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Computational Analysis of Tumour Heterogeneity
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.ORCID iD: 0000-0002-3889-5589
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University , 2020. , p. 79
Keywords [en]
tumour heterogeneity, human genome and gene expression analyses, pathway annotation, fusion transcript detection, copy number calling, and high-resolution data
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-176074ISBN: 978-91-7797-943-2 (print)ISBN: 978-91-7797-944-9 (electronic)OAI: oai:DiVA.org:su-176074DiVA, id: diva2:1384891
Public defence
2020-03-20, Wangari, Widerströmska huset (KI), Tomtebodavägen 18, Solna, 14:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.

Available from: 2020-02-26 Created: 2020-01-12 Last updated: 2020-02-19Bibliographically approved
List of papers
1. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity
Open this publication in new window or tab >>Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity
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2018 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, article id 2419Article in journal (Refereed) Published
Abstract [en]

Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor microenvironment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies.

National Category
Biological Sciences Cancer and Oncology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-158253 (URN)10.1038/s41467-018-04724-5 (DOI)000435650800010 ()29925878 (PubMedID)
Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2020-01-24Bibliographically approved
2. Spatio-temporal analysis of prostate tumours suggests the pre-existence of ADT-resistant expression clones
Open this publication in new window or tab >>Spatio-temporal analysis of prostate tumours suggests the pre-existence of ADT-resistant expression clones
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(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-177920 (URN)
Available from: 2020-01-12 Created: 2020-01-12 Last updated: 2020-02-03Bibliographically approved
3. Fusion transcript detection using spatial transcriptomics
Open this publication in new window or tab >>Fusion transcript detection using spatial transcriptomics
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Fusion transcripts are involved in tumourigenesis and play a crucial role in tumour heterogeneity, tumour evolution and cancer treatment resistance. However, fusion transcripts have not been studied at high spatial resolution in tissue sections due to the lack of full-length transcripts with spatial information. New high-throughput technologies like spatial transcriptomics measure the transcriptome of tissue sections on almost single-cell level. While this technique does not allow for direct detection of fusion transcripts, we show that they can be inferred using the relative poly(A) tail abundance of the involved parental genes.

We present a new method STfusion, which uses spatial transcriptomics to infer the presence and absence of poly(A) tails. A fusion transcript lacks a poly(A) tail for the 5´ gene and has an elevated number of poly(A) tails for the 3´ gene. Its expression level is defined by the upstream promoter of the 5´ gene. STfusion measures the difference between the observed and expected number of poly(A) tails with a novel C-score. 

We verified the STfusion ability to predict fusion transcripts on HeLa cells with known fusions. STfusion and C-sore applied to clinical prostate cancer data revealed the spatial distribution of the cis-SAGe SLC45A3-ELK4 in 12 tissue sections with almost single-cell resolution. The cis-SAGe occured in the centre or periphery of inflamed, prostatic intraepithelial neoplastic, or cancerous areas, and occasionally in normal glands.

Keywords
Fusion transcript detection, Spatial Transcriptomics, gene fusion, cis-SAGE, oncogenes
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-177919 (URN)
Available from: 2020-01-12 Created: 2020-01-12 Last updated: 2020-01-24Bibliographically approved
4. MetaCNV - a consensus approach to infer accurate copy numbers from low coverage data
Open this publication in new window or tab >>MetaCNV - a consensus approach to infer accurate copy numbers from low coverage data
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background: The majority of copy number callers requires high read coverage data that is often achieved with elevated material input, which increases the heterogeneity of tissue samples. However, to gain insights into smaller areas within a tissue sample, e.g a cancerous area in a heterogeneous tissue sample, less material is used for sequencing, which results in lower read coverage. Therefore, more focus needs to be put on copy number calling that is sensitive enough for low coverage data. 

Results: We present MetaCNV, a copy number caller that infers reliable copy numbers for human genomes with a consensus approach. MetaCNV specializes in low coverage data, but also performs well on normal and high coverage data. MetaCNV integrates the results of multiple copy number callers and infers absolute and unbiased copy numbers for the entire genome. MetaCNV is based on a meta-model that bypasses the weaknesses of current calling models while combining the strengths of existing approaches. Here we apply MetaCNV based on ReadDepth, SVDetect, and CNVnator to real and simulated datasets in order to demonstrate how the approach improves copy number calling. 

Conclusions: MetaCNV, available at https://bitbucket.org/sonnhammergroup/metacnv, provides accurate copy number prediction on low coverage data and performs well on high coverage data.

Keywords
Human genome analysis, copy number calling, low coverage data
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-177917 (URN)
Available from: 2020-01-12 Created: 2020-01-12 Last updated: 2020-02-04Bibliographically approved

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