RESEARCH Open Access
Cancer cell line microarray as a novel
screening method for identification of
radioresistance biomarkers in head and
neck squamous cell carcinoma
Johannes Routila1,2,3, Karri Suvila1, Reidar Grénman2, Ilmo Leivo4, Jukka Westermarck1,4 and Sami Ventelä1,2,4*
Abstract
Background: Currently, no clinically useful biomarkers for radioresistance are available in head and neck squamous
cell carcinoma (HNSCC). This study assesses the usefulness of Cell Line Microarray (CMA) method to enhance
immunohistochemical screening of potential immunohistochemical biomarkers for radioresistance in HNSCC cell lines.
Methods: Twenty-nine HNSCC cell lines were cultured, cell pellets formalin-fixed, paraffin-embedded, and arrayed.
Radioresistance features of the cell lines were combined to immunohistochemical stains for p53, NDFIP1, EGFR, stem
cell marker Oct4, and PP2A inhibitor CIP2A.
Results: Expression of p53, EGFR or CIP2A did not indicate intrinsic radioresistance in vitro. Stem cell marker Oct4
nuclear positivity and NDFIP1 nuclear positivity was correlated with increased intrinsic radioresistance.
Conclusion: The usefulness of CMA in analysis of HNSCC cell lines and discovery of biomarkers is demonstrated. CMA
is very well adapted to both testing of antibodies in a large panel of cell lines as well as correlating staining results with
other cell line characteristics. In addition, CMA-based antibody screening proved an efficient and relatively simple
method to identify potential radioresistance biomarkers in HNSCC.
Keywords: Radiosensitivity, Radioresistance, Head and neck cancer, Oct4, Stemness
Background
Radiotherapy has a major role in the treatment of
head and neck squamous cell carcinoma (HNSCC).
Albeit the general radiosensitivity of HNSCC, radiore-
sistance of a subset of the tumors is a major clinical
problem requiring further study both for an enhanced
mechanistic understanding of radioresistance, and to
overcome clinical radioresistance in patient therapy.
Putative mechanisms responsible for clinical radiore-
sistance include hypoxic environment, EGFR-pathway
alterations, epithelial-mesenchymal transition, deregu-
lation of p53, angiogenesis, and cancer stem cells [1].
Despite extensive characterization, investigations into
clinical biomarkers for HNSCC radioresistance have
proven disappointing [2, 3].
The inability to correctly identify clinically signifi-
cant molecular events in cell line studies remains a
significant problem, since virtually all in vivo cancer
studies are preceded by in vitro cell line investigation
into cancer behavior and characteristics. The genomic
and molecular diversity of different cell lines compli-
cates the selection of cell lines for in vitro studies.
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* Correspondence: satuve@utu.fi
1Turku Bioscience Centre, University of Turku and Åbo Akademi university,
Turku, Finland
2Department for Otorhinolaryngology – Head and Neck Surgery, Turku
University Hospital and University of Turku, Kiinamyllynkatu 4-8, 20521 Turku,
Finland
Full list of author information is available at the end of the article
Routila et al. BMC Cancer          (2021) 21:868 
https://doi.org/10.1186/s12885-021-08618-6
Behavior of different cell lines may vary according to
the planned experiment e.g. due to differences in pro-
tein expression or genomic features. Thus, selection
from the multitude of available cell lines happens
often by trial and error. Thus, there is a high demand
for high-throughput screening method for cell line
characteristics.
Tissue microarray (TMA) methods have proven to
be an excellent tool for profiling and screening tumor
samples [4, 5]. Recently, to answer the demand for
high-throughput screening methods for cell line char-
acteristics, cell-based microarrays have been intro-
duced [6, 7]. The cell microarray (CMA) offers a
powerful tool in the evaluation of cell lines. The ab-
sence of interference of extracellular matrix facilitates
the study of novel antibodies or genomic methods
and allows for more reliable specificity analysis of
antibodies. The validity of stainings can be assessed
by comparing the results to previously acquired data.
The included cell lines can be cultured in various
conditions, e.g., to include shRNA-treated cells to aid
in validation of analysis of proteins of interest. The
function of CMA as a fixed, paraffin-embedded bio-
bank of cell lines is of special importance, as it can
be stored virtually without limits for later use after
the relatively tedious original cell cultures.
In this study, we aimed to assess a carefully se-
lected panel of five immunohistochemical stains of
putative radiotherapy biomarkers – p53, EGFR, Oct4,
NDFIP1 and CIP2A – in head and neck cancer cell
lines. Mutations of the tumor suppressor p53 occur
frequently in HNSCC tumors and cell lines [8, 9].
While the exact type of p53 genetic alteration may
predict HNSCC radioresistance, the role of p53 ex-
pression is unclear [10]. The role of epidermal
growth factor receptor (EGFR) for therapy resistance
of HNSCC has been an active field of investigation,
since EGFR inhibitor cetuximab was introduced and
approved for use in HNSCC [11, 12]. The results of
cetuximab-based chemo- or chemoradiotherapy have
proven disappointing, despite that EGFR expression
seems to associate with HNSCC clinical radioresis-
tance [12]. Oct4 is a stem cell marker which has
been linked to radioresistance through cancer stem
cell phenomenon and epithelial-mesenchymal transi-
tion in several previous studies [13–15]. The prog-
nostic relevance of CIP2A to HNSCC has previously
been established [13, 16]. CIP2A mediates radioresis-
tance in HNSCC and colorectal cancer, and is linked
to several oncogenic signaling mechanisms such as
c-Myc, p53, EGFR, mTOR signaling and Oct4 [17,
18]. Nedd4 family interacting protein NDFIP1 is
previously linked to radioresistance of HNSCC
through PTEN regulation [19]. Overexpression of
NDFIP1 RNA is associated with an unfavorable
prognosis in The Cancer Genome Atlas (TCGA)
RNA-Seq data [20].
