Evidence map›Paper›PMID 37685320›Full record

ReviewDiagnostics (Basel, Switzerland)2023

Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer.

Moonsik Kim, Ji Yun Jeong, An Na Seo

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. 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.

Moonsik KimDepartment of Pathology, School of Medicine, Kyungpook National University, 136-gil 90, Chilgokjungang-daero, Buk-gu, Daegu 41405, Republic of Korea.
Ji Yun JeongDepartment of Pathology, School of Medicine, Kyungpook National University, 136-gil 90, Chilgokjungang-daero, Buk-gu, Daegu 41405, Republic of Korea.ORCID 0000-0001-6167-4998
An Na SeoDepartment of Pathology, School of Medicine, Kyungpook National University, 136-gil 90, Chilgokjungang-daero, Buk-gu, Daegu 41405, Republic of Korea.ORCID 0000-0001-6412-3067

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite advances in diagnostic imaging, surgical techniques, and systemic therapy, gastric cancer (GC) is the third leading cause of cancer-related death worldwide. Unfortunately, molecular heterogeneity and, consequently, acquired resistance in GC are the major causes of failure in the development of biomarker-guided targeted therapies. However, by showing promising survival benefits in some studies, the recent emergence of immunotherapy in GC has had a significant impact on treatment-selectable procedures. Immune checkpoint inhibitors (ICIs), widely indicated in the treatment of several malignancies, target inhibitory receptors on T lymphocytes, including the programmed cell death protein (PD-1)/programmed death-ligand 1 (PD-L1) axis and cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and release effector T-cells from negative feedback signals. In this article, we review currently available predictive biomarkers (including PD-L1, microsatellite instability, Epstein-Barr virus, and tumor mutational burden) that affect the ICI treatment response, focusing on PD-L1 expression. We further briefly describe other potential biomarkers or mechanisms for predicting the response to ICIs in GC. This review may facilitate the expansion of the understanding of biomarkers for predicting the response to ICIs and help select the appropriate therapeutic approaches for patients with GC.

Indexed as

biomarkergastric cancerimmunotherapymolecular pathologyprogrammed cell death-ligand 1

Identifiers

PMID37685320
PMCPMC10487043

What OpenQuestion holds

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Registered trials

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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.