REVIEW
published: 17 August 2021
doi: 10.3389/fonc.2021.676948
Edited by:
Davide Melisi,
University of Verona, Italy
Reviewed by:
Maud Kamal,
Institut Curie, France
Marcelo A. Soares,
National Cancer Institute
(INCA), Brazil
*Correspondence:
Rana P. Singh
ranasingh@mail.jnu.ac.in
Vibha Tandon
vtandon@mail.jnu.ac.in;
vibhadelhi6@gmail.com
Rakesh K. Tyagi
rkt2300@mail.jnu.ac.in;
rktyagi@yahoo.com
†
These authors have contributed
equally to this work and
share first authorship
Specialty section:
This article was submitted to
Head and Neck Cancer,
a section of the journal
Frontiers in Oncology
Received: 06 March 2021
Accepted: 19 July 2021
Published: 17 August 2021
Citation:
Jawa Y, Yadav P, Gupta S,
Mathan SV, Pandey J, Saxena AK,
Kateriya S, Tiku AB, Mondal N,
Bhattacharya J, Ahmad S,
Chaturvedi R, Tyagi RK, Tandon V and
Singh RP (2021) Current Insights and
Advancements in Head and Neck
Cancer: Emerging Biomarkers and
Therapeutics With Cues From Single
Cell and 3D Model Omics Profiling.
Front. Oncol. 11:676948.
doi: 10.3389/fonc.2021.676948
Frontiers in Oncology | www.frontiersin.org
Current Insights and Advancements
in Head and Neck Cancer: Emerging
Biomarkers and Therapeutics with
Cues from Single Cell and 3D Model
Omics Profiling
Yashika Jawa 1†, Pooja Yadav 1†, Shruti Gupta 2, Sivapar V. Mathan 3, Jyoti Pandey 4,
Ajay K. Saxena 3, Suneel Kateriya 4, Ashu B. Tiku 3, Neelima Mondal 3,
Jaydeep Bhattacharya 4, Shandar Ahmad 2, Rupesh Chaturvedi 4,
Rakesh K. Tyagi 1*, Vibha Tandon 1* and Rana P. Singh 3*
1 Special Center for Molecular Medicine, Jawaharlal Nehru University, New Delhi, India, 2 School of Computational and
Integrative Sciences, Jawaharlal Nehru University, New Delhi, India, 3 School of Life Sciences, Jawaharlal Nehru University,
New Delhi, India, 4 School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
Head and neck cancer (HNC) is among the ten leading malignancies worldwide, with India
solely contributing one-third of global oral cancer cases. The current focus of all cuttingedge strategies against this global malignancy are directed towards the heterogeneous
tumor microenvironment that obstructs most treatment blueprints. Subsequent to the
portrayal of established information, the review details the application of single cell
technology, organoids and spheroid technology in relevance to head and neck cancer
and the tumor microenvironment acknowledging the resistance pattern of the
heterogeneous cell population in HNC. Bioinformatic tools are used for study of
differentially expressed genes and further omics data analysis. However, these tools
have several challenges and limitations when analyzing single-cell gene expression data
that are discussed briefly. The review further examines the omics of HNC, through
comprehensive analyses of genomics, transcriptomics, proteomics, metabolomics, and
epigenomics profiles. Patterns of alterations vary between patients, thus heterogeneity
and molecular alterations between patients have driven the clinical significance of
molecular targeted therapies. The analyses of potential molecular targets in HNC are
discussed with connotation to the alteration of key pathways in HNC followed by a
comprehensive study of protein kinases as novel drug targets including its ATPase and
additional binding pockets, non-catalytic domains and single residues. We herein review,
the therapeutic agents targeting the potential biomarkers in light of new molecular
targeted therapies. In the final analysis, this review suggests that the development of
improved target-specific personalized therapies can combat HNC’s global plight.
Keywords: head and neck (H&N) cancer, single cell analysis (SCA), organoid technology, 3D culture, omics
analyses, therapeutics of HNC
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Current Insights and Advancements in HNC
effective treatment strategies by mapping individual genetic
profiles of heterogeneous tumor cells.
INTRODUCTION
The origin of cancer is traced to the characteristic unresponsive
cellular behavior towards signals that regulate survival,
proliferation, differentiation, and eventual evasion of death (1).
The research into the biological mechanisms of cancer
progression has advanced our knowledge of disease biology,
and new developments in effective anti-tumor therapies have
generated a stream of possibilities and strategies to tackle a wide
range of cancer types. Despite these advances, head and neck
cancer (HNC) remains among the ten most common
malignancies worldwide with higher rankings in developing
countries (2). The HNCs are categorized by origin in the head,
neck, or the upper aero-digestive tract including oral cavity,
para-nasal sinuses, pharynx, larynx, cervical esophagus, thyroid,
associated lymph nodes, soft tissues, and bone (3). A broad
spectrum of tumors arising from different head and neck tissues
are designated as HNC, with >90% of malignancies being
squ amous cell carcinomas (SCC) and its variants.
Histopathologically, other tumor of the head and neck region
include adenocarcinomas, sarcomas, anaplastic carcinoma,
plasmacytoma, lymphomas, and malignant melanoma.
Approximately 75% of the cases of HNC worldwide can be
associated with its classical causative agents; heavy tobacco or
alcohol consumption (3, 4). Human papillomaviruses (HPV) are
also important causative agents for the development of
oropharyngeal tumor of the tonsils or the tongue basal area
(5). HPV positive tumors exhibit better prognosis and show little
correlation with tobacco and alcohol exposure unlike HPV
negative tumors (6). Likely, other unknown factors could also
play essential roles in tumorigenesis, tumor progression, and
metastasis of HNC, such as alteration in microbial diversity and
function, genetic polymorphisms in enzymes involved in alcohol
and tobacco metabolism (7, 8) or, genetic predisposition as is in
Li Fraumeni’s syndrome, Fanconi’s anemia and ataxia
telangiectasia (9).
Though several drugs are presently in clinical trials (10, 11),
most treatment strategies are hamstrung by limited patient
response and the complex tumor microenvironment.
Therefore, in-depth studies to elucidate the mechanism of
action of drugs, and the challenges that cripple their efficacy,
are necessary before devising new molecules with increased
efficacy. Till very recently, predicting clone genotypes from
tumor bulk sequencing of multiple samples was cardinal to the
delineation of tumor profiles. Since drug-resistant clones develop
throughout the tumor growth process, their presence often
precedes a drug treatment regimen strategy; enabling single
tumor cells to evade drug treatment camouflaged by their
divergent profiles. Single-cell analysis, spheroids, organoids
technology are emerging as solutions that can be exploited for
HETEROGENEITY: A CHALLENGE IN THE
TREATMENT OF HEAD AND NECK
CANCER AND ROAD TOWARDS
SOLUTIONS
Head and neck cancers, notorious for their heterogeneity and
relapsing nature require an improved understanding and
characterization in order to counter recurrence, resistance
and disparities in therapeutic responses. This heterogeneity
and anatomical diversity makes the treatment protocol a
virtual nightmare and also demands linking of phenotypic
assay data with clinical outcomes in order to optimize the
treatment and translate benefits to the patients (12). Though
the Cancer Genome Atlas has increased perception of intertumoral heterogeneity across scores of patients, the knowledge of
intra-tumoral heterogeneity stays very rudimentary.
The conventional diagnostic techniques analyze the tumor
population as a whole and, as a result, derive an inference which
averages the effects of all different types of cells in the population.
Until recently, genotypes were predicted using tumor sequencing
from multiple and bulk samples (13). However, the average
targeting of cancer is grossly inadequate and strategies are
required to characterize individual cancer cells and
subsequently optimize treatment regimens. The development
of models that consider as well as provide the interactions with
ECM and cells of the microenvironment (like cancer-associated
fibroblasts (CAFs), myeloid derived suppressor cells (MDSCs)
and immune cells like Th1, Th2, Treg cells & cytotoxic T cells,
M1 & M2 macrophages, N1 & N2 neutrophils, natural killer cells
(NK cells), dendritic cells etc.) (14) becomes necessary. These
models necessarily require to mimic other in vivo conditions as
well, such as hypoxia which is said to be responsible for stemness
(15) and radio-resistance (16), both prominently seen in HNCs.
The single-cell analytical methods and spheroids/organoid
models are being found particularly useful in cancer biology and
clinical oncology. Aiming to improve the understanding of two
key areas, cancer research and, drug discovery, the latter provides
suitable models to reproduce the tumor microenvironment while
the former gives an accurate measure of cell properties and
minimizes adulteration or approximation associated with bulk
measurements. The conventional 2D cultures include growing
transformed cells derived from tissues in monolayer cultures.
Although characterized by easy maintenance and experimental
modifications, the extended survival of cancer cell lines in these
monolayer cultures allows for the development of undefined
mutations and the consequent loss of parental cells’ genetic
characteristics (17). Also, the cellular heterogeneity and tissue
architecture found in tissues or tumor of their origin is lacking in
2D cultures. On the other hand, organoid and spheroid cultures
can mimic or recapitulate the tumor microenvironment
signaling by partially permitting vital cell-cell contacts, cell
signaling, and cell-ECM interactions. Their higher
Abbreviations: ASCs, Adult Stem Cells; HNSCC, Head and Neck Squamous Cell
Carcinoma; ESCs, Embryonic Stem Cells; ECM, Extracellular Matrix; HNC, Head
and Neck Cancer; HPV, Human Papilloma Viruses; iPSCs, induced Pluripotent
Stem Cells; OSCC, Oral Squamous Cell Carcinoma; PDOs, Patientderived Organoids.
Frontiers in Oncology | www.frontiersin.org
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Current Insights and Advancements in HNC
approaches used for single single-cell isolation vary from
targeting either their physical or biological characteristics
(Figure 2). The physical characteristics like electric charges,
density, size, and flexibility, are exploited by the microchipbased capture platforms, membrane filtration, and density
gradient centrifugation. On the contrary, the cells’ biological
characteristics such as cell surface markers, size, granularity help
in single-cell isolation via affinity chromatography, fluorescenceactivated cell sorting (FACS), and magnetic–activated cell sorting
(MACS) methods (18). To characterize the heterogeneity in
tumor mass and microenvironment, single-cell separation and
culturing techniques are significant. These methods not only
physiological relevance, susceptibility to manipulation of niche
components, signaling pathways and genome editing, makes
them an important bridge between 2D culture and in vivo
animal models (Figure 1).
In view of the above, it is reasonable to hypothesize that the
organoid and single cell technologies have applicative potential
in HNC where identifying, understanding and, addressing the
tumor heterogeneity is the primary concern. These technologies
can be applied either independently or in combination to
discover novel biomarkers and specific molecular targets.
Subsequently, the information so retrieved can be supportive
of streamlining the drug development procedure (Figure 1). The
A
B
FIGURE 1 | Comparison between two different cell culture systems initiated from the same source. (A) Cells isolated from various tumor sites are grown in 2D and
3D culture systems, and their trajectories are tabulated on the right and left side, respectively. Analysis of mutations in each organoid grown from a single cell may be
used to construct the phylogenetic tree. (B) When compared to PDXs or slice cultures, the 3D cultures are amenable for easier manipulation, identification of
heterogenous population, and high throughput screening (HTS).
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FIGURE 2 | Overview of single-cell isolation technologies. (A) Schematic of fluorescence-activated cell sorting; FACS employs two separate techniques, streamlining
the fluorescently labeled cells to pass through a micro-spectrophotometer one cell at a time, and a second to record the emission of the signal. The signals are
based on cell dimensions, coarseness, and fluorescence. The technique allows both qualitative and quantitative analysis of a cell population. After the initial sample
preparation, the cell suspension is passed in a monolayer in a manner that each cell is subjected to exposure by a laser which permits the fluorescent labels to be
identified by the instrument. The instrument applies a charge depending on the nature of the cell, which deflects a droplet containing the cell of interest from the
entire flow. This charged droplet is then collected by collection tubes. (B) Magnetic-activated cell sorting differs from FACS in the way that instead of fluorophore
tagged labels, this technique uses magnetic bead conjugated with antibodies, streptavidin, lectins, or enzymes. The cells are channelized under an applied magnetic
field that allows non-conjugated cells to pass freely. The magnetic bead conjugated cells are then eluted by turning the magnetic field off. Separation can be both
positive and negative. Positive separation employs a technique where the cells of interest are conjugated with the magnetic beads. (C) Laser capture microdissection uses an inverted microscope, an infrared or ultraviolet laser, and an extraction system. After visual identification of the cell of interest, through a userdefined pattern, the laser cuts the cell from the population. Various extraction methods are used, one of them being the laser activating an adhesive on a thin film
kept over the tissue, which in turn sticks to the cell of interest, and the cell can be removed by picking up the film. (D) Manual cell picking also employs an inverted
microscope, but instead of lasers, automated micropipettes are used for the cell extraction. MCP’s main advantage over LCM is that live cell cultures can be
isolated, in contrast to fixed cells in LCM and (E) A microfluidic device depends on the capture of single cells from the suspension so well diluted that the probability
of one cell going into one well is maximum. The microwell technique can accommodate single-cell imaging along with analysis. Automated devices streamline the
cells in a microflow and sort the cells according to specific properties like size, charge, or ligand affinity into different populations.
were measured in response to different drug treatments.
Therefore, the study suggested the use of organoids as a
complementary tool to perform rapid comparisons between
treated and non-treated samples, to observe metabolic response
to drugs and to characterize heterogeneity (20). Another study by
Tanaka et al. used CTOS (Cancer tissue originated spheroids)
method to establish HNC organoids. The study also
characterized marker expression profile in spheroids in
comparison to the original tumor cell, finally showing similar
marker expression of cancer stem cell to in vivo. Exposure to
drugs like cisplatin and docetaxel was able to accurately define
drug sensitivity in vivo (21). The Driehuis et al. provided a
standardized protocol for generation of HNC organoids using
patient tumor samples and their subsequent use in drug
screenings. This allowed comparison of differential drug
responses in different patients. The study also floats the idea
that organoids may potentially predict patient clinical responses
(22). The same group in a previous study primarily focused on
utilize the physical properties of cells but have added advantage
of being label-free techniques. Thus, single cell-sorting and omics
analysis techniques have become the backbone of current
investigations in the direction of personalized treatment in all
forms of cancer including HNC. The single-cell technologies
operate on the dual platforms of ‘single-cell separation’ and
‘single-cell analysis’. These technologies are certainly warranted
for detection and analysis of intra-tumor heterogeneity (ITH)
and decipher the mechanisms of tumor metastasis, investigate
omics alterations, and discover precise treatment strategies (19).
