Evidence map›Paper›PMID 35837091›Full record

ArticleFrontiers in oncology2022

Development of a Multicellular 3D Tumor Model to Study Cellular Heterogeneity and Plasticity in NSCLC Tumor Microenvironment.

Leena Arora, Moyna Kalia, Suman Dasgupta, Navneet Singh, Anita K Verma, Durba Pal

Abstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

6 authors.

Leena AroraDepartment of Biomedical Engineering, Indian Institute of Technology Ropar, Punjab, India.
Moyna KaliaDepartment of Biomedical Engineering, Indian Institute of Technology Ropar, Punjab, India.
Suman DasguptaDepartment of Molecular Biology & Biotechnology, Tezpur University, Assam, India.
Navneet SinghDepartment of Pulmonary Medicine, Postgraduate Institute of Medical Education & Research (PGIMER), Chandigarh, India.
Anita K VermaDepartment of Zoology, Kirori Mal College, University of Delhi, Delhi, India.
Durba PalDepartment of Biomedical Engineering, Indian Institute of Technology Ropar, Punjab, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heterogeneity is a characteristic feature of solid tumors. Intra-tumor heterogeneity includes phenotypic diversity, epigenetic abnormalities, cell proliferation, and plasticity that eventually drives disease progression. Studying tumor heterogeneity in 2D culture is challenging as it cannot simulate the microenvironmental features, such as hypoxia, nutrient unavailability, and cell-ECM interactions. We propose the development of multicellular (tri-culture) 3D spheroids using a hanging drop method to study the non-tumorigenic (BEAS-2B) vs. tumorigenic NSCLC (A549/NCI-H460)cells' interaction with lung fibroblasts (MRC-5) and monocytes (THP-1). Unlike the non-tumorigenic model, the tumorigenic 3D spheroids show significant induction of cell proliferation, hypoxia, pluripotency markers, notable activation of cancer-associated fibroblasts, and tumor-associated macrophages. CD68+ macrophages isolated from tumorigenic spheroids exhibited profound induction of phenotypic endothelial characteristics. The results are zebrafish tumor xenograft model and by using human patient samples. This multicellular 3D tumor model is a promising tool to study tumor-stroma interaction and cellular plasticity, targeting tumor heterogeneity, and facilitating cancer therapy success against NSCLC.

Indexed as

cellular plasticitymulticellular 3D spheroidsNSCLCtumor heterogeneitytumor microenvironment

Identifiers

PMID35837091
PMCPMC9273950

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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.