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. 2021 Sep 9;25(9):605–616. doi: 10.1089/omi.2021.0057

How to Achieve Therapeutic Response in Erlotinib-Resistant Head and Neck Squamous Cell Carcinoma? New Insights from Stable Isotope Labeling with Amino Acids in Cell Culture-Based Quantitative Tyrosine Phosphoproteomics

Ankit P Jain 1,2, Aneesha Radhakrishnan 1, Sneha Pinto 1,5, Krishna Patel 1,3, Manish Kumar 1,4, Vishalakshi Nanjappa 1, Remya Raja 1,4, Thottethodi Subrahmanya Keshava Prasad 1,5,6, Premendu P Mathur 2,7, David Sidransky 8, Aditi Chatterjee 1,4,5,*,✉, Harsha Gowda 1,4,5,*,✉
PMCID: PMC8591087  PMID: 34432535

Abstract

Resistance to cancer chemotherapy is a major global health burden. Epidermal growth factor receptor (EGFR) is a proven therapeutic target for multiple cancers of epithelial origin. Despite its overexpression in >90% of head and neck squamous cell carcinoma (HNSCC) patients, tyrosine kinase inhibitors such as erlotinib have shown a modest response in clinical trials. Cellular heterogeneity is thought to play an important role in HNSCC therapeutic resistance. Genomic alterations alone cannot explain all resistance mechanisms at play in a heterogeneous system. It is thus important to understand the biochemical mechanisms associated with drug resistance to determine potential strategies to achieve clinical response. We investigated tyrosine kinase signaling networks in erlotinib-resistant cells using quantitative tyrosine phosphoproteomics approach. We observed altered phosphorylation of proteins involved in cell adhesion and motility in erlotinib-resistant cells. Bioinformatics analysis revealed enrichment of pathways related to regulation of the actin cytoskeleton, extracellular matrix (ECM)–receptor interaction, and endothelial migration. Of importance, enrichment of the focal adhesion kinase (PTK2) signaling pathway downstream of EGFR was also observed in erlotinib-resistant cells. To the best of our knowledge, we present the first report of tyrosine phosphoproteome profiling in erlotinib-resistant HNSCC, with an eye to inform new ways to achieve clinical response. Our findings suggest that common signaling networks are at play in driving resistance to EGFR-targeted therapies in HNSCC and other cancers. Most notably, our data suggest that the PTK2 pathway genes may potentially play a significant role in determining clinical response to erlotinib in HNSCC tumors.

Keywords: erlotinib resistance, cancer research, head and neck cancer, quantitative tyrosine phosphoproteomics, focal adhesion kinase, tyrosine kinase inhibitors

Introduction

Targeted therapies have significantly improved the management and treatment of cancer. However, resistance to these targeted therapies remains a major barrier to achieve durable response. Data from the past decade indicate intratumoral heterogeneity as a major driver of therapeutic resistance in cancer (Ding et al., 2012; Mroz and Rocco, 2016; Navin et al., 2011). For example, differential sensitivity of the primary and recurrent tumor to anticancer drugs was observed in head and neck squamous cell carcinoma (HNSCC) patients. Single-cell clones from these tumors exhibited different gene expression profiles and differential sensitivity to anticancer drugs (Suzuki et al., 2011). Intratumoral heterogeneity has also been demonstrated to play a role in cisplatin resistance in HNSCC (Niehr et al., 2018).

In a previous study, we reported multiple genetic and proteomic alterations in epidermal growth factor receptor (EGFR)-MAPK pathway in a cell line model of erlotinib resistance in HNSCC (Jain et al., 2019). Targeting EGFR-MAPK pathway with Map2k1 inhibitor was effective in inhibiting the growth of erlotinib-resistant cells. However, only a subset of cells responded to inhibition of EGFR-MAPK pathway even at high inhibitor concentrations. We hypothesized one of the potential reasons for erlotinib resistance in a subset of cells was heterogeneity. Different cell subsets might be dependent on different mechanisms for survival. Therefore, investigating these mechanisms and identifying alternate druggable targets is of clinical relevance for better management of erlotinib-resistant HNSCC tumors.

Genomic analysis using NGS technologies has significantly aided our understanding of anticancer drug resistance mechanisms. However, genomic alterations alone cannot explain all the mechanisms involved in driving drug resistance. Aberrant activation of signaling pathways has been reported to play a critical role in drug resistance. For example, increased expression of receptor tyrosine kinases including Her2, Her3, and Axl are known to be associated with resistance to tyrosine kinase inhibitors (TKIs) such as EGFR-TKIs in HNSCC (Cooper and Cohen, 2009; Giles et al., 2013). Mass spectrometry (MS)-based phosphoproteomics is a powerful tool to understand global alterations in signaling pathways (Rajagopalan et al., 2018). Immunoaffinity enrichment followed by MS analysis has been extensively used for phosphotyrosine profiling (Pinto et al., 2015; Sathe et al., 2016).