Methods
Cell lines
The included 26 UT-SCC cell lines are summarized
in Table 1. UT-SCC cell lines were established as
previously reported from various HNSCC tumors ac-
cording to ethical approval by the Ethics Committee
of the Hospital District of Southwest Finland as well
as patient informed consent [21]. All experiments
were carried out according to institutional guidelines
and the Declaration of Helsinki. The clinical course
of every donor patient’s disease was monitored and
clinical data from patient was gathered [22, 23]. Cell
lines were cultured at 37 °C and 5% CO2 in Dulbec-
co’s Modified Eagle’s Medium (DMEM), with 10%
FCS, glutamine and antibiotics (penicillin and
streptomycin). The intrinsic radioresistance of UT-
SCC cell lines was previously determined using the
96-well clonogenic assay for radioresistance [8, 9,
22]. In short, the cells were cultured, plated on 96-
well plates, exposed to photon irradiation doses ran-
ging from 0.75 to 7.5 Gy the number of dividing
cells calculated, and area under the curve (AUC) of
cell survival was calculated. For analysis, the mean
inactivation dose defined as the AUC of survival
curve is used.
shRNA cell lines
Three previously established stable shRNA-transfected
UT-SCC cell lines were included, two shCIP2A-
silenced variants of UT-SCC-24A and one shCIP2A-
silenced variant of UT-SCC-14A. shRNA cell lines
were generated using pGIPZ lentiviral vectors consist-
ing of a GFP tag and puromycin resistance (Open
Biosystems). Cells transfected with lentiviral vector
pGIPZ.NS shRNA containing non-silencing shRNA
served as control cells expressing high CIP2A. Two
stable cell lines for low CIP2A expression were gener-
ated using pGIPZ.shRNA (#556) and pGIPZ.shRNA
(#557) containing targeting CIP2A antisense se-
quences TACATCAGCAGCAAGTTTG and TACTCA
ATGTCTTTATGTG, respectively. Lentivirus produc-
tion followed the standard protocol. At the time of
transduction, cell line confluency was approximately
40–50%. After infection, the cells were selected for
puromycin resistance, and if the number of GFP-
positive cells was low, the cells were sorted with
FACS for further experiments. Finally, all cell lines
were tested negative for replication competent viruses
(RCV test) as well as for Mycoplasma, Acholeplasma,
Entomoplasma and Spiroplasma (The MycoAlert™
Routila et al. BMC Cancer          (2021) 21:868 Page 2 of 11
Mycoplasma Detection kit, Lonza). Western blot using
anti-CIP2A antibody (dilution 1:1000, 2G10-3B5, sc-
80,659, SantaCruz) was used to confirm the successful
silencing of CIP2A expression (Fig. 4A).
CMA construction
Cultured cells were harvested by trypsinization before
pelleting the cells. Approximately 40 × 106 cells were
used for each cell pellet. Pellets were washed with
PBS and resuspended in 120–160 μl of 10% neutral-
buffered formalin. Cells were then added into a
microfuge tube containing a conical fill made of 2%
agarose in PBS. After the spin (1000 rpm × 5min) the
supernatant was removed and 10 ml of buffered for-
malin was added for 48 h, after which the pellets were
stored in PBS (+ 4 °C). Microfuge tubes were cut
open, the pellet transferred into a tissue cassette, and
submitted for paraffin embedding (Fig. 1). A micro-
array was assembled by using the services of Auria
Biobank (Auria Biobank, Turku, Finland). The
formalin-fixed, paraffin-embedded cell pellets were cut
for 6 μm sections and haematoxylin-eosin stained.
The slides were scanned and annotated for 0.6 mm
cores using Pannoramic Viewer software. The anno-
tated cores were combined into duplicate receiver
blocks using TMA Grand Master (3D Histech). Sam-
ples from normal human liver were included for
orientation.
