1
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Gao W, Sun L, Gai J, Cao Y, Zhang S. Exploring the resistance mechanism of triple-negative breast cancer to paclitaxel through the scRNA-seq analysis. PLoS One 2024; 19:e0297260. [PMID: 38227591 PMCID: PMC10791000 DOI: 10.1371/journal.pone.0297260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND The triple negative breast cancer (TNBC) is the most malignant subtype of breast cancer with high aggressiveness. Although paclitaxel-based chemotherapy scenario present the mainstay in TNBC treatment, paclitaxel resistance is still a striking obstacle for cancer cure. So it is imperative to probe new therapeutic targets through illustrating the mechanisms underlying paclitaxel chemoresistance. METHODS The Single cell RNA sequencing (scRNA-seq) data of TNBC cells treated with paclitaxel at different points were downloaded from the Gene Expression Omnibus (GEO) database. The Seurat R package was used to filter and integrate the scRNA-seq expression matrix. Cells were further clustered by the FindClusters function, and the gene marker of each subset was defined by FindAllMarkers function. Then, the hallmark score of each cell was calculated by AUCell R package, the biological function of the highly expressed interest genes was analyzed by the DAVID database. Subsequently, we performed pseudotime analysis to explore the change patterns of drug resistance genes and SCENIC analysis to identify the key transcription factors (TFs). Finally, the inhibitors of which were also analyzed by the CTD database. RESULTS We finally obtained 6 cell subsets from 2798 cells, which were marked as AKR1C3+, WNT7A+, FAM72B+, RERG+, IDO1+ and HEY1+HCC1143 cell subsets, among which the AKR1C3+, IDO1+ and HEY1+ cell subsets proportions increased with increasing treatment time, and then were regarded as paclitaxel resistance subsets. Hallmark score and pseudotime analysis showed that these paclitaxel resistance subsets were associated with the inflammatory response, virus and interferon response activation. In addition, the gene regulatory networks (GRNs) indicated that 3 key TFs (STAT1, CEBPB and IRF7) played vital role in promoting resistance development, and five common inhibitors targeted these TFs as potential combination therapies of paclitaxel were identified. CONCLUSION In this study, we identified 3 paclitaxel resistance relevant IFs and their inhibitors, which offers essential molecular basis for paclitaxel resistance and beneficial guidance for the combination of paclitaxel in clinical TNBC therapy.
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Affiliation(s)
- Wei Gao
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Linlin Sun
- Day Surgery Center, Dalian Municipal Central Hospital, Dalian, China
| | - Jinwei Gai
- Day Surgery Center, Dalian Municipal Central Hospital, Dalian, China
| | - Yinan Cao
- Graduate School of Dalian Medical University, Dalian, China
| | - Shuqun Zhang
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
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2
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Groves SM, Quaranta V. Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1225736. [PMID: 37731743 PMCID: PMC10507267 DOI: 10.3389/fnetp.2023.1225736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/25/2023] [Indexed: 09/22/2023]
Abstract
Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.
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Affiliation(s)
- Sarah M. Groves
- Department of Pharmacology, Vanderbilt University, Nashville, TN, United States
| | - Vito Quaranta
- Department of Pharmacology, Vanderbilt University, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
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3
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Copperman J, Gross SM, Chang YH, Heiser LM, Zuckerman DM. Morphodynamical cell state description via live-cell imaging trajectory embedding. Commun Biol 2023; 6:484. [PMID: 37142678 PMCID: PMC10160022 DOI: 10.1038/s42003-023-04837-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 04/10/2023] [Indexed: 05/06/2023] Open
Abstract
Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories-that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.
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Affiliation(s)
- Jeremy Copperman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA.
| | - Sean M Gross
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA.
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA.
| | - Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA.
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA.
