Evidence map›Paper›PMID 38169551›Full record

ReviewJournal of Cancer2024

Predictive Biomarkers of Immune Checkpoint Inhibitor-Based Mono- and Combination Therapies for Hepatocellular Carcinoma.

Long-Bin Jeng, John Wang, Chiao-Fang Teng

Open access · goldAbstract readReview
In one paragraph

Review in Journal of Cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. 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 2 institutions in 1 country.

Long-Bin JengOrgan Transplantation Center, China Medical University Hospital, Taichung, Taiwan.
John WangDepartment of Pathology, China Medical University Hospital, Taichung, Taiwan.
Chiao-Fang TengOrgan Transplantation Center, China Medical University Hospital, Taichung, Taiwan.
China Medical University · TWChina Medical University Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is among the most frequent and deadly human cancers worldwide. It has been shown that interaction between immune checkpoint receptors and ligands plays a crucial role in inhibition of T cell-mediated anti-tumor immune responses, thereby assisting tumor cells to evade the host immune surveillance. Therefore, several immune checkpoint inhibitors (ICIs) that selectively block immune checkpoint receptors or ligands have been developed as clinically effective and safe immunotherapeutic agents for treating HCC, including the inhibitors targeting cytotoxic T lymphocyte-associated antigen 4, programmed death 1, and programmed death ligand 1. In addition, various combinations of ICIs and other ICIs or tyrosine kinase inhibitors or vascular endothelial growth factor inhibitors have also emerged as clinically beneficial treatments for HCC. However, the overall response rates of ICI mono-therapy and combination therapy in HCC patients remain unsatisfied, highlighting the urgent need for discovering valuable predictive biomarkers to achieve personalized therapy. This review comprehensively summarizes the literature-based evidence validating a variety of biomarkers with predictive significance for treatment responses and outcomes in HCC patients receiving various ICI-based mono- and combination therapies.

Indexed as

combination therapyhepatocellular carcinomaimmune checkpoint inhibitormono-therapypredictive biomarker

Identifiers

PMID38169551
PMCPMC10758026
OpenAlexW4389340184

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.