Whole genome shallow sequencing of ctDNA from leiomyosarcoma patients
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ABSTRACT:
PROVIDER: PRJEB22076 | ENA |
REPOSITORIES: ENA
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Project description:Whole genome sequencing by Complete Genomics was performed on one primary leiomyosarcoma samples.
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Project description:The Oxford Nanopore (ONT) platform provides portable and rapid genome sequencing, and its ability to natively profile DNA methylation without complex sample processing is attractive for clinical sequencing. We recently demonstrated ONT shallow whole-genome sequencing to detect copy number alterations (CNA) from the circulating tumor DNA (ctDNA) of cancer patients. Here, we show that cell-type and cancer-specific methylation changes can also be detected, as well as cancer-associated fragmentation signatures. This feasibility study suggests that ONT shallow WGS could be a powerful tool for liquid biopsy, especially real-time medical applications.
2022-04-27 | GSE185307 | GEO
Project description:Background: Leiomyosarcomas are among the most common histological types of soft tissue sarcoma (STS), with no effective treatment available for advanced patients. Lung metastasis, the most common site of distant metastasis, is the primary prognostic factor. We analysed the immune environment targeting lung metastasis of STS to explore new targets for immunotherapy. Methods: We analysed the immune environment of primary and lung metastases in 38 patients with STS using immunohistochemistry. Next, we performed gene expression analyses on primary and lung metastatic tissues from six patients with leiomyosarcoma. Using human leiomyosarcoma cell lines, the effects of the identified genes on immune cells were assessed in vitro. Results: Immunohistochemistry showed a significant decrease in CD8⁺ cells in the lung metastases of leiomyosarcoma. Among the genes upregulated in lung metastases, epithelial cellular adhesion molecule (EPCAM) showed the strongest negative correlation with the number of CD8⁺ cells. Transwell assay results showed that the migration of CD8⁺ T cells was significantly increased in the conditioned media obtained after inhibition or knock down of EPCAM. Conclusions: EPCAM was upregulated in lung metastases of leiomyosarcoma, suggesting inhibition of CD8⁺ T cell migration. Our findings suggest that EPCAM could serve as a potential novel therapeutic target for leiomyosarcoma.
2023-12-22 | GSE250089 | GEO
Project description:Objective: The objective of this study was to estimate the accuracy of transcriptome-based classifier in differential diagnosis of uterine leiomyoma and leiomyosarcoma. Methods: We manually selected 114 normal uterine tissue and 31 leiomyosarcoma samples from publicly available transcriptome data in UCSC Xena as training/validation sets. We developed pre-processing procedure and gene selection method to sensitively find genes of larger variance in leiomyosarcoma than normal uterine tissues. Through our method, twenty genes were selected to build transcriptome-based classifier. The prediction accuracies of deep feedforward neural network (DNN), support vector machine (SVM), Random Forest (RF), and Gradient Boosting (GB) models were examined. We interpret the biological functionality of selected genes via network-based analysis using Gene-Mania. To validate the performance of trained model, we additionally collected 35 clinical samples of leiomyosarcoma and leiomyoma as a test set (18 + 17 as 1st and 2nd test sets). Results: We discovered genes expressed in a highly variable way in leiomyosarcoma while these genes are expressed in a conserved way in normal uterine samples. These genes were mainly associated with DNA replication, cell cycle, and DNA damage checkpoint. Among evaluated machine learning classifiers, the DNN had the highest accuracy and average AUC value in training data set. As gene selection and model training were made in leiomyosarcoma and uterine normal tissue, proving discriminant of ability between leiomyosarcoma and leiomyoma is necessary. Thus, further validation of trained model was conducted in newly collected clinical samples of leiomyosarcoma and leiomyoma. The DNN classifier performed AUC of 0.917 and 0.914 supporting that the selected genes in conjunction with DNN classifier are well discriminating the difference between leiomyosarcoma and leiomyoma in clinical sample. Conclusion: The transcriptome-based classifier accurately distinguished uterine leiomyoma from leiomyosarcoma.
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Project description:To develop diagnostic and prognostic biomarkers, we compared methylation profiles of HCC tissues and normal blood by analyzing 485,000 CpG markers and identified a HCC enriched methylation marker panel compared to that of normal blood. We found there was a highly correlation of methylation profiles between DNA from HCC cancer tissue and matched plasma ctDNA within the same patient. We then selected 10 markers from this panel and created a combined diagnosis score (cd-score) which showed high diagnostic specificity and sensitivity in both a training cohort and an independent validation cohort. We also showed the cd-score correlate highly with tumor load, treatment response and stage and is superior to that by AFP. We also showed the cd-score correlate highly with tumor load, treatment response and stage and is superior to that by AFP. Additional, we generated 8 markers from unicox and LASSO-cox analysis and created a combined prognosis score (cp-score) which could predict prognosis and survival. Together, these findings demonstrated the utility of ctDNA methylation markers in the diagnosis, treatment evaluation and prognosis of HCC.
2019-01-01 | GSE93203 | GEO
Project description:Uterine leiomyosarcoma (ULMS) is a poorly understood gynecologic cancer with few effective treatments. This study explores molecular events involved in ULMS with the goal of identifying strategies. Genome-wide transcriptional profiling were used to compare clinically well-annotated specimens of myometrium, leiomyoma and leiomyosarcoma.
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