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Pudełek M, Ryszawy D, Piwowarczyk K, Lasota S, Madeja Z, Kędracka-Krok S, Czyż J. Metabolic reprogramming of poly(morpho)nuclear giant cells determines glioblastoma recovery from doxorubicin-induced stress. J Transl Med 2024; 22:757. [PMID: 39135106 PMCID: PMC11318163 DOI: 10.1186/s12967-024-05541-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Accepted: 07/26/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Multi-drug resistance of poly(morpho)nuclear giant cells (PGCs) determines their cytoprotective and generative potential in cancer ecosystems. However, mechanisms underlying the involvement of PGCs in glioblastoma multiforme (GBM) adaptation to chemotherapeutic regimes remain largely obscure. In particular, metabolic reprogramming of PGCs has not yet been considered in terms of GBM recovery from doxorubicin (DOX)-induced stress. METHODS Long-term proteomic and metabolic cell profiling was applied to trace the phenotypic dynamics of GBM populations subjected to pulse DOX treatment in vitro, with a particular focus on PGC formation and its metabolic background. The links between metabolic reprogramming, drug resistance and drug retention capacity of PGCs were assessed, along with their significance for GBM recovery from DOX-induced stress. RESULTS Pulse DOX treatment triggered the transient formation of PGCs, followed by the appearance of small expanding cell (SEC) clusters. Development of PGCs was accompanied by the mobilization of their metabolic proteome, transient induction of oxidative phosphorylation (OXPHOS), and differential intracellular accumulation of NADH, NADPH, and ATP. The metabolic background of PGC formation was confirmed by the attenuation of GBM recovery from DOX-induced stress following the chemical inhibition of GSK-3β, OXPHOS, and the pentose phosphate pathway. Concurrently, the mobilization of reactive oxygen species (ROS) scavenging systems and fine-tuning of NADPH-dependent ROS production systems in PGCs was observed. These processes were accompanied by perinuclear mobilization of ABCB1 and ABCG2 transporters and DOX retention in the perinuclear PGC compartments. CONCLUSIONS These data demonstrate the cooperative pattern of GBM recovery from DOX-induced stress and the crucial role of metabolic reprogramming of PGCs in this process. Metabolic reprogramming enhances the efficiency of self-defense systems and increases the DOX retention capacity of PGCs, potentially reducing DOX bioavailability in the proximity of SECs. Consequently, the modulation of PGC metabolism is highlighted as a potential target for intervention in glioblastoma treatment.
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Affiliation(s)
- Maciej Pudełek
- Department of Cell Biology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa 7, Krakow, 30-387, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland
| | - Damian Ryszawy
- Department of Cell Biology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa 7, Krakow, 30-387, Poland
| | - Katarzyna Piwowarczyk
- Department of Cell Biology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa 7, Krakow, 30-387, Poland
| | - Sławomir Lasota
- Department of Cell Biology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa 7, Krakow, 30-387, Poland
| | - Zbigniew Madeja
- Department of Cell Biology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa 7, Krakow, 30-387, Poland
| | - Sylwia Kędracka-Krok
- Department of Physical Biochemistry, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Kraków, Poland
| | - Jarosław Czyż
- Department of Cell Biology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa 7, Krakow, 30-387, Poland.
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Marin JJG, Serrano MA, Herraez E, Lozano E, Ortiz-Rivero S, Perez-Silva L, Reviejo M, Briz O. Impact of genetic variants in the solute carrier ( SLC) genes encoding drug uptake transporters on the response to anticancer chemotherapy. CANCER DRUG RESISTANCE (ALHAMBRA, CALIF.) 2024; 7:27. [PMID: 39143954 PMCID: PMC11322974 DOI: 10.20517/cdr.2024.42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/14/2024] [Accepted: 06/20/2024] [Indexed: 08/16/2024]
Abstract
Cancer drug resistance constitutes a severe limitation for the satisfactory outcome of these patients. This is a complex problem due to the co-existence in cancer cells of multiple and synergistic mechanisms of chemoresistance (MOC). These mechanisms are accounted for by the expression of a set of genes included in the so-called resistome, whose effectiveness often leads to a lack of response to pharmacological treatment. Additionally, genetic variants affecting these genes further increase the complexity of the question. This review focuses on a set of genes encoding members of the transportome involved in drug uptake, which have been classified into the MOC-1A subgroup of the resistome. These proteins belong to the solute carrier (SLC) superfamily. More precisely, we have considered here several members of families SLC2, SLC7, SLC19, SLC22, SLCO, SLC28, SLC29, SLC31, SLC46, and SLC47 due to the impact of their expression and genetic variants in anticancer drug uptake by tumor cells or, in some cases, general bioavailability. Changes in their expression levels and the appearance of genetic variants can contribute to the Darwinian selection of more resistant clones and, hence, to the development of a more malignant phenotype. Accordingly, to address this issue in future personalized medicine, it is necessary to characterize both changes in resistome genes that can affect their function. It is also essential to consider the time-dependent dimension of these features, as the genetic expression and the appearance of genetic variants can change during tumor progression and in response to treatment.
