Head and neck squamous cell carcinoma (HNSCC) is the eighth most common neoplasia worldwide, accounting for 377,000 new cases and 177,000 deaths from this disease every year [1
]. HNSCC can be associated with tobacco and alcohol consumption, representing 75% of all cases. However, infection with the Human Papilloma Virus (HPV) high-risk types are increasingly detected and currently considered one oncogenic driver in a subset of cases [2
]. HNSCCs also harbor a high genetic heterogeneity, with mutations in tumor suppressor genes such as TP53
and activation of oncogenes, such as the epidermal growth factor receptor (EGFR
) and PIK3CA
EGFR is a transmembrane protein that belongs to the HER/ErbB family of tyrosine kinases receptors (RTK), including ErbB2/Neu/Her2, ErbB3/Her3, and ErbB4/Her4 [6
]. Ligand binding triggers homo/heterodimerization with other HER phosphorylation, activating downstream signaling pathways promoting proliferation members and subsequent cell cycle differentiation, survival, angiogenesis, invasion, and metastasis in cancer [3
]. Moreover, EGFR can translocate to the nucleus and acts as a transcriptional factor [7
]. EGFR is overexpressed in 90% of all HNSCCs and correlates with poor survival and treatment outcomes [3
Currently, the only anti-EGFR therapy FDA approved for HNSCC is Cetuximab [3
]. Cetuximab is a chimeric monoclonal antibody that targets EGFR by competitively inhibiting their natural ligands, fostering EGFR internalization, and altering EGFR-dependent signaling [6
]. Cetuximab demonstrated a 13% response rate in recurrent or metastatic HNSCC as monotherapy [10
]. The combination of cetuximab and the first-line chemotherapy (platinum–fluorouracil) also improves response rates, progression-free survival (PFS), and overall survival (OS) (10.1 months versus 7.4 months; p
= 0.04) in recurrent or metastatic HNSCC patients [11
]. Moreover, in patients with advanced locoregionally disease, cetuximab plus radiotherapy improves OS compared with radiotherapy alone (49.0 months versus 29.3 months; p
= 0.03) [12
]. In addition, the locoregional control of HNSCC patients was 24.4 months among patients treated with cetuximab plus radiotherapy and 14.9 months among those given radiotherapy alone [12
]. Despite encouraging response rates, cetuximab has shown high recurrence levels in HNSCC. Although PIK3CA
mutations and PTEN
expression were previously related as potentially biomarkers [13
], the molecular mechanisms underlying cetuximab resistance remain unclear [14
Herein, to identify new biomarkers related to the acquired cetuximab resistance in HNSCC, we developed a cetuximab-resistant in vitro model, performed an integrated molecular investigation and compared the newly acquired features with the parental cells to uncover particular genes or pathways involved in the acquired cetuximab resistance that could be related to the disease development.
2. Materials and Methods
2.1. Cell Lines and Cetuximab Resistance Model Development
FaDu cell line was obtained from American Type Culture Collection (ATCC catalog number HTB-43). The cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM 1X, high glucose; Gibco, Invitrogen, Grand Island, NY, USA) supplemented with 10% Fetal Bovine Serum (FBS, Gibco, Invitrogen Grand Island, NY, USA) and 1% penicillin/streptomycin solution (P/S, Sigma-Aldrich, San Luis, MO, USA), at 37 °C and 5% CO2
. The Department of Molecular Diagnostics, Barretos Cancer Hospital performed authentication of cells in June 2019, as previously reported [15
]. Moreover, all cell lines were tested for mycoplasma through MycoAlertTm Mycoplasma Detection Kit (Lonza, Basel, Switzerland), following the manufacturer’s instructions.
