Pankova Valeriya, Krasny Lukas, Kerrison William, Tam Yuen B, Chadha Madhumeeta, Burns Jessica, Wilding Christopher P, Chen Liang, Chowdhury Avirup, Perkins Emma, Lee Alexander T J, Howell Louise, Guljar Nafia, Sisley Karen, Fisher Cyril, Chudasama Priya, Thway Khin, Jones Robin L, Huang Paul H
Division of Molecular Pathology, The Institute of Cancer Research, London, United Kingdom.
Precision Sarcoma Research Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Clin Cancer Res. 2024 Aug 1;30(15):3229-3242. doi: 10.1158/1078-0432.CCR-23-3960.
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.
软组织肉瘤（STS）中细胞外基质（ECM）改变的情况仍未得到充分描述。我们旨在研究STS中存在的肿瘤ECM和黏附信号网络及其临床意义。
分析了来自11种组织学亚型的321例患者的蛋白质组学和临床数据，以确定ECM和整合素黏附网络。对平滑肌肉瘤（LMS）、去分化脂肪肉瘤（DDLPS）和未分化多形性肉瘤（UPS）进行了亚组分析。
该分析确定了亚型特异性的ECM谱，包括LMS中基底膜蛋白的富集和UPS中ECM蛋白酶的富集。在整个队列中，我们确定了三个不同的共调节ECM网络，它们与肿瘤恶性程度和组织学亚型相关。对LMS细胞系和患者蛋白质组学数据的比较分析确定淋巴细胞胞质蛋白1细胞骨架蛋白为LMS的一个预后因素。DDLPS中ECM网络事件的特征揭示了具有不同致癌信号通路和生存结果的三个亚型。对预后最差的DDLPS亚型的评估将ECM重塑蛋白指定为候选的抗基质治疗靶点。最后，我们定义了一种蛋白聚糖特征，它是DDLPS和UPS总生存的独立预后因素。
STS包含异质性的ECM信号网络和基质特异性特征，这些特征可用于风险分层和治疗选择，未来可能会指导这些罕见癌症的精准医学。
