Project description:Purpose: To reveal multi-omics features and disease subtypes associated with brain metastasis outcomes following craniotomy Methods: We executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization. Results: We discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference. Conclusions: A multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease.
Project description:As Asians are underrepresented across many omics databases limiting the potential of precision medicine of the global population. It is thus important for multi-omics derived quantitative trait loci (QTLs) to fill the knowledge gap of complex traits in the Asian ancestry. Integrating omics data from genomics and epigenomics (including DNA methylation and RNA-seq from blood), we developed iMOMdb, an open-acesss database to enhance disease prediction models and precision medicine outcomes in Asian pregnant women.
Project description:Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. These emerging multi-omics techniques help the investigation of cell-type resolved gene regulatory mechanisms. Here, we developed in situ SHERRY after ATAC-seq (ISSAAC-seq), a highly sensitive and flexible single cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single cell. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from thousands of nuclei from the mouse cerebral cortex, we uncovered major and rare cell types together with their cell-type specific regulatory elements and expression profiles. Finally, we revealed distinct dynamics and relationships of transcription and chromatin accessibility during an oligodendrocyte maturation trajectory.
Project description:Multi-omics molecular profiling was performed on post-radical prostatectomy material from a cohort of 132 patients with localized prostate adenocarcinoma. Unsupervised classification techniques were used to build a comprehensive classification of prostate tumours based on three molecular levels: DNA copy number, DNA methylation, and mRNA expression.
Project description:Multi-omics molecular profiling was performed on post-radical prostatectomy material from a cohort of 132 patients with localized prostate adenocarcinoma. Unsupervised classification techniques were used to build a comprehensive classification of prostate tumours based on three molecular levels: DNA copy number, DNA methylation, and mRNA expression.
Project description:Multi-omics molecular profiling was performed on post-radical prostatectomy material from a cohort of 132 patients with localized prostate adenocarcinoma. Unsupervised classification techniques were used to build a comprehensive classification of prostate tumours based on three molecular levels: DNA copy number, DNA methylation, and mRNA expression.
Project description:Multi-omics molecular profiling was performed on post-radical prostatectomy material from a cohort of 132 patients with localized prostate adenocarcinoma. Unsupervised classification techniques were used to build a comprehensive classification of prostate tumours based on three molecular levels: DNA copy number, DNA methylation, and mRNA expression.