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4
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Yang EY, Howard GR, Brock A, Yankeelov TE, Lorenzo G. Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin. Front Mol Biosci 2022; 9:972146. [PMID: 36172049 PMCID: PMC9510895 DOI: 10.3389/fmolb.2022.972146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/17/2022] [Indexed: 11/20/2022] Open
Abstract
The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the cytotoxic effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that treatment with doxorubicin induces a compartmentalization of the breast cancer cell population into surviving cells, which continue proliferating after treatment, and irreversibly damaged cells, which gradually transition from proliferating to treatment-induced death. The model is fit to experimental data including variations in drug concentration, inter-treatment interval, and number of doses. Our model recapitulates tumor cell dynamics in all these scenarios (as quantified by the concordance correlation coefficient, CCC > 0.95). In particular, superior tumor control is observed with higher doxorubicin concentrations, shorter inter-treatment intervals, and a higher number of doses (p < 0.05). Longer inter-treatment intervals require adapting the model parameterization after each doxorubicin dose, suggesting the promotion of chemoresistance. Additionally, we propose promising empirical formulas to describe the variation of model parameters as functions of doxorubicin concentration (CCC > 0.78). Thus, we conclude that our mathematical model could deepen our understanding of the cytotoxic effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.
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Affiliation(s)
- Emily Y. Yang
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Grant R. Howard
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
- Interdisciplinary Life Sciences Program, The University of Texas at Austin, Austin, TX, United States
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States
- Department of Oncology, The University of Texas at Austin, Austin, TX, United States
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
- *Correspondence: Guillermo Lorenzo, ,
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5
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Asnaashari TG, Chang YH. Strategies to Reduce Long-Term Drug Resistance by Considering Effects of Differential Selective Treatments. MATHEMATICAL AND COMPUTATIONAL ONCOLOGY : THIRD INTERNATIONAL SYMPOSIUM, ISMCO 2021, VIRTUAL EVENT, OCTOBER 11-13, 2021 : PROCEEDINGS. ISMCO (SYMPOSIUM) (3RD : 2021 : ONLINE) 2021; 13060:49-60. [PMID: 37342205 PMCID: PMC10280777 DOI: 10.1007/978-3-030-91241-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
Despite great advances in modeling and cancer therapy using optimal control theory, tumor heterogeneity and drug resistance are major obstacles in cancer treatments. Since recent biological studies demonstrated the evidence of tumor heterogeneity and assessed potential biological and clinical implications, tumor heterogeneity should be taken into account in the optimal control problem to improve treatment strategies. Here, first we study the effects of two different treatment strategies (i.e., symmetric and asymmetric) in a minimal two-population model to examine the long-term effects of these treatment methods on the system. Second, by considering tumor adaptation to treatment as a factor of the cost function, the optimal treatment strategy is derived. Numerical examples show that optimal treatment decreases tumor burden for the long-term by decreasing rate of tumor adaptation over time.
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Affiliation(s)
- Tina Ghodsi Asnaashari
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, USA
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6
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Jolly MK, Murphy RJ, Bhatia S, Whitfield HJ, Redfern A, Davis MJ, Thompson EW. Measuring and Modelling the Epithelial- Mesenchymal Hybrid State in Cancer: Clinical Implications. Cells Tissues Organs 2021; 211:110-133. [PMID: 33902034 DOI: 10.1159/000515289] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 01/25/2021] [Indexed: 11/19/2022] Open
Abstract
The epithelial-mesenchymal (E/M) hybrid state has emerged as an important mediator of elements of cancer progression, facilitated by epithelial mesenchymal plasticity (EMP). We review here evidence for the presence, prognostic significance, and therapeutic potential of the E/M hybrid state in carcinoma. We further assess modelling predictions and validation studies to demonstrate stabilised E/M hybrid states along the spectrum of EMP, as well as computational approaches for characterising and quantifying EMP phenotypes, with particular attention to the emerging realm of single-cell approaches through RNA sequencing and protein-based techniques.