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Affiliation(s)
- Jose J. G. Marin
- Experimental Hepatology and Drug Targeting (HEVEPHARM), University of Salamanca, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca 37007, Spain
- Center for the Study of Liver and Gastrointestinal Diseases (CIBEREHD), Carlos III National Institute of Health, Madrid 28029, Spain
| | - Maria A. Serrano
- Experimental Hepatology and Drug Targeting (HEVEPHARM), University of Salamanca, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca 37007, Spain
- Center for the Study of Liver and Gastrointestinal Diseases (CIBEREHD), Carlos III National Institute of Health, Madrid 28029, Spain
| | - Elisa Herraez
- Experimental Hepatology and Drug Targeting (HEVEPHARM), University of Salamanca, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca 37007, Spain
- Center for the Study of Liver and Gastrointestinal Diseases (CIBEREHD), Carlos III National Institute of Health, Madrid 28029, Spain
| | - Elisa Lozano
- Experimental Hepatology and Drug Targeting (HEVEPHARM), University of Salamanca, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca 37007, Spain
- Center for the Study of Liver and Gastrointestinal Diseases (CIBEREHD), Carlos III National Institute of Health, Madrid 28029, Spain
| | - Sara Ortiz-Rivero
- Experimental Hepatology and Drug Targeting (HEVEPHARM), University of Salamanca, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca 37007, Spain
- Center for the Study of Liver and Gastrointestinal Diseases (CIBEREHD), Carlos III National Institute of Health, Madrid 28029, Spain
| | - Laura Perez-Silva
- Experimental Hepatology and Drug Targeting (HEVEPHARM), University of Salamanca, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca 37007, Spain
| | - Maria Reviejo
- Experimental Hepatology and Drug Targeting (HEVEPHARM), University of Salamanca, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca 37007, Spain
- Center for the Study of Liver and Gastrointestinal Diseases (CIBEREHD), Carlos III National Institute of Health, Madrid 28029, Spain
| | - Oscar Briz
- Experimental Hepatology and Drug Targeting (HEVEPHARM), University of Salamanca, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca 37007, Spain
- Center for the Study of Liver and Gastrointestinal Diseases (CIBEREHD), Carlos III National Institute of Health, Madrid 28029, Spain
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3
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Junior MGV, Côrtes AMDA, Carneiro FRG, Carels N, da Silva FAB. Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity. Int J Mol Sci 2024; 25:4894. [PMID: 38732140 PMCID: PMC11084314 DOI: 10.3390/ijms25094894] [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: 03/08/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 05/13/2024] Open
Abstract
Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.
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Affiliation(s)
- Marcos Guilherme Vieira Junior
- Graduate Program in Computational and Systems Biology, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, Brazil;
| | - Adriano Maurício de Almeida Côrtes
- Department of Applied Mathematics, Institute of Mathematics, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-909, Brazil;
- Systems Engineering and Computer Science Program, Coordination of Postgraduate Programs in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-972, Brazil
| | - Flávia Raquel Gonçalves Carneiro
- Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-361, Brazil;
- Laboratório Interdisciplinar de Pesquisas Médicas, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, Brazil
- Program of Immunology and Tumor Biology, Brazilian National Cancer Institute (INCA), Rio de Janeiro 20231-050, Brazil
| | - Nicolas Carels
- Laboratory of Biological System Modeling, Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-361, Brazil
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Mohanty A, Nam A, Srivastava S, Jones J, Lomenick B, Singhal SS, Guo L, Cho H, Li A, Behal A, Mirzapoiazova T, Massarelli E, Koczywas M, Arvanitis LD, Walser T, Villaflor V, Hamilton S, Mambetsariev I, Sattler M, Nasser MW, Jain M, Batra SK, Soldi R, Sharma S, Fakih M, Mohanty SK, Mainan A, Wu X, Chen Y, He Y, Chou TF, Roy S, Orban J, Kulkarni P, Salgia R. Acquired resistance to KRAS G12C small-molecule inhibitors via genetic/nongenetic mechanisms in lung cancer. SCIENCE ADVANCES 2023; 9:eade3816. [PMID: 37831779 PMCID: PMC10575592 DOI: 10.1126/sciadv.ade3816] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/08/2023] [Indexed: 10/15/2023]
Abstract
Inherent or acquired resistance to sotorasib poses a substantialt challenge for NSCLC treatment. Here, we demonstrate that acquired resistance to sotorasib in isogenic cells correlated with increased expression of integrin β4 (ITGB4), a component of the focal adhesion complex. Silencing ITGB4 in tolerant cells improved sotorasib sensitivity, while overexpressing ITGB4 enhanced tolerance to sotorasib by supporting AKT-mTOR bypass signaling. Chronic treatment with sotorasib induced WNT expression and activated the WNT/β-catenin signaling pathway. Thus, silencing both ITGB4 and β-catenin significantly improved sotorasib sensitivity in tolerant, acquired, and inherently resistant cells. In addition, the proteasome inhibitor carfilzomib (CFZ) exhibited synergism with sotorasib by down-regulating ITGB4 and β-catenin expression. Furthermore, adagrasib phenocopies the combination effect of sotorasib and CFZ by suppressing KRAS activity and inhibiting cell cycle progression in inherently resistant cells. Overall, our findings unveil previously unrecognized nongenetic mechanisms underlying resistance to sotorasib and propose a promising treatment strategy to overcome resistance.
