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  • 1. Berger, Ashton C
    et al.
    Korkut, Anil
    Kanchi, Rupa S
    Hegde, Apurva M
    Lenoir, Walter
    Liu, Wenbin
    Liu, Yuexin
    Fan, Huihui
    Shen, Hui
    Ravikumar, Visweswaran
    Rao, Arvind
    Schultz, Andre
    Li, Xubin
    Sumazin, Pavel
    Williams, Cecilia
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Mestdagh, Pieter
    Gunaratne, Preethi H
    Yau, Christina
    Bowlby, Reanne
    Robertson, A Gordon
    Tiezzi, Daniel G
    Wang, Chen
    Cherniack, Andrew D
    Godwin, Andrew K
    Kuderer, Nicole M
    Rader, Janet S
    Zuna, Rosemary E
    Sood, Anil K
    Lazar, Alexander J
    Ojesina, Akinyemi I
    Adebamowo, Clement
    Adebamowo, Sally N
    Baggerly, Keith A
    Chen, Ting-Wen
    Chiu, Hua-Sheng
    Lefever, Steve
    Liu, Liang
    MacKenzie, Karen
    Orsulic, Sandra
    Roszik, Jason
    Shelley, Carl Simon
    Song, Qianqian
    Vellano, Christopher P
    Wentzensen, Nicolas
    Weinstein, John N
    Mills, Gordon B
    Levine, Douglas A
    Akbani, Rehan
    A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers.2018In: Cancer Cell, ISSN 1535-6108, E-ISSN 1878-3686, Vol. 33, no 4, p. 690-705.e9, article id S1535-6108(18)30119-3Article in journal (Refereed)
    Abstract [en]

    We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories.

  • 2.
    Danielsson, Frida
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. Royal Inst Technol, Sci Life Lab, S-17165 Stockholm, Sweden..
    Peterson, McKenzie Kirsten
    Univ Utah, Sch Med, Dept Pathol, Salt Lake City, UT 84112 USA..
    Araujo, Helena Caldeira
    Univ Madeira, Ctr Quim, P-9020105 Funchal, Portugal..
    Lautenschlaeger, Franziska
    Saarland Univ, Leibniz Inst Neue Mat gGmbH INM & Expt Phys, NT Fac, Campus D2 2,E 2 6, D-66123 Saarbrucken, Germany..
    Britt Gad, Annica Karin
    Univ Madeira, Ctr Quim, P-9020105 Funchal, Portugal.;Uppsala Univ, Dept Med Biochem & Microbiol, S-75237 Uppsala, Sweden..
    Vimentin Diversity in Health and Disease2018In: CELLS, ISSN 2073-4409, Vol. 7, no 10, article id 147Article, review/survey (Refereed)
    Abstract [en]

    Vimentin is a protein that has been linked to a large variety of pathophysiological conditions, including cataracts, Crohn's disease, rheumatoid arthritis, HIV and cancer. Vimentin has also been shown to regulate a wide spectrum of basic cellular functions. In cells, vimentin assembles into a network of filaments that spans the cytoplasm. It can also be found in smaller, non-filamentous forms that can localise both within cells and within the extracellular microenvironment. The vimentin structure can be altered by subunit exchange, cleavage into different sizes, re-annealing, post-translational modifications and interacting proteins. Together with the observation that different domains of vimentin might have evolved under different selection pressures that defined distinct biological functions for different parts of the protein, the many diverse variants of vimentin might be the cause of its functional diversity. A number of review articles have focussed on the biology and medical aspects of intermediate filament proteins without particular commitment to vimentin, and other reviews have focussed on intermediate filaments in an in vitro context. In contrast, the present review focusses almost exclusively on vimentin, and covers both ex vivo and in vivo data from tissue culture and from living organisms, including a summary of the many phenotypes of vimentin knockout animals. Our aim is to provide a comprehensive overview of the current understanding of the many diverse aspects of vimentin, from biochemical, mechanical, cellular, systems biology and medical perspectives.

  • 3. Mönnich, M.
    et al.
    Borgeskov, L.
    Breslin, L.
    Jakobsen, L.
    Rogowski, M.
    Doganli, C.
    Schrøder, J. M.
    Mogensen, J. B.
    Blinkenkjær, L.
    Harder, L. M.
    Lundberg, Emma
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Geimer, S.
    Christensen, S. T.
    Andersen, J. S.
    Larsen, L. A.
    Pedersen, L. B.
    CEP128 Localizes to the Subdistal Appendages of the Mother Centriole and Regulates TGF-β/BMP Signaling at the Primary Cilium2018In: Cell reports, ISSN 2211-1247, E-ISSN 2211-1247, Vol. 22, no 10, p. 2601-2614Article in journal (Refereed)
    Abstract [en]

    The centrosome is the main microtubule-organizing center in animal cells and comprises a mother and daughter centriole surrounded by pericentriolar material. During formation of primary cilia, the mother centriole transforms into a basal body that templates the ciliary axoneme. Ciliogenesis depends on mother centriole-specific distal appendages, whereas the role of subdistal appendages in ciliary function is unclear. Here, we identify CEP128 as a centriole subdistal appendage protein required for regulating ciliary signaling. Loss of CEP128 did not grossly affect centrosomal or ciliary structure but caused impaired transforming growth factor-β/bone morphogenetic protein (TGF-β/BMP) signaling in zebrafish and at the primary cilium in cultured mammalian cells. This phenotype is likely the result of defective vesicle trafficking at the cilium as ciliary localization of RAB11 was impaired upon loss of CEP128, and quantitative phosphoproteomics revealed that CEP128 loss affects TGF-β1-induced phosphorylation of multiple proteins that regulate cilium-associated vesicle trafficking. Mönnich et al. show that CEP128 localizes to the subdistal appendages of the mother centriole and basal body of the primary cilium. CEP128 regulates vesicular trafficking and targeting of RAB11 to the primary cilium. CEP128 loss leads to impaired TGF-β/BMP signaling, which, in zebrafish, is associated with defective organ development.

