Evidence map›Paper›PMID 38339311›Full record

ReviewCancers2024

Therapeutic Immunomodulation in Gastric Cancer.

Venu Akkanapally, Xue-Feng Bai, Sujit Basu

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
5.4field-weighted citation impact, top 3% 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

14 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Observational
  6. Review
  7. Article
  8. Article
  9. Review
  10. Review
  11. Article
  12. Review
  13. Review
  14. 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

3 authors at 1 institution in 1 country.

Venu AkkanapallyDepartment of Pathology, The Ohio State University, Columbus, OH 43210, USA.ORCID 0009-0003-0324-1790
Xue-Feng BaiDepartment of Pathology, The Ohio State University, Columbus, OH 43210, USA.ORCID 0000-0001-9476-8376
Sujit BasuDepartment of Pathology, The Ohio State University, Columbus, OH 43210, USA.ORCID 0000-0002-8272-0807
The Ohio State University · US

Funding

United States Department of Defense W81XWH-21-1-0874
6 · The paper itself

Abstract

Gastric carcinoma, being one of the most prevalent types of solid tumors, has emerged as the third leading cause of death worldwide. The symptoms of gastric cancer (GC) are typically complex, which makes early detection challenging. Immune checkpoint inhibition has become the new standard targeted therapy for advanced or metastatic GC. It is currently being explored in various combinations, both with and without chemotherapy, across multiple therapies in clinical trials. Immunotherapy can stimulate immune responses in GC patients, leading to the destruction of cancer cells. Compared with traditional therapies, immunotherapy has shown strong effectiveness with tolerable toxicity levels. Hence, this innovative approach to the treatment of advanced GC has gained popularity. In this review, we have outlined the recent advancements in immunotherapy for advanced GC, including immune checkpoint inhibitors, cancer vaccines, vascular endothelial growth factor-A inhibitors, and chimeric antigen receptor T-cell therapy. Our current emphasis is on examining the immunotherapies presently employed in clinical settings, addressing the existing challenges associated with these therapeutic approaches, and exploring promising strategies to overcome their limitations.

Indexed as

biomarkersCTLA-4gastric cancerimmune checkpoint inhibitorsimmunotherapyPD-1PD-L1treatment

Identifiers

PMID38339311
PMCPMC10854796
OpenAlexW4391350825

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.