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Affiliation(s)
- Atish Mohanty
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Arin Nam
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Saumya Srivastava
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Jeff Jones
- Proteome Exploration Laboratory, California Institute of Technology, Pasadena, CA 91125, USA
| | - Brett Lomenick
- Proteome Exploration Laboratory, California Institute of Technology, Pasadena, CA 91125, USA
| | - Sharad S. Singhal
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Linlin Guo
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Hyejin Cho
- Integrative Genomics Core, Beckman Research Institute, City of Hope, Monrovia, CA 91016, USA
| | - Aimin Li
- Department of Pathology, City of Hope National Medical Center, Duarte, CA 91010,USA
| | - Amita Behal
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Tamara Mirzapoiazova
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Erminia Massarelli
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Marianna Koczywas
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | | | - Tonya Walser
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Victoria Villaflor
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Stanley Hamilton
- Department of Pathology, City of Hope National Medical Center, Duarte, CA 91010,USA
| | - Isa Mambetsariev
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Martin Sattler
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Mohd W. Nasser
- Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Maneesh Jain
- Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Surinder K. Batra
- Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Raffaella Soldi
- Applied Cancer Research and Drug Discovery Division, Translational Genomics Research Institute (TGen) of City of Hope, Phoenix, AZ 850043, USA
| | - Sunil Sharma
- Applied Cancer Research and Drug Discovery Division, Translational Genomics Research Institute (TGen) of City of Hope, Phoenix, AZ 850043, USA
| | - Marwan Fakih
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Saswat Kumar Mohanty
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, India
| | - Avijit Mainan
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, India
| | - Xiwei Wu
- Integrative Genomics Core, Beckman Research Institute, City of Hope, Monrovia, CA 91016, USA
| | - Yihong Chen
- W. M. Keck Laboratory for Structural Biology, University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
| | - Yanan He
- W. M. Keck Laboratory for Structural Biology, University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
| | - Tsui-Fen Chou
- Proteome Exploration Laboratory, California Institute of Technology, Pasadena, CA 91125, USA
| | - Susmita Roy
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, India
| | - John Orban
- W. M. Keck Laboratory for Structural Biology, University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
| | - Prakash Kulkarni
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Ravi Salgia
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA
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Stochastic Fluctuations Drive Non-genetic Evolution of Proliferation in Clonal Cancer Cell Populations. Bull Math Biol 2022; 85:8. [PMID: 36562835 DOI: 10.1007/s11538-022-01113-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022]
Abstract
Evolutionary dynamics allows us to understand many changes happening in a broad variety of biological systems, ranging from individuals to complete ecosystems. It is also behind a number of remarkable organizational changes that happen during the natural history of cancers. These reflect tumour heterogeneity, which is present at all cellular levels, including the genome, proteome and phenome, shaping its development and interrelation with its environment. An intriguing observation in different cohorts of oncological patients is that tumours exhibit an increased proliferation as the disease progresses, while the timescales involved are apparently too short for the fixation of sufficient driver mutations to promote explosive growth. Here, we discuss how phenotypic plasticity, emerging from a single genotype, may play a key role and provide a ground for a continuous acceleration of the proliferation rate of clonal populations with time. We address this question by combining the analysis of real-time growth of non-small-cell lung carcinoma cells (N-H460) together with stochastic and deterministic mathematical models that capture proliferation trait heterogeneity in clonal populations to elucidate the contribution of phenotypic transitions on tumour growth dynamics.
