Figure 1 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas

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posted on 2024-10-30, 14:40 authored by Valeriya Pankova, Lukas Krasny, William Kerrison, Yuen B. Tam, Madhumeeta Chadha, Jessica Burns, Christopher P. Wilding, Liang Chen, Avirup Chowdhury, Emma Perkins, Alexander T.J. Lee, Louise Howell, Nafia Guljar, Karen Sisley, Cyril Fisher, Priya Chudasama, Khin Thway, Robin L. Jones, Paul H. Huang

The matrisome and adhesome landscape of soft tissue sarcomas (STS). A, Annotated heatmap illustrating the unsupervised clustering (Pearson’s correlation distance) of 302 matrisome and adhesome components in the STS cohort. Top annotation panel correspond to histological subtype. The annotation on the (left) side shows proteins belonging to matrisome or adhesome databases and the breakdown into matrisome and adhesome functional classes. B, Heatmap showing matrisome and adhesome proteins uniquely upregulated (indicated by black boxes) in histological subtypes (false discovery rate <0.01, fold change ≥2), arranged by histological subtype. A selection of matrisome proteins which are upregulated in each histological subtype is shown. AS, angiosarcoma; ASPS, alveolar soft part sarcoma; CCS, clear cell sarcoma; DSRCT, desmoplastic small round cell tumor; ES, epithelioid sarcoma; RT, rhabdoid tumor.

Funding

Sarcoma UK (SUK)

Cancer Research UK (CRUK)

NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research (BRC)

Royal Marsden Cancer Charity (The Royal Marsden Cancer Charity)

Institute of Cancer Research (ICR)

German Research Foundation

German Federal Ministry of Education

Sarcoma Foundation of America (SFA)

History

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DOI - Is supplement to Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas

ARTICLE ABSTRACT

The landscape of extracellular matrix (ECM) alterations in soft tissue sarcomas (STS) remains poorly characterized. We aimed to investigate the tumor ECM and adhesion signaling networks present in STS and their clinical implications. Proteomic and clinical data from 321 patients across 11 histological subtypes were analyzed to define ECM and integrin adhesion networks. Subgroup analysis was performed in leiomyosarcomas (LMS), dedifferentiated liposarcomas (DDLPS), and undifferentiated pleomorphic sarcomas (UPS). This analysis defined subtype-specific ECM profiles including enrichment of basement membrane proteins in LMS and ECM proteases in UPS. Across the cohort, we identified three distinct coregulated ECM networks which are associated with tumor malignancy grade and histological subtype. Comparative analysis of LMS cell line and patient proteomic data identified the lymphocyte cytosolic protein 1 cytoskeletal protein as a prognostic factor in LMS. Characterization of ECM network events in DDLPS revealed three subtypes with distinct oncogenic signaling pathways and survival outcomes. Evaluation of the DDLPS subtype with the poorest prognosis nominates ECM remodeling proteins as candidate antistromal therapeutic targets. Finally, we define a proteoglycan signature that is an independent prognostic factor for overall survival in DDLPS and UPS. STS comprise heterogeneous ECM signaling networks and matrix-specific features that have utility for risk stratification and therapy selection, which could in future guide precision medicine in these rare cancers.