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Biomarkers & Diagnostic Imaging

  • Open Access
    Specific biomarkers for C9orf72 FTD/ALS could expedite the journey towards effective therapies
    Specific biomarkers for <em>C9orf72 </em>FTD/ALS could expedite the journey towards effective therapies
    1. Rubika Balendra1,2,
    2. Thomas G Moens1,2 and
    3. Adrian M Isaacs (a.isaacs{at}ucl.ac.uk)1
    1. 1Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
    2. 2Department of Genetics, Evolution and Environment, Institute of Healthy Ageing, University College London, London, UK

    A hexanucleotide repeat expansion in the C9orf72 gene is a common genetic cause of ALS and FTD. The repeats are translated into five different dipeptide repeat proteins (DPRs). In this issue, Lehmer et al (2017) demonstrate that one of these DPRs, poly(GP), can be measured in the CSF of individuals with C9orf72 mutations. In conjunction with the findings from another recent study (Gendron et al, 2017), these DPR biomarkers may prove to be extremely valuable in the quest for effective therapies for C9FTD/ALS.

    See also: C Lehmer et al and TF Gendron et al

    Isaacs and colleagues discuss two recent studies showing how a dipeptide repeat protein, poly(GP), can be measured in the CSF of individuals with C9orf72 mutations and used as a valuable biomarker in the quest for effective therapies for C9FTD/ALS.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Rubika Balendra, Thomas G Moens, Adrian M Isaacs
    Published online 22.05.2017
  • Open Access
    Poly‐GP in cerebrospinal fluid links C9orf72‐associated dipeptide repeat expression to the asymptomatic phase of ALS/FTD
    Poly‐GP in cerebrospinal fluid links <em>C9orf72</em>‐associated dipeptide repeat expression to the asymptomatic phase of ALS/FTD
    1. Carina Lehmer1,†,
    2. Patrick Oeckl2,†,
    3. Jochen H Weishaupt2,
    4. Alexander E Volk3,
    5. Janine Diehl‐Schmid4,
    6. Matthias L Schroeter5,6,
    7. Martin Lauer7,
    8. Johannes Kornhuber8,
    9. Johannes Levin1,9,
    10. Klaus Fassbender10,
    11. Bernhard Landwehrmeyer2,
    12. German Consortium for Frontotemporal Lobar Degeneration‡,
    13. Martin H Schludi1,
    14. Thomas Arzberger1,11,12,
    15. Elisabeth Kremmer13,
    16. Andrew Flatley14,
    17. Regina Feederle1,14,
    18. Petra Steinacker2,
    19. Patrick Weydt2,15,
    20. Albert C Ludolph2,
    21. Dieter Edbauer (dieter.edbauer{at}dzne.de)*,1,† and
    22. Markus Otto (markus.otto{at}uni-ulm.de)*,2,†
    1. 1German Center for Neurodegenerative Diseases (DZNE) and Munich Cluster for System Neurology (SyNergy), Munich, Germany
    2. 2Department of Neurology, Ulm University Hospital, Ulm, Germany
    3. 3Institute of Human Genetics, University Medical Centre Hamburg‐Eppendorf, Hamburg, Germany
    4. 4Department of Psychiatry and Psychotherapy, Technical University of Munich, München, Germany
    5. 5Clinic for Cognitive Neurology, University Clinic Leipzig, Leipzig, Germany
    6. 6Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    7. 7Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Würzburg, Würzburg, Germany
    8. 8Department of Psychiatry and Psychotherapy, Friedrich‐Alexander‐University of Erlangen‐Nuremberg, Erlangen, Germany
    9. 9Department of Neurology, Ludwig‐Maximilians‐University Universität München, Munich, Germany
    10. 10Department of Neurology, Saarland University, Homburg, Germany
    11. 11Center for Neuropathology and Prion Research, Ludwig‐Maximilians‐University Munich, Munich, Germany
    12. 12Department of Psychiatry and Psychotherapy, Ludwig‐Maximilians‐University Munich, Munich, Germany
    13. 13Institute of Molecular Immunology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Munich, Germany
    14. 14Monoclonal Antibody Core Facility and Research Group, Institute for Diabetes and Obesity, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Munich, Germany
    15. 15Department of Neurodegenerative Diseases and Gerontopsychiatry, Bonn University Hospital, Bonn, Germany
    1. ↵* Corresponding author. Tel: +49 89 4400 46510; E‐mail: dieter.edbauer{at}dzne.de
      Corresponding author. Tel: +49 731 500 63010; Fax: +49 731 500 63012; E‐mail: markus.otto{at}uni-ulm.de