In this study, we used quantitative tyrosine phosphoproteomics analysis to characterize altered kinome networks in erlotinib-resistant HNSCC cell line model. We identified hyperphosphorylation of kinases such as Axl and c-Met along with other kinases and their substrates that are known to be associated with resistance to TKIs in HNSCC and other epithelial cancers. We observed enrichment of focal adhesion kinase (PTK2) pathway in erlotinib-resistant cell line. In addition, we observed amplification and overexpression of several genes related to PTK2 pathway in HNSCC tumors from publicly available The Cancer Genome Atlas (TCGA) datasets. These observations further warrant investigation of PTK2 pathway as a potential target for EGFR-TKI-resistant HNSCC tumors.

Materials and Methods

Cell culture and SILAC labeling

Isogenic human oral cancer cell line SCC-R (resistant to erlotinib) and SCC-S (sensitive to erlotinib) derived from UMSCC1 were used for this study (Chang et al., 2013; Jain et al., 2019). Both cell lines were maintained in Dulbecco's modified Eagle's medium (DMEM)–high glucose medium containing 10% fetal bovine serum (Clontech, Mountain View, CA) and 1% penicillin/streptomycin mixture at 37°C in a humidified 5% CO2 incubator. For SILAC-based proteomics experiments, SCC-S cells were adapted to DMEM-SILAC media supplemented with l-lysine-2HCl (13C6, 15N2, 98% isotopic purity) and l-arginine-HCl (13C6, 98% isotopic purity). SCC-R cells were maintained in regular media (Ong et al., 2002).

Trypsin digestion and Sep-Pak C18 column-based cleanup

SCC-S and SCC-R cell lines were grown in DMEM-SILAC (K8R6) and DMEM media, respectively, and were maintained in serum-free media for 12 h before harvesting. After 12 h of serum starvation, cells were harvested and lysed in urea lysis buffer (20 mM HEPES pH 8.0, 9 M urea, 1 mM sodium orthovanadate, 2.5 mM sodium pyrophosphate, 1 mM phosphoglycerophosphate). Protein concentration was determined using bicinchoninic acid (BCA) assay and 10 mg equivalent of lysate from each of the conditions were pooled. The mixture was reduced with 5 mM dithiothreitol at 60°C for 30 min and alkylated using 20 mM iodoacetamide for 10 min at room temperature.

The concentration of urea was brought to <2 M using HEPES buffer, pH 8.0, and proteins were digested overnight at 37°C using sequencing grade-modified trypsin (Promega). The protein digests were desalted using Sep-Pak C18 column (with 40% acetonitrile [ACN] with 0.1% TFA as the final elution buffer). Peptide digests were then lyophilized.

Immunoaffinity purification (IAP) of phosphotyrosyl peptides

The lyophilized peptide mixtures were dissolved in immunoaffinity purification (IAP) buffer containing 50 mM MOPS pH 7.2, 10 mM sodium phosphate, and 50 mM NaCl. Before phosphotyrosine enrichment, the P-Tyr-1000 beads (Cell Signaling Technology, Danvers, MA) were washed twice with IAP buffer at 4°C. Then P-Tyr-1000 beads were added to the peptide mixture and incubated for 30 min with gentle rotation. The supernatant was discarded and the beads were washed thrice with ice-cold IAP buffer and twice with ice-cold water to remove unbound peptides. The enriched tyrosine phosphopeptides were then eluted from beads using 0.15% TFA at room temperature. This step was repeated twice. This was followed by cleanup of samples using C18 StageTips as described earlier and lyophilized (Rappsilber et al., 2003; Sathe et al., 2016).

MS analysis of enriched phosphotyrosyl peptides

The enriched phosphotyrosine-containing peptides were analyzed on Orbitrap Fusion Tribrid mass spectrometer (Thermo Electron, Bremen, Germany) interfaced with Easy-nLC II nanoflow liquid chromatography system (Thermo Scientific, Odense, Denmark). Peptide digests were reconstituted in 0.1% formic acid and loaded onto trap column packed (75 μm × 2 cm) with Magic C18 AQ 5 μm particle (Michrom Bioresources, Inc., Auburn, CA) at a flow rate of 4 μL/min. Peptides were separated on an analytical column (75 μm × 12 cm) at a flow rate of 300 nL/min using a step gradient of 5–30% solvent B (0.1% formic acid in 95% acetonitrile) for the first 75 min and 30–40% solvent B from 75 to 90 min. The total run time was set to 120 min and the mass spectrometer was operated in data-dependent acquisition mode.

