135. Tumor deconvolution using comprehensive single-cell RNA sequencing cell type signatures

Aly Abdelkareem

Bianca Xue

Bianca Xue is a 4th year PhD candidate in Biomolecular Engineering and Bioinformatics department in UC Santa Cruz, Stuart lab. Her research focuses on single cell RNA-seq and cancer biology, with her thesis work focus on tumor microenvironment deconvolution using comprehensive cell type profile. She is also working with TCGA groups and contributed various analyses.

Bianca has industrial experience in early cancer detection field, she worked on extracting important cancer biomarkers to build machine learning models with multi-omics data in leading early cancer detection companies.


Yuanqing (Bianca) Xue, Verena Friedl

University of California, Santa Cruz, Santa Cruz, CA, USA

There is ever-growing evidence that the cell types present in a tumor microenvironment influence the outcomes for cancer patients. In recent years since single-cell sequencing became available, the characterization of the various cell types in the human body improved immensely. The growing number of public single-cell sequencing datasets provides a more accurate and comprehensive definition of the human cell type repertoire. We created a highly comprehensive set of human cell type signatures derived from single-cell RNA-Sequencing experiments. We successfully validated the integration of the different datasets and mitigation of batch effects from technological differences. By using these cell-type signatures for deconvolution of bulk tumor samples, we are able to stratify multiple cancer types into groups with distinct survival outcomes that do not recapitulate known cancer subtypes. Our cell-type signatures also capture the tissue-specific signals to cancer patients. Based on the microenvironment similarity in cancer samples, we will present a pan-can tumor microenvironment map that enables intuitive and interactive browsing of TCGA. Thus, these comprehensive human cell type signatures may offer a new measure of classifying tumors to inform treatment decisions in the clinic.