    1. ↵† These authors contributed equally to this work

    The C9orf72 hexanucleotide repeat expansion causing amyotrophic lateral sclerosis and frontotemporal dementia is translated into five dipeptide repeat (DPR) proteins, including poly‐GP. Analysis of poly‐GP levels in the CSF of patients and presymptomatic carriers support an early role of DPRs in pathogenesis.

    Synopsis

    The C9orf72 hexanucleotide repeat expansion causing amyotrophic lateral sclerosis and frontotemporal dementia is translated into five dipeptide repeat (DPR) proteins, including poly‐GP. Analysis of poly‐GP levels in the CSF of patients and presymptomatic carriers support an early role of DPRs in pathogenesis.

    • Monoclonal immunoassay detects poly‐GP in CSF of C9orf72 patients.

    • Poly‐GP levels in asymptomatic carriers and c9ALS/FTD patients are similar.

    • Neurofilaments but not poly‐GP levels correlate with clinical disease progression.

    • amyotrophic lateral sclerosis
    • biomarker
    • C9orf72
    • cerebrospinal fluid
    • frontotemporal dementia
    • Received December 19, 2016.
    • Revision received March 27, 2017.
    • Accepted March 28, 2017.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Carina Lehmer, Patrick Oeckl, Jochen H Weishaupt, Alexander E Volk, Janine Diehl‐Schmid, Matthias L Schroeter, Martin Lauer, Johannes Kornhuber, Johannes Levin, Klaus Fassbender, Bernhard Landwehrmeyer, German Consortium for Frontotemporal Lobar Degeneration
    Published online 13.04.2017
  • Open Access
    Non‐invasive lung cancer diagnosis by detection of GATA6 and NKX2‐1 isoforms in exhaled breath condensate
    Non‐invasive lung cancer diagnosis by detection of <em>GATA6</em> and <em>NKX2‐1</em> isoforms in exhaled breath condensate
    1. Aditi Mehta1,‡,
    2. Julio Cordero1,‡,
    3. Stephanie Dobersch1,
    4. Addi J Romero‐Olmedo1,2,
    5. Rajkumar Savai3,4,†,
    6. Johannes Bodner5,
    7. Cho‐Ming Chao6,
    8. Ludger Fink7,†,
    9. Ernesto Guzmán‐Díaz8,
    10. Indrabahadur Singh1,
    11. Gergana Dobreva9,
    12. Ulf R Rapp3,†,
    13. Stefan Günther10,
    14. Olga N Ilinskaya11,
    15. Saverio Bellusci6,11,†,
    16. Reinhard H Dammann12,†,
    17. Thomas Braun10,†,
    18. Werner Seeger3,4,†,
    19. Stefan Gattenlöhner13,
    20. Achim Tresch14,15,
    21. Andreas Günther4,16,† and
    22. Guillermo Barreto (guillermo.barreto{at}mpi-bn.mpg.de)*,1,11,†
    1. 1LOEWE Research Group Lung Cancer Epigenetic, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
    2. 2Facultad de Ciencias Químicas, Universidad Autonoma “Benito Juarez” de Oaxaca, Oaxaca, Mexico
    3. 3Department of Lung Development and Remodeling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
    4. 4Pulmonary and Critical Care Medicine, Department of Internal Medicine, Justus Liebig University, Giessen, Germany
    5. 5Section Thoracic Surgery, Justus Liebig University, Giessen, Germany
    6. 6Chair for Lung Matrix Remodeling, Excellence Cluster Cardio Pulmonary System, Justus Liebig University, Giessen, Germany
    7. 7Institute of Pathology and Cytology, UEGP, Wetzlar, Germany
    8. 8Regional Hospital of High Specialties of Oaxaca (HRAEO), Oaxaca, Mexico
    9. 9Emmy Noether Research Group Origin of Cardiac Cell Lineages, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
    10. 10Department of Cardiac Development and Remodeling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
    11. 11Institute of Fundamental Medicine and Biology, Kazan (Volga Region) Federal University, Kazan, Russian Federation
    12. 12Institute for Genetics, Justus Liebig University, Giessen, Germany
    13. 13Institute for Pathology, Justus Liebig University, Giessen, Germany
    14. 14Max Planck Institute for Plant Breeding Research, Cologne, Germany
    15. 15University of Cologne, Cologne, Germany
    16. 16Agaplesion Lung Clinic Waldhof Elgershausen, Greifenstein, Germany
    1. ↵*Corresponding author. Tel: +49 6032 705259; E‐mail: guillermo.barreto{at}mpi-bn.mpg.de

