Transcriptomics

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Computational identification of preneoplastic cells displaying high stemness and risk of cancer progression


ABSTRACT: Evidence points towards the differentiate state of cells being a marker of cancer risk and progression, in line with the cancer-stem-cell hypothesis. Measuring the differentiation state of single cells in a preneoplastic population could thus enable novel strategies for early detection and risk prediction. Here we present a novel computational method called CancerStemID that estimates a stemness index of cells from single-cell RNA-Seq data. We validate CancerStemID in two human esophageal squamous cell carcinoma (ESCC) cohorts, demonstrating how it can identify undifferentiated preneoplastic cells whose transcriptomic state is overrepresented in invasive cancer. We demonstrate decreased differentiation activity of tissue-specific TFs in cancer cells compared to the basal cell-of-origin layer, and that the differentiation state correlates with differential DNA methylation at the promoters of such TFs independently of underlying NOTCH1 and TP53 mutations. In summary, these data support an epigenetic stem-cell model of oncogenesis and highlight a novel computational strategy in which to identify stem-like preneoplastic cells that undergo positive selection.

ORGANISM(S): Homo sapiens

PROVIDER: GSE199654 | GEO | 2022/04/01

REPOSITORIES: GEO

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