Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Exploring genetic heterogeneity in cancer using high-throughput DNA and RNA sequencing
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

High-throughput sequencing (HTS) technology has revolutionised the biomedical sciences, where it is used to analyse the genetic makeup and gene expression patterns of both primary patient tissue samples and models cultivated in vitro. This makes it especially useful for research on cancer, a disease that is characterised by its deadliness and genetic heterogeneity. This inherent genetic variation is an important aspect that warrants exploration, and the depth and breadth that HTS possesses makes it well-suited to investigate this facet of cancer.

The types of analyses that may be accomplished with HTS technologies are many, but they may be divided into two groups: those that analyse the DNA of the sample in question, and those that work on the RNA. While DNA-based methods give information regarding the genetic landscape of the sample, RNA-based analyses yield data regarding gene expression patterns; both of these methods have already been used to investigate the heterogeneity present in cancer. While RNA-based methods are traditionally used exclusively for expression analyses, the data they yield may also be utilised to investigate the genetic variation present in the samples. This type of RNA-based analysis is seldom performed, however, and valuable information is thus ignored.

The aim of this thesis is the development and application of DNA- and RNA- based HTS methods for analysing genetic heterogeneity within the context of cancer. The present investigation demonstrates that not only may RNA-based sequencing be used to successfully differentiate different in vitro cancer models through their genetic makeup, but that this may also be done for primary patient data. A pipeline for these types of analyses is established and evaluated, showing it to be both robust to several technical parameters as well as possess a broad scope of analytical possibilities. Genetic variation within cancer models in public databases are evaluated and demonstrated to affect gene expression in several cases. Both inter- and intra-patient genetic heterogeneity is shown using the established pipeline, in addition to demonstrating that cancerous cells are more heterogeneous than their normal neighbours. Finally, two bioinformatic open source software packages are presented.

The results presented herein demonstrate that genetic analyses using RNA-based methods represent excellent complements to already existing DNA-based techniques, and further increase the already large scope of how HTS technologies may be utilised.

Place, publisher, year, edition, pages
Stockholm: Kungliga tekniska högskolan, 2018. , p. 83
Series
TRITA-CBH-FOU ; 2018:31
Keywords [en]
Biotechnology, bioinformatics, RNA-seq, WGS, WES, systems biology, variant analysis, single nucleotide variant, gene expression, machine learning, clustering, open source, R, bioconductor, Python
National Category
Medical Biotechnology Bioinformatics and Systems Biology
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-234265ISBN: 978-91-7729-918-9 (print)OAI: oai:DiVA.org:kth-234265DiVA, id: diva2:1245671
Public defence
2018-10-05, FR4, Oskar Klein's Auditorium, Albanova, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20180906

Available from: 2018-09-06 Created: 2018-09-05 Last updated: 2018-09-06Bibliographically approved
List of papers
1. A novel RNA sequencing data analysis method for cell line authentication
Open this publication in new window or tab >>A novel RNA sequencing data analysis method for cell line authentication
Show others...
2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 2, article id e0171435Article in journal (Refereed) Published
Abstract [en]

We have developed a novel analysis method that can interrogate the authenticity of biological samples used for generation of transcriptome profiles in public data repositories. The method uses RNA sequencing information to reveal mutations in expressed transcripts and subsequently confirms the identity of analysed cells by comparison with publicly available cell-specific mutational profiles. Cell lines constitute key model systems widely used within cancer research, but their identity needs to be confirmed in order to minimise the influence of cell contaminations and genetic drift on the analysis. Using both public and novel data, we demonstrate the use of RNA-sequencing data analysis for cell line authentication by examining the validity of COLO205, DLD1, HCT15, HCT116, HKE3, HT29 and RKO colorectal cancer cell lines. We successfully authenticate the studied cell lines and validate previous reports indicating that DLD1 and HCT15 are synonymous. We also show that the analysed HKE3 cells harbour an unexpected KRAS-G13D mutation and confirm that this cell line is a genuine KRAS dosage mutant, rather than a true isogenic derivative of HCT116 expressing only the wild type KRAS. This authentication method could be used to revisit the numerous cell line based RNA sequencing experiments available in public data repositories, analyse new experiments where whole genome sequencing is not available, as well as facilitate comparisons of data from different experiments, platforms and laboratories.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE, 2017
National Category
Biological Sciences
Identifiers
urn:nbn:se:kth:diva-204084 (URN)10.1371/journal.pone.0171435 (DOI)000394423800024 ()28192450 (PubMedID)2-s2.0-85012231859 (Scopus ID)
Note

QC 20170329

Available from: 2017-03-29 Created: 2017-03-29 Last updated: 2018-09-05Bibliographically approved
2. Adaptive rewiring of protein-protein interactions and signal flow in the EGFR signaling network by mutant RAS
Open this publication in new window or tab >>Adaptive rewiring of protein-protein interactions and signal flow in the EGFR signaling network by mutant RAS
Show others...
(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics and Systems Biology Medical Biotechnology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-234291 (URN)
Note