    1. ↵‡ These authors contributed equally to this work

    Early diagnosis is critical to improving lung cancer (LC) patient prognosis. An accurate, non‐invasive LC diagnostic test was developed based on expression analysis of embryonic vs. adult GATA6 and NKX2‐1 gene isoforms in exhaled breath condensates.

    Synopsis

    Early diagnosis is critical to improving lung cancer (LC) patient prognosis. An accurate, non‐invasive LC diagnostic test was developed based on expression analysis of embryonic vs. adult GATA6 and NKX2‐1 gene isoforms in exhaled breath condensates.

    • Embryonic and adult isoforms of GATA6 and NKX2‐1 are differentially expressed during mouse lung development and in lung cancer.

    • RNA purified from exhaled breath condensates (EBC) can be used for qRT–PCR‐based expression analysis, despite the relatively high fragmentation of the isolated RNA.

    • Embryonic and adult isoforms of GATA6 and NKX2‐1 detected in EBC can be combined into the LC score for LC diagnosis at stages I and II.

    • EBC
    • GATA6
    • lung cancer
    • molecular diagnostics
    • NKX2‐1

    EMBO Mol Med (2016) 8: 1380–1389

    • Received March 8, 2016.
    • Revision received September 23, 2016.
    • Accepted September 28, 2016.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Aditi Mehta, Julio Cordero, Stephanie Dobersch, Addi J Romero‐Olmedo, Rajkumar Savai, Johannes Bodner, Cho‐Ming Chao, Ludger Fink, Ernesto Guzmán‐Díaz, Indrabahadur Singh, Gergana Dobreva, Ulf R Rapp, Stefan Günther, Olga N Ilinskaya, Saverio Bellusci, Reinhard H Dammann, Thomas Braun, Werner Seeger, Stefan Gattenlöhner, Achim Tresch, Andreas Günther, Guillermo Barreto
    Published online 01.12.2016
  • Open Access
    New CSF biomarkers on the block
    New CSF biomarkers on the block
    1. Charlotte E Teunissen (c.teunissen{at}vumc.nl)1 and
    2. Lucilla Parnetti2
    1. 1Neurochemistry Lab and Biobank, Department of Clinical Chemistry, Neuroscience Amsterdam, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
    2. 2Centro Disturbi della Memoria, Laboratorio di Neurochimica Clinica, Clinica Neurologica, Università di Perugia, Perugia, Italy

    A growing number of cerebrospinal fluid biomarkers are now available to capture different aspects of Alzheimer's disease. People are increasingly aware that these biomarkers represent a real‐time reflection of pathological mechanisms that are ongoing in the brain. These novel markers can be added to the panel of existing ones like amyloid beta, total Tau and phosphoTau that are currently used for sensitive diagnosis of Alzheimer's disease, either alone or in combination.