Immunohistochemistry
Six micrometer sections were obtained from the final
CMA and TMA block for IHC stainings. p53 and
EGFR immunohistochemical stainings was obtained
from local clinical pathology department laboratory
and carried out in Ventana staining automate. CIP2A
immunohistochemical staining was carried out after
protocol optimization in Ventana BenchMark XT
staining automate (Ventana, Tucson, AZ) with Opti-
View DAB kit and with 64-min CC1 preparation and
32-min antibody incubation. Mouse monoclonal anti-
Table 1 The main characteristics of the UT-SCC cell lines included in the study
Cell line Sex Age Primary tumor site Grade T N M Specimen site Type Radioresistance, AUC
UT-SCC-2 m 60 floor of mouth 2 4 1 0 floor of mouth primary 1.8 ± 0.2
UT-SCC-5 m 58 tongue 2 1 1 0 tongue primary 2.3 ± 0.3
UT-SCC-7 m 67 temporal skin 2 1 0 0 neck metastasis 2.0 ± 0.2
UT-SCC-8 m 42 supraglottic 1 2 0 0 larynx primary 1.9 ± 0.1
UT-SCC-9 m 81 glottic 1 2 1 0 neck metastasis 1.4 ± 0.1
UT-SCC-14 m 25 tongue 2 3 1 0 tongue primary 1.7 ± 0.3
UT-SCC-16A f 77 tongue 3 3 0 0 tongue primary 1.8 ± 0.1
UT-SCC-17 m 65 supraglottic 3 2 0 0 sternum metastasis 1.8. ± 0.1
UT-SCC-19A m 44 glottic 2 4 0 0 larynx primary 1.7 ± 0.1
UT-SCC-20A f 58 floor of mouth 2 1 0 0 floor of mouth primary 2.1 ± 0.2
UT-SCC-24A m 41 tongue 2 2 0 0 tongue primary 2.6 ± 0.3
UT-SCC-25 m 50 tongue 1 2 0 0 tongue recidive
UT-SCC-30 f 77 tongue 1 3 1 0 tongue primary 2.0 ± 0.1
UT-SCC-32 m 66 tongue 2 3 0 0 tongue primary 1.7 ± 0.3
UT-SCC-34 m 63 supraglottic 1 4 0 0 larynx primary 2.0 ± 0.1
UT-SCC-36 m 46 floor of mouth 3 4 1 0 floor of mouth primary 2.2 ± 0.2
UT-SCC-45 m 76 floor of mouth 3 3 1 0 floor of mouth primary 2.0 ± 0.1
UT-SCC-46A m 62 retromolar gingiva 3 1 0 0 gingiva primary 1.6 ± 0.1
UT-SCC-47 m 78 floor of mouth 3 2 0 0 floor of mouth primary 2.0 ± 0.2
UT-SCC-50 m 70 glottic 3 2 0 0 larynx (rT2N0) recidive
UT-SCC-60B m 59 tonsil 1 4 1 0 neck metastasis 2.2 ± 0.3
UT-SCC-72 m 50 mandibular gingiva 2 4 2 0 gingiva primary 2.8 ± 0.2
UT-SCC-74A m 31 tongue 2 3 1 0 tongue primary
UT-SCC-76A m 52 tongue 2 3 0 0 tongue primary 2.5 ± 0.2
UT-SCC-79A f 80 parotid 2 0 2 0 parotid metastasis 2.4 ± 0.2
UT-SCC-79B f 80 parotid 2 0 2 0 neck metastasis 2.5 ± 0.1
Routila et al. BMC Cancer          (2021) 21:868 Page 3 of 11
CIP2A antibody (dilution 1:25, 2G10-3B5, sc-80,659,
SantaCruz) was used. For PME-1, SET, LIMA1, NDFI
P1, and Oct4 stainings, immunohistochemical stain-
ings were done as previously described [13, 24]. The
antibodies used were rabbit polyclonal anti-SET (H-
120) antibody sc-25,564 (diluted 1:1000, Santa Cruz
Biotechnology), mouse monoclonal anti-PME-1 (B-12)
antibody sc-25,278 (diluted 1:1000, Santa Cruz Bio-
technology), rabbit polyclonal anti-NDFIP1 antibody
HPA009682 (diluted 1:500, Sigma-Aldrich), and
Fig. 1 A Schematic representation of the CMA construction. Cores from each cell line block are combined into a single paraffin-embedded array.
B Overview of the completed CMA (p16 immunohistochemical staining)
Routila et al. BMC Cancer          (2021) 21:868 Page 4 of 11
mouse monoclonal anti-Oct4 antibody sc5279 (diluted
1:200, Santa Cruz Biotechnology).
Scoring of immunohistochemistry
All stainings were analyzed independently by at least
two authors (JR, KS, IL, SV), and contradictory cases
were discussed until consensus was reached. For micro-
scopic photographs, slides were scanned using Pannora-
mic 250 Flash slide scanner. Using the CaseViewer
software, photographs of either 10-fold or 20-fold mag-
nification were exported in 300 dpi quality. Images were
cropped in image editing software, while no color adjust-
ments were performed.
p53 staining was analyzed using a previously estab-
lished 3-tier system consisting of deletion-like absence
of p53 staining, wild-type pattern of staining, and muta-
tion/amplification-associated aberrant-type overexpres-
sion. EGFR staining was scored in a 4-tier system (0, +,
++, +++) for cytoplasic/membraneous staining pattern.
For CIP2A, the intensity of the cytoplasmic/membran-
eous staining was scored on a scale of 1 to 3 as weak/
negative, intermediate or strong, taking into account the
number of positive cells. In patient samples, CIP2A was
uniformly present and was scored similarly based on
cytoplasmic/membraneous staining intensity. Oct4 im-
munostaining was analyzed for the presence of positive
nuclei. Oct4 positivity was present only in a subpopula-
tion of cells, and thus the presence of strong nuclear im-
munoreactivity of individual cells was regarded positive.
NDFIP1 staining was scored positive, when strong, uni-
form nuclear staining pattern was present, whereas cyto-
plasmic staining was not taken into account. A three-tier
scoring system was used for uniform nuclear PME-1 and
SET staining [24].
Statistical analysis
Cell line data, patient data, and results of immunohisto-
chemistry were entered in SPSS 25 software. Depend-
ence of cell line radioresistance to the staining results
was analyzed using General Linear Model statistics. Both
main effects and interactions were observed. Estimated
marginal means were calculated and 95% confidence in-
tervals determined using bootstrapping. Correlation was
determined by Spearman’s method. Throughout, p value
< 0.05 was deemed significant.
