At EMBL, experts from institutes throughout the world speak on a wide range of scientific and technical topics. Abstract Characterizing and understanding drivers of cancer transcriptional and epigenetic heterogeneity is key to advancing personalized medicine and developing effective therapies In this presentation I will discuss our recent work on designing a computational methodology to extract gene signatures for distinct transcriptional states of cancer cells from single cell RNA sequencing data scRNA Abstract[Characterizing and understanding drivers of cancer transcriptional and epigenetic heterogeneity is key to advancing personalized medicine and developing effective therapies.
In this presentation, I will discuss our recent work on designing a computational methodology to extract gene signatures for distinct transcriptional states of cancer cells from single-cell RNA sequencing data scRNA-seq and show examples of linking intratumor transcriptional heterogeneity to tumor microenvironment and clinical variables. In this context, I will talk about the best-performing existing approaches for the integration of scRNA-seq data from malignant cells across cancer patients and also present our recently developed scRNA-seq-based CancerFoundation model, which, in addition to being capable of data integration across patients, can be used for predicting drug responses.
Finally, I will Abstract [Characterizing and understanding drivers of cancer transcriptional and epigenetic heterogeneity is key to advancing personalized medicine and developing effective therapies. In this context, I will talk about the best-performing existing approaches for the integration of scRNA-seq data from malignant cells across cancer patients and also present our recently developed scRNA-seq-based CancerFoundation model, which , in addition to being capable of data integration across patients, can be used for predicting drug responses.
Finally, I will conclude with our latest efforts in spatial transcriptomics and demonstrate how supervised machine-learning approaches that use spatial information can further resolve the complexity of cancer and provide explainable clinical biomarkers ]. About the speaker [Prof. Her research focuses on developing computational methods for multi-omics data integration to understand the epigenetic and transcriptional plasticity of cancer cells.
She holds a Ph. Throughout her career, Prof. Boeva has made contributions to the field of computational cancer genomics with developing methods for the analysis of DNA sequencing data, bulk and single-cell transcriptomics and epigenomics data, and, recently, spatial transcriptomics and proteomics ].
