Advent of Single Tumor Cell Analysis in Oral Squamous Cell Carcinoma – An Insight

Introduction

Oral squamous cell carcinoma (OSCC) is the common type of head and neck squamous cell carcinoma, associated with high mortality and morbidity. The incidence of lip and oral cavity cancer among all ages and both the genders has been reported to be 377713; the number of deaths attributed towards the same is 177757 across the world (Globocan 2020). In India, lip and oral cavity cancer ranks second with an incidence of 135929 cases (10.3%) among both the sexes and in all ages (Globocan 2020).¹

Various chairside investigations and molecular techniques are in day-to-day use for the effective diagnosis, management and prognostication of oral cancer.² However, there is a need for better understanding of the complex nature of tumor microenvironment and a requirement for tools and techniques to study tumor heterogeneity.

Owing to its presentation at an advanced stage and delay in diagnosis, the management of OSCC poses a challenge. Despite numerous advances in diagnostics and therapeutics, unfavorable outcomes and lower survival rates seem to be unfortunate.³ Hence there is a continuous need for development of novel methods for early diagnosis, therapeutics and better prognostication of oral cancer.

The OSCC is characterized by complex orchestration of genetic and epigenetic changes.⁴ The gene expression studies assist in molecular typing of oral cancer. The traditional studies on cancer gene expression and therapeutic response obtained from whole cell populations yield average values of the differences between individual cells.

The newer tools and technologies (such as ‘omics’, lab-on-chip/microfluidics, Single nucleotide variation analysis (SNVs), single cell RNA sequencing (scRNAseq) using Single Tumor Cell Analysis (STCA) allows identification and analysis of individual cell type within a system, to better comprehend various aspects of carcinogenesis, to characterize cancer and predict the progression of precancer to cancer. This STCA has further facilitated the study of complex tumor microenvironment both in vitro and in vivo. ^5-11 Therefore the present paper aimed to throw light on available evidence on single tumor cell analysis in OSCC, in disease diagnosis, therapeutic decision making and prognosis.

Tumor heterogeneity and its microenvironment

Tumor cells do exhibit diverse phenotypes seen in proliferation, invasion, migration, stemness, epithelial mesenchymal transition (EMT), immune evasion, apoptosis, metabolism, and hypoxia (Hanahan and Weinberg, 2011). This diversity could play a significant role in tumor invasion, progression, metastasis and therapeutic response.¹²

The tumor microenvironment (TME) is composed of a wide variety of cells such as fibroblasts, endothelial cells, and immune cells. The fibroblasts are often transformed into cancer-associated fibroblasts (CAFs) that would either promote or inhibit tumor growth (Kalluri, 2016). These functions of CAFs in TME are in the form of angiogenesis, matrix construction and regulation of immune cells (Elyada et al., 2019; Bartoschek et al., 2018; Lambrechts et al., 2018; Puram et al., 2017). Another cell of interest in TME is the endothelial cell that gets reprogrammed as tumor endothelial cell (TEC). This TEC promotes tumor growth by inducing vasculature and modulating immune cells (Hida et al., 2018).¹³

Within the same tumor, the cells exhibit dysregulation with respect to genetic, epigenetic, transcription and proteomic characteristics. All these could affect the overall tumor progression.

The targeted treatment may fail if the evaluation is based on the average of whole tumor cell analysis as the most oncogenic subtypes could have been missed out from the analysis. This may further compromise the therapeutic efficacy and the overall patient survival.

A possible solution for this could be resolution of the complexity of stromal and immune components of TME by using certain single cell analytical methods.

The scRNA-seq (single-cell RNA Sequencing) and scATAC-seq (Single-cell sequencing assay for transposase-accessible chromatin) have the potential to resolve the plasticity and phenotypic diversity of the tumor cells.^14-15

Discussion

General remarks

Single tumor cell analysis is the study of cancer gene expression on individual tumor cells, isolated from tissues in multi-cellular organisms, providing a much deeper and more specific view of individual cells. STCA has been extensively carried out in carcinomas of breast, prostrate, pancreas, colon and lung; much needs to be known yet in OSCC.

For single tumor cell analysis, isolation of individual cells from the specimen is important. For isolation, mechanical and enzymatic methods are used. The commonly employed methods are mechanical micromanipulation, laser capture microdissection, fluorescence-activated cell sorting and microfluidics. For analysis of isolated single tumor cells, Multi-Omics, Lab-on-chip/Microfluidics, Flow-cytometry, Single Nucleotide Variation (SNV) analysis, RNA & DNA sequencing are in practice.^16-18

The many advantages of STCA enabled it to dominate the conventional genetic studies. The traditional studies are done on whole cell populations and the results are averaged. However, STCA allows identification and analysis of each cell type within a system and aids in the investigation of genome wide changes occurring in them. STCA has facilitated the analysis of cancer regulating phenotypes. There is better unfolding of tumor heterogeneity with STCA by pin-pointing the driver mutations responsible for tumor onset, metastasis or progression. Further, STCA has enabled deeper understanding of tumor micro-environment with the use of many amplification methods. In addition, STCA has facilitated the isolation and identification of Cancer Stem Cells (CSCs) and has also enabled the detection of EMT markers on Circulating Tumor Cells (CTCs) in patients associated with metastatic disease.