QC 20180906

Available from: 2018-09-06 Created: 2018-09-06 Last updated: 2018-09-06Bibliographically approved
3. Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia
Open this publication in new window or tab >>Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia
Show others...
2018 (English)In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 9, no 28, p. 19730-19744Article in journal (Refereed) Published
Abstract [en]

In tumor tissues, hypoxia is a commonly observed feature resulting from rapidly proliferating cancer cells outgrowing their surrounding vasculature network. Transformed cancer cells are known to exhibit phenotypic alterations, enabling continuous proliferation despite a limited oxygen supply. The four-step isogenic BJ cell model enables studies of defined steps of tumorigenesis: the normal, immortalized, transformed, and metastasizing stages. By transcriptome profiling under atmospheric and moderate hypoxic (3% O2) conditions, we observed that despite being highly similar, the four cell lines of the BJ model responded strikingly different to hypoxia. Besides corroborating many of the known responses to hypoxia, we demonstrate that the transcriptome adaptation to moderate hypoxia resembles the process of malignant transformation. The transformed cells displayed a distinct capability of metabolic switching, reflected in reversed gene expression patterns for several genes involved in oxidative phosphorylation and glycolytic pathways. By profiling the stage-specific responses to hypoxia, we identified ASS1 as a potential prognostic marker in hypoxic tumors. This study demonstrates the usefulness of the BJ cell model for highlighting the interconnection of pathways involved in malignant transformation and hypoxic response.

Place, publisher, year, edition, pages
Impact Journals LLC, 2018
Keywords
Hypoxia, Malignant transformation, Transcriptomics
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-227616 (URN)10.18632/oncotarget.24808 (DOI)2-s2.0-85045315705 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20180522

Available from: 2018-05-22 Created: 2018-05-22 Last updated: 2019-04-26Bibliographically approved
4. Analysis of public RNA-sequencing data reveals biological consequences of genetic heterogeneity in cell line populations
Open this publication in new window or tab >>Analysis of public RNA-sequencing data reveals biological consequences of genetic heterogeneity in cell line populations
2018 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 11226Article in journal (Refereed) Published
Abstract [en]

Meta-analysis of datasets available in public repositories are used to gather and summarise experiments performed across laboratories, as well as to explore consistency of scientific findings. As data quality and biological equivalency across samples may obscure such analyses and consequently their conclusions, we investigated the comparability of 85 public RNA-seq cell line datasets. Thousands of pairwise comparisons of single nucleotide variants in 139 samples revealed variable genetic heterogeneity of the eight cell line populations analysed as well as variable data quality. The H9 and HCT116 cell lines were found to be remarkably stable across laboratories (with median concordances of 99.2% and 98.5%, respectively), in contrast to the highly variable HeLa cells (89.3%). We show that the genetic heterogeneity encountered greatly affects gene expression between same-cell comparisons, highlighting the importance of interrogating the biological equivalency of samples when comparing experimental datasets. Both the number of differentially expressed genes and the expression levels negatively correlate with the genetic heterogeneity. Finally, we demonstrate how comparing genetically heterogeneous datasets affect gene expression analyses and that high dissimilarity between same-cell datasets alters the expression of more than 300 cancer-related genes, which are often the focus of studies using cell lines.

Place, publisher, year, edition, pages
Nature Publishing Group, 2018
National Category
Medical Genetics
Identifiers
urn:nbn:se:kth:diva-232882 (URN)10.1038/s41598-018-29506-3 (DOI)000439686700049 ()30046134 (PubMedID)2-s2.0-85050698721 (Scopus ID)
Note

QC 20180809

Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2019-05-15Bibliographically approved
5. seqCAT: a Bioconductor R-package for variant analysis of high throughput sequencing data
Open this publication in new window or tab >>seqCAT: a Bioconductor R-package for variant analysis of high throughput sequencing data
(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics and Systems Biology Medical Biotechnology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-234292 (URN)
Note

QC 20180906

Available from: 2018-09-06 Created: 2018-09-06 Last updated: 2018-09-06Bibliographically approved
6. Single cell RNA-seq variant analysis for exploration of inter- and intra-tumour genetic heterogeneity
Open this publication in new window or tab >>Single cell RNA-seq variant analysis for exploration of inter- and intra-tumour genetic heterogeneity
(English)Manuscript (preprint) (Other academic)
National Category
Medical Biotechnology Bioinformatics and Systems Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-234293 (URN)
Note

QC 20180906

Available from: 2018-09-06 Created: 2018-09-06 Last updated: 2018-09-06Bibliographically approved

Open Access in DiVA

PhD Thesis Erik Fasterius(13570 kB)95 downloads
File information
File name FULLTEXT01.pdfFile size 13570 kBChecksum SHA-512
4519c8339d6ec4bd532a5ee764b4ac5b3fb7163b984bb9d515c44bbc9d39441f7eaecc0c838fa8f5e5f55db0fcb54003cf1a40ec671df94d631c7cb1a9a7a946
Type fulltextMimetype application/pdf

Authority records BETA

Fasterius, Erik

Search in DiVA

By author/editor
Fasterius, Erik
By organisation
School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH)
Medical BiotechnologyBioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar
Total: 95 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 1288 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf