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  • 1.
    Charitou, Theodosia
    et al.
    South Australian Hlth & Med Res Inst, EMBL Australia Grp, Adelaide, SA 5000, Australia..
    Srihari, Sriganesh
    South Australian Hlth & Med Res Inst, EMBL Australia Grp, Adelaide, SA 5000, Australia..
    Lynn, Miriam A.
    South Australian Hlth & Med Res Inst, EMBL Australia Grp, Adelaide, SA 5000, Australia..
    Jarboui, Mohamed-Ali
    Univ Tubingen, Inst Ophthalm Res, Tubingen, Germany.;Univ Tubingen, Werner Siemens Imaging Ctr, Tubingen, Germany..
    Fasterius, Erik
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Moldovan, Max
    South Australian Hlth & Med Res Inst, EMBL Australia Grp, Adelaide, SA 5000, Australia..
    Shirasawa, Senji
    Fukuoka Univ, Fac Med, Fukuoka, Fukuoka 8140133, Japan..
    Tsunoda, Toshiyuki
    Fukuoka Univ, Fac Med, Fukuoka, Fukuoka 8140133, Japan..
    Ueffing, Marius
    Univ Tubingen, Inst Ophthalm Res, Tubingen, Germany..
    Xie, Jianling
    South Australian Hlth & Med Res Inst, Nutr Diabet & Metab, Adelaide, SA 5000, Australia..
    Xin, Jin
    South Australian Hlth & Med Res Inst, Nutr Diabet & Metab, Adelaide, SA 5000, Australia..
    Wang, Xuemin
    South Australian Hlth & Med Res Inst, Nutr Diabet & Metab, Adelaide, SA 5000, Australia.;Univ Adelaide, Sch Biol Sci, Adelaide, SA 5000, Australia..
    Proud, Christopher G.
    South Australian Hlth & Med Res Inst, Nutr Diabet & Metab, Adelaide, SA 5000, Australia.;Univ Adelaide, Sch Biol Sci, Adelaide, SA 5000, Australia..
    Boldt, Karsten
    Univ Tubingen, Inst Ophthalm Res, Tubingen, Germany..
    Al-Khalili Szigyarto, Cristina
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Kolch, Walter
    Univ Coll Dublin, Syst Biol Ireland, Dublin, Ireland.;Univ Coll Dublin, Sch Med, Dublin, Ireland.;Univ Coll Dublin, Conway Inst, Dublin, Ireland..
    Lynn, David J.
    South Australian Hlth & Med Res Inst, EMBL Australia Grp, Adelaide, SA 5000, Australia.;Flinders Univ S Australia, Coll Med & Publ Hlth, Sch Med, Bedford Pk, SA 5042, Australia..
    Transcriptional and metabolic rewiring of colorectal cancer cells expressing the oncogenic KRAS(G13D) mutation2019In: British Journal of Cancer, ISSN 0007-0920, E-ISSN 1532-1827, Vol. 121, no 1, p. 37-50Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Activating mutations in KRAS frequently occur in colorectal cancer (CRC) patients, leading to resistance to EGFRtargeted therapies. METHODS: To better understand the cellular reprogramming which occurs in mutant KRAS cells, we have undertaken a systems-level analysis of four CRC cell lines which express either wild type (wt) KRAS or the oncogenic KRAS(G13D) allele (mtKRAS). RESULTS: RNAseq revealed that genes involved in ribosome biogenesis, mRNA translation and metabolism were significantly upregulated in mtKRAS cells. Consistent with the transcriptional data, protein synthesis and cell proliferation were significantly higher in the mtKRAS cells. Targeted metabolomics analysis also confirmed the metabolic reprogramming in mtKRAS cells. Interestingly, mtKRAS cells were highly transcriptionally responsive to EGFR activation by TGF alpha stimulation, which was associated with an unexpected downregulation of genes involved in a range of anabolic processes. While TGF alpha treatment strongly activated protein synthesis in wtKRAS cells, protein synthesis was not activated above basal levels in the TGF alpha-treated mtKRAS cells. This was likely due to the defective activation of the mTORC1 and other pathways by TGF alpha in mtKRAS cells, which was associated with impaired activation of PKB signalling and a transient induction of AMPK signalling. CONCLUSIONS: We have found that mtKRAS cells are substantially rewired at the transcriptional, translational and metabolic levels and that this rewiring may reveal new vulnerabilities in oncogenic KRAS CRC cells that could be exploited in future.