Precursor MS scan (m/z 350–1800) and MS/MS (m/z 350–1800) was acquired with a mass resolution of 120K and 30K at 200 m/z in the Orbitrap mass analyzer. The automatic gain control (AGC) target was set to 500K for MS and 50K for MS/MS, and maximum ion injection time was set to 60 ms for MS and 200 ms for MS/MS scan events. In each duty cycle, 10 most intense peaks were selected for MS/MS fragmentation using higher energy collision dissociation mode at 32% normalized collision energy and isolation width was set to 2.0 m/z. Precursor ions with single or unassigned charges were rejected. Dynamic exclusion was set for 45 sec with a 10 ppm mass window. The lock mass option was enabled using polysiloxane ion (m/z = 445.12002) from ambient air for internal calibration.

Processing of raw MS data

The tandem MS data were searched using MASCOT (v 2.2) and Sequest-HT search algorithms against a Human RefSeq database (RefSeq 65) supplemented with frequently observed contaminants through the Proteome Discoverer platform (version 1.4.1.14; Thermo Fisher Scientific, Bremen, Germany). For both algorithms, the search parameters included a maximum of two missed cleavages; carbamidomethylation at cysteine as a fixed modification, oxidation at methionine, phosphorylation at serine, threonine, and tyrosine and SILAC labels 13C6, 15N2-lysine (K8); 13C6-arginine (R6) as variable modifications. The MS error tolerance was set at 10 ppm and MS/MS error tolerance to 0.05 Da. The data were also searched against a decoy database and peptide spectral matches (PSMs) that passed 1% false discovery rate score threshold were considered for further analysis.

A precursor ion quantifier node with a mass precision of 3 ppm was used for peptide and protein quantification. The probability of phosphorylation for each Tyr-site on each peptide was calculated using PhosphoRS node (version 3.0) in the Proteome Discoverer (Taus et al., 2011). Data from both the replicates were combined and phosphopeptides with ≥75% phosphosite probability were considered for further analysis.

LC-MS/MS data availability

Raw data are submitted to ProteomeXchange Consortium using the PRIDE public data repository (Vizcaino et al., 2013) and can be accessed using the data identifier—PXD007769.

Bioinformatics analysis

Gene Ontology-based functional enrichment analysis of dysregulated phosphoproteins was carried out using g:Profiler (version e96_eg43_p13_563554d) with g:SCS multiple testing correction method applying a significance threshold of 0.05 (Raudvere et al., 2019). Protein–protein interaction network analysis of dysregulated phosphoproteins was carried out using String database version 11.0 (Szklarczyk et al., 2019) using text mining, experiments, databases, and coexpression as active interaction sources with query proteins only. A high confidence interaction score cutoff of 0.9 was used.

Pathway analysis was performed using DAVID version 6.8 using KEGG pathway database as background (Huang da et al., 2009; Kanehisa et al., 2012). Gene expression analysis from HNSCC cohort from TCGA data was carried out using UALCAN and cBioPortal (Cancer Genome Atlas, 2015; Cerami et al., 2012; Chandrashekar et al., 2017; Gao et al., 2013).

Western blotting

Whole-cell extracts of SCC-S and SCC-R cells were prepared using modified RIPA lysis buffer (Merck Millipore, Billerica, MA) containing protease inhibitors (Roche, Indianapolis, IN) and phosphatase inhibitors (Thermo Scientific, Bremen, Germany). Western blot analysis was performed as previously described using 30 μg protein lysates (Chang et al., 2011; Radhakrishnan et al., 2017). Nitrocellulose membranes were hybridized with primary antibodies and developed using Luminol reagent (Santa Cruz Biotechnology, Dallas, TX) as per the manufacturer's instructions.

Anti-Ptk2, anti-phospho Ptk2 (Y397), anti-phospho Ptk2 (Y576/577), anti-Bcar1, anti-phospho Bcar1 (Y410), anti-Crkl, anti-phospho Crkl (Y207), anti-Pxn (Y118), anti-phospho Pxn, anti-Pak4, and anti-Itgb1 antibodies were all sourced from Cell Signaling Technology (Beverly, MA). The beta-actin antibody was obtained from Sigma (St. Louis, MO).