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Affiliation(s)
- Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Ryan J Murphy
- Queensland University of Technology, School of Mathematical Sciences, Brisbane, Queensland, Australia
| | - Sugandha Bhatia
- Queensland University of Technology, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Brisbane, Queensland, Australia.,Queensland University of Technology, Translational Research Institute, Brisbane, Queensland, Australia.,The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Holly J Whitfield
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia
| | - Andrew Redfern
- Department of Medicine, School of Medicine, University of Western Australia, Fiona Stanley Hospital Campus, Perth, Washington, Australia
| | - Melissa J Davis
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia.,Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - Erik W Thompson
- Queensland University of Technology, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Brisbane, Queensland, Australia.,Queensland University of Technology, Translational Research Institute, Brisbane, Queensland, Australia
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7
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Changes in Tumor Stem Cell Markers and Epithelial-Mesenchymal Transition Markers in Nonluminal Breast Cancer after Neoadjuvant Chemotherapy and Their Correlation with Contrast-Enhanced Ultrasound. BIOMED RESEARCH INTERNATIONAL 2020; 2020:3869538. [PMID: 33282946 PMCID: PMC7685800 DOI: 10.1155/2020/3869538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 10/12/2020] [Accepted: 10/30/2020] [Indexed: 12/24/2022]
Abstract
Nonluminal breast cancer has high early metastasis and treatment resistance, and neoadjuvant chemotherapy (NAC) is needed. The presence of cancer stem cells (CSC) and epithelial-mesenchymal transition (EMT) leads to poor prognosis. This study investigated the changes in CSC markers and EMT markers after NAC in nonluminal breast cancer and their correlation with contrast-enhanced ultrasound (CEUS) features and chemotherapy efficacy. Before NAC, the range of nonluminal breast cancer on CEUS was larger than that of two-dimensional ultrasound, but after NAC, it was significantly smaller than that of two-dimensional ultrasound and closer to the postoperative pathological size. After NAC, the enlarged lesions and perfusion defects were significantly less than those before NAC. The time-intensity curve showed the characteristics of slow-in, low enhancement, and low perfusion. Nonluminal breast cancer downregulated the expression of CSC markers and EMT markers after NAC, but the epithelial phenotype of nonluminal breast cancer with good response to chemotherapy was upregulated. In nonluminal breast cancer with poor response to chemotherapy, markers of CSC and EMT were highly expressed before chemotherapy. In conclusion, CEUS is better than conventional ultrasound in estimating NAC efficacy in this mode. CEUS can also predict the prognosis of nonluminal breast cancer before NAC with the characteristics of enhanced enlargement and perfusion defects. The contrast-enhanced time-intensity curve of lesions with relatively poor blood supply may have more CSC and EMT characteristics.
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8
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Contreras HR, López-Moncada F, Castellón EA. Cancer stem cell and mesenchymal cell cooperative actions in metastasis progression and hormone resistance in prostate cancer: Potential role of androgen and gonadotropin‑releasing hormone receptors (Review). Int J Oncol 2020; 56:1075-1082. [PMID: 32319606 DOI: 10.3892/ijo.2020.5008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 01/09/2020] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the leading cause of male cancer‑associated mortality worldwide. Mortality is associated with metastasis and hormone resistance. Cellular, genetic and molecular mechanisms underlying metastatic progression and hormone resistance are poorly understood. Studies have investigated the local effects of gonadotropin‑releasing hormone (GnRH) analogs (used for androgen deprivation treatments) and the presence of the GnRH receptor (GnRH‑R) on PCa cells. Furthermore, cell subpopulations with stem‑like properties, or cancer stem cells, have been isolated and characterized using a cell culture system derived from explants of human prostate tumors. In addition, the development of preclinical orthotopic models of human PCa in a nonobese diabetic/severe combined immunodeficiency mouse model of compromised immunity has enabled the establishment of a reproducible system of metastatic progression in vivo. There is increasing evidence that metastasis is a complex process involving the cooperative actions of different cancer cell subpopulations, in which cancer stem‑like cells would be responsible for the final step of colonizing premetastatic niches. It has been hypothesized that PCa cells with stemness and mesenchymal signatures act cooperatively in metastatic progression and the inhibition of stemness genes, and that overexpression of androgen receptor (AR) and GnRH‑R decreases the rate the metastasis and sensitizes tumors to hormone therapy. The aim of the present review is to analyze the evidence regarding this cooperative process and the possible influence of stem‑like cell phenotypes, AR and GnRH‑R in metastatic progression and hormone resistance. These aspects may represent an important contribution in the understanding of the mechanisms underlying metastasis and hormone resistance in PCa, and potential routes to blocking these processes, enabling the development of novel therapies that would be particularly relevant for patients with metastatic and castration‑resistant PCa.