The cetuximab resistance model was developed by growing the sensitive cell line FaDu in increasing (200–3200 µg/mL) sub-lethal concentrations of cetuximab (Merck, Darmstadt, Germany) in the growth medium. The starting dose was approximately the IC50 (inhibitory concentration, 50%) of the cell line for 72 h. The medium was then removed to allow the cells to recover for a further 72 h. This development phase was conducted for approximately eight months, and each clone was removed from the culture at a specific concentration, and then the cell viability assay was re-assessed. In the end, we obtained seven stable resistant clones. The resistant cell obtained (FaDu resistant) was cultivated in the same conditions as the parental cell line. Fadu Parental (FaDu P) was used as non-resistant control for all experiments. The stock solutions of cetuximab were prepared in phosphate-buffered saline (PBS, Sigma-Aldrich, San Luis, MO, USA) and stored at 4 °C.
2.2. Chromosome Preparation and G-Banding
The karyotyping analysis was performed as described previously [16
]. Fadu parental and FaDu resistant were incubated with 120 ng/mL colcemid (Life Technologies–Gibco, Invitrogen Grand Island, NY, USA) for 120 min. The cells were harvested by treatment with 0.025% acutase, suspended in KCl 0.075 M solution at 37 °C temperature for 8 min, and fixed with methanol/acetic acid (3:1) five times. The G banding was obtained using 0.0125 g/mL trypsin and 4% Giemsa solution. Twenty metaphases were analyzed using the Olympus BX61, BandView 7.2.7 (Applied Spectral Imaging/Genasis), and the result was described according to the International Human Cytogenetic Nomenclature System (ISCN 2016).
2.3. DNA Isolation and Whole Exome Sequencing (WES) Analysis
The DNA from FaDu parental and FaDu resistant was used for WES, with the input of 1 µg on the Illumina NovaSeq™ System by a commercial company (Sophia Genetics, Switzerland). Sequence reads were aligned using human reference genome build 37 (hs37d5-decoy) applying BWA-MEM with Burrows–Wheeler Aligner version 0.7.10-789 [17
]. Duplicate reads were marked with Picard-Tools 1.92 (http://broadinstitute.github.io/picard/
, accessed on 19 October 2021). MuTect version 1.1.4; (http://www.broadinstitute.org/cancer/cga/mutect
, accessed on 19 October 2021) and Varscan2 [18
] were used to call somatic SNVs and indels parental cell line, respectively. MuTect was run using default parameters with files from COSMIC version 54 and dbSNP version 132 included as input [19
]. We used Ensembl Variant Effect Predictor (VEP) [21
] to annotate and determine tumor-specific variants’ functional effects. The results from SIFT, Polyphen-2, ClinVar were considered. It also excluded variants likely to be germline, i.e., listed in ESP6500 (http://evs.gs.washington.edu/EVS/
, accessed on 19 October 2021), 1000 Genome, or ExAC [22
]. The candidate mutations were manually curated using the Integrated Genomics Viewer (IGV) [24
]. Copy number abnormalities (CNA) were identified using Nexus Copy Number version 9.0 (BioDiscovery; El Segundo, CA, USA; https://www.biodiscovery.com/products/Nexus-Copy-Number
, accessed on 19 October 2021) with a default parameter for BAM ngCGH (matched) input with homozygous frequency threshold and value at 0.97 and 0.8, respectively, hemizygous loss threshold at −0.18, single copy gain at 0.18 and high copy gain at 0.6.