    See also: N Mattsson et al (October 2016)

    A growing number of cerebrospinal fluid biomarkers are becoming available for Alzheimer's disease. In this issue of EMBO Molecular Medicine, Mattsson et al propose a new combination of these to capture different aspects of Alzheimer's disease in patients.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Charlotte E Teunissen, Lucilla Parnetti
    Published online 01.10.2016
  • Open Access
    Cerebrospinal fluid tau, neurogranin, and neurofilament light in Alzheimer's disease
    Cerebrospinal fluid tau, neurogranin, and neurofilament light in Alzheimer's disease
    1. Niklas Mattsson (niklas.mattsson{at}med.lu.se)*,1,2,
    2. Philip S Insel1,3,
    3. Sebastian Palmqvist1,2,
    4. Erik Portelius4,
    5. Henrik Zetterberg4,5,
    6. Michael Weiner3,
    7. Kaj Blennow4,
    8. Oskar Hansson (oskar.hansson{at}med.lu.se)*,1,2,
    9. the Alzheimer's Disease Neuroimaging Initiative†
    1. 1Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
    2. 2Department of Neurology, Skåne University Hospital, Lund, Sweden
    3. 3Department of Radiology, University of California San Francisco, San Francisco, CA, USA
    4. 4Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
    5. 5Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK
    1. ↵* Corresponding author. Tel: +46 72 575 9329; E‐mail: niklas.mattsson{at}med.lu.se
      Corresponding author. Tel: +46 40 33 50 36; E‐mail: oskar.hansson{at}med.lu.se

    Different cerebrospinal fluid (CSF) biomarkers have been proposed to measure neurodegeneration in AD. These biomarkers include tau (total tau, T‐tau), neurofilament light (NFL) and neurogranin (Ng), but it is unknown to what degree they provide overlapping or complementing information in AD.

    Synopsis

    Different cerebrospinal fluid (CSF) biomarkers have been proposed to measure neurodegeneration in AD. These biomarkers include tau (total tau, T‐tau), neurofilament light (NFL) and neurogranin (Ng), but it is unknown to what degree they provide overlapping or complementing information in AD.

    • Combinations of CSF T‐tau, Ng and NFL may improve the diagnostic accuracy for AD.

    • CSF T‐tau, Ng and NFL have significant and different associations with distinct hallmarks of neurodegeneration, including cognitive decline and structural and functional brain changes.

    • CSF T‐tau and Ng are closely associated with β‐amyloid‐dependent degeneration, while NFL is associated with neurodegeneration independently of β‐amyloid pathology.

    • Alzheimer's
    • biomarker
    • CSF
    • neurodegeneration

    EMBO Mol Med (2016) 8: 1184–1196

    • Received April 25, 2016.
    • Revision received June 22, 2016.
    • Accepted July 8, 2016.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Niklas Mattsson, Philip S Insel, Sebastian Palmqvist, Erik Portelius, Henrik Zetterberg, Michael Weiner, Kaj Blennow, Oskar Hansson, the Alzheimer's Disease Neuroimaging Initiative
    Published online 01.10.2016
  • Open Access
    CSF sTREM2: marking the tipping point between preclinical AD and dementia?
    CSF sTREM2: marking the tipping point between preclinical AD and dementia?
    1. Suzanne E Schindler1,2,3 and
    2. David M Holtzman (holtzman{at}neuro.wustl.edu)1,2,3
    1. 1Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
    2. 2Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
    3. 3Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA

    Biomarkers for Alzheimer's disease (AD) have improved our understanding of the temporal sequence of biological events that lead to AD dementia (Jack et al, 2013). AD is characterized neuropathologically by amyloid plaques comprised of the amyloid‐β peptide and neurofibrillary tangles comprised of tau. Brain amyloid deposition, as evidenced by a decline in amyloid‐β peptide 42 (Aβ42) in the cerebrospinal fluid (CSF) or by binding of amyloid PET ligands, is thought to be a key initiating event in AD and begins many years prior to the onset of dementia. A rise in CSF tau and phosphorylated tau in the setting of Aβ deposition appears to reflect neurodegeneration and also begins years prior to the onset of dementia but after Aβ deposition has begun to accumulate. Individuals with “preclinical AD,” that is, normal cognition but abnormal AD biomarkers, have a much higher risk for developing AD dementia but may remain cognitively normal for years (Vos et al, 2013). While deposition of amyloid and formation of tau tangles are necessary for AD to occur, it is likely that additional events involving inflammation or other processes contribute to crossing the tipping point from preclinical AD to AD dementia. Current efforts are aimed at defining the biomarker(s) that best predict the transition from cognitive normality to abnormality. A biomarker that is closely associated with the onset of cognitive decline could help us to understand the biological events that connect amyloid deposition and tangle formation to cognitive decline and could have significant practical value in AD diagnosis and clinical trial design.

    See also: M Suárez-Calvet et al (May 2016)

    Schindler and Holtzman highlight the work of Suarez‐Calvet et al in this issue as they report that cerebrospinal fluid sTREM2 levels are associated with the onset of cognitive decline in Alzheimer's disease, a significant finding for AD diagnostics and drug trials.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    ICI
    Suzanne E Schindler, David M Holtzman
    Published online 01.05.2016
  • Open Access
    sTREM2 cerebrospinal fluid levels are a potential biomarker for microglia activity in early‐stage Alzheimer's disease and associate with neuronal injury markers
    sTREM2 cerebrospinal fluid levels are a potential biomarker for microglia activity in early‐stage Alzheimer's disease and associate with neuronal injury markers
    1. Marc Suárez‐Calvet1,2,
    2. Gernot Kleinberger1,3,
    3. Miguel Ángel Araque Caballero4,
    4. Matthias Brendel5,
    5. Axel Rominger3,5,
    6. Daniel Alcolea6,7,
    7. Juan Fortea6,7,
    8. Alberto Lleó6,7,
    9. Rafael Blesa6,7,
    10. Juan Domingo Gispert8,9,
    11. Raquel Sánchez‐Valle10,11,
    12. Anna Antonell10,11,
    13. Lorena Rami10,11,
    14. José L Molinuevo8,9,10,11,
    15. Frederic Brosseron12,
    16. Andreas Traschütz13,
    17. Michael T Heneka12,13,
    18. Hanne Struyfs14,15,
    19. Sebastiaan Engelborghs14,15,
    20. Kristel Sleegers16,17,
    21. Christine Van Broeckhoven16,17,
    22. Henrik Zetterberg18,19,
    23. Bengt Nellgård20,
    24. Kaj Blennow18,
    25. Alexander Crispin21,
    26. Michael Ewers*,4,† and
    27. Christian Haass*,1,2,3,†
    1. 1BioMedical Center (BMC), Biochemistry, Ludwig‐Maximilians‐University Munich, Munich, Germany
    2. 2German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
    3. 3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
    4. 4Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig‐Maximilians‐University Munich, Munich, Germany
    5. 5Department of Nuclear Medicine, Klinikum der Universität München, Ludwig‐Maximilians‐University Munich, Munich, Germany
    6. 6Department of Neurology, Institut d'Investigacions Biomèdiques Hospital de la Santa Creu i Sant Pau Universitat Autònoma de Barcelona, Barcelona, Spain
    7. 7Center for Networked Biomedical Research for Neurodegenerative Diseases, CIBERNED, Madrid, Spain
    8. 8Clinical and Neuroimaging Departments, Barcelona Beta Brain Research Center Pasqual Maragall Foundation, Barcelona, Spain
    9. 9Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER‐BBN), Barcelona, Spain
    10. 10Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, ICN Hospital Clinic i Universitari, Barcelona, Spain
    11. 11Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
    12. 12German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
    13. 13Neurology Department, Universitätsklinikum Bonn, Bonn, Germany
    14. 14Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born‐Bunge University of Antwerp, Antwerp, Belgium
    15. 15Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
    16. 16Neurodegenerative Brain Diseases Group, Department of Molecular Genetics, VIB, Antwerp, Belgium
    17. 17Laboratory of Neurogenetics, Institute Born‐Bunge University of Antwerp, Antwerp, Belgium
    18. 18Clinical Neurochemistry Lab, Institute of Neuroscience and Physiology The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
    19. 19Reta Lila Weston Laboratories and Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK
    20. 20Department of Anaesthesiology and Intensive Care, Institute of Clinical Sciences Sahlgrenska Academy Gothenburg University, Gothenburg, Sweden
    21. 21Institute of Medical Informatics, Biometry, and Epidemiology, Munich, Germany
    1. ↵* Corresponding author. Tel: +49 89 4400 46221; Fax: +49 89 4400 46113; E‐mail: michael.ewers{at}med.uni-muenchen.de
      Corresponding author. Tel: +49 89 4400 46549; Fax: +49 89 4400 46546; E‐mail: christian.haass{at}mail03.med.uni-muenchen.de

