Genetic Heterogeneity in Oropharyngeal Cancer Revealed by Single-Cell RNA Sequencing

Introduction

Oropharyngeal squamous cell carcinoma (OPSCC) is a heterogenous tumour type due to the high mutation rate in HPV- and the process of viral integration in HPV+ tumours. This heterogeneity contributes to drug resistance and tumour progression. Single-cell RNA sequencing (scRNASeq) allows analysis of tumours with an unprecedented resolution, enabling identification of small but biologically significant populations of cancer and stromal cells that have an impact on prognosis and drug sensitivity.

Our aim was to prove the feasibility of large-scale scRNASeq in OPSCC through characterising these subpopulations and their functions in a large number of patients with the end goal of improved patient stratification to avoid overtreatment as well as identifying new treatment targets.

Materials and Methods

16 patients with OPSCC, 3 HPV- and 13 HPV16+, undergoing curative surgery 2018-19 were included in the study. Fresh tumour samples (and adjacent normal samples in three patients) were dissociated into a single-cell suspension. Cells were barcoded using the Chromium 10x system, followed by Illumina sequencing. The generated sequences were aligned to the human transcriptome as well as the transcriptomes of HPV 16, 18, 31, 33 and 35.

Cells were clustered and their gene expression patterns examined to define the major cell types. Cancer cells were defined by the presence of copy number aberrations (CNA) inferred through mRNA expression across chromosomal regions. Gene expression patterns recurring in cell populations from multiple patients were combined into metaprograms representing biological functions.

Results and Discussion

More than 60000 cells were identified and could be classified according to cell type. In cancer cells, metaprograms representing senescence, proliferation, epithelial-mesenchymal transition and stemness were found. Clonal tumour evolution could be traced through identifying cells with differing CNA in the same tumour.

Particularly interesting were the facts that one tumour classified as HPV+ by pathologists due to p16 expression could be reclassified as HPV- due to absence of HPV transcripts, while another tumour showed two highly distinct CNA patterns, suggesting two biologically unrelated tumours in the same location. We also found both HPV transcripts and CNA in epithelial cells from a pathologically normal sample taken beyond the surgical margin, suggesting cancer spread beyond the margin.

Conclusion

This is, to date, the largest single-cell transcriptomic study of head and neck cancer. We provide a comprehensive map of the tumour ecosystem and identify distinct subpopulations of biological significance in all the major cell types that make up the tumour. Our re-classification of a p16-positive tumour, tumour evolution tracing and identification of cancer cell populations with different biological functions, as well as finding cancer cells beyond the surgical margins all highlight the potential for single-cell technology to be used in pathology for improved patient stratification and treatment selection.