Liang Chen

Heidelberg University

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Yuen B. Tam

Institute of Cancer Research

Priya Chudasama

Heidelberg University

Jessica Burns

Moffitt Cancer Center

Paul H. Huang

Institute of Cancer Research

Khin Thway

Royal Marsden NHS Foundation Trust

Christopher P. Wilding

Institute of Cancer Research

Valeriya Pankova

Institute of Cancer Research

Madhumeeta Chadha

Institute of Cancer Research

Robin L. Jones

Royal Marsden Hospital

Emma Perkins

Skidmore College

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Institute of Cancer Research

Royal Marsden NHS Foundation Trust

Royal Marsden Hospital

Institute of Cancer Research

China Jiliang University

Fondazione IRCCS Istituto Nazionale dei Tumori

National Health Service

University of Edinburgh

University of California, San Francisco

Istituti di Ricovero e Cura a Carattere Scientifico

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Supplementary Table S9 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas

Valeriya Pankova Lukáš Krásný William Kerrison Yuen B. Tam Madhumeeta Chadha

Supplementary Table S9: Summary of multivariable (MVA) Cox regression analyses assessing the association of clinicopathological factors and dedifferentiated liposarcoma (DDLPS) subgroups with local recurrence-free survival (LRFS), overall survival (OS) and metastasis-free survival (MFS).

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10.1158/1078-0432.26423117.v1

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Supplementary Table S2 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas

Valeriya Pankova Lukáš Krásný William Kerrison Yuen B. Tam Madhumeeta Chadha

Supplementary Table S2: Matrisome and adhesome cluster membership.

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10.1158/1078-0432.26423138.v1

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Supplementary Table S13 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas

Valeriya Pankova Lukáš Krásný William Kerrison Yuen B. Tam Madhumeeta Chadha

Supplementary Table S13. Summary of univariable (UVA) and multivariable (MVA) Cox regression analyses assessing the association of clinicopathological factors and ssGSEA-derived proteoglycan score with overall survival (OS).

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10.1158/1078-0432.26423141.v1

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Supplementary Figure S3 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas

Valeriya Pankova Lukáš Krásný William Kerrison Yuen B. Tam Madhumeeta Chadha

Supplementary Figure S3. Expression of matrisome proteins in a desmoid tumour cohort (n=37) split into 3 groups based on the % tumour content.

10.1158/1078-0432.27335682

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Figure 5 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas

Valeriya Pankova Lukáš Krásný William Kerrison Yuen B. Tam Madhumeeta Chadha

Proteoglycan protein expression identifies a high-risk STS group. A, Summary of log-rank tests used to assess significant associations of matrisome-related gene sets with LRFS, MFS, and OS. The scores for each patient were obtained by taking the median expression focusing on 10 matrisome-related gene sets from the Molecular Signatures Database. B, Identities of the 11 proteoglycans included in the proteoglycan score. C, Kaplan–Meier plot of OS with stratification by the median expression of 11 proteoglycans in a combined UPS and DDLPS cohort (n = 92). D, Kaplan–Meier plot of OS with stratification by the ssGSEA score of 11 proteoglycans in a combined UPS and DDLPS cohort (n = 92). Hazard ratio, 95% CI, and P-values were determined by univariate Cox regression with a two-sided Wald test.

Matrix (chemical analysis)

10.1158/1078-0432.27335691

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

Valeriya Pankova Lukáš Krásný William Kerrison Yuen B. Tam Madhumeeta Chadha

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.

10.1158/1078-0432.27335703

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Supplementary Methods S1 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas

Valeriya Pankova Lukáš Krásný William Kerrison Yuen B. Tam Madhumeeta Chadha

Supplemental Methods

10.1158/1078-0432.26423156

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Supplementary Figure S2 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas

Valeriya Pankova Lukáš Krásný William Kerrison Yuen B. Tam Madhumeeta Chadha

Supplementary Figure S2. Validation of MMP14 expression in undifferentiated pleomorphic sarcoma (UPS), dedifferentiated liposarcoma (DDLPS) and leiomyosarcoma (LMS) by immunohistochemistry (IHC).

Undifferentiated Pleomorphic Sarcoma

10.1158/1078-0432.26423177

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Supplementary Table S2 from Clinical Implications and Molecular Features of Extracellular Matrix Networks in Soft Tissue Sarcomas

Valeriya Pankova Lukáš Krásný William Kerrison Yuen B. Tam Madhumeeta Chadha

Supplementary Table S2: Matrisome and adhesome cluster membership.

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10.1158/1078-0432.26423138

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

Clinical Cancer Research (2024)

Valeriya Pankova Lukáš Krásný William Kerrison Yuen B. Tam Madhumeeta Chadha

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.

10.1158/1078-0432.ccr-23-3960

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