Results
CMA construction
For the CMA construction, UT-SCC cell lines with pre-
vious data on radiosensitivity were preferentially se-
lected. Altogether 26 UT-SCC cell lines were included,
the majority of which were derived from male patients
with oral cavity cancer (Table 1). Six cell lines were de-
rived from metastatic samples, and two cell lines from
recurring cancers. In addition, three stable CIP2A
shRNA-silenced cell lines were included. The cell lines
were cultured until a sufficient number of cells was ob-
tained, whereafter cells were pelleted, fixed in formalin
and embedded in paraffin. The following formalin-fixed,
paraffin-embedded cell blocks were annotated, and a
CMA was arrayed. The CMA construction is demon-
strated in Fig. 1A and an example of immunohistochem-
ical assessment in Fig. 1B.
Association of biomarker staining intensities and intrinsic
radioresistance
Immunohistochemical stains for p53, EGFR, Oct4,
CIP2A, and NDFIP1 were analyzed (Fig. 2A-L, Table 2).
There were significant correlations between Oct4 and
NDFIP1 stains (ρ 0.46, p = 0.020), but no correlation be-
tween the expression of other biomarkers. Patient age
and gender or tumor site, T class or nodal positivity
were not directly linked with the expression of any in-
vestigated biomarkers.
The associations between staining results and intrinsic
radioresistance of the cell lines were analyzed. Expres-
sion of p53, EGFR, and CIP2A were not associated with
radioresistance (Fig. 3A-B and D). Interestingly, nuclear
NDFIP1 expression was associated with a significant in-
crease in radioresistance (Fig. 3C). In accordance with
our previous findings, also Oct4 expression was associ-
ated with a significant increase in radioresistance (Fig.
3E). Interaction effects of the biomarkers were analyzed
to further explore the possible links between different
potential radioresistance mechanisms. There was a sig-
nificant interaction effect between Oct4 and p53
(Fig. 3F). A trend for a comparable interaction effect be-
tween p53 and high EGFR expression was noted (Fig.
3G).
Analysis of CIP2A-shRNA-silenced cell lines
To test the functionality of CMA in the validation of
antibody specificity, and to study protein interactions in
genetically modified cancer cell lines, three CIP2A
shRNA-silenced cell lines were included in the CMA.
CIP2A shRNA-silencing was confirmed by Western blot
(Fig. 4A) and is demonstrated by the minimal immuno-
reactivity of the silenced cell lines compared to the par-
ental non-silenced lines (Fig. 4B-F). Parallel
immunohistochemistry of the potential radioresistance
biomarkers did not reveal significant correlations be-
tween CIP2A silencing and p53, EGFR, NDFIP1, or Oct4
expression (Table 3).
Since CIP2A failed to predict radioresistance in the
CMA and since CIP2A silencing did not affect other pu-
tative biomarkers expression, we were interested,
whether CIP2A would have an association with other
PP2A inhibitors and performed immunohistochemistry
Routila et al. BMC Cancer          (2021) 21:868 Page 5 of 11
of two well-established endogenous PP2A inhibitors,
PME-1 and SET, which are not pronouncedly identified
as radiotherapy biomarkers (Supplemental Figure 1).
Surprisingly, in all three silenced cell lines the expression
of both PME-1 and SET was reduced as well, suggesting
a CIP2A-mediated circuitry leading to a more universal
loss of PP2A inhibitor expression (Table 3). However,
CIP2A, PME-1 and SET expression levels were not cor-
related across other UT-SCC cell lines of the CMA.
Neither PME-1 nor SET was associated with intrinsic
radioresistance of the cell lines.
Discussion
HNSCC consists of a genetically and behaviorally het-
erogeneous group of malignancies. The observed gen-
omic instability is due to mutagenic insults on the
mucosal lining of the upper aerodigestive tract such as
tobacco and alcohol [25]. Accordingly, HNSCC-derived
Fig. 2 Representative images of immunohistochemical stains of UT-SCC cell line CMA. p53 A absent (deletion-type staining), B wild-type, C
mutated (mutation/amplification type staining). EGFR (D) low, and (E) strong cytoplasmic/membraneous staining. NDFIP1 (F) low nuclear staining,
and (G) strong positive nuclear stain, despite only negligible difference in cytoplasmic staining. CIP2A (H) low, (I) moderate, and (J) strong
cytoplasmic/membraneous staining. Nuclear Oct4 (K) negative and (L) positive
Routila et al. BMC Cancer          (2021) 21:868 Page 6 of 11
cell lines exhibit highly variable genomic changes [26–
29]. The field cancerization phenomenon makes radio-
therapy an especially inviting treatment option, and it is
included in the treatment plan of nearly one half of head
and neck cancer patients [30]. Since no biomarkers can
currently be used to identify patients benefitting of dif-
ferent treatment strategies, or to explain the clinical di-
versity in radiotherapy outcomes, the question of
radioresistance identification is of tremendous import-
ance for treatment of HNSCC.
Prior studies presenting similar methods of cell line
array construction have mostly included relatively small
amounts of cell lines, and have been constructed with
other than head and neck cancers [6, 31, 32]. To our
knowledge, this is the first CMA constructed with pri-
marily HNSCC cell lines to this large extent. CMA is a
practical, cost-efficient and time-saving way to deter-
mine the expression of a protein of interest using a spe-
cific antibody in a large number of cell lines
simultaneously. In this study, we demonstrate that
formalin-fixed, paraffin-embedded cell lines may be han-
dled with similar staining protocols used for clinical
samples. Cells retained their morphology and overall cel-
lular architecture, leading to good quality staining result,
allowing for a reliable evaluation of IHC staining inten-
sity. Loss of CMA spots is low and comparable to TMA
methods.
In addition to efficiency, CMA provides other notable
benefits as well. Use of exclusively cellular material al-
lows strictly focusing the evaluation of IHC staining
positivity on cancer cells and removing the majority of
unspecific sources of unwanted IHC signaling positivity.
