Background

The concept of immune evasion has led to a plethora of research on cancer-immune cell interactions. Multiple interactions between cancer and immune cells occur, including the formation of an immunosuppressive environment, paracrine signaling that influences the cancer microenvironment, biochemical and mechanical support and secretion of inhibiting and promoting factors (Tan and Naylor 2022).

Infiltrating immune cells regulate several cellular events during the progression of cancer. The major anti-tumour immune cells that infiltrate the tumour microenvironment are cytotoxic T-lymphocytes, dendritic cells, NK cells, type 1 helper T-cells, and cytotoxic macrophages, which attack tumours through the release of anti-tumour cytokines, phagocytosis and tumour-specific cytotoxicity. In contrast, regulatory T-cells, tumour-associated macrophages, myeloid-derived suppressor cells and tumour-associated neutrophils secrete pro-tumour cytokines that exacerbate the progression of cancer (Shelton et al. 2020).

Ancillary immune-mediated factors such as tumour necrosis factor (TNF/TNFR1), interleukins (IL-16, IL-11 and IL-22) and epidermal growth factor (EGF) and oncogenic tyrosine kinases such as Src and c-MET help to activate STAT3 in cancer cells (Byers et al. 2020). The paracrine effect induced by transforming growth factor-beta dampens anti-tumour immune cell reactivity (Grivennikov et al. 2010).

In contrast, Hsp70, Hsp90, Hsp110, gp96, GRP170 and CRT promote the anti-tumour reactivity of immune cells. IL-1β is part of adaptive immunity, hindering the progression of cancer by priming interferon gamma (IFN-γ)–producing CD8+ T-cells (Liu and Zeng 2012).

Protein–protein interactions (PPIs) modulate immune evasion (Lu et al. 2020). Protein interactions are affected by various post-translational modifications of mechanism related proteins (Wang et al. 2022). It has been found that post-translational modification induced EGFR isoforms influenced cetuximab treatment outcome in head and neck squamous cell carcinoma (Nelhűbel et al. 2021). Significant presence of immunosuppressive proteins found in OSCC cells (Chakraborty et al. 2024).

Protein tyrosine kinases add a phosphate group to proteins, resulting in phosphorylation (a post-translational modifications of protein) (Sivaganesh et al. 2021). Protein phosphorylation leads to the activation of several cell signalling molecules that induce proliferation, differentiation, survival and epithelial mesenchymal transition, resulting in metastasis. Several aberrant signalling pathways have been previously characterised in head and neck SCC (Frederick et al. 2011). The EGF receptor (EGFR) pathway is the most widely studied because EGFR overexpression occurs in approximately 70–80% of cancers of the head and neck region (Zimmermann et al. 2006). In addition, abnormal signalling of the EGFR is implicated in the activation of several downstream pathways such as PI3K/Akt, Ras/Raf/MEK and ERK signalling. Interestingly, these pathways are all phosphorylated during activation.

Moreover, the phosphorylation of proteins directly involved in the apoptotic machinery has been extensively documented (Pfeffer and Singh 2018). Interestingly, these post-translational modification events often affect the functioning of the apoptotic cascade and the onset of apoptosis (Niemi and MacKeigan 2013). It has been found that k-Ras signalling leads to phosphorylation of the pro-apoptotic BAD protein, activating BAD and resulting in cell apoptosis. In contrast, the phosphorylation of the BAX protein at S184 by Akt blocks apoptosis in cancer cell lines. In other words, phosphorylation transforms BAX from pro-apoptotic to anti-apoptotic (Kale et al. 2018). Therefore, investigating the phosphorylation of apoptotic proteins was imperative to understand the progression of oral cancer.

Cetuximab and nivolumab (PD-1 inhibitor)-treated patients with recurrent and metastatic head and neck squamous cell carcinoