UC Santa Cruz

The cellular components of tumors and their microenvironment play pivotal roles intumor progression, patient survival, and the response to cancer treatments. In my doctoral thesis, I describe a new way to extract transcriptional signatures from gene expression data of tumor components and microenvironments and access their influence on cancer patients. Tumor immune infiltration has been studied for years for its high correlation with patients’ survival. Many immune therapies are dependent on the detection and quantification of cell types present in bulk tumors. Bulk tumor microenvironment deconvolution has been largely limited by the low number of cell-type signatures. Leveraging cell type signatures derived from scRNA-seq data provides a broader range of cell types in the detection and quantification of tumor infiltration, therefore helping in developing cancer immunotherapy and targeted cancer therapies. I developed a new method called scBeacon, a novel tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures from public data consortiums while minimizing batch effects. I derived a comprehensive set of human cell-type signatures from Single Cell Expression Atlas and performed TCGA bulk tumors deconvolution analysis using the cell-type signature profile. These cell type estimates enable the detection of a pan-cancer high-risk sample group that is not detected by traditional gene expression analysis. Cancers are traditionally classified into types and subtypes by the organ and cell of origin. However, there are tumor samples that consist of a mixture of cancer subtypes which raise challenges in characterizing the subtype profile for mixture samples. Inspired by the scBeacon deconvolution analysis, I used a similar approach to detect and quantify the subtypes in testicular germ cell cancer mixture samples. In addition, I used single-cell RNA-seq signatures to characterize the major cell types and their differential states in testicular germ cell cancer samples. For glioblastoma, I used predefined cell state marker genes to deconvolute bulk glioblastoma tumors using a hierarchical deconvolution approach. It proved using hierarchical deconvolution addresses the nature of cell type differentiation, which could give a finer resolution for deconvolution, especially when some rare cell types come from a subpopulation of a very similar cell type. This approach is particularly useful for brain tissue deconvolution because of the complexity of cell type lineages in brain development.