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O'Neill H, Lee H, Gupta I, Rodger EJ, Chatterjee A. Single-Cell DNA Methylation Analysis in Cancer. Cancers (Basel) 2022; 14:6171. [PMID: 36551655 PMCID: PMC9777108 DOI: 10.3390/cancers14246171] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/07/2022] [Accepted: 12/10/2022] [Indexed: 12/23/2022] Open
Abstract
Morphological, transcriptomic, and genomic defects are well-explored parameters of cancer biology. In more recent years, the impact of epigenetic influences, such as DNA methylation, is becoming more appreciated. Aberrant DNA methylation has been implicated in many types of cancers, influencing cell type, state, transcriptional regulation, and genomic stability to name a few. Traditionally, large populations of cells from the tissue of interest are coalesced for analysis, producing averaged methylome data. Considering the inherent heterogeneity of cancer, analysing populations of cells as a whole denies the ability to discover novel aberrant methylation patterns, identify subpopulations, and trace cell lineages. Due to recent advancements in technology, it is now possible to obtain methylome data from single cells. This has both research and clinical implications, ranging from the identification of biomarkers to improved diagnostic tools. As with all emerging technologies, distinct experimental, bioinformatic, and practical challenges present themselves. This review begins with exploring the potential impact of single-cell sequencing on understanding cancer biology and how it could eventually benefit a clinical setting. Following this, the techniques and experimental approaches which made this technology possible are explored. Finally, the present challenges currently associated with single-cell DNA methylation sequencing are described.
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Affiliation(s)
- Hannah O'Neill
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand
| | - Heather Lee
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, NSW 2308, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia
| | - Ishaan Gupta
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Euan J Rodger
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand
| | - Aniruddha Chatterjee
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand
- School of Health Sciences and Technology, University of Petroleum and Energy Studies (UPES), Dehradun 248007, India
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Lavia P, Sciamanna I, Spadafora C. An Epigenetic LINE-1-Based Mechanism in Cancer. Int J Mol Sci 2022; 23:14610. [PMID: 36498938 PMCID: PMC9738484 DOI: 10.3390/ijms232314610] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 11/24/2022] Open
Abstract
In the last fifty years, large efforts have been deployed in basic research, clinical oncology, and clinical trials, yielding an enormous amount of information regarding the molecular mechanisms of cancer and the design of effective therapies. The knowledge that has accumulated underpins the complexity, multifactoriality, and heterogeneity of cancer, disclosing novel landscapes in cancer biology with a key role of genome plasticity. Here, we propose that cancer onset and progression are determined by a stress-responsive epigenetic mechanism, resulting from the convergence of upregulation of LINE-1 (long interspersed nuclear element 1), the largest family of human retrotransposons, genome damage, nuclear lamina fragmentation, chromatin remodeling, genome reprogramming, and autophagy activation. The upregulated expression of LINE-1 retrotransposons and their protein products plays a key role in these processes, yielding an increased plasticity of the nuclear architecture with the ensuing reprogramming of global gene expression, including the reactivation of embryonic transcription profiles. Cancer phenotypes would thus emerge as a consequence of the unscheduled reactivation of embryonic gene expression patterns in an inappropriate context, triggering de-differentiation and aberrant proliferation in differentiated cells. Depending on the intensity of the stressing stimuli and the level of LINE-1 response, diverse degrees of malignity would be generated.
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Affiliation(s)
- Patrizia Lavia
- Institute of Molecular Biology and Pathology (IBPM), CNR Consiglio Nazionale delle Ricerche, c/o Department of Biology and Biotechnology, Sapienza University of Rome, 00185 Rome, Italy
| | - Ilaria Sciamanna
- Center for Animal Research and Welfare (BENA), ISS Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Corrado Spadafora
- Institute of Translational Pharmacology (IFT), CNR Consiglio Nazionale delle Ricerche, 00133 Rome, Italy
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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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In Vitro Characterization of Renal Drug Transporter Activity in Kidney Cancer. Int J Mol Sci 2022; 23:ijms231710177. [PMID: 36077583 PMCID: PMC9456511 DOI: 10.3390/ijms231710177] [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: 08/10/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/17/2022] Open
Abstract
The activity of drug transporters is central to the secretory function of the kidneys and a defining feature of renal proximal tubule epithelial cells (RPTECs). The expression, regulation, and function of these membrane-bound proteins is well understood under normal renal physiological conditions. However, the impact of drug transporters on the pathophysiology of kidney cancer is still elusive. In the present study, we employed different renal cell carcinoma (RCC) cell lines and a prototypical non-malignant RPTEC cell line to characterize the activity, expression, and potential regulatory mechanisms of relevant renal drug transporters in RCC in vitro. An analysis of the uptake and efflux activity, the expression of drug transporters, and the evaluation of cisplatin cytotoxicity under the effects of methylation or epidermal growth factor receptor (EGFR) inhibition showed that the RCC cells retained substantial drug transport activity. In RCC cells, P-glycoprotein was localized in the nucleus and its pharmacological inhibition enhanced cisplatin toxicity in non-malignant RPTECs. On the other hand, methylation inhibition enhanced cisplatin toxicity by upregulating the organic cation uptake activity in RCC cells. Differential effects of methylation and EGFR were observed in transporter expression, showing regulatory heterogeneity in these cells. Interestingly, the non-malignant RPTEC cell line that was used lacked the machinery responsible for organic cation transport, which reiterates the functional losses that renal cells undergo in vitro.
