Evidence map›Paper›PMID 41146557›Full record

ArticleMolecular oncology2026

In vitro models of cancer-associated fibroblast heterogeneity uncover subtype-specific effects of CRISPR perturbations.

Elysia Saputra, Shamsudheen Karuthedath Vellarikkal, Lixia Li, Hong Sun, Khoa Nguyen, Amber Montano, Suchitra Natarajan, Federica Piccioni, Alex Michael Tamburino, Xin Yu and 1 more

Abstract read
In one paragraph

Article in Molecular oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

11 authors.

Elysia SaputraDepartment of Data, AI, and Genome Sciences, Merck & Co., Inc., Rahway, NJ, USA.
Shamsudheen Karuthedath VellarikkalDepartment of Data, AI, and Genome Sciences, Merck & Co., Inc., Rahway, NJ, USA.
Lixia LiDepartment of Data, AI, and Genome Sciences, Merck & Co., Inc., Rahway, NJ, USA.
Hong SunDepartment of Data, AI, and Genome Sciences, Merck & Co., Inc., Rahway, NJ, USA.
Khoa NguyenDepartment of Discovery Oncology, Merck & Co., Inc., Rahway, NJ, USA.
Amber MontanoDepartment of Discovery Oncology, Merck & Co., Inc., Rahway, NJ, USA.
Suchitra NatarajanDepartment of Discovery Oncology, Merck & Co., Inc., Rahway, NJ, USA.
Federica PiccioniDepartment of Data, AI, and Genome Sciences, Merck & Co., Inc., Rahway, NJ, USA.
Alex Michael TamburinoDepartment of Data, AI, and Genome Sciences, Merck & Co., Inc., Rahway, NJ, USA.
Xin YuDepartment of Discovery Oncology, Merck & Co., Inc., Rahway, NJ, USA.ORCID 0000-0003-1300-0117
Aleksandra Katarzyna OlowDepartment of Data, AI, and Genome Sciences, Merck & Co., Inc., Rahway, NJ, USA.ORCID 0000-0002-0113-8306

Funding

Merck
6 · The paper itself

Abstract

Cancer-associated fibroblasts (CAFs) are sought after as potential therapeutic targets due to their pro- and antitumorigenic functions, which are attributed to specializations in CAF subtypes. A precise targeting of specific subtypes would be required to design therapies that effectively modulate CAF phenotypes, necessitating translatable model systems to support target discovery efforts. However, not only is our knowledge of CAF heterogeneity in solid tumors lacking, particularly in pancreatic tumors, but the translatability of CAF models has also not been rigorously evaluated. Here, we develop a coculturing model with primary CAFs and immortalized tumor cell lines that can reliably represent CAF phenotypes observed in tumors, with correlations to immuno-resistant and immunomodulatory phenotypes. Using single-cell transcriptomics, we characterize the CAF subtype heterogeneity in the in vitro CAF cell lines isolated from pancreatic cancer patients and investigate the impact of perturbing potential stromal genes on different CAF subtypes. We also infer the continuum of state changes underlying the interconvertibility of CAF subtypes. Finally, we use immortalized CAF cell lines to perform single-cell CRISPR perturbations of stromal targets, revealing the subtype-specific effects of perturbations and the impact of model-type selection on the translatability of insights.

Indexed as

Cancer-Associated FibroblastsClustered Regularly Interspaced Short Palindromic RepeatsCRISPR-Cas SystemsGenetic HeterogeneityModels, BiologicalPancreatic NeoplasmsCell Line, TumorCoculture TechniquesHumansSingle-Cell AnalysisTumor MicroenvironmentCAF heterogeneitycancer‐associated fibroblastpancreatic tumorPerturb‐seqsingle‐cell transcriptomicstumor microenvironment

Identifiers

PMID41146557
PMCPMC13155148

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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.