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Plaugher D, Murrugarra D. Pancreatic cancer mutationscape: revealing the link between modular restructuring and intervention efficacy amidst common mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.27.577546. [PMID: 38352601 PMCID: PMC10862704 DOI: 10.1101/2024.01.27.577546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/04/2024]
Abstract
There is increasing evidence that biological systems are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time, as well as influence phenotypic fate transitions. In this work, we use a stochastic multicellular signaling network of pancreatic cancer (PC) to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease in silico. We finally present evidence that the impact and location of mutations with respect to PC modular structure directly corresponds to the efficacy of single agent treatments in silico, because topologically deep mutations require deep targets for control.
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Affiliation(s)
- Daniel Plaugher
- Department of Toxicology and Cancer Biology, University of Kentucky
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Plaugher D, Aguilar B, Murrugarra D. Uncovering potential interventions for pancreatic cancer patients via mathematical modeling. J Theor Biol 2022; 548:111197. [PMID: 35752283 DOI: 10.1016/j.jtbi.2022.111197] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 05/20/2022] [Accepted: 06/08/2022] [Indexed: 12/24/2022]
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) is widely known for its poor prognosis because it is often diagnosed when the cancer is in a later stage. We built a Boolean model to analyze the microenvironment of pancreatic cancer in order to better understand the interplay between pancreatic cancer, stellate cells, and their signaling cytokines. Specifically, we have used our model to study the impact of inducing four common mutations: KRAS, TP53, SMAD4, and CDKN2A. After implementing the various mutation combinations, we used our stochastic simulator to derive aggressiveness scores based on simulated attractor probabilities and long-term trajectory approximations. These aggression scores were then corroborated with clinical data. Moreover, we found sets of control targets that are effective among common mutations. These control sets contain nodes within both the pancreatic cancer cell and the pancreatic stellate cell, including PIP3, RAF, PIK3 and BAX in pancreatic cancer cell as well as ERK and PIK3 in the pancreatic stellate cell. Many of these nodes were found to be differentially expressed among pancreatic cancer patients in the TCGA database. Furthermore, literature suggests that many of these nodes can be targeted by drugs currently in circulation. The results herein help provide a proof of concept in the path towards personalized medicine through a means of mathematical systems biology. All data and code used for running simulations, statistical analysis, and plotting is available on a GitHub repository athttps://github.com/drplaugher/PCC_Mutations.
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Affiliation(s)
- Daniel Plaugher
- Department of Mathematics, University of Kentucky, Lexington, KY, USA.
| | | | - David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, KY, USA.
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Aguilar B, Gibbs DL, Reiss DJ, McConnell M, Danziger SA, Dervan A, Trotter M, Bassett D, Hershberg R, Ratushny AV, Shmulevich I. A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma. Gigascience 2020; 9:giaa075. [PMID: 32696951 PMCID: PMC7374045 DOI: 10.1093/gigascience/giaa075] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 02/14/2020] [Accepted: 06/21/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. RESULTS By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type-specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. CONCLUSIONS The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.
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Affiliation(s)
- Boris Aguilar
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| | - David L Gibbs
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| | - David J Reiss
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Mark McConnell
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Samuel A Danziger
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Andrew Dervan
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Matthew Trotter
- BMS Center for Innovation and Translational Research Europe (CITRE), Pabellon de Italia, Calle Isaac Newton 4, Sevilla 41092, Spain
| | - Douglas Bassett
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Robert Hershberg
- Formerly Celgene Corporation, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Alexander V Ratushny
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Ilya Shmulevich
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
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Palma A, Jarrah AS, Tieri P, Cesareni G, Castiglione F. Gene Regulatory Network Modeling of Macrophage Differentiation Corroborates the Continuum Hypothesis of Polarization States. Front Physiol 2018; 9:1659. [PMID: 30546316 PMCID: PMC6278720 DOI: 10.3389/fphys.2018.01659] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 11/02/2018] [Indexed: 01/22/2023] Open
Abstract
Macrophages derived from monocyte precursors undergo specific polarization processes which are influenced by the local tissue environment: classically activated (M1) macrophages, with a pro-inflammatory activity and a role of effector cells in Th1 cellular immune responses, and alternatively activated (M2) macrophages, with anti-inflammatory functions and involved in immunosuppression and tissue repair. At least three different subsets of M2 macrophages, namely, M2a, M2b, and M2c, are characterized in the literature based on their eliciting signals. The activation and polarization of macrophages is achieved through many, often intertwined, signaling pathways. To describe the logical relationships among the genes involved in macrophage polarization, we used a computational modeling methodology, namely, logical (Boolean) modeling of gene regulation. We integrated experimental data and knowledge available in the literature to construct a logical network model for the gene regulation driving macrophage polarization to the M1, M2a, M2b, and M2c phenotypes. Using the software GINsim and BoolNet, we analyzed the network dynamics under different conditions and perturbations to understand how they affect cell polarization. Dynamic simulations of the network model, enacting the most relevant biological conditions, showed coherence with the observed behavior of in vivo macrophages. The model could correctly reproduce the polarization toward the four main phenotypes as well as to several hybrid phenotypes, which are known to be experimentally associated to physiological and pathological conditions. We surmise that shifts among different phenotypes in the model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the extremes of an uninterrupted sequence of states. Furthermore, model simulations suggest that anti-inflammatory macrophages are resilient to shift back to the pro-inflammatory phenotype.
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Affiliation(s)
- Alessandro Palma
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Abdul Salam Jarrah
- Department of Mathematics and Statistics, American University of Sharjah, Sharjah, United Arab Emirates
| | - Paolo Tieri
- Institute for Applied Computing, National Research Council of Italy, Rome, Italy.,Data Science Program, Sapienza University of Rome, Rome, Italy
| | - Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Rome, Italy.,Fondazione Santa Lucia Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Filippo Castiglione
- Institute for Applied Computing, National Research Council of Italy, Rome, Italy
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Cappuccio A, Tieri P, Castiglione F. Multiscale modelling in immunology: a review. Brief Bioinform 2015; 17:408-18. [PMID: 25810307 DOI: 10.1093/bib/bbv012] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 01/30/2015] [Indexed: 01/26/2023] Open
Abstract
One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.
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Affiliation(s)
- Antonio Cappuccio
- Laboratory of Integrative biology of human dendritic cells and T cells, U932 Immunity and cancer, Institut Curie, 26 Rue d`Ulm, 75005 Paris, France
| | - Paolo Tieri
- Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy
| | - Filippo Castiglione
- Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy
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