Evidence map›Paper›PMID 23481268›Full record

ReviewMolecular oncology2013

Mouse models for lung cancer.

Min-chul Kwon, Anton Berns

Open access · bronzeAbstract readReview
In one paragraph

Review in Molecular oncology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed
10.2field-weighted citation impact, top 1% 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

93 citing papers in PubMed, 154 citations in OpenAlex.

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33 more citing papers are in PubMed but not listed here.

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.

Min-chul KwonDivision of Molecular Genetics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.
Anton Berns
The Netherlands Cancer Institute · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is a devastating disease and a major therapeutic burden with poor survival rates. It is responsible for 30% of all cancer deaths. Lung cancer is strongly associated with smoking, although some subtypes are also seen in non-smokers. Tumors in the latter group are mostly adenocarcinomas with many carrying mutations in the epidermal growth factor receptor (EGFR). Survival statistics of lung cancer are grim because of its late detection and frequent local and distal metastases. Although DNA sequence information from tumors has revealed a number of frequently occurring mutations, affecting well-known tumor suppressor genes and proto-oncogenes, many of the driver mutations remain ill defined. This is likely due to the involvement of numerous rather infrequently occurring driver mutations that are difficult to distinguish from the very large number of passenger mutations detected in smoking-related lung cancers. Therefore, experimental model systems are indispensable to validate putative driver lesions and to gain insight into their mechanisms of action. Whereas a large fraction of these analyzes can be performed in cell cultures in vitro, in many cases the consequences of the mutations have to be assessed in the context of an intact organism, as this is the context in which the Mendelian selection process of the tumorigenic process took place and the advantages of particular mutations become apparent. Current mouse models for cancer are very suitable for this as they permit mimicking many of the salient features of human tumors. The capacity to swiftly re-engineer complex sets of lesions found in human tumors in mice enables us to assess the contribution of defined combinations of lesions to distinct tumor characteristics such as metastatic behavior and response to therapy. In this review we will describe mouse models of lung cancer and how they are used to better understand the disease and how they are exploited to develop better intervention strategies.

Indexed as

Disease Models, AnimalAnimalsHumansLung NeoplasmsMiceXenograft Model Antitumor Assays

Identifiers

PMID23481268
PMCPMC5528410
OpenAlexW2029764427

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.