Evidence map›Paper›PMID 30122997›Full record

ReviewCancer management and research2018

Current landscape and future directions of biomarkers for predicting responses to immune checkpoint inhibitors.

Yingming Zhu, Fen Zhao, Zhenxiang Li, Jinming Yu

Abstract readReview
In one paragraph

Review in Cancer management and research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Microbial signatures in metastatic cancer.Frontiers in medicine · 2025
    Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
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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.

Yingming ZhuDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong University, Jinan, China, yujinmingsd@126.com.
Fen ZhaoDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong University, Jinan, China, yujinmingsd@126.com.
Zhenxiang LiDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong University, Jinan, China, yujinmingsd@126.com.
Jinming YuDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong University, Jinan, China, yujinmingsd@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immune checkpoint inhibitors (ICIs), represented by anti-CTLA-4 or anti-PD-1/anti-PD-L1 pathway antibodies, have led to a revolution in cancer treatment modalities. ICIs have unique clinical benefits, such as effectiveness against a broad range of tumor types, strong overall impact on survival, and persistent responses after the cessation of therapy. However, only a subset of patients responds to these therapies, and a small proportion of patients even experience rapid progression or an increased risk of death. Therefore, it is imperative to optimize patient selection for treatment. This review focuses on the mechanisms of tumor escape from immune surveillance, the composition and activity of a preexisting immune infiltrate, the degree of tumor foreignness (as reflected by the mutational burden, expression of viral genes, and driver gene mutations), and host factors (including peripheral blood biomarkers, genetic polymorphisms, and gut microbiome) to summarize current evidence on the biomarkers of responses to ICIs and explore the future prospects in this field.

Indexed as

biomarkercytotoxic T-lymphocyte-associated antigen-4efficacyimmune checkpoint inhibitorprogrammed death-1programmed death ligand-1

Identifiers

PMID30122997
PMCPMC6086110

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.