Unknown,Transcriptomics,Genomics,Proteomics

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Two New Stromal Signatures Stratify Breast Cancers with Different Prognosis

ABSTRACT: Purpose: Multiple studies from last decades have shown that the microenvironment of carcinomas plays an important role in the initiation, progression and metastasis of cancer. Our group has previously identified novel cancer stroma gene expression signatures associated with outcome differences in breast cancer by gene expression profiling of two tumors of fibroblasts as surrogates for physiologic stromal expression patterns. The aim of this study is to find additional new types of tumor stroma gene expression patterns. Results: 53 tumors were sequenced by 3SEQ with an average of 29 million reads per sample. Both the elastofibroma (EF) and fibroma of tendon sheath (FOTS) gene signatures demonstrated robust outcome results for survival in the four breast cancer datasets. The EF signature positive breast cancers (20-33% of the cohort) demonstrated significantly better outcome for survival. In contrast, the FOTS signature positive breast cancers (11-35% of the cohort) had a worse outcome. The combined stromal signatures of EF, FOTS, and our previously identified DTF, and CSF1 signatures characterize, in part, the stromal expression profile for the tumor microenvironment for between 74%-90% of all breast cancers. Conclusions: We defined and validated two new stromal signatures in breast cancer (EF and FOTS), which are significantly associated with prognosis. Gene expression profiling by 3SEQ was performed on 8 additional types of fibrous tumors, to identify different fibrous tumor specific gene expression signatures. We then determined the significance of the fibrous tumor gene signatures in four publically available breast cancer datasets (GSE1456, GSE4922, GSE3494, NKI Dataset).

ORGANISM(S): Homo sapiens

SUBMITTER: Xiangqian Guo

PROVIDER: E-GEOD-42948 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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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Two New Stromal Signatures Stratify Breast Cancers with Different Prognosis

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