Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Stratification of Leiomyosarcoma molecular subtypes by 3' end RNA-sequencing: Toward precision medicine

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

SUBMITTER: Xiangqian Guo

PROVIDER: E-GEOD-45510 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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]

PMID: 25896974

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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.

Project description:Background: Intrinsic glioma subtypes (IGS) are molecularly similar tumors that can be identified based on unsupervised gene-expression analysis. Here, we have evaluated the clinical relevance of these subtypes within EORTC26951, a randomized phase III clinical trial investigating adjuvant procarbazine, CCNU (lomustine) and vincristine (PCV) chemotherapy in anaplastic oligodendroglial tumors. Our study is the first to include gene-expression profiles of formalin-fixed and paraffin-embedded (FFPE) clinical trial samples. Methods: Gene-expression profiling was performed in 140 samples: 47 fresh frozen and 93 FFPE, on HU133_Plus_2.0 and HuEx_1.0_st arrays (Affymetrix), respectively. Results: All previously identified six intrinsic glioma subtypes are present in EORTC26951. This confirms that different molecular subtypes are present within a well-defined histological subtype. Intrinsic subtypes are highly prognostic for overall- (OS) and progression-free survival (PFS). They are prognostic for PFS independent of clinical (age, performance, tumor location), molecular (1p19qLOH, IDH1 mutation, MGMT methylation) and histological parameters. Combining known molecular (1p19LOH, IDH1) prognostic parameters with intrinsic subtypes improves outcome prediction (Proportion of Explained Variation 30% v 23%). Specific genetic changes (IDH1, 1p19qLOH and EGFR amplification) segregate into different subtypes. We identified one subtype, IGS-9 (characterized by a high percentage of 1p19qLOH and IDH1 mutations), that especially benefits from PCV chemotherapy. Median OS in this subtype was 5.5 years after radiotherapy (RT) alone v 12.8 years after RT/PCV; P=0.0349; HR 2.18, 95% CI [1.06, 4.50]. Conclusion: Intrinsic subtypes are highly prognostic in EORTC26951 and improve outcome prediction when combined with other prognostic factors. Tumors assigned to IGS-9 benefit from adjuvant PCV A total of 140 samples were included in this study, profiles of 95 were FFPE derived samples run on exon arrays. 12 samples (part 2) can also be found in GSE16011 (PMID 19920198 and 16357140), and 6 from Oncomine (part 3).

Dataset Information

Stratification of Leiomyosarcoma molecular subtypes by 3' end RNA-sequencing: Toward precision medicine

Publications

Clinically Relevant Molecular Subtypes in Leiomyosarcoma.

Similar Datasets

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