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Affiliation(s)
- Héctor R Contreras
- Laboratory of Cellular and Molecular Oncology, Department of Basic and Clinical Oncology, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Fernanda López-Moncada
- Laboratory of Cellular and Molecular Oncology, Department of Basic and Clinical Oncology, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Enrique A Castellón
- Laboratory of Cellular and Molecular Oncology, Department of Basic and Clinical Oncology, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
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9
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Tripathi S, Chakraborty P, Levine H, Jolly MK. A mechanism for epithelial-mesenchymal heterogeneity in a population of cancer cells. PLoS Comput Biol 2020; 16:e1007619. [PMID: 32040502 PMCID: PMC7034928 DOI: 10.1371/journal.pcbi.1007619] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 02/21/2020] [Accepted: 12/20/2019] [Indexed: 12/15/2022] Open
Abstract
Epithelial-mesenchymal heterogeneity implies that cells within the same tumor can exhibit different phenotypes-epithelial, mesenchymal, or one or more hybrid epithelial-mesenchymal phenotypes. This behavior has been reported across cancer types, both in vitro and in vivo, and implicated in multiple processes associated with metastatic aggressiveness including immune evasion, collective dissemination of tumor cells, and emergence of cancer cell subpopulations with stem cell-like properties. However, the ability of a population of cancer cells to generate, maintain, and propagate this heterogeneity has remained a mystifying feature. Here, we used a computational modeling approach to show that epithelial-mesenchymal heterogeneity can emerge from the noise in the partitioning of biomolecules (such as RNAs and proteins) among daughter cells during the division of a cancer cell. Our model captures the experimentally observed temporal changes in the fractions of different phenotypes in a population of murine prostate cancer cells, and describes the hysteresis in the population-level dynamics of epithelial-mesenchymal plasticity. The model is further able to predict how factors known to promote a hybrid epithelial-mesenchymal phenotype can alter the phenotypic composition of a population. Finally, we used the model to probe the implications of phenotypic heterogeneity and plasticity for different therapeutic regimens and found that co-targeting of epithelial and mesenchymal cells is likely to be the most effective strategy for restricting tumor growth. By connecting the dynamics of an intracellular circuit to the phenotypic composition of a population, our study serves as a first step towards understanding the generation and maintenance of non-genetic heterogeneity in a population of cancer cells, and towards the therapeutic targeting of phenotypic heterogeneity and plasticity in cancer cell populations.
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Affiliation(s)
- Shubham Tripathi
- PhD Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, TX, United States of America
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States of America
- Department of Physics, Northeastern University, Boston, MA, United States of America
| | - Priyanka Chakraborty
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, Karnataka, India
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States of America
- Department of Physics, Northeastern University, Boston, MA, United States of America
- * E-mail: (H.L.); (M.K.J.)
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, Karnataka, India
- * E-mail: (H.L.); (M.K.J.)
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10
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Tripathi S, Levine H, Jolly MK. The Physics of Cellular Decision Making During Epithelial-Mesenchymal Transition. Annu Rev Biophys 2020; 49:1-18. [PMID: 31913665 DOI: 10.1146/annurev-biophys-121219-081557] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The epithelial-mesenchymal transition (EMT) is a process by which cells lose epithelial traits, such as cell-cell adhesion and apico-basal polarity, and acquire migratory and invasive traits. EMT is crucial to embryonic development and wound healing. Misregulated EMT has been implicated in processes associated with cancer aggressiveness, including metastasis. Recent experimental advances such as single-cell analysis and temporal phenotypic characterization have established that EMT is a multistable process wherein cells exhibit and switch among multiple phenotypic states. This is in contrast to the classical perception of EMT as leading to a binary choice. Mathematical modeling has been at the forefront of this transformation for the field, not only providing a conceptual framework to integrate and analyze experimental data, but also making testable predictions. In this article, we review the key features and characteristics of EMT dynamics, with a focus on the mathematical modeling approaches that have been instrumental to obtaining various useful insights.
