Single-cell sequencing and its applications in head and neck cancer

Abstract

Head and neck squamous cell carcinoma (HNSCC), like many tumors, is characterized by significant intra-tumoral heterogeneity, namely transcriptional, genetic, and epigenetic differences that define distinct cellular subpopulations. While it has been established that intra-tumoral heterogeneity may have prognostic significance in HNSCC, we are only beginning to describe and define such heterogeneity at a cellular resolution. Recent advances in single-cell sequencing technologies have been critical in this regard, opening new avenues in our understanding of more nuanced tumor biology by identifying distinct cellular subpopulations, dissecting signaling within the tumor microenvironment, and characterizing cellular genomic mutations and copy number aberrations. The combined effect of these insights is likely to be robust and meaningful changes in existing diagnostic and treatment algorithms through the application of novel biomarkers as well as targeted therapeutics. Here, we review single-cell technological and computational advances at the genomic, transcriptomic, and epigenomic levels, and discuss their applications in cancer research and clinical practice, with a specific focus on HNSCC.

Keywords: Diagnostic biomarker; Head and neck cancer; Immunotherapy; Single cell; Single cell RNA sequencing; Single cell sequencing; Squamous cell carcinoma; Technology development; Tumor heterogeneity.

Figure 1.. Overview of the technologies applied at different clinical sample collections.

WGS: whole genome sequencing; WES: whole exome sequencing; CTC: circulating tumor cell; sc: single cell; PDX: patient-derived xenograft.

Figure 2.. A schematic overview of scRNA-seq analysis workflow.

scRNA-seq data is noisy due to technical and biological factors. The analysis workflow involves (a) demultiplexing the sequencing reads by cell barcode and collapsing on UMI, (b) removing low quality cells (doublets, multiplets or dead cells), (c) aligning trimmed reads and generating count matrix, (d) normalization by scaling factors and imputation for sparse counts, (e) cell level analysis including clustering and trajectory inference and (f) gene level analysis including differential gene expression and regulatory network.

Figure 3.. Schematic illustration of applications of single-cell sequencing to malignant cells.

The applications of single-cell sequencing are numbered as: 1) Identifying cancer stem cell; 2) Charactering Intra-tumor heterogeneity; 3) Charactering tumor microenvironment; 4) Deciphering clonal evolution; 5) Monitoring cancer progress via circulating tumor cells (CTCs); 6) Dissecting cancer metastasis; 7) Interrogating therapy resistance.