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  • 1.
    Larsson, Ida
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neurooncology and neurodegeneration.
    Integrative modeling of intratumoral heterogeneity, plasticity and regulation in nervous system cancers2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The adult brain tumor glioblastoma (GBM) is characterized by short survival and a lack of efficient treatments. Median survival is 15 months from time of diagnosis and the 5-year survival rate is only 7 %. There is an urgent need for more efficient treatment against GBM, but there are many challenges, including the high extent of heterogeneity of GBM. The tumoral heterogeneity of GBM ranges from interpatient to intratumoral. The aim of this thesis has been to address unanswered questions relating to the intratumoral heterogeneity of GBM, with three specific focuses; (1) the organization of GBM cell state transitions (paper I and III), (2) the regulation of cell states and cell state transitions (paper II), and (3) targeted interventions against cell states (paper II and IV).

    In paper I, we develop an experimental-computational method to measure and quantify cell state transitions. We find that GBM cell states organize hierarchically, with a clear “source state” feeding cells downwards in the hierarchy towards a “sink state” with negative growth rate, but with multi-directional transitions between intermediate states. 

    In paper II, we address the lack of computational methods to identify regulators of intratumoral heterogeneity by developing an algorithm called scRegClust that uses scRNA-seq data to estimate regulatory programs. Through an integrative study of the regulatory landscape of neuro-oncology we find two potential regulators of the macrophage-induced mesenchymal transition in GBM.

    In paper III, we explore the energy-concept as a way of measuring differentiation potential of single cells, instead of relying on gene markers or gene signatures of stemness. We fit a model called the Ising model from statistical mechanics to scRNA-seq data and show both on synthetic and real data that the estimated Ising energy is a good measure of a cell’s differentiation potential, where high Ising energy indicate a high degree of stemness.

    Finally, in paper IV, another experimental-computational method is developed to investigate drug-induced effects on both inter- and intratumoral heterogeneity. 

    In summary, the high extent of intratumoral heterogeneity in nervous system cancer is a major caveat for the development of more efficient treatments. In this thesis we have taken a systems biology approach to understand how this heterogeneity is structured and how we can exploit that knowledge for therapeutic purposes. 

    List of papers
    1. Modeling glioblastoma heterogeneity as a dynamic network of cell states
    Open this publication in new window or tab >>Modeling glioblastoma heterogeneity as a dynamic network of cell states
    Show others...
    2021 (English)In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 17, no 9, article id e10105Article in journal (Refereed) Published
    Abstract [en]

    Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single-cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time-dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time-dependent transcriptional variation of patient-derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3 weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient-specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time-dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition.

    Place, publisher, year, edition, pages
    John Wiley & SonsWILEY, 2021
    Keywords
    cell state, cellular barcoding, patient-derived brain tumor cells, single-cell lineage tracing, time-dependent computational models
    National Category
    Cell Biology Cell and Molecular Biology
    Identifiers
    urn:nbn:se:uu:diva-495138 (URN)
    Funder
    Swedish Cancer SocietySwedish Research CouncilSwedish Foundation for Strategic Research
    Available from: 2023-01-24 Created: 2023-01-24 Last updated: 2024-01-15
    2. Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers
    Open this publication in new window or tab >>Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers
    Show others...
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Nervous system cancers contain a large spectrum of transcriptional cell states, reflecting processes active during normal development, injury response and growth. However, we lack a good understanding of these states' regulation and pharmacological importance. Here, we describe the integrated reconstruction of such cellular regulatory programs and their therapeutic targets from extensive collections of single-cell RNA sequencing data (scRNA-seq) from both tumors and developing tissues. Our method, termed single-cell Regulatory-driven Clustering (scRegClust), predicts essential kinases and transcription factors in little computational time thanks to a new efficient optimization strategy. Using this method, we analyze scRNA-seq data from both adult and childhood brain cancers to identify transcription factors and kinases that regulate distinct tumor cell states.  In adult glioblastoma, our model predicts that blocking the activity of PDGFRA, DDR1, ERBB3 or SOX6, or increasing YBX1-activity, would potentiate temozolomide treatment. We further perform an integrative study of scRNA-seq data from both cancer and the developing brain to uncover the regulation of emerging meta-modules. We find a meta-module regulated by the transcription factors SPI1 and IRF8 and link it to an immune-mediated mesenchymal-like state. Our algorithm is available as an easy-to-use R package and companion visualization tool that help uncover the regulatory programs underlying cell plasticity in cancer and other diseases.