Thus, STCA is a promising tool for the investigation of genome wide changes in carcinogenesis phenomenon, heading the way towards improved diagnosis, biomarker development, better treatment outcomes and prognosis in oncology.^19-23

Single Tumor Cell Analysis in Oral Squamous Cell Carcinoma

Evidence from animal models

An animal research (Huang et al., 2020) was intended to study the sequential events of oral tongue cell carcinogenesis with minimal individual heterogeneity. In this study, 4-nitroquinoline 1-oxide and arecoline were used to induce oral tongue carcinogenesis in experimental mice. With continuous administration of the carcinogens, a white lesion suggestive of oral potentially malignant disorder (OPMD) was noticed on the tongue which eventually turned into invasive carcinoma. Through single-cell RNA sequencing (scRNA-seq), the key factors for gene expression controlling the carcinogenesis were recognized. The lesions on the tongue were analyzed from 16 – and 29 – week experimental groups and compared with that of control group. Further, the cell subtypes were investigated through gene set enrichment analysis. Seventeen cell subtypes that were implied in carcinogenesis were recognized; the seventh (stem cell) and ninth (keratinocyte) oncogenic subtypes were observed to be most importantly involved in MYC related pathway. The association of MYC_targets_v1 pathway and the target genes was confirmed by cisplatin-resistant nasopharyngeal carcinoma cell lines (HONE1-CIS6) with significant overexpression of RANBP1, MCM5 and HSP90AB1. This study indicated the utilization of the cell subtype biomarkers of oral carcinogenesis to predict the risk of malignant transformation of OPMDs and also as therapeutic targets. It highlighted the importance of single tumor cell analysis in oral cancer research.⁸

Evidence from human tissue samples and cell lines

While exploring the heterogeneity of tumor microenvironment, Chen and team (March 2021) had investigated the intricacy of infiltrated T-cell subpopulations in oral squamous cell carcinoma. The scRNA-seq was performed on Fluorescence Activated Cell Sorting (FACS)-sorted T cells of tumor and adjacent normal tissues. A greater percentage of CD8+ T cells was found in the tumors (70.6%) when compared to their paired adjacent normal tissues (52.4%). The higher affinity of CD8+ T cells for tumors in the microenvironment was attributed to the stimulation triggered by tumor antigens. A reduced CD4 to CD8 ratio (0.91 vs 0.42) had indicated intense antigen-experienced antitumor immune response. The tumor progression was indicated based on the CD8+ T cells in the tumor microenvironment. The functional and molecular differences between the tumor-infiltrating T cells and normal tissue-resident T cells could be dissected by single-cell technologies in high-throughput manner. Thus, the analysis of tumor-infiltrating lymphocytes is a crucial component evolved in the progress of treatment planning of OSCC. The use of single tumor cell analysis has been well appreciated in the current study.²⁴

STCA in circulating tumor cell analysis

The analysis of circulating tumor cells (CTCs) in cancer is more of prognostic significance. The CTC assay in oral squamous cell carcinoma signifies metastatic disease and also helps in planning precision medicine. There are various tools and techniques available to study and monitor circulating tumor cells in oral / head and neck carcinoma. In addition to determining the overall patient survival rate, the CTC analysis helps us to understand inter- and intra-tumoral heterogeneity. In this aspect, the isolation devices used for CTC analysis would combine an efficient enrichment of tumor cells. This would further facilitate the real-time monitoring of the disease by the analysis of single tumor cells. Thus, STCA of CTCs in oral / head and neck squamous cell carcinoma facilitates cancer diagnostics, individual therapy approaches and real-time monitoring.^25-28

Much needs to be explored with STCA in OSCC. With advancing research, STCA could enable us to devise novel diagnostic methods and targeted therapies in OSCC.

As expected with any tool or technique, STCA is also associated with certain limitations. The cellular dissociation during isolation of single cells is said to disrupt valuable spatial information and slows down pace of STCA. Techniques like scRNA-seq, demand freshly isolated cells and are not feasible on cryopreserved and fixed tissue samples. Despite progress in bioinformatics, appropriate algorithms for data analysis are yet to be developed and cross disciplinary collaborations are required to critically validate the data.⁶ Artefacts may be encountered due to (i) suboptimal sample sizes, (ii) non-representative starting samples, (iii) number of sample molecules used and, (iv) limited guidelines to define quality control standards, to remove technical artefacts and to interpret results. With expansions in scale of experiment, it is also challenging to implement STCA in larger cohorts as there is high cost involved in the procedure. All these suggest the need for highthroughput, low-cost technologies to delineate the overall tumor tissue landscape and facilitate designing of better therapeutic strategies.

Some of the proposed future prospects for research using STCA include:

1. Development of STCA techniques that retain spatial and temporal information during the isolation of single cells
2. Cryopreserved and fixed tissue samples could be used for STCA
3. Concurrent analysis of both epigenome and transcriptome of the same cell
4. Direct sequencing of unamplified DNA and RNA derived from single cells and
5. Development of powerful and precise algorithms for the interpretation of large data sets.

Therefore, significant advances in bioinformatics and computational methods are necessary and in turn sharing of data around the world would help speed up solutions to aid in early diagnosis, treatment and prognosis of OSCC.^29-32

Conclusion

Thus, the high throughput information generated by the blend of single tumor cell analysis with cutting edge technologies could be better comprehended by computational analysis. All of these would facilitate the integration of multidimensional datasets in the diagnosis, risk stratification, therapeutics and prognostication of patients with oral squamous cell carcinoma across the globe.

Conflict of Interest

None