    1. ↵† These authors contributed equally to this study

    TREM2 is an innate immune receptor selectively expressed by microglia in the brain. Measuring its soluble variant in the CSF (sTREM2) may be a candidate as a marker of microglial activity. This study aimed to investigate how CSF sTREM2 levels change during the course of Alzheimer's disease (AD).

    Synopsis

    TREM2 is an innate immune receptor selectively expressed by microglia in the brain. Measuring its soluble variant in the CSF (sTREM2) may be a candidate as a marker of microglial activity. This study aimed to investigate how CSF sTREM2 levels change during the course of Alzheimer's disease (AD).

    • CSF sTREM2 levels are increased in the mild cognitive impairment (MCI) stage of AD compared to controls (P = 0.002), and to the preclinical (trend level, P = 0.062), and dementia stage of AD (P = 0.013).

    • CSF sTREM2 levels are increased in individuals with suspected non‐AD pathology (SNAP) compared to controls (P = 0.0004).

    • CSF sTREM2 levels increase with aging.

    • Increased CSF sTREM2 levels are associated with higher levels of T‐tau and P‐tau181P, markers of neuronal cell injury, and neurofibrillary tangles.

    • Alzheimer's disease
    • biomarkers
    • microglia
    • neurodegeneration
    • TREM2

    EMBO Mol Med (2016) 8: 466–476

    • Received December 3, 2015.
    • Revision received January 27, 2016.
    • Accepted January 28, 2016.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    ICI
    Marc Suárez‐Calvet, Gernot Kleinberger, Miguel Ángel Araque Caballero, Matthias Brendel, Axel Rominger, Daniel Alcolea, Juan Fortea, Alberto Lleó, Rafael Blesa, Juan Domingo Gispert, Raquel Sánchez‐Valle, Anna Antonell, Lorena Rami, José L Molinuevo, Frederic Brosseron, Andreas Traschütz, Michael T Heneka, Hanne Struyfs, Sebastiaan Engelborghs, Kristel Sleegers, Christine Van Broeckhoven, Henrik Zetterberg, Bengt Nellgård, Kaj Blennow, Alexander Crispin, Michael Ewers, Christian Haass
    Published online 01.05.2016
  • Open Access
    Concise gene signature for point‐of‐care classification of tuberculosis
    Concise gene signature for point‐of‐care classification of tuberculosis
    1. Jeroen Maertzdorf*,1,†,
    2. Gayle McEwen1,†,
    3. January Weiner 3rd1,
    4. Song Tian2,
    5. Eric Lader2,
    6. Ulrich Schriek3,
    7. Harriet Mayanja‐Kizza4,
    8. Martin Ota5,†,
    9. John Kenneth6 and
    10. Stefan HE Kaufmann*,1
    1. 1Max Planck Institute for Infection Biology, Berlin, Germany
    2. 2Qiagen, Frederick, MD, USA
    3. 3Qiagen GmbH, Hilden, Germany
    4. 4Makerere University, Kampala, Uganda
    5. 5Medical Research Council, Banjul, The Gambia
    6. 6St. John's Research Institute, Bangalore, Karnataka, India
    1. ↵* Corresponding author. Tel: +49 30 28460502, E‐mail: kaufmann{at}mpiib-berlin.mpg.de
      Corresponding author. Tel: +49 30 28460514, E‐mail: maertzdorf{at}mpiib-berlin.mpg.de