Several gene expression, metabolic and drug response based
studies have reiterated the importance of 3D culture, as it
mimicks in vivo cell environment in a better manner as
compared to 2D culture (Figure 3). A study by Shah et al.
characterized head and neck cancer organoids metabolically. The
cell metabolism was analyzed by measuring the intrinsic
fluorescence of NAD(P)H and FAD on a single cell level
before and after treatment. The redox ratios of the organoids
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FIGURE 3 | Characteristic features of 2D and organoid culture. Schematic representation showing cells grown in 2D monolayer culture and in 3D culture. An
organoid mimics the tumor microenvironment by forming different zones (viz. proliferating zone, quiescent viable zone, and necrotic core) and gradients, which gives
a realistic response compared to 2D monolayer cultures.
tumor initiation, progression and metastasis (25). Similarly,
paracrine signaling between stromal and cancer cells is known
to mutually stimulate proliferation and induction of drug
resistance. Table 1 discusses some of the model systems used
in HNC related studies. Different models allow for
customizations relevant to parameters under investigation and
provide an edge over conventional techniques. For instance, to
elucidate monocyte action, they can be cultured along with HNC
cells (26–29). Similarly, fibroblasts and PBMCs can also be cocultured with HNC cells for EGFR based studies (30) and for
testing antibodies (31), respectively. Other studies have shown
the role of TAMs (Tumor associated macrophages) and HDFs
(Human Dermal Fibroblasts) in cancer stemness and invasion
respectively (32, 33). Differential drug response towards EGFR
targeting drugs is studied using CAFs (34). Different organoid
model systems of HNC were established to explore ERK1/2 and
Nanog signaling (35), HSV1 and HPV16 (36), invasiveness in
cancer (37), drug screening (38), and other characteristic
hallmarks (39). Hydrodynamic shuttling chip (HSC) is a
microfluidics platform through which single-cell squamous
using 3D models for testing in vitro targeted PDT
(photodynamic therapy). Since EGFR is primarily targeted in
PDT, its expression levels were compared in organoids to that of
cell lines used previously. The levels in organoids recapitulated
both tumor and normal patient samples. In fact, organoids from
tumor were found to be more sensitive to PDT than their
corresponding normal/wild type tissues. This suggests that the
therapy may prove more significant as it will leave surrounding
normal epithelia of tumor unaffected. Therefore, also
highlighting the use of EGFR as a major molecular target in
HNC which is already suggested by multiple studies (23).
Multiple studies have also reiterated that organoids are not
relevant only because they grow in 3D spatial arrangement
mimicking in vivo conditions but also because they capture
distinct behaviors of respective tumor they arise from (24).
Co-culture systems in the form of 3D organoid models are
gaining more attention recently and are being used for assessing
the anticancer effects. Within the tumor microenvironment, the
cell-cell interactions between Cancer associated fibroblast
(CAFs) and cancer cells contribute to carcinogenesis, via
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TABLE 1 | Different 3D models of HNC.
S.No.
3D culture models
1
Malignant/benign HNC + Mucosa
monocytes
2
3
4
HNC cell line + fibroblasts
HNC cell line +PBMC
HNC spheroids + tumor Associated
macrophages (TAMs)
HNC OECM-1 cell line + human dermal
fibroblasts (HDFs)
HNC spheroids + CAFs
HNC spheroids
Oral mucosal Organoids and HNC patientderived tumoroids
HNC Multicellular tumor spheroid (MCTS)
5
6
7
8
9
Application
IL-6 secretion and prediction of prognosis
Mechanisms for monocyte activation
Tumor-associated macrophages are a source of monocyte chemotactic protein (MCP-1) in HNC tumors.
Anti-EGFR monoclonal antibody
Trifunctional bispecific antibody catumaxomab
CD44 signaling and mechanistic link of TAMs in cancer stemness
Cancer cell invasion in collagen microenvironment
Differential drug response to cetuximab and mTOR inhibitor
Role of ERK1/2-Nanog signaling in head and neck cancer stemness
To study oral mucosal pathology by infecting with HSV1 and HPV16 and HNC patient-derived tumoroids utilized
for drug screening and personalized medicine.
Drug screening
targeted therapy that evolved due to omics studies. This review
provides a panorama of the target landscape for the development
of treatments of HNC. The gaps in the HNC treatment are being
identified and future strategies to fill those gaps are suggested.
carcinoma cells are separated and co-cultured with lymphatic
endothelial cells to observe the motility and cell-cell
communications (40).
State of the art methods for culturing 3D cells are classified on
the basis of source materials used, 3D environment, kind of
scaffold and the types of cultures generated. Various methods
have evolved with date and used for 3D cell culturing are
scaffold-dependent methods (41) viz. hydrogel method (42),
agarose coating method (43); and scaffold-independent
methods (44) like hanging drop method, rotary cell culture
system, micropatterning, microfluidics (45), low-attachment
plates method (46), magnetic-cell leviation (47). Organoids
derived from single cells can generate enough biomass for
investigating tumor heterogeneity at the single cell level
(Figure 1). Patient-derived organoids (PDOs) are particularly
useful as models for specific diseases or infections, which
otherwise are difficult to generate or probe in animal models.
Despite many applications 3D cultures cannot mimic in vivo
growth factor, biomechanical forces etc. Thus, organoids, in spite
of their potential as near-physiological cell culture models, are
difficult to culture with unknown or unfamiliar niche or growth
factors, and necessitate high technical skill and elaborate
experimental set up in most cases. In addition to these, the
field of HNC still requires more comprehensive studies using
organoid technology as the literature available is less compared
to other cancers like breast, colon, prostate etc.
Both the above discussed technologies may be applied to
HNC, where understanding the heterogeneity is the major
concern. The technologies can be used individually or in a
combinatorial approach (48) to first identify biomarkers and
molecular targets specific to HNC and then to perform drug
screenings/assays which will help in validating novel therapeutic
agents and maximizing the success of a proposed therapeutic
regimen in the patients (Figure 1). These techniques despite
being very promising are limited by the lack of studies specific to
HNC i.e. the literature is scarce. The review aims to encourage
more such studies in the field of HNC research. This review also
encompasses the omics profiles of single cell and is compared
with bulk-cell analysis in HNC. We have discussed the single-cell
derived spheroid based therapeutic advances and emerging
Frontiers in Oncology | www.frontiersin.org
Issues and Challenges in Bioinformatic
Analysis With Reference to HNC
The omics analysis with reference of single cells, spheroids and
organoids from HNC patient samples is a major challenge. One
of the primary objectives of any omics analysis is to find reliable
targets for therapeutic intervention. Such a task becomes possible
with the identification of cell-specific genes which need to
regulated specifically. Identification of biomarkers from a
transcriptomics data typically start with the computational
analysis of highly differentially expressed genes. This
computational analysis becomes possible through wellestablished and benchmarked bioinformatics strategies, which
face specific challenges in the case of single cell data emerging
from HNSCC. Three issues require special attention viz. (i) much
subtler changes in expression levels in single cell populations
when compared to bulk expression data, (ii) sparsely collected
data with lots of missing values, and (iii) absence of largescale
relationships between single cell changes of expression and gene
sets such as pathways or ontology terms, frequently used in
interpreting bulk expression data outcomes.
To address the first of these issues (weak biomarker signal in
differential expression), many computational tools, dedicated to
the scRNA-seq data analysis have been developed (49, 50), which
have allowed for significant advances in investigating
heterogeneity and single-cell specific markers. A database of
tools employed for scRNA-seq analysis has been reported and
can be accessed via URL http://www.scRNA-tools.org (51). Tools
like SCANPY (52) and Scatter (53) are some of the powerful
robust pipelines that are well-integrated for comprehensive
analysis (pre-processing and post-processing analysis) of
scRNA-seq data. Many of these tools and resources provide
expression data analysis of single cells, which takes into account
the subtle gene expression level changes.
The second bioinformatics analysis issue is that of sparseness
in the data sets. Poor coverage of expression values from each
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the intrinsic cellular heterogeneity. Intra-tumoral heterogeneity
and tumor nest architecture was largely recapitulated within
lymph node metastases. Specifically, it was observed that high
heterogeneity measured by mutant-allele tumor heterogeneity
(MATH) scores leads to poor patient outcomes, thereby
highlighting the need to understand the cell population
composition of HNSC cells to improve the patient survival
rates. Authors also found that malignant cells’ expression
patterns could not be distinguished from those of basal
tumors, suggesting that most tumors could be defined as a
single ‘malignant-basal’ cohort in OSCC. This contrasts with
the glioblastoma multiforme (GBM) tumor, in which the
malignant cells map to multiple different subtypes. These
findings suggest that HNC tumor consists of lower diversity in
malignant subtypes or because the subtypes have not been
confidently resolved at this stage. Another study was
performed to examine the change in tumor properties by
simulating single-cell events leading to macroscopic tumor
development. The model was able to successfully observe
adhesion-driven cell movements and nutrition dependent
heterogeneous tumor growth. Different treatment plans
strongly influenced the final tumor cell type composition. The
growth rate was observed to be significantly decreased when
metabolism in tumor cells was upregulated. The mutation rates
were adjusted, and low mutation rates cell types with higher
division rate and delayed cell death started dominating the
tumor. The models were also used to probe treatment
regimens. Shorter pulses of chemotherapy were observed to
have a better effect than a uniform application. The tumor size
was significantly reduced by a single strong radiotherapy pulse as
compared to multiple weaker pulses. The presence of tumor stem
cells was confirmed to impact treatment outcome by increasing
tumor size as well as heterogeneity. In view of the above results,
the single-cell simulations can be a source of information to
determine the heterogeneity and also predict treatment strategy
and outcomes. These proved to be highly useful in improving the
understanding of tumor development on a single-cell level but
also the differences/similarities from bulk tumor analysis (62).
Since, cellular heterogeneity is so critical to HNC
characterization and personalized treatment, researchers have
tried to establish general patterns of cellular heterogeneity so prior
therapeutics for each group can be developed. Bioinformatics work
has concluded that the non-malignant cells from HNC patients
could be grouped into eight main clusters by cell type viz. (a) T cells,
(b) B/plasma cells, (c) macrophages, (d) dendritic cells, (e) mast
cells, (f) endothelial cells, (g) fibroblasts, and (h) myocytes.
However, the computational analysis so far has found that these
non-malignant cells did not cluster as per their origin, when their
expression profiles are used for automatic grouping, suggesting that
the cell types and their expression states are consistent across tumor
when their expression profiles are used for automatic grouping. On
the other hand, malignant cells clustered well by the patient,
suggesting expression changes across patients are more diverse
than across cells of the same patient. In summary, malignant cells
carry patient identity. The origin of cell from the 08 groups was not
well encoded into the gene expression program. In the same context,
sample has two implications, (a) the very absence of the
expression values may lead to missing the biomarkers
altogether as only 10—20% values are reliably captured,
(b) these dropouts adversely impact a confident grouping of
cellular profiles into their subclasses as each transcript is
described by a different set of genes. Few genes are present in
one face while others are available in another. One of the
solutions that has been proposed by bioinformaticians to
address the sparseness of expression data in scRNA-seq is to
reconstruct or predict the missing gene expression values, a
process well-known as “imputation” in computer science.
Traditional computational methods of imputation in general
have dealt with a few or a small proportion of missing values in a
data set. This problem is, however far more acute in the singlecell data due to much less information available to impute the
missing ones. Imputing a missing value often relies on adopting a
derived value from carefully selected similar samples. In single
cell analysis, groups of samples are not known a priori. Hence,
the question of identifying subclasses and imputing the missing
values becomes a cyclic problem. Early computational
techniques, developed for imputing gene expression values
have included ZIFA (Zero-Inflated Factor Analyst) (55) and
CIDR (clustering through imputation and dimensionality
reduction) (56). Recently, SAVER, MAGIC and scImpute
dedicated specifically to reconstructing a large number of
missing values or imputations, were developed (57–59) and
were successful in recovering the true expression of spike-ins
transcripts improving and data quality.
Beyond the algorithms and tools for scRNA-seq analysis by
addressing its sparseness, a number of data resources comprising
transcriptomic and genomic information are also available in the
public domain. The Table 2 has listed the Gene Expression
Omnibus (GEO) Dataset collection of transcriptome and
associated data from HNC. Also, TCGA HNC dataset, which
includes 527 cases, is a vast resource containing comprehensive
integrative datasets of SNV, CNV, methylation, and slide images
as well, complementing the transcriptome data. These datasets
can be accessed from the GDC Data Portal (https://portal.gdc.
cancer.gov/).
The discussion above is based on the review of works on the
issues of scRNA-seq analysis in general, which must anyway be
addressed in HNC samples as well. However, so far there is only
one published study that has specifically addressed the issue of
single cell transcriptomics in HNC (60), while another study
from the same group has comprehensively reviewed the
bioinformatic approaches and key findings derived from for
the single-cell technology-based study of cancer (61). These
twin papers suggest that the HNC computational analysis and
results can be broadly classified into three groups viz. (a) study of
cellular heterogeneity and gene expression analysis (b) study of
micro-environment of cancer cells and (c) process of invasion
and metastasis of cancer cells.
Among the insights gained from bioinformatics analysis of
HNC data sets at single cell levels, the foremost finding arguably
is reported by Qi et al. (61) in which it was shown that patient
outcomes under all treatment regimens are highly dependent on
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TABLE 2 | List of GEO DataSets (expression profiling by array) related to HNC studies for data re- analysis.
S.No.
1
2
3
4
5
6
Title
Details
Platform
Series
#Samples
DNA methyltransferase
inhibitor 5-aza-2’deoxycytidine effect on
oral cancer cell lines
Oral squamous cell
carcinoma- derived cell
lines
Squamous cell
carcinoma of the tongue:
tumor and histologically
normal surgical margins
Head and neck
squamous cell
carcinoma
Bleomycin effect on
mutagen- sensitive
lymphoblastoid cell lines
Analysis of oral squamous cell carcinoma (OSCC) cell lines: OC3, SAS, SCC-15, and HSC3,
treated with 5-aza-2’- deoxycytidine (AzC), an inhibitor of DNA methyltransferase. Results
provide insight into potential tumor suppressor genes silenced by DNA hypermethylation in
OSCC.
