Evidence map›Paper›PMID 35116101›Full record

ReviewWorld journal of gastrointestinal oncology2022

Biomarkers for response to immune checkpoint inhibitors in gastrointestinal cancers.

Meng Li, Denis Kaili, Lei Shi

Open access · diamondAbstract readReview
In one paragraph

Review in World journal of gastrointestinal oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

7 citing papers in PubMed, 10 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Meng LiSchool of Life Sciences, Chongqing University, Chongqing 400044, China.
Denis KailiDepartment of Surgery, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, United States.
Lei ShiSchool of Life Sciences, Chongqing University, Chongqing 400044, China. shil@cqu.edu.cn.
Chongqing University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastrointestinal (GI) cancers account for a large proportion of cancer deaths worldwide and pose a major public health challenge. Immunotherapy is considered to be one of the prominent and successful approaches in cancer treatment in recent years. Among them, immune checkpoint inhibitor (ICI) therapy, has received widespread attention, and many clinical findings support the feasibility of ICIs, with sustained responses and significantly prolonged lifespan observed in a wide range of tumors. However, patients treated with ICIs have not fully benefited, and therefore, the identification and development of biomarkers for predicting ICI treatment response have received further attention and exploration. From tumor genome to molecular interactions in the tumor microenvironment, and further expanding to circulating biomarkers and patient characteristics, the exploration of biomarkers is evolving with high-throughput sequencing as well as bioinformatics. More large-scale prospective and specific studies are needed to explore biomarkers in GI cancers. In this review, we summarize the known biomarkers used in ICI therapy for GI tumors. In addition, some ICI biomarkers applied to other tumors are included to provide insights and further validation for GI tumors. Moreover, we present single-cell analysis and machine learning approaches that have emerged in recent years. Although there are no clear applications yet, it can be expected that these techniques will play an important role in the application of biomarker prediction.

Indexed as

BiomarkerGastrointestinal cancerImmune checkpoint inhibitorImmunotherapyPredictive response

Identifiers

PMID35116101
PMCPMC8790411
OpenAlexW4206127480

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.