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Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.ORCID iD: 0000-0001-5422-4243
Mathematical Sciences, Chalmers University of Technology, Gothenburg.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.ORCID iD: 0000-0001-9804-4041
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
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(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 [en]
regulatory programs, regulatory-driven clustering, cell state, phenotypic plasticity
National Category
Bioinformatics (Computational Biology)
Research subject
Oncology
Identifiers
URN: urn:nbn:se:uu:diva-498235DOI: 10.1101/2023.03.10.532041OAI: oai:DiVA.org:uu-498235DiVA, id: diva2:1742828
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)
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Available from: 2023-04-12 Created: 2023-03-12 Last updated: 2023-04-12

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