1
|
Park JH, Hothi P, de Lomana ALG, Pan M, Calder R, Turkarslan S, Wu WJ, Lee H, Patel AP, Cobbs C, Huang S, Baliga NS. Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells. SCIENCE ADVANCES 2024; 10:eadj7706. [PMID: 38848360 PMCID: PMC11160475 DOI: 10.1126/sciadv.adj7706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 05/03/2024] [Indexed: 06/09/2024]
Abstract
Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell-state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing nongenetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupt acquired resistance in GBM.
Collapse
Affiliation(s)
| | - Parvinder Hothi
- Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | | | - Min Pan
- Institute for Systems Biology, Seattle, WA, USA
| | | | | | - Wei-Ju Wu
- Institute for Systems Biology, Seattle, WA, USA
| | - Hwahyung Lee
- Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | - Anoop P. Patel
- Department of Neurosurgery, Preston Robert Tisch Brain Tumor Center, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Charles Cobbs
- Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | - Sui Huang
- Institute for Systems Biology, Seattle, WA, USA
| | - Nitin S. Baliga
- Institute for Systems Biology, Seattle, WA, USA
- Departments of Microbiology, Biology, and Molecular Engineering Sciences, University of Washington, Seattle, WA, USA
| |
Collapse
|
2
|
da Silva Rosa SC, Barzegar Behrooz A, Guedes S, Vitorino R, Ghavami S. Prioritization of genes for translation: a computational approach. Expert Rev Proteomics 2024; 21:125-147. [PMID: 38563427 DOI: 10.1080/14789450.2024.2337004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 02/21/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation. AREAS COVERED In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes. EXPERT OPINION Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.
Collapse
Affiliation(s)
- Simone C da Silva Rosa
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
| | - Amir Barzegar Behrooz
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
- Electrophysiology Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Rui Vitorino
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal
- Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, Aveiro, Portugal
- UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Saeid Ghavami
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
- Faculty of Medicine in Zabrze, Academia of Silesia, Katowice, Poland
- Research Institute of Oncology and Hematology, Cancer Care Manitoba, University of Manitoba, Winnipeg, Canada
| |
Collapse
|
3
|
Kim HJ, Batara DC, Jeon YJ, Lee S, Beck S, Kim SH. The impact of MEIS1 TALE homeodomain transcription factor knockdown on glioma stem cell growth. Anim Cells Syst (Seoul) 2024; 28:93-109. [PMID: 38487309 PMCID: PMC10939110 DOI: 10.1080/19768354.2024.2327340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 02/28/2024] [Indexed: 03/17/2024] Open
Abstract
Myeloid ecotropic virus insertion site 1 (MEIS1) is a HOX co-factor necessary for organ development and normal hematopoiesis. Recently, MEIS1 has been linked to the development and progression of various cancers. However, its role in gliomagenesis particularly on glioma stem cells (GSCs) remains unclear. Here, we demonstrate that MEIS1 is highly upregulated in GSCs compared to normal, and glioma cells and to its differentiated counterparts. Inhibition of MEIS1 expression by shRNA significantly reduced GSC growth in both in vitro and in vivo experiments. On the other hand, integrated transcriptomics analyses of glioma datasets revealed that MEIS1 expression is correlated to cell cycle-related genes. Clinical data analysis revealed that MEIS1 expression is elevated in high-grade gliomas, and patients with high MEIS1 levels have poorer overall survival outcomes. The findings suggest that MEIS1 is a prognostic biomarker for glioma patients and a possible target for developing novel therapeutic strategies against GBM.