Results

Quantitative tyrosine phosphoproteomic analysis of erlotinib-resistant HNSCC cell line

In our previous study, we used genomic, proteomic, and phosphoproteomic approach to characterize molecular alterations associated with erlotinib resistance in HNSCC cell line. Our phosphoproteomic strategy preferentially enriched for serine/threonine phosphopeptides. We demonstrated MAPK pathway activation conferred resistance to erlotinib (Jain et al., 2019). However, MAPK pathway-mediated resistance was observed only in a subset of cells suggesting resistant cells were heterogeneous and other mechanisms could be involved in conferring erlotinib resistance.

In this study, we investigated changes in cellular tyrosine kinase networks to delineate additional molecular mechanisms associated with erlotinib resistance in HNSCC. We carried out quantitative tyrosine-phosphoproteomic analysis of SCC-R (erlotinib-resistant) and SCC-S (erlotinib-sensitive) cells using stable isotope labeling with amino acids in cell culture (SILAC). Both SCC-S and SCC-R cells were serum starved for a period of 12 h before phosphopeptide enrichment to study basal signaling events without interference from growth factors in serum.

Tyrosine phosphopeptides enriched by immunoaffinity method were analyzed in duplicates (R2 = 0.95) (Fig. 1a). High-resolution MS-based analysis lead to the identification of 527 unique phosphopeptides and 441 unique phosphosites corresponding to 241 proteins. A total of 97 phosphosites belonging to 62 proteins were hyperphosphorylated and 63 phosphosites belonging to 49 proteins were hypophosphorylated in SCC-R cells.

FIG. 1.

FIG. 1.

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Tyrosine phosphoproteomics analysis of erlotinib-resistant HNSCC cell line. (a) Correlation analysis of technical replicates for commonly identified phosphosites. (b) Scatterplot of log2-transformed phosphosite/protein expression changes between SCC-R and SCC-S cells. (c) Human Kinome map showing altered kinases identified in tyrosine phosphoproteomics data.

We compared tyrosine phosphoproteomics data with global proteomics data from our previous study (Jain et al., 2019). Relative protein expression and tyrosine phosphorylation of commonly quantified proteins between the two datasets are given in Figure 1b. A complete list of identified phosphopeptides along with changes in tyrosine phosphorylation and protein expression values are provided in Supplementary Table S1 and S2. We observed decreased expression (0.6-fold) of Egfr and hypophosphorylation of the known activation site (Y869, 0.4-fold) in erlotinib-resistant SCC-R cells. Alteration in phosphorylation status of 26 kinases belonging to 15 kinase families was observed in erlotinib-resistant SCC-R cells (Fig. 1c) (Eid et al., 2017; Manning et al., 2002). This included Tnk2#Y827 (3.8-fold) and Ptk2#Y397 (2.7-fold), Tyk2#Y292 (0.4-fold), Yes1#Y222 (0.5-fold) and Lyn#Y397 (0.2-fold).

We analyzed genomic alterations in these 26 kinases using whole-exome sequencing data of SCC-R cells from our previous study (Supplementary Table S3). None of the kinases showed genomic alterations except for AXL gene, which was amplified in SCC-R cells. This shows most biochemical alterations are not necessarily driven by genomic alterations.

Erlotinib-resistant cell line shows enrichment of proteins related to cellular adhesion and motility

Gene ontology-based functional enrichment analysis of differentially phosphorylated proteins using g:Profiler showed significant enrichment (p ≤ 0.05) of proteins related to cell adhesion and cell motility and cell junctions along with enrichment of proteins with receptor tyrosine kinase activity (Fig. 2a) (Raudvere et al., 2019). Proteins associated with cell motility and adhesion were classified under molecular classes of actin binding (Actn1#Y193, 4.4-fold; Itgb1#Y783, 4.5-fold; Ctnnd1#Y120, 0.5-fold; Ctnnd1#Y174, 0.2-fold; Ctnnd1#Y203, 0.4-fold; Ctnnd1#Y242, 0.3-fold) and cadherin binding (Krt18#Y36, 0.1-fold; Tagln2#Y192, 4.3-fold; Epn2#Y186, 0.4-fold; Cav1#Y14, 4.4-fold; Cav1#Y25, 8-fold).

FIG. 2.

FIG. 2.

