Evidence map›Paper›PMID 28611939›Full record

ArticleFrontiers in oncology2017

Selective Targeting of the L858R Mutation (EGFR) in Non-Small Cell Lung Cancer: A Mechanism for Advancing Targeted Chemotherapy.

Rohan Arora, Venkat Krishnan

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 38% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
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.

Rohan AroraAmerican High School, Fremont, CA, United States.
Venkat KrishnanAmerican High School, Fremont, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer remains one of today's most deadly and intractable cancers. Non-small cell lung cancer (NSCLC) accounts for roughly 85% of lung cancers, with an extremely poor survival rate. To ensure patient comfort and survival, the development of a selective therapy is imperative. However, lung cancer does not display surface proteins associated uniquely with tumor cells; thus, it is very difficult to develop a tumor-specific drug. Current techniques that target overexpression of proteins or inhibit growth pathways are either non-specific or prone to rapid drug resistance. The goal was to design a drug targeted to structural mutations expressed by tumor-associated general surface proteins, thereby combating the lack of tumor-unique markers in lung cancer. Mutant EGFR was identified as a potential target due to its prominence in tumor cells. Due to their size, it was determined that small molecules would be most effective at targeting isolated changes in protein structure, and thereby differentiating between the tumor-associated mutant EGFR and the healthy wild type. Conformational analysis of a virtual binding study conducted in VINA predicted a set of drug-like small molecules specific for the L858R mutation in EGFR. One molecule (ZN47) was then acquired and conjugated to a carrier protein to form a multifaceted hapten-protein conjugate. Multiple ELISAs were conducted to confirm the specificity of the conjugate to both tumor-associated mutant EGFRs. The results indicate that the identified molecule may be highly selective for tumor-associated L858R-EGFR, but further research, including a complete dosage-binding study, is necessary for full validation.

Indexed as

EGFRL858Rlung cancersmall molecule–cytotoxin conjugatetargeted therapy

Identifiers

PMID28611939
PMCPMC5447734
OpenAlexW2612876924

What OpenQuestion holds

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LicenceCC BY
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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.