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Computational Analysis of Tumour Heterogeneity Stefanie Friedrich Computational Analysis of Tumour Heterogeneity Computational Analysis of Tumour Heterogeneity Tumour of Analysis Computational Stefanie Friedrich Stefanie Friedrich ISBN 978-91-7797-943-2 Department of Biochemistry and Biophysics Doctoral Thesis in Biochemistry towards Bioinformatics at Stockholm University, Sweden 2020 Computational Analysis of Tumour Heterogeneity Stefanie Friedrich Academic dissertation for the Degree of Doctor of Philosophy in Biochemistry towards Bioinformatics at Stockholm University to be publicly defended on Friday 20 March 2020 at 14.00 in Wangari, Widerströmska huset (KI), Tomtebodavägen 18. Abstract Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of non- responding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity. The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity. Keywords: tumour heterogeneity, human genome and gene expression analyses, pathway annotation, fusion transcript detection, copy number calling, and high-resolution data. Stockholm 2020 http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-176074 ISBN 978-91-7797-943-2 ISBN 978-91-7797-944-9 Department of Biochemistry and Biophysics Stockholm University, 106 91 Stockholm COMPUTATIONAL ANALYSIS OF TUMOUR HETEROGENEITY Stefanie Friedrich Computational Analysis of Tumour Heterogeneity Stefanie Friedrich ©Stefanie Friedrich, Stockholm University 2020 ISBN print 978-91-7797-943-2 ISBN PDF 978-91-7797-944-9 Front page: Painting by Osnat Tazdok (2019). Inside My Mind (#6158) Printed in Sweden by Universitetsservice US-AB, Stockholm 2020 Abstract Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of non-responding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity. The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity. List of papers The following papers and manuscripts, referred to in the text by their Roman numerals, are included in this thesis: PAPER I: Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity Emelie Berglund*, Jonas Maaskola*, Niklas Schultz*, Stefanie Friedrich*, Maja Marklund, Joseph Bergenstråhle, Firas Tarish, Anna Tanoglidi, Sanja Vickovic, Ludvig Larsson, Fredrik Salmén, Christoph Ogris, Karolina Wallenborg, Jens Lagergren, Patrik Ståhl, Erik Sonnhammer, Thomas Helleday, and Joakim Lundeberg (2018) Nature communications, 9(1), 2419. PAPER II: Spatio-temporal analysis of prostate tumours suggests the pre- existence of ADT-resistant expression clones Maja Marklund*, Niklas Schultz*, Stefanie Friedrich*, Emelie Berglund, Firas Tarish, Jonas Maaskola, Joseph Bergenstråhle, Yao Liu, Anna Tanoglidi, Patrik Ståhl, Thomas Helleday, Erik Sonnhammer, and Joakim Lundeberg (2020) manuscript. PAPER III: Fusion transcript detection using spatial transcriptomics Stefanie Friedrich and Erik Sonnhammer (2020) BMC Medical Genomics ‒ under revision. PAPER IV: MetaCNV ‒ a consensus approach to infer accurate copy numbers from low coverage data Stefanie Friedrich, Remus Barbulescu, Thomas Helleday, and Erik Sonnhammer (2020) BMC Medical Genomics ‒ under revision. * Contributed equally Reprints were made with permission from the publisher Contents Introduction................................................................................................................ 1 Background................................................................................................................................ 1 Problem statement.................................................................................................................... 3 Limitations.................................................................................................................................. 5 Synopsis and structure of the thesis.......................................................................................6 Literature review........................................................................................................9 Cancer research........................................................................................................................ 9 Cancer...................................................................................................................................... 11 Pathways in cancer...........................................................................................................12 A primary tumour site........................................................................................................13 Tumour micro-environment..............................................................................................15 Tumour development........................................................................................................15 Tumour initiation.......................................................................................................... 16 Tumour promotion and progression..........................................................................17 Tumour heterogeneity.......................................................................................................18 Models of tumour heterogeneity................................................................................19 Types of tumour heterogeneity..................................................................................20 Spatial heterogeneity............................................................................................ 21 Spatio-temporal heterogeneity............................................................................ 27 Investigation of tumour heterogeneity.................................................................................. 29 Approaches........................................................................................................................ 29 Technologies......................................................................................................................29 Standard sequence-based methods: bulk sequencing..........................................30 High-resolution sequence-based methods.............................................................. 30 Single-cell sequencing..........................................................................................31 Spatially resolved omics: in situ capturing methods.........................................31 Computational methods....................................................................................................32 Alignment and data preprocessing........................................................................... 33 Mutation calling............................................................................................................34
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