This also provides a fixed, paraffin-embedded biobank of
cell lines. It is possible to manipulate and modify the cell
lines before constructing CMA, allowing e.g. molecular
profiling of cell lines by inhibiting or enhancing the ex-
pression or effect of certain genes. This may also be ap-
plied to drug development and discovery studies, since
Table 2 Immunohistochemistry results of the CMA
Cell line Primary site AUC p53
wt
DNA
p53 mutation Immunohistochemistry
p53 LIMA1 EGFR NDFIP1 Oct4 CIP2A
UT-SCC-2 floor of mouth 1.8 ± 0.2 no c.275 CCC > CAT mutated 1 3 0 1 2
UT-SCC-5 tongue 2.3 ± 0.3 no c.151 CCC > CAT mutated 2 3 1 1 1
UT-SCC-7 temporal skin 2.0 ± 0.2 yes c.266 GGA > GAA mutated 1 3 1 1 3
UT-SCC-8 supraglottic 1.9 ± 0.1 no c.255 ATC > TTC mutated 1 3 1 1 2
UT-SCC-9 glottic 1.4 ± 0.1 no total deletion absent 1 2 0 0 2
UT-SCC-14 tongue 1.7 ± 0.3 yes deletion, frameshift mutated 1 3 0 1 2
UT-SCC-16A tongue 1.8 ± 0.1 c.110 CGT > TGT / c.232 ATC > AAC mutated 1 2 0 0 1
UT-SCC-17 supraglottic 1.8. ± 0.1 c.110 CGT > CTT / c.257 CTG > CAG mutated 2 3 1 1 3
UT-SCC-19A glottic 1.7 ± 0.1 no c.285 GAG>AAG mutated 2 3 0 0 3
UT-SCC-20A floor of mouth 2.1 ± 0.2 yes c.248 CGG > TGG mutated 2 2 1 1 2
UT-SCC-24A tongue 2.6 ± 0.3 yes 47 bp insertion, frameshift absent 2 3 1 1 3
UT-SCC-25 tongue no c.248 CGG > TGG mutated 2 3 1 1 2
UT-SCC-30 tongue 2.0 ± 0.1 no c.282 CGG > CCG mutated 1 2 0 1 1
UT-SCC-32 tongue 1.7 ± 0.3 no c.266 GGA > GAA mutated 1 3 1 0 1
UT-SCC-34 supraglottic 2.0 ± 0.1 yes total deletion absent 1 3 1 1 1
UT-SCC-36 floor of mouth 2.2 ± 0.2 no c.244 GGC > AGC mutated 1 2 1 1 3
UT-SCC-45 floor of mouth 2.0 ± 0.1 wt 1 3 0 1 1
UT-SCC-46A retromolar gingiva 1.6 ± 0.1 wt 1 1 0 0 1
UT-SCC-47 floor of mouth 2.0 ± 0.2 absent 2 2 1 1 3
UT-SCC-50 glottic wt 2 3 1 1 2
UT-SCC-60B tonsil 2.2 ± 0.3 absent 2 2 0 1 2
UT-SCC-72 mandibular gingiva 2.8 ± 0.2 absent 2 3 1 1 2
UT-SCC-74A tongue wt 1 3 0 1 2
UT-SCC-76A tongue 2.5 ± 0.2 mutated 1 3 0 0 1
UT-SCC-79A parotid 2.4 ± 0.2 yes deletion, frameshift wt 2 3 1 1 1
UT-SCC-79B parotid 2.5 ± 0.1 yes deletion, frameshift wt 2 2 1 1 1
Routila et al. BMC Cancer          (2021) 21:868 Page 7 of 11
Fig. 3 Association of biomarker stains and radioresistance was investigated across 23 cell lines. Bars represent estimated marginal means and
error bars indicate 95% confidence intervals determined using bootstrapping. Sample sizes, F values and p values are indicated. A p53, B EGFR,
and D CIP2A demonstrate no association with radioresistance, whereas C NDFIP1, and E Oct4 have a significant (*) association with the intrinsic
radioresistance of the cell lines. F A highly significant interaction effect between p53 and Oct4 was revealed, implying a predictive role of Oct4 in
the absence of p53 mutation/amplification type staining. G The interaction trend between p53 and EGFR did not reach significance
Routila et al. BMC Cancer          (2021) 21:868 Page 8 of 11
cell lines may be exposed to drug of interest during cul-
ture. Moreover, in a CMA, all types of analysis available
for FFPE patient samples can be used. As the CMA par-
affin block is handled similarly to FFPE samples, cell line
results can directly be compared with patient staining
results. Our UT-SCC CMA included three CIP2A-
shRNA-silenced cell lines, which demonstrate a highly
successful silencing after lengthy cell line culture. CMA
offers a powerful tool for the characterization of cellular
effects of targeted silencing.
A major weakness of cell line experiments in general
and thus also of this study, is that cell line models are
unable to take into account patient-related factors such
as immunological responses, that may decisively affect
the success of radiotherapy in HNSCC patients. How-
ever, the intrinsic radioresistance of the cancer cells is
an important phenomenon in clinical radioresistance [1,
9, 22]. In the present study, expression of the
radioresistance-associated Oct4 and NDFIP1 in patient-
derived UT-SCC cell lines could not be explained by
correlation with clinical factors, supporting the notion,
that radiotherapy response cannot be sufficiently pre-
dicted by analysis of the patient clinical parameters
alone. Interestingly, other putative radioresistance bio-
markers – p53, EGFR, and CIP2A – failed to be associ-
ated with intrinsic radioresistance of the cell lines in this
relatively large panel of HNSCC cell lines.