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Achuthan S, Chatterjee R, Kotnala S, Mohanty A, Bhattacharya S, Salgia R, Kulkarni P. Leveraging deep learning algorithms for synthetic data generation to design and analyze biological networks. J Biosci 2022. [DOI: 10.1007/s12038-022-00278-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy. Biomolecules 2021; 12:biom12010008. [PMID: 35053156 PMCID: PMC8773639 DOI: 10.3390/biom12010008] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/14/2021] [Accepted: 12/17/2021] [Indexed: 12/21/2022] Open
Abstract
Drug resistance, a major challenge in cancer therapy, is typically attributed to mutations and genetic heterogeneity. Emerging evidence suggests that dynamic cellular interactions and group behavior also contribute to drug resistance. However, the underlying mechanisms remain poorly understood. Here, we present a new mathematical approach with game theoretical underpinnings that we developed to model real-time growth data of non-small cell lung cancer (NSCLC) cells and discern patterns in response to treatment with cisplatin. We show that the cisplatin-sensitive and cisplatin-tolerant NSCLC cells, when co-cultured in the absence or presence of the drug, display dynamic group behavior strategies. Tolerant cells exhibit a 'persister-like' behavior and are attenuated by sensitive cells; they also appear to 'educate' sensitive cells to evade chemotherapy. Further, tolerant cells can switch phenotypes to become sensitive, especially at low cisplatin concentrations. Finally, switching treatment from continuous to an intermittent regimen can attenuate the emergence of tolerant cells, suggesting that intermittent chemotherapy may improve outcomes in lung cancer.
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12
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Global analysis of a cancer model with drug resistance due to Lamarckian induction and microvesicle transfer. J Theor Biol 2021; 527:110812. [PMID: 34129816 DOI: 10.1016/j.jtbi.2021.110812] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 04/30/2021] [Accepted: 06/08/2021] [Indexed: 11/20/2022]
Abstract
Development of resistance to chemotherapy in cancer patients strongly effects the outcome of the treatment. Due to chemotherapeutic agents, resistance can emerge by Darwinian evolution. Besides this, acquired drug resistance may arise via changes in gene expression. A recent discovery in cancer research uncovered a third possibility, indicating that this phenotype conversion can occur through the transfer of microvesicles from resistant to sensitive cells, a mechanism resembling the spread of an infectious agent. We present a model describing the evolution of sensitive and resistant tumour cells considering Darwinian selection, Lamarckian induction and microvesicle transfer. We identify three threshold parameters which determine the existence and stability of the three possible equilibria. Using a simple Dulac function, we give a complete description of the dynamics of the model depending on the three threshold parameters. We also establish an agent based model as a spatial version of the ODE model and compare the outputs of the two models. We find that although the ODE model does not provide spatial information about the structure of the tumour, it is capable to determine the outcome in terms of tumour size and distribution of cell types. We demonstrate the possible effects of increasing drug concentration, and characterize the possible bifurcation sequences. Our results show that the presence of microvesicle transfer cannot ruin a therapy that otherwise leads to extinction, however it may doom a partially successful therapy to failure.
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13
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Graw S, Chappell K, Washam CL, Gies A, Bird J, Robeson MS, Byrum SD. Multi-omics data integration considerations and study design for biological systems and disease. Mol Omics 2021; 17:170-185. [PMID: 33347526 PMCID: PMC8058243 DOI: 10.1039/d0mo00041h] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.
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Affiliation(s)
- Stefan Graw
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Kevin Chappell
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Charity L Washam
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA. and Arkansas Children's Research Institute, 13 Children's Way, Little Rock, AR 72202, USA
| | - Allen Gies
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Jordan Bird
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Michael S Robeson
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
| | - Stephanie D Byrum
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA. and Arkansas Children's Research Institute, 13 Children's Way, Little Rock, AR 72202, USA
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14
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Zhang Y, Wang D, Peng M, Tang L, Ouyang J, Xiong F, Guo C, Tang Y, Zhou Y, Liao Q, Wu X, Wang H, Yu J, Li Y, Li X, Li G, Zeng Z, Tan Y, Xiong W. Single-cell RNA sequencing in cancer research. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2021; 40:81. [PMID: 33648534 PMCID: PMC7919320 DOI: 10.1186/s13046-021-01874-1] [Citation(s) in RCA: 133] [Impact Index Per Article: 44.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 02/08/2021] [Indexed: 02/06/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq), a technology that analyzes transcriptomes of complex tissues at single-cell levels, can identify differential gene expression and epigenetic factors caused by mutations in unicellular genomes, as well as new cell-specific markers and cell types. scRNA-seq plays an important role in various aspects of tumor research. It reveals the heterogeneity of tumor cells and monitors the progress of tumor development, thereby preventing further cellular deterioration. Furthermore, the transcriptome analysis of immune cells in tumor tissue can be used to classify immune cells, their immune escape mechanisms and drug resistance mechanisms, and to develop effective clinical targeted therapies combined with immunotherapy. Moreover, this method enables the study of intercellular communication and the interaction of tumor cells and non-malignant cells to reveal their role in carcinogenesis. scRNA-seq provides new technical means for further development of tumor research and is expected to make significant breakthroughs in this field. This review focuses on the principles of scRNA-seq, with an emphasis on the application of scRNA-seq in tumor heterogeneity, pathogenesis, and treatment.