Collapse
Affiliation(s)
- Hyun-Jin Kim
- Animal Molecular Biochemistry Laboratory, Department of Animal Science, College of Agriculture and Life Sciences, Chonnam National University, Gwangju, Republic of Korea
| | - Don Carlo Batara
- Animal Molecular Biochemistry Laboratory, Department of Animal Science, College of Agriculture and Life Sciences, Chonnam National University, Gwangju, Republic of Korea
| | - Young-Jun Jeon
- Department of Integrative Biotechnology, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Seongsoo Lee
- Gwangju Center, Korea Basic Science Institute (KBSI), Gwangju, Republic of Korea
- Department of Systems Biotechnology, Chung-Ang University, Anseong-si, Gyeonggi-do, Republic of Korea
| | - Samuel Beck
- Department of Dermatology, Center for Aging Research, Chobanian & Avedisian School of Medicine, Boston University, Boston, USA
| | - Sung-Hak Kim
- Animal Molecular Biochemistry Laboratory, Department of Animal Science, College of Agriculture and Life Sciences, Chonnam National University, Gwangju, Republic of Korea
| |
Collapse
|
4
|
Park JH, Hothi P, Lopez Garcia de Lomana A, Pan M, Calder R, Turkarslan S, Wu WJ, Lee H, Patel AP, Cobbs C, Huang S, Baliga NS. Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578510. [PMID: 38370784 PMCID: PMC10871280 DOI: 10.1101/2024.02.02.578510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing non-genetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupts acquired resistance in GBM.
Collapse
Affiliation(s)
| | - Parvinder Hothi
- Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA
| | | | - Min Pan
- Institute for Systems Biology, Seattle, WA
| | | | | | - Wei-Ju Wu
- Institute for Systems Biology, Seattle, WA
| | - Hwahyung Lee
- Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA
| | - Anoop P Patel
- Department of Neurosurgery, Preston Robert Tisch Brain Tumor Center, Duke University, Durham, NC
- Center for Advanced Genomic Technologies, Duke University, Durham, NC
| | - Charles Cobbs
- Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA
| | - Sui Huang
- Institute for Systems Biology, Seattle, WA
| | - Nitin S Baliga
- Institute for Systems Biology, Seattle, WA
- Departments of Microbiology, Biology, and Molecular Engineering Sciences, University of Washington, Seattle, WA
| |
Collapse
|
5
|
Liu L, van Schaik TA, Chen KS, Rossignoli F, Borges P, Vrbanac V, Wakimoto H, Shah K. Establishment and immune phenotyping of patient-derived glioblastoma models in humanized mice. Front Immunol 2024; 14:1324618. [PMID: 38274817 PMCID: PMC10808686 DOI: 10.3389/fimmu.2023.1324618] [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: 10/19/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
Glioblastoma (GBM) is the most aggressive and common type of malignant brain tumor diagnosed in adults. Preclinical immunocompetent mouse tumor models generated using mouse tumor cells play a pivotal role in testing the therapeutic efficacy of emerging immune-based therapies for GBMs. However, the clinical translatability of such studies is limited as mouse tumor lines do not fully recapitulate GBMs seen in inpatient settings. In this study, we generated three distinct, imageable human-GBM (hGBM) models in humanized mice using patient-derived GBM cells that cover phenotypic and genetic GBM heterogeneity in primary (invasive and nodular) and recurrent tumors. We developed a pipeline to first enrich the tumor-initiating stem-like cells and then successfully established robust patient-derived GBM tumor engraftment and growth in bone marrow-liver-thymus (BLT) humanized mice. Multiplex immunofluorescence of GBM tumor sections revealed distinct phenotypic features of the patient GBM tumors, with myeloid cells dominating the immune landscape. Utilizing flow cytometry and correlative immunofluorescence, we profiled the immune microenvironment within the established human GBM tumors in the BLT mouse models and showed tumor infiltration of variable human immune cells, creating a unique immune landscape compared with lymphoid organs. These findings contribute substantially to our understanding of GBM biology within the context of the human immune system in humanized mice and lay the groundwork for further translational studies aimed at advancing therapeutic strategies for GBM.
