Clinical implications and molecular features of extracellular matrix networks in soft tissue sarcomas.

PURPOSE: The landscape of extracellular matrix (ECM) alterations in soft tissue sarcomas (STS) remains poorly characterised. We aimed to investigate the tumour ECM and adhesion signalling networks present in STS and their clinical implications. EXPERIMENTAL DESIGN: Proteomic and clinical data from 321 patients across 11 histological subtypes were analysed to define ECM and integrin adhesion networks. Subgroup analysis was performed in leiomyosarcomas (LMS), dedifferentiated liposarcomas (DDLPS) and undifferentiated pleiomorphic sarcomas (UPS). RESULTS: 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 co-regulated ECM networks which are associated with tumour malignancy grade and histological subtype. Comparative analysis of LMS cell line and patient proteomic data identified the LCP1 cytoskeletal protein as a prognostic factor in LMS. Characterisation of ECM network events in DDLPS revealed three subtypes with distinct oncogenic signalling pathways and survival outcomes. Evaluation of the DDLPS subtype with the poorest prognosis nominates ECM remodelling proteins as candidate anti-stromal therapeutic targets. Finally, we define a proteoglycan signature which is an independent prognostic factor for overall survival in DDLPS and UPS. CONCLUSIONS: STS comprise heterogeneous ECM signalling networks and matrix-specific features have utility for risk stratification and therapy selection which could in future guide precision medicine in these rare cancers.

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 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.

Characterisation of a Novel Cell Line (ICR-SS-1) Established from a Patient-Derived Xenograft of Synovial Sarcoma.

Synovial sarcoma is a rare translocation-driven cancer with poor survival outcomes, particularly in the advanced setting. Previous synovial sarcoma preclinical studies have relied on a small panel of cell lines which suffer from the limitation of genomic and phenotypic drift as a result of being grown in culture for decades. Patient-derived xenografts (PDX) are a valuable tool for preclinical research as they retain many histopathological features of their originating human tumour; however, this approach is expensive, slow, and resource intensive, which hinders their utility in large-scale functional genomic and drug screens. To address some of these limitations, in this study, we have established and characterised a novel synovial sarcoma cell line, ICR-SS-1, which is derived from a PDX model and is amenable to high-throughput drug screens. We show that ICR-SS-1 grows readily in culture, retains the pathognomonic SS18::SSX1 fusion gene, and recapitulates the molecular features of human synovial sarcoma tumours as shown by proteomic profiling. Comparative analysis of drug response profiles with two other established synovial sarcoma cell lines (SYO-1 and HS-SY-II) finds that ICR-SS-1 harbours intrinsic resistance to doxorubicin and is sensitive to targeted inhibition of several oncogenic pathways including the PI3K-mTOR pathway. Collectively, our studies show that the ICR-SS-1 cell line model may be a valuable preclinical tool for studying the biology of anthracycline-resistant synovial sarcoma and identifying new salvage therapies following failure of doxorubicin.

Proteomic Profiling Identifies Co-Regulated Expression of Splicing Factors as a Characteristic Feature of Intravenous Leiomyomatosis.

Intravenous leiomyomatosis (IVLM) is a rare benign smooth muscle tumour that is characterised by intravenous growth in the uterine and pelvic veins. Previous DNA copy number and transcriptomic studies have shown that IVLM harbors unique genomic and transcriptomic alterations when compared to uterine leiomyoma (uLM), which may account for their distinct clinical behaviour. Here we undertake the first comparative proteomic analysis of IVLM and other smooth muscle tumours (comprising uLM, soft tissue leiomyoma and benign metastasizing leiomyoma) utilising data-independent acquisition mass spectrometry. We show that, at the protein level, IVLM is defined by the unique co-regulated expression of splicing factors. In particular, IVLM is enriched in two clusters composed of co-regulated proteins from the hnRNP, LSm, SR and Sm classes of the spliceosome complex. One of these clusters (Cluster 3) is associated with key biological processes including nascent protein translocation and cell signalling by small GTPases. Taken together, our study provides evidence of co-regulated expression of splicing factors in IVLM compared to other smooth muscle tumours, which suggests a possible role for alternative splicing in the pathogenesis of IVLM.

Proteomic profiling of soft tissue sarcomas with SWATH mass spectrometry.

Soft tissue sarcomas (STS) are a group of rare and heterogeneous cancers. While large-scale genomic and epigenomic profiling of STS have been undertaken, proteomic analysis has thus far been limited. Here we utilise sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) for proteomic profiling of formalin fixed paraffin embedded (FFPE) specimens from a cohort of STS patients (n = 36) across four histological subtypes (leiomyosarcoma, synovial sarcoma, undifferentiated pleomorphic sarcoma and dedifferentiated liposarcoma). We quantified 2951 proteins across all cases and show that there is a significant enrichment of gene sets associated with smooth muscle contraction in leiomyosarcoma, RNA splicing regulation in synovial sarcoma and leukocyte activation in undifferentiated pleomorphic sarcoma. We further identified a subgroup of STS cases that have a distinct expression profile in a panel of proteins, with worse survival outcomes when compared to the rest of the cohort. Our study highlights the value of comprehensive proteomic characterisation as a means to identify histotype-specific STS profiles that describe key biological pathways of clinical and therapeutic relevance; as well as for discovering new prognostic biomarkers in this group of rare and difficult-to-treat diseases.

Extracting DNA from FFPE Tissue Biospecimens Using User-Friendly Automated Technology: Is There an Impact on Yield or Quality?

DNA extracted from formalin-fixed, paraffin-embedded (FFPE) tissue blocks is amenable to analytical techniques, including sequencing. DNA extraction protocols are typically long and complex, often involving an overnight proteinase K digest. Automated platforms that shorten and simplify the process are therefore an attractive proposition for users wanting a faster turn-around or to process large numbers of biospecimens. It is, however, unclear whether automated extraction systems return poorer DNA yields or quality than manual extractions performed by experienced technicians. We extracted DNA from 42 FFPE clinical tissue biospecimens using the QiaCube (Qiagen) and ExScale (ExScale Biospecimen Solutions) automated platforms, comparing DNA yields and integrities with those from manual extractions. The QIAamp DNA FFPE Spin Column Kit was used for manual and QiaCube DNA extractions and the ExScale extractions were performed using two of the manufacturer's magnetic bead kits: one extracting DNA only and the other simultaneously extracting DNA and RNA. In all automated extraction methods, DNA yields and integrities (assayed using DNA Integrity Numbers from a 4200 TapeStation and the qPCR-based Illumina FFPE QC Assay) were poorer than in the manual method, with the QiaCube system performing better than the ExScale system. However, ExScale was fastest, offered the highest reproducibility when extracting DNA only, and required the least intervention or technician experience. Thus, the extraction methods have different strengths and weaknesses, would appeal to different users with different requirements, and therefore, we cannot recommend one method over another.