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Figure 2 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

Matrisome and adhesome networks in STS. A, Heatmap showing a similarity matrix of Pearson’s correlation coefficients for all pairwise comparisons of matrisome and adhesome proteins. Heatmap is split into three clusters (C1, C2, and C3) identified by consensus clustering analysis. B, Pie charts showing breakdown of proteins within the clusters into adhesome, core matrisome, or matrisome-associated proteins (top), breakdown by matrisome class (middle) and by functional annotation of adhesome (bottom). C, Selected protein–protein interaction networks (colored by clusters) are shown for each cluster as identified by enrichment analysis (reactome pathways). D, Box plots showing distributions of median expression of C1, C2 and C3 proteins across tumor grades. Boxes indicate 25th and 75th percentile, with the median line in the middle, whiskers extending from 25th percentile − [1.5 × interquartile range (IQR)] to 75th percentile + (1.5 × IQR), and outliers plotted as points. Significance determined by Mann–Whitney U test. ***, P < 0.001; ****, P < 0.0001. E, Box plots showing distributions of median expression of C1, C2 and C3 proteins across histological subtypes. Boxes indicate 25th and 75th percentile, with the median line in the middle, whiskers extending from 25th percentile − (1.5 × IQR) to 75th percentile + (1.5 × IQR), and outliers plotted as points. Significance determined by Kruskal–Wallis tests with Dunn’s multiple corrections tests. *, P < 0.05; **, P < 0.01; ****, P < 0.0001. Other, 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

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.

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