Oct4 is a transcription factor essential for pluripotency
in embryonic stem cells and testicular maturation. Oct4-
related stemness has been found to have a role in radio-
therapy resistance of various cancers, including HNSCC
[13, 14, 33–35]. Especially interesting, regarding HNSC
C, is that Oct4 expression is universally acknowledged to
be a favourable biomarker for cisplatin sensitivity in tes-
ticular cancer, but seems to predict poor cisplatin re-
sponse in other cancers [36–38]. Since radioresistance is
one of the most important issues in the treatment of
HNSCC, further studies should investigate the potential
Fig. 4 Analysis of CIP2A-shRNA-silenced cell lines. A Succesful silencing was confirmed using Western blot. B-C Loss of CIP2A
immunohistochemical staining in shRNA-silenced cell line in comparison with the non-silenced cell line UT-SCC-14. D-F Loss of CIP2A
immunohistochemical staining in both shRNA-silenced cell line versions of UT-SCC-24A
Table 3 Immunohistochemistry of UT-SCC-14 and UT-SCC-24A and corrresponding CIP2A-shRNA-silenced cell lines
Cell line Immunohistochemistry
CIP2A p53 LIMA1 EGFR NDFIP1 Oct4 SET PME-1
Non-treated UT-SCC-14 2 mutated 1 3 0 1 2 2
CIP2A-shRNA-silenced UT-SCC-14 (plasmid 557) 1 mutated 1 3 0 1 1 1
Non-treated UT-SCC-24A 3 absent 2 3 1 1 1 2
CIP2A-shRNA-silenced UT-SCC-24A (plasmid 556) 1 absent 2 3 0 1 1 1
CIP2A-shRNA-silenced UT-SCC-24A (plasmid 557) 1 absent 2 3 1 1 1 1
Routila et al. BMC Cancer          (2021) 21:868 Page 9 of 11
role of Oct4-related stemness in the clinical radiosensi-
tivity and chemoradiosensitivity of HNSCC. Further-
more, since Oct4 is expressed by a minority of cells as
predicted by the cancer stem cell hypothesis, especially
Oct4 overexpression experiments would provide essen-
tial information on the relationship of therapy resistance
and stemness-like characteristics.
Conclusions
This study, in addition to confirming the feasibility of
CMA methodology to screen for new predictive factors
in head and neck cancer, revealed potential immunohis-
tochemical biomarkers associated with radioresistance in
head and neck cancer cell lines. Especially interesting for
its potential clinical implications is the stem cell marker
Oct4 which has been demonstrated to have a significant
predictive role in previous studies [13, 14]. In conclu-
sion, our parallel immunohistochemical analysis of 29
HNSCC cell lines suggests, that radioresistance of
HNSCC is regulated by stemness-related mechanisms.
The relatively straightforward Oct4 immunohistochemi-
cal staining might offer a novel and intriguing way to
identify cancer radioresistance in HNSCC.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12885-021-08618-6.
Additional file 1: Figure S1. Representative examples of low and
moderate PME-1 and moderate and strong SET stainings.
Acknowledgements
We thank the Auria Biobank in Turku for kind cooperation in preparation of
the tissue and cell microarrays.
Authors’ contributions
JR and JW conceived the study and designed the analysis of the data. RG
offered the patient-derived cell lines and patient information for the study.
KS did the cell cultures and planned the cell line microarray. JR, KS, IL and SV
did the immunohistochemical analysis. JR and KS wrote the original manu-
script. JR, KS, RG, IL, JW and SV edited the draft manuscript. All authors read
and approved the final manuscript.
Funding
This study was funded by Turku University Hospital Research Funds (EVO)
and The Finnish Medical Foundation.
Availability of data and materials
All data generated or analyzed during this study are included in this
published article.
Declarations
Ethics approval and consent to participate
UT-SCC cell lines were established according to ethical approval by Ethics
Committee of the Hospital District of Southwest Finland as well as patient
informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1Turku Bioscience Centre, University of Turku and Åbo Akademi university,
Turku, Finland. 2Department for Otorhinolaryngology – Head and Neck
Surgery, Turku University Hospital and University of Turku, Kiinamyllynkatu
4-8, 20521 Turku, Finland. 3Department for Otorhinolaryngology, Satakunta
Central Hospital, Pori, Finland. 4Institute of Biomedicine, University of Turku,
Kiinamyllynkatu 10, 20520 Turku, Finland.
Received: 17 December 2020 Accepted: 15 July 2021
References
1. Perri F, Pacelli R, Della Vittoria Scarpati G, Cella L, Giuliano M, Caponigro F,
et al. Radioresistance in head and neck squamous cell carcinoma: biological
bases and therapeutic implications. Head Neck. 2015;37(5):763–70. https://
doi.org/10.1002/hed.23837.
2. Chow LQM. Head and neck Cancer. N Engl J Med. 2020;382(1):60–72.
https://doi.org/10.1056/NEJMra1715715.
3. Kim KY, McShane LM, Conley BA. Designing biomarker studies for head and
neck cancer. Head Neck. 2014;36(7):1069–75. https://doi.org/10.1002/
hed.23444.
4. Kononen J, Bubendorf L, Kallioniemi A, Bärlund M, Schraml P, Leighton S,
et al. Tissue microarrays for high-throughput molecular profiling of tumor
specimens. Nat Med. 1998;4:844–7. https://www.ncbi.nlm.nih.gov/pubmed/
9662379. https://doi.org/10.1038/nm0798-844.