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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
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, USA; .,Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, Karnataka 560012, India;
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11
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Chapman MP, Risom T, Aswani AJ, Langer EM, Sears RC, Tomlin CJ. Correction: Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer. PLoS Comput Biol 2019; 15:e1007441. [PMID: 31596847 PMCID: PMC6785055 DOI: 10.1371/journal.pcbi.1007441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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12
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Meyer AS, Heiser LM. Systems biology approaches to measure and model phenotypic heterogeneity in cancer. CURRENT OPINION IN SYSTEMS BIOLOGY 2019; 17:35-40. [PMID: 32864511 PMCID: PMC7449235 DOI: 10.1016/j.coisb.2019.09.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The recent wide-spread adoption of single cell profiling technologies has revealed that individual cancers are not homogenous collections of deregulated cells, but instead are comprised of multiple genetically and phenotypically distinct cell subpopulations that exhibit a wide range of responses to extracellular signals and therapeutic insult. Such observations point to the urgent need to understand cancer as a complex, adaptive system. Cancer systems biology studies seek to develop the experimental and theoretical methods required to understand how biological components work together to determine how cancer cells function. Ultimately, such approaches will lead to improvements in how cancer is managed and treated. In this review, we discuss recent advances in cancer systems biology approaches to quantify, model, and elucidate mechanisms of heterogeneity.
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Affiliation(s)
- Aaron S. Meyer
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Laura M. Heiser
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine, OHSU, Portland, OR, USA
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13
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Bhatia S, Monkman J, Blick T, Pinto C, Waltham M, Nagaraj SH, Thompson EW. Interrogation of Phenotypic Plasticity between Epithelial and Mesenchymal States in Breast Cancer. J Clin Med 2019; 8:E893. [PMID: 31234417 PMCID: PMC6617164 DOI: 10.3390/jcm8060893] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 06/19/2019] [Accepted: 06/20/2019] [Indexed: 12/21/2022] Open
Abstract
Dynamic interconversions between transitional epithelial and mesenchymal states underpin the epithelial mesenchymal plasticity (EMP) seen in some carcinoma cell systems. We have delineated epithelial and mesenchymal subpopulations existing within the PMC42-LA breast cancer cell line by their EpCAM expression. These purified but phenotypically plastic states, EpCAMHigh (epithelial) and EpCAMLow (mesenchymal), have the ability to regain the phenotypic equilibrium of the parental population (i.e., 80% epithelial and 20% mesenchymal) over time, although the rate of reversion in the mesenchymal direction (epithelial-mesenchymal transition; EMT) is higher than that in the epithelial direction (mesenchymal-epithelial transition; MET). Single-cell clonal propagation was implemented to delineate the molecular and cellular features of this intrinsic heterogeneity with respect to EMP flux. The dynamics of the phenotypic proportions of epithelial and mesenchymal states in single-cell generated clones revealed clonal diversity and intrinsic plasticity. Single cell-derived clonal progenies displayed differences in their functional attributes of proliferation, stemness marker (CD44/CD24), migration, invasion and chemo-sensitivity. Interrogation of genomic copy number variations (CNV) with whole exome sequencing (WES) in the context of chromosome count from metaphase spread indicated that chromosomal instability was not influential in driving intrinsic phenotypic plasticity. Overall, these findings reveal the stochastic nature of both the epithelial and mesenchymal subpopulations, and the single cell-derived clones for differential functional attributes.
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Affiliation(s)
- Sugandha Bhatia
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4059, Australia.
- School of Biological/Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD 4000, Australia.
- Translational Research Institute, Brisbane, QLD 4102, Australia.
| | - James Monkman
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4059, Australia.
- School of Biological/Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD 4000, Australia.
- Translational Research Institute, Brisbane, QLD 4102, Australia.
| | - Tony Blick
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4059, Australia.
- School of Biological/Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD 4000, Australia.
- Translational Research Institute, Brisbane, QLD 4102, Australia.
| | - Cletus Pinto
- Invasion and Metastasis Unit, St. Vincent's Institute, Melbourne, VIC 3065, Australia.
- University of Melbourne Department of Surgery, St. Vincent's Hospital, Melbourne, VIC 3065, Australia.
| | - Mark Waltham
- Invasion and Metastasis Unit, St. Vincent's Institute, Melbourne, VIC 3065, Australia.
- University of Melbourne Department of Surgery, St. Vincent's Hospital, Melbourne, VIC 3065, Australia.
| | - Shivashankar H Nagaraj
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4059, Australia.
- School of Biological/Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD 4000, Australia.
- Translational Research Institute, Brisbane, QLD 4102, Australia.
| | - Erik W Thompson
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4059, Australia.
- School of Biological/Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD 4000, Australia.
- Translational Research Institute, Brisbane, QLD 4102, Australia.
- Invasion and Metastasis Unit, St. Vincent's Institute, Melbourne, VIC 3065, Australia.
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