Evidence map›Paper›PMID 30850937›Full record

ArticleEndocrine2019

BRAF

Rosa Falcone, Federica Conte, Giulia Fiscon, Valeria Pecce, Marialuisa Sponziello, Cosimo Durante, Lorenzo Farina, Sebastiano Filetti, Paola Paci, Antonella Verrienti

Abstract read
PubMed Publisher
In one paragraph

Article in Endocrine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
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

21 citing papers in PubMed.

  1. Article
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  5. Article
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  7. Article
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  10. Article
  11. Article
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  13. In silico drug repurposing in COVID-19: A network-based analysis.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2021
    Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes.International journal of molecular sciences · 2020
    Article
  20. 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

10 authors.

Rosa FalconeDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.ORCID 0000-0001-9679-4711
Federica ConteInstitute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.ORCID 0000-0003-0427-1476
Giulia FisconInstitute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.ORCID 0000-0002-3354-8203
Valeria PecceDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
Marialuisa SponzielloDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.ORCID 0000-0003-0736-1047
Cosimo DuranteDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.ORCID 0000-0002-1791-5915
Lorenzo FarinaDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.ORCID 0000-0001-8314-6029
Sebastiano FilettiDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
Paola PaciInstitute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy. paola.paci@iasi.cnr.it.ORCID 0000-0002-9393-2047
Antonella VerrientiDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.ORCID 0000-0002-3603-5972

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSeveral studies have shown that different tumour types sharing a driver gene mutation do not respond uniformly to the same targeted agent. Our aim was to use an unbiased network-based approach to investigate this fundamental issue using BRAF

methodsWe applied SWIM, a software able to identify putative regulatory (switch) genes involved in drastic changes to the cell phenotype, to gene expression profiles of different BRAF

resultsWe identified lung adenocarcinoma as the tumour with the highest number of switch genes (298) compared to its normal counterpart. By looking for switch genes encoding for kinases with homology sequences similar to known vemurafenib targets, we found that thyroid cancer and lung adenocarcinoma have a similar number of putative targetable switch gene kinases (5 and 6, respectively) whereas colorectal cancer has just one.

conclusionsWe are persuaded that our network analysis may aid in the comprehension of molecular mechanisms underlying the different responses to vemurafenib in BRAF

Indexed as

Models, TheoreticalAdenocarcinoma of LungAntineoplastic AgentsHumansLung NeoplasmsPredictive Value of TestsProto-Oncogene Proteins B-rafThyroid NeoplasmsVemurafenibAntineoplastic AgentsBRAF protein, humanProto-Oncogene Proteins B-rafVemurafenibBRAF V600ENetwork medicinePrediction of responseVemurafenib

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.