Evidence map›Paper›PMID 27494133›Full record

ArticlePLoS computational biology2016

Identification of Cell Type-Specific Differences in Erythropoietin Receptor Signaling in Primary Erythroid and Lung Cancer Cells.

Ruth Merkle, Bernhard Steiert, Florian Salopiata, Sofia Depner, Andreas Raue, Nao Iwamoto, Max Schelker, Helge Hass, Marvin Wäsch, Martin E Böhm and 8 more

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.0field-weighted citation impact, top 7% 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

20 citing papers in PubMed, 42 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. STAT5 as a Key Protein of Erythropoietin Signalization.International journal of molecular sciences · 2021
    Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Extensions of ℓBMC bioinformatics · 2019
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Logic Modeling in Quantitative Systems Pharmacology.CPT: pharmacometrics & systems pharmacology · 2017
    Article
  18. Article
  19. Article
  20. Article
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

18 authors at 2 institutions in 1 country.

Ruth MerkleDivision Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.
Bernhard SteiertInstitute of Physics, University of Freiburg, Germany & BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany.
Florian SalopiataDivision Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.
Sofia DepnerDivision Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.
Andreas RaueInstitute of Physics, University of Freiburg, Germany & BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany.ORCID 0000-0002-9198-2489
Nao IwamotoDivision Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.ORCID 0000-0003-3362-375X
Max SchelkerInstitute of Physics, University of Freiburg, Germany & BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany.ORCID 0000-0002-3723-5722
Helge HassInstitute of Physics, University of Freiburg, Germany & BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany.ORCID 0000-0003-0577-2432
Marvin WäschDivision Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.
Martin E BöhmDivision Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.
Oliver MückeDivision Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.
Daniel B LipkaRegulation of Cellular Differentiation Group, Division Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.ORCID 0000-0001-5081-7869
Christoph PlassDivision Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.
Wolf D LehmannDivision Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.
Clemens KreutzInstitute of Physics, University of Freiburg, Germany & BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany.
Jens TimmerInstitute of Physics, University of Freiburg, Germany & BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany.
Marcel SchillingDivision Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.ORCID 0000-0002-9517-5166
Ursula KlingmüllerDivision Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, Heidelberg, Germany.
German Cancer Research Center · DEUniversity of Freiburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer, with its most prevalent form non-small-cell lung carcinoma (NSCLC), is one of the leading causes of cancer-related deaths worldwide, and is commonly treated with chemotherapeutic drugs such as cisplatin. Lung cancer patients frequently suffer from chemotherapy-induced anemia, which can be treated with erythropoietin (EPO). However, studies have indicated that EPO not only promotes erythropoiesis in hematopoietic cells, but may also enhance survival of NSCLC cells. Here, we verified that the NSCLC cell line H838 expresses functional erythropoietin receptors (EPOR) and that treatment with EPO reduces cisplatin-induced apoptosis. To pinpoint differences in EPO-induced survival signaling in erythroid progenitor cells (CFU-E, colony forming unit-erythroid) and H838 cells, we combined mathematical modeling with a method for feature selection, the L1 regularization. Utilizing an example model and simulated data, we demonstrated that this approach enables the accurate identification and quantification of cell type-specific parameters. We applied our strategy to quantitative time-resolved data of EPO-induced JAK/STAT signaling generated by quantitative immunoblotting, mass spectrometry and quantitative real-time PCR (qRT-PCR) in CFU-E and H838 cells as well as H838 cells overexpressing human EPOR (H838-HA-hEPOR). The established parsimonious mathematical model was able to simultaneously describe the data sets of CFU-E, H838 and H838-HA-hEPOR cells. Seven cell type-specific parameters were identified that included for example parameters for nuclear translocation of STAT5 and target gene induction. Cell type-specific differences in target gene induction were experimentally validated by qRT-PCR experiments. The systematic identification of pathway differences and sensitivities of EPOR signaling in CFU-E and H838 cells revealed potential targets for intervention to selectively inhibit EPO-induced signaling in the tumor cells but leave the responses in erythroid progenitor cells unaffected. Thus, the proposed modeling strategy can be employed as a general procedure to identify cell type-specific parameters and to recommend treatment strategies for the selective targeting of specific cell types.

Indexed as

Receptors, ErythropoietinCarcinoma, Non-Small-Cell LungCell Line, TumorComputational BiologyErythroid CellsHumansLung NeoplasmsSignal TransductionReceptors, Erythropoietin

Identifiers

PMID27494133
PMCPMC4975441
OpenAlexW2483655230

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.