Analysis of oral squamous cell carcinoma (OSCC)-derived cell lines. OSCC is a lethal disease
with early death typically occurring as a result of local invasion and regional lymph node
metastases. Results provide insight into the molecular mechanisms underlying OSCC.
Analysis of oral carcinoma, histologically normal margins, and adjacent normal tissues from
patients with squamous cell carcinoma (OSCC) of the tongue (training set). Results provide
insight into molecular signature in histologically normal margins that are predictive of oral
carcinoma recurrence.
Analysis of paired normal tissues and tumor samples from patients with head and neck
squamous cell carcinoma (HNC). Results were used to assess the effectiveness of using a
combinatorial approach to analyze microarray data in identifying differentially expressed genes.
Analysis of mutagen-sensitive lymphoblastoid cell lines after exposure to bleomycin. Mutagensensitive cells exhibit a high number of bleomycin- induced chromatid breaks. Mutagen
sensitivity (MS) reflects an individual's susceptibility to sporadic cancers. Results identify genes
involved in MS.
Microarray analysis of cells obtained with LCM from 16 patients and compared these results
with 4 control cell epithelium identified expression profiles differentially expressed between
normal and tissues.
GPL6883
GSE38823
16
GPL96
GSE31853
11
GPL10526 GSE31056
96
GPL8300
GSE6631
44
GPL2902
GSE3598
28
GPL96
GSE3524
20
Analysis of 8 head and neck squamous cell carcinoma (HNC) tumor positive for human
papillomavirus (HPV). 28 HNC HPV negative tumor examined. Between 15% and 35% of
HNCs harbor HPV DNA. Results provide insight into the effect of HPV in HNC.
Genome wide expression profiling of 84 HNCs, CCs and site-matched normal epithelial
samples. LCM was used to enrich samples for tumor derived versus normal epithelial
expression of a large subset of cell cycle genes was found to be upregulated in HPV+ HNC.
GPL570
GSE3292
36
GPL570
GSE6791
84
Methylation analysis of 21 HPV+ and 21 HPV- samples was performed.
GPL13534 GSE38266
42
Global and stratified pooled analysis of epigenome wide data was performed to identify tissue
specific components and common viral epigenetic targets. Analysis revealed a novel
epigenetic signature of HPV infection.
GPL13534 GSE95036
11
Total RNA was isolated from formalin fixed paraffin embedded (FFPE) samples. The
expression data for 20818 genes was obtained using whole genome array.
GPL14951 GSE34105
78
To identify novel potential prognostic markers, 20 patients were grouped into stage (early vs.
late) and nodal disease (node positive vs. node negative) subgroups and genes differentially
expressed in tumor vs. normal and between the subgroups were identified.
Tumors with different HPV16 DNA and RNA (E6*I) status from 290 consecutively recruited
HNSCC patients was compared by gene expression profiling and targeted sequencing of 50
genes. The study confirmed that the HPV16 DNA+ RNA+ tumors are HPV-negative (DNA-)
HNSCC and have elevated expression of cell cycle genes and rare TP53 mutations.
GPL8300
GSE13601
58
GPL10558 GSE65858
270
12
Association between
gene expression profile
and tumor invasion in
OSCC
Head and neck
squamous carcinoma
harboring papillomavirus
Gene expression profiles
of HPV - positive and Negative Head/Neck
Cervical cancers
450K analysis of 42
FFPE HPV+ and HPVHNSCC samples
Unique DNA methylation
signature in HPV positive Head and Neck
Squamous Cell
Carcinomas
Gene expression profiling
of archival tongue
carcinoma and normal
tongue tissue.
Oral tongue cancer
13
Gene expression
7
8
9
10
11
Total
Casasent et al. (65) has reported a method called Topographic
Single Cell Sequencing (TSCS), which utilizes a combination of
LCM (66) and single-cell DNA-sequencing to measure genomic
copy number profiles of single tumor cells in breast cancer patients.
This approach preserves single cells’ spatial context, which is critical
to the location-specific therapeutic targeting strategies. Although the
studies mentioned here are performed on cancers other than HNC,
they successfully present strategies to combat the challenges
associated with bioinformatics analysis.
Recently, a review published on the applications of single cell
RNA sequencing in the field of otolaryngology, self-analyzed the
another study by Yost et al. (63) on basal cell carcinoma using a
combination of scRNA-seq and TCR sequencing, have implicated
cancer-associated fibroblasts (CAFs) in tumorigenesis, tumor
survival, ECM remodeling, immune system suppression, and
tumor invasion system suppression, and tumor invasion. Another
study by Leung et al. (64) focused on single-cell DNA sequencing,
exome sequencing, and targeted deep-sequencing has investigated
clonal evolution during metastatic dissemination in two colorectal
patients. This study has highlighted that understanding the clonality
at a single-cell level in a tumor is essential to simultaneously
capturing and maintaining spatial information. Another study by
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794
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Current Insights and Advancements in HNC
temporal progression of tumor heterogeneity. In response to
systemic therapy, the issue of recurrence of tumor and overall
temporal dynamics are other issues of transcription data analysis
that heavily rely on suitable computational strategies, which are
still under development.
single cell RNA seq data of HNC patients taken from the study
by Puram et al. The analysis gave following findings that were
relevant for clinicians 1) The scRNA-Seq data not only
distinguished the disease causing cells from native tissue but
also revealed the heterogeneity within diseased tissue samples.
2) Malignant cells from 10 HNC patients, when mixed, formed
patient specific clusters i.e. with the cells of their original native
tissue only. This suggested that clonal evolution is unique to each
patient, and therefore the treatment strategy needs to be
personalized. 3) Cells from the tumor microenvironment
(TME) were also profiled along with malignant cells. However,
these were not found to be clustering on patient-specific basis but
rather on a cell-type basis. These cells could thus represent
shared disease pathogenesis between all HNC patients that can
be targeted using a similar therapy. 4) Rare cell types like stem
cells, progenitor cells, CD4+ T-regulatory cells or exhausted Tcells were also identified from TME. These helped in
understanding the disease maintenance, immune evasion and
decreased efficacy of immune therapies. 5) Most importantly, the
cell type specific biomarkers can be identified by investigating
gene expression in heterogenous cell clusters detected by scRNASeq. For example, Puram et al. identified partial-EMT signature
detected in a subset of malignant cells which was also present in
existing bulk RNA-Seq tumor data. Such identifications can
enable clinicians to determine the risks of nodal dissections on
the basis of signatures indicating risk of metastasis. The
prognostic signatures predicting survival, metastasis,
chemoresistance can vary patient to patient. Such signatures
can also be identified as markers to monitor drug response,
emergence of resistance etc. before and after treatment. 6)
Looking for genetic targets of FDA-approved drugs or small
molecules in clusters of malignant sub-populations or TME cells
can help identifying new druggable targets. A new database
called Pharos describes 20,000 gene/protein targets and the
drugs molecules available which can be further repurposed for
use in HNC treatment (67).
Some bioinformatics studies have gone beyond biomarker
discovery and cellular heterogeneity. Few researchers have used
appropriate bioinformatics tools in creating and maintaining the
tumor ecosystem’s spatial organization. Researchers have found
that partial-EMT (P-EMT) cells were loosely arranged, and
positioned in between malignant cells and CAFs. The study
attributed the compactness of HNC tumor architecture to the
expression of CD63 (68). Studies by Ligorio et al. (69) and
Wagner et al. (70) in pancreatic and breast cancer respectively,
have highlighted the need to utilize single cell separation method
(SCS methods) with preserved spatial information, to gain
insights into the role of intercellular interactions.
Another study by Navin et al. elucidated the tumor evolution
process in breast cancer through sequencing of 100 single cells
and revealed 3 distinct clonal sub-populations that represent
sequential expansions. Contrasting to the gradual models of
tumor progression their data indicated that tumors grow by
punctuated clonal expansions. The study was performed on
breast cancer and its liver metastases (71). More such studies
on HNC will help in developing an understanding of the
Frontiers in Oncology | www.frontiersin.org
Limitations of scRNA-Seq in
Clinical Medicine
The scRNA-Seq is a stride towards personalized medicine, but is
still daunted by several challenges. Lack of large cohorts of
scRNA-Seq data from human patient samples, high costs, userfriendliness, and tissue preservation are some of the major issues.
The use of scRNA-Seq on individual patient tumors for drug
selection is now feasible but more studies are still needed to
establish personalized drug selection and drug repurposing using
scRNA-Seq results for improved patient outcomes.
The cost of scRNA-Seq varies based on the chosen
methodology, and hence depends on the cost of equipment,
reagents, and sequencing. The costs of isolation and sequencing
per cell have dropped significantly, but the throughput of
sequencing machines has also increased, so the cost per run
with more cells still remains high. Most of the platforms are
available only in science laboratories and require a large
investment and planning to procure for hospital use. In
addition to cost, analysis of scRNA-Seq data requires basic
bioinformatics knowledge and coding skills. Furthermore,
standardization of different pipelines is also required for
clinical use.
Tissue preservation is a major issue because of its fragility and
cell viability. Currently, the use of frozen tissue samples or
methanol fixed tissues for scRNA-Seq platforms is in its
infancy. However, a few other options to aid tissue
preservation are available and includes, temporary tissue
stabilization buffers that can preserve cells for sequencing for
48 hours.
Generally, single nucleus sequencing (sNuc-seq) usually
involves tissue disruption and cell lysis, carried out in cold
conditions, followed by centrifugation and separation of the
nuclei from the debris. It minimizes the skewing effect of
degraded mRNA or cell-stress response genes on the data. Cell
lysis in sNUC-Seq allows for potentially more efficient cell type
delineation that includes for even the most interdigitated cell
types. These advantages potentially make sNuc-Seq a better
alternative to SCRNA-Seq. strategy.
OMICS OF THE HEAD AND NECK
CANCER
Genomics of HNC
Single cell DNA (scDNA) sequencing is focused mainly on the
copy number variations (CNVs) and identification of singlenucleotide variations (SNVs). These are the driving forces in
biological processes which cause genomic heterogeneity and thus
necessitate study of the cell at an individual level. The whole
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nucleotide polymorphism (SNPs) with non-synonymous type
variations such as in FAT1& 2, TP53, NOTCH2, Cadherin 3
(CDH3), and ATM; ii) synonymous type variations in
Adenomatous Polyposis Coli gene (APC) (a tumor suppressor
gene) and IL12B (cytokine gene). SNPs were also observed in
non-coding regions, located in or near EGFR, STAT5B, Cyclin
dependent kinase 5 (CDK5), and a protooncogene, MYCL1
(Figure 4). Sayans et al. (88) analyzed 528 tumors of HNSCC
subset in TCGA database and found 3491 deregulated genes. The
somatic copy number alteration analysis showed CDKN2A,
CDKN2B, PPFIA1, FADD, and ANO1 as the most altered
HNSCC genes. At the same time, genes with the most somatic
mutations were TP53, TTN, FAT1 and, MUC16. Another
relevant result from the study was the mutual exclusivity
pattern found between TP53 and PIK3CA mutations. The
difference in expression profiles between different studies i.e.,
the heterogeneity in the results could be attributed to the nature
of the cancer.
genome wide analysis of HNC identified mutations in many gene
families, but the most significant percentage of mutations were
observed in the NOTCH gene family (72–74), especially
NOTCH1. NOTCH and many other known oncogenes,
including cyclin E, MYC, and JUN are targets of FBXW7, a
ubiquitin ligase. FBXW7 is known to be mutated in 4.7% of
cancers of HNC (74). Apart from this, more than 60% of
mutations were observed in serine/phosphatidylinositol 3kinase (PI3K) pathway genes such as PTEN and PI3KCA (75,
76). In fact, this is the most commonly affected pathway in HNC,
and a more aggressive form of the disease can be attributed to
multiple mutations in this pathway (77). Approximately 8-23%
of HNCs possess mutation in PTEN that causes down-regulation
and constitutive activation of threonine-specific protein kinase
Akt and mammalian target of rapamycin (mTOR) (74, 78). It
increases the susceptibility of the oral epithelium to carcinogens.
The genome analysis in HPV positive HNSSC tumor showed
mutations in PI3KCA gene leading to an increase in mTOR
activity rather than Akt phosphorylation and hence helps
explains the better efficacy of dual inhibitors against PI3K/
mTOR (79). Interestingly, p53 was not found expressed in
HNC tumors with PTEN downregulation, implying the
exclusion of p53 gene mutation (80).
The Epidermal Growth Factor Receptor (EGFR), a receptor
tyrosine kinase (RTK) gene found upregulated in 80% of the
patients suffering with HNC. The EGFR on activation causes
cellular proliferation via either RAS/RAF/MAPK pathway, JAK/
STAT, or PI3K/AKT/mTOR axis. Its over-expression in many
HNSSC tumors is correlated to poor prognosis (81). In nearly
20% of HNC, oxidative stress genes are altered by mutation or
variation in copy number. NRF2 (encoded by the NFE2L2 locus)
is a transcription factor that activates a cellular antioxidant
response. It is overexpressed in 90% of the tumors leading to
poor prognosis (82). Elevated NRF2 levels are shown to cause
chemoresistance in a variety of cancer cell lines that is reversible
with siRNA inhibition of NRF2 (83). Several chromatin-related
genes in HNC viz, MLL2 (a histone methyltransferase), NSD1
(another histone methyltransferase), EP300 (a histone
acetyltransferase) and FAT1 were also found to be repeatedly
mutated in 19%, 10%, 7% and 23% of tumors respectively (84,
85). A recent study on HNSCC patients assessed the prognostic
value of altered immune gene expression using a cohort of 96
patients (86). The expression of 46 immune-related genes was
analyzed and, 4-1BB, IDO1, OX40L, GITR, FOXP3 were found
significantly overexpressed along with PD-1, TIGIT, and CTLA4. Almost half of the immune related genes had deregulated
mRNA levels. The study assessed that a combination of high
OX40-L and low PD-1 mRNA levels, high PDGFRB, and low
CD3E mRNA levels are associated with increased tumor
recurrence. While CD8A was observed to be associated with
poor prognosis, the increased expression of PD-1 was associated
with a good prognosis. These findings offer a therapeutic strategy
in the treatment of HNSCC through the application of a
combination of immune checkpoint inhibitors. Genetic
alterations due to tobacco and betel quid chewing were also
reported in oral cancer patients (87). These included i) single
Frontiers in Oncology | www.frontiersin.org
Transcriptomics of HNC
One of the recent applications of transcriptomics in cancer is the
study of the cellular heterogeneity in tumor towards better
understanding to achieve precision treatment. HPV positive
HNC is a vital cancer type and has been identified with different
gene expression patterns compared to HPV negative HNC.