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Bioinformatics analysis of dysregulated phosphoproteins in SCC-R cells. (a) Gene ontology-based functional enrichment analysis of dysregulated phosphoproteins. (b) Protein–protein interaction-based analysis of dysregulated phosphoproteins. (c) MS/MS spectra of Axl Y598 phosphopeptide. (d) MS/MS spectra of c-Met Y1234 phosphopeptide (inset—relative precursor ion intensities depicting fold change). MS, mass spectrometry.

Alterations in cell adhesion and cell motility are some of the characteristic features observed in epithelial-to-mesenchymal transition, a known mechanism associated with erlotinib resistance. In our previous study, we demonstrated that erlotinib-resistant SCC-R cells showed a mesenchymal phenotype.

Protein–protein interaction analysis of differentially phosphorylated proteins

Protein–protein interaction network analysis of dysregulated phosphoproteins was performed using String database version 11.0 (Szklarczyk et al., 2019). Text mining-based annotation of dysregulated phosphoproteins from erlotinib-resistant SCC-R cells with proteins associated with resistance to EGFR-based targeted therapies in multiple cancers revealed a common set of interacting proteins (Fig. 2b) (Abe et al., 2017; Astsaturov et al., 2010; Solanki et al., 2018; Wilson et al., 2014; Yoshida et al., 2014). These included receptor tyrosine kinases such as tyrosine–protein kinase receptor UFO (Axl), Ephrin type-A receptor 2 (Epha2) and hepatocyte growth factor receptor (c-Met). Axl was overexpressed (11.2-fold) and hyperphosphorylated at Y598 (43-fold) in SCC-R cells (Fig. 2c). We also observed c-Met hyperphosphorylation at Y1003 (3.8-fold) and Y1234 (2.7-fold) (Fig. 2d) with a 1.4-fold change in protein expression in SCC-R cells.

Dysregulation in focal adhesion kinase pathway in SCC-R cells

Pathway enrichment analysis of dysregulated phosphoproteins using DAVID (Huang da et al., 2009) keeping KEGG as background pathway database revealed significant enrichment of multiple tyrosine kinase-mediated signaling pathways such as focal adhesion kinase (PTK2), ERBB, RAP1, VEGF, FoxO, and PI3K-AKT signaling pathways in SCC-R cells. In addition to kinase-mediated signaling pathways, pathways regulating cell migration/invasion including actin cytoskeleton regulation, adherens junction, and transendothelial migration were also enriched in SCC-R cells (Fig. 3a).

FIG. 3.

FIG. 3.

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Dysregulation of focal adhesion kinase (PTK2) pathway in SCC-R cells. (a) List of signaling pathways significantly enriched in SCC-R cells based on bioinformatics analysis of dysregulated phosphoproteins (≥2-fold). (b) PTK2 signaling pathway showing dysregulated proteins and phosphosites.

Significant enrichment of focal adhesion kinase (PTK2) pathway that lies downstream of EGFR and ITGB1 signaling was observed in SCC-R cells. By combining tyrosine phosphoproteomics data with data from our previous study, we observed 44 proteins of PTK2 pathway to be dysregulated or differentially phosphorylated in SCC-R cells (fold change ≥2) (Fig. 3b) (Jain et al., 2019). Protein tyrosine kinase 2 (Ptk2) is an important mediator of growth-factor signaling, cell proliferation and survival (McLean et al., 2005). We observed increased phosphorylation of Ptk2 activation sites Y397, Y576/577, and Y861 in SCC-R cells.

We also observed more than twofold overexpression of integrin β1 (Itgb1) in SCC-R cells. The cytoplasmic side of clustered integrins is known to bind and phosphorylate Ptk2, initiating further downstream changes (Shi and Boettiger, 2003; Sieg et al., 2000). Activation of Ptk2 by autophosphorylation at Y397 is followed by binding of Src family kinases and phosphorylation of downstream proteins such as Bcar1 and paxillin (Pxn) (Lim et al., 2004; Mitra et al., 2005). Concurrently, with increased Ptk2 activation, we observed an increased phosphorylation of Bcar1 and Bcar3 at multiple sites. Hyperphosphorylation of Ptk2 substrate paxillin (Pxn) at Y88 and Y118 was also observed in SCC-R cells. We also observed a twofold increase in phosphorylation at Y198 and Y207 of crk-like protein (Crkl) downstream of Bcar1 and Bcar3.

Increased expression of Itgb1, as well as overexpression and activation of Ptk2 in SCC-R cells, was validated by immunoblotting (Fig. 4a). Protein expression and phosphorylation of downstream targets related to the PTK2 pathway were also validated using immunoblotting. In concordance with proteomics and phosphoproteomics data, we observed overexpression and hyperphosphorylation of Bcar1/Bcar3, Crkl, Pxn, and Pak4 downstream of Ptk2 (Fig. 4b). Overall, we observed activation of PTK2 pathway in erlotinib-resistant HNSCC cells.

