Improved detection of tumor suppressor events in single-cell RNA-Seq data

Andrew E Teschendorff,Ning Wang

doi:10.1038/s41525-020-00151-y

Abstract

Tissue-specific transcription factors are frequently inactivated in cancer. To fully dissect the heterogeneity of such tumor suppressor events requires single-cell resolution, yet this is challenging because of the high dropout rate. Here we propose a simple yet effective computational strategy called SCIRA to infer regulatory activity of tissue-specific transcription factors at single-cell resolution and use this tool to identify tumor suppressor events in single-cell RNA-Seq cancer studies. We demonstrate that tissue-specific transcription factors are preferentially inactivated in the corresponding cancer cells, suggesting that these are driver events. For many known or suspected tumor suppressors, SCIRA predicts inactivation in single cancer cells where differential expression does not, indicating that SCIRA improves the sensitivity to detect changes in regulatory activity. We identify NKX2-1 and TBX4 inactivation as early tumor suppressor events in normal non-ciliated lung epithelial cells from smokers. In summary, SCIRA can help chart the heterogeneity of tumor suppressor events at single-cell resolution.

Highlights

Tissue-specific transcription factors (TFs) are required for the differentiated state of cells in a given tissue[1]
To justify inferring TFs and their regulons from bulk-tissue data, we performed a careful power calculation, which revealed that SCIRA has reasonable sensitivity to detect tissue-specific TFs that are highly expressed even if only in a relatively underrepresented cell type within the tissue (“Methods,” Fig. 1b)
In the context of cancer risk prediction, the ability to measure gene expression in single normal cells from individuals exposed to an environmental risk factor could help identify those at most risk of cancer development

Summary

Introduction

Tissue-specific transcription factors (TFs) are required for the differentiated state of cells in a given tissue[1]. They are often inactivated in cancer, which is associated with a lack of differentiation, a well-known cancer hallmark[2,3,4,5,6]. Many of these tissue-specific TFs encode tumor suppressors and their inactivation may constitute driver events that are thought to occur in the earliest stages of carcinogenesis[7,8,9]. To fully characterize cancer heterogeneity requires identifying putative tumor suppressor events at the most fundamental scale, i.e., the single cell[15,16,17,18]

Methods

Results

Conclusion