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Affiliation(s)
- Yijie Zhang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, the Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Dan Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Miao Peng
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Le Tang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Jiawei Ouyang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Fang Xiong
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Can Guo
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Yanyan Tang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Yujuan Zhou
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Qianjin Liao
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Xu Wu
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Hui Wang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Jianjun Yu
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China
| | - Yong Li
- Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Xiaoling Li
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China
| | - Guiyuan Li
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, the Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhaoyang Zeng
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, the Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yixin Tan
- Department of Dermatology, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Wei Xiong
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, China. .,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, Hunan, China. .,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, the Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
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15
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A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors. PLoS Comput Biol 2021; 17:e1008266. [PMID: 33566821 PMCID: PMC7901744 DOI: 10.1371/journal.pcbi.1008266] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 02/23/2021] [Accepted: 01/16/2021] [Indexed: 12/12/2022] Open
Abstract
Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.
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16
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Jiménez-Sánchez J, Bosque JJ, Jiménez Londoño GA, Molina-García D, Martínez Á, Pérez-Beteta J, Ortega-Sabater C, Honguero Martínez AF, García Vicente AM, Calvo GF, Pérez-García VM. Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers. Proc Natl Acad Sci U S A 2021; 118:e2018110118. [PMID: 33536339 PMCID: PMC8017959 DOI: 10.1073/pnas.2018110118] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/04/2021] [Indexed: 01/09/2023] Open
Abstract
Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, normalized distance from 18F-fluorodeoxyglucose (18F-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using 18F-FDG PET-computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of 18F-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non-small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NHOC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome.
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Affiliation(s)
- Juan Jiménez-Sánchez
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain
| | - Jesús J Bosque
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain
| | | | - David Molina-García
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain
| | - Álvaro Martínez
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain
- Nuclear Medicine Unit, Hospital General Universitario de Ciudad Real, Ciudad Real, 13005, Spain
| | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain
| | - Carmen Ortega-Sabater
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain
| | | | - Ana M García Vicente
- Thoracic Surgery Unit, Hospital General Universitario de Albacete, Albacete, 02006, Spain
| | - Gabriel F Calvo
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain;
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain;
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17
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Craig M, Jenner AL, Namgung B, Lee LP, Goldman A. Engineering in Medicine To Address the Challenge of Cancer Drug Resistance: From Micro- and Nanotechnologies to Computational and Mathematical Modeling. Chem Rev 2020; 121:3352-3389. [PMID: 33152247 DOI: 10.1021/acs.chemrev.0c00356] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Drug resistance has profoundly limited the success of cancer treatment, driving relapse, metastasis, and mortality. Nearly all anticancer drugs and even novel immunotherapies, which recalibrate the immune system for tumor recognition and destruction, have succumbed to resistance development. Engineers have emerged across mechanical, physical, chemical, mathematical, and biological disciplines to address the challenge of drug resistance using a combination of interdisciplinary tools and skill sets. This review explores the developing, complex, and under-recognized role of engineering in medicine to address the multitude of challenges in cancer drug resistance. Looking through the "lens" of intrinsic, extrinsic, and drug-induced resistance (also referred to as "tolerance"), we will discuss three specific areas where active innovation is driving novel treatment paradigms: (1) nanotechnology, which has revolutionized drug delivery in desmoplastic tissues, harnessing physiochemical characteristics to destroy tumors through photothermal therapy and rationally designed nanostructures to circumvent cancer immunotherapy failures, (2) bioengineered tumor models, which have benefitted from microfluidics and mechanical engineering, creating a paradigm shift in physiologically relevant environments to predict clinical refractoriness and enabling platforms for screening drug combinations to thwart resistance at the individual patient level, and (3) computational and mathematical modeling, which blends in silico simulations with molecular and evolutionary principles to map mutational patterns and model interactions between cells that promote resistance. On the basis that engineering in medicine has resulted in discoveries in resistance biology and successfully translated to clinical strategies that improve outcomes, we suggest the proliferation of multidisciplinary science that embraces engineering.
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Affiliation(s)
- Morgan Craig
- Department of Mathematics and Statistics, University of Montreal, Montreal, Quebec H3C 3J7, Canada.,Sainte-Justine University Hospital Research Centre, Montreal, Quebec H3S 2G4, Canada
| | - Adrianne L Jenner
- Department of Mathematics and Statistics, University of Montreal, Montreal, Quebec H3C 3J7, Canada.,Sainte-Justine University Hospital Research Centre, Montreal, Quebec H3S 2G4, Canada
| | - Bumseok Namgung
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States.,Department of Medicine, Harvard Medical School, Boston, Massachusetts 02139, United States
| | - Luke P Lee
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States.,Department of Medicine, Harvard Medical School, Boston, Massachusetts 02139, United States
| | - Aaron Goldman
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States.,Department of Medicine, Harvard Medical School, Boston, Massachusetts 02139, United States
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18
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Mohanty A, Nam A, Pozhitkov A, Yang L, Srivastava S, Nathan A, Wu X, Mambetsariev I, Nelson M, Subbalakshmi A, Guo L, Nasser MW, Batra SK, Orban J, Jolly MK, Massarelli E, Kulkarni P, Salgia R. A Non-genetic Mechanism Involving the Integrin β4/Paxillin Axis Contributes to Chemoresistance in Lung Cancer. iScience 2020; 23:101496. [PMID: 32947124 PMCID: PMC7502350 DOI: 10.1016/j.isci.2020.101496] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 06/08/2020] [Accepted: 08/20/2020] [Indexed: 02/07/2023] Open
Abstract
Tumor heterogeneity and cisplatin resistance are major causes of tumor relapse and poor survival. Here, we show that in lung cancer, interaction between paxillin (PXN) and integrin β4 (ITGB4), components of the focal adhesion (FA) complex, contributes to cisplatin resistance. Knocking down PXN and ITGB4 attenuated cell growth and improved cisplatin sensitivity, both in 2D and 3D cultures. PXN and ITGB4 independently regulated expression of several genes. In addition, they also regulated expression of common genes including USP1 and VDAC1, which are required for maintaining genomic stability and mitochondrial function, respectively. Mathematical modeling suggested that bistability could lead to stochastic phenotypic switching between cisplatin-sensitive and resistant states in these cells. Consistently, purified subpopulations of sensitive and resistant cells re-created the mixed parental population when cultured separately. Altogether, these data point to an unexpected role of the FA complex in cisplatin resistance and highlight a novel non-genetic mechanism.
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Affiliation(s)
- Atish Mohanty
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Arin Nam
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Alex Pozhitkov
- Department of Computational and Quantitative Medicine, City of Hope, 1500 East Duarte Road, Duarte, CA, USA
| | - Lu Yang
- Department of Systems Biology, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, USA
| | - Saumya Srivastava
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Anusha Nathan
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Xiwei Wu
- Genomics Core Facility, Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Isa Mambetsariev
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Michael Nelson
- Department of Molecular Imaging and Therapy, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, USA
| | - A.R. Subbalakshmi
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Linlin Guo
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Mohd W. Nasser
- Department of Biochemistry and Molecular Biology, Division of Thoracic Surgery, University of Nebraska College of Medicine, Omaha, NE, USA
| | - Surinder K. Batra
- Department of Biochemistry and Molecular Biology, Division of Thoracic Surgery, University of Nebraska College of Medicine, Omaha, NE, USA
| | - John Orban
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
| | - Mohit Kumar Jolly
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Erminia Massarelli
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Prakash Kulkarni
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Ravi Salgia
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
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19
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Rabé M, Dumont S, Álvarez-Arenas A, Janati H, Belmonte-Beitia J, Calvo GF, Thibault-Carpentier C, Séry Q, Chauvin C, Joalland N, Briand F, Blandin S, Scotet E, Pecqueur C, Clairambault J, Oliver L, Perez-Garcia V, Nadaradjane A, Cartron PF, Gratas C, Vallette FM. Identification of a transient state during the acquisition of temozolomide resistance in glioblastoma. Cell Death Dis 2020; 11:19. [PMID: 31907355 PMCID: PMC6944699 DOI: 10.1038/s41419-019-2200-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/05/2019] [Accepted: 12/05/2019] [Indexed: 12/17/2022]
Abstract
Drug resistance limits the therapeutic efficacy in cancers and leads to tumor recurrence through ill-defined mechanisms. Glioblastoma (GBM) are the deadliest brain tumors in adults. GBM, at diagnosis or after treatment, are resistant to temozolomide (TMZ), the standard chemotherapy. To better understand the acquisition of this resistance, we performed a longitudinal study, using a combination of mathematical models, RNA sequencing, single cell analyses, functional and drug assays in a human glioma cell line (U251). After an initial response characterized by cell death induction, cells entered a transient state defined by slow growth, a distinct morphology and a shift of metabolism. Specific genes expression associated to this population revealed chromatin remodeling. Indeed, the histone deacetylase inhibitor trichostatin (TSA), specifically eliminated this population and thus prevented the appearance of fast growing TMZ-resistant cells. In conclusion, we have identified in glioblastoma a population with tolerant-like features, which could constitute a therapeutic target.
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Affiliation(s)
- Marion Rabé
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France
| | - Solenne Dumont
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France.,GenoBiRD, SFR François Bonamy, Université de Nantes, Nantes, France
| | - Arturo Álvarez-Arenas
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-la Mancha, Ciudad Real, Spain
| | - Hicham Janati
- Laboratoire Jacques-Louis Lions, Inria, Mamba team and Sorbonne Université, Paris 6, UPMC, Paris, France
| | - Juan Belmonte-Beitia
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-la Mancha, Ciudad Real, Spain
| | - Gabriel F Calvo
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-la Mancha, Ciudad Real, Spain
| | | | - Quentin Séry
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France.,Laboratoire de Biologie des Cancers et Théranostic, Institut de Cancérologie de l'Ouest-St Herblain, 44805, Saint-Herblain, France
| | - Cynthia Chauvin
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France
| | - Noémie Joalland
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France
| | - Floriane Briand
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France
| | - Stéphanie Blandin
- Plate-Forme MicroPICell, SFR François Bonamy, Université de Nantes, Nantes, France
| | - Emmanuel Scotet
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France
| | - Claire Pecqueur
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France
| | - Jean Clairambault
- Laboratoire Jacques-Louis Lions, Inria, Mamba team and Sorbonne Université, Paris 6, UPMC, Paris, France
| | - Lisa Oliver
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France.,CHU Nantes, 44093, Nantes, France
| | - Victor Perez-Garcia
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-la Mancha, Ciudad Real, Spain
| | - Arulraj Nadaradjane
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France.,Laboratoire de Biologie des Cancers et Théranostic, Institut de Cancérologie de l'Ouest-St Herblain, 44805, Saint-Herblain, France
| | - Pierre-François Cartron
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France.,Laboratoire de Biologie des Cancers et Théranostic, Institut de Cancérologie de l'Ouest-St Herblain, 44805, Saint-Herblain, France
| | - Catherine Gratas
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France. .,CHU Nantes, 44093, Nantes, France.
| | - François M Vallette
- CRCINA, INSERM, Université d'Angers, Université de Nantes, Nantes, France. .,Laboratoire de Biologie des Cancers et Théranostic, Institut de Cancérologie de l'Ouest-St Herblain, 44805, Saint-Herblain, France.
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Budia I, Alvarez-Arenas A, Woolley TE, Calvo GF, Belmonte-Beitia J. Radiation protraction schedules for low-grade gliomas: a comparison between different mathematical models. J R Soc Interface 2019; 16:20190665. [PMID: 31822220 DOI: 10.1098/rsif.2019.0665] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
We optimize radiotherapy (RT) administration strategies for treating low-grade gliomas. Specifically, we consider different tumour growth laws, both with and without spatial effects. In each scenario, we find the optimal treatment in the sense of maximizing the overall survival time of a virtual low-grade glioma patient, whose tumour progresses according to the examined growth laws. We discover that an extreme protraction therapeutic strategy, which amounts to substantially extending the time interval between RT sessions, may lead to better tumour control. The clinical implications of our results are also presented.
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Affiliation(s)
- I Budia
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - A Alvarez-Arenas
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - T E Woolley
- School of Mathematics, Cardiff University, Senghennydd Road, Cardiff CF24 4AG, UK
| | - G F Calvo
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - J Belmonte-Beitia
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
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21
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Zaman A, Wu W, Bivona TG. Targeting Oncogenic BRAF: Past, Present, and Future. Cancers (Basel) 2019; 11:E1197. [PMID: 31426419 PMCID: PMC6721448 DOI: 10.3390/cancers11081197] [Citation(s) in RCA: 125] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 08/13/2019] [Accepted: 08/13/2019] [Indexed: 12/20/2022] Open
Abstract
Identifying recurrent somatic genetic alterations of, and dependency on, the kinase BRAF has enabled a "precision medicine" paradigm to diagnose and treat BRAF-driven tumors. Although targeted kinase inhibitors against BRAF are effective in a subset of mutant BRAF tumors, resistance to the therapy inevitably emerges. In this review, we discuss BRAF biology, both in wild-type and mutant settings. We discuss the predominant BRAF mutations and we outline therapeutic strategies to block mutant BRAF and cancer growth. We highlight common mechanistic themes that underpin different classes of resistance mechanisms against BRAF-targeted therapies and discuss tumor heterogeneity and co-occurring molecular alterations as a potential source of therapy resistance. We outline promising therapy approaches to overcome these barriers to the long-term control of BRAF-driven tumors and emphasize how an extensive understanding of these themes can offer more pre-emptive, improved therapeutic strategies.
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Affiliation(s)
- Aubhishek Zaman
- Department of Medicine, University of California, San Francisco, CA 94143, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Wei Wu
- Department of Medicine, University of California, San Francisco, CA 94143, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, CA 94143, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA.
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