Transcriptomic data analysis between HPV positive and
negative tumors provided important insights into the expression
profiles (76, 89).Activated receptor (RTKs)-RAS-PI3K pathways
and inactivated TP53 and CDKN2A in HPV-negative tumors were
observed. In HPV-positive tumors, PIK3CA, FGFR3, and E2F1
were found to be activated while TP53 and RB1 were inactivated
by viral oncoproteins E6 and E7 respectively. PI3K activation in
HNC is reported by either of these mechanisms, receptor- tyrosine
kinases, such as EGFR or mutation occurring in PI3K catalytic
subunit, p110a (encoded by PIK3CA gene). Mutations often target
one of two hotspot locations in the kinase or in helical domain,
thereby promoting constitutive signaling through the pathway
(90). Yu et al. (91) reported results from a network-based metaanalysis, identifying the biological signatures of HNC in pathways
like integrin signaling, tight-junction regulation, antigen
presentation, chemokine signaling, leucocyte extravasation, and
vascular endothelial growth factor (VEGF) signaling.
Another transcriptomics study in HNC suggested the
upregulation of genes involved in digestion and remodeling of
the ECM, such as matrix metalloproteinases (MMP) 1-3, 9, 10,
13, urokinase plasminogen activator (uPA), Integrin alpha
(ITGA) 3 and ITGA5. Both neoplastic and stromal cells secrete
MMPs that digest certain components of the ECM (92) and
promote cell migration and metastases in early stages of
tumorigenesis (93, 94). Overexpression and activation of
MMPs is critical in cancer progression and the pro-MMP-9/
NGAL complex has been identified as a potential prognostic
marker (95). A related study on a cohort of 145 oral cancer
patients exhibited high levels of MMP2 in severe patients when
compared to non-severe oral cancer patients. High levels of
CD276 and low levels of CXCL10 and STAT1 were also
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Current Insights and Advancements in HNC
FIGURE 4 | Genes altered in HNC at genomic and transcript levels. (A) Mutations in NOTCH gene pathway leads to cell growth and evasion of apoptosis, whereas
(B) in RTKs (VEGFR, EGFR, FGFR) lead to alterations through RAS/RAF/MAPK pathway or PI3K/AKT/mTOR axis, eventually leading to uncontrolled cell proliferation,
(C) in JAK/STAT pathway increase angiogenesis. (D) The integrins (ITGA 3 and ITGA 5), uPA, and MMP 1,2,3,9.10,13 are all involved in ECM digestion and
remodeling. (E) MHC I and MHC II expression is altered to evade recognition by immune cells. (F) Oxidative stress is increased due to mutations in genes like NRF2,
whereas (G) mutations in NSD1, MLL2, ATM are characteristic of genomic instability. HPV proteins E6 and E7 inhibit TP53 and RB1. All eventually leading to
uncontrolled cell proliferation and (H), multiple other genes are altered, producing significant effects.
lymph node metastases. The significant finding of the study was
to distinguish among non-malignant (3363) and malignant
(2215) cells on the basis of copy-number variations (CNVs)
and epithelial cells where stromal and immune cells were
excluded (60, 99, 100). Clinical and genomic meta-analysis of
multicohort HNSCC gene expression profile has clearly
demonstrated that HPV+ and HPV- HNSCCs are not only
derived from tissues of different anatomical regions, but also
present with different mutation profiles, molecular
characteristics, immune landscapes, and clinical prognosis. Cell
lines and primary cells of HNC have been explored at single-cell
transcriptomics (60, 101). The datasets have significantly
improved the identification of distinct cells which are highly
tumorigenic in nature in the HNC ecosystem. In the pool of cells,
including malignant and non-malignant type, intra-tumoral
variations at cell cycle, partial-EMT, proliferation, hypoxiarelated genes have been observed. In this context, scRNA-seq
is becoming a reliable technique for exploring HNC
heterogeneity both at the genetic and functional levels. All the
tumor influencing factors, such as circulating tumor cells
(CTCs), immune cells, cancer stem cells (CSCs), present
within, or in surroundings are investigated to gain clarity at a
single cell level.
observed to be associated with reduced overall survival. However,
when compared MMP2 appeared to be a superior and
independent prognostic marker (96).
The upregulation of interleukin (IL) 8, chemokine C-X-C ligand
1 (CXCL1), CD28, CD3D, CD4, IL-18, and IL-2 is observed in
chemotaxis and lymphocyte activation while downregulation of
MHC 1 &2 are hallmarks of invasive HNCs. Also, upregulation of
VEGF and interleukin-8 (IL-8) connoted tumor cell angiogenesis,
while EGFR, STAT-3, PI3K, and NOTCH upregulation influenced
signal transduction pathways (97).
One hundred forty-six novel miRNAs expressed in HNC have
been identified; but expression patterns among smokers and
non-smokers remained undistinguishable. The three novel
miRNAs significantly associated with HPV status, were
mapped to chromosome 12 between genes Keratin 6C
(KRT6C) and KRT6B (98).
Puram et al. have reclassified HNC into three malignant
subtypes: classical, basal-mesenchymal and atypical. Single-cell
transcriptomics from 18 HNC patients identified p-EMT as an
independent predictor of grade, metastasis and critical
pathological features (60). They performed the scRNA-seq
analysis by considering 6,000 single cells from eighteen HNC
patients containing five sets of matched primary tumor and
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Current Insights and Advancements in HNC
as a source for identification of bio-markers in cancer, and its
proteomic analysis is considered to be a promising tool for HNC
diagnosis; for example, over-expression of PLUNC and zincalpha-2-glycoprotein (110). To better understand the process of
tumor progression and to make detection of cancer with
precision, a technical triad of laser microdissection, protein
chip technology and immunohistochemistry have been
employed to identify the tumor relevant biomarkers. This
study encompasses the protein profiling of calgranulin A and
calgranulin B which are implicated in cancer pathology. Thus,
such combinatorial approaches open up the possibility towards
accurate prediction of metastasizing ability of a cell population
(111, 112).
The proteins like Hsp90, VIM and keratin are already
established bio-markers and drug targets while prelamin-A/C
and PGAM1, have been recently suggested as potential markers
(113). Bohnenberger et al. (114) identified distinct proteomic
profiles between lung metastasis of HNC (metHNC) and
squamous cell lung carcinoma (SQCLC). On classifying 51
squamous cell lung tumors, as either primary SQCLC or
metHNC using proteomic approaches, 518 proteins with
significantly different expression levels in HNC and SQCLC
were identified. These proteins belonged to pathways involved
in (i) vesicle transport, (ii) glycosylation, or (iii) RNA-processing.
The FAM83H expression generally upregulated in cancers, was
correlated to poor prognosis in HNC as well (115). The
locoregional recurrence after chemotherapy (platinum-based
concurrent chemoradiation) frequently occurs in HNC
patients. It was observed that the intra-tumoral heterogeneity
is linked to clonal evolution, and it is actually responsible for
cisdiamminedichloridoplatinum (II) (CDDP) resistance in HNC
(115). Niehr and co-workers (116) have applied targeted nextgeneration sequencing, fluorescence in situ hybridization,
microarray-based transcriptome, and mass spectrometry-based
phosphor-proteome analysis to elucidate the molecular basis of
CDDP resistance. This resistance was observed to be associated
with aneuploidy of chromosome 17, increased TP53 copynumber, overexpression of the gain-of-function (GOF) mutant
variant p53R248L and increased activity of the PI3K–AKT–
mTOR pathway, which were also considered as molecular
targets for treatment optimization (116). Furthermore, labelfree profiling of proteins in oral cancer has been performed by
relative quantitation and employing nano-UPLC-Q-TOF ion
mobility mass spectrometry hence, enabling rapid and
simultaneous identification of multiple cancer biomarkers
(117). This approach appears to have promising implications
on tumor diagnosis. Single cell proteomics approach has
encouraged system-wide protein profiling, direct assessment of
immune cell health and tumor–immune interactions. This
further helped augmenting evaluation of immunotherapy
(118). Moreover, profiling of every single individual cell
appears to indicate its role in tumor progression and molecular
basis of the disease (119). The p53 tumor suppressor proteins
have been counted in single colorectal cancer cells with 88%
accuracy using the MAC chip (microfluidic antibody capture)
(120). However, MAC chip utility in HNC is yet to be
The scRNA-seq data may be used for understanding the drug
response, as well as, drug resistance in individual HNC patients.
The cetuximab-treated and untreated HNC cells yielded
heterogeneous expressions of TFAP2A and EMT during the
early stage of treatments, indicating onset of resistance. The
expression variation analysis (EVA) analysis of scRNA-seq data
suggests that cetuximab treatment increases cell heterogeneity,
leading to evolution of different clonal cells with differentially
activated pathways, thereby preventing EGFR inhibition (102).
A comprehensive multi-omics, single-cell analysis was
performed in HNC cell lines by Kagohara et al., to identify
responses to cetuximab, an anti-EGFR drug (102). It was
observed that hundreds of genes altered their expression
pattern as a response to the drug within 5 days of treatment.
scRNA seq analysis identified onset of resistance following
changes in various signaling pathways including regulation of
receptor tyrosine kinases by Transcription Factor AP-2
(TFAP2A) and epithelial-to-mesenchymal transition (EMT)
pathway. Different squamous cell carcinoma cell lines exhibited
cell type dependent differential expression of TFAP2A and
Vimentin (VIM) genes that corroborates inter cell line
heterogeneity. The available HNC data bases provide clinical
and genomic information on HNC cell systems (102–104). A
holistic HNdb database curates all major omics data and
literature on HNC-related genes (105). This database has laid
the foundation for identification of possible biomarkers and
development of HNC personalized medicine. It is interesting
to note that a few genes are common in genomics,
transcriptomics and scRNA-seq analysis of the HNC
(Figure 4). These finding have stemmed from the independent
studies. Therefore, it is imperative to perform integrated multiomics studies and visualize molecular linkages using systems
medicine for paving a way for personalized medicine.
The CSCs are responsible for failures of cancer therapeutics,
drug resistance, and tumor recurrence. The single-cell
transcriptomic data from salivary gland squamous cell
carcinoma reported luminal and basal epithelial cells, as well
as, small populations of CSCs. Overall, the study indicated that
the process of tumorigenesis followed ‘gain-of-function’ by bcatenin and ‘loss-of–function’ by Bmpr1A mutations in basal
cells, EMT markers expression, and activated Wnt signaling in
CSCs of luminal cells (106).
Proteomics of HNC
In order to minimize variables arising from HNC intra-tumor
heterogeneity, analysis of differentially expressed proteins have
been strategized. Bhat et al. (107) identified 286 biomolecules,
having relevance in HNC. A few of these included i) insulin like
growth factor binding protein (IGFBP) ii) downstream signaling
components ERK, COX2, STAT, PFN2, EPCAM, SERPINH1,
MCM2, iii) genes involved in prolactin signaling iv) angiogenesis
v) DNA repair genes using integrated transcriptomics and
proteomics approach. It has been reported that the ERK,
COX2 and STAT1 proteins are important in progression and
development of chemo resistance in HNC. Hence, these may be
potential targets for effective therapy (108, 109). The saliva serves
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Current Insights and Advancements in HNC
tryptophan and cadaverine) were manifested in both the studies.
Altered levels of urea and 3-hydroxybutyric acid were also
reported for the first time in the later study (124).
A study by Wei (125), identified a signature panel of salivary
metabolites (phenylalanine, valine, g-aminobutyric acid, neicosanoic acid and lactic acid) whose levels were significantly
altered in oral squamous cell carcinomas (OSCC). Hence these
could potentially be used as biomarkers to distinguish between
healthy and disease physiologies (125). While increase in lactic
acid is simply explained by Warburg effect in glycolysis, valine
and other amino acids are found significantly to be decreased
presumably due to increased metabolic utilization. Increased
ketone bodies, abnormal lipolysis, TCA cycle and amino acid
metabolism have been reported in blood serum from OSCC
patients (126). Patients with disease relapse exhibited increase in
glucose, ribose, fructose, and tagatose with decrease in lysine,
hippurate, trans-4-hydroxy-L-proline, and 4-hydroxymandelate
in serum samples. A GC-MS based serum screening of OSCC
revealed differences in 38 metabolites at pre-operative levels in
comparison to healthy individuals. Furthermore, a comparison
of pre-operative and post-operative metabolite profiles yielded
significant differences in 32 metabolites. Seven potential
biomarker candidates were found, i glyceric acid, lauric acid,
N-acetyl-L-aspartic acid, ornithine, heptadecanoate, serine and
asparagines. The sensitivity and specificity of biomarker pairs
were assessed as 94.4% and 82.8% for ornithine+asparagine,
88.8% and 85.7% ornithine+glyceric acid, 88.8% and 97.1%
ornithine+N-acetyl-L-aspartic acid, and 88.8% and 82.8% for
ornithine+serine; endorsing their potential in early detection and
stage identification in OSCC (127). An increase in choline
compounds in OSCC implies its significant role in cancer
feedback cell signaling. These increased choline levels renders
it as a potential biomarker for cancer cell proliferation, survival
and malignancy (128). Decreased levels of PUFA and creatine,
and increased levels of amino acids and glutathione, were also
observed in a study in tissues through proton high-resolution
magic angle spinning magnetic resonance (HR-MAS MR) (129).
