Evidence map›Paper›PMID 36072895›Full record

ArticleDisease markers2022

Construction of a Humanized PBMC-PDX Model to Study the Efficacy of a Bacterial Marker in Lung Cancer Immunotherapy.

Chengwei Wu, Xinning Wang, Haitao Shang, Hong Wei

Open access · hybridAbstract read
In one paragraph

Article in Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.9field-weighted citation impact, top 16% 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

10 citing papers in PubMed, 13 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

4 authors at 1 institution in 1 country.

Chengwei WuPrecision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.ORCID https://orcid.org/0000-0002-1795-915X
Xinning WangPrecision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.ORCID https://orcid.org/0000-0003-1907-0642
Haitao ShangPrecision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.ORCID https://orcid.org/0000-0001-5532-8335
Hong WeiPrecision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.ORCID https://orcid.org/0000-0001-6069-1067
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Commensal microbiome is a key factor of lung cancer immunotherapy efficacy. Elucidating the role of specific strains as bacterial markers in immunotherapy has drawn great attention from the academia. At present, most preclinical studies about the relationship between bacterial markers and immunotherapy rely on the syngeneic mouse models. However, mice differ greatly from humans in immune system and tumor characteristics. In this study, humanized mouse models based on peripheral blood mononuclear cells (PBMCs) immune reconstitution and lung cancer cell line-derived xenograft (CDX) or patient-derived xenograft (PDX) were constructed. The PBMC-PDX model was shown to be superior to the PBMC-CDX model in preserving tumor heterogeneity and construction time-saving. Through optimizing the experimental process, the time it took for humanized models to evaluate the effect of cancer treatment was reduced to 42 days. Next, by utilizing PBMC-PDX mice treated with antibiotics (ATB), the role of

Indexed as

Leukocytes, MononuclearLung NeoplasmsAnimalsBacteriaBiomarkersDisease Models, AnimalHumansImmunologic FactorsImmunotherapyMiceXenograft Model Antitumor AssaysBiomarkersImmunologic Factors

Identifiers

PMID36072895
PMCPMC9441396
OpenAlexW4293403762

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.