2.4. Copy Number Alterations (CNA)
CNA was analyzed using the nCounter®
v2 Cancer CN Assay panel (NanoString Technologies, Seattle, WA, USA). This panel counts the Copy Number Variation (CNV) of 87 involved genes commonly amplified or deleted in several cancers (www.nanostring.com/products/CNV
, accessed on 19 October 2021). As a control, the genomic DNA from FaDu parental was used. First 600 ng of genomic DNA was submitted to DNA fragmentation by AluI digestion followed by a denaturation step, which yields single-stranded templates. Then, single-stranded DNA templates were submitted to hybridization with a reporter probe, which carries the color-code, and a capture probe, which allows the immobilization of the targets over the cartridge for data capture. After hybridization, samples were transferred to the nCounter®
Prep Station, an automated platform, to remove probes excess and probe/target complexes alignment and immobilization over the nCounter®
cartridge. Then, cartridges were placed in the nCounter®
Digital Analyzer for data capture. Raw data was captured by the nSolverAnalysis Software v4.0®
(NanoString Technologies) and normalized by the average of 54 probes targeting invariant regions (invariant controls). For final data estimation, CNA estimated twice the ratio of the average probe count per gene in the resistant cell line than the parental cell line as previously described [25
2.5. Cell Surface Markers Screening
The cell surface markers were analyzed according to the manufacturers’ instructions. BD Lyoplate™ Human Cell Surface Marker Screening Panel was utilized (cat. 560747; BD Biosciences, Franklin Lakes, NJ, USA) containing 242 purified monoclonal antibodies and corresponding isotype controls following the manufacturer’s instructions. Briefly, 5 × 103 cells were seeded in a 96-well plate in fluorescence-activated cell sorting (FACS) buffer containing 10 mg/mL DNAse. Primary antibody incubation was carried out in 27 mL volume for 30 min on ice, followed by two washes in FACS buffer washes. Next, cells were incubated with biotinylated secondary antibodies (goat anti-mouse 1:200, goat anti-mouse 1:200). The cells were fixed in 1000 µL of 4% paraformaldehyde in 1× PBS and washed in FACS buffer washes. FACS analysis gating allowed the elimination of debris. The analysis was performed using the BD ACCURI™C6 flow cytometer using at least 10,000 events per well.
2.6. Western Blot, Human RTK, Subcellular Fraction Separation, and Cytokines Arrays
To assess the effect of cetuximab resistance in the intracellular signaling pathways and RTKs, the cells were cultured in DMEM 10% FBS in T25 culture flasks, which grew to 85% confluence and then serum starved for 2 h. The cells were washed and scraped in cold PBS and lysed in a buffer containing 50 mM Tris (pH 7.6–8), 150 mM NaCl, 5 mM EDTA, 1 mM Na3VO4, 10 mM NaF, 10 mM sodium pyrophosphate, and 1% NP-40 and protease cocktail inhibitors. Western blot analysis was performed using a standard 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis, loading 20 μg of protein per lane. All the antibodies were used as recommended by the manufacturer. All the antibodies were used as recommended by the manufacturer (Supplementary Table S3
). We used the Cell Fractionation Kit (cell signaling; #9038) for subcellular fraction separation according to the manufacturer’s instructions. Concerning the RTK phosphorylation assessment, a proteome human RTK Phosphorylation Antibody Array (ab193662; Abcam, Cambridge, UK) and Human XL Cytokine Array Kit (ARY022; R&D systems, Minneapolis, MM, USA) was used according to the manufacturer’s instructions. A total of 500 μg of fresh protein lysates was briefly incubated overnight at 4 °C with nitrocellulose membranes dotted with duplicated spots for 71 anti-RTK, 102 anti-Cytokines, and control antibodies. Bound phospho-RTKs and cytokines were incubated with a pan anti-phosphotyrosine-HRP antibody for 2 h at room temperature. Blot detection was performed by chemiluminescence (ECL Western Blotting Detection Reagents, RPN2109; GE Healthcare, Piscataway, NJ, USA) in ImageQuant LAS 4000 mini (GE Healthcare, Chicago, IL, USA). The bands were quantified by densitometry using the Image J software (1.49v). For subcellular fraction assay first, the total protein was normalized by the loading control (β-actin) or by loading control for each cellular compartment (lamin B1 for nuclear and FADD for cytoplasm). Additionally, relative ratios (phospho/total) were determined.
