Webinar | Analysis of Single-Cell Transcriptomes in Colorectal Cancer

Shyam Prabhakar explores signatures of patient survival and EMT in colorectal tumors

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Intra-tumor heterogeneity is a major obstacle to cancer treatment. Existing single-cell studies of intra-tumor heterogeneity have largely focused on DNA mutations; functional heterogeneity is thus well understood.

We performed an unbiased analysis of functional diversity in colorectal cancer cells and their microenvironment using RNA-seq profiling of over 1,500 unsorted single cells from 11 primary tumors and matched normal mucosa (NM). To robustly interpret single-cell transcriptomes, we developed novel algorithms for normalizing expression estimates (pQ), clustering cells (RCA) and identifying differentially expressed genes (NODES). RCA identified 6 major cell types and multiple subtypes within colorectal samples.

Single-cell differential expression analysis yielded results that were substantially different from bulk-sample analysis. Notably, epithelial-mesenchymal transition (EMT) genes were upregulated in tumor fibroblasts, but not in cancer cells. Though cancer cells generally lay on a continuum of transcriptomic states, a small "tail" of cells showed exceptionally high stemness signatures.

Importantly, colorectal tumors previously assigned to a single subtype based on bulk transcriptomics could be divided into subgroups with divergent survival probability based on single-cell signatures, thus underscoring the prognostic value of our approach. Going forward, we see single-cell transcriptomics becoming an essential tool for cancer biology, biomarker discovery and personalized oncology.

About the presenter:

Shyam Prabhakar

Shyam Prabhakar, PhD
Associate Director/Integrative Genomics and Group Leader
Genome Institute of Singapore

Shyam Prabhakar joined the Genome Institute of Singapore in 2007, where his group uses single-cell RNA-seq, cohort-scale ChIP-seq and other high-throughput assays to uncover molecular mechanisms and diagnostic or prognostic markers of human diseases. In parallel, the group develops computational algorithms for deriving biological insights from functional genomics data.