Evidence map›Paper›PMID 36923306›Full record

ArticleCancer research communications2022

Brain Neural Progenitors are New Predictive Biomarkers for Breast Cancer Hormonotherapy.

Agnes Basseville, Chiara Cordier, Fadoua Ben Azzouz, Wilfried Gouraud, Hamza Lasla, Fabien Panloup, Mario Campone, Pascal Jézéquel

Open access · goldFull text read
In one paragraph

Article in Cancer research communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 44% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. Article
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 at 3 institutions in 1 country.

Agnes BassevilleOmics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.ORCID 0000-0002-9349-1819
Chiara CordierOmics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.
Fadoua Ben AzzouzOmics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.
Wilfried GouraudOmics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.
Hamza LaslaOmics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.
Fabien PanloupLaboratoire Angevin de Recherche en Mathématiques (LAREMA), Université d'Angers, Angers, France.
Mario CamponeSIRIC ILIAD, Angers-Nantes, France.ORCID 0000-0002-5196-5908
Pascal JézéquelOmics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.ORCID 0000-0002-6068-6441
Université Nantes Angers Le Mans · FRCentre National de la Recherche Scientifique · FRLaboratoire Angevin de Recherche en Mathématiques · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heterogeneity of the tumor microenvironment (TME) is one of the major causes of treatment resistance in breast cancer. Among TME components, nervous system role in clinical outcome has been underestimated. Identifying neuronal signatures associated with treatment response will help to characterize neuronal influence on tumor progression and identify new treatment targets. The search for hormonotherapy-predictive biomarkers was implemented by supervised machine learning (ML) analysis on merged transcriptomics datasets from public databases. ML-derived genes were investigated by pathway enrichment analysis, and potential gene signatures were curated by removing the variables that were not strictly nervous system specific. The predictive and prognostic abilities of the generated signatures were examined by Cox models, in the initial cohort and seven external cohorts. Generated signature performances were compared with 14 other published signatures, in both the initial and external cohorts. Underlying biological mechanisms were explored using deconvolution tools (CIBERSORTx and xCell). Our pipeline generated two nervous system-related signatures of 24 genes and 97 genes (NervSign24 and NervSign97). These signatures were prognostic and hormonotherapy-predictive, but not chemotherapy-predictive. When comparing their predictive performance with 14 published risk signatures in six hormonotherapy-treated cohorts, NervSign97 and NervSign24 were the two best performers. Pathway enrichment score and deconvolution analysis identified brain neural progenitor presence and perineural invasion as nervous system-related mechanisms positively associated with NervSign97 and poor clinical prognosis in hormonotherapy-treated patients. Transcriptomic profiling has identified two nervous system-related signatures that were validated in clinical samples as hormonotherapy-predictive signatures, meriting further exploration of neuronal component involvement in tumor progression. Significance: The development of personalized and precision medicine is the future of cancer therapy. With only two gene expression signatures approved by FDA for breast cancer, we are in need of new ones that can reliably stratify patients for optimal treatment. This study provides two hormonotherapy-predictive and prognostic signatures that are related to nervous system in TME. It highlights tumor neuronal components as potential new targets for breast cancer therapy.

Indexed as

Breast NeoplasmsBiomarkers, TumorBrainFemaleGene Expression ProfilingHumansNervous SystemTumor MicroenvironmentBiomarkers, Tumor

Identifiers

PMID36923306
PMCPMC10010318
OpenAlexW4288096554

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read24
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.