  • 4. O'Hagan, Steve
    et al.
    Muelas, Marina Wright
    Day, Philip J.
    Lundberg, Emma
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Kell, Douglas B.
    GeneGini: Assessment via the Gini Coefficient of Reference "Housekeeping'' Genes and Diverse Human Transporter Expression Profiles2018In: Cell Systems, ISSN 2405-4712, Vol. 6, no 2, p. 230-+Article in journal (Refereed)
    Abstract [en]

    The expression levels of SLC or ABC membrane transporter transcripts typically differ 100- to 10,000-fold between different tissues. The Gini coefficient characterizes such inequalities and here is used to describe the distribution of the expression of each transporter among different human tissues and cell lines. Many transporters exhibit extremely high Gini coefficients even for common substrates, indicating considerable specialization consistent with divergent evolution. The expression profiles of SLC transporters in different cell lines behave similarly, although Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying selection. Transporter genes are significantly more heterogeneously expressed than the members of most non-transporter gene classes. Transcripts with the stablest expression have a low Gini index and often differ significantly from the "housekeeping'' genes commonly used for normalization in transcriptomics/qPCR studies. PCBP1 has a low Gini coefficient, is reasonably expressed, and is an excellent novel reference gene. The approach, referred to as GeneGini, provides rapid and simple characterization of expression-profile distributions and improved normalization of genome-wide expression-profiling data.

  • 5.
    Sullivan, Devin P.
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Winsnes, Casper F.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Åkesson, Lovisa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hjelmare, Martin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Wiking, Mikaela
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schutten, Rutger
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Campbell, Linzi
    CCP Hf, Reyjkavik, Iceland..
    Leifsson, Hjalti
    CCP Hf, Reyjkavik, Iceland..
    Rhodes, Scott
    CCP Hf, Reyjkavik, Iceland..
    Nordgren, Andie
    CCP Hf, Reyjkavik, Iceland..
    Smith, Kevin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Revaz, Bernard
    MMOS Sarl, Monthey, Switzerland..
    Finnbogason, Bergur
    CCP Hf, Reyjkavik, Iceland..
    Szantner, Attila
    MMOS Sarl, Monthey, Switzerland..
    Lundberg, Emma
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics.
    Deep learning is combined with massive-scale citizen science to improve large-scale image classification2018In: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 36, no 9, p. 820-+Article in journal (Refereed)
    Abstract [en]

    Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.

  • 6.
    Thul, Peter
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Åkesson, Lovisa
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Mahdessian, Diana
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bäckström, Anna
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Danielsson, Frida
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Gnann, Christian
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hjelmare, Martin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schutten, Rutger
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Stadler, Charlotte
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sullivan, Devin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Winsnes, Casper
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Galea, Gabriella
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Pepperkok, R.
    Uhlén, Mathias
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundberg, Emma
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Exploring the Proteome of Multilocalizing Proteins2017In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 28Article in journal (Other academic)
  • 7.
    Zheng, Daoshan
    et al.
    Dept Canc Biol, 4500 San Pablo Rd, Jacksonville, FL 32224 USA..
    Williams, Cecilia
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska Institute.
    Vold, Jeremy A.
    Mayo Canc Registry, 4500 San Pablo Rd, Jacksonville, FL 32224 USA..
    Nguyen, Justin H.
    Mayo Clin, Dept Surg, 4500 San Pablo Rd, Jacksonville, FL 32224 USA.;Mayo Clin, Mayo Clin Canc Ctr, 4500 San Pablo Rd, Jacksonville, FL 32224 USA..
    Harnois, Denise M.
    Mayo Clin, Dept Surg, 4500 San Pablo Rd, Jacksonville, FL 32224 USA.;Mayo Clin, Mayo Clin Canc Ctr, 4500 San Pablo Rd, Jacksonville, FL 32224 USA..
    Bagaria, Sanjay P.
    Mayo Clin, Dept Surg, 4500 San Pablo Rd, Jacksonville, FL 32224 USA.;Mayo Clin, Mayo Clin Canc Ctr, 4500 San Pablo Rd, Jacksonville, FL 32224 USA..
    McLaughlin, Sarah A.
    Mayo Clin, Dept Surg, 4500 San Pablo Rd, Jacksonville, FL 32224 USA.;Mayo Clin, Mayo Clin Canc Ctr, 4500 San Pablo Rd, Jacksonville, FL 32224 USA..
    Li, Zhaoyu
    Dept Canc Biol, 4500 San Pablo Rd, Jacksonville, FL 32224 USA..
    Regulation of sex hormone receptors in sexual dimorphism of human cancers2018In: Cancer Letters, ISSN 0304-3835, E-ISSN 1872-7980, Vol. 438, p. 24-31Article, review/survey (Refereed)
    Abstract [en]

    Gender differences in the incidences of cancers have been found in almost all human cancers. However, the mechanisms that underlie gender disparities in most human cancer types have been under-investigated. Here, we provide a comprehensive overview of potential mechanisms underlying sexual dimorphism of each cancer regarding sex hormone signaling. Fully addressing the mechanisms of sexual dimorphism in human cancers will greatly benefit current development of precision medicine. Our discussions of potential mechanisms underlying sexual dimorphism in each cancer will be instructive for future cancer research on gender disparities.

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