Evidence map›Paper›PMID 42622615›Full record

ArticleFASEB journal : official publication of the Federation of American Societies for Experimental Biology2026

Patient-Derived Organoid and Single-Cell Multiomics Reveal Evolution Driven by CEACAM6/KRT19-Mediated Heterogeneity.

Changwen Jing, Zhuo Wang, Haixia Cao, Yuan Zhang, Yuchun Huang, Yuetong Yu, Bingzhe Li, Rong Ma

Abstract read
In one paragraph

Article in FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Changwen JingMolecular Pathology Laboratory, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.ORCID https://orcid.org/0000-0002-5503-9426
Zhuo WangMolecular Pathology Laboratory, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
Haixia CaoMolecular Pathology Laboratory, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
Yuan ZhangScience and Technology Department, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research & the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
Yuchun HuangMolecular Pathology Laboratory, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
Yuetong YuCollege of Public Health, Medical University, Nanjing, China.
Bingzhe LiYangzhou University Medical College, Yangzhou, China.
Rong MaMolecular Pathology Laboratory, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.

Funding

JST | Jiangsu Provincial Key Research and Development Program (Key Technologies R&D Program of Jiangsu Province) BE2021745Ministry of Health of China | Wu Jieping Medical Foundation (WJMF) 320.6750.2024-24-7MOST | National Natural Science Foundation of China (NSFC) 82203045
6 · The paper itself

Abstract

Colorectal cancer (CRC) remains a leading cause of cancer mortality globally, with therapeutic efficacy hindered by tumor heterogeneity and drug resistance. While organoid technology offers unprecedented opportunities to model tumor complexity, systematic platforms for chemo-targeted combination screening and molecular mechanisms underlying chemoresistance remain underexplored in Chinese populations. Here, we established a biobank of 17 patient-derived CRC organoids (85% success rate) from treatment-naïve surgical specimens, preserving histopathological fidelity and molecular diversity of parental tumors. High-throughput drug screening across eight clinical regimens revealed different responses. To dissect chemoresistance mechanisms, six organoids representing extreme phenotypes (three sensitive and three resistant, based on IC50 difference for irinotecan + raltitrexed) were selected from this cohort for single-cell transcriptomic analysis. Single-cell transcriptomics of irinotecan + raltitrexed-treated organoids identified chemoresistance-associated clonal expansion of epithelial subpopulations (cluster_0), characterized by overexpression of REG4/TFF1/TFF3 and epithelial keratins (KRT19/KRT8/KRT18). Intercellular network analysis revealed intra-epithelial communication as a resistance hub, mediated by 32 ligand-receptor pairs. Integration with TCGA-COAD cohort (n = 378) demonstrated intratumoral heterogeneity (ITH) score as an independent prognostic determinant, outperforming stemness/angiogenesis metrics. Machine learning consensus identified CEACAM6 and KRT19 as dual regulators of mortality and ITH through epithelial plasticity networks. Our multi-omics framework establishes organoid-guided chemoresistance mechanisms while proposing ITH quantification targeting as precision strategies. This study bridges critical gaps in CRC therapeutic personalization, offering clinically actionable insights for overcoming treatment failure.

Indexed as

Antigens, CDCell Adhesion MoleculesColorectal NeoplasmsOrganoidsDrug Resistance, NeoplasmFemaleGPI-Linked ProteinsHumansIrinotecanKeratin-19MultiomicsQuinazolinesSingle-Cell AnalysisAntigens, CDCEACAM6 protein, humanCell Adhesion MoleculesGPI-Linked ProteinsIrinotecanKeratin-19KRT19 protein, humanQuinazolinesCEACAM6CRCITHKRT19resistancesingle‐cell sequencing

Identifiers

PMID42622615
PMCPMC13492355

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Registered trials

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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.