Deconvolution of sarcoma methylomes reveals varying degrees of immune cell infiltrates with association to genomic aberrations.


Journal

Journal of translational medicine
ISSN: 1479-5876
Titre abrégé: J Transl Med
Pays: England
ID NLM: 101190741

Informations de publication

Date de publication:
12 05 2021
Historique:
received: 13 03 2021
accepted: 26 04 2021
entrez: 13 5 2021
pubmed: 14 5 2021
medline: 22 5 2021
Statut: epublish

Résumé

Soft-tissue sarcomas (STS) are a heterogeneous group of mesenchymal tumors for which response to immunotherapies is not well established. Therefore, it is important to risk-stratify and identify STS patients who will most likely benefit from these treatments. To reveal shared and distinct methylation signatures present in STS, we performed unsupervised deconvolution of DNA methylation data from the TCGA sarcoma and an independent validation cohort. We showed that leiomyosarcoma can be subclassified into three distinct methylation groups. More importantly, we identified a component associated with tumor-infiltrating leukocytes, which suggests varying degrees of immune cell infiltration in STS subtypes and an association with prognosis. We further investigated the genomic alterations that may influence tumor infiltration by leukocytes including RB1 loss in undifferentiated pleomorphic sarcomas and ELK3 amplification in dedifferentiated liposarcomas. In summary, we have leveraged unsupervised methylation-based deconvolution to characterize the immune compartment and molecularly stratify subtypes in STS, which may benefit precision medicine in the future.

Sections du résumé

BACKGROUND
Soft-tissue sarcomas (STS) are a heterogeneous group of mesenchymal tumors for which response to immunotherapies is not well established. Therefore, it is important to risk-stratify and identify STS patients who will most likely benefit from these treatments.
RESULTS
To reveal shared and distinct methylation signatures present in STS, we performed unsupervised deconvolution of DNA methylation data from the TCGA sarcoma and an independent validation cohort. We showed that leiomyosarcoma can be subclassified into three distinct methylation groups. More importantly, we identified a component associated with tumor-infiltrating leukocytes, which suggests varying degrees of immune cell infiltration in STS subtypes and an association with prognosis. We further investigated the genomic alterations that may influence tumor infiltration by leukocytes including RB1 loss in undifferentiated pleomorphic sarcomas and ELK3 amplification in dedifferentiated liposarcomas.
CONCLUSIONS
In summary, we have leveraged unsupervised methylation-based deconvolution to characterize the immune compartment and molecularly stratify subtypes in STS, which may benefit precision medicine in the future.

Identifiants

pubmed: 33980253
doi: 10.1186/s12967-021-02858-7
pii: 10.1186/s12967-021-02858-7
pmc: PMC8117561
doi:

Substances chimiques

Elk3 protein, human 0
Proto-Oncogene Proteins c-ets 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

204

Subventions

Organisme : CIHR
ID : 825835
Pays : Canada

Commentaires et corrections

Type : ErratumIn

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Auteurs

Malte Simon (M)

Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.

Sadaf S Mughal (SS)

Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Peter Horak (P)

Division of Translational Medical Oncology, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.

Sebastian Uhrig (S)

Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Jonas Buchloh (J)

Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Bogac Aybey (B)

Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Albrecht Stenzinger (A)

Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.

Hanno Glimm (H)

Department of Translational Medical Oncology, NCT Dresden, Dresden, Germany.
University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany.

Stefan Fröhling (S)

Division of Translational Medical Oncology, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.

Benedikt Brors (B)

Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.

Charles D Imbusch (CD)

Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. charles@imbusch.net.

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Classifications MeSH