Evidence map›Paper›PMID 41655669›Full record

ArticleActa biomaterialia2026

Tumoroid model recreates clinically relevant phenotypes of high grade serous ovarian cancer cells, carcinoma associated fibroblasts, and macrophages.

Kathleen M Burkhard, Ayush Semwal, Benjamin K Johnson, Kristina C Chu, Riley J Kranick, Mihika Rayan, Analisa DiFeo, Hui Shen, Geeta Mehta

Abstract read
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In one paragraph

Article in Acta biomaterialia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Kathleen M BurkhardDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI.
Ayush SemwalDepartment of Epigenetics, Van Andel Institute, Grand Rapids, MI.
Benjamin K JohnsonDepartment of Epigenetics, Van Andel Institute, Grand Rapids, MI.
Kristina C ChuDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI.
Riley J KranickDepartment of Materials Science and Engineering, University of Michigan, Ann Arbor, MI.
Mihika RayanDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI.
Analisa DiFeoDepartment of Pathology, Michigan Medicine, University of Michigan, Ann Arbor, MI; Department of Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI.
Hui ShenDepartment of Epigenetics, Van Andel Institute, Grand Rapids, MI. Electronic address: huishen@vai.org.
Geeta MehtaDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI; Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI; Macromolecular Science and Engineering, University of Michigan, Ann Arbor, MI; Rogel Cancer Center, University of Michigan, Ann Arbor, MI; Precision Health, University of Michigan, Ann Arbor, MI. Electronic address: mehtagee@umich.edu.

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Eric R. Fearon · 1988 to 2026
$178.2M
High-throughput Epigenomic Mapping of Regulatory Elements in Ovarian Cancer at Basepair ResolutionR37CA230748 · NCI · VAN ANDEL RESEARCH INSTITUTE · PI SHEN, HUI · 2018 to 2024
$3.4M
Profiling the Fluid Assisted Dissemination of Pre-malignant cells in Fallopian TubesR37CA282790 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Geeta Mehta · 2023 to 2026
$2.6M
NCI NIH HHS P30 CA046592NCI NIH HHS R37 CA230748NCI NIH HHS R37 CA282790
6 · The paper itself

Abstract

Ovarian cancer, the gynecological malignancy with the lowest survival rate, is significantly influenced by the tumor microenvironment. The mesenchymal subtype of high-grade serous carcinoma (HGSC) shows poor outcomes due to high stromal and low immune response. Single-cell RNA sequencing (scRNA-seq) of HGSC metastatic ascites has identified carcinoma-associated fibroblasts (CAFs), macrophages, and carcinoma-associated mesenchymal stem cells (CA-MSCs) as crucial drivers of immune exclusion, chemotherapy resistance, metastasis, and stem-like cell propagation. To explore this complex signaling, we developed heterogeneous tri-component tumoroids, incorporating HGSC cells (OVCAR3, OVCAR4, OVCAR8), primary MSCs, and U937-derived M2-like macrophages (M2) in defined ratios, each labeled with a fluorescent protein for distinct analysis. Upon a 48-hour treatment with carboplatin and/or paclitaxel, HGSC cells in tri-component tumoroids exhibited higher chemoresistance than HGSC-only tumoroids. Flow cytometry revealed significant increases in cancer stem-like cell (CSC) markers CD44 and CD90 in the tri-component tumoroids. Conditioned medium from the tri-component tumoroids significantly enhanced HGSC cell migration. Invasion assays further demonstrated that tri-component tumoroids penetrated monolayer of mCherry-labeled LP-9 mesothelial cells more effectively than HGSC-only tumoroid. Additionally, scRNA-seq of tri-component tumoroids identified a unique cancer cell cluster enriched in epithelial-mesenchymal transition (EMT) and matrisome signatures, featuring a 14-gene signature linked to poor survival. MSCs in these tri-component tumoroids displayed a myofibroblastic-CAF signature, while macrophages indicated an ECM-associated and immunosuppressive phenotype. In conclusion, our 3D heterogenous tri-component tumoroids replicate key HGSC phenotypes, such as chemoresistance, CSC enrichment, migration, invasion, and EMT. This platform is invaluable for studying HGSC microenvironment interactions and preclinical testing of targeted therapies. STATEMENT OF SIGNIFICANCE: The cellular composition of the ovarian tumor microenvironment has a profound effect on patients' clinical outcomes, yet effective therapies that target its cellular components remain underexplored. In this study, we introduce a highly tunable 3D in vitro tumoroid model for ovarian cancer that integrates stromal cells, such as mesenchymal stem cells (MSCs) and macrophages, with cancer cells to more accurately recapitulate the complex cell-cell interactions of these heterogeneous tumors. This model facilitates detailed investigation of intercellular signaling pathways, enabling the identification of previously unexplored therapeutic targets. Generated using a 384-well hanging drop array, these tri-component tumoroids are compatible with high-throughput drug screening. This versatile platform can be adapted for a range of stromal cell types, making it broadly applicable for studying ovarian as well as other solid tumor microenvironments.

Indexed as

Cancer-Associated FibroblastsCystadenocarcinoma, SerousMacrophagesModels, BiologicalOvarian NeoplasmsCell Line, TumorDrug Resistance, NeoplasmFemaleHumansMesenchymal Stem CellsNeoplastic Stem CellsPaclitaxelPhenotypeTumor MicroenvironmentPaclitaxelAlternately activated macrophagesCarcinoma-associated mesenchymal stem cells (MSC) (CA-MSC)High grade serous ovarian cancerMolecular subtypingOrganoidsscRNA-seqSpheroidsTri-component tumoroidsTumor associated macrophages (TAM)Tumor microenvironment

Identifiers

PMID41655669

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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.