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Fusion transcript detection using spatial transcriptomics
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
(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 [en]
Fusion transcript detection, Spatial Transcriptomics, gene fusion, cis-SAGE, oncogenes
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-177919OAI: oai:DiVA.org:su-177919DiVA, id: diva2:1384889
Available from: 2020-01-12 Created: 2020-01-12 Last updated: 2020-01-24Bibliographically approved
In thesis
1. Computational Analysis of Tumour Heterogeneity
Open this publication in new window or tab >>Computational Analysis of Tumour Heterogeneity
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
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:nbn:se:su:diva-176074 (URN)978-91-7797-943-2 (ISBN)978-91-7797-944-9 (ISBN)
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

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