Different molecular pathways along with pathogenesis of three subtypes of Leiomyosarcoma
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Xiangqian Guo
PROVIDER: E-GEOD-54511 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Publications
Clinically Relevant Molecular Subtypes in Leiomyosarcoma.
Guo Xiangqian X Jo Vickie Y VY Mills Anne M AM Zhu Shirley X SX Lee Cheng-Han CH Espinosa Inigo I Nucci Marisa R MR Varma Sushama S Forgó Erna E Hastie Trevor T Anderson Sharon S Ganjoo Kristen K Beck Andrew H AH West Robert B RB Fletcher Christopher D CD van de Rijn Matt M
Clinical cancer research : an official journal of the American Association for Cancer Research 20150420 15
<h4>Purpose</h4>Leiomyosarcoma is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for leiomyosarcoma. Recognition of different molecular subtypes is necessary to evaluate novel therapeutic options. In a previous study on 51 leiomyosarcomas, we identified three molecular subtypes in leiomyosarcoma. The current study was performed to determine whether the existence of these subtypes could be confirm ...[more]
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Project description:Leiomyosarcoma (LMS) is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for LMS. We demonstrate the existence of 3 molecular subtypes in a cohort of 99 cases and an independent cohort of 82 LMS. Two new FFPE tissue-compatible diagnostic immunohistochemical markers are identified: LMOD1 for subtype I LMS and ARL4C for subtype II LMS. Subtype I and subtype II LMS are associated with good and poor prognosis, respectively. The LMS subtypes show significant differences in expression levels for genes for which novel targeted therapies are being developed. Gene expression profiling was performed by 3' End RNA Sequencing (3SEQ), a next generation sequencing approach that does not rely on frozen tissue but can be performed on archival FFPE tissue. Samples included 99 LMS, 6 Undifferentiated Pleomorphic Sarcomas (UPS), 3 leiomyomas, 4 normal myometrium samples, and 1 case of Lymphangioleiomyomatosis (LAM). This study only includes the 99 LMS Samples. After gene expression levels were quantified by 3SEQ analysis pipeline, Consensus Clustering with bootstrap method was used to determine that the dataset contained three robust subtypes, and Silhouette analysis was performed to validate the subtype assignments. Two class SAM analysis (Significance Analysis of Microarrays) was performed to identify genes expressed differentially between each subtype of LMS with FDR of 0.05. Immunohistochemical staining was used to validate the potential diagnostic and prognostic markers from 3SEQ data on a tissue microarray.
2015-05-19 | E-GEOD-45510 | biostudies-arrayexpress
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.
2015-05-19 | E-GEOD-53844 | biostudies-arrayexpress
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2013-11-21 | E-GEOD-42948 | biostudies-arrayexpress
Project description:Leiomyosarcoma (LMS) is a malignant neoplasm with smooth muscle differentiation, and there are three molecular subtypes of LMS which have been defined previously by our lab. To further validate these subtypes and identify potential therapeutic targets in each subtype, we profiled the LMS cases from each subtype with RNA-Seq technology.
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Project description:Molecular characterization of tumors has been critical for identifying important genes in cancer biology and for improving tumor classification and diagnosis. Long non-coding RNAs (lncRNAs), as a new, relatively unstudied class of transcripts, provide a rich opportunity to identify both functional drivers and cancer-type specific biomarkers. However, despite the potential importance of lncRNAs to the cancer field, no comprehensive survey of lncRNA expression across various cancers has been reported. We used 3'-End Sequencing for Expression Quantification (3SEQ) to quantify transcript abundance across 64 solid tumors representing 17 diagnostic subtypes of adenocarcinomas, squamous cell carcinomas, and sarcomas. We identified hundreds of transcripts from among the known 1,065 lncRNAs surveyed that show variability in transcript levels between the tumor types, and therefore, make potential biomarker candidates. We discovered 1,071 novel intergenic transcribed regions and demonstrate that these show similar patterns of variability between tumor types. We find that many of these differentially expressed cancer transcripts are also expressed in normal tissues. One such novel transcript specifically expressed in breast tissue was further evaluated using RNA in situ hybridization on a panel of breast tumors and shown to correlate with low tumor grade and estrogen receptor expression, thereby representing a potentially important new breast cancer biomarker. This study provides the first large survey of lncRNA expression within a panel of solid cancers and also identifies a number of novel transcribed regions differentially expressed across distinct cancer types that represent candidate biomarkers for future research. 3SEQ was performed on 64 formalin-fixed, paraffin-embedded (FFPE) human tumors representing 17 diagnostic cancer subtypes. Duplicate libraries were prepared for two of the tumors (ESS STT5520 and LMS STT516). 3SEQ was also performed on 27 normal human tissue samples. RNA-Seq was performed on 6 breast cancer cell lines for the examination of the breast-specific transcript on chr10. Series supplementary files: 'GSE28866_raw_counts_54511_peaks_cancer_and_normal.txt': Raw_counts: Total 3SEQ reads in each peak for each sample. File includes read counts for 54,511 peaks for the 66 cancer libraries and the 27 normal libraries. 'GSE28866_36048_normalized_peaks_cancer_and_normal.txt': Normalized_peaks: Normalized 3SEQ expression data for the 36,048 filtered peaks for the 66 cancer libraries and the 27 normal libraries. Peaks were classified as coding, lncRNA (Rinn, Bartel, Hughes, Gencode_lnc), other known transcripts (ref_known_Gencode_nc, all_mrna, other known), downstream, intron, promoter, or novel_intergenic. Peaks determined to be differentially expressed in one of the 17 cancer subtypes using a series of 2-Class SAM analyses are noted along with the type of cancer showing upregulation. Expression data was normalized using the sequencing depth of each sample by scaling the data using the mean value of each sample. Data was further compressed to reduce outliers by taking the square root of each value.
