Single-Cell Deconvolution of Head and Neck Squamous Cell Carcinoma

Abstract

Complexities in cell-type composition have rightfully led to skepticism and caution in the interpretation of bulk transcriptomic analyses. Recent studies have shown that deconvolution algorithms can be utilized to computationally estimate cell-type proportions from the gene expression data of bulk blood samples, but their performance when applied to tumor tissues, including those from head and neck, remains poorly characterized. Here, we use single-cell data (~6000 single cells) collected from 21 head and neck squamous cell carcinoma (HNSCC) samples to generate cell-type-specific gene expression signatures. We leverage bulk RNA-seq data from >500 HNSCC samples profiled by The Cancer Genome Atlas (TCGA), and using single-cell data as a reference, apply two newly developed deconvolution algorithms (CIBERSORTx and MuSiC) to the bulk transcriptome data to quantitatively estimate cell-type proportions for each tumor in TCGA. We show that these two algorithms produce similar estimates of constituent/major cell-type proportions and that a high T-cell fraction correlates with improved survival. By further characterizing T-cell subpopulations, we identify that regulatory T-cells (Tregs) were the major contributor to this improved survival. Lastly, we assessed gene expression, specifically in the Treg population, and found that TNFRSF4 (Tumor Necrosis Factor Receptor Superfamily Member 4) was differentially expressed in the core Treg subpopulation. Moreover, higher TNFRSF4 expression was associated with greater survival, suggesting that TNFRSF4 could play a key role in mechanisms underlying the contribution of Treg in HNSCC outcomes.

Identification of Immune-Related Risk Signatures for the Prognostic Prediction in Oral Squamous Cell Carcinoma

“…CTSG was reported as a potential immune-related biomarker in OSCC by a recent study [ 23 ], and it has also been found to be associated with the survival of other cancer types such as soft tissue sarcoma [ 24 ], muscle-invasive bladder cancer [ 25 ], and node-negative breast cancer [ 26 ]. Consistently, TNFRSF4 and PLAU exhibited a potential association with OS in HNSCC [ 27 , 28 ]. Based on our collected data, LCORL was characterized as a marker gene of γδ T cell and encoded a transcription factor, according to a previous study [ 29 ].…”

Single‐cell RNA sequencing technologies and applications: A brief overview

“…So far, there are more advanced approaches reported on alterations of immune cells in tumours such as in lung adenocarcinoma, 111 breast cancer, 112 , 113 head and neck squamous cell carcinoma (HNSCC), 114 nasopharyngeal carcinoma, 115 head and neck cancer, 116 pancreatic cancer. 117 Non‐tumour cells have been investigated starting from the first study on metastatic melanoma where almost 5000 cells both malignant and non‐malignant were analyzed, 105 following the study led by Lambrechts and colleagues where they identified 52 subtypes of stromal cells, including new subsets of cells.…”

Cell type ontologies of the Human Cell Atlas

“…This link, in a broader sense, represents cross-community research whereby a common cell-type reference can be referred. For example, a well-defined cell-type classification of human head and neck tumours, which covered major immune and non-immune cell populations, was utilized as the reference to interrogate the cellular signals contributing to bulk samples of head and neck squamous cell carcinoma from The Cancer Genome Atlas (TCGA) 45 . This analysis revealed the association of tumour-infiltrating regulatory T cells with improved survival in head and neck cancer 45 .…”