    1. ↵† These authors contributed equally to this work

    The ongoing tuberculosis pandemic stresses the urgent need for new tools to better control the global disease burden. A simple molecular test could provide a rapid point‐of‐care diagnostic tool to help reduce tuberculosis incidence rate in endemic areas.

    Synopsis

    The ongoing tuberculosis pandemic stresses the urgent need for new tools to better control the global disease burden. A simple molecular test could provide a rapid point‐of‐care diagnostic tool to help reduce tuberculosis incidence rate in endemic areas.

    • A small set of 4 genes provided excellent classification performance between tuberculosis patients and healthy individuals in an easily implemented RT–PCR setup.

    • High classification power was observed in multiple cohorts from different geographical regions and ethnicities and was maintained in HIV+ populations.

    • The 4‐gene signature also distinguished tuberculosis from several other pulmonary diseases.

    • disease classification
    • genomics
    • molecular diagnosis
    • real‐time PCR
    • tuberculosis

    EMBO Mol Med (2016) 8: 86–95

    • Received August 28, 2015.
    • Revision received November 13, 2015.
    • Accepted November 17, 2015.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Jeroen Maertzdorf, Gayle McEwen, January Weiner, Song Tian, Eric Lader, Ulrich Schriek, Harriet Mayanja‐Kizza, Martin Ota, John Kenneth, Stefan HE Kaufmann
    Published online 01.02.2016
  • Open Access
    Prediction of colorectal cancer diagnosis based on circulating plasma proteins
    Prediction of colorectal cancer diagnosis based on circulating plasma proteins
    1. Silvia Surinova111,
    2. Meena Choi2,
    3. Sha Tao312,
    4. Peter J Schüffler4,
    5. Ching‐Yun Chang2,
    6. Timothy Clough2,
    7. Kamil Vysloužil5,
    8. Marta Khoylou6,
    9. Josef Srovnal6,
    10. Yansheng Liu1,
    11. Mariette Matondo1,
    12. Ruth Hüttenhain1,
    13. Hendrik Weisser1,
    14. Joachim M Buhmann4,
    15. Marián Hajdúch6,
    16. Hermann Brenner3,7,
    17. Olga Vitek*,2,8,9 and
    18. Ruedi Aebersold*,1,10
    1. 1Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
    2. 2Department of Statistics, Purdue University, West Lafayette, IN, USA
    3. 3Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
    4. 4Department of Computer Science, Institute for Machine Learning, ETH Zurich, Zurich, Switzerland
    5. 5Department of Surgery, University Hospital in Olomouc, Olomouc, Czech Republic
    6. 6Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University, Olomouc, Czech Republic
    7. 7German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
    8. 8Department of Computer Science, Purdue University, West Lafayette, IN, USA
    9. 9College of Science and College of Computer and Information Science, Northeastern University, Boston, MA, USA
    10. 10Faculty of Science, University of Zurich, Zurich, Switzerland
    11. 11UCL Cancer Institute University College London, London, UK
    12. 12 Department of Preventive Medicine, Center for Genomic Translational Medicine and Prevention, School of Public Health, Fudan University, Shanghai, China
    1. ↵* Corresponding author. Tel: +1 617 373 21 94; E‐mail: o.vitek{at}neu.edu
      Corresponding author. Tel: +41 44 633 31 70; E‐mail: aebersold{at}imsb.biol.ethz.ch

    A five‐protein biomarker signature discovered and validated by mass spectrometry can accurately predict colorectal cancer (CRC) diagnosis non‐invasively from a blood sample.

