Evidence map›Paper›PMID 31598791›Full record

ArticleJournal of cancer research and clinical oncology2019

Malignant ascites-derived organoid (MADO) cultures for gastric cancer in vitro modelling and drug screening.

Jie Li, Huawei Xu, Lixing Zhang, Lele Song, Dan Feng, Xiaobo Peng, Meihong Wu, Yang Zou, Bin Wang, Lixing Zhan and 2 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cancer research and clinical oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
1.6field-weighted citation impact, top 14% 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

31 citing papers in PubMed, 39 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

12 authors at 4 institutions in 1 country.

Jie LiDepartment of Oncology, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China.
Huawei XuDepartment of Oncology, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China.
Lixing ZhangResearch and Early Development, Shanghai 121Biomed Inc, Shanghai, 200235, China.
Lele SongDepartment of Oncology, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China.
Dan FengDepartment of Oncology, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China.
Xiaobo PengDepartment of Oncology, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China.
Meihong WuDepartment of Oncology, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China.
Yang ZouShanghai Key Laboratory of Regulatory Biology, School of Life Sciences, Institute of Biomedical Sciences, East China Normal University, Shanghai, 200241, China.
Bin WangDepartment of Oncology, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China.
Lixing ZhanCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
Guoqiang HuaInstitute of Radiation Medicine and Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 230032, China. guoqianghua@fudan.edu.cn.
Xianbao ZhanDepartment of Oncology, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China. zhan.xianbao@smmu.edu.cn.ORCID http://orcid.org/0000-0002-0033-8808
Second Military Medical University · CNShanghai Medical Information Center · CNEast China Normal University · CNFudan University Shanghai Cancer Center · CN

Funding

National Major Scientific and Technological Special Project for "Significant New Drugs Development" 2017ZX09304030National Natural Science Foundation of China 31470826Scientific and Innovative Action Plan of Shanghai 19411970700
6 · The paper itself

Abstract

purposeMalignant ascites (MA) is a common manifestation in advanced gastric cancer with peritoneal carcinomatosis and usually indicates a poor prognosis. However, lack of in vitro models that can faithfully recapitulate the characteristics of tumour cells in ascites hinders related researches. Tumour organoids have emerged as a robust in vitro model for tumour research and drug screening. Hence, we aimed to generate a 3-D in vitro organoid cultures from malignant ascites of gastric cancer for disease modelling and drug screening.

methodsEleven MADOs were generated from the MA tumour cells of gastric cancer patients. We made comparisons between MADOs and original MA tumour cells in histopathology by immunohistochemistry and genomics by whole-exome sequencing. In order to evaluate MADOs as functional in vitro disease models, we tested whether MADOs could be used for drug sensitivity screens.

resultsEleven MADO cultures from human gastric cancer were established. MADOs demonstrated divergent growth characteristics and morphologies. MADO cultures preserve the histological architecture, genomic landscape of the corresponding MA tumour cells. MADOs exhibited heterogeneous responses to standard-of-care chemotherapeutics.

conclusionsWe generated MADOs modelling characteristics and mutated genes of MA tumour cells. A broad range of intrinsic MADO response to conventional chemotherapeutics suggests MADOs are amenable to drug screening.

Indexed as

Antineoplastic AgentsAscitesCell ProliferationDrug Screening Assays, AntitumorExome SequencingHumansIn Vitro TechniquesOrgan Culture TechniquesOrganoidsStomach NeoplasmsTumor Cells, CulturedAntineoplastic AgentsDrug screeningGastric cancerMalignant ascitesOrganoidPersonalised medicine

Identifiers

PMID31598791
PMCPMC11810372
OpenAlexW2979526048

What OpenQuestion holds

Textmetadata
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.