5. Kallioniemi OP, Wagner U, Kononen J, Sauter G. Tissue microarray
technology for high-throughput molecular profiling of cancer. Hum Mol
Genet. 2001;10(7):657–62. https://www.ncbi.nlm.nih.gov/pubmed/11257096.
https://doi.org/10.1093/hmg/10.7.657.
6. Ferrer B, Bermudo R, Thomson T, Nayach I, Soler M, Sanchez M, et al.
Paraffin-embedded cell line microarray (PECLIMA): development and
validation of a high-throughput method for antigen profiling of cell lines.
Pathobiology. 2005;72(5):225–32. https://doi.org/10.1159/000089416.
7. Jonczyk R, Kurth T, Lavrentieva A, Walter JG, Scheper T, Stahl F. Living Cell
Microarrays: An Overview of Concepts. Microarrays (Basel). 2016;5:1. https://
doi.org/10.3390/microarrays5020011.
8. Servomaa K, Kiuru A, Grénman R, Pekkola-Heino K, Pulkkinen JO, Rytömaa T.
p53 mutations associated with increased sensitivity to ionizing radiation in
human head and neck cancer cell lines. Cell Prolif. 1996;29(5):219–30.
https://doi.org/10.1046/j.1365-2184.1996.01009.x.
9. Pekkola-Heino K, Servomaa K, Kiuru A, Grenman R. Sublethal damage repair
capacity in carcinoma cell lines with p53 mutations. Head Neck. 1998;20(4):
298–303. https://www.ncbi.nlm.nih.gov/pubmed/9588701. https://doi.org/1
0.1002/(SICI)1097-0347(199807)20:4<298::AID-HED3>3.0.CO;2-U.
10. Hashmi AA, Hussain ZF, Hashmi SK, Irfan M, Khan EY, Faridi N, et al.
Immunohistochemical over expression of p53 in head and neck squamous
cell carcinoma: clinical and prognostic significance. BMC Res Notes. 2018;
11(1):433. https://doi.org/10.1186/s13104-018-3547-7.
11. Ang KK, Berkey BA, Tu X, Zhang H-Z, Katz R, Hammond EH, et al. Impact of
epidermal growth factor receptor expression on survival and pattern of
relapse in patients with advanced head and neck carcinoma. Cancer Res.
2002;62(24):7350–6. https://pubmed.ncbi.nlm.nih.gov/12499279.
12. Rieckmann T, Kriegs M. The failure of cetuximab-based de-intensified
regimes for HPV-positive OPSCC: a radiobiologists perspective. Clin Transl
Radiat Oncol. 2019;17:47–50. https://doi.org/10.1016/j.ctro.2019.05.003.
13. Ventelä S, Sittig E, Mannermaa L, Mäkelä J-A, Kulmala J, Löyttyniemi E, et al.
CIP2A is an Oct4 target gene involved in head and neck squamous cell
cancer oncogenicity and radioresistance. Oncotarget. 2015;6:144–58. https://
doi.org/10.18632/oncotarget.2670.
14. Shen L, Huang X, Xie X, Su J, Yuan J, Chen X. High expression of SOX2 and
OCT4 indicates radiation resistance and an independent negative prognosis
in cervical squamous cell carcinoma. J Histochem Cytochem. 2014;62(7):
499–509. https://doi.org/10.1369/0022155414532654.
15. Shao M, Bi T, Ding W, Yu C, Jiang C, Yang H, et al. OCT4 potentiates radio-
resistance and migration activity of rectal Cancer cells by improving
epithelial-mesenchymal transition in a ZEB1 dependent manner. Biomed
Res Int. 2018;2018:3424956–12. https://doi.org/10.1155/2018/3424956.
Routila et al. BMC Cancer          (2021) 21:868 Page 10 of 11
16. Routila J, Bilgen T, Saramäki O, Grénman R, Visakorpi T, Westermarck J,
et al. Copy number increase of oncoprotein CIP2A is associated with
poor patient survival in human head and neck squamous cell
carcinoma. J Oral Pathol Med. 2016;45(5):329–37. https://doi.org/10.1111/
jop.12372.
17. Birkman E-M, Elzagheid A, Jokilehto T, Avoranta T, Korkeila E, Kulmala J, et al.
Protein phosphatase 2A (PP2A) inhibitor CIP2A indicates resistance to
radiotherapy in rectal cancer. Cancer Med. 2018;7(3):698–706. https://doi.
org/10.1002/cam4.1361.
18. Khanna A, Pimanda JE, Westermarck J. Cancerous inhibitor of protein
phosphatase 2A, an emerging human oncoprotein and a potential cancer
therapy target. Cancer Res. 2013;73(22):6548–53. https://doi.org/10.1158/
0008-5472.CAN-13-1994.
19. Ahmad P, Sana J, Slavik M, Slampa P, Smilek P, Slaby O. MicroRNAs
involvement in Radioresistance of head and neck Cancer. Dis Markers. 2017;
2017:8245345.
20. Uhlen M, Zhang C, Lee S, Sjöstedt E, Fagerberg L, Bidkhori G, et al. A
pathology atlas of the human cancer transcriptome. Science. 2017;357:
eaan2507. https://doi.org/10.1126/science.aan2507.
21. Krause CJ, Carey TE, Ott RW, Hurbis C, McClatchey KD, Regezi JA. Human
squamous cell carcinoma. Establishment and characterization of new
permanent cell lines. Arch Otolaryngol. 1981;107(11):703–10. https://www.
ncbi.nlm.nih.gov/pubmed/7295166. https://doi.org/10.1001/archotol.1981.