    Synopsis

    A five‐protein biomarker signature discovered and validated by mass spectrometry can accurately predict colorectal cancer (CRC) diagnosis non‐invasively from a blood sample.

    • Secreted protein biomarker candidates discovered in tumor tissue were detected in the circulation of healthy and CRC subjects.

    • A five‐protein predictive diagnostic signature was developed in a cohort of 100 healthy and 100 CRC subjects, and independently validated in an external cohort of 67 healthy and 202 CRC subjects.

    • The protein biomarker signature was found to be more effective for CRC subjects with larger tumors.

    • colorectal cancer
    • diagnostic protein biomarker
    • discovery‐driven and targeted proteomics

    EMBO Mol Med (2015) 7: 1166–1178

    • Received November 18, 2014.
    • Revision received July 1, 2015.
    • Accepted July 7, 2015.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Silvia Surinova, Meena Choi, Sha Tao, Peter J Schüffler, Ching‐Yun Chang, Timothy Clough, Kamil Vysloužil, Marta Khoylou, Josef Srovnal, Yansheng Liu, Mariette Matondo, Ruth Hüttenhain, Hendrik Weisser, Joachim M Buhmann, Marián Hajdúch, Hermann Brenner, Olga Vitek, Ruedi Aebersold
    Published online 01.09.2015
  • Open Access
    Non‐invasive prognostic protein biomarker signatures associated with colorectal cancer
    Non‐invasive prognostic protein biomarker signatures associated with colorectal cancer
    1. Silvia Surinova19,
    2. Lenka Radová210,
    3. Meena Choi3,
    4. Josef Srovnal2,
    5. Hermann Brenner4,5,
    6. Olga Vitek*,3,6,7,
    7. Marián Hajdúch2 and
    8. Ruedi Aebersold*,1,8
    1. 1Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
    2. 2Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University, Olomouc, Czech Republic
    3. 3Department of Statistics, Purdue University, West Lafayette, IN, USA
    4. 4Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
    5. 5German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
    6. 6Department of Computer Science, Purdue University, West Lafayette, IN, USA
    7. 7College of Science and College of Computer and Information Science, Northeastern University, Boston, MA, USA
    8. 8Faculty of Science, University of Zurich, Zurich, Switzerland
    9. 9UCL Cancer Institute, University College London, London, UK
    10. 10 Central European Institute of Technology, Masaryk University, Brno, Czech Republic
    1. ↵* Corresponding author. Tel: +1 617 373 21 94; E‐mail: o.vitek{at}neu.edu
      Corresponding author. Tel: +41 44 633 31 70; E‐mail: aebersold{at}imsb.biol.ethz.ch

    Prognostic signatures comprising a handful of secreted proteins extracted non‐invasively from a blood sample, are shown to predict outcome and other clinical features of colorectal cancer patients.

    Synopsis

    Prognostic signatures comprising a handful of secreted proteins extracted non‐invasively from a blood sample, are shown to predict outcome and other clinical features of colorectal cancer patients.

    • Predictive models of prognostic endpoints were developed from a cohort of 202 patients with multiplexed proteomic measurements of 88 glycoprotein biomarker candidates.

    • The outcome signature was able to predict patient survival beyond the clinical stage and stratify patients of localized disease into low and high risk prognostic groups.

    • The predictive ability of the outcome signature was confirmed at the transcript level.

    • Recently defined transcriptional CRC subtypes with good and poor prognosis could be predicted with the outcome signature.

    • colorectal cancer
    • prognostic protein biomarker
    • targeted proteomics

    EMBO Mol Med (2015) 7: 1153–1165

    • Received November 18, 2014.
    • Revision received May 11, 2015.
    • Accepted June 7, 2015.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Silvia Surinova, Lenka Radová, Meena Choi, Josef Srovnal, Hermann Brenner, Olga Vitek, Marián Hajdúch, Ruedi Aebersold
    Published online 01.09.2015

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