Collapse
Affiliation(s)
- Longsha Liu
- Center for Stem Cell and Translational Immunotherapy (CSTI), Harvard Medical School, Boston, MA, United States
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Thijs A. van Schaik
- Center for Stem Cell and Translational Immunotherapy (CSTI), Harvard Medical School, Boston, MA, United States
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Kok-Siong Chen
- Center for Stem Cell and Translational Immunotherapy (CSTI), Harvard Medical School, Boston, MA, United States
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Filippo Rossignoli
- Center for Stem Cell and Translational Immunotherapy (CSTI), Harvard Medical School, Boston, MA, United States
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Paulo Borges
- Center for Stem Cell and Translational Immunotherapy (CSTI), Harvard Medical School, Boston, MA, United States
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Vladimir Vrbanac
- Humanized Immune System Mouse Program, Ragon Institute, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Hiroaki Wakimoto
- Center for Stem Cell and Translational Immunotherapy (CSTI), Harvard Medical School, Boston, MA, United States
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Khalid Shah
- Center for Stem Cell and Translational Immunotherapy (CSTI), Harvard Medical School, Boston, MA, United States
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA, United States
| |
Collapse
|
6
|
Castillo SP, Galvez-Cancino F, Liu J, Pollard SM, Quezada SA, Yuan Y. The tumour ecology of quiescence: Niches across scales of complexity. Semin Cancer Biol 2023; 92:139-149. [PMID: 37037400 DOI: 10.1016/j.semcancer.2023.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/06/2023] [Accepted: 04/08/2023] [Indexed: 04/12/2023]
Abstract
Quiescence is a state of cell cycle arrest, allowing cancer cells to evade anti-proliferative cancer therapies. Quiescent cancer stem cells are thought to be responsible for treatment resistance in glioblastoma, an aggressive brain cancer with poor patient outcomes. However, the regulation of quiescence in glioblastoma cells involves a myriad of intrinsic and extrinsic mechanisms that are not fully understood. In this review, we synthesise the literature on quiescence regulatory mechanisms in the context of glioblastoma and propose an ecological perspective to stemness-like phenotypes anchored to the contemporary concepts of niche theory. From this perspective, the cell cycle regulation is multiscale and multidimensional, where the niche dimensions extend to extrinsic variables in the tumour microenvironment that shape cell fate. Within this conceptual framework and powered by ecological niche modelling, the discovery of microenvironmental variables related to hypoxia and mechanosignalling that modulate proliferative plasticity and intratumor immune activity may open new avenues for therapeutic targeting of emerging biological vulnerabilities in glioblastoma.
Collapse
Affiliation(s)
- Simon P Castillo
- Centre for Evolution and Cancer & Division of Molecular Pathology, The Institute of Cancer Research, London SM2 5NG, UK
| | - Felipe Galvez-Cancino
- Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London WC1E 6DD, UK
| | - Jiali Liu
- Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London WC1E 6DD, UK
| | - Steven M Pollard
- Centre for Regenerative Medicine and Cancer Research UK Scotland Centre, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh EH16 4UU, UK
| | - Sergio A Quezada
- Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London WC1E 6DD, UK
| | - Yinyin Yuan
- Centre for Evolution and Cancer & Division of Molecular Pathology, The Institute of Cancer Research, London SM2 5NG, UK.
| |
Collapse
|
7
|
Lauko A, Lo A, Ahluwalia MS, Lathia JD. Cancer cell heterogeneity & plasticity in glioblastoma and brain tumors. Semin Cancer Biol 2022; 82:162-175. [PMID: 33640445 PMCID: PMC9618157 DOI: 10.1016/j.semcancer.2021.02.014] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/22/2021] [Indexed: 12/25/2022]
Abstract
Brain tumors remain one of the most difficult tumors to treat and, depending on the diagnosis, have a poor prognosis. Of brain tumors, glioblastoma (GBM) is the most common malignant glioma and has a dismal prognosis, with only about 5% of patients alive five years after diagnosis. While advances in targeted therapies and immunotherapies are rapidly improving outcomes in a variety of other cancers, the standard of care for GBM has largely remained unaltered since 2005. There are many well-studied challenges that are either unique to brain tumors (i.e., blood-brain barrier and immunosuppressive environment) or amplified within GBM (i.e., tumor heterogeneity at the cellular and molecular levels, plasticity, and cancer stem cells) that make this disease particularly difficult to treat. While we touch on all these concepts, the focus of this review is to discuss the immense inter- and intra-tumoral heterogeneity and advances in our understanding of tumor cell plasticity and epigenetics in GBM. With each improvement in technology, our understanding of the complexity of tumoral heterogeneity and plasticity improves and we gain more clarity on the causes underlying previous therapeutic failures. However, these advances are unlocking new therapeutic opportunities that scientists and physicians are currently exploiting and have the potential for new breakthroughs.
