Evidence map›Paper›PMID 26187108›Full record

ReviewMolecular aspects of medicine2015

Biomarkers and targeted systemic therapies in advanced non-small cell lung cancer.

Mukesh Kumar, Vinicius Ernani, Taofeek K Owonikoko

Abstract readReview
In one paragraph

Review in Molecular aspects of medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. 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.

Mukesh KumarDepartment of Hematology & Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA.
Vinicius ErnaniDepartment of Hematology & Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA.
Taofeek K OwonikokoDepartment of Hematology & Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA. Electronic address: towonik@emory.edu.

Funding

Novel systemic therapy to improve clinical outcome in small cell lung cancerK23CA164015 · NCI · EMORY UNIVERSITY · PI OWONIKOKO, TAOFEEK K · 2011 to 2015
$815k
NCI NIH HHS K23 CA164015
6 · The paper itself

Abstract

The last decade has witnessed significant growth in therapeutic options for patients diagnosed with lung cancer. This is due in major part to our improved technological ability to interrogate the genomics of cancer cells, which has enabled the development of biologically rational anticancer agents. The recognition that lung cancer is not a single disease entity dates back many decades to the histological subclassification of malignant neoplasms of the lung into subcategories of small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). While SCLC continues to be regarded as a single histologic and therapeutic category, the NSCLC subset has undergone additional subcategorizations with distinct management algorithms for specific histologic and molecular subtypes. The defining characteristics of these NSCLC subtypes have evolved into important tools for prognosis and for predicting the likelihood of benefit when patients are treated with anticancer agents.

Indexed as

Molecular Targeted TherapyAntineoplastic AgentsBiomarkers, TumorCarcinoma, Non-Small-Cell LungErb-b2 Receptor Tyrosine KinasesHumansProtein-Tyrosine KinasesProto-Oncogene ProteinsProto-Oncogene Proteins c-retRandomized Controlled Trials as TopicAntineoplastic AgentsBiomarkers, TumorERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesProtein-Tyrosine KinasesProto-Oncogene ProteinsProto-Oncogene Proteins c-retRET protein, humanROS1 protein, humanALKBiomarkersEGFRLung cancerNSCLCRETROS1

Identifiers

PMID26187108
PMCPMC4637259

What OpenQuestion holds

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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.