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Project description:Leiomyosarcoma (LMS) is a malignant neoplasm of smooth muscle and is an aggressive soft tissue tumor, have complex genetic abnormalities and could be defined as three molecular subtypes. Since that the molecular heterogeneity of LMS, the pathogenesis analysis per subtype will be highly necessary and helpful to understand the etiology of this more common sarcoma.
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Project description:Leiomyosarcoma (LMS) is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for LMS. We demonstrate the existence of 3 molecular subtypes in a cohort of 99 cases and an independent cohort of 82 LMS. Two new FFPE tissue-compatible diagnostic immunohistochemical markers are identified: LMOD1 for subtype I LMS and ARL4C for subtype II LMS. Subtype I and subtype II LMS are associated with good and poor prognosis, respectively. The LMS subtypes show significant differences in expression levels for genes for which novel targeted therapies are being developed.
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Project description:Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We employed integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering demonstrated 3 reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and demonstrated that the molecular subtypes defined by gene-expression showed distinct genomic changes. Tumors from the "muscle-enriched" cluster showed significantly increased copy number changes (p=0.04). Most muscle-enriched cases showed loss at 16q24 which contains FANCA, known to play an important role in DNA repair, and loss at 1p36 which contains PRDM16, whose loss promotes muscle differentiation. Immunohistochemistry was performed on LMS tissue microarrays (n= 377) for five markers with high levels of mRNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP,CFL2, MYLK) and demonstrated significantly correlated expression of the 5 proteins (all pairwise p < 0.005). Expression of the 5 markers was associated with improved disease-specific survival (DSS) in a multivariate Cox regression analysis (p < 0.04). In this analysis that combined gene expression profiling, aCGH and immunohistochemistry, we characterized distinct molecular LMS subtypes, provided insight into their pathogenesis, and identified prognostic biomarkers. This set contains microarray slides from expression studies. VARIABLES: Phenotype: Tumour morphology Age: age at the time of the primary lesion diagnosis (*:Age of diagnosis at the time of tumor resection) Individual Compound Based Treatment: Patient received treatment (CT: exposure to chemotherapy, RT: exposure to radiation therapy) Organism Part: Primary tumour site (UT: uterine origin, ST: soft tissue origin; **:clinically most likely anatomic origin of the tumor) Disease State: Tumour status (P: primary tumor, M: metastatic tumor, LR: local recurrence, N/A: information not available.) Sex/Mating type: M(ale)/F(emale) Experiment type: aCGH or Expression type II A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied.
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Project description:Leiomyosarcoma (LMS) is a mesenchymal neoplasm with complex copy number alterations and characteristic loss of tumor suppressor genes without known recurrent activating mutations. Clinical management of advanced LMS relies on chemotherapy and complementary palliative approaches, and research efforts to date have had limited success identifying clinically actionable biomarkers or targeted therapeutic vulnerabilities. To explore the biological underpinning of LMS, we evaluated gene expression patterns of this disease in comparison to diverse sarcomas, non-mesenchymal neoplasms and normal myogenic tissues. We identified a recurrent gene expression program in LMS, with evidence of oncogenic evolution of an underlying smooth muscle lineage-derived program characterized by activation of E2F1 and downstream effectors. Recurrently amplified or highly expressed genes in LMS were identified that may represent therapeutic vulnerabilities, including targeting of IGF1R and retinoid signaling pathways. Though the majority of expressed transcripts were conserved across LMS samples, three separate subtypes were identified that were enriched for muscle-associated transcripts (conventional-LMS), immune markers (inflammatory-LMS) or a uterine-like gene expression program (uterogenic-LMS). Each of these subtypes express a unique subset of genes that may be useful in the managemeny of LMS: IGF1R was enriched in conventional LMS with prognostic implications, worse disease-specific survival was observed in inflammatory LMS, and prolactin was elaborated by uterogenic LMS and may represent a disease biomarker. These results extend our understanding of LMS biology and identify several strategies and challenges for further translational investigation.
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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.
2015-05-19 | GSE53844 | GEO