a high correlation between quantitative predictions and experimental data. Applying cSTAR to different types of perturbation and omics datasets, including single-cell data, demonstrates its flexibility and scalability and provides new biological insights. The ability of cSTAR to identify targeted perturbations that interconvert cell fates will enable designer approaches for manipulating cellular development pathways and mechanistically underpinned therapeutic interventions.
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Affiliation(s)
- Oleksii S Rukhlenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Melinda Halasz
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Nora Rauch
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Vadim Zhernovkov
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Thomas Prince
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Kieran Wynne
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Stephanie Maher
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Eugene Kashdan
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Kenneth MacLeod
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Walter Kolch
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Boris N Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland.
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.
- Department of Pharmacology, Yale University School of Medicine, New Haven, USA.
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Kholodenko BN, Rauch N, Kolch W, Rukhlenko OS. A systematic analysis of signaling reactivation and drug resistance. Cell Rep 2021; 35:109157. [PMID: 34038718 PMCID: PMC8202068 DOI: 10.1016/j.celrep.2021.109157] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 02/24/2021] [Accepted: 04/29/2021] [Indexed: 01/07/2023] Open
Abstract
Increasing evidence suggests that the reactivation of initially inhibited signaling pathways causes drug resistance. Here, we analyze how network topologies affect signaling responses to drug treatment. Network-dependent drug resistance is commonly attributed to negative and positive feedback loops. However, feedback loops by themselves cannot completely reactivate steady-state signaling. Newly synthesized negative feedback regulators can induce a transient overshoot but cannot fully restore output signaling. Complete signaling reactivation can only occur when at least two routes, an activating and inhibitory, connect an inhibited upstream protein to a downstream output. Irrespective of the network topology, drug-induced overexpression or increase in target dimerization can restore or even paradoxically increase downstream pathway activity. Kinase dimerization cooperates with inhibitor-mediated alleviation of negative feedback. Our findings inform drug development by considering network context and optimizing the design drug combinations. As an example, we predict and experimentally confirm specific combinations of RAF inhibitors that block mutant NRAS signaling. Kholodenko et al. uncover signaling network circuitries and molecular mechanisms necessary and sufficient for complete reactivation or overshoot of steady-state signaling after kinase inhibitor treatment. The two means to revive signaling output fully are through network topology or reactivation of the kinase activity of the primary drug target. Blocking RAF dimer activity by a combination of type I½ and type II RAF inhibitors efficiently blocks mutant NRAS-driven ERK signaling.
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Affiliation(s)
- Boris N Kholodenko
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland; Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin, Ireland; Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
| | - Nora Rauch
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland
| | - Walter Kolch
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland; Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin, Ireland
| | - Oleksii S Rukhlenko
- Systems Biology Ireland, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland
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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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Santra T, Rukhlenko O, Zhernovkov V, Kholodenko BN. Reconstructing static and dynamic models of signaling pathways using Modular Response Analysis. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.coisb.2018.02.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Shayeghi N, Ng T, Coolen A. Direct Response Analysis in cellular signalling networks. J Theor Biol 2012; 304:219-25. [DOI: 10.1016/j.jtbi.2012.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Revised: 03/17/2012] [Accepted: 04/02/2012] [Indexed: 11/25/2022]
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Ourfali O, Shlomi T, Ideker T, Ruppin E, Sharan R. SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments. ACTA ACUST UNITED AC 2007; 23:i359-66. [PMID: 17646318 DOI: 10.1093/bioinformatics/btm170] [Citation(s) in RCA: 84] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION The complex program of gene expression allows the cell to cope with changing genetic, developmental and environmental conditions. The accumulating large-scale measurements of gene knockout effects and molecular interactions allow us to begin to uncover regulatory and signaling pathways within the cell that connect causal to affected genes on a network of physical interactions. RESULTS We present a novel framework, SPINE, for Signaling-regulatory Pathway INferencE. The framework aims at explaining gene expression experiments in which a gene is knocked out and as a result multiple genes change their expression levels. To this end, an integrated network of protein-protein and protein-DNA interactions is constructed, and signaling pathways connecting the causal gene to the affected genes are searched for in this network. The reconstruction problem is translated into that of assigning an activation/repression attribute with each protein so as to explain (in expectation) a maximum number of the knockout effects observed. We provide an integer programming formulation for the latter problem and solve it using a commercial solver. We validate the method by applying it to a yeast subnetwork that is involved in mating. In cross-validation tests, SPINE obtains very high accuracy in predicting knockout effects (99%). Next, we apply SPINE to the entire yeast network to predict protein effects and reconstruct signaling and regulatory pathways. Overall, we are able to infer 861 paths with confidence and assign effects to 183 genes. The predicted effects are found to be in high agreement with current biological knowledge. AVAILABILITY The algorithm and data are available at http://cs.tau.ac.il/~roded/SPINE.html.
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Affiliation(s)
- Oved Ourfali
- School of Computer Science, School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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