FIG. 4.

FIG. 4.

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Overexpression and activation of PTK2 and downstream proteins. (a) Western blot analysis of Ptk2, p-Ptk2 (Y397andY576/577), Itgb1 in SCC-S and SCC-R cells. (b) Western blot analysis of indicated proteins and phosphoproteins related to PTK2 pathway in SCC-S and SCC-R cells. β-Actin served as a loading control. (c) Amplification and overexpression of PTK2 in HNSCC patients. (d) mRNA overexpression of PTK2 in HNSCC samples across individual cancer stages. (e) Genomic and transcriptomic alterations of PTK2 pathway-related proteins in HNSCC patients.

PTK2 and genes related to focal adhesion kinase pathway are overexpressed in HNSCC patients

To determine the relevance of our findings, we evaluated the expression of PTK2 as well as other genes of the PTK2 pathway in HNSCC tissue samples using data from TCGA. We observed amplification and corresponding overexpression of PTK2 in HNSCC tumors (Fig. 4c). Furthermore, we observed significant overexpression of PTK2 across all stages of HNSCC compared with normal tissue (Fig. 4d). Genomic and transcriptomic alterations in PTK2 were observed in 26% of HNSCC patients. Genes related to the PTK2 pathway (PTK2, ITGB1, BCAR1, BCAR3, CRKL, PXN, and PAK4) were altered in ∼46% of HNSCC tumors (Fig. 4e). This indicates that PTK2 pathway may be of clinical significance and may play an important role in HNSCC.

Discussion

HNSCC tumors almost ubiquitously express high EGFR levels. However, EGFR-TKIs such as erlotinib have shown modest response in clinical trials (Cohen et al., 2009; Junior et al., 2011). Research over the past decade has documented intratumoral heterogeneity as a mechanism of therapeutic resistance in multiple cancers including HNSCC (Mroz and Rocco, 2016; Ramon et al., 2020; Suzuki et al., 2011). A recent study from our group reported multiple genomic and proteomic alterations in the EGFR-MAPK pathway in a cell line model of erlotinib resistance in HNSCC (Jain et al., 2019). Although targeting EGFR-MAPK pathway was effective, a subpopulation did not respond to the drug even at high concentration. These findings indicated that erlotinib-resistant cells might be a heterogeneous population where different mechanisms might be contributing to cancer cell survival.

Activation of various kinase signaling pathways are known to be associated with resistance to EGFR-targeted therapies (Cooper and Cohen, 2009; Giles et al., 2013). We used a quantitative tyrosine phosphoproteomics approach to compare SCC-R (erlotinib-resistant) and SCC-S (erlotinib-sensitive) cell line models. We observed decreased expression and phosphorylation of Egfr Y869 in SCC-R cells. In addition, we observed changes in tyrosine phosphorylation of multiple kinases and their interacting proteins in erlotinib-resistant SCC-R cells.

For instance, we observed more than twofold decrease in expression as well as phosphorylation of Lyn at both Y173 and Y397. Decreased Lyn expression and phosphorylation is known to inhibit autophosphorylation of Egfr at Y1068 site (Sutton et al., 2013). Apart from amplification of Axl kinase, no genomic alterations were observed in any of the dysregulated kinases in SCC-R cells. This indicated that the activity profiling of cellular kinome can provide unique insights into alternate mechanisms driving drug resistance that cannot be determined by genomic approaches alone.

Functional enrichment analysis of dysregulated phosphoproteins based on gene ontology showed significant enrichment of terms related to cell adhesion, cell motility, and cell junctions in all three classes of gene ontology through cellular component, molecular class, and biological process. We observed hyperphosphorylation of actin-binding proteins such as Actn1, Lasp1, and Itgb1. Although functional implication of hyperphosphorylation at Y193 are not known, overexpression of Actn1 is associated with destabilization of E-cadherin-based adhesion and increased migration and invasion in breast cancer (Kovac et al., 2018).

Similarly, we observed hypophosphorylation at multiple sites of Ctnnd1 (Y120, Y174, Y203, and Y242) along with protein downregulation in SCC-R cells. Downregulation of Ctnnd1 has been frequently observed in HNSCC and its downregulation has been correlated with tumor progression and poor prognosis (Lo Muzio et al., 2013). Ctnnd1 is known to control cadherin turnover and its loss is also known to be associated with loss of E-cadherin and may have other roles that modulate cadherin adhesiveness (Mariner et al., 2004).

