Evidence map›Paper›PMID 29662665›Full record

ArticleOncotarget2018

Molecular mechanism of action and potential biomarkers of growth inhibition of synergistic combination of afatinib and dasatinib against gefitinib-resistant non-small cell lung cancer cells.

Miao Wang, Alex Yuang-Chi Chang

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

19 citing papers in PubMed, 41 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Miao WangDepartment of Oncology, Johns Hopkins Singapore International Medical Center, Singapore.
Alex Yuang-Chi ChangDepartment of Oncology, Johns Hopkins Singapore International Medical Center, Singapore.
Johns Hopkins University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epidermal growth factor receptor - tyrosine kinase inhibitor (EGFR-TKI) is the first choice of treatment for advanced non-small cell lung cancer (NSCLC) patients harbouring activating EGFR mutations. However, single agent usually has limited efficacy due to heterogeneous resistant mechanisms of cancer cells. Thus drug combination therapy would offer more benefits by synergistic interactions and avoidance of resistance emergence. In this study, we selected 8 NSCLC cell lines with different genetic characteristics as research models to investigate the efficacy of 4 agents (gefitinib, cetuximab, afatinib and dasatinib) and their combinations. As a single agent, both afatinib and dasatinib showed more inhibition against cell proliferation than gefitinib and cetuximab. Afatinib combined with dasatinib demonstrated significantly high efficacy against 7 gefitinib-resistant NSCLC cell lines. Moreover, it reversed the resistance to the 4 studied single agents in PTEN mutated NSCLC cells. By studying the activity of EGFR, Src and their downstream signalling pathways including PI3K/PTEN/Akt, Ras/Raf/MEK/ERK, Src/FAK and JAK/Stat, we demonstrated the synergistic interaction between afatinib and dasatinib was not only due to their blockage of different signalling pathways but also the complemental inhibition of the related signalling molecules such as Stat3. We also found that the level of Src, Stat3, and MAPK may be useful biomarkers predicating synergism between afatinib and dasatinib for the treatment of gefitinib-resistant NSCLC cells.

Indexed as

gefitinib resistancegrowth inhibitionmolecular mechanismnon-small cell lung cancersynergistic combination

Identifiers

PMID29662665
PMCPMC5893260
OpenAlexW2794633020

What OpenQuestion holds

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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.