Evidence map›Paper›PMID 36505480›Full record

ReviewFrontiers in immunology2022

Recent developments in PD-1/PD-L1 blockade research for gastroesophageal malignancies.

Meng Chen, Chenyan Li, Mingjun Sun, Yiling Li, Xuren Sun

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Meng ChenDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, China.
Chenyan LiDepartment of Endocrinology and Metabolism, First Affiliated Hospital of China Medical University, Shenyang, China.
Mingjun SunDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, China.
Yiling LiDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, China.
Xuren SunDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastroesophageal cancers (GECs) comprise malignancies in the stomach, esophagus, and gastroesophageal junction. Despite ongoing improvements in chemoradiotherapy, the clinical outcomes of GEC have not significantly improved over the years, and treatment remains challenging. Immune checkpoint inhibitors (ICIs) have been the subject of clinical trials worldwide for several years. Encouraging results have been reported in different countries, but further research is required to apply ICIs in the clinical care of patients with GEC. This review summarizes completed and ongoing clinical trials with programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) pathway blockers in GEC and current biomarkers used for predicting PD-1/PD-L1 blockade efficacy. This review captures the main findings of PD-1/PD-L1 antibodies combined with chemotherapy as an effective first-line treatment and a monotherapy in second-line or more treatment and in maintenance therapy. This review aims to provide insight that will help guide future research and clinical trials, thereby improving the outcomes of patients with GEC.

Indexed as

B7-H1 AntigenEsophageal NeoplasmsEsophagogastric JunctionHumansProgrammed Cell Death 1 ReceptorB7-H1 AntigenCD274 protein, humanProgrammed Cell Death 1 Receptorbiomarkersesophageal cancergastric cancerimmune checkpoint inhibitorsPD-1PD-L1

Identifiers

PMID36505480
PMCPMC9731511

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.