We also observed hyperphosphorylation of cadherin-binding proteins, Tagnl2 and Cav1. Although functional effects of hyperphosphorylation of Y192 are not known, silencing of Tagln2 is known to significantly inhibit cell proliferation and invasion in HNSCC cells (Nohata et al., 2011b). Furthermore, specific extracellular matrix (ECM)–integrin interactions and Y14 phosphorylation are essential for Cav1-driven migration, invasion, transendothelial cell migration, and metastasis (Nohata et al., 2011a; Ortiz et al., 2016).

We also observed hypophosphorylation and downregulation of cytoskeletal proteins such as Krt18 in SCC-R cells. Loss of epithelial phenotype-specific markers such as Krt18 is associated with changes in cell migration pattern and invasiveness along with epithelial-to-mesenchymal transition (Fortier et al., 2013). These results are in concordance with our previous study demonstrating changes in the migration pattern of erlotinib-resistant cells. Changes in cell migration pattern is associated with epithelial-to-mesenchymal transition, a well-established mechanism of resistance to EGFR inhibitors (Chaffer et al., 2016; Fuchs et al., 2008; McConkey et al., 2009).

To understand biological networks that play a role in driving erlotinib resistance in HNSCC, protein–protein interaction network analysis of aberrantly phosphorylated proteins was carried out using STRING network analysis tool. Text-mining-based annotations of the proteins identified in studies related to erlotinib and EGFR-targeted therapy resistance in other cancers revealed that several proteins identified in our study were previously reported in the context of EGFR therapy resistance (Abe et al., 2017; Astsaturov et al., 2010; Solanki et al., 2018; Wilson et al., 2014; Yoshida et al., 2014).

For instance, we observed Axl overexpression and hyperphosphorylation in erlotinib-resistant SCC-R cells. Increased Axl expression and activity have been associated with acquired erlotinib resistance in HNSCC cell lines (Giles et al., 2013). Similarly, we observed overexpression and hyperphosphorylation of c-Met in SCC-R cells. Activation of c-Met is known to overcome EGFR blockade in HNSCC patients (Krumbach et al., 2011; Madoz-Gurpide et al., 2015; Rothenberger and Stabile, 2017; Stabile et al., 2013). Furthermore, c-Met also drives epithelial–mesenchymal transition, which is associated with cetuximab resistance in HNSCC (Basu et al., 2013). It has been shown that Met receptor activation eliminates EGFR signaling in cancer cells and helps Egfr interact with other receptor molecules including Axl and Epha2 (Gusenbauer et al., 2013).

These results show that common mechanisms of resistance are at play in both HNSCC and other tumor types that show resistance to EGFR-targeted therapies. Our findings support previous reports in other cancers and offer the first report of altered tyrosine kinase networks in HNSCC.

Bioinformatics analysis of dysregulated phosphoproteins using DAVID and KEGG revealed enrichment of pathways related to cytoskeletal reorganization, adherens junction, and leukocyte migration that are related to changes in cellular cytoskeleton and migration. Furthermore, we observed significant enrichment of focal adhesion kinase (PTK2) pathway downstream of Egfr among several kinase-mediated signaling pathways that were dysregulated in SCC-R cells. Overexpression of focal adhesion kinase (PTK2) has been reported in 62% of HNSCC patients and also in other tumors such as lung, colon, breast, ovarian, and thyroid (Canel et al., 2006; Golubovskaya, 2010).

Synergistic inhibition of PTK2 and EGFR signaling cascades is also shown to overcome erlotinib resistance in EGFR wild-type non-squamous cell lung cancer (NSCLC), gastric cancer, and glioblastoma by enhancing the anti-tumor activity of erlotinib (Feng and Yang, 2016; Howe et al., 2016; Solanki et al., 2018; Srikanth et al., 2013; Tong et al., 2019). Ptk2 has also been demonstrated as an alternate target in third-generation EGFR-TKI-resistant NSCLC (Ichihara et al., 2017). Several members of the PTK2 pathway such as Crkl and Pxn, which are either overexpressed or hyperphosphorylated in our study, are also reported to be involved in resistance to TKIs (Cheung et al., 2011; Huang and Fu, 2015; Wu et al., 2016).

