Dataset Information

Identification of Novel Fusion Genes in Bone and Soft Tissue Sarcoma and Their Implication in the Generation of a Mouse Model.

ABSTRACT: Fusion genes induced by chromosomal aberrations are common mutations causally associated with bone and soft tissue sarcomas (BSTS). These fusions are usually disease type-specific, and identification of the fusion genes greatly helps in making precise diagnoses and determining therapeutic directions. However, there are limitations in detecting unknown fusion genes or rare fusion variants when using standard fusion gene detection techniques, such as reverse transcription-polymerase chain reaction (RT-PCR) and fluorescence in situ hybridization (FISH). In the present study, we have identified 19 novel fusion genes using target RNA sequencing (RNA-seq) in 55 cases of round or spindle cell sarcomas in which no fusion genes were detected by RT-PCR. Subsequent analysis using Sanger sequencing confirmed that seven out of 19 novel fusion genes would produce functional fusion proteins. Seven fusion genes detected in this study affect signal transduction and are ideal targets of small molecule inhibitors. YWHAE-NTRK3 expression in mouse embryonic mesenchymal cells (eMCs) induced spindle cell sarcoma, and the tumor was sensitive to the TRK inhibitor LOXO-101 both in vitro and in vivo. The combination of target RNA-seq and generation of an ex vivo mouse model expressing novel fusions provides important information both for sarcoma biology and the appropriate diagnosis of BSTS.

SUBMITTER: Teramura Y

PROVIDER: S-EPMC7565474 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Identification of Novel Fusion Genes in Bone and Soft Tissue Sarcoma and Their Implication in the Generation of a Mouse Model.

Teramura Yasuyo Y Tanaka Miwa M Yamazaki Yukari Y Yamashita Kyoko K Takazawa Yutaka Y Ae Keisuke K Matsumoto Seiichi S Nakayama Takayuki T Kaneko Takao T Musha Yoshiro Y Nakamura Takuro T

Cancers 20200819 9

Fusion genes induced by chromosomal aberrations are common mutations causally associated with bone and soft tissue sarcomas (BSTS). These fusions are usually disease type-specific, and identification of the fusion genes greatly helps in making precise diagnoses and determining therapeutic directions. However, there are limitations in detecting unknown fusion genes or rare fusion variants when using standard fusion gene detection techniques, such as reverse transcription-polymerase chain reaction ...[more]

PMID: 32825119

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Project description:BackgroundSoft-tissue sarcomas (STS) constitute a rare group of heterogeneous mesenchymal tumours containing more than 100 histologic subtypes. Here, we investigate whether, and if so, to what extent, skeletal metastases affect the outcome of patients with advanced or metastatic disease.Materials and methodsSelected patients participated in five clinical trials of EORTC-STBSG. Individuals were included if they started treatment with an active drug and had advanced/metastatic STS. The endpoints of interest were overall survival (OS) and progression-free survival (PFS). Univariate and multivariate pooled analyses (after correcting for 12 covariates) were employed with Kaplan-Meier and Cox regression to model the impact of bone metastasis at presentation per treatment line stratified by study. For the subset of patients with bone metastasis, the impact of another metastatic organ site was explored with multivariate Cox regression models.Results565 out of 1034 (54.6%) patients received first-line systemic treatment for locally advanced or metastatic disease. Bone metastases were present in 140 patients (77 first-line, 63 second-line or higher). The unadjusted difference in OS/PFS with or without bone metastasis was statistically significant only for first-line patients. For OS, the adjusted hazard ratios for bone metastasis presence were 1.33 (95%-CI: 0.99-1.78) and 1.11 (95%-CI: 0.81-1.52) for first-line/second-line or higher treated patients, respectively. Likewise, the adjusted hazard ratios for PFS were 1.31 (95%-CI: 1.00-1.73) and 1.07 (95%-CI: 0.80-1.43). Effects were not statistically significant, despite a trend in first-line patients for both endpoints. Subgroup analyses indicated bone and lymph node metastasis as the most detrimental combination for OS and bone and lung metastasis for PFS.ConclusionsAdult STS patients receiving palliative systemic therapy with bone metastasis carried an overall worse prognosis than STS patients without bone metastases. Skeletal metastasis was detrimental for both OS and PFS, independent of the treatment line. Findings may have implications for the management of these patients.

Project description:BackgroundImmune-related genes (IRGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of IRGs and their clinical significance in soft tissue sarcoma (STS) patients is lacking.MethodsGene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed immune-related genes (DEIRGs) were determined by matching the DEG and ImmPort gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEIRGs was conducted, and associations with prognosis, the tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for progression free survival (PFS), were established and validated in an independent set. Finally, two transcription factor (TF)-IRG regulatory networks were constructed, and a crucial regulatory axis was validated.ResultsIn total, 364 DEIRGs and four clusters were identified. OS, TME scores, five immune checkpoints, and 12 types of immune cells were found to be significantly different among the four clusters. The two prognostic signatures incorporating 20 DEIRGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.879 (95%CI 0.832 ~ 0.926) and 0.825 (95%CI 0.776 ~ 0.874) for the OS and PFS signatures, respectively. Finally, TF-IRG regulatory networks were established, and the MYH11-ADM regulatory axis was verified in three independent datasets.ConclusionThis comprehensive analysis of the IRG landscape in soft tissue sarcoma revealed novel IRGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

Project description:BackgroundVarious fusion strategies (feature-level fusion, matrix-level fusion, and image-level fusion) were used to fuse PET and MR images, which might lead to different feature values and classification performance. The purpose of this study was to measure the classification capability of features extracted using various PET/MR fusion methods in a dataset of soft-tissue sarcoma (STS).MethodsThe retrospective dataset included 51 patients with histologically proven STS. All patients had pre-treatment PET and MR images. The image-level fusion was conducted using discrete wavelet transformation (DWT). During the DWT process, the MR weight was set as 0.1, 0.2, 0.3, 0.4, …, 0.9. And the corresponding PET weight was set as 1- (MR weight). The fused PET/MR images was generated using the inverse DWT. The matrix-level fusion was conducted by fusing the feature calculation matrix during the feature extracting process. The feature-level fusion was conducted by concatenating and averaging the features. We measured the predictive performance of features using univariate analysis and multivariable analysis. The univariate analysis included the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. The multivariable analysis was used to develop the signatures by jointing the maximum relevance minimum redundancy method and multivariable logistic regression. The area under the ROC curve (AUC) value was calculated to evaluate the classification performance.ResultsBy using the univariate analysis, the features extracted using image-level fusion method showed the optimal classification performance. For the multivariable analysis, the signatures developed using the image-level fusion-based features showed the best performance. For the T1/PET image-level fusion, the signature developed using the MR weight of 0.1 showed the optimal performance (0.9524(95% confidence interval (CI), 0.8413-0.9999)). For the T2/PET image-level fusion, the signature developed using the MR weight of 0.3 showed the optimal performance (0.9048(95%CI, 0.7356-0.9999)).ConclusionsFor the fusion of PET/MR images in patients with STS, the signatures developed using the image-level fusion-based features showed the optimal classification performance than the signatures developed using the feature-level fusion and matrix-level fusion-based features, as well as the single modality features. The image-level fusion method was more recommended to fuse PET/MR images in future radiomics studies.

Dataset Information

Identification of Novel Fusion Genes in Bone and Soft Tissue Sarcoma and Their Implication in the Generation of a Mouse Model.

Publications

Identification of Novel Fusion Genes in Bone and Soft Tissue Sarcoma and Their Implication in the Generation of a Mouse Model.

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