Collapse
Affiliation(s)
- Adam Lauko
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, United States; Medical Scientist Training Program, Case Western Reserve University School of Medicine, Cleveland, OH, United States
| | - Alice Lo
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Manmeet S Ahluwalia
- Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, Cleveland, OH, United States; Case Comprehensive Cancer Center, Cleveland, OH, United States
| | - Justin D Lathia
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, United States; Medical Scientist Training Program, Case Western Reserve University School of Medicine, Cleveland, OH, United States; Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, Cleveland, OH, United States; Case Comprehensive Cancer Center, Cleveland, OH, United States.
| |
Collapse
|
8
|
Abstract
Drug resistance and metastasis—the major complications in cancer—both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.
Collapse
Affiliation(s)
- Aseel Shomar
- Department of Chemical Engineering, Israel Institute of Technology, Haifa 32000, Israel
- Network Biology Research Laboratory, Israel Institute of Technology, Haifa 32000, Israel
| | - Omri Barak
- Network Biology Research Laboratory, Israel Institute of Technology, Haifa 32000, Israel
- Rappaport Faculty of Medicine Technion, Israel Institute of Technology, Haifa 32000, Israel
| | - Naama Brenner
- Department of Chemical Engineering, Israel Institute of Technology, Haifa 32000, Israel
- Network Biology Research Laboratory, Israel Institute of Technology, Haifa 32000, Israel
- Corresponding author
| |
Collapse
|
9
|
Lam KHB, Leon AJ, Hui W, Lee SCE, Batruch I, Faust K, Klekner A, Hutóczki G, Koritzinsky M, Richer M, Djuric U, Diamandis P. Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity. Nat Commun 2022; 13:116. [PMID: 35013227 PMCID: PMC8748638 DOI: 10.1038/s41467-021-27667-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
Glioblastoma is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance and progression. While regional and single cell transcriptomic variations of glioblastoma have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned across glioblastoma's hallmark histomorphologic niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to distinct histologic patterns across 20 patients and propose diverse molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Moreover, we highlight differential drug sensitivities and relative chemoresistance in glioblastoma cell lines with enhanced KRAS programs. Importantly, these pharmacological differences are less pronounced in transcriptional glioblastoma subgroups suggesting that this model may provide insights for targeting heterogeneity and overcoming therapy resistance.
Collapse
Affiliation(s)
- K H Brian Lam
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
| | - Alberto J Leon
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada
| | - Weili Hui
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada
| | - Sandy Che-Eun Lee
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, #2374-1 King's College Circle, M5S 1A8, Canada
| | - Ihor Batruch
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, M5G 1×5, Canada
| | - Kevin Faust
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada
- Department of Computer Science, University of Toronto, 40 St.George Street, Toronto, Ontario, M5S 2E4, Canada
| | - Almos Klekner
- Department of Neurosurgery, Faculty of Medicine, University of Debrecen, 4032, Debrecen, Hungary
| | - Gábor Hutóczki
- Department of Neurosurgery, Faculty of Medicine, University of Debrecen, 4032, Debrecen, Hungary
| | - Marianne Koritzinsky
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, #2374-1 King's College Circle, M5S 1A8, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, #504-149 College Street, M5T1P5, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
| | - Maxime Richer
- Department of Pathology, Centre Hospitalier Universitaire de Sherbrooke, 3001, 12e avenue Nord, Sherbrooke, QC, J1H 5N4, Canada
- Axe neurosciences du Centre de recherche du Centre hospitalier universitaire (CHU) de Québec-Université Laval et Département de biologie moléculaire, biochimie et pathologie de l'Université Laval, Québec, QC, G1V 4G2, Canada
| | - Ugljesa Djuric
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada
- Laboratory Medicine Program, University Health Network, 200 Elizabeth Street, Toronto, ON, Toronto, Ontario, M5G 2C4, Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, M5S 1A8, Canada.
