1
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BV H, Jolly MK. Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma. iScience 2024; 27:109184. [PMID: 38433919 PMCID: PMC10905000 DOI: 10.1016/j.isci.2024.109184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/31/2023] [Accepted: 02/06/2024] [Indexed: 03/05/2024] Open
Abstract
The aggressive nature of glioblastoma (GBM) - one of the deadliest forms of brain tumors - is majorly attributed to underlying phenotypic heterogeneity. Early attempts to classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed the existence of four distinct molecular subtypes: Proneural, Neural, Classical, and Mesenchymal. Further, a single-cell RNA sequencing (scRNA-seq) analysis of primary tumors also reported similar four subtypes mimicking neurodevelopmental lineages. However, it remains unclear whether these four subtypes identified via bulk and single-cell transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes and gene signatures corresponding to these proposed subtypes and show that the subtypes are not distinctly mutually antagonistic in either TCGA or scRNA-seq data. We observed that the proneural (or neural progenitor-like)-mesenchymal axis is the most prominent antagonistic pair, with the other two subtypes lying on this spectrum. These results are reinforced through a meta-analysis of over 100 single-cell and bulk transcriptomic datasets as well as in terms of functional association with metabolic switching, cell cycle, and immune evasion pathways. Finally, this proneural-mesenchymal antagonistic trend percolates to the association of relevant transcription factors with patient survival. These results suggest rethinking GBM phenotypic characterization for more effective therapeutic targeting efforts.
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Affiliation(s)
- Harshavardhan BV
- IISc Mathematics Initiative, Indian Institute of Science, Bengaluru, Karnataka 560012, India
| | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India
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2
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Guo H, Li W, Guo Y, Chen N, Cui J. Molecular classification of small cell lung cancer subtypes: Characteristics, prognostic factors, and clinical translation. Chin Med J (Engl) 2024; 137:130-139. [PMID: 37660289 PMCID: PMC10798698 DOI: 10.1097/cm9.0000000000002693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Indexed: 09/04/2023] Open
Abstract
ABSTRACT Small cell lung cancer (SCLC) is a highly malignant tumor with a very poor prognosis; therefore, more effective treatments are urgently needed for patients afflicted with the disease. In recent years, emerging molecular classifications based on key transcription factors of SCLC have provided more information on the tumor pathophysiology, metastasis, immune microenvironment, and acquired therapeutic resistance and reflected the intertumoral heterogeneity of the various SCLC phenotypes. Additionally, advances in genomics and single-cell sequencing analysis have further revealed the high intratumoral heterogeneity and plasticity of the disease. Herein, we review and summarize these recent lines of evidence and discuss the possible pathogenesis of SCLC.
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Affiliation(s)
| | | | | | | | - Jiuwei Cui
- Cancer Center, The First Hospital of Jilin University, Changchun, Jilin 130021, China
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3
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Pernice S, Maglione A, Tortarolo D, Sirovich R, Clerico M, Rolla S, Beccuti M, Cordero F. A new computational workflow to guide personalized drug therapy. J Biomed Inform 2023; 148:104546. [PMID: 37984546 DOI: 10.1016/j.jbi.2023.104546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVE Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics. METHODS GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations. RESULTS To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months. CONCLUSION GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.
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Affiliation(s)
- Simone Pernice
- Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy; CINI Infolife laboratory, Turin, Italy
| | - Alessandro Maglione
- Department of Clinical and Biological Sciences, Regione Gonzole 10, Orbassano, 10043, Italy
| | - Dora Tortarolo
- Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy
| | - Roberta Sirovich
- Department of Mathematics, University of Turin, Via Carlo Alberto 10, Turin, 10123, Italy
| | - Marinella Clerico
- Department of Clinical and Biological Sciences, Regione Gonzole 10, Orbassano, 10043, Italy
| | - Simona Rolla
- Department of Clinical and Biological Sciences, Regione Gonzole 10, Orbassano, 10043, Italy.
| | - Marco Beccuti
- Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy; CINI Infolife laboratory, Turin, Italy
| | - Francesca Cordero
- Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy; CINI Infolife laboratory, Turin, Italy
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4
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Sherekar S, Todankar CS, Viswanathan GA. Modulating the dynamics of NFκB and PI3K enhances the ensemble-level TNFR1 signaling mediated apoptotic response. NPJ Syst Biol Appl 2023; 9:57. [PMID: 37973854 PMCID: PMC10654705 DOI: 10.1038/s41540-023-00318-0] [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: 04/26/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023] Open
Abstract
Cell-to-cell variability during TNFα stimulated Tumor Necrosis Factor Receptor 1 (TNFR1) signaling can lead to single-cell level pro-survival and apoptotic responses. This variability stems from the heterogeneity in signal flow through intracellular signaling entities that regulate the balance between these two phenotypes. Using systematic Boolean dynamic modeling of a TNFR1 signaling network, we demonstrate that the signal flow path variability can be modulated to enable cells favour apoptosis. We developed a computationally efficient approach "Boolean Modeling based Prediction of Steady-state probability of Phenotype Reachability (BM-ProSPR)" to accurately predict the network's ability to settle into different phenotypes. Model analysis juxtaposed with the experimental observations revealed that NFκB and PI3K transient responses guide the XIAP behaviour to coordinate the crucial dynamic cross-talk between the pro-survival and apoptotic arms at the single-cell level. Model predicted the experimental observations that ~31% apoptosis increase can be achieved by arresting Comp1 - IKK* activity which regulates the NFκB and PI3K dynamics. Arresting Comp1 - IKK* activity causes signal flow path re-wiring towards apoptosis without significantly compromising NFκB levels, which govern adequate cell survival. Priming an ensemble of cancerous cells with inhibitors targeting the specific interaction involving Comp1 and IKK* prior to TNFα exposure could enable driving them towards apoptosis.
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Affiliation(s)
- Shubhank Sherekar
- Department of Chemical Engineering, Indian Institute of Technology Bombay Powai, Mumbai, 400076, India
| | - Chaitra S Todankar
- Department of Chemical Engineering, Indian Institute of Technology Bombay Powai, Mumbai, 400076, India
| | - Ganesh A Viswanathan
- Department of Chemical Engineering, Indian Institute of Technology Bombay Powai, Mumbai, 400076, India.
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5
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Jain P, Pillai M, Duddu AS, Somarelli JA, Goyal Y, Jolly MK. Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity. Semin Cancer Biol 2023; 96:48-63. [PMID: 37788736 DOI: 10.1016/j.semcancer.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/24/2023] [Accepted: 09/28/2023] [Indexed: 10/05/2023]
Abstract
Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.
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Affiliation(s)
- Paras Jain
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India
| | - Maalavika Pillai
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India; Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL 60611, USA
| | | | - Jason A Somarelli
- Department of Medicine, Duke Cancer Institute, Duke University, Durham, NC 27710, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL 60611, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India.
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6
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Ozen M, Lopez CF. Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways. NPJ Syst Biol Appl 2023; 9:55. [PMID: 37907529 PMCID: PMC10618210 DOI: 10.1038/s41540-023-00316-2] [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: 07/18/2023] [Accepted: 10/12/2023] [Indexed: 11/02/2023] Open
Abstract
Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.
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Affiliation(s)
- Mustafa Ozen
- Dept. of Biochemistry, Vanderbilt University, Nashville, TN, USA
- Multiscale Modeling Group, SI3, Altos Labs, Redwood City, CA, USA
| | - Carlos F Lopez
- Dept. of Biochemistry, Vanderbilt University, Nashville, TN, USA.
- Multiscale Modeling Group, SI3, Altos Labs, Redwood City, CA, USA.
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7
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Yoodee S, Thongboonkerd V. Epigenetic regulation of epithelial-mesenchymal transition during cancer development. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2023; 380:1-61. [PMID: 37657856 DOI: 10.1016/bs.ircmb.2023.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
Epithelial-mesenchymal transition (EMT) plays essential roles in promoting malignant transformation of epithelial cells, leading to cancer progression and metastasis. During EMT-induced cancer development, a wide variety of genes are dramatically modified, especially down-regulation of epithelial-related genes and up-regulation of mesenchymal-related genes. Expression of other EMT-related genes is also modified during the carcinogenic process. Especially, epigenetic modifications are observed in the EMT-related genes, indicating their involvement in cancer development. Mechanically, epigenetic modifications of histone, DNA, mRNA and non-coding RNA stably change the EMT-related gene expression at transcription and translation levels. Herein, we summarize current knowledge on epigenetic regulatory mechanisms observed in EMT process relate to cancer development in humans. The better understanding of epigenetic regulation of EMT during cancer development may lead to improvement of drug design and preventive strategies in cancer therapy.
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Affiliation(s)
- Sunisa Yoodee
- Medical Proteomics Unit, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Visith Thongboonkerd
- Medical Proteomics Unit, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
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8
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Grody EI, Abraham A, Shukla V, Goyal Y. Toward a systems-level probing of tumor clonality. iScience 2023; 26:106574. [PMID: 37192968 PMCID: PMC10182304 DOI: 10.1016/j.isci.2023.106574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023] Open
Abstract
Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.