  • 2.
    Danielsson, Frida
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Fasterius, Erik
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Transcriptome profiling of a cell line model for malignant transformation in response to moderate hypoxiaManuscript (preprint) (Other academic)
  • 3.
    Danielsson, Frida
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Fasterius, Erik
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Sullivan, Devin
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hases, Linnea
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska Institute, Huddinge, Sweden.
    Sanli, Kemal
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Cheng
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Al-Khalili Szigyarto, Cristina
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101).
    Huss, M.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics (closed 20130101). KTH, Centres, Science for Life Laboratory, SciLifeLab. Technical University of Denmark, Hørsholm, Denmark.
    Williams, Cecilia
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska Institute, Huddinge, Sweden.
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia2018In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 9, no 28, p. 19730-19744Article in journal (Refereed)
    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.

  • 4.
    Fasterius, Erik
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
    Exploring genetic heterogeneity in cancer using high-throughput DNA and RNA sequencing2018Doctoral 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.

  • 5.
    Fasterius, Erik
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    seqCAT: a Bioconductor R-package for variant analysis of high throughput sequencing dataManuscript (preprint) (Other academic)
  • 6.
    Fasterius, Erik
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Al-Khalili Szigyarto, Cristina
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Analysis of public RNA-sequencing data reveals biological consequences of genetic heterogeneity in cell line populations2018In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 11226Article in journal (Refereed)
    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.

  • 7.
    Fasterius, Erik
    et al.
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Raso, Cinzia
    Kennedy, Susan
    Rauch, Nora
    Lundin, Par
    Kolch, Walter
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    Al-Khalili Szigyarto, Cristina
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.
    A novel RNA sequencing data analysis method for cell line authentication2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 2, article id e0171435Article in journal (Refereed)
    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.

  • 8.
    Fasterius, Erik
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Uhlén, Mathias
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Al-Khalili Szigyarto, Cristina
    KTH, Superseded Departments (pre-2005), Biotechnology.
    Single cell RNA-seq variant analysis for exploration of inter- and intra-tumour genetic heterogeneityManuscript (preprint) (Other academic)
  • 9.
    Fasterius, Erik
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.
    Al-Khalili Szigyarto, Cristina
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Single-cell RNA-seq variant analysis for exploration of genetic heterogeneity in cancer2019In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 9524Article in journal (Refereed)
    Abstract [en]

    Inter-and intra-tumour heterogeneity is caused by genetic and non-genetic factors, leading to severe clinical implications. High-throughput sequencing technologies provide unprecedented tools to analyse DNA and RNA in single cells and explore both genetic heterogeneity and phenotypic variation between cells in tissues and tumours. Simultaneous analysis of both DNA and RNA in the same cell is, however, still in its infancy. We have thus developed a method to extract and analyse information regarding genetic heterogeneity that affects cellular biology from single-cell RNA-seq data. The method enables both comparisons and clustering of cells based on genetic variation in single nucleotide variants, revealing cellular subpopulations corroborated by gene expression-based methods. Furthermore, the results show that lymph node metastases have lower levels of genetic heterogeneity compared to their original tumours with respect to variants affecting protein function. The analysis also revealed three previously unknown variants common across cancer cells in glioblastoma patients. These results demonstrate the power and versatility of scRNA-seq variant analysis and highlight it as a useful complement to already existing methods, enabling simultaneous investigations of both gene expression and genetic variation.

  • 10. Kennedy, S
    et al.
    Fasterius, Erik
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Al-Khalili Szigyarto, Cristina
    KTH, Superseded Departments (pre-2005), Biotechnology.
    Kolch, W
    et al.,
    Adaptive rewiring of protein-protein interactions and signal flow in the EGFR signaling network by mutant RASManuscript (preprint) (Other academic)
1 - 10 of 10
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