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1, Canada.
- Institute of Medical Science, University of Toronto, Toronto, Ontario, #2374-1 King's College Circle, M5S 1A8, Canada.
- Laboratory Medicine Program, University Health Network, 200 Elizabeth Street, Toronto, ON, Toronto, Ontario, M5G 2C4, Canada.
| |
Collapse
|
10
|
Jermakowicz AM, Rybin MJ, Suter RK, Sarkaria JN, Zeier Z, Feng Y, Ayad NG. The novel BET inhibitor UM-002 reduces glioblastoma cell proliferation and invasion. Sci Rep 2021; 11:23370. [PMID: 34862404 PMCID: PMC8642539 DOI: 10.1038/s41598-021-02584-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 10/19/2021] [Indexed: 11/23/2022] Open
Abstract
Bromodomain and extraterminal domain (BET) proteins have emerged as therapeutic targets in multiple cancers, including the most common primary adult brain tumor glioblastoma (GBM). Although several BET inhibitors have entered clinical trials, few are brain penetrant. We have generated UM-002, a novel brain penetrant BET inhibitor that reduces GBM cell proliferation in vitro and in a human cerebral brain organoid model. Since UM-002 is more potent than other BET inhibitors, it could potentially be developed for GBM treatment. Furthermore, UM-002 treatment reduces the expression of cell-cycle related genes in vivo and reduces the expression of invasion related genes within the non-proliferative cells present in tumors as measured by single cell RNA-sequencing. These studies suggest that BET inhibition alters the transcriptional landscape of GBM tumors, which has implications for designing combination therapies. Importantly, they also provide an integrated dataset that combines in vitro and ex vivo studies with in vivo single-cell RNA-sequencing to characterize a novel BET inhibitor in GBM.
Collapse
Affiliation(s)
- Anna M Jermakowicz
- Department of Neurological Surgery, Miami Project To Cure Paralysis, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Matthew J Rybin
- Department of Psychiatry and Behavioral Sciences, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Robert K Suter
- Department of Neurological Surgery, Miami Project To Cure Paralysis, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Jann N Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Zane Zeier
- Department of Psychiatry and Behavioral Sciences, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Yangbo Feng
- Department of Molecular and Cellular Pharmacology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, 33136, USA.
| | - Nagi G Ayad
- Department of Neurological Surgery, Miami Project To Cure Paralysis, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, 33136, USA. .,Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, 20057, USA.
| |
Collapse
|
11
|
Khajanchi S, Nieto JJ. Spatiotemporal dynamics of a glioma immune interaction model. Sci Rep 2021; 11:22385. [PMID: 34789751 PMCID: PMC8599515 DOI: 10.1038/s41598-021-00985-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 10/20/2021] [Indexed: 12/20/2022] Open
Abstract
We report a mathematical model which depicts the spatiotemporal dynamics of glioma cells, macrophages, cytotoxic-T-lymphocytes, immuno-suppressive cytokine TGF-β and immuno-stimulatory cytokine IFN-γ through a system of five coupled reaction-diffusion equations. We performed local stability analysis of the biologically based mathematical model for the growth of glioma cell population and their environment. The presented stability analysis of the model system demonstrates that the temporally stable positive interior steady state remains stable under the small inhomogeneous spatiotemporal perturbations. The irregular spatiotemporal dynamics of gliomas, macrophages and cytotoxic T-lymphocytes are discussed extensively and some numerical simulations are presented. Performed some numerical simulations in both one and two dimensional spaces. The occurrence of heterogeneous pattern formation of the system has both biological and mathematical implications and the concepts of glioma cell progression and invasion are considered. Simulation of the model shows that by increasing the value of time, the glioma cell population, macrophages and cytotoxic-T-lymphocytes spread throughout the domain.