2.7. mRNA NanoStringTM Analysis
Gene expression analysis on FaDu parental and FaDu resistant was performed using the NanoString nCounter PanCancer Pathways panel (730 gene transcripts) according to the manufacturers’ instruction (NanoString Technologies, Seattle, WA, USA). This panel assesses thirteen canonical pathways (Notch, Wnt, Hedgehog, Chromatin modification, Transcriptional Regulation, DNA Damage Control, TGF-beta, MAPK, STAT, PI3K, RAS, Cell Cycle and Apoptosis). Briefly, 100 ng aliquots of RNA were hybridized with probe pools, hybridization buffer, and codeset reagents in a total volume of 30 μL and incubated at 65 °C for 20 h. After codeset hybridization overnight, the samples were washed and immobilized to a cartridge using the Nanostring nCounter Prep Station (NanoString Technologies, Seattle, WA, USA) for 4 h. Finally, the cartridges containing immobilized and aligned reporter complexes were scanned in the nCounter Digital Analyzer (NanoString Technologies), and image data were subsequently generated using the high-resolution setting. Quality control assessment of raw NanoString gene expression counts was performed with nSolver Analysis Software version 4.0 and the default settings (NanoString Technologies, Seattle, WA, USA). Quantile normalization and differential expression were performed within the NanoStringNorm package (v18.104.22.168) [26
] in the R statistical environment (v3.6.3). The normalized log2 mRNA expression values were used for subsequent data analysis. Genes with fold change (FC) ≥ ±2 and p
< 0.01 were considered significant. Heatmap with hierarchical clustering of differentially expressed genes was built in the ComplexHeatmap package (v2.0.0) [27
2.8. Expression Microarray
The expression profile of the FaDu cells was analyzed by microarray slides Gene Expression Microarray, 4 × 44 K (Agilent Technologies, Santa Clara, CA, USA) in dual-color methodology (Cy3 and Cy5), following the manufacturer’s recommendations. Total RNA (500 ng) with the addition of control RNA (spike-in) were carried out to reverse transcription with the aid of the enzyme MMLV-RT (Maloney Murine Leukemia Virus—Reverse Transcriptase). Subsequently, nucleotides marked with the fluorochromes cyanine 5 (Cy5) were added to the T7 polymerase enzyme to the parental and resistant cells cDNA. The respective cDNAs were purified with commercial columns and then hybridized on the slides for 17 h at a constant temperature of 65 °C in a hybridization oven (Agilent Technologies). Finally, according to the manufacturer’s recommendations, the hybridized slides were washed in Wash Buffer 1 and Wash Buffer 2. The slides were scanned at 550 nm (green spectrum—-Cy3) and 640 nm (red spectrum-Cy5) on the Agilent Scanner Surescan (Agilent Technologies). Data extraction and quality control were performed using the Feature Extraction software, version 12 (Agilent Technologies). Oligonucleotides were identified by the customized protocol GE2_107_Sep09. After quantification, the raw data means (gMeanSignal) were selected to subtract the background means (gBGMeanSignal) used for future analyses. The expression data were analyzed in an R environment, version 4. The limma package was used to remove positive and negative controls, values that overlapped the background, and data conversion on a logarithmic scale for the inclusion of data, identification of flags, and generation of expression matrices. Then, the data were normalized by the quantile methodology, and the moderated t test was applied, followed by false-discovery rate (FDR) correction. The category that presented at least three genes and a classification p ≤ 0.05 was considered significant after Benjamini–Hochberg correction.
2.9. Sanger Sequencing
The analysis of ROS1 mutation (c.6341A>G) was performed by PCR followed by direct Sanger sequencing, as described previously [28
]. Briefly, using a specific pair of primers (forward 5′-3′: AGCATTACTCTGTGTCCCGT; reverse 5′-3′: AGGGATCTGGCAGCTAGAAA), the target region was amplified by PCR using a Veriti PCR thermal cycler (Applied Biosystems, Foster City, CA). We used an initial denaturation at 95 °C for 1 min followed by 35 cycles of 95 °C denaturation for 30 s; specific annealing temperature was for 30 s and the 72 °C elongation phase for 30 s, followed by a 72 °C final elongation for 2 min. Amplification of PCR products was confirmed by gel electrophoresis. Sequencing PCR was performed using the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, Waltham, MA, USA) and a 3500 xL Genetic Analyzer (Applied Biosystems, Waltham, MA, USA).