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Affiliation(s)
- Emanuelle I. Grody
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Ajay Abraham
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Vipul Shukla
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Corresponding author
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Corresponding author
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9
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Ozen M, Lopez CF. Data-driven structural analysis of Small Cell Lung Cancer transcription factor network suggests potential subtype regulators and transition pathways. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.01.535226. [PMID: 37066351 PMCID: PMC10104011 DOI: 10.1101/2023.04.01.535226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.
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Affiliation(s)
- Mustafa Ozen
- Dept. of Biochemistry, Vanderbilt University, Nashville, TN 37212, USA
- Currently at: Computational Innovation Hub, Multiscale Modeling Group, Altos Labs, Redwood City, CA 94065, USA
| | - Carlos F. Lopez
- Dept. of Biochemistry, Vanderbilt University, Nashville, TN 37212, USA
- Currently at: Computational Innovation Hub, Multiscale Modeling Group, Altos Labs, Redwood City, CA 94065, USA
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10
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Pillai M, Hojel E, Jolly MK, Goyal Y. Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. NATURE COMPUTATIONAL SCIENCE 2023; 3:301-313. [PMID: 38177938 DOI: 10.1038/s43588-023-00427-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 03/03/2023] [Indexed: 01/06/2024]
Abstract
Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.
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Affiliation(s)
- Maalavika Pillai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Emilia Hojel
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India.
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA.
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11
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Groves SM, Panchy N, Tyson DR, Harris LA, Quaranta V, Hong T. Involvement of Epithelial-Mesenchymal Transition Genes in Small Cell Lung Cancer Phenotypic Plasticity. Cancers (Basel) 2023; 15:1477. [PMID: 36900269 PMCID: PMC10001072 DOI: 10.3390/cancers15051477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 03/03/2023] Open
Abstract
Small cell lung cancer (SCLC) is an aggressive cancer recalcitrant to treatment, arising predominantly from epithelial pulmonary neuroendocrine (NE) cells. Intratumor heterogeneity plays critical roles in SCLC disease progression, metastasis, and treatment resistance. At least five transcriptional SCLC NE and non-NE cell subtypes were recently defined by gene expression signatures. Transition from NE to non-NE cell states and cooperation between subtypes within a tumor likely contribute to SCLC progression by mechanisms of adaptation to perturbations. Therefore, gene regulatory programs distinguishing SCLC subtypes or promoting transitions are of great interest. Here, we systematically analyze the relationship between SCLC NE/non-NE transition and epithelial to mesenchymal transition (EMT)-a well-studied cellular process contributing to cancer invasiveness and resistance-using multiple transcriptome datasets from SCLC mouse tumor models, human cancer cell lines, and tumor samples. The NE SCLC-A2 subtype maps to the epithelial state. In contrast, SCLC-A and SCLC-N (NE) map to a partial mesenchymal state (M1) that is distinct from the non-NE, partial mesenchymal state (M2). The correspondence between SCLC subtypes and the EMT program paves the way for further work to understand gene regulatory mechanisms of SCLC tumor plasticity with applicability to other cancer types.
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Affiliation(s)
- Sarah M. Groves
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Nicholas Panchy
- Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN 37996, USA
| | - Darren R. Tyson
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37235, USA
| | - Leonard A. Harris
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR 72701, USA
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, AR 72701, USA
- Cancer Biology Program, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Vito Quaranta
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37235, USA
| | - Tian Hong
- Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN 37996, USA
- National Institute for Mathematical and Biological Synthesis, Knoxville, TN 37996, USA
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12
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Caranica C, Lu M. A data-driven optimization method for coarse-graining gene regulatory networks. iScience 2023; 26:105927. [PMID: 36698721 PMCID: PMC9868542 DOI: 10.1016/j.isci.2023.105927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/19/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
One major challenge in systems biology is to understand how various genes in a gene regulatory network (GRN) collectively perform their functions and control network dynamics. This task becomes extremely hard to tackle in the case of large networks with hundreds of genes and edges, many of which have redundant regulatory roles and functions. The existing methods for model reduction usually require the detailed mathematical description of dynamical systems and their corresponding kinetic parameters, which are often not available. Here, we present a data-driven method for coarse-graining large GRNs, named SacoGraci, using ensemble-based mathematical modeling, dimensionality reduction, and gene circuit optimization by Markov Chain Monte Carlo methods. SacoGraci requires network topology as the only input and is robust against errors in GRNs. We benchmark and demonstrate its usage with synthetic, literature-based, and bioinformatics-derived GRNs. We hope SacoGraci will enhance our ability to model the gene regulation of complex biological systems.
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Affiliation(s)
- Cristian Caranica
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA,Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA,Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA,The Jackson Laboratory, Bar Harbor, ME 04609, USA,Corresponding author
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13
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Minimal frustration underlies the usefulness of incomplete regulatory network models in biology. Proc Natl Acad Sci U S A 2023; 120:e2216109120. [PMID: 36580597 PMCID: PMC9910462 DOI: 10.1073/pnas.2216109120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Regulatory networks as large and complex as those implicated in cell-fate choice are expected to exhibit intricate, very high-dimensional dynamics. Cell-fate choice, however, is a macroscopically simple process. Additionally, regulatory network models are almost always incomplete and/or inexact, and do not incorporate all the regulators and interactions that may be involved in cell-fate regulation. In spite of these issues, regulatory network models have proven to be incredibly effective tools for understanding cell-fate choice across contexts and for making useful predictions. Here, we show that minimal frustration-a feature of biological networks across contexts but not of random networks-can compel simple, low-dimensional steady-state behavior even in large and complex networks. Moreover, the steady-state behavior of minimally frustrated networks can be recapitulated by simpler networks such as those lacking many of the nodes and edges and those that treat multiple regulators as one. The present study provides a theoretical explanation for the success of network models in biology and for the challenges in network inference.
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14
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NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity. Genome Biol 2022; 23:270. [PMID: 36575445 PMCID: PMC9793520 DOI: 10.1186/s13059-022-02835-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022] Open
Abstract
A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.
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15
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Gopal P, Petty A, Rogacki K, Bera T, Bareja R, Peacock CD, Abazeed ME. Multivalent state transitions shape the intratumoral composition of small cell lung carcinoma. SCIENCE ADVANCES 2022; 8:eabp8674. [PMID: 36516249 PMCID: PMC9750150 DOI: 10.1126/sciadv.abp8674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 10/26/2022] [Indexed: 06/17/2023]
Abstract
Studies to date have not resolved how diverse transcriptional programs contribute to the intratumoral heterogeneity of small cell lung carcinoma (SCLC), an aggressive tumor associated with a dismal prognosis. Here, we identify distinct and commutable transcriptional states that confer discrete functional attributes in individual SCLC tumors. We combine an integrative approach comprising the transcriptomes of 52,975 single cells, high-resolution measurement of cell state dynamics at the single-cell level, and functional and correlative studies using treatment naïve xenografts with associated clinical outcomes. We show that individual SCLC tumors contain distinctive proportions of stable cellular states that are governed by bidirectional cell state transitions. Using drugs that target the epigenome, we reconfigure tumor state composition in part by altering individual state transition rates. Our results reveal new insights into how single-cell transition behaviors promote cell state equilibrium in SCLC and suggest that facile plasticity underlies its resistance to therapy and lethality.
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Affiliation(s)
- Priyanka Gopal
- Department of Radiation Oncology, Northwestern University, Feinberg School of Medicine, 251 E. Huron St., Galter Pavilion LC-178, Chicago, IL 60611, USA
| | - Aaron Petty
- Department of Translational Hematology Oncology Research, Cleveland Clinic, 2111 East 96th St./NE-6, Cleveland, OH 44195, USA
| | - Kevin Rogacki
- Department of Radiation Oncology, Northwestern University, Feinberg School of Medicine, 251 E. Huron St., Galter Pavilion LC-178, Chicago, IL 60611, USA
| | - Titas Bera
- Department of Radiation Oncology, Northwestern University, Feinberg School of Medicine, 251 E. Huron St., Galter Pavilion LC-178, Chicago, IL 60611, USA
| | - Rohan Bareja
- Institute for Computational Biomedicine, Weill Cornell Medicine, 1305 York Ave., New York, NY 10021, USA
| | - Craig D. Peacock
- Department of Genetics and Genome Sciences, Case Western Reserve University, 2109 Adelbert Road, Biomedical Research Building 647B, Cleveland, OH 44106, USA
| | - Mohamed E. Abazeed
- Department of Radiation Oncology, Northwestern University, Feinberg School of Medicine, 251 E. Huron St., Galter Pavilion LC-178, Chicago, IL 60611, USA
- Robert H. Lurie Cancer Center, Northwestern University, 303 E. Superior St./Lurie 7, Chicago, IL 60611, USA
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16
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Groves SM, Ildefonso GV, McAtee CO, Ozawa PMM, Ireland AS, Stauffer PE, Wasdin PT, Huang X, Qiao Y, Lim JS, Bader J, Liu Q, Simmons AJ, Lau KS, Iams WT, Hardin DP, Saff EB, Holmes WR, Tyson DR, Lovly CM, Rathmell JC, Marth G, Sage J, Oliver TG, Weaver AM, Quaranta V. Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer. Cell Syst 2022; 13:690-710.e17. [PMID: 35981544 PMCID: PMC9615940 DOI: 10.1016/j.cels.2022.07.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 05/10/2022] [Accepted: 07/25/2022] [Indexed: 01/26/2023]
Abstract
Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.
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Affiliation(s)
- Sarah M Groves
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Geena V Ildefonso
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Caitlin O McAtee
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Patricia M M Ozawa
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Abbie S Ireland
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Philip E Stauffer
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Perry T Wasdin
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Xiaomeng Huang
- Utah Center for Genetic Discovery, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Yi Qiao
- Utah Center for Genetic Discovery, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Jing Shan Lim
- Department of Pediatrics and Genetics, Stanford University, Stanford, CA 94305, USA
| | - Jackie Bader
- Department of Pathology, Microbiology, and Immunology, Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Qi Liu
- Department of Biostatistics and Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Alan J Simmons
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37235, USA
| | - Ken S Lau
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37235, USA
| | - Wade T Iams
- Division of Hematology-Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Doug P Hardin
- Department of Mathematics and Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37235, USA
| | - Edward B Saff
- Department of Mathematics, Vanderbilt University, Nashville, TN 37235, USA
| | - William R Holmes
- Department of Mathematics, Vanderbilt University, Nashville, TN 37235, USA; Department of Physics, Vanderbilt University, Nashville, TN 37235, USA
| | - Darren R Tyson
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Christine M Lovly
- Department of Mathematics and Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37235, USA; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Jeffrey C Rathmell
- Department of Pathology, Microbiology, and Immunology, Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Gabor Marth
- Utah Center for Genetic Discovery, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
| | - Julien Sage
- Department of Pediatrics and Genetics, Stanford University, Stanford, CA 94305, USA
| | - Trudy G Oliver
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Alissa M Weaver
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37235, USA; Department of Pathology, Microbiology, and Immunology, Vanderbilt University, Nashville, TN 37235, USA
| | - Vito Quaranta
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA.
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17
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Newby E, Tejeda Zañudo JG, Albert R. Structure-based approach to identifying small sets of driver nodes in biological networks. CHAOS (WOODBURY, N.Y.) 2022; 32:063102. [PMID: 35778133 DOI: 10.1063/5.0080843] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In network control theory, driving all the nodes in the Feedback Vertex Set (FVS) by node-state override forces the network into one of its attractors (long-term dynamic behaviors). The FVS is often composed of more nodes than can be realistically manipulated in a system; for example, only up to three nodes can be controlled in intracellular networks, while their FVS may contain more than 10 nodes. Thus, we developed an approach to rank subsets of the FVS on Boolean models of intracellular networks using topological, dynamics-independent measures. We investigated the use of seven topological prediction measures sorted into three categories-centrality measures, propagation measures, and cycle-based measures. Using each measure, every subset was ranked and then evaluated against two dynamics-based metrics that measure the ability of interventions to drive the system toward or away from its attractors: To Control and Away Control. After examining an array of biological networks, we found that the FVS subsets that ranked in the top according to the propagation metrics can most effectively control the network. This result was independently corroborated on a second array of different Boolean models of biological networks. Consequently, overriding the entire FVS is not required to drive a biological network to one of its attractors, and this method provides a way to reliably identify effective FVS subsets without the knowledge of the network dynamics.
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Affiliation(s)
- Eli Newby
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | | | - Réka Albert
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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18
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Hassan M, Awan FM, Naz A, deAndrés-Galiana EJ, Alvarez O, Cernea A, Fernández-Brillet L, Fernández-Martínez JL, Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. Int J Mol Sci 2022; 23:4645. [PMID: 35563034 PMCID: PMC9104788 DOI: 10.3390/ijms23094645] [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: 02/28/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.
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Affiliation(s)
- Mubashir Hassan
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
| | - Faryal Mehwish Awan
- Department of Medical Lab Technology, The University of Haripur, Haripur 22620, Pakistan;
| | - Anam Naz
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Oscar Alvarez
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | - Ana Cernea
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | | | - Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43205, USA
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19
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Cancer: More than a geneticist’s Pandora’s box. J Biosci 2022. [DOI: 10.1007/s12038-022-00254-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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20
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Mertins SD. Capturing Biomarkers and Molecular Targets in Cellular Landscapes From Dynamic Reaction Network Models and Machine Learning. Front Oncol 2022; 11:805592. [PMID: 35127516 PMCID: PMC8813744 DOI: 10.3389/fonc.2021.805592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/31/2021] [Indexed: 12/02/2022] Open
Abstract
Computational dynamic ODE models of cell function describing biochemical reactions have been created for decades, but on a small scale. Still, they have been highly effective in describing and predicting behaviors. For example, oscillatory phospho-ERK levels were predicted and confirmed in MAPK signaling encompassing both positive and negative feedback loops. These models typically were limited and not adapted to large datasets so commonly found today. But importantly, ODE models describe reaction networks in well-mixed systems representing the cell and can be simulated with ordinary differential equations that are solved deterministically. Stochastic solutions, which can account for noisy reaction networks, in some cases, also improve predictions. Today, dynamic ODE models rarely encompass an entire cell even though it might be expected that an upload of the large genomic, transcriptomic, and proteomic datasets may allow whole cell models. It is proposed here to combine output from simulated dynamic ODE models, completed with omics data, to discover both biomarkers in cancer a priori and molecular targets in the Machine Learning setting.
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Affiliation(s)
- Susan D. Mertins
- Department of Science, Mount St. Mary’s University, Emmitsburg, MD, United States
- Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Limited Liability Company (LLC), Frederick, MD, United States
- BioSystems Strategies, Limited Liability Company (LLC), Frederick, MD, United States
- *Correspondence: Susan D. Mertins,
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21
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Payapilly A, Guilbert R, Descamps T, White G, Magee P, Zhou C, Kerr A, Simpson KL, Blackhall F, Dive C, Malliri A. TIAM1-RAC1 promote small-cell lung cancer cell survival through antagonizing Nur77-induced BCL2 conformational change. Cell Rep 2021; 37:109979. [PMID: 34758330 PMCID: PMC8595642 DOI: 10.1016/j.celrep.2021.109979] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/30/2021] [Accepted: 10/20/2021] [Indexed: 12/17/2022] Open
Abstract
Small-cell lung cancer (SCLC), an aggressive neuroendocrine malignancy, has limited treatment options beyond platinum-based chemotherapy, whereafter acquired resistance is rapid and common. By analyzing expression data from SCLC tumors, patient-derived models, and established cell lines, we show that the expression of TIAM1, an activator of the small GTPase RAC1, is associated with a neuroendocrine gene program. TIAM1 depletion or RAC1 inhibition reduces viability and tumorigenicity of SCLC cells by increasing apoptosis associated with conversion of BCL2 from its pro-survival to pro-apoptotic function via BH3 domain exposure. This conversion is dependent upon cytoplasmic translocation of Nur77, an orphan nuclear receptor. TIAM1 interacts with and sequesters Nur77 in SCLC cell nuclei and TIAM1 depletion or RAC1 inhibition promotes Nur77 translocation to the cytoplasm. Mutant TIAM1 with reduced Nur77 binding fails to suppress apoptosis triggered by TIAM1 depletion. In conclusion, TIAM1-RAC1 signaling promotes SCLC cell survival via Nur77 nuclear sequestration.
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MESH Headings
- Animals
- Apoptosis
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Cell Proliferation
- Female
- Gene Expression Profiling
- Gene Expression Regulation, Neoplastic
- Humans
- Lung Neoplasms/genetics
- Lung Neoplasms/metabolism
- Lung Neoplasms/pathology
- Mice
- Mice, Inbred NOD
- Mice, SCID
- Nuclear Receptor Subfamily 4, Group A, Member 1/genetics
- Nuclear Receptor Subfamily 4, Group A, Member 1/metabolism
- Protein Conformation
- Proto-Oncogene Proteins c-bcl-2/chemistry
- Proto-Oncogene Proteins c-bcl-2/genetics
- Proto-Oncogene Proteins c-bcl-2/metabolism
- Small Cell Lung Carcinoma/genetics
- Small Cell Lung Carcinoma/metabolism
- Small Cell Lung Carcinoma/pathology
- T-Lymphoma Invasion and Metastasis-inducing Protein 1/genetics
- T-Lymphoma Invasion and Metastasis-inducing Protein 1/metabolism
- Tumor Cells, Cultured
- Xenograft Model Antitumor Assays
- rac1 GTP-Binding Protein/genetics
- rac1 GTP-Binding Protein/metabolism
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Affiliation(s)
- Aishwarya Payapilly
- Cell Signalling Group, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK; Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK
| | - Ryan Guilbert
- Cell Signalling Group, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK; Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK
| | - Tine Descamps
- Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK; Cancer Research UK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Alderley Park SK10 4TG, UK
| | - Gavin White
- Cell Signalling Group, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK; Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK
| | - Peter Magee
- Cell Signalling Group, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK; Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK
| | - Cong Zhou
- Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK; Cancer Research UK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Alderley Park SK10 4TG, UK
| | - Alastair Kerr
- Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK; Cancer Research UK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Alderley Park SK10 4TG, UK
| | - Kathryn L Simpson
- Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK; Cancer Research UK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Alderley Park SK10 4TG, UK
| | - Fiona Blackhall
- Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Caroline Dive
- Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK; Cancer Research UK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Alderley Park SK10 4TG, UK
| | - Angeliki Malliri
- Cell Signalling Group, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK; Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK.
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22
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Pillai M, Jolly MK. Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma. iScience 2021; 24:103111. [PMID: 34622164 PMCID: PMC8479788 DOI: 10.1016/j.isci.2021.103111] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/03/2021] [Accepted: 09/08/2021] [Indexed: 02/07/2023] Open
Abstract
Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple "attractor" states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different "attractors" (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.
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Affiliation(s)
- Maalavika Pillai
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
- Undergraduate Programme, Indian Institute of Science, Bangalore, India
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
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23
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Yan W, Chung CY, Xie T, Ozeck M, Nichols TC, Frey J, Udyavar AR, Sharma S, Paul TA. Intrinsic and acquired drug resistance to LSD1 inhibitors in small cell lung cancer occurs through a TEAD4-driven transcriptional state. Mol Oncol 2021; 16:1309-1328. [PMID: 34669238 PMCID: PMC8936524 DOI: 10.1002/1878-0261.13124] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 07/20/2021] [Accepted: 10/19/2021] [Indexed: 11/10/2022] Open
Abstract
Small-cell lung cancer (SCLC) is a heterogeneous disease, consisting of intratumoral and intertumoral neuroendocrine (ASCL1 and/or NEUROD1), mesenchymal-like, and YAP-driven transcriptional states. Lysine-specific demethylase 1 (LSD1; also known as KDM1A) inhibitors have recently been progressed to clinical trials in SCLC based on a promising preclinical antitumor activity. A potential clinical limitation of LSD1 inhibitors is the heterogeneous drug responses that have been observed in SCLC cell lines and patient-derived models. Based on these observations, we studied molecular and transcriptional signatures that predict patient response to this class of drug. Employing SCLC patient-derived transcriptional signatures, we define that SCLC cell lines sensitive to LSD1 inhibitors are enriched in neuroendocrine transcriptional markers, whereas cell lines enriched in a mesenchymal-like transcriptional program demonstrate intrinsic resistance to LSD1 inhibitors. We have identified a reversible, adaptive resistance mechanism to LSD1 inhibitors through epigenetic reprogramming to a TEAD4-driven mesenchymal-like state. Our data suggest that only a segment of SCLC patients, with a defined neuroendocrine differentiation state, will likely benefit from LSD1 inhibitors. It provides novel evidence for the selection of a TEAD4-driven mesenchymal-like subpopulation resistant to LSD1 inhibitors in SCLC patients that may require effective drug combinations to sustain effective clinical responses.
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Affiliation(s)
- Wen Yan
- Oncology Research Discovery, Pfizer Worldwide Research and Development, San Diego, CA, USA
| | - Chi-Yeh Chung
- Oncology Research Discovery, Pfizer Worldwide Research and Development, San Diego, CA, USA
| | - Tao Xie
- Oncology Research Discovery, Pfizer Worldwide Research and Development, San Diego, CA, USA
| | - Mark Ozeck
- Oncology Research Discovery, Pfizer Worldwide Research and Development, San Diego, CA, USA
| | - Timothy C Nichols
- Oncology Research Discovery, Pfizer Worldwide Research and Development, San Diego, CA, USA
| | - Jessica Frey
- Oncology Research Discovery, Pfizer Worldwide Research and Development, San Diego, CA, USA
| | | | - Shikhar Sharma
- Oncology Research Discovery, Pfizer Worldwide Research and Development, San Diego, CA, USA
| | - Thomas A Paul
- Oncology Research Discovery, Pfizer Worldwide Research and Development, San Diego, CA, USA
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24
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Lang J, Nie Q, Li C. Landscape and kinetic path quantify critical transitions in epithelial-mesenchymal transition. Biophys J 2021; 120:4484-4500. [PMID: 34480928 PMCID: PMC8553640 DOI: 10.1016/j.bpj.2021.08.043] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/04/2021] [Accepted: 08/30/2021] [Indexed: 01/11/2023] Open
Abstract
Epithelial-mesenchymal transition (EMT), a basic developmental process that might promote cancer metastasis, has been studied from various perspectives. Recently, the early warning theory has been used to anticipate critical transitions in EMT from mathematical modeling. However, the underlying mechanisms of EMT involving complex molecular networks remain to be clarified. Especially, how to quantify the global stability and stochastic transition dynamics of EMT and what the underlying mechanism for early warning theory in EMT is remain to be fully clarified. To address these issues, we constructed a comprehensive gene regulatory network model for EMT and quantified the corresponding potential landscape. The landscape for EMT displays multiple stable attractors, which correspond to E, M, and some other intermediate states. Based on the path-integral approach, we identified the most probable transition paths of EMT, which are supported by experimental data. Correspondingly, the results of transition actions demonstrated that intermediate states can accelerate EMT, consistent with recent studies. By integrating the landscape and path with early warning concept, we identified the potential barrier height from the landscape as a global and more accurate measure for early warning signals to predict critical transitions in EMT. The landscape results also provide an intuitive and quantitative explanation for the early warning theory. Overall, the landscape and path results advance our mechanistic understanding of dynamical transitions and roles of intermediate states in EMT, and the potential barrier height provides a new, to our knowledge, measure for critical transitions and quantitative explanations for the early warning theory.
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Affiliation(s)
- Jintong Lang
- Institute of Science and Technology for Brain-Inspired Intelligence, Shanghai, China; Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, California
| | - Chunhe Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Shanghai, China; Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China; School of Mathematical Sciences, Fudan University, Shanghai, China.
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25
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Rozum JC, Gómez Tejeda Zañudo J, Gan X, Deritei D, Albert R. Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks. SCIENCE ADVANCES 2021; 7:7/29/eabf8124. [PMID: 34272246 PMCID: PMC8284893 DOI: 10.1126/sciadv.abf8124] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 06/03/2021] [Indexed: 05/14/2023]
Abstract
We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-standing open question of how attractor count in critical random Boolean networks scales with network size and whether the scaling matches biological observations. Via 80-fold improvement in probed network size (N = 16,384), we find the unexpectedly low scaling exponent of 0.12 ± 0.05, approximately one-tenth the analytical upper bound. We demonstrate a general principle: A system's relationship to its time reversal and state-space inversion constrains its repertoire of emergent behaviors.
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Affiliation(s)
- Jordan C Rozum
- Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.
| | - Jorge Gómez Tejeda Zañudo
- Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Xiao Gan
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Dávid Deritei
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Réka Albert
- Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA
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26
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Katebi A, Ramirez D, Lu M. Computational systems-biology approaches for modeling gene networks driving epithelial-mesenchymal transitions. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2021; 1:e1021. [PMID: 34164628 PMCID: PMC8219219 DOI: 10.1002/cso2.1021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Epithelial-mesenchymal transition (EMT) is an important biological process through which epithelial cells undergo phenotypic transitions to mesenchymal cells by losing cell-cell adhesion and gaining migratory properties that cells use in embryogenesis, wound healing, and cancer metastasis. An important research topic is to identify the underlying gene regulatory networks (GRNs) governing the decision making of EMT and develop predictive models based on the GRNs. The advent of recent genomic technology, such as single-cell RNA sequencing, has opened new opportunities to improve our understanding about the dynamical controls of EMT. In this article, we review three major types of computational and mathematical approaches and methods for inferring and modeling GRNs driving EMT. We emphasize (1) the bottom-up approaches, where GRNs are constructed through literature search; (2) the top-down approaches, where GRNs are derived from genome-wide sequencing data; (3) the combined top-down and bottom-up approaches, where EMT GRNs are constructed and simulated by integrating bioinformatics and mathematical modeling. We discuss the methodologies and applications of each approach and the available resources for these studies.
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Affiliation(s)
- Ataur Katebi
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, Massachusetts, USA
| | - Daniel Ramirez
- Center for Theoretical Biological Physics, Northeastern University, Boston, Massachusetts, USA
- College of Health Solutions, Arizona State University, Tempe, Arizona, USA
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, Massachusetts, USA
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Does Cancer Biology Rely on Parrondo's Principles? Cancers (Basel) 2021; 13:cancers13092197. [PMID: 34063648 PMCID: PMC8125342 DOI: 10.3390/cancers13092197] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 04/29/2021] [Accepted: 04/29/2021] [Indexed: 12/17/2022] Open
Abstract
Simple Summary Parrondo’s paradox, whereby losing strategies or deleterious effects can combine to provide a winning outcome, has been increasingly applied by biologists to explain complex adaptations in many living systems. Here, we suggest that considering this paradox in oncology, particularly in relation to the phenotypic diversity of malignant cells, could also be a promising approach to understand several puzzling aspects of cancer biology. For example, the high genetic and epigenetic instability of cancer cells, their metastatic behavior and their capacity to enter dormancy could be explained by Parrondo’s theory. We also discuss the relevance of Parrondo’s paradox in a therapeutical framework using different examples. This work provides a compelling argument that the traditional separation between medicine and other disciplines remains a fundamental limitation that needs to be overcome if complex processes, such as oncogenesis, are to be completely understood. Abstract Many aspects of cancer biology remain puzzling, including the proliferative and survival success of malignant cells in spite of their high genetic and epigenetic instability as well as their ability to express migrating phenotypes and/or enter dormancy despite possible fitness loss. Understanding the potential adaptive value of these phenotypic traits is confounded by the fact that, when considered separately, they seem to be rather detrimental at the cell level, at least in the short term. Here, we argue that cancer’s biology and success could frequently be governed by processes underlying Parrondo’s paradox, whereby combinations of intrinsically losing strategies may result in winning outcomes. Oncogenic selection would favor Parrondo’s dynamics because, given the environmental adversity in which malignant cells emerge and evolve, alternating between various less optimal strategies would represent the sole viable option to counteract the changing and deleterious environments cells are exposed to during tumorigenesis. We suggest that malignant processes could be viewed through this lens, and we discuss how Parrondo’s principles are also important when designing therapies against cancer.
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Sherekar S, Viswanathan GA. Boolean dynamic modeling of cancer signaling networks: Prognosis, progression, and therapeutics. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2021. [DOI: 10.1002/cso2.1017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Shubhank Sherekar
- Department of Chemical Engineering Indian Institute of Technology Bombay, Powai Mumbai India
| | - Ganesh A. Viswanathan
- Department of Chemical Engineering Indian Institute of Technology Bombay, Powai Mumbai India
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Schaff DL, Singh S, Kim KB, Sutcliffe MD, Park KS, Janes KA. Fragmentation of Small-Cell Lung Cancer Regulatory States in Heterotypic Microenvironments. Cancer Res 2021; 81:1853-1867. [PMID: 33531375 PMCID: PMC8137564 DOI: 10.1158/0008-5472.can-20-1036] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 12/02/2020] [Accepted: 01/28/2021] [Indexed: 11/16/2022]
Abstract
Small-cell lung cancers derive from pulmonary neuroendocrine cells, which have stem-like properties to reprogram into other cell types upon lung injury. It is difficult to uncouple transcriptional plasticity of these transformed cells from genetic changes that evolve in primary tumors or secondary metastases. Profiling of single cells is also problematic if the required sample dissociation activates injury-like signaling and reprogramming. Here we defined cell-state heterogeneities in situ through laser capture microdissection-based 10-cell transcriptomics coupled with stochastic-profiling fluctuation analysis. In labeled cells from a small-cell lung cancer mouse model initiated by neuroendocrine deletion of Rb1-Trp53, variations in transcript abundance revealed cell-to-cell differences in regulatory state in vitro and in vivo. Fluctuating transcripts in spheroid culture were partly shared among Rb1-Trp53-null models, and heterogeneities increased considerably when cells were delivered intravenously to colonize the liver. Colonization of immunocompromised animals drove a fractional appearance of alveolar type II-like markers and poised cells for paracrine stimulation from immune cells and hepatocytes. Immunocompetency further exaggerated the fragmentation of tumor states in the liver, yielding mixed stromal signatures evident in bulk sequencing from autochthonous tumors and metastases. Dozens of transcript heterogeneities recurred irrespective of biological context; their mapped orthologs brought together observations of murine and human small-cell lung cancer. Candidate heterogeneities recurrent in the liver also stratified primary human tumors into discrete groups not readily explained by molecular subtype but with prognostic relevance. These data suggest that heterotypic interactions in the liver and lung are an accelerant for intratumor heterogeneity in small-cell lung cancer. SIGNIFICANCE: These findings demonstrate that the single-cell regulatory heterogeneity of small-cell lung cancer becomes increasingly elaborate in the liver, a common metastatic site for the disease.See related articles by Singh and colleagues, p. 1840 and Sutcliffe and colleagues, p. 1868.
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Affiliation(s)
- Dylan L Schaff
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Shambhavi Singh
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Kee-Beom Kim
- Department of Microbiology, Immunology & Cancer Biology, University of Virginia, Charlottesville, Virginia
| | - Matthew D Sutcliffe
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Kwon-Sik Park
- Department of Microbiology, Immunology & Cancer Biology, University of Virginia, Charlottesville, Virginia
| | - Kevin A Janes
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia.
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia
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Chauhan L, Ram U, Hari K, Jolly MK. Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer. eLife 2021; 10:e64522. [PMID: 33729159 PMCID: PMC8012062 DOI: 10.7554/elife.64522] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 03/16/2021] [Indexed: 02/07/2023] Open
Abstract
Phenotypic (non-genetic) heterogeneity has significant implications for the development and evolution of organs, organisms, and populations. Recent observations in multiple cancers have unraveled the role of phenotypic heterogeneity in driving metastasis and therapy recalcitrance. However, the origins of such phenotypic heterogeneity are poorly understood in most cancers. Here, we investigate a regulatory network underlying phenotypic heterogeneity in small cell lung cancer, a devastating disease with no molecular targeted therapy. Discrete and continuous dynamical simulations of this network reveal its multistable behavior that can explain co-existence of four experimentally observed phenotypes. Analysis of the network topology uncovers that multistability emerges from two teams of players that mutually inhibit each other, but members of a team activate one another, forming a 'toggle switch' between the two teams. Deciphering these topological signatures in cancer-related regulatory networks can unravel their 'latent' design principles and offer a rational approach to characterize phenotypic heterogeneity in a tumor.
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Affiliation(s)
- Lakshya Chauhan
- Centre for BioSystems Science and Engineering, Indian Institute of ScienceBangaloreIndia
- Undergraduate Programme, Indian Institute of ScienceBangaloreIndia
| | - Uday Ram
- Centre for BioSystems Science and Engineering, Indian Institute of ScienceBangaloreIndia
- Undergraduate Programme, Indian Institute of ScienceBangaloreIndia
| | - Kishore Hari
- Centre for BioSystems Science and Engineering, Indian Institute of ScienceBangaloreIndia
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of ScienceBangaloreIndia
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Neuroendocrine-Related Circulating Transcripts in Small-Cell Lung Cancers: Detection Methods and Future Perspectives. Cancers (Basel) 2021; 13:cancers13061339. [PMID: 33809582 PMCID: PMC8061767 DOI: 10.3390/cancers13061339] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 02/28/2021] [Accepted: 03/11/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary The recent implementation of techniques to study circulating tumor cells allowed a rapid increase in knowledge about the molecular basis of Small-Cell Lung Cancer (SCLC), which appears to be more heterogeneous and dynamic than expected. Here, we present a summary of current knowledge and new findings about some of the neuroendocrine-related transcripts expressed in SCLC patients that could offer a great opportunity in distinguishing and managing different SCLC phenotypes. Abstract No well-established prognostic or predictive molecular markers of small-cell lung cancer (SCLC) are currently available; therefore, all patients receive standard treatment. Adequate quantities and quality of tissue samples are frequently unavailable to perform a molecular analysis of SCLC, which appears more heterogeneous and dynamic than expected. The implementation of techniques to study circulating tumor cells could offer a suitable alternative to expand the knowledge of the molecular basis of a tumor. In this context, the advantage of SCLC circulating cells to express some specific markers to be explored in blood as circulating transcripts could offer a great opportunity in distinguishing and managing different SCLC phenotypes. Here, we present a summary of published data and new findings about the detection methods and potential application of a group of neuroendocrine related transcripts in the peripheral blood of SCLC patients. In the era of new treatments, easy and rapid detection of informative biomarkers in blood warrants further investigation, since it represents an important option to obtain essential information for disease monitoring and/or better treatment choices.
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Chedere A, Hari K, Kumar S, Rangarajan A, Jolly MK. Multi-Stability and Consequent Phenotypic Plasticity in AMPK-Akt Double Negative Feedback Loop in Cancer Cells. J Clin Med 2021; 10:jcm10030472. [PMID: 33530625 PMCID: PMC7865639 DOI: 10.3390/jcm10030472] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/07/2021] [Accepted: 01/21/2021] [Indexed: 12/23/2022] Open
Abstract
Adaptation and survival of cancer cells to various stress and growth factor conditions is crucial for successful metastasis. A double-negative feedback loop between two serine/threonine kinases AMPK (AMP-activated protein kinase) and Akt can regulate the adaptation of breast cancer cells to matrix-deprivation stress. This feedback loop can significantly generate two phenotypes or cell states: matrix detachment-triggered pAMPKhigh/ pAktlow state, and matrix (re)attachment-triggered pAkthigh/ pAMPKlow state. However, whether these two cell states can exhibit phenotypic plasticity and heterogeneity in a given cell population, i.e., whether they can co-exist and undergo spontaneous switching to generate the other subpopulation, remains unclear. Here, we develop a mechanism-based mathematical model that captures the set of experimentally reported interactions among AMPK and Akt. Our simulations suggest that the AMPK-Akt feedback loop can give rise to two co-existing phenotypes (pAkthigh/ pAMPKlow and pAMPKhigh/pAktlow) in specific parameter regimes. Next, to test the model predictions, we segregated these two subpopulations in MDA-MB-231 cells and observed that each of them was capable of switching to another in adherent conditions. Finally, the predicted trends are supported by clinical data analysis of The Cancer Genome Atlas (TCGA) breast cancer and pan-cancer cohorts that revealed negatively correlated pAMPK and pAkt protein levels. Overall, our integrated computational-experimental approach unravels that AMPK-Akt feedback loop can generate multi-stability and drive phenotypic switching and heterogeneity in a cancer cell population.
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Affiliation(s)
- Adithya Chedere
- Department of Molecular Reproduction, Development, and Genetics, Indian Institute of Science, Bangalore 560012, India; (A.C.); (S.K.)
| | - Kishore Hari
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India;
| | - Saurav Kumar
- Department of Molecular Reproduction, Development, and Genetics, Indian Institute of Science, Bangalore 560012, India; (A.C.); (S.K.)
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India;
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Annapoorni Rangarajan
- Department of Molecular Reproduction, Development, and Genetics, Indian Institute of Science, Bangalore 560012, India; (A.C.); (S.K.)
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India;
- Correspondence: (A.R.); (M.K.J.)
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India;
- Correspondence: (A.R.); (M.K.J.)
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Domanskyi S, Hakansson A, Bertus TJ, Paternostro G, Piermarocchi C. Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics. PeerJ 2021; 9:e10670. [PMID: 33520459 PMCID: PMC7811293 DOI: 10.7717/peerj.10670] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/08/2020] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.e., without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. These new approaches are integrated in a software platform that includes many other state-of-the-art methodologies and provides a self-contained toolkit for scRNA-seq analysis. RESULTS We present a suite of software elements for the analysis of scRNA-seq data. This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. We illustrate the capability of the software using data from large datasets of peripheral blood mononuclear cells (PBMC), as well as plasma cells of bone marrow samples from healthy donors and multiple myeloma patients. We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data. AVAILABILITY The DCS toolkit is available for download and installation through the Python Package Index (PyPI). The software can be deployed using the Python import function following installation. Source code is also available for download on Zenodo: DOI 10.5281/zenodo.2533377. SUPPLEMENTARY INFORMATION Supplemental Materials are available at PeerJ online.
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Affiliation(s)
- Sergii Domanskyi
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA
| | - Alex Hakansson
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Thomas J. Bertus
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA
| | | | - Carlo Piermarocchi
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA
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Capp JP, Thomas F. A Similar Speciation Process Relying on Cellular Stochasticity in Microbial and Cancer Cell Populations. iScience 2020; 23:101531. [PMID: 33083761 PMCID: PMC7502340 DOI: 10.1016/j.isci.2020.101531] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Similarities between microbial and cancer cells were noticed in recent years and serve as a basis for an atavism theory of cancer. Cancer cells would rely on the reactivation of an ancestral "genetic program" that would have been repressed in metazoan cells. Here we argue that cancer cells resemble unicellular organisms mainly in their similar way to exploit cellular stochasticity to produce cell specialization and maximize proliferation. Indeed, the relationship between low stochasticity, specialization, and quiescence found in normal differentiated metazoan cells is lost in cancer. On the contrary, low stochasticity and specialization are associated with high proliferation among cancer cells, as it is observed for the "specialist" cells in microbial populations that fully exploit nutritional resources to maximize proliferation. Thus, we propose a model where the appearance of cancer phenotypes can be solely due to an adaptation and a speciation process based on initial increase in cellular stochasticity.
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Affiliation(s)
- Jean-Pascal Capp
- Toulouse Biotechnology Institute, University of Toulouse, INSA, CNRS, INRAE, 31077 Toulouse, France
| | - Frédéric Thomas
- CREEC, UMR IRD 224, CNRS 5290, University of Montpellier, 34394 Montpellier, France
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35
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Tripathi S, Kessler DA, Levine H. Biological Networks Regulating Cell Fate Choice Are Minimally Frustrated. PHYSICAL REVIEW LETTERS 2020; 125:088101. [PMID: 32909810 DOI: 10.1103/physrevlett.125.088101] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 07/14/2020] [Indexed: 06/11/2023]
Abstract
Characterization of the differences between biological and random networks can reveal the design principles that enable the robust realization of crucial biological functions including the establishment of different cell types. Previous studies, focusing on identifying topological features that are present in biological networks but not in random networks, have, however, provided few functional insights. We use a Boolean modeling framework and ideas from the spin glass literature to identify functional differences between five real biological networks and random networks with similar topological features. We show that minimal frustration is a fundamental property that allows biological networks to robustly establish cell types and regulate cell fate choice, and that this property can emerge in complex networks via Darwinian evolution. The study also provides clues regarding how the regulation of cell fate choice can go awry in a disease like cancer and lead to the emergence of aberrant cell types.
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Affiliation(s)
- Shubham Tripathi
- PhD Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, USA
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, USA
- Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA
| | - David A Kessler
- Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, USA
- Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA
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36
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Konstorum A, Tesfay L, Paul BT, Torti FM, Laubenbacher RC, Torti SV. Systems biology of ferroptosis: A modeling approach. J Theor Biol 2020; 493:110222. [PMID: 32114023 PMCID: PMC7254156 DOI: 10.1016/j.jtbi.2020.110222] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 02/22/2020] [Accepted: 02/26/2020] [Indexed: 02/06/2023]
Abstract
Ferroptosis is a recently discovered form of iron-dependent regulated cell death (RCD) that occurs via peroxidation of phospholipids containing polyunsaturated fatty acid (PUFA) moieties. Activating this form of cell death is an emerging strategy in cancer treatment. Because multiple pathways and molecular species contribute to the ferroptotic process, predicting which tumors will be sensitive to ferroptosis is a challenge. We thus develop a mathematical model of several critical pathways to ferroptosis in order to perform a systems-level analysis of the process. We show that sensitivity to ferroptosis depends on the activity of multiple upstream cascades, including PUFA incorporation into the phospholipid membrane, and the balance between levels of pro-oxidant factors (reactive oxygen species, lipoxogynases) and antioxidant factors (GPX4). We perform a systems-level analysis of ferroptosis sensitivity as an outcome of five input variables (ACSL4, SCD1, ferroportin, transferrin receptor, and p53) and organize the resulting simulations into 'high' and 'low' ferroptosis sensitivity groups. We make a novel prediction corresponding to the combinatorial requirements of ferroptosis sensitivity to SCD1 and ACSL4 activity. To validate our prediction, we model the ferroptotic response of an ovarian cancer stem cell line following single- and double-knockdown of SCD1 and ACSL4. We find that the experimental outcomes are consistent with our simulated predictions. This work suggests that a systems-level approach is beneficial for understanding the complex combined effects of ferroptotic input, and in predicting cancer susceptibility to ferroptosis.
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Affiliation(s)
- Anna Konstorum
- Center for Quantitative Medicine, UConn Health, 263 Farmington Ave., Farmington, CT, United States of America.
| | - Lia Tesfay
- Department of Molecular Biology and Biophysics, UConn Health, 263 Farmington Ave., Farmington, CT, United States of America
| | - Bibbin T Paul
- Department of Molecular Biology and Biophysics, UConn Health, 263 Farmington Ave., Farmington, CT, United States of America
| | - Frank M Torti
- Department of Medicine, UConn Health, 263 Farmington Ave., Farmington, CT, United States of America
| | - Reinhard C Laubenbacher
- Center for Quantitative Medicine, UConn Health, 263 Farmington Ave., Farmington, CT, United States of America; Jackson Laboratory for Genomic Medicine, 263 Farmington Ave., Farmington, CT, United States of America
| | - Suzy V Torti
- Department of Molecular Biology and Biophysics, UConn Health, 263 Farmington Ave., Farmington, CT, United States of America
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37
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Simpson KL, Stoney R, Frese KK, Simms N, Rowe W, Pearce SP, Humphrey S, Booth L, Morgan D, Dynowski M, Trapani F, Catozzi A, Revill M, Helps T, Galvin M, Girard L, Nonaka D, Carter L, Krebs MG, Cook N, Carter M, Priest L, Kerr A, Gazdar AF, Blackhall F, Dive C. A biobank of small cell lung cancer CDX models elucidates inter- and intratumoral phenotypic heterogeneity. NATURE CANCER 2020; 1:437-451. [PMID: 35121965 DOI: 10.1038/s43018-020-0046-2] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 02/26/2020] [Indexed: 12/17/2022]
Abstract
Although small cell lung cancer (SCLC) is treated as a homogeneous disease, biopsies and preclinical models reveal heterogeneity in transcriptomes and morphology. SCLC subtypes were recently defined by neuroendocrine transcription factor (NETF) expression. Circulating-tumor-cell-derived explant models (CDX) recapitulate donor patients' tumor morphology, diagnostic NE marker expression and chemotherapy responses. We describe a biobank of 38 CDX models, including six CDX pairs generated pretreatment and at disease progression revealing complex intra- and intertumoral heterogeneity. Transcriptomic analysis confirmed three of four previously described subtypes based on ASCL1, NEUROD1 and POU2F3 expression and identified a previously unreported subtype based on another NETF, ATOH1. We document evolution during disease progression exemplified by altered MYC and NOTCH gene expression, increased 'variant' cell morphology, and metastasis without strong evidence of epithelial to mesenchymal transition. This CDX biobank provides a research resource to facilitate SCLC personalized medicine.
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Affiliation(s)
- Kathryn L Simpson
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Ruth Stoney
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
- Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Kristopher K Frese
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Nicole Simms
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - William Rowe
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Simon P Pearce
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Sam Humphrey
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Laura Booth
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Derrick Morgan
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Marek Dynowski
- Scientific Computing Core Facility, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK
| | - Francesca Trapani
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Alessia Catozzi
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Mitchell Revill
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Thomas Helps
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Melanie Galvin
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Luc Girard
- Hamon Center for Therapeutic Oncology Research, Department of Pharmacology, UT Southwestern Medical Center, Dallas, TX, USA
| | | | - Louise Carter
- The Christie NHS Foundation Trust, Manchester, UK
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Matthew G Krebs
- The Christie NHS Foundation Trust, Manchester, UK
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Natalie Cook
- The Christie NHS Foundation Trust, Manchester, UK
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Mathew Carter
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Lynsey Priest
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Alastair Kerr
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Adi F Gazdar
- Hamon Center for Therapeutic Oncology Research, Department of Pharmacology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Fiona Blackhall
- The Christie NHS Foundation Trust, Manchester, UK
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK
| | - Caroline Dive
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK.
- Cancer Research UK Lung Cancer Centre of Excellence, Manchester, UK.
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Domanskyi S, Hakansson A, Paternostro G, Piermarocchi C. Modeling disease progression in Multiple Myeloma with Hopfield networks and single-cell RNA-seq. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2019; 2019:2129-2136. [PMID: 35574240 PMCID: PMC9097163 DOI: 10.1109/bibm47256.2019.8983325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
UNLABELLED Associative memories in Hopfield's neural networks are mapped to gene expression pattern to model different paths of disease progression towards Multiple Myeloma (MM). The model is built using single cell RNA-seq data from bone marrow aspirates of MM patients as well as patients diagnosed with Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), two medical conditions that often progress to full MM. RESULTS We identify different clusters of MGUS, SMM, and MM cells, map them to Hopfield associative memory patterns, and model the dynamics of transition between the different patterns. The model is then used to identify genes that are differentialy expressed across different MM stages and whose simultaneous inhibition is associated to a delayed disease progression.
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Wooten DJ, Groves SM, Tyson DR, Liu Q, Lim JS, Albert R, Lopez CF, Sage J, Quaranta V. Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers. PLoS Comput Biol 2019; 15:e1007343. [PMID: 31671086 PMCID: PMC6860456 DOI: 10.1371/journal.pcbi.1007343] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 11/18/2019] [Accepted: 08/19/2019] [Indexed: 01/15/2023] Open
Abstract
Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.
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Affiliation(s)
- David J. Wooten
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Sarah M. Groves
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Darren R. Tyson
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Qi Liu
- Departments of Biomedical Informatics and Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jing S. Lim
- Departments of Pediatrics and Genetics, Stanford University, Stanford, California, United States of America
| | - Réka Albert
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Carlos F. Lopez
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Julien Sage
- Departments of Pediatrics and Genetics, Stanford University, Stanford, California, United States of America
| | - Vito Quaranta
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
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40
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Lehman JM, aMassion PP. Somatostatin receptor 2 targeting in small cell lung carcinoma: perspectives. Oncotarget 2019; 10:4727-4730. [PMID: 31413814 PMCID: PMC6677662 DOI: 10.18632/oncotarget.27107] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 06/29/2019] [Indexed: 12/30/2022] Open
Abstract
Small Cell lung carcinoma (SCLC) is the most lethal and aggressive subtype of lung cancer. Novel targeting approaches and agents are desperately needed. In this perspectives, we briefly explore recent data published in the International Journal of Cancer suggesting Somatostatin Receptor 2 (SSTR2) as a viable target for SCLC, summarize the current clinical trial space, and describe promising new research and clinical directions for Somatostatin Receptor 2 targeting in SCLC.
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Affiliation(s)
- Jonathan M Lehman
- Division of Medical Oncology, Thoracic Program, Vanderbilt Ingram Cancer Center, Nashville, TN, USA.,Thoracic Program, Vanderbilt Ingram Cancer Center, Nashville, TN, USA.,Veterans Affairs, Tennessee Valley Healthcare System, Nashville Campus, Nashville, TN, USA
| | - Pierre P aMassion
- Division of Medical Oncology, Thoracic Program, Vanderbilt Ingram Cancer Center, Nashville, TN, USA.,Thoracic Program, Vanderbilt Ingram Cancer Center, Nashville, TN, USA.,Veterans Affairs, Tennessee Valley Healthcare System, Nashville Campus, Nashville, TN, USA
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41
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Rudin CM, Poirier JT, Byers LA, Dive C, Dowlati A, George J, Heymach JV, Johnson JE, Lehman JM, MacPherson D, Massion PP, Minna JD, Oliver TG, Quaranta V, Sage J, Thomas RK, Vakoc CR, Gazdar AF. Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data. Nat Rev Cancer 2019; 19:289-297. [PMID: 30926931 PMCID: PMC6538259 DOI: 10.1038/s41568-019-0133-9] [Citation(s) in RCA: 634] [Impact Index Per Article: 126.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Small cell lung cancer (SCLC) is an exceptionally lethal malignancy for which more effective therapies are urgently needed. Several lines of evidence, from SCLC primary human tumours, patient-derived xenografts, cancer cell lines and genetically engineered mouse models, appear to be converging on a new model of SCLC subtypes defined by differential expression of four key transcription regulators: achaete-scute homologue 1 (ASCL1; also known as ASH1), neurogenic differentiation factor 1 (NeuroD1), yes-associated protein 1 (YAP1) and POU class 2 homeobox 3 (POU2F3). In this Perspectives article, we review and synthesize these recent lines of evidence and propose a working nomenclature for SCLC subtypes defined by relative expression of these four factors. Defining the unique therapeutic vulnerabilities of these subtypes of SCLC should help to focus and accelerate therapeutic research, leading to rationally targeted approaches that may ultimately improve clinical outcomes for patients with this disease.
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Affiliation(s)
| | - John T Poirier
- Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | | | | | | | | | | | - Jane E Johnson
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | | | | | - John D Minna
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Trudy G Oliver
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Vito Quaranta
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | - Adi F Gazdar
- University of Texas Southwestern Medical Center, Dallas, TX, USA
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42
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Zañudo JGT, Guinn MT, Farquhar K, Szenk M, Steinway SN, Balázsi G, Albert R. Towards control of cellular decision-making networks in the epithelial-to-mesenchymal transition. Phys Biol 2019; 16:031002. [PMID: 30654341 PMCID: PMC6405305 DOI: 10.1088/1478-3975/aaffa1] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
We present the epithelial-to-mesenchymal transition (EMT) from two perspectives: experimental/technological and theoretical. We review the state of the current understanding of the regulatory networks that underlie EMT in three physiological contexts: embryonic development, wound healing, and metastasis. We describe the existing experimental systems and manipulations used to better understand the molecular participants and factors that influence EMT and metastasis. We review the mathematical models of the regulatory networks involved in EMT, with a particular emphasis on the network motifs (such as coupled feedback loops) that can generate intermediate hybrid states between the epithelial and mesenchymal states. Ultimately, the understanding gained about these networks should be translated into methods to control phenotypic outcomes, especially in the context of cancer therapeutic strategies. We present emerging theories of how to drive the dynamics of a network toward a desired dynamical attractor (e.g. an epithelial cell state) and emerging synthetic biology technologies to monitor and control the state of cells.
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Affiliation(s)
- Jorge Gómez Tejeda Zañudo
- Department of Physics, Pennsylvania State University, University Park, PA 16802, USA
- Department of Medical Oncology, Dana-Farber Cancer Center, Boston, MA 02215, USA
- Cancer Program, Eli and Edythe L. Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - M. Tyler Guinn
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11794 USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
- Stony Brook Medical Scientist Training Program, 101 Nicolls Road, Stony Brook, NY 11794, USA
| | - Kevin Farquhar
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Mariola Szenk
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11794 USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Steven N. Steinway
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Gábor Balázsi
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11794 USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Réka Albert
- Department of Physics, Pennsylvania State University, University Park, PA 16802, USA
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
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43
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Udyavar AR, Wooten DJ, Hoeksema M, Bansal M, Califano A, Estrada L, Schnell S, Irish JM, Massion PP, Quaranta V. Correction: Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity. Cancer Res 2019; 79:1014. [PMID: 30824677 DOI: 10.1158/0008-5472.can-19-0069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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De Bastiani MA, Pfaffenseller B, Klamt F. Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning. Front Pharmacol 2018; 9:697. [PMID: 30034338 PMCID: PMC6043797 DOI: 10.3389/fphar.2018.00697] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 06/08/2018] [Indexed: 01/09/2023] Open
Abstract
Drug discovery is a very expensive and time-consuming endeavor. Fortunately, recent omics technologies and Systems Biology approaches introduced interesting new tools to achieve this task, facilitating the repurposing of already known drugs to new therapeutic assignments using gene expression data and bioinformatics. The inherent role of transcription factors in gene expression modulation makes them strong candidates for master regulators of phenotypic transitions. However, transcription factors expression itself usually does not reflect its activity changes due to post-transcriptional modifications and other complications. In this aspect, the use of high-throughput transcriptomic data may be employed to infer transcription factors-targets interactions and assess their activity through co-expression networks, which can be further used to search for drugs capable of reverting the gene expression profile of pathological phenotypes employing the connectivity maps paradigm. Following this idea, we argue that a module-oriented connectivity map approach using transcription factors-centered networks would aid the query for new repositioning candidates. Through a brief case study, we explored this idea in bipolar disorder, retrieving known drugs used in the usual clinical scenario as well as new candidates with potential therapeutic application in this disease. Indeed, the results of the case study indicate just how promising our approach may be to drug repositioning.
