Evidence map›Paper›PMID 37707389›Full record

ArticleCancer research communications2023

An Early Neoplasia Index (ENI10), Based on Molecular Identity of CD10 Cells and Associated Stemness Biomarkers, is a Predictor of Patient Outcome in Many Cancers.

Boris Guyot, Flora Clément, Youenn Drouet, Xenia Schmidt, Sylvain Lefort, Emmanuel Delay, Isabelle Treilleux, Jean-Philippe Foy, Sandrine Jeanpierre, Emilie Thomas and 10 more

Abstract read
In one paragraph

Article in Cancer research communications, 2023. 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

20 authors.

Boris GuyotCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0002-4512-7123
Flora ClémentCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0002-0312-8320
Youenn DrouetCentre Léon Bérard, Lyon, France.ORCID 0000-0003-2963-558X
Xenia SchmidtCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0003-2851-5519
Sylvain LefortCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0001-7320-4256
Emmanuel DelayCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0002-3784-4560
Isabelle TreilleuxCentre Léon Bérard, Lyon, France.ORCID 0000-0003-3919-5506
Jean-Philippe FoyCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0002-4271-5093
Sandrine JeanpierreCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0009-0004-5793-8240
Emilie ThomasBioinformatics Platform, Synergie Lyon Cancer Foundation, Lyon, France.ORCID 0000-0002-8313-7218
Janice KielbassaBioinformatics Platform, Synergie Lyon Cancer Foundation, Lyon, France.ORCID 0009-0007-0229-1111
Laurie TononBioinformatics Platform, Synergie Lyon Cancer Foundation, Lyon, France.ORCID 0009-0005-8794-7276
Helen He ZhuState Key Laboratory of Oncogenes and Related Genes, Renji-Med-X Stem Cell Research Center, Shanghai Cancer Institute and Department of Urology, Ren Ji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China.ORCID 0000-0003-0290-5527
Pierre SaintignyCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0002-8090-9323
Wei-Qiang GaoState Key Laboratory of Oncogenes and Related Genes, Renji-Med-X Stem Cell Research Center, Shanghai Cancer Institute and Department of Urology, Ren Ji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China.ORCID 0000-0002-1989-4927
Arnaud de la FouchardiereCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0003-2251-8241
Franck TirodeCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0003-4731-7817
Alain ViariBioinformatics Platform, Synergie Lyon Cancer Foundation, Lyon, France.ORCID 0000-0002-1329-7777
Jean-Yves BlayCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0001-7190-120X
Véronique Maguer-SattaCNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Lyon, France.ORCID 0000-0002-1556-068X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An accurate estimate of patient survival at diagnosis is critical to plan efficient therapeutic options. A simple and multiapplication tool is needed to move forward the precision medicine era. Taking advantage of the broad and high CD10 expression in stem and cancers cells, we evaluated the molecular identity of aggressive cancer cells. We used epithelial primary cells and developed a breast cancer stem cell–based progressive model. The superiority of the early-transformed isolated molecular index was evaluated by large-scale analysis in solid cancers. BMP2-driven cell transformation increases CD10 expression which preserves stemness properties. Our model identified a unique set of 159 genes enriched in G2–M cell-cycle phases and spindle assembly complex. Using samples predisposed to transformation, we confirmed the value of an early neoplasia index associated to CD10 (ENI10) to discriminate premalignant status of a human tissue. Using a stratified Cox model, a large-scale analysis (>10,000 samples, The Cancer Genome Atlas Pan-Cancer) validated a strong risk gradient (HRs reaching HR = 5.15; 95% confidence interval: 4.00–6.64) for high ENI10 levels. Through different databases, Cox regression model analyses highlighted an association between ENI10 and poor progression-free intervals for more than 50% of cancer subtypes tested, and the potential of ENI10 to predict drug efficacy. The ENI10 index constitutes a robust tool to detect pretransformed tissues and identify high-risk patients at diagnosis. Owing to its biological link with refractory cancer stem cells, the ENI10 index constitutes a unique way of identifying effective treatments to improve clinical care. SIGNIFICANCE: We identified a molecular signature called ENI10 which, owing to its biological link with stem cell properties, predicts patient outcome and drugs efficiency in breast and several other cancers. ENI10 should allow early and optimized clinical management of a broad number of cancers, regardless of the stage of tumor progression.

Indexed as

NeoplasmsBiomarkers, TumorHumansNeprilysinBiomarkers, TumorNeprilysin

Identifiers

PMID37707389
PMCPMC10540743

What Socratic holds

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LicenceCC BY
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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 Socratic graph.