Evidence map›Paper›PMID 24930887›Full record

ReviewOncotarget2014

HGF/MET-directed therapeutics in gastroesophageal cancer: a review of clinical and biomarker development.

Stephen P Hack, Jean-Marie Bruey, Hartmut Koeppen

Open access · diamondAbstract readReview
In one paragraph

Review in Oncotarget, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
4.3field-weighted citation impact, top 4% 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

39 citing papers in PubMed, 65 citations in OpenAlex.

  1. Article
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  5. Review
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  9. c-MetBiomedicines · 2023
    Article
  10. Article
  11. Article
  12. Review
  13. Predictive biomarkers in gastric cancer.Journal of cancer research and clinical oncology · 2023
    Review
  14. Article
  15. Article
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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

3 authors.

Stephen P HackProduct Development, Genentech Inc., South San Francisco, CA, USA.
Jean-Marie Bruey
Hartmut Koeppen

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aberrant activation of the HGF/MET signaling axis has been strongly implicated in the malignant transformation and progression of gastroesophageal cancer (GEC). MET receptor overexpression in tumor samples from GEC patients has been consistently correlated with an aggressive metastatic phenotype and poor prognosis. In preclinical GEC models, abrogation of HGF/MET signaling has been shown to induce tumor regression as well as inhibition of metastatic dissemination. Promising clinical results in patient subsets in which MET is overexpressed have spurned several randomized studies of HGF/MET-directed agents, including two pivotal global Phase III trials. Available data highlight the need for predictive biomarkers in order to select patients most likely to benefit from HGF/MET inhibition. In this review, we discuss the current knowledge of mechanisms of MET activation in GEC, the current status of the clinical evaluation of MET-targeted therapies in GEC, characteristics of ongoing randomized GEC trials and the associated efforts to identify and validate biomarkers. We also discuss the considerations and challenges for HGF/MET inhibitor drug development in the GEC setting.

Indexed as

AdenocarcinomaAnimalsAntineoplastic AgentsBiomarkers, TumorDrug DesignEsophageal NeoplasmsHepatocyte Growth FactorHumansMolecular Targeted TherapyProto-Oncogene Proteins c-metSignal TransductionStomach NeoplasmsAntineoplastic AgentsBiomarkers, TumorHepatocyte Growth FactorHGF protein, humanProto-Oncogene Proteins c-met

Identifiers

PMID24930887
PMCPMC4102777
OpenAlexW2130229658

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.