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  • 1. Berglund, Emelie
    et al.
    Maaskola, Jonas
    Schultz, Niklas
    Friedrich, Stefanie
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Marklund, Maja
    Bergenstråhle, Joseph
    Tarish, Firas
    Tanoglidi, Anna
    Vickovic, Sanja
    Larsson, Ludvig
    Salmén, Fredrik
    Ogris, Christoph
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Wallenborg, Karolina
    Lagergren, Jens
    Ståhl, Patrik
    Sonnhammer, Erik
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Helleday, Thomas
    Lundeberg, Joakim
    Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity2018Ingår i: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, artikel-id 2419Artikel i tidskrift (Refereegranskat)
    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.

  • 2.
    Friedrich, Stefanie
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Computational Analysis of Tumour Heterogeneity2020Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    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. 

  • 3.
    Friedrich, Stefanie
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Fusion transcript detection using spatial transcriptomicsManuskript (preprint) (Övrigt vetenskapligt)
    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.

  • 4.
    Friedrich, Stefanie
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Barbulescu, Remus
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Helleday, Thomas
    Sonnhammer, Erik
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    MetaCNV - a consensus approach to infer accurate copy numbers from low coverage dataManuskript (preprint) (Övrigt vetenskapligt)
    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.

  • 5.
    Friedrich, Stefanie
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Dalianis, Hercules
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Adverse drug event classification of health records using dictionary-based pre-processing and machine learning2015Ingår i: Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis / [ed] Cyril Grouin, Thierry Hamon, Aurélie Névéol, Pierre Zweigenbaum, Association for Computational Linguistics, 2015, s. 121-130Konferensbidrag (Refereegranskat)
    Abstract [en]

    A method to find adverse drug reactions in electronic health records written in Swedish is presented. A total of 14,751 health records were manually classified into four groups. The records are normalised by pre-processing using both dic- tionaries and manually created word lists. Three different supervised machine learning algorithm were used to find the best results; decision tree, random forest and LibSVM. The best performance on a test dataset was with LibSVM obtaining a pre- cision of 0.69 and a recall of 0.66, and a F-score of 0.67. Our method found 865 of 981 true positives (88.2%) in a 3-class dataset which is an improvement of 49.5% over previous approaches.

  • 6. Marklund, Maja
    et al.
    Schultz, Niklas
    Friedrich, Stefanie
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Berglund, Emelie
    Tarish, Firas
    Maaskola, Jonas
    Bergenstråhle, Jonas
    Liu, Yao
    Tanoglidi, Anna
    Ståhl, Patrik
    Helleday, Thomas
    Sonnhammer, Erik
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Lundeberg, Joakim
    Spatio-temporal analysis of prostate tumours suggests the pre-existence of ADT-resistant expression clonesManuskript (preprint) (Övrigt vetenskapligt)
  • 7. Yan, Jing
    et al.
    Friedrich, Stefanie
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Kurgan, Lukasz
    A comprehensive comparative review of sequence-based predictors of DNA- and RNA-binding residues2016Ingår i: Briefings in Bioinformatics, ISSN 1467-5463, E-ISSN 1477-4054, Vol. 17, nr 1, s. 88-105Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    Motivated by the pressing need to characterize protein-DNA and protein-RNA interactions on large scale, we review a comprehensive set of 30 computational methods for high-throughput prediction of RNA- or DNA-binding residues from protein sequences. We summarize these predictors from several significant perspectives including their design, outputs and availability. We perform empirical assessment of methods that offer web servers using a new benchmark data set characterized by a more complete annotation that includes binding residues transferred from the same or similar proteins. We show that predictors of DNA-binding (RNA-binding) residues offer relatively strong predictive performance but they are unable to properly separate DNA- from RNA-binding residues. We design and empirically assess several types of consensuses and demonstrate that machine learning (ML)-based approaches provide improved predictive performance when compared with the individual predictors of DNA-binding residues or RNA-binding residues. We also formulate and execute first-of-its-kind study that targets combined prediction of DNA- and RNA-binding residues. We design and test three types of consensuses for this prediction and conclude that this novel approach that relies on ML design provides better predictive quality than individual predictors when tested on prediction of DNA- and RNA-binding residues individually. It also substantially improves discrimination between these two types of nucleic acids. Our results suggest that development of a new generation of predictors would benefit from using training data sets that combine both RNA- and DNA-binding proteins, designing new inputs that specifically target either DNA- or RNA-binding residues and pursuing combined prediction of DNA- and RNA-binding residues.

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