Activation of PTK2 pathway in SCC-R cells in the absence of Egfr activation or overexpression indicated activation of other upstream regulators of Ptk2. Itgb1 is an upstream regulator of Ptk2 where integrin β1-PTK2 signaling is known to direct proliferation in metastatic lung cancer (Shibue and Weinberg, 2009). Also, integrin β1 overexpression is associated with acquired resistance to erlotinib in NSCLC and the integrin β1/Src/Akt signaling pathway has been identified as a key mediator (Fu et al., 2020; Ju et al., 2010; Kanda et al., 2013).

Concurrently, we identified and validated overexpression of Itgb1 along with hyperphosphorylation of Pxn and Bcar1 downstream of Ptk2 in SCC-R cells. Furthermore, we also observed amplification as well as overexpression of PTK2 in HNSCC tissue samples using data from TCGA. Significant overexpression of PTK2 was also observed across different cancer stages compared with normal tissues. We also observed genomic and transcriptomic alteration in other genes related to PTK2 pathway in a subset of HNSCC patients.

Conclusions

We present the first report of tyrosine phosphoproteome profiling in erlotinib-resistant HNSCC. Our findings suggest that common signaling networks are at play in driving resistance to EGFR-targeted therapies in HNSCC and other cancers. Furthermore, our data also demonstrate activation of PTK2 pathway in erlotinib in HNSCC model. Data from TCGA also suggest that PTK2 pathway genes are altered in HNSCC. Further studies are warranted to investigate if Ptk2 inhibition would overcome erlotinib resistance in HNSCC.

Supplementary Material

Supplemental data
Supp_TableS1.pdf (484.8KB, pdf)
Supplemental data
Supp_TableS2.pdf (3.6MB, pdf)
Supplemental data
Supp_TableS3.pdf (512KB, pdf)

Acknowledgments

The authors thank the Department of Biotechnology, Government of India for research support to the Institute of Bioinformatics, Bangalore.

Abbreviations Used

ACN

acetonitrile

AGC

automatic gain control

BCA

bicinchoninic acid

DMEM

Dulbecco's modified Eagle's medium

EGFR

epidermal growth factor receptor

EMT

epithelial–mesenchymal transition

HCD

higher energy collisional dissociation

HEPES

4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid

HNSCC

head and neck squamous cell carcinoma

IAP buffer

immunoaffinity purification buffer

MOPS

3-(N-morpholino)propanesulfonic acid

MS

mass spectrometry

NSCLC

non-squamous cell lung cancer

MTT

3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide

RIPA

radioimmunoprecipitation assay buffer

SILAC

stable isotope labeling with amino acids in cell culture

TCGA

The Cancer Genome Atlas

TEABC

triethylammonium bicarbonate

TKIs

tyrosine kinase inhibitors

Authors' Contributions

A.C. and H.G. participated in study conception and study design. A.P.J., A.R., and V.N. were involved in cell culture and performed all assays. A.P.J. and S.P. performed proteomics experiments and fractionation. M.K. carried out the mass spectrometric analysis of samples. A.P.J., S.P., and K.P. were involved in data analysis and interpretation. A.P.J., A.R., and R.R. carried validation experiments. A.P.J. and A.R. prepared the article and article figures. A.C., H.G., D.S., P.P.M., and T.S.K.P. edited, critically read, and revised the article. All the authors made a significant intellectual contribution and have read and approved the final version of the article.

Author Disclosure Statement

The authors declare they have no conflicting financial interests.

Funding Information

Institute of Bioinformatics is supported by Department of Biotechnology, Government of India Program Support on Neuroproteomics and infrastructure for proteomic data analysis (BT/01/COE/08/05). This work was supported by FAMRI-funded 072017_YCSA. K.P. is a recipient of Senior Research Fellowship from Council of Scientific and Industrial Research (CSIR), India. V.N. is a recipient of INSPIRE Faculty Award from the Department of Science and Technology, Government of India.

R.R. is a recipient of fellowship from Science and Engineering Research Board, Department of Science and Technology (YSS/2014/000395), Government of India. H.G. is a recipient of NHMRC R.D. Wright Biomedical Career Development Fellowship. The results shown in the study are in part based upon data generated by the TCGA Research Network (https://www.cancer.gov/tcga). KinMap illustration reproduced courtesy of Cell Signaling Technology, Inc. (www.cellsignal.com).

Supplementary Material

Supplementary Table S1

Supplementary Table S2

Supplementary Table S3

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Associated Data

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Supplementary Materials

Supplemental data
Supp_TableS1.pdf (484.8KB, pdf)
Supplemental data
Supp_TableS2.pdf (3.6MB, pdf)
Supplemental data
Supp_TableS3.pdf (512KB, pdf)

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