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Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia
KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-7692-1100
KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska Institute, Huddinge, Sweden.
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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. Vol. 9, no 28, p. 19730-19744
Keywords [en]
Hypoxia, Malignant transformation, Transcriptomics
National Category
Cell and Molecular Biology
Identifiers
URN: urn:nbn:se:kth:diva-227616DOI: 10.18632/oncotarget.24808Scopus ID: 2-s2.0-85045315705OAI: oai:DiVA.org:kth-227616DiVA, id: diva2:1209147
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: 2018-09-05Bibliographically approved
In thesis
1. Exploring genetic heterogeneity in cancer using high-throughput DNA and RNA sequencing
Open this publication in new window or tab >>Exploring genetic heterogeneity in cancer using high-throughput DNA and RNA sequencing
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
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:nbn:se:kth:diva-234265 (URN)978-91-7729-918-9 (ISBN)
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

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Danielsson, FridaFasterius, ErikSanli, KemalZhang, ChengMardinoglu, AdilAl-Khalili Szigyarto, CristinaUhlén, MathiasWilliams, CeciliaLundberg, Emma

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Danielsson, FridaFasterius, ErikSullivan, DevinHases, LinneaSanli, KemalZhang, ChengMardinoglu, AdilAl-Khalili Szigyarto, CristinaUhlén, MathiasWilliams, CeciliaLundberg, Emma
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Science for Life Laboratory, SciLifeLabProteomics (closed 20130101)
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