Collapse
Affiliation(s)
- Subhas Khajanchi
- Department of Mathematics, Presidency University, 86/1 College Street, Kolkata, 700073, India.
| | - Juan J Nieto
- Instituto de Matem\acute{a}ticas, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| |
Collapse
|
12
|
Association of Circadian Clock Gene Expression with Glioma Tumor Microenvironment and Patient Survival. Cancers (Basel) 2021; 13:cancers13112756. [PMID: 34199348 PMCID: PMC8199552 DOI: 10.3390/cancers13112756] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 05/29/2021] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Gliomas are the most common type of malignant primary brain tumors and are classified according to the cell of origin and genetic features, which can help predict the prognosis and treatment sensitivity. Improving the prognosis remains a challenge; however, chronobiology is a promising field for future works, as circadian clock genes are linked to the tumor biology and outcomes in multiple cancers, including glioma. Here, we examined the relationship of circadian clock genes, IDH mutational status, and prognosis in glioma patients by using unsupervised clustering of the expression of 13 clock genes. We further explored the expression of the clock genes across the tumor regions and cell subpopulations, highlighting the importance of the tumor microenvironment in researching circadian rhythms in cancer. Our research is important for understanding how best to target circadian rhythms to improve patient outcomes in neuro-oncology. Abstract Circadian clock genes have been linked to clinical outcomes in cancer, including gliomas. However, these studies have not accounted for established markers that predict the prognosis, including mutations in Isocitrate Dehydrogenase (IDH), which characterize the majority of lower-grade gliomas and secondary high-grade gliomas. To demonstrate the connection between circadian clock genes and glioma outcomes while accounting for the IDH mutational status, we analyzed multiple publicly available gene expression datasets. The unsupervised clustering of 13 clock gene transcriptomic signatures from The Cancer Genome Atlas showed distinct molecular subtypes representing different disease states and showed the differential prognosis of these groups by a Kaplan–Meier analysis. Further analyses of these groups showed that a low period (PER) gene expression was associated with the negative prognosis and enrichment of the immune signaling pathways. These findings prompted the exploration of the relationship between the microenvironment and clock genes in additional datasets. Circadian clock gene expression was found to be differentially expressed across the anatomical tumor location and cell type. Thus, the circadian clock expression is a potential predictive biomarker in glioma, and further mechanistic studies to elucidate the connections between the circadian clock and microenvironment are warranted.
Collapse
|
13
|
Tumor cell plasticity, heterogeneity, and resistance in crucial microenvironmental niches in glioma. Nat Commun 2021; 12:1014. [PMID: 33579922 PMCID: PMC7881116 DOI: 10.1038/s41467-021-21117-3] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 01/08/2021] [Indexed: 12/15/2022] Open
Abstract
Both the perivascular niche (PVN) and the integration into multicellular networks by tumor microtubes (TMs) have been associated with progression and resistance to therapies in glioblastoma, but their specific contribution remained unknown. By long-term tracking of tumor cell fate and dynamics in the live mouse brain, differential therapeutic responses in both niches are determined. Both the PVN, a preferential location of long-term quiescent glioma cells, and network integration facilitate resistance against cytotoxic effects of radiotherapy and chemotherapy—independently of each other, but with additive effects. Perivascular glioblastoma cells are particularly able to actively repair damage to tumor regions. Population of the PVN and resistance in it depend on proficient NOTCH1 expression. In turn, NOTCH1 downregulation induces resistant multicellular networks by TM extension. Our findings identify NOTCH1 as a central switch between the PVN and network niche in glioma, and demonstrate robust cross-compensation when only one niche is targeted. Whether the perivascular niche (PVN) and the integration into multicellular networks by tumor microtubes (TMs) have a different role in glioblastoma progression and resistance to therapies is currently unclear. Here, the authors, by long-term tracking of individual glioma, demonstrate that both niches can partially compensate for each other and that glioma cells localized in both niches are resistant to radio- and chemotherapy.
Collapse
|
14
|
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.
