Evidence map›Paper›PMID 34900526›Full record

ReviewActa pharmaceutica Sinica. B2021

Personalized medicine of non-gene-specific chemotherapies for non-small cell lung cancer.

Wenxiao Jiang, Guiqing Cai, Peter Hu, Yue Wang

Open access · diamondAbstract readReview
In one paragraph

Review in Acta pharmaceutica Sinica. B, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

12 citing papers in PubMed, 29 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Unveiling the mechanisms and challenges of cancer drug resistance.Cell communication and signaling : CCS · 2024
    Review
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. 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

4 authors at 3 institutions in 1 country.

Wenxiao JiangOtogenetics Corp., Atlanta, GA 30360, USA.
Guiqing CaiQuest Diagnostics, San Juan Capistrano, CA 92675, USA.
Peter HuThe University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Yue WangDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
Baylor College of Medicine · USQuest Diagnostics (United States) · USThe University of Texas MD Anderson Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-small cell lung cancer is recognized as the deadliest cancer across the globe. In some areas, it is more common in women than even breast and cervical cancer. Its rise, vaulted by smoking habits and increasing air pollution, has garnered much attention and resource in the medical field. The first lung cancer treatments were developed more than half a century ago. Unfortunately, many of the earlier chemotherapies often did more harm than good, especially when they were used to treat genetically unsuitable patients. With the introduction of personalized medicine, physicians are increasingly aware of when, how, and in whom, to use certain anti-cancer agents. Drugs such as tyrosine kinase inhibitors, anaplastic lymphoma kinase inhibitors, and monoclonal antibodies possess limited utility because they target specific oncogenic mutations, but other drugs that target mechanisms universal to all cancers do not. In this review, we discuss many of these non-oncogene-targeting anti-cancer agents including DNA replication inhibitors (

Indexed as

DNA replication inhibitorsDrug resistanceNon-small cell lung cancerPersonalized medicinePharmacogeneticsPharmacogenomicsToxoidsVinca alkaloids

Identifiers

PMID34900526
PMCPMC8642451
OpenAlexW3127361683

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.