    Keywords
    regulatory programs, regulatory-driven clustering, cell state, phenotypic plasticity
    National Category
    Bioinformatics (Computational Biology)
    Research subject
    Oncology
    Identifiers
    urn:nbn:se:uu:diva-498235 (URN)10.1101/2023.03.10.532041 (DOI)
    Available from: 2023-03-12 Created: 2023-03-12 Last updated: 2023-03-23
    3. Estimating the differentiation potential and plasticity of cancer cells using statistical mechanics
    Open this publication in new window or tab >>Estimating the differentiation potential and plasticity of cancer cells using statistical mechanics
    Show others...
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Cell differentiation is a crucial property of both normal and cancerous cells, that is driven by complex underlying processes. A number of computational methods can score the differentiation potential of individual cells based on their RNA expression. However, we lack a unifying model to explain how differentiation arises from underlying gene regulation and external perturbations. Here, we show that an adaptation of the Ising model, commonly used in statistical mechanics, can bridge this gap, thereby offering a way to identify normal and cancer stem cells. Our new model states that every cell updates its gene expression pattern according to a Boltzmann distribution, influenced by the gene-gene network and an external perturbation field. We first show that this model can be fitted to scRNAseq data sets. We apply the model to a range of data sets to demonstrate its efficacy in separating cells with varying differentiation potential and creating a pseudo-temporal ordering of cells in a GBM data set. Additionally, we explore other aspects of the model to identify known chromosomal aberrations of GBM from single cells and predict therapeutic interventions. This framework has potential applications in many cancer types and can be used to identify CSCs and measure differentiation potential without relying on stemness signatures or marker genes. 

    Keywords
    differentiation potential, plasticity, single-cell profiling, the ising model, computational modeling, gene perturbations
    National Category
    Bioinformatics (Computational Biology)
    Research subject
    Oncology; Oncology; Oncology
    Identifiers
    urn:nbn:se:uu:diva-498237 (URN)
    Available from: 2023-03-12 Created: 2023-03-12 Last updated: 2023-03-23
    4. Using drug-induced cell states to build therapeutic combinations against nervous system cancers
    Open this publication in new window or tab >>Using drug-induced cell states to build therapeutic combinations against nervous system cancers
    Show others...
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Evidence is amounting that nervous system cancers are heterogeneous at the single cell level, yet data are currently scarce on how therapeutic agents affect this heterogeneity. Here, we describe a new, data-driven strategy to identify drugs that modulate the intratumoral heterogeneity of nervous system cancers. First, we demonstrate that drugs elicit structured changes in pathway activation in patient-derived cells from glioblastomas, neuroblastomas and medulloblastomas.  Second, we present a mathematical model to estimate how drugs induce changes in tumor heterogeneity, as defined by single cell RNA sequencing atlases of each disease. Finally, as an evaluation of our method we use it to identify candidate synergistic drug pairs based on the drugs' effects on intratumoral heterogeneity.

    Keywords
    Glioblastoma, Neuroblastoma, Medulloblastoma, DRUG-Seq
    National Category
    Bioinformatics (Computational Biology)
    Research subject
    Oncology
    Identifiers
    urn:nbn:se:uu:diva-498238 (URN)
    Available from: 2023-03-12 Created: 2023-03-12 Last updated: 2023-03-23
    Download full text (pdf)
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  • 2.
    Larsson, Ida
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Dalmo, Erika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden..
    Elgendy, Ramy
    Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden..
    Niklasson, Mia
    Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden..
    Doroszko, Milena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden..
    Segerman, Anna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden.;Uppsala Univ Hosp, Dept Med Sci Canc Pharmacol & Computat Med, Uppsala, Sweden..
    Jornsten, Rebecka
    Chalmers Univ Technol, Math Sci, Gothenburg, Sweden..
    Westermark, Bengt
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Nelander, Sven
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Modeling glioblastoma heterogeneity as a dynamic network of cell states2021In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 17, no 9, article id e10105Article in journal (Refereed)
    Abstract [en]

    Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single-cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time-dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time-dependent transcriptional variation of patient-derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3 weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient-specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time-dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition.