Collapse
|
15
|
Suter RK, Rodriguez-Blanco J, Ayad NG. Epigenetic pathways and plasticity in brain tumors. Neurobiol Dis 2020; 145:105060. [DOI: 10.1016/j.nbd.2020.105060] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/31/2020] [Accepted: 08/20/2020] [Indexed: 12/11/2022] Open
|
16
|
Zhang Y, Ma Y, Huang Y, Zhang Y, Jiang Q, Zhou M, Su J. Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data. Comput Struct Biotechnol J 2020; 18:2953-2961. [PMID: 33209207 PMCID: PMC7642725 DOI: 10.1016/j.csbj.2020.10.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/29/2020] [Accepted: 10/02/2020] [Indexed: 12/16/2022] Open
Abstract
Biological pathway analysis provides new insights for cell clustering and functional annotation from single-cell RNA sequencing (scRNA-seq) data. Many pathway analysis algorithms have been developed to transform gene-level scRNA-seq data into functional gene sets representing pathways or biological processes. Here, we collected seven widely-used pathway activity transformation algorithms and 32 available datasets based on 16 scRNA-seq techniques. We proposed a comprehensive framework to evaluate their accuracy, stability and scalability. The assessment of scRNA-seq preprocessing showed that cell filtering had the less impact on scRNA-seq pathway analysis, while data normalization of sctransform and scran had a consistent well impact across all tools. We found that Pagoda2 yielded the best overall performance with the highest accuracy, scalability, and stability. Meanwhile, the tool PLAGE exhibited the highest stability, as well as moderate accuracy and scalability.
Collapse
Affiliation(s)
- Yaru Zhang
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Yunlong Ma
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Yukuan Huang
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Yan Zhang
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Qi Jiang
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Meng Zhou
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Jianzhong Su
- Institute of Biomedical Big Data, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China
| |
Collapse
|
17
|
Vollmann-Zwerenz A, Leidgens V, Feliciello G, Klein CA, Hau P. Tumor Cell Invasion in Glioblastoma. Int J Mol Sci 2020; 21:E1932. [PMID: 32178267 PMCID: PMC7139341 DOI: 10.3390/ijms21061932] [Citation(s) in RCA: 143] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/02/2020] [Accepted: 03/09/2020] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is a particularly devastating tumor with a median survival of about 16 months. Recent research has revealed novel insights into the outstanding heterogeneity of this type of brain cancer. However, all GBM subtypes share the hallmark feature of aggressive invasion into the surrounding tissue. Invasive glioblastoma cells escape surgery and focal therapies and thus represent a major obstacle for curative therapy. This review aims to provide a comprehensive understanding of glioma invasion mechanisms with respect to tumor-cell-intrinsic properties as well as cues provided by the microenvironment. We discuss genetic programs that may influence the dissemination and plasticity of GBM cells as well as their different invasion patterns. We also review how tumor cells shape their microenvironment and how, vice versa, components of the extracellular matrix and factors from non-neoplastic cells influence tumor cell motility. We further discuss different research platforms for modeling invasion. Finally, we highlight the importance of accounting for the complex interplay between tumor cell invasion and treatment resistance in glioblastoma when considering new therapeutic approaches.
Collapse
Affiliation(s)
- Arabel Vollmann-Zwerenz
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, University Hospital Regensburg, 93053 Regensburg, Germany; (A.V.-Z.); (V.L.)
| | - Verena Leidgens
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, University Hospital Regensburg, 93053 Regensburg, Germany; (A.V.-Z.); (V.L.)
| | - Giancarlo Feliciello
- Fraunhofer-Institute for Toxicology and Experimental Medicine, Division of Personalized Tumor Therapy, 93053 Regensburg, Germany; (G.F.); (C.A.K.)
| | - Christoph A. Klein
- Fraunhofer-Institute for Toxicology and Experimental Medicine, Division of Personalized Tumor Therapy, 93053 Regensburg, Germany; (G.F.); (C.A.K.)
- Experimental Medicine and Therapy Research, University of Regensburg, 93053 Regensburg, Germany
| | - Peter Hau
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, University Hospital Regensburg, 93053 Regensburg, Germany; (A.V.-Z.); (V.L.)
| |
Collapse
|