The proteomic landscape of soft tissue sarcomas
Soft tissue sarcomas (STS) are rare and diverse mesenchymal cancers with limited treatment options. Here we undertake comprehensive proteomic profiling of tumour specimens from 321 STS patients representing 11 histological subtypes. Within leiomyosarcomas, we identify three proteomic subtypes with d...
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Published in: | Nature communications Vol. 14; no. 1; p. 3834 |
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Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
London
Nature Publishing Group UK
29-06-2023
Nature Publishing Group Nature Portfolio |
Subjects: | |
Online Access: | Get full text |
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Summary: | Soft tissue sarcomas (STS) are rare and diverse mesenchymal cancers with limited treatment options. Here we undertake comprehensive proteomic profiling of tumour specimens from 321 STS patients representing 11 histological subtypes. Within leiomyosarcomas, we identify three proteomic subtypes with distinct myogenesis and immune features, anatomical site distribution and survival outcomes. Characterisation of undifferentiated pleomorphic sarcomas and dedifferentiated liposarcomas with low infiltrating CD3 + T-lymphocyte levels nominates the complement cascade as a candidate immunotherapeutic target. Comparative analysis of proteomic and transcriptomic profiles highlights the proteomic-specific features for optimal risk stratification in angiosarcomas. Finally, we define functional signatures termed Sarcoma Proteomic Modules which transcend histological subtype classification and show that a vesicle transport protein signature is an independent prognostic factor for distant metastasis. Our study highlights the utility of proteomics for identifying molecular subgroups with implications for risk stratification and therapy selection and provides a rich resource for future sarcoma research.
Characterising the molecular profile of soft tissue sarcomas (STS) remains critical. Here, the authors analyse samples from 321 STS patients across 11 histological subtypes using proteomics and identify prognostic signatures that can be applied to multiple subtypes. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-023-39486-2 |