00790470051012.
22. Grénman R, Carey TE, McClatchey KD, Wagner JG, Pekkola-Heino K,
Schwartz DR, et al. In vitro radiation resistance among cell lines established
from patients with squamous cell carcinoma of the head and neck. Cancer.
1991;67(11):2741–7. https://doi.org/10.1002/1097-0142(19910601)67:11<
2741::AID-CNCR2820671105>3.0.CO;2-S.
23. Grenman R, Burk D, Virolainen E, Wagner JG, Lichter AS, Carey TE.
Radiosensitivity of head and neck cancer cells in vitro. A 96-well plate
clonogenic cell assay for squamous cell carcinoma. Arch Otolaryngol Head
Neck Surg. 1988;114(4):427–31. https://doi.org/10.1001/archotol.1988.018601
60071024.
24. Routila J, Mäkelä J-A, Luukkaa H, Leivo I, Irjala H, Westermarck J, et al.
Potential role for inhibition of protein phosphatase 2A tumor suppressor in
salivary gland malignancies. Genes Chromosom Cancer. 2016;55(1):69–81.
https://doi.org/10.1002/gcc.22312.
25. Gollin SM. Cytogenetic alterations and their molecular genetic correlates in
head and neck squamous cell carcinoma: a next generation window to the
biology of disease. Genes Chromosom Cancer. 2014;53(12):972–90. https://
doi.org/10.1002/gcc.22214.
26. Ruutu M, Johansson B, Grenman R, Syrjänen S. Two different global gene
expression profiles in cancer cell lines established from etiologically
different oral carcinomas. Oncol Rep. 2005;14(6):1511–7. https://www.ncbi.
nlm.nih.gov/pubmed/16273247.
27. Ludwig ML, Kulkarni A, Birkeland AC, Michmerhuizen NL, Foltin SK, Mann JE,
et al. The genomic landscape of UM-SCC oral cavity squamous cell
carcinoma cell lines. Oral Oncol. 2018;87:144–51. https://doi.org/10.1016/j.
oraloncology.2018.10.031.
28. Mann JE, Kulkarni A, Birkeland AC, Kafelghazal J, Eisenberg J, Jewell BM,
et al. The molecular landscape of the University of Michigan laryngeal
squamous cell carcinoma cell line panel. Head Neck. 2019;41(9):3114–24.
https://doi.org/10.1002/hed.25803.
29. Lepikhova T, Karhemo P-R, Louhimo R, Yadav B, Murumägi A, Kulesskiy E,
et al. Drug-sensitivity screening and genomic characterization of 45 HPV-
negative head and neck carcinoma cell lines for novel biomarkers of drug
efficacy. Mol Cancer Ther. 2018;17(9):2060–71. https://doi.org/10.1158/1535-
7163.MCT-17-0733.
30. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer
statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide
for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.
https://doi.org/10.3322/caac.21492.
31. Gately K, Kerr K, O’Byrne K. Design, construction, and analysis of cell
line arrays and tissue microarrays for gene expression analysis.
Methods Mol Biol. 2011;784:139–53. https://doi.org/10.1007/978-1-61
779-289-2_10.
32. Wu X, Zahari MS, Renuse S, Jacob HK, Sakamuri S, Singal M, et al. A breast
cancer cell microarray (CMA) as a rapid method to characterize candidate
biomarkers. Cancer Biol Ther. 2014;15(12):1593–9. https://doi.org/10.4161/1
5384047.2014.961886.
33. Koo BS, Lee SH, Kim JM, Huang S, Kim SH, Rho YS, et al. Oct4 is a critical
regulator of stemness in head and neck squamous carcinoma cells.
Oncogene. 2015;34(18):2317–24. https://doi.org/10.1038/onc.2014.174.
34. Mishra S, Tiwari V, Arora A, Gupta S, Anand N, Husain N. Increased
expression of Oct4, Nanog and CD24 predicts poor response to chemo-
radiotherapy and Unfavourable prognosis in locally advanced Oral
squamous cell carcinoma. Asian Pac J Cancer Prev. 2020;21(9):2539–47.
https://doi.org/10.31557/APJCP.2020.21.9.2539.
35. Xing N, Qiao T, Zhuang X, Yuan S, Zhang Q, Xu G. CpG
oligodeoxyribonucleotide 7909 enhances radiosensitivity via
downregulating Oct-4 expression in radioresistant lung cancer cells. Onco
Targets Ther. 2015;8:1443–9. https://doi.org/10.2147/OTT.S84467.
36. Tsai L-L, Yu C-C, Chang Y-C, Yu C-H, Chou M-Y. Markedly increased Oct4
and Nanog expression correlates with cisplatin resistance in oral squamous
cell carcinoma. J Oral Pathol Med. 2011;40(8):621–8. https://doi.org/10.1111/
j.1600-0714.2011.01015.x.
37. Gao W, Li JZ-H, Chen S-Q, Chu C-Y, Chan JY-W, Wong T-S. BEX3 contributes
to cisplatin chemoresistance in nasopharyngeal carcinoma. Cancer Med.
2017;6(2):439–51. https://doi.org/10.1002/cam4.982.
38. de Vries G, Rosas-Plaza X, van Vugt MATM, Gietema JA, de Jong S. Testicular
cancer: determinants of cisplatin sensitivity and novel therapeutic
opportunities. Cancer Treat Rev. 2020;88:102054. https://doi.org/10.1016/j.
ctrv.2020.102054.
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