Evidence map›Paper›PMID 31819186›Full record

ArticleBritish journal of cancer2020

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer.

Nikolaus Berndt, Antje Egners, Guido Mastrobuoni, Olga Vvedenskaya, Athanassios Fragoulis, Aurélien Dugourd, Sascha Bulik, Matthias Pietzke, Chris Bielow, Rob van Gassel and 6 more

Open access · hybridAbstract read
In one paragraph

Article in British journal of cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.2field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Kinetic Modeling of Hepatic Metabolism and Simulation of Treatment Effects.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  5. Advancements in Pulsed Stable Isotope-Resolved Metabolomics.Handbook of experimental pharmacology · 2023
    Article
  6. Review
  7. On the relationship between metabolic capacities andFrontiers in bioengineering and biotechnology · 2022
    Article
  8. Article
  9. Shining a light on metabolic vulnerabilities in non-small cell lung cancer.Biochimica et biophysica acta. Reviews on cancer · 2021
    Review
  10. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 7 institutions in 3 countries.

Nikolaus Berndt *Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biochemistry, 10117, Berlin, Germany.
Antje Egners *Molecular Tumor Biology, Department of General, Visceral and Transplantation Surgery, RWTH University Hospital, 52074, Aachen, Germany.
Guido Mastrobuoni *Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine, 10115, Berlin, Germany.
Olga VvedenskayaBerlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine, 10115, Berlin, Germany.
Athanassios FragoulisMolecular Tumor Biology, Department of General, Visceral and Transplantation Surgery, RWTH University Hospital, 52074, Aachen, Germany.
Aurélien DugourdJoint Research Centre for Computational Biomedicine, Faculty of Medicine, RWTH Aachen University, 52074, Aachen, Germany.
Sascha BulikCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biochemistry, 10117, Berlin, Germany.
Matthias PietzkeBerlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine, 10115, Berlin, Germany.
Chris BielowBerlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine, 10115, Berlin, Germany.
Rob van GasselDepartment of Surgery, Maastricht University Medical Centre, 6229 ER, Maastricht, The Netherlands.
Steven W Olde DaminkDepartment of Surgery, Maastricht University Medical Centre, 6229 ER, Maastricht, The Netherlands.
Merve ErdemMolecular Tumor Biology, Department of General, Visceral and Transplantation Surgery, RWTH University Hospital, 52074, Aachen, Germany.
Julio Saez-RodriguezJoint Research Centre for Computational Biomedicine, Faculty of Medicine, RWTH Aachen University, 52074, Aachen, Germany.
Hermann-Georg Holzhütter *Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biochemistry, 10117, Berlin, Germany.
Stefan Kempa *Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine, 10115, Berlin, Germany.
Thorsten Cramer *Molecular Tumor Biology, Department of General, Visceral and Transplantation Surgery, RWTH University Hospital, 52074, Aachen, Germany. tcramer@ukaachen.de.
Max Delbrück Center · DERWTH Aachen University · DEFreie Universität Berlin · DEJoint Research Centre · DEMaastricht University · NLCharité - Universitätsmedizin Berlin · DEHumboldt-Universität zu Berlin · DE

Funding

Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 0316172Deutsche Forschungsgemeinschaft (German Research Foundation) Cr 133/2-1 and Cr 133/3-1Deutsche Krebshilfe (German Cancer Aid) 109160EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) MSCA-ITN-2015-ETN #675585
6 · The paper itself

Abstract

backgroundMetabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation.

methodsWe applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer.

resultsWe show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing.

conclusionsOur systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.

Indexed as

AnimalsCellular ReprogrammingComputer SimulationDisease Models, AnimalHepatocytesHumansLiver NeoplasmsMass SpectrometryMetabolic Networks and PathwaysMiceMice, TransgenicProteomeProteome

Identifiers

PMID31819186
PMCPMC7052204
OpenAlexW2993140700

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.