The significant data on HNC metabolomics, is hindered by
differences in detection and analytical methods. In addition, the
inherent heterogeneity in HNC has obstructed the identification
of an accurate biomarker for its early detection (130). Studies
based on single cell analysis have shown significant differences
from average pattern in bulk samples. Most metabolic changes in
single malignant cells are not captured through bulk
measurements as they tend to underestimate the highly
complicated cellular composition of bulk samples. Though
there is a universal upregulation of metabolic pathways, the
over-expressions of certain genes (for example, OXPHOS i.e.
oxidative phosphorylation pathway genes) are evidenced only at
single cell level. Their absence at the bulk level is credited to the
probable fallout of bulk measurements, enmeshed in the
complexity of tumor composition. Differential expression from
bulk level is also observed in genes involved in Vitamin b6
metabolism, lysine degradation, synthesis of aromatic amino
acids, drug metabolism through cytochrome P450, degradation
of fatty acids, oxidative phosphorylation, TCA cycle etc.
established. Multiplexing of protein markers at single-cell level
using immunofluorescence methods have also been applied.
However, single cell proteomics methods are in developing
state and the proteome coverage is smaller in comparison to
single-cell transcriptomics. In the context of precision medicine,
integrating the protein based prognostic biomarkers is emerging
as a supporting strategy for the treatment of cancer patients.
Most head and neck cancers expressing elevated levels of
desmoglein 3 (DSG3) metastasize to the neck lymph nodes. The
IHC and H&E reports may not always detect DSG3 during the
initial metastasis process when metastatic lesions are less than
2mm in size. The use of sensitive methods like RT-PCR, scRNAseq, and next-generation sequencing (NGS) is costlier and time
consuming. Measuring the protein expression of tumor
metastasis marker during the earlier phase of cell growth at the
single-cell level for therapeutics provides additional advantages.
The 3D printed microfluidics immune-array has a 10,000-fold
higher sensitivity, which is superior to ELISA. This does not even
requires any sorting experiments prior detection of proteins from
a single cell. Not only it detects DSG-3, VEGF-A, and VEGF-C at
lower concentrations, but its automated operations also provide
results at a fast pace and lower cost. In addition to delivering
information about HNC, it also quickly reproduces the results
with minimal errors (121).
Metabolomics of HNC
A comprehensive analysis of metabolites or metabolomic study is
cardinal to cancer pathology as metabolome is a summary
manifestation of all the other upstream omic profiles (122).
In a tissue metabolite profiling of HNC, 41 out of 109
metabolites screened were observed to be higher in tumorous
versus non-tumorous tissues, while 15 appeared lower. Serum
levels of glycolytic pathway metabolites increased (glucose,
fructose, tagatose etc.), while that of several amino acids for
example, lysine decreased significantly. Conversely, in tissue
samples the glycolytic pathway metabolites decreased, and
amino acids (valine, phenylalanine, threonine etc.) increased in
tumorous versus non-tumorous tissues (122). Since, cancer cells
depend more on aerobic glycolysis rather than oxidative
phosphorylation for energy, and also use glutamine as major
source of energy, they deplete glucose in hypo-vascular
microenvironment. Also, amino acid levels are higher due to
degradation of ECM in tumors. Another study showed the
increased levels of polyamines in saliva of oral cancer patients
in comparison to that of other cancer types. The choline to
creatinine ratio revealed oral cancer specific elevation. In
addition to this, 28 metabolites that accurately differentiate oral
cancers from control samples were also identified. However, oral
cancer may have higher impact on the metabolite composition of
saliva in comparison to other cancers simply because of its
location. Therefore, to confirm this a concurrent and
comparative metabolic profile from saliva, blood and cancer
tissue is warranted to confirm the oral cancer specific role of
choline-creatinine ratio (123). Additional conformation was
derived from another serum based study of 25 metabolites, of
which 7 metabolites (leucine, isoleucine, taurine, valine, choline,
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achieved using single-cell technology but is viable only at an
early stage of cancer. In this context the degree of single cell
chromatin accessibility also constitutes a significant
challenge (136).
The epigenetic modifications are known to control
programmed developmental changes and the ability of the
genome to register, signal and perpetuate environmental cues
(132). In order to sustain the inheritance of gene expression and
biological functions, epigenetic mechanisms are linked to the
transmission of cell lineage and phenotype from progenitor to
progeny. These modifications are now known to be transmitted
to the progeny cells with the epigenetic marks or genome
bookmarking by transcription factors and other gene
regulatory proteins (137, 138). The deviation from the
transmission of normal epigenetic marking is suggested to be
relevant not only in cell differentiation but also in the onset of
several diseases, including cancer. In this context, some other
vital chemical modifications altering chromatin states and
subsequent gene expression patterns include DNA
methylation, histone modifications, small non-coding RNAs,
and chromatin remodeling factors. This is currently a subject
of intensive study.
However, where the expression at single cell and bulk level is
different in purine metabolism, it was found similar in
pyrimidine metabolism. Twenty-four out of fifty-six pathways
show similar patterns of up-regulation or downregulation upon
comparison between single malignant cells and bulk tumors,
while 25 pathways that were reported downregulated through
bulk tumor analysis were found upregulated on single cell level
(131). Figure 5 represents the major metabolic pathways
upregulated in single cell and bulk tumor analyses. The intersection in the Venn is indicative of pathways similarly
upregulated or downregulated in both. The major cause of
heterogeneity is the variations in mitochondrial metabolic
activity (TCA cycle and Oxidative phosphorylation). Also, the
metabolic features of immune and stromal cell sub-types were
found distinct when the mean expression level of genes within
these pathways were compared. Therefore, more single cellbased studies are required to not only gain better insights but
also eliminate existing discrepancies, and to help identify
different metabolic phenotypes in cell sub-populations.
Epigenomics of HNC
Notably, intra tumor heterogeneity is the most significant hurdle
in developing effective anticancer drugs, as targeted drugs and
chemotherapy are effective until the development of drug
resistance (133, 134). Tools like single-cell pharmacokinetic
imaging have emerged as a powerful means to elucidate the
mechanism of drug resistance in the tumor that may help
overcoming the resistance (135). Characterization of cancer
heterogeneity in epigenomic sub-populations appears to be
relevant as cancer evolution, drug sensitivity, etc. are
necessarily impacted by epigenetic alterations. This can be
Methylation
Both, DNA and chromatin-associated proteins are modified to
modulate DNA accessibility and chromatin structure (139).
Methyltransferases like DNMT3A and DNMT3B are generally
altered in malignancies (140). Abnormal expression of genes in
many cancers is attributed to promoter-specific hypermethylation for gene suppression, and genome-wide hypomethylation (particularly in repetitive DNA) leading to gene
FIGURE 5 | A study by Xiao et al. shows that major metabolic pathways found up-regulated in single cell analysis were found downregulated in bulk-tumor analyses
and vice-versa. Twenty-four pathways showed similar up-regulation/down-regulation patterns in both as represented by the intersection in Venn (132).
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The putative role of gene promoter methylations or other
epigenetic modifications provides favorable options for relevant
therapeutic interventions. A study published in 2018
demonstrated an increased efficacy of immune therapy when
combined with epigenetic therapy. The sensitivities of immune
agents pembrolizumab and nivolumab were reported to be
enhanced in a pre-clinical HNC model when combined with
epigenetic drugs 5-azacytidine (DNA methyltransferase
inhibitor) and romidepsin (histone deacetylase inhibitor) (156).
Cancer specific DNA methylation patterns are effective early
detection tools based on biomarkers generated from blood or
epithelial cells shed in the lumen. The methylation status of 5
neuropeptide gene promoters (SST, TAC1, HCRT, NPY, and
GAL) are also reported to be prospective alternative prognostic
markers. For example, the methylation of TAC1, HCRT and GAL
are indicative of poor survival in oral, laryngeal, and
oropharyngeal cancers, respectively (157). The available
information on methylated gene promoters is limited to a data
subset, and the CpG island methylator phenotype (CIMP) is still
under-investigated in HNC (158). Promoter hypermethylation
has been observed in oropharyngeal cancers with HPV infection.
A study by Esposti et al. (159), performed an epigenome-wide
analysis using Illumina human methylation bead array data to
identify differentially methylated CpGs associated with HPV
infections. Five CpGs capable of predicting HPV status and
survival were found in hypomethylated regions independent of
anatomical site. This may help bypassing the issues associated
with heterogeneity, arising due to different anatomies of HNSCC.
It was observed that HPV has a genome-wide effect on the
methylome that is independent of other risk factors. On the basis
of DNA methylation patterns in 528 samples, 5 sub-clusters were
identified. Of these pertained to HPV- cancers. Although 60% of
differentially methylated genes were hypomethylated, the study
also identified hypermethylation in genes CDH18 and CTNND2
that were found to be associated with HPV status. Promoter
upregulation (141, 142). DNA methylation is reported to affect
most HNC genes involved in classical oncogenic pathways, cell
cycle regulation (143–146), DNA repair (147, 148), Wnt
signaling (149, 150), transmembrane proteins (151), tumor
suppressors (152, 153), etc. (Table 3). A recent gene
comprehensive bioinformatics analysis using microarray data
of DNA methylation and gene expression identified 27
aberrantly methylated genes with altered expression levels.
FAM135B among them was hypomethylated and hence highly
expressed. Multivariate cox proportional hazards analysis
indicated that FAM135B could be a favorable independent
prognostic biomarker for the overall survival of HNC patients
(154). The primary risk factors like tobacco and alcohol use,
human papillomavirus and Epstein-Barr virus infection can
cause genetic and epigenetic alterations leading to the
pathogenesis of HNC. Costa et al. (155) used TCGA data to
identify distinct genetic and epigenetic particularities between
HPV+ and HPV- HNSCC. The study primarily focused on gene
promoter methylation patterns and was able to identify three
different co-expression modules associated with HPV status. The
genes were not only differentially expressed in HPV+ and HPVcancers but also varied significantly between different stages of
cancer. This indicated modulation of specific gene expression at
different levels during cancer progression. However, a general
pattern of expression (over or under) was observed throughout
the stages (I-IV). Also, epigenetic modifications appeared pivotal
for HPV infection as the association between methylation and
gene expression was more potent in HPV+ cancers. TP53,
CDKN2A, and FAT1 appeared to be significantly mutated in
HPV- cancers compared to the HPV+ ones. CCNA1, PITX2,
GJB6, and FLRT3 were found under-expressed and
hypermethylated in HPV+ cancers while SYCP2 was observed
to be overexpressed in HPV+ oropharyngeal cancers. However,
contrary to some reports, no association between PIK3CA and
HPV+ cancers was observed in this study.
TABLE 3 | The names and functions of genes modified epigenetically through methylation in HNC and their effects on development and prognosis of HNC.
S.
No
1
Major classes of
genes
Cell cycle regulation
2
DNA repair
3
Wnt signaling
4
5
Transmembrane
proteins
Tumor suppressor
Member
Function of genes
Epigenetic changes
p16
Inhibit CDK4 & CDK6
p15
Inhibit CDK
TP53
CHFR
DAP-K
RASSF1A
WIF 1
Tumor suppressor
Early G2/M checkpoint.
p53 dependent apoptosis
Pro-apoptotic, negative RAS
effector
Formation of adherence
junctions
Secreted Wnt antagonist
Cox-2
Prostaglandin synthesis
Frequent methylation in OSCC, correlated with shorter survival of OSCC patients, possible
prognosis marker
Increased expression, inverse relationship with E-cadherin
DCC
KIF1A
Pro-apoptotic
Motor protein
Hyper-methylation in the promoter region in 75% of primary HNC
Methylation of promoters is frequent in HNC, observed in 38% of salivary rinses
ECAD
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Hyper-methylation in promoter region leads to CDKN2A inactivation, frequent in HNC, deregulation in cell cycle
Promoter hyper-methylation in histologically normal epithelium of chronic smokers and
drinkers
G to T transversions, patterns, differ between smokers and non-smokers
Aberrant methylation, a potential biomarker
Hyper-methylation, a biomarker for early detection and prognosis
Inactivated TSG, promotes the development of cancer
Promoter hyper-methylation, loss of E-cadherin
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Current Insights and Advancements in HNC
might be alternative targets for the development of effective
drugs for HNC.
hypermethylation was also observed in ZNF733. The study not
only highlighted hypomethylation of 60% genes for the first time
but also suggested more pronounced effect of hypomethylation
on gene expression than the hypermethylation. In addition,
hypomethylation of many cMyc target genes was observed, and
CpG island shore of SYCP2 was found to be associated with
increased gene expression. This observed role of SYCP2 with
another previously reported study (155). The 5 CpGs proposed
as an epigenetic signature to identify HPV+ cases encompassed 3
genetic loci (B3GALT6-SDF4, SYCP2-FAM127B HTLF-HLTFAS1). This predicted signature was able to integrate different
epigenetic alterations and multiple exposure levels and hence this
signature appeared as a better predictor of survival.
A study by Talukdar et al. (160) performed genome-wide
DNA methylation profiling for esophageal squamous cell
carcinoma (ESCC) using samples from 9 high incidence
countries of Asia, Africa and South America. In the discovery
phase, 108 tumors and 51 normal adjacent tissue while in
replication phase 132 tumors and 36 normal tissues were
analyzed. The study identified 6,796 differentially methylated
positions and 866 differently methylated regions. Pathways
important for cancer development like WNT and hippo
signaling, cell communication pathways etc. were found
enriched. PAX9, SIM2, THSD4 were identified as top genes
with crucial DNA methylation events, and were observed to be
downregulated in tumors. Among all differentially methylated
regions, 88% were found differentially expressed between normal
and tumor tissues. The study also reported THSD4, PHYHD1,
GPT, KCNJ15, and TP53AIP1 for the first time in ESCC.
However, there is ample scope for more such studies in
HNSCC to identify non-random tumor specific methylation
events to provide attractive avenues for biomarker
development and therapeutic intervention.
Non-Coding RNAs
Non-coding RNAs do not code for proteins like RNA, but have
enzymatic, regulatory, and structural functions (143). It is now
known that microRNAs regulate cellular processes like
proliferation, differentiation, and apoptosis via altered signaling
in malignancies. Levels of miR-21, miR-16, and miR-30a-5p have
been reported to be increased in HNC. Likewise, miR-205 and
let-7a were also reported increased in both benign and malignant
squamous epithelia (163). Conceivably, microRNAs act both as
tumor suppressors or oncogenes. Epigenetic silencing of tumor
suppressor mRNAs by CpG island hypermethylation is now
emerging as a hallmark for human tumors. Hypermethylation in
miR-148a, miR-34b/c and miR-9 was observed to be associated
with downregulation of CMYC, E2F3, CDK6 etc. (164).
A long non-coding RNA LINC00312 is significantly downregulated in nasopharyngeal carcinoma. Since it inhibits the
progression of the G1 to S phase, its reduced expression leads
to tumor progression (165). HOX antisense intergenic RNA
(HOTAIR) influences progression, metastasis and drug
resistance in many cancer types. It is a prime candidate for a
therapeutic target in cancer, as tumor cells contain significantly
increased levels of HOTAIR, and its inhibition induces their
apoptosis (166). The emerging understanding of HNC
epigenetics is expected to benefit in understanding the
prognosis and susceptibility of cancer to different therapies in
isolation or their combinations.
The levels of complexity in epigenetic modifications have
impeded their translation into instruments of cancer prognosis
and therapeutics. Also, the bulk methodologies fail to capture the
cellular diversity and tumor heterogeneity. Epigenome
sequencing on single cell level can identify epigenetic and
chromatin marks in single cells. A recent single cell based
study identified the role of miR-142-3p in repressing CLIC4.
CLIC4 was found expressed more in tumor associated fibroblasts
and endothelial cells as compared to tumor epithelial cells. The
discrete patterns of localization and inverse co-relation of
expression in both indicates the ambiguity related to bulk
measurements (167). Development of advanced techniques like
i) single cell genome-wide bisulphite sequencing (scBS-seq),
ii) single cell chromatin integration labelling followed by
sequencing (scCHIL-seq), and iii) single cell sequencing for
transposable accessible chromatin (scATAC-seq) might
provide insight into contribution of epigenomics in cellular
heterogeneity. While these technologies uncover many aspects
of cancer biology, further studies for HNC are still awaited. The
applications of advanced techniques remains limited due to
challenges in the unbiased amplification of a small amount of
genetic material from a single cell (61). Table 4 summarizes all
the major biomarkers identified at bulk and single cell level.
However, only limited studies are performed at single cell level,
and therefore literature available is still limited. Therefore,
reiterating the necessity of more studies at single-cell level to
help remove discrepancies and facilitate accurate identification
of biomarkers.
Post-Translational Covalent Histone Modifications
Histone modifications such as acetylation, methylation, and
ubiquitination of lysines, serine, threonine phosphorylation,
etc., modify the accessibility of DNA for transcription factors
and associated machinery. On comparing OSCC with healthy
tissues, altered levels of histones H3K4me2 and me3 were
observed (161). The significance of post-translational histone
modifications can be understood by understanding their role in
the development of chemoresistance which is also observed to be
mediated by NFƘB. Studies have shown that chemo-resistant
HNC cells have increased deacetylation of histones, that leads to
chromatin compaction and further to impaired DNA damage
repair. Subsequently, increased accumulation of histone gH2AX
through serine phosphorylation, increases genomic instability.
This implies chemoresistance may be prevented by HDAC
inhibitors (162).
SENP5, a desumoylating enzyme, is overexpressed in OSCC
and is related to poor prognosis (143). Likewise, lysine-specific
demethylase 1 (LSD1) expression is upregulated in HNC, leading
to increased growth and metastasis. Therefore, pharmacological
attenuation of LSD1 should inhibit growth specific target genes
and signaling pathways (161). Therefore, it is reasonable to
speculate that epigenetic regulators and histone modulators
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TABLE 4 | Major biomarkers identified using different forms of omics profiling in head and neck cancer at single-cell level and bulk tumor level.
OMIC PROFILING
GENE/PROTEIN
SINGLE CELL ANALYSIS/BULK-TUMOR ANALYSIS
Genomics
NOTCH
FBXW7
PI3KCA
Akt
mTOR
EGFR
FGFR
VEGFR
NRF2
MLL2
NSD1
EP300
FAT1
TP53
CDKN2A/2B
PPFIA1
FADD
ANO1
Bulk tumor analysis
Transcriptomics
TP53
CDKN2A
E2F1
RB1
p110a
MMP
uPA
ITGA3/ITGA5
IL-8/IL-18/IL-2
CXCL1
CD4/CD28
MHC-1/2
KRT6C/KRT6B
Bulk tumor analysis
VIM
TFP2A
Single cell based analysis
IGFBP
ERK
COX2
STAT
PFN2
EPCAM
MCM2
SEPINNH1
Bulk tumor analysis
PLUNC
Zinc-alpha-2-glycoprotein
Saliva and serum
Calgranulin A/B
Hsp90
VIM
Keratin
Pre-lamin A/C
PGAM-1
Bulk tumor analysis
DNMT3A/3B
FAM135B
SENP5
HDAC
LINC00312
H3K4me2/me3
GH2AX
LSD1
miR-21/16/30a-5p
HOTAIR
TP53/TP53AIP1
Bulk tumor analysis
Proteomics
Epigenomics
(Continued)
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TABLE 4 | Continued
OMIC PROFILING
GENE/PROTEIN
SINGLE CELL ANALYSIS/BULK-TUMOR ANALYSIS
CDKN2A
FAT1
CCNA1
PITX2
GJB6
FLRT3
SYCP2
CDH18
CTNND2
ZNF733
B3GALT6SDF4
HTLF/HLTF-AS1
PAX9,
GPT,
SIM2
THSD4
PHYHD1
KCNJ15
CLIC4
miR-142-3p
Single cell analysis
SINGLE CELL DERIVED SPHEROIDS FOR
DRUG ASSESSMENT/DEVELOPMENT
resistance and heterogeneity, it is important to identify the response
of a tumor to any anti-cancer drug. The scRNA seq is powerful tool
to investigate varying modes of chemoresistance in tumor cells
derived from oral squamous cell carcinoma patients (OSCC). The
cells isolated from the HNC patients undergoing cisplatin treatment
were studied for drug resistance pattern, ITH, tumorogenic
properties, and metastasis. Epithelial (ECAD+/VIM−) to
mesenchymal (ECAD+/VIM+) transitions were identified in
tumor and patient-derived cell lines. Also, it was determined that
resistant cells can acquire metastatic characteristics and vice versa.
The study highlights the predictive power of OSC7C patient derived
primary cell line and scRNA-seq technology in revealing not only the
course of tumor evolution in the clinic, but also in predicting
mechanistic insight that can be exploited to design the next
generation therapeutic strategies (169).
In another study, stem cell enriched 3D spheroid model was
generated from cells taken from fresh tumor biopsies with
different techniques such as hanging Drop (HD) and ultralow
attachment (ULA) assays. The goal was to determine the ideal
therapy regimen and identify mutation status specific to patients
and therapy targets (170). In their approach, firstly the radiation
treatment (2 Gy) plus cisplatin (2.5/5/10 mM) was given while in
2nd approach chemotherapeutics alone were given. The study
observed spheroids generated from ULA to be more reproducible
and reliable than HD method. The spheroid model was found to
be much better method for the study of drug effectiveness and
mechanism behind drug resistance. But how the spheroids are
developed are also important factor in drug screening and
development. The two important spheroid growing techniques
are culture free floating spheres (171) and multicellular tumor
spheroid (MCTS) (172) which was earlier used for screening of
several anti-cancer compounds. Both techniques have their own
limitations. Thus, to screen the active compounds targeting
cancer stem cells (CSC), stem cell-enriched spheroid model
The current strategies for drug assessment and development
involves the use of in vitro 2D techniques and animal models
that are not only challenging in terms of genetic alteration and
cellular heterogeneity but also are expensive approaches. The
limitations of the 2D cultures are already discussed in
Heterogeneity: A Challenge in the Treatment of Head and Neck
Cancer and Road Towards Solutions. Single-cell and spheroid
technology is an evolving science in HNSCC treatment, its
therapeutic application comes in to play when selecting a chemical
or biological agent. The gene expression patterns could be studied by
using RNA sequencing from single cell derived spheroid, which can
then be used to determine the most appropriate course of treatment
on patient to patient basis. The data from recent melanoma studies
suggests the presence of unique malignant cell signatures that are
able to define the response to immune checkpoint inhibition (ICI),
which is usually highly variable and difficult to predict, this could be a
provocative possibility if extended to HNSCC (168). In other study
an integrated analysis of cancer cells has been shown in HNSCC,
where transcriptomes of ~6,000 single cells were profiled from 18
HNSCC patients to provide knowledge of the HNSCC ecosystem
and define stromal interactions and a p-EMT process associated with
metastasis, providing a detailed, molecularly-based predictor of
adverse biologic features that drives clinical decision-making. Here,
computational approach for inferring malignant cell-specific profiles
from bulk expression data was used to refine HNSCC sub-types and
provide a general scheme to extract information from other cancer
datasets (60). Such study proves to be stepping stones in enhancing
the understanding of intra-tumoral expression heterogeneity in
epithelial tumors and might be able to guide future diagnostic
strategies and treatment algorithms. Since even same type of
tumor shows different response to the same therapy because of
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(SCESM) were generated using FaDu cells exploiting selective
properties of both the techniques by Gorican et al. (173).
Treatment of SCESM spheroid with all-trans retinoic acid
(ATRA), a differentiating agent also used in HNSCC therapy
reduced the stem cell marker expression, thus confirms the
sensitivity and specificity of the spheroid.
In a study by Melissaridou et al. (174), cisplatin (1, 2 and 4 mg/
ml) and cetuximab (60, 90 and 120 nM) treatment response were
investigated on 3D tumor spheroids and 2D monolayers cells using
a MTS-based assay. The cells cultured on 3D were found to be less
sensitive to cisplatin compared to cells in 2D. The 3D spheroids
were checked for the expression of three cancer stem cell (CSC)
markers viz. CD44, SOX2 and NANOG and six EMT-associated
genes (CDH1, CDH2, VIM, FN1, TWIST, FOXC2). A higher
expression of CSC marker, CDH1 in 3D cultures was observed.
EMT profile in HNSCC has been linked to drug resistance (175),
however no evident pattern was observed in the study depicting
towards other co-factors causing drug resistance.
Organoids developed by Driehuis et al. offered wide range of
applications, which includes drugs screening of conventional drugs
such as cisplatin, docetaxel, and fluorouracil or experimental
targeted agents as well as predicting drug response of individual
HNSCC patients. The study established starting point where
chemo/RT response from multiple organoids generated from
tumor biopsies of same patient can be directly compared to
patient’s clinical response. Thus, establishment of an organoid
model can lead to important advances in HNSCC diagnostics and
treatment (36). In another study single-cell RNA sequencing was
used in advanced melanoma to analyze sub-populations of T-cells.
This study found significantly higher expression of TCF7 in
treatment responders versus non-responders suggesting that the
transcription factor TCF7 was among the chief markers predictive
of a good clinical response (61, 176). As per the current scenario
there is no similar multi-omics study, performed for HNSCC
patients. Such studies could guide in analyzing changes in intratumoral heterogeneity with exogenous agents such as various forms
of chemotherapy (e.g. cisplatin), biologic therapies (e.g. cetuximab),
radiation, and ICI and could also help in patient selection for
systemic chemotherapy or immunotherapy (61). From target
identification to hit identification, single-cell spheroid has made
its way as a new and emerging technique having significance at
various levels in drug discovery. Although advancements in singlecell and spheroid technology are relatively encouraging, nonetheless
there are no reports till date exemplifying validation and application
of these technologies in clinical setups. Not much data is available
on clinical application with patient-derived single cell spheroids and
organoid in HNC. Hence, implementation and therapeutic
application for the treatment of HNC in clinical routine is awaited.
strategies. Though many targets are now being explored under
different experimental set-ups for HNC treatment, the available
drugs are against a minimal number of targets. In the past decade,
several genetic mutation studies have identified specific essential
genes that are mutated in the key biological pathways (Table 5)
and could be potential targets for future drug development in
HNC. The potential drug targets can be identified using integrated
omics and mutational analysis to identify alterations in genes and
pathways specific to HNC. Several mutational studies report most
cancer-causing mutations in tumor suppressor genes instead of
oncogenes (73, 90). Recently the targeted therapy (Precision
Medicines) and gene therapy approaches have received a lot of
interest from researchers. The targeted therapy approach takes
advantage of differences between normal cells and cancer cells,
interfering with specific “molecular targets” and blocking the
growth and spread. The best known targeted therapies are
Epidermal growth factor receptor (EGFR), monoclonal
antibodies (cetuximab, panitumumab, zalutumumab, and
nimotuzumab), checkpoint inhibitors (pembrolizumab and
nivolumab), EGFR tyrosine kinase inhibitors (gefitinib, erlotinib,
lapatinib, afatinib, and dacomitinib), vascular endothelial growth
factor (VEGF) inhibitor (bevacizumab) or vascular endothelial
growth factor receptor (VEGFR) inhibitors (sorafenib, sunitinib
and vandetanib) and inhibitors of phosphatidylinositol 3-kinase/
serine/threonine-specific protein kinase/mammalian target of
rapamycin against HNC. On the other hand, gene therapy is an
efficient anti-tumor treatment that uses genes to treat or prevent
disease, and is rapidly evolving in cancer therapy (177). The indepth transcriptomics and genomics studies could further
determine the essential genes that could be considered for gene
therapy in HNC treatment. The adenovirus vector carrying the
p53 tumor-suppressor gene is one of the product for gene therapy
approved in China for HNC treatment since 2003 (178). DNA
damage response (DDR) pathway is another potential target for
anticancer therapy. During the progression, most cancers lose one
or more DDR pathways leading to greater genetic instability and
increased dependence on other pathways. Targeting different
proteins involved in the DDR pathway has shown efficacy in
treating cancer.
Protein Kinases as a Drug Target in HNC
Protein kinases are involved in cancer metabolism and have been
the second most important drug targets in the pharmaceutical
industry after G-protein-coupled receptors. The crystal
structures of kinase-inhibitor complexes of different families
have been determined, these include (i) receptor tyrosine
kinase (EGFR, HER2), vascular endothelial growth factor
receptor (VEGFR), JAK2, JAK3, Syk, ZAP-70, Tie2, EGFR, VEGFR, FGFR) (ii) non-receptor tyrosine kinase (Bcr-Abl)
NOTCH1, Janus kinase (JAK) (iii) serine-Threonine kinase
(Clk, Dyrk, Chk1, IKK2, CDK1, CDK2, PLK, JNK3, GSK3,
mTOR, p38 MAPK, PKB) (iii) Rho-kinase (iv) Cyclindependent kinases (179). In these structures, active and
inactive state of the protein kinases, ATPase pocket, point
mutations, catalytic and non-catalytic domains of the kinases
have been used as targets by kinase inhibitors and provided the
mechanism of inhibition. A well-documented crystallographic
POTENTIAL MOLECULAR TARGETS IN
HEAD AND NECK CANCER
In cancer treatment, selection of therapy, drug administration,
and dosing is a complex process varying on a case-to-case basis.