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Affiliation(s)
- Marco A De Bastiani
- Laboratory of Cellular Biochemistry, Department of Biochemistry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Science and Technology for Translational Medicine, Porto Alegre, Brazil
| | - Bianca Pfaffenseller
- Laboratory of Cellular Biochemistry, Department of Biochemistry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,Laboratory of Molecular Psychiatry, Clinicas Hospital of Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Fabio Klamt
- Laboratory of Cellular Biochemistry, Department of Biochemistry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Science and Technology for Translational Medicine, Porto Alegre, Brazil
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45
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Enhanced Delta-Notch Lateral Inhibition Model Incorporating Intracellular Notch Heterogeneity and Tension-Dependent Rate of Delta-Notch Binding that Reproduces Sprouting Angiogenesis Patterns. Sci Rep 2018; 8:9519. [PMID: 29934586 PMCID: PMC6015056 DOI: 10.1038/s41598-018-27645-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 06/06/2018] [Indexed: 01/17/2023] Open
Abstract
Endothelial cells adopt unique cell fates during sprouting angiogenesis, differentiating into tip or stalk cells. The fate selection process is directed by Delta-Notch lateral inhibition pathway. Classical Delta-Notch models produce a spatial pattern of tip cells separated by a single stalk cell, or the salt-and-pepper pattern. However, classical models cannot explain alternative tip-stalk patterning, such as tip cells that are separated by two or more stalk cells. We show that lateral inhibition models involving only Delta and Notch proteins can also recapitulate experimental tip-stalk patterns by invoking two mechanisms, specifically, intracellular Notch heterogeneity and tension-dependent rate of Delta-Notch binding. We introduce our computational model and analysis where we establish that our enhanced Delta-Notch lateral inhibition model can recapitulate a greater variety of tip-stalk patterning which is previously not possible using classical lateral inhibition models. In our enhanced Delta-Notch lateral inhibition model, we observe the existence of a hybrid cell type displaying intermediate tip and stalk cells’ characteristics. We validate the existence of such hybrid cells by immuno-staining of endothelial cells with tip cell markers, Delta and CD34, which substantiates our enhanced model.
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46
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Ross BC, Boguslav M, Weeks H, Costello JC. Simulating heterogeneous populations using Boolean models. BMC SYSTEMS BIOLOGY 2018; 12:64. [PMID: 29879983 PMCID: PMC5992775 DOI: 10.1186/s12918-018-0591-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 05/24/2018] [Indexed: 11/10/2022]
Abstract
Background Certain biological processes, such as the development of cancer and immune activation, can be controlled by rare cellular events that are difficult to capture computationally through simulations of individual cells. Information about such rare events can be gleaned from an attractor analysis, for which a variety of methods exist (in particular for Boolean models). However, explicitly simulating a defined mixed population of cells in a way that tracks even the rarest subpopulations remains an open challenge. Results Here we show that when cellular states are described using a Boolean network model, one can exactly simulate the dynamics of non-interacting, highly heterogeneous populations directly, without having to model the various subpopulations. This strategy captures even the rarest outcomes of the model with no sampling error. Our method can incorporate heterogeneity in both cell state and, by augmenting the model, the underlying rules of the network as well (e.g., introducing loss-of-function genetic alterations). We demonstrate our method by using it to simulate a heterogeneous population of Boolean networks modeling the T-cell receptor, spanning ∼ 1020 distinct cellular states and mutational profiles. Conclusions We have developed a method for using Boolean models to perform a population-level simulation, in which the population consists of non-interacting individuals existing in different states. This approach can be used even when there are far too many distinct subpopulations to model individually. Electronic supplementary material The online version of this article (10.1186/s12918-018-0591-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Brian C Ross
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, 12801 E. 17th Ave., Aurora, CO, 80045, USA.,Department of Pharmacology, University of Colorado Anschutz Medical Campus, 12800 E. 19th Ave., Aurora, CO, 80045, USA
| | - Mayla Boguslav
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, 12801 E. 17th Ave., Aurora, CO, 80045, USA.,Department of Pharmacology, University of Colorado Anschutz Medical Campus, 12800 E. 19th Ave., Aurora, CO, 80045, USA
| | - Holly Weeks
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, Aurora, CO, 80045, USA
| | - James C Costello
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, 12801 E. 17th Ave., Aurora, CO, 80045, USA. .,Department of Pharmacology, University of Colorado Anschutz Medical Campus, 12800 E. 19th Ave., Aurora, CO, 80045, USA.
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47
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G T Zañudo J, Steinway SN, Albert R. Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer. ACTA ACUST UNITED AC 2018; 9:1-10. [PMID: 32954058 PMCID: PMC7487767 DOI: 10.1016/j.coisb.2018.02.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer. Cancer is rooted in incorrect cellular decisions caused by genetic alterations. Dynamic models of signaling networks can map the relevant repertoire of alterations. Discrete dynamic network models can predict therapeutic interventions. Progress in personalized medicine needs integration of multiple data and model types.
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Affiliation(s)
- Jorge G T Zañudo
- Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute and Broad Institute of Harvard and MIT, Boston MA, USA
| | - Steven N Steinway
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Réka Albert
- Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.,Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA
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Abstract
Small cell lung cancer (SCLC) is a devastating and aggressive neuroendocrine carcinoma of the lung. It accounts for ~15% of lung cancer mortality and has had no improvement in standard treatment options for nearly 30 years. However, there is now hope for change with new therapies and modalities of therapy. Immunotherapies and checkpoint inhibitors are entering clinical practice, selected targeted therapies show promise, and "smart bomb"-based drug/radioconjugates have led to good response in early clinical trials. Additionally, new research insights into the genetics and tumor heterogeneity of SCLC alongside the availability of new tools such as patient-derived or circulating tumor cell xenografts offer the potential to shine light on this beshadowed cancer.
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Jolly MK, Kulkarni P, Weninger K, Orban J, Levine H. Phenotypic Plasticity, Bet-Hedging, and Androgen Independence in Prostate Cancer: Role of Non-Genetic Heterogeneity. Front Oncol 2018; 8:50. [PMID: 29560343 PMCID: PMC5845637 DOI: 10.3389/fonc.2018.00050] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2017] [Accepted: 02/19/2018] [Indexed: 12/21/2022] Open
Abstract
It is well known that genetic mutations can drive drug resistance and lead to tumor relapse. Here, we focus on alternate mechanisms-those without mutations, such as phenotypic plasticity and stochastic cell-to-cell variability that can also evade drug attacks by giving rise to drug-tolerant persisters. The phenomenon of persistence has been well-studied in bacteria and has also recently garnered attention in cancer. We draw a parallel between bacterial persistence and resistance against androgen deprivation therapy in prostate cancer (PCa), the primary standard care for metastatic disease. We illustrate how phenotypic plasticity and consequent mutation-independent or non-genetic heterogeneity possibly driven by protein conformational dynamics can stochastically give rise to androgen independence in PCa, and suggest that dynamic phenotypic plasticity should be considered in devising therapeutic dosing strategies designed to treat and manage PCa.
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Affiliation(s)
- Mohit Kumar Jolly
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States
| | - Prakash Kulkarni
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, United States
| | - Keith Weninger
- Department of Physics, North Carolina State University, Raleigh, NC, United States
| | - John Orban
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, United States
- Department of Chemistry and Biochemistry, University of Maryland, College Park, College Park, United States
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States
- Department of Bioengineering, Rice University, Houston, TX, United States
- Department of Physics and Astronomy, Rice University, Houston, TX, United States
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50
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Bloomingdale P, Nguyen VA, Niu J, Mager DE. Boolean network modeling in systems pharmacology. J Pharmacokinet Pharmacodyn 2018; 45:159-180. [PMID: 29307099 PMCID: PMC6531050 DOI: 10.1007/s10928-017-9567-4] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 12/29/2017] [Indexed: 01/01/2023]
Abstract
Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.
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Affiliation(s)
- Peter Bloomingdale
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, 431 Kapoor Hall, Buffalo, NY, 14214, USA
| | - Van Anh Nguyen
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, 431 Kapoor Hall, Buffalo, NY, 14214, USA
| | - Jin Niu
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, 431 Kapoor Hall, Buffalo, NY, 14214, USA
| | - Donald E Mager
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, 431 Kapoor Hall, Buffalo, NY, 14214, USA.
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