2.10. Cell Viability Assay
The cell viability was performed by MTS (Promega, Madison, WI, USA) as previously described [15
]. To determine the IC50
values, cells were seeded into 96-well plates at a density of 5 × 103 cells per well and allowed to adhere overnight in DMEM 10% FBS. Subsequently, the cells were treated with increasing concentration of cetuximab (0, 20, 30, 50, 100, 200 e 250 µg/mL) diluted in DMEM-0.5% FBS for 72 h. The results were expressed as mean viable cells relative to PBS alone (considered 100% viability) ± SD. The IC50
concentration was calculated by nonlinear regression analysis using GraphPad Prism software version 7. The assays were performed in triplicate at least three times.
2.11. Morphological Data
The images of the cell lines’ morphological changes after treatment with cetuximab were obtained in Olympus XT01 (Olympus, Shinjuku, Tokyo, Japan) microscope at 100× magnification.
2.12. Wound Healing Migration Assay
The cells were seeded in six-well plates and cultured in DMEM-10%FBS to at least 95% of confluence. Monolayer cells were washed with PBS and scraped with a plastic 100 μL pipette tip. The “wounded” areas were photographed by phase-contrast microscopy after 0, 24, 48, and 72 h. The following formula calculated the relative migration distance: percentage of wound closure (%) = 100(A − B)/A, where A is the width of cell wounds before incubation, and B is the width of cell wounds after incubation [29
2.13. Colony Formation Assay Anchorage-Dependent
FaDu parental and FaDu resistant cells were seeded at a density of 5 × 103
/well in six-well plates and maintained in a culture medium for 8–10 days at 37 °C. Finally, colonies were stained with 0.125% crystal violet solution. Colonies larger than 50 cells were photographed under the light microscope Eclipse 2200 (Nikon, Minato, Tokyo, Japan), and the number of colonies was analyzed by the open-source software CFU (Plos One—http://opencfu.sourceforge.net/
, accessed on 20 October 2021) [30
]. The results represent the mean of at least three independent experiments.
2.14. Adhesion Assay
Adhesion of FaDu parental and FaDu resistant cell lines was evaluated as described previously [31
]. Briefly, a 96-well plate was coated for 24 h with PBS, BSA (bovine serum albumin, 10 μg/mL, Sigma-Aldrich), Matrigel®
was diluted at 1:10 in PBS1X. The next day the excess liquids were removed, and the plates were incubated with 100 μL/well of 0.1% BSA for two h and washed with PBS. Then, 6 × 103
cells of FaDu parental and FaDu resistant cells were added to each well in a serum-free medium and incubated at 37 °C in a 5% CO2
humidified atmosphere for 2 h. The non-adherent cells were rinsed off, and the remaining cells were fixed with 10% trichloroacetic acid (TCA), stained with crystal violet and quantified using an ELISA reader at 540 nm.
2.15. Immunofluorescence Analysis
Epidermal growth factor receptor (EGFR) was stained as described [32
], 2.5 × 104
cells were seeded in 12-well plate in DMEM 10%. The next day, cells were washed in PBS and permeabilized with 0.3% Triton X-100 for 10 min. Cells were incubated with primary antibodies, Goat anti-EGFR (1:1000, Dako, Santa Clara, CA, USA), and Rabbit anti-phospho-mTOR (1:1000, cell signaling) for 24 h at 4 °C. Cetuximab (CTX) was detected using anti-human IgG Fc antibody (1:1000; R&D systems) for 24 h at 4 °C. After the wash step with PBS1X, cells were incubated for 1 h at room temperature with Alexa 560 labeled anti-goat secondary antibodies (1:400, Invitrogen). Sections were then stained with Hoechst (Thermo Scientific, Waltham, MA, USA) and washed with PBS three times. The cells were analyzed using fluorescence microscopy (Olympus, Fluoview FV10, Shinjuku, Tokyo, Japan) at 600× magnification.
