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Estimating the differentiation potential and plasticity of cancer cells using statistical mechanics
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.ORCID iD: 0000-0001-5422-4243
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
Mathematical Sciences, Chalmers University of Technology, Gothenburg.
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(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 [en]
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: urn:nbn:se:uu:diva-498237OAI: oai:DiVA.org:uu-498237DiVA, id: diva2:1742829
Available from: 2023-03-12 Created: 2023-03-12 Last updated: 2023-03-23
In thesis
1. Integrative modeling of intratumoral heterogeneity, plasticity and regulation in nervous system cancers
Open this publication in new window or tab >>Integrative modeling of intratumoral heterogeneity, plasticity and regulation in nervous system cancers
2023 (English)Doctoral 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. 

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2023. p. 53
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1920
Keywords
Nervous system cancer, Glioblastoma, Heterogeneity, Plasticity, Mathematical modeling
National Category
Bioinformatics and Systems Biology
Research subject
Oncology
Identifiers
urn:nbn:se:uu:diva-498239 (URN)978-91-513-1753-3 (ISBN)
Public defence
2023-05-05, Rudbecksalen, Rudbecklaboratoriet, Dag Hammarskjölds väg 20, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2023-04-12 Created: 2023-03-12 Last updated: 2023-04-12

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