The current treatment regimen used for HNC treatment aims at
preserving organ and function, unlike the past treatment
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TABLE 5 | Alterations in key pathways in head and neck squamous cell cancer.
S. No.
Alterations of Pathway
Genes Involved
% Frequency of Mutations
1
Mitogenic
2
Cell Cycle
3
NOTCH Signaling
4
Oxidative Stress Response gene
EGFR
PIK3CA
HRAS
PTEN
CDKN2A
Cyclin D 1(CCND1)
Retinoblastoma (RB1)
F-Box and WD Repeat Domain Containing 7, E3 Ubiqutin Ligase (FBXW7)
NOTCH1
TP63
KEAP1
Cullin -3 (CUL3)
NFE2L2
analysis of the cAbl kinase domain with Gleevec inhibitor
revealed locking of the protein kinase’s inactive conformation
(180). The conformational flexibility and stability of protein
kinases are central to their inhibition and subsequent drug
designing strategies. IIdentifying the diagnostic significance of
p38 isoforms in HNC and the subsequent design of specific
peptide inhibitors against p38a MAPK aims to contribute to
anti-kinase drug development, and the expanding expertise offers
optimistic prospects for future cancer treatment.
3
20
3
2
15
0
3
5
19
2
4
4
6
structure of HER2 receptor in complex with herceptin Fab (186)
shows that Fab binds at a specific site at the C-terminal of
domain IV, which is involved in binding to other domains in
closed conformation of HER1 and HER3 receptors. The
Herceptin Fab binding to this pocket (close to the membrane)
will engage the HER2 receptor with endocytosis machinery and
inhibit the receptor signaling. In combination with radiotherapy,
other EGFR antibodies IMC-C225 (cetuximab, Erbitux),
Thermacin h-R3 (Cimaher) based on the given principle are
getting quite successful in treating HNC cancer.
ATPase Pocket of Protein Kinase as a Drug Target
The ATPase pockets of the protein kinases are quite conserved
and offer an attractive target for drug design. It is important to
understand whether a unique combination of specific amino
acids or only a few conserved residues in ATPase site are
involved in ATPase binding mechanism present in various
protein kinases. The structures of protein kinase-A in complex
with Fasudil and a more potent Rho-kinase inhibitor H-1152P
were determined (182). The structural analysis shows the
characteristic binding site within the ATP site, though the
difference is of only two methyl groups between both
the complexes.
Single Residue in Protein Kinase as a Drug Target
Single residue in active sites of protein kinase plays a key role in
inhibitor mechanism and can be used as a drug target. The
BIRB796 (diarylurea) inhibitor binds to a specific sub-site in
ATPase pocket of p38 kinase structure, incompatible for ATP
binding (187). This BIRB796 inhibitor binds to Phe residue in
the conserved DFG motif buried in a hydrophobic pocket located
between two lobes of p38 kinase. In another case, from the
structure of the complex between SU5402 and FGFR1 tyrosine
kinase (188) the inhibitory binding modes of the indole-2-one
family of anti-angiogenesis molecules (SU5416, SU668, SU1248)
were identified. The methyl pyrrole ring attached to C3 in these
inhibitors stabilized by an intra-molecular hydrogen bond
between pyrrole nitrogen and the O2 atom of the oxindole
ring. In SU5402-FGFR1 complex structure, a hydrogen bond
between carboxyl group of SU5402 and the side chain of Asn568
of FGFR1 is important for inhibition.
Despite various encouraging results by kinase inhibitors, the
critical challenges are drug resistance that mostly occurs through
acquired resistance after initial treatment, toxicity, and
compromised efficacy at the clinical level (189). In the clinical
assessment, the key challenges are to develop efficient
combinations of treatment after recognizing the kinase targets
for particular cancer.
Additional Binding Pockets of Protein Kinase as a
Drug Target
An additional hydrophobic pocket close to the ATPase pocket in
the protein kinase structure plays a crucial role in the inhibition
mechanism. In p38 and C-Abl structures, a small threonine
(Thr) residue lies as a gatekeeper and it interact with designed
inhibitors to block ATP entry in the kinase domain. In the crystal
structure of CDK2 with roscovitine (183), the isopropyl group of
inhibitor interacts with gatekeeper Thr residue. In EGFR-tarceva
crystal structure (184) the tarceva binds similar to anilinequianzolines binding to CDK2 and p38 kinases (185), and
acetyl moiety interacts with gatekeeper Thr residue. This
pocket is similar to as observed in Gleevec-6-methyl group
bound to C-Abl kinase.
G Protein-Coupled Receptors (GPCRs) as
a Drug Target
Non-Catalytic Domain of Protein Kinase
as a Drug Target
G protein-coupled receptors (GPCRs) are involved in signaling
pathways and can elicit both cytostatic and cytotoxic effects. Four
of the GPCRs, (i) galanin receptor type 1 (GALR1) (ii) GALR2,
(iii) tachykinin receptor type 1 (TACR1), and (iv) somatostatin
In protein kinase structures, non-catalytic domains have been
observed, which play key role in kinase activity. The crystal
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Current Insights and Advancements in HNC
inhibitors of PI3K and mTOR pathways, e.g., px-866, an
inhibitor of PI3K that binds to ATP catalytic site (194).
Another antiproliferative and immunosuppressive drug
sirolimus (extracted from streptomyces bacteria) demonstrated
critical challenges in the form of poor bioavailability and long
half-life in patients leading to frequent monitoring of the drug
(195). Thus, substituting the drug with its better analogs with
improved pharmacokinetic properties seems desirable.
A study by our group on the effects of a combination of two
drugs against HNC showed that a combination of resveratrol and
quercetin improved cytotoxicity and altered gene expression in
oral cancer cells (196). The above combination of drugs was
found to modify the epigenetic markers by downregulating
histone deacetylases such as HDAC1, HDAC3, and HDAC8.
The cetuximab is an approved targeted therapeutic against
HNC. It is accredited for first-line use with platinum-based
chemotherapy: the chemotherapy plus cetuximab appreciably
extended basic survival compared to chemotherapy alone.
Significant improvements were visible within the progressionfree survival and goal response prices. In a retrospective analysis
of the trial, the enhancements observed with cetuximab were
regarded on par with tumors being HPV positive against tumor
being HPV negative (197). The single-cell analysis following
treatment with cetuximab to different squamous carcinoma cell
lines identified a heterogeneous cell population (198). Resistance
to cetuximab appeared to be cell-type-specific which was
attributed to altered gene expression of TFAP2A and EMT.
However, resistance to cetuximab was found to be very
common in HNC. Various evading mechanisms such as
mutations in receptors may act in accordance to restore
original oncogene dependence. A gain in copy number of
target genes is another factor that counteracts the action of
inhibitors. It has been found that altered copy number by
amplification of chromosome 7p11.2 which encompasses
EGFR gene, causes various cases of changes in EGFR
activation in HNC (199). Gillison et al. (200) observed that
with HPV-positive oropharyngeal squamous cell carcinoma
(OPSCC) patients, cetuximab and radiotherapy demonstrated
an inferior overall survival when compared with radiotherapy
plus cisplatin.
The cisplatin plus fluorouracil treatments were given to 657
patients in the SPECTRUM phase III trial, with or without
panitumumab, another monoclonal antibody targeting the
EGFR receptor (201). A statistically non-significant trend
indicated increased overall survival with the addition of
panitumumab. As with the EXTREME trial using cetuximab,
there was slightly more toxicity in the panitumumab arm than in
the control arm. In the phase II trial (202) however, a
comparison was made to identify the efficacy of panitumumab
plus radiotherapy with chemoradiotherapy groups in locally
advanced HNC patients. In the combined study, the efficacy of
panitumumab was found to be inferior to cisplatin. It cannot be
considered as its substitute for the treatment of unresected stage
III–IVb HNC.
Larotrectinib is another type of targeted therapy that does not
target specific cancer types but focuses on specific genetic
receptor type 1 (SST1) are the most studied and promising
therapeutic target in a wide variety of cancer. GALR1 & 2 both
inhibit cell proliferation and apoptosis of HNC cells. GALR1 act
through ERK1/2-mediated activation of cell cycle control
proteins such as p27, p57, and suppression of cyclin D1
protein. In p53 mutant HNC cells, GALR2 was found to have
anti-proliferative and pro-apoptotic effects (190). The significant
reduced disease-free survival and a higher recurrence rate is
associated with hypermethylation of GALR1, GALR2, TACR1,
and SST1. Methylation of GAL, TAC, and SST and its
investigation as potential prognostic markers in HNC has
already been discussed in Epigenomics of HNC.
THERAPEUTIC AGENTS TARGETING
POTENTIAL BIOMARKER IN HNC
PATIENTS
The primary treatment of HNC patients includes surgery,
radiotherapy, and chemotherapy. However, a high recurrence
rate, resistance to radiotherapy, and reduced life quality are major
issues. Surgery and radiotherapy are the key treatments for earlystage tumors. However, therapeutic interventions are completely
based on accessibility to the tumor, i.e., the tumor location, and not
on the specific biology of the tumor. An increased understanding of
cancer biology has led to the discovery of biomarkers, which can be
efficaciously targeted to improve patient outcomes. Patients
experiencing recurrence unable to be treated with surgery or
radiotherapy, having limited overall median survival of one year,
have shown better response to immune check point inhibitors
targeting programmed cell death in HNC. Now clinicians need to
determine that how targeted therapy can be best included/combined
with immunotherapy. Clinical trials evaluating the combination of
molecular targeted therapy with immunotherapy are emerging
regularly. The results of such clinical trials will suggest us whether
molecular targeted therapy and immunotherapy benefit different
patients with different molecular alterations or can be used in
combinations (191) (Tables 6 and 7).
The standard systemic treatment regimens for HNC include a
combination of different drugs. However, overall survival rates
are still very low, and due to the use of combinations of several
drugs, the upper limit of toxicity seems to have been reached,
causing the death of patients (192).
Targeted Chemotherapy
The chemotherapeutic agents such as, afatinib, poziotinib,
vandetanib, nintedanib, gefitnib, erlotinib, lapatinib,
dacomitinib, alpelisib, PX-866 are the multitargeted inhibitors
of protein kinases that regulate Ras/Raf/MEK/ERK/PI3K
signaling pathways (193). The immunotherapeutic approaches
such as specific antibodies targeting tumor, cytokines, cancer
vaccines, and immune-modulating agents are other cancer
treatment strategies, discussed below and in Tables 6 and 7.
A continuous flow of new molecules, explicitly targeting the
upcoming biomarkers, is required as few of the promising agents
have failed to show desired results in clinical trials. These include
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Current Insights and Advancements in HNC
TABLE 6 | A List of Therapeutic agents and Their Mechanism of Action against HNC.
S.
Therapeutic
No. agents
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
Clinical
Phase
Cisplatin
FDA
Approved
Methotrexate
FDA
Approved
5-fluorouracil
FDA
Approved
Bleomycin
FDA
Approved
Docetaxel
FDA
Approved
Pembrolizumab FDA
Approved
Nivolumab
FDA
Approved
Cetuximab
FDA
Approved
Zalutumumab
Phase III
Panitumumab
Phase III
Pimotuzumab
Phase III
AV-203
Phase I
Afatinib
Phase I
Poziotinib
Phase II
Vandetanib
Phase II
Nintedanib
Phase II
Gefitnib
Phase III
Erlotinib
Phase III
Lapatinib
Phase III
Dacomitinib
EGFR
antisense DNA
Foretinib
Figitumumab
Sunitinib
Sfatinib
Bevacizumab
SCH-58500
Advexin
H-101
Gendicine
ONYX-015
Sorafenib
Phase II
Phase II
Dasatinib
Buparlisib
Alpelisib
(BYL719)
PX-866
Copanlisib
Temsirolimus
Everolimus
Sirolimus
Gedatolisib
Ficlatuzumab
IRX-2
INO-3112
MEDI4736
Durvalumab
MAGE-A3
MAGE-A3
HPV16 Vaccine
DC vaccine
Phase II
Phase II
Phase II
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
II
II
II
III
III
III
III
III
II
III
II
II
II
II
II
I
I
II
II
II
II
III
I
I
Phase I
Mechanisms of Action
Interferes with DNA replication, kills carcinogenic cells. It acts through ERBB signaling pathways
Inhibits folic acid reductase, leading to inhibition of DNA synthesis and replication. It acts via interaction with enzymes of folate
pathway.
Thymidylate synthase inhibitor. Inhibits deoxythymidine mono-phosphate (dTMP) production. dTMP is essential for DNA replication
and repair and therefore its depletion causes cytotoxicity
DNA inhibition by induction of DNA strand breaks
Interferes with the normal function of microtubule growth by hyper-stabilizing their structure. It activates JNK signaling pathway and
inhibits Hypoxia-inducible factor (HIF-a) and cancerous cell death under hypoxic conditions.
Targets programmed cell death protein (PD-1). PD-1, a member of the B7/CD28 family of co-stimulatory receptors, regulates the
activation of T-cell.
Anti-PD-1 monoclonal antibody. It acts via inhibition of T cell proliferation and cytokine production
EGFR antagonist
Tyrosine Kinase Inhibitor (TKI). It targets the EGFR, HER2, and HER4
Multitarget TKI. Targets EGFR and VEGFR
It targets VEGFR1–3, Platelet-derived growth factor receptor (PDGFR), and Fibroblast Growth factor receptor (FGFR1–2)
Selective, reversible inhibitor of EGFR tyrosine kinase domain
Inhibits the intracellular phosphorylation of tyrosine kinase of the EGFR
Inhibitor of the intracellular tyrosine kinase domains of both epidermal growth factor receptor and human epidermal growth factor
receptor type 2
Irreversible, potent inhibitor of HER1/EGFR, HER2, and HER4 tyrosine kinase.
Antisense DNA
Target multiple RTKs
Human IgG2 mAb act against Insulin-like growth factor type I receptor (IGF-1R) pathway
Multi-target tyrosine kinase inhibitor
Potent, selective, and irreversible ErbB family blocker
Anti –VEGF mab. It blocks the binding of circulating VEGF to its receptor.
p53 stimulants (Recombinant adenovirus that encodes for gene human tumor-suppressor p53)
Inhibit multiple intracellular and cell surface kinases in the Ras/Raf/MEK/ERK signaling pathways. The drug inhibits Raf-1, B-Raf, and
kinase activity, PDGFR-b, VEGFR 2, hepatocyte factor receptor (c-KIT), and other proteins to inhibit tumor angiogenesis.
Inhibit multiple kinases
Inhibitor of PI3K signaling pathway (P13K inhibitor)
P110a inhibitor.
Inhibitor of PI3K pathway that binds to ATP catalytic site.
Inhibitor of class I PI3K (preferential activity against PI3Ka and PI3Kd)
mTOR kinase Inhibitor
class I PI3K and mTOR dual inhibitor
Hepatocyte growth factor inhibitor
multi cytokine stimulant that enhances the immune response. Akt/PI3K pathway is the prominent downstream target
It is a combination of two previously developed DNA vaccines, VGX-3100 and INO-9012. It acts as an Immunostimulant.
Human IgG1 mAb that binds to Programmed cell death ligand 1(PD-L1) and blocks its binding to PD-1
mAb that targets PD-L1
Peptide epitope vaccine. It elicits spontaneous cellular or humoral immune response.
Peptide epitope vaccine
Immunostimulant
(Continued)
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Current Insights and Advancements in HNC
TABLE 6 | Continued
S.
Therapeutic
No. agents
Clinical
Phase
50
51
52
53
54
55
Phase
Phase
Phase
Phase
Phase
Phase
Tavolimab
Urelumab
Utomilumab
Ipilimumab
Tremelimumab
Motolimod
I
I
I
I
I
I
Mechanisms of Action
IgG1 agonist antibody targeted against OX40 (a member of Tumor necrosis factor receptor family)
Human IgG4 mAb that targets CD137 receptor
Humanized IgG2 mAb.Engages the immune costimulatory molecule 4-1BB/CD137
Human mAb. It Blocks the interaction of cytotoxic T –lymphocyte antigen (CTLA-4) with its ligands (CD80/CD86)
An IgG2 Ab. Involved in Immune activation by blocking the CTLA-4 negative costimulatory receptor.
TLR-8 agonist that stimulates antigen-presenting cells that express TLR-8
against ligand is bevacizumab that targets VEGF. Bevacizumab,
has shown some evidence of activity combined with platinumbased chemotherapy. However, bevacizumab does not have a
role in managing advanced or metastatic HNC outside of a
clinical trial setting. Combining bevacizumab with
chemotherapy in the first line of treatment of advanced
metastatic HNC showed enhanced response rate and increased
toxicity. In the E1305 trial, 403 patients without prior systemic
therapy for advanced HNC were randomly assigned to platinumbased chemotherapy with or without bevacizumab (216). Thus,
cisplatin with either fluorouracil or docetaxel or carboplatin with
either fluorouracil or docetaxel were used. The primary endpoint
was overall improved survival.
There has been substantial progress in the development of
mAbs targeting FGFR pathway. Trastuzumab, a mAb targeting
HER2, binds to domain IV of HER2 and blocks the homodimerization. In a phase II clinical trial study the effectiveness of
trastuzumab on patients with advanced/metastatic salivary gland
cancer was conducted, however no result is posted till date
(NCT00004163) (217) (Table 6).
The transmembrane glycoprotein Trop2 is involved in several
cell signaling pathways and is upregulated in a variety of cancers,
including HNC. The overexpression of Trop-2 is associated with
poor disease-free and overall survival in several solid tumors.
IMMU-132 (hRS7-SN38 or Sacituzumab govitecan) is an
Antibody Drug Conjugate (ADC) that target Trop-2. It
consists of an antibody, hRS7 linked to SN38. SN38 is the
active metabolite of irinotecan. The preclinical data
demonstrated 136-fold more SN-38 delivery by IMMU-132 to
a xenograft mouse model than irinotecan with lower toxicity,
including lesser cases of severe diarrhea than irinotecan alone.
IMMU-132 is under phase I/II clinical trials for evaluation of the
safety and efficacy in patients of HNC (NCT03964727 &
NCT01631552) (218–220). Table 7 enlists clinical trials with
combination of small molecules along with immunogens.
changes in neurotrophic tropomyosin receptor kinase (NTRK)
genes. This uncommon genetic change was found in head and
neck cancer. NTRK is observed to be highly expressed in
aggressive cancer and is used as a predictive biomarker and
drug target (203). This FDA- approved TRK inhibitor is used for
tumor-agnostic treatment after pembrolizumab (204).
Gene therapy is another targeted approach used to treat a variety
of cancers, including head and neck cancer. The p53, a tumor
suppressor gene, is mutated in over 50% of all types of cancers in
humans. It plays a critical role in suppressing malignancy. Thus,
restoration of functional wild-type p53 gene appears as a promising
strategy for cancer treatment. The commonly used p53 stimulants
are advexin, gendicine, ONYX-015, and H-101 (205).
Monoclonal Antibodies as Targeting Agent
Monoclonal antibodies play a major role in the treatment of
HNSCC. Monoclonal antibodies, such as antibody-drug conjugate
to cytotoxic agents (206), are used to target particular cell surface
proteins conferring tumor specificity by identifying selective targets.
Clinically useful agents that target cell surface proteins in HNC such
as AVID100 for EGFR; BAY1129980 for C4.4a; IMMU-132 target
for TROP-2 antigen, and tisotumab vedotin are being developed
and investigated (207–210).
The other approved targeted antibodies have been developed
against specific targets such as CTLA-4 and programmed cell death
protein 1(PD-1) that can stimulate co-stimulatory signals. The later
one includes the agonistic mAb against OX-40 such as tavolimab
and CD137 (urelumab, utomilumab) (211) or toll-like receptor 8
(TLR-8) agonist (motolimod) that mimics the viral ssRNA, the
natural ligand of TLR8 and enhances immune response (212). The
motolimod plus cetuximab plus was found to be safe in a phase I
trial metastatic HNC patients (https://clinicaltrials.gov/ct2/show/
NCT04272333) (213). The mAbs, pembrolizumab, and
nivolumab are the approved PD-1 inhibitors. These have shown
lasting responses in many cancers and were rapidly expanded for
use in HNC treatments (214).
Another mAb that targets the EGFR domain, prevents a
change in its conformation required for its activation. A
randomized phase III trial with zalutumumab has failed to meet
its endpoint of improved overall survival or no disease-specific
survival and thus was suspended for further development (215).
Other antibodies that targets different pathways/receptors and are
under evaluation in head and neck cancer clinical trials. These
include DLL/Notch pathway, FGF/FGF-R, HER2, TROP2 protein
and VEGF/VEGF-R pathway and are discussed underneath.
Angiogenesis is important process in tumor growth and
metastasis. The first-in-class anti-angiogenic mAb directed
Frontiers in Oncology | www.frontiersin.org
Small Molecules as Targeting Agents
Small molecules have emerged as an important class of targeting
agents that target multiple TK. The well-known molecular
targets which have shown promising results are EGFR, EGFR
TK and VEGF/VEGFR inhibitors and protein kinases or PI3K.
Gefitinib and erlotinib are the most common EGFR TKIs that
are being used in clinical studies (phase II) for treatment of
HNSCC. Lapatinib is another TKI that targets ErbB1/ErB2. The
phase II study of lapatinib plus chemoradiotherapy in HNSCC
has showed beneficial effect in HPV negative tumors (221). The
lapatinib plus capecitabine combination has demonstrated best
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August 2021 | Volume 11 | Article 676948
Jawa et al.
Current Insights and Advancements in HNC
activity in the metastatic/recurrent HNSCC. Afatinib is an
irreversible TKI that blocks the signaling originating from
ErbB family. It is also used in other cancers with high EGFR
mutations. In the stage III & IVa HNSCC, it is evaluated as
adjuvant following radiotherapy (222).
Sorafenib, sunitinib and vandetanib are small molecules that
targets VEGF (223). Sorafenib also act as radiosensitizer of HNSCC
cells (224). Other VEGF inhibitors in clinical trials for treating
HNSCC are linifanib, axitinib, pazopanib and nilotinib (225).
Several studies in vitro and in vivo demonstrated that
temsirolimus, an mTOR inhibitor, inhibits proliferation of
HNC. A study with HNSCC cell lines demonstrated beneficial
effect of mTOR inhibitors plus cetuximab in the treatment of
tumor with low EGFR expression or those that acquired
resistance due to cetuximab/cisplatin (226). However, in other
studies, temsirolimus failed to demonstrate significant changes in
patients with advanced malignancies due to toxicity and
subsequent death of patients. Everolimus, another mTOR
inhibitor demonstrated antitumor effect in phase II clinical
trial in patients with advanced HNSCC (NCT01111058) (227).
The other small molecules that targets BCR-Abl kinase and are
under clinical trial for treatment of HNSCC include imatinib,
dasatinib, nilotinib, ponatinib inhibitors (193).
Although for the last several years, a large number of small
molecules are being scrutinized against HNC (Table 6) with
diverse heterocyclic structures, still a preferred specific and
effective pharmacophore is yet to be assigned by drug
development scientists. Current treatment is still associated
with significant toxicities and includes chemotherapy mainly
with platinum compounds, radiation, surgery, and a few targeted
treatments. The scarcity of highly efficient drugs prompts
researchers to identify novel targets for single-agent or for
combined therapy.
research as they not only get the measure of this heterogeneity
but also provide a means to encounter the problems it stems. The
organoid technology has expanded to embrace genetic
manipulation, various omics, drug-screening analyses, and
diverse co-culture systems. In fact, multiple studies have shown
similarities in patient responses and in vitro organoid studies.
Single-cell technology, on the other hand, promises to identify
and characterize alterations in sub-clone profiles. Given the rapid
technology development in the field despite the remaining
challenges, the combinatorial approach, including both these
technologies, remains novel, innovative, and assuring in cancer
treatment. The suitable assays for clinical implementation can be
developed. Treating the model system with anti-cancer drugs
may help distinguish responders from non-responders and
hence, help find the right drug for the right patients potentially
be leading to significant developments in the field of precision
medicine. Multi-omics studies have shifted the focus on cancer
driven perturbations at the whole cellular level. This helps
identify molecular subtypes of the tumor, molecular signatures,
and cellular responses at the clinic-pathological level based on a
gene-protein expression. The multi-tiered approaches using the
genomes, transcriptomes, and methylomes from carcinomas
have aided our understanding of disease progression. However,
integrating all the multi-omics data is crucial in identifying
predictive signatures, i.e. integrating all molecular data and
determining a minimal gene signature that distinguishes a
tumor group. Patterns of alterations vary between patients as a
result; it becomes essential to identify patient subsets with
differential prognosis or the ones responding to different
treatments (targeting therapies). The significant challenge still
is the low availability of patient-derived models specific to head
and neck cancers, the variability and diversity in treatment
tested, and the absence of a standardized set of protocols to be
followed. The clinical parameters tested vary inter-studies, and
the quality needs to be ensured, primarily for drug screening
assays. Also, the data available is limited mainly to Caucasian
populations, while ironically, HNC constitutes 30-40% of total
cancer cases in India. It reiterates the need for multi-omics based
studies using organoid technology and single-cell analysis to
identify unique biomarkers, drug targets, and signatures specific
to Indian populations. The review aims to act as a compendium
CONCLUSION AND FUTURE
PERSPECTIVES OF HNC THERAPY
The major hindrance in the treatment of head and neck cancers
comes with the associated heterogeneity. Organoid and singlecell technologies hold great potential in clinical translational
TABLE 7 | Clinical trials of the combination of small molecules with immunogens against HNC.
Clinical trial number
(NCT)
NCT02551159
NCT02369874
NCT02741570
NCT02952586
NCT03040999
NCT0276459
NCT02641093
NCT02777385
NCT02892201
NCT03085719
NCT02823574
Therapeutic agent
Clinical Trial
Phase
Durvalumab (MEDI4736) ± tremelimumab vs standard of care (SOC) EXTREME regimen (cetuximab + cisplatin/
carboplatin + fluoruracil)
Durvalumab (MEDI4736) ± tremelimumab vs standard of care
Nivolumab + ipilimumab vs SOC EXTREME regimen
Avelumab+ cisplatin/RT vs cisplatin/RT alone
Pembrolizumab + chemo/RT vs chemo/RT alone
Cisplatin/RT± nivolumab
Adjuvant cisplatin/pembrolizumab/RT
Concurrent vs sequential pembrolizumab combined with cisplatin/IMRT
Pembrolizumab
Pembrolizumab with high vs high and low dose RT
Nivolumab+ ipilimumab vs Nivolumab+ ipilimumab placebo
Frontiers in Oncology | www.frontiersin.org
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III
III
III
III
III
III
II
II
II
II
II
August 2021 | Volume 11 | Article 676948
Jawa et al.
Current Insights and Advancements in HNC
support for the project leading to this publication was done by
RS, VT. All authors contributed to the article and approved the
submitted version.
on the above technical advancements and their potential to
identify biomarkers and test drug regimens.
AUTHOR CONTRIBUTIONS
FUNDING
RS, VT, and RT were involved in formulation and
conceptualization and execution of the review article. Abstract
and conclusion was written by RS. Introduction was written by
NM and AT. Single cell technology, 2D and 3D technology is
written by SM, RC, and JP. Genomics, transcriptomics &
proteomics were written by PY and SK. Epigenomics &
metabolomics done by RT and YJ. Potential drug target
identification was done by AS and PY. PY and YJ compiled the
manuscript, done referencing, creation and presentation of all
tables and figures. The bioinformatic approach for data analysis
was written by SA and SG. The coordination and management
between authors was done by VT. Acquisition of the financial
We acknowledge the funding support from DST-DPRP
[No. VI-D&P/546/2016-17/TDT(c)], BSR MID Career Award
[No. F.19.226/2018 (BSR)] India for single cell-derived spheroid
work in HNC.
ACKNOWLEDGMENTS
We would like to thank Souvik Sur, Ritish Syanti, Titas
Mondal and Ashwani for contribution to the ideas in
the manuscript.
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