  • 3.
    Larsson, Ida
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Held, Felix
    Mathematical Sciences, Chalmers University of Technology, Gothenburg.
    Popova, Gergana
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Koc, Alper
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Jörnsten, Rebecka
    Mathematical Sciences, Chalmers University of Technology, Gothenburg.
    Nelander, Sven
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancersManuscript (preprint) (Other academic)
    Abstract [en]

    Nervous system cancers contain a large spectrum of transcriptional cell states, reflecting processes active during normal development, injury response and growth. However, we lack a good understanding of these states' regulation and pharmacological importance. Here, we describe the integrated reconstruction of such cellular regulatory programs and their therapeutic targets from extensive collections of single-cell RNA sequencing data (scRNA-seq) from both tumors and developing tissues. Our method, termed single-cell Regulatory-driven Clustering (scRegClust), predicts essential kinases and transcription factors in little computational time thanks to a new efficient optimization strategy. Using this method, we analyze scRNA-seq data from both adult and childhood brain cancers to identify transcription factors and kinases that regulate distinct tumor cell states.  In adult glioblastoma, our model predicts that blocking the activity of PDGFRA, DDR1, ERBB3 or SOX6, or increasing YBX1-activity, would potentiate temozolomide treatment. We further perform an integrative study of scRNA-seq data from both cancer and the developing brain to uncover the regulation of emerging meta-modules. We find a meta-module regulated by the transcription factors SPI1 and IRF8 and link it to an immune-mediated mesenchymal-like state. Our algorithm is available as an easy-to-use R package and companion visualization tool that help uncover the regulatory programs underlying cell plasticity in cancer and other diseases.

  • 4.
    Larsson, Ida
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Lundin, Erika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden..
    Elgendy, Ramy
    Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden..
    Niklasson, Mia
    Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden..
    Doroszko, Milena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden..
    Segerman, Anna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden.;Uppsala Univ Hosp, Dept Med Sci Canc Pharmacol & Computat Med, Uppsala, Sweden..
    Jornsten, Rebecka
    Chalmers Univ Technol, Math Sci, Gothenburg, Sweden..
    Westermark, Bengt
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Nelander, Sven
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Modeling glioblastoma heterogeneity as a dynamic network of cell states2021In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 17, no 9, article id e10105Article in journal (Refereed)
    Abstract [en]

    Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single-cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time-dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time-dependent transcriptional variation of patient-derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3 weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient-specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time-dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition.

    Download full text (pdf)
    fulltext
  • 5.
    Larsson, Ida
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Popova, Gergana
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Elgendy, Ramy
    Sundström, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Krona, Cecilia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Jörnsten, Rebecka
    athematical Sciences, Chalmers University of Technology, Gothenburg.
    Nelander, Sven
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Using drug-induced cell states to build therapeutic combinations against nervous system cancersManuscript (preprint) (Other academic)
    Abstract [en]

    Evidence is amounting that nervous system cancers are heterogeneous at the single cell level, yet data are currently scarce on how therapeutic agents affect this heterogeneity. Here, we describe a new, data-driven strategy to identify drugs that modulate the intratumoral heterogeneity of nervous system cancers. First, we demonstrate that drugs elicit structured changes in pathway activation in patient-derived cells from glioblastomas, neuroblastomas and medulloblastomas.  Second, we present a mathematical model to estimate how drugs induce changes in tumor heterogeneity, as defined by single cell RNA sequencing atlases of each disease. Finally, as an evaluation of our method we use it to identify candidate synergistic drug pairs based on the drugs' effects on intratumoral heterogeneity.

  • 6.
    Lång, Adam
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Larsson, Ida
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Skeppås, Madeleine
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Jörnsten, Rebecka
    Mathematical Sciences, Chalmers University of Technology, Gothenburg.
    Nelander, Sven
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Estimating the differentiation potential and plasticity of cancer cells using statistical mechanicsManuscript (preprint) (Other academic)
    Abstract [en]

    Cell differentiation is a crucial property of both normal and cancerous cells, that is driven by complex underlying processes. A number of computational methods can score the differentiation potential of individual cells based on their RNA expression. However, we lack a unifying model to explain how differentiation arises from underlying gene regulation and external perturbations. Here, we show that an adaptation of the Ising model, commonly used in statistical mechanics, can bridge this gap, thereby offering a way to identify normal and cancer stem cells. Our new model states that every cell updates its gene expression pattern according to a Boltzmann distribution, influenced by the gene-gene network and an external perturbation field. We first show that this model can be fitted to scRNAseq data sets. We apply the model to a range of data sets to demonstrate its efficacy in separating cells with varying differentiation potential and creating a pseudo-temporal ordering of cells in a GBM data set. Additionally, we explore other aspects of the model to identify known chromosomal aberrations of GBM from single cells and predict therapeutic interventions. This framework has potential applications in many cancer types and can be used to identify CSCs and measure differentiation potential without relying on stemness signatures or marker genes. 

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