Project description:20 tumor samples run in duplicates, consisting of pleomorphic sarcomas; classfied as leiomyosarcoma or high-grade undifferentiated pleomorphic sarcoma.
Project description:In this work, 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 in 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 (independent of histological subtype) that have a distinct expression profile in a panel of 133 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.
Project description:Soft tissue sarcoma diagnostics and prognostics are challenging, particularly in highly malignant and pleomorphic subtypes such as undifferentiated pleomorphic sarcoma (UPS) and leiomyosarcoma (LMS). We applied 32K BAC-arrays and gene expression profiling to 18 extremity soft tissue LMS and 31 extremity soft tissue UPS with the aim to identify molecular subtype signatures and genomic prognostic markers. Both the gains/losses and gene expression signatures revealed striking similarities between UPS and LMS, which were indistinguishable using unsupervised hierarchical cluster analysis and significance analysis for microarrays. Gene expression analysis revealed just 9 genes, among them Tropomyosin beta, that were differentially expressed. Loss of 4q31 (encompassing the SMAD1 locus), loss of 18q22 and tumor necrosis were identified as independent predictors of metastasis in multivariate stepwise Cox regression analysis. Combined analysis applying loss of 4q31 and 18q22 and presence of necrosis improved the area under receiver operating characteristic curve for metastasis prediction from 0.64 to 0.86. The extensive genetic similarities between extremity soft tissue UPS and LMS suggest a shared lineage of these STS subtypes and the new and independent genetic prognosticators identified hold promise for refined prognostic determination in high-grade, genetically complex STS. Genomic DNA was extracted from a total of 49 samples, 31 Undifferentiated pleomorphic Sarcomas and 18 Leiomyosarcomas. The DNA was labeled using Bioprime array CGH labeling kit (Invitrogen). Promega pooled male DNA was used as reference. Labeled DNA was hybridized onto BAC arrays containing ~32 000 BAC clones printed in singlets. BAC arrays were produced at the SWEGENE DNA Microarray Facility at Lund University.
Project description:Soft tissue sarcoma diagnostics and prognostics are challenging, particularly in highly malignant and pleomorphic subtypes such as undifferentiated pleomorphic sarcoma (UPS) and leiomyosarcoma (LMS). We applied 32K BAC-arrays and gene expression profiling to 18 extremity soft tissue LMS and 31 extremity soft tissue UPS with the aim to identify molecular subtype signatures and genomic prognostic markers. Both the gains/losses and gene expression signatures revealed striking similarities between UPS and LMS, which were indistinguishable using unsupervised hierarchical cluster analysis and significance analysis for microarrays. Gene expression analysis revealed just 9 genes, among them Tropomyosin beta, that were differentially expressed. Loss of 4q31 (encompassing the SMAD1 locus), loss of 18q22 and tumor necrosis were identified as independent predictors of metastasis in multivariate stepwise Cox regression analysis. Combined analysis applying loss of 4q31 and 18q22 and presence of necrosis improved the area under receiver operating characteristic curve for metastasis prediction from 0.64 to 0.86. The extensive genetic similarities between extremity soft tissue UPS and LMS suggest a shared lineage of these STS subtypes and the new and independent genetic prognosticators identified hold promise for refined prognostic determination in high-grade, genetically complex STS.
Project description:Analysis of undifferentiated pleomorphic sarcoma/malignant fibrous histiocytoma like tumors from BrafCa, p53Fl/Fl mouse model of soft tissue sarcoma
Project description:Analysis of undifferentiated pleomorphic sarcoma/malignant fibrous histiocytoma-like tumors from LSL-KrasG12D, p53Fl/Fl mouse model of soft tissue sarcoma.
Project description:Background: Undifferentiated pleomorphic sarcoma (UPS), used to be called malignant fibrous histiocytoma (MFH), is a malignant soft tissue tumor of uncertain origin, and is characterized by morphology. UPS often share similar morphological characters with other sarcomas, especially Leiomyosarcoma. Leiomyosarcoma (LMS) is another malignant soft tissue sarcoma with complex genomic abnormalities, origin from smooth muscle. As a result, development of gene signature and/or biomarkers distinguishing UPS and LMS will definitely help the pathologist to precisely diagnose those patients. However, in the past, UPS was reported to be indistinguishable with LMS by genomic profiles. Methods and Results: In this study, 3’ end RNA Sequencing (3SEQ) was used to expression profile 6 UPS and 99 LMS cases. Overall, UPS was undistinguished with LMS by 3SEQ data, however, when we stratified LMS into three subtypes, UPS was shown to share similar expression pattern with Subtype II LMS, but had distinct molecular expression patterns with Subtype I and Subtype III LMS. Additional Immunohistochemistry staining by using LMS Subtype I and Subtype II markers validated that UPSs were positive for Subtype II marker ARL4C, but negative for Subtype I marker LMOD1. Furthermore, CD4 was shown to be significantly more highly expressed in UPS than LMS in both mRNA and protein levels. Conclusion: This study first reported that UPS shared similar gene expression pattern with subtype II LMS and UPS recapitulated the expression profiles of subtype II LMS. In this study, 3’ end RNA Sequencing (3SEQ) was used to expression profile 6 UPS and 99 LMS cases. In order to explore the molecular differences between UPS and LMS, We analyzed the expression data by SAMseq to identify the genes which were significantly differently expressed between UPS and LMS, between UPS and each LMS subtype.