ABSTRACT
A challenge with studying cancer transcriptomes is in distilling the wealth of information down into manageable portions of information. In this resource, we develop an approach that creates and assembles cancer type-specific gene expression modules into flexible barcodes, allowing for adaptation to a wide variety of uses. Specifically, we propose that modules derived organically from high-quality gold standards such as The Cancer Genome Atlas (TCGA) can accurately capture and describe functionally related genes that are relevant to specific cancer types. We show that such modules can: (1) uncover novel gene relationships and nominate new functional memberships, (2) improve and speed up analysis of smaller or lower-resolution datasets, (3) re-create and expand known cancer subtyping schemes, (4) act as a "decoder" to bridge seemingly disparate established gene signatures, and (5) efficiently apply single-cell RNA sequencing information to other datasets. Moreover, such modules can be used in conjunction with native spreadsheet program commands to create a powerful and rapid approach to hypothesis generation and testing that is readily accessible to non-bioinformaticians. Finally, we provide tools for users to create and interpret their own modules. Overall, the flexible modular nature of the proposed barcoding provides a user-friendly approach to rapidly decoding transcriptome-wide data for research or, potentially, clinical uses.
ABSTRACT
Cancer stem cells (CSC) play a critical role in metastasis, relapse, and therapy resistance in colorectal cancer. While characterization of the normal lineage of cell development in the intestine has led to the identification of many genes involved in the induction and maintenance of pluripotency, recent studies suggest significant heterogeneity in CSC populations. Moreover, while many canonical colorectal cancer CSC marker genes have been identified, the ability to use these classical markers to annotate stemness at the single-cell level is limited. In this study, we performed single-cell RNA sequencing on a cohort of 6 primary colon, 9 liver metastatic tumors, and 11 normal (nontumor) controls to identify colorectal CSCs at the single-cell level. Finding poor alignment of the 11 genes most used to identify colorectal CSC, we instead extracted a single-cell stemness signature (SCS_sig) that robustly identified "gold-standard" colorectal CSCs that expressed all marker genes. Using this SCS_sig to quantify stemness, we found that while normal epithelial cells show a bimodal distribution, indicating distinct stem and differentiated states, in tumor epithelial cells stemness is a continuum, suggesting greater plasticity in these cells. The SCS_sig score was quite variable between different tumors, reflective of the known transcriptomic heterogeneity of CRC. Notably, patients with higher SCS_sig scores had significantly shorter disease-free survival time after curative intent surgical resection, suggesting stemness is associated with relapse. IMPLICATIONS: This study reveals significant heterogeneity of expression of genes commonly used to identify colorectal CSCs, and identifies a novel stemness signature to identify these cells from scRNA-seq data.