Evidence map›Paper›PMID 33910926›Full record

ArticleCancer discovery2021

Cancer Cells Retrace a Stepwise Differentiation Program during Malignant Progression.

Sadegh Saghafinia, Krisztian Homicsko, Annunziata Di Domenico, Stephan Wullschleger, Aurel Perren, Ilaria Marinoni, Giovanni Ciriello, Iacovos P Michael, Douglas Hanahan

Open access · bronzeAbstract read
In one paragraph

Article in Cancer discovery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
11.7field-weighted citation impact, top 1% 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

25 citing papers in PubMed, 48 citations in OpenAlex.

  1. Article
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  7. Novel concepts of cell-of origin in neuroendocrine neoplasms.Virchows Archiv : an international journal of pathology · 2026
    Review
  8. Review
  9. Overview of high mobility group box 3 (HMGB3] protein.Molecular genetics and genomics : MGG · 2025
    Review
  10. Review
  11. Molecular principles underlying aggressive cancers.Signal transduction and targeted therapy · 2025
    Review
  12. Article
  13. Article
  14. Article
  15. Article
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  18. Review
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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

9 authors at 4 institutions in 1 country.

Sadegh Saghafinia *Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID 0000-0001-5695-9825
Krisztian HomicskoSwiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Annunziata Di DomenicoInstitute of Pathology, University of Bern, Bern, Switzerland.ORCID 0000-0003-1433-4127
Stephan WullschlegerSwiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Aurel PerrenInstitute of Pathology, University of Bern, Bern, Switzerland.ORCID 0000-0002-6819-6092
Ilaria MarinoniInstitute of Pathology, University of Bern, Bern, Switzerland.ORCID 0000-0003-1721-6636
Giovanni CirielloDepartment of Computational Biology, University of Lausanne (UNIL), Lausanne, Switzerland.
Iacovos P Michael *Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. iacovosm@gmail.com douglas.hanahan@epfl.ch.ORCID 0000-0001-7674-1233
Douglas HanahanSwiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. iacovosm@gmail.com douglas.hanahan@epfl.ch.
École Polytechnique Fédérale de Lausanne · CHUniversity of Bern · CHSIB Swiss Institute of Bioinformatics · CHLudwig Cancer Research · CH

Funding

Swiss National Science Foundation 135420Swiss National Science Foundation 152834Swiss National Science Foundation 166358Swiss National Science Foundation 185382
6 · The paper itself

Abstract

Pancreatic neuroendocrine tumors (PanNET) comprise two molecular subtypes, relatively benign islet tumors (IT) and invasive, metastasis-like primary (MLP) tumors. Until now, the origin of aggressive MLP tumors has been obscure. Herein, using multi-omics approaches, we revealed that MLP tumors arise from IT via dedifferentiation following a reverse trajectory along the developmental pathway of islet β cells, which results in the acquisition of a progenitor-like molecular phenotype. Functionally, the miR-181cd cluster induces the IT-to-MLP transition by suppressing expression of the Meis2 transcription factor, leading to upregulation of a developmental transcription factor, Hmgb3. Notably, the IT-to-MLP transition constitutes a distinct step of tumorigenesis and is separable from the classic proliferation-associated hallmark, temporally preceding accelerated proliferation of cancer cells. Furthermore, patients with PanNET with elevated HMGB3 expression and an MLP transcriptional signature are associated with higher-grade tumors and worse survival. Overall, our results unveil a new mechanism that modulates cancer cell plasticity to enable malignant progression. SIGNIFICANCE: Dedifferentiation has long been observed as a histopathologic characteristic of many cancers, albeit inseparable from concurrent increases in cell proliferation. Herein, we demonstrate that dedifferentiation is a mechanistically and temporally separable step in the multistage tumorigenesis of pancreatic islet cells, retracing the developmental lineage of islet β cells.

Indexed as

Cell Transformation, NeoplasticGene Expression RegulationAnimalsDisease Models, AnimalMiceNeuroendocrine TumorsPancreatic Neoplasms

Identifiers

PMID33910926
PMCPMC7611766
OpenAlexW3159642248

What OpenQuestion holds

Textmetadata
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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.