2.16. Statistical Analysis
Single comparisons between the conditions studied were made using Student’s t test, and the differences between the groups were tested using variance analysis. The statistical analysis was performed using GraphPad Prism version 8.4.3. The level of significance in all statistical analyses was set as p < 0.05.
Targeted therapies are the key to the personalized treatment of cancer patients [39
]. Herein, using an in vitro model of HNSCC, we performed a comprehensive evaluation of the molecular profile and biological mechanisms of cetuximab resistance using a long-term cetuximab exposure model. We showed that a cetuximab acquired-resistance model was successfully established and demonstrated that the overexpression of mTOR-PI3K-AKT related genes and the acquired mesenchymal and stem cell signatures might be potentially novel cetuximab-resistance (acquired) biomarkers.
Using an in vitro model to create a cetuximab resistant cell line by incremental dose exposition, we found 17-fold-higher IC50
for the resistant cell line, similar to the previous study conducted in HNSCC cell lines that showed a 10-fold IC50
for cetuximab [40
]. We exposed the FaDu cell line for eight months in incremental doses to obtain cetuximab-acquired-resistant cells. Some studies have reported a resistant phenotype in 6 or 7 months at 100 and 5µg/mL of cetuximab or even in periods of shorter than 15 days [41
]. We believe that resistance models based on long exposure times generate a persistent resistance to cetuximab.
We observed several chromosomal numerical aberrations with a gain of chromosomes 3, 7, and 12. Some of these alterations are known to be frequent in HNSCC, such as the gain of chromosome 7, as well as its tetrasomy [16
]. Moreover, we observed specific CNA in several gene loci associated with gene overexpression in the cetuximab resistant cells, including KRAS
, and RASGRF1
. In addition, we found a loss in PTEN
. KRAS activating mutations and loss of PTEN protein expression was previously reported as a predicted biomarker of cetuximab resistance that lead to mTOR/PI3K/AKT pathway activation [13
]. Taken together, these results suggest that where KRAS mutation is a biomarker of cetuximab response in colorectal cancer [45
], in HNSCC, KRAS
amplification may be a predictive marker of cetuximab resistance.
In our study, RHOA was the most overexpressed gene in resistant cells. We highlighted that RhoA has not been described for HNSCC as cetuximab predictive factor previously. RHOA belongs to the Rho GTPases family, which is also involved in the RAS and mTOR-PIK3-AKT signaling and controls all aspects of cellular motility and invasion, including cellular polarity, cytoskeletal organization, and transduction of signals [46
]. Recently, Pan and coworkers reported that dysregulation of Rho GTPases, particularly Rho-A, Rho-C, and Rac2, resulted in an aggressive HNSCC phenotype [47
]. Rho-A expression may indicate a poor prognosis due to the high probability to irinotecan and doxorubicin resistance in colorectal cancer [48
]. Furthermore, Rho-A has been described to modulate the EMT by TGF-β and mTOR [50
]. EMT is a cellular transformation process in which epithelial cells lose epithelial polarity and intercellular adhesiveness, gaining migratory potential and acquire mesenchymally and stem cell signatures [52
]. In the current study, cetuximab-resistant cells showed a morphological change and increased levels of TGF-β, SLUG, and CD44, similar to EMT cell activation. We also observed an increase in adhesion rates in FaDu resistant and increased levels of mTOR, and CD44, essential regulators of EMT and metastasis [38
]. Overall, this dataset reinforces that the EMT transition phenotype and overexpression of Rho-A are involved in HNSCC cetuximab resistance.
Another important therapy resistance mechanism is associated with cancer stem cells (CSCs) [54
]. Our study observed a significant difference in mesenchymal stem cell markers and cell surface markers in FaDu resistant cells compared to parental cells such as CD44, ICAM-1, ENG, PD-L1 LAMP1, and LAMP2. CD44 is an important marker of CSC’s in carcinomas, and it plays a role in the mediation of resistance to drug therapy, including anti-EGFR inhibitors [55
]. In the HNSCC context, cancer stem cells are responsible for treatment failure in which CD44 has been reported to represent a candidate of resistance marker [57
]. Moreover, it has been demonstrated that CD44 regulates EGFR, activating the mTOR-PI3K/Akt signaling pathway [58
Additionally, in our resistance model, PD-L1 (CD274) amplification/overexpression may involve the adaptive immune resistance to cetuximab. PD-L1 upregulation was associated with a higher risk for nodal metastasis at diagnosis, overall tumor-related death, and recurrence in HNSCC and associated with TKI’s resistance in lung cancer [59
]. Importantly, it can be suggested that immune checkpoint blockers, such as pembrolizumab, which targets PD-L1, could activate an immune response and overcome the cetuximab response. Alterations of the EGFR signaling cascade are known factors influencing cetuximab response [61
]. We observed a decreased EGFR phosphorylation in the resistant cells, despite the absence of variation of total EGFR and the upregulation of genes involved in the mTOR/PI3K/AKT pathway. In concordance with our results, Lida et al. showed constitutive activation of the mTOR/PI3K/AKT signaling axis in cetuximab resistant lung cell lines [62
]. The upregulation of the mTOR/PI3K/AKT pathway was also reported using UM-SCC-6R cetuximab-resistant cells that demonstrated EGF-independent signaling [43
]. Conversely, it was previously reported that the mTOR/PI3K/AKT pathway’s constitutive activation leads to EGFR trafficking [63
]. Nuclear EGFR translocation is reported to be related to anti-EGFR therapy resistance [64
]; thus, our results corroborate this hypothesis.
As mentioned, the mTOR pathway seems to play a significant role in cetuximab resistance in HNSCC. A recent study associated MTOR overexpression in the TKI inhibitors resistance process, demonstrating that stress-induced mutagenesis contributes to adaptive evolution in cancer for drug resistance [65
]. Our study found an increase in mTOR levels in whole cellular compartments, including in the membrane, cytoplasm, and mainly in the nucleus of cetuximab-resistant cells. Besides, we also identified an upregulation of genes involved in mTOR signaling, namely RHOA, PIK3R3, SKP2, and a frameshift mutation in IRS2 that was not present in the parental cell line. IRS2 acts as a protein scaffold that activates the phosphoinositide 3-kinase (PI3K)/AKT/mTOR pathway, leading to proliferation and migration [66
]. Thus, the inability of IRS2 to respond to negative feedback signals could contribute to higher PI3K/mTOR activity associated with cetuximab resistance in HNSCC. Of note, IRS2 mutations were recently associated with invasion in pleomorphic invasive lobular carcinoma [67
]. Overall, mTOR-PIK3-AKT family members’ overexpression could be involved with cetuximab resistance, and combination regimens with drugs that block mTOR may overcome cetuximab resistance in HNSCC.
We also found a ROS1 variant (c.6341A>G) associated with resistant cells. ROS1 was previously described as regulated and associated with metastasis to lung and lymph nodes in oral squamous cell carcinoma [68
]. Of note, ROS1 mutations were associated with response to anti-ROS1 inhibitors, such as Lorlatinib in pancreatic cancer [69
]. Recently, Davies and coworkers demonstrated a compensatory mechanism of growth control involving ROS and EGFR, where cells resistant to ROS1 inhibition have been resensitized by inhibiting EGFR by a switch between these pathways [70
]. Thus, co-targeting of ROS1 and EGFR could potentially offer an effective option to overcome cetuximab resistance. Further validation in other cell line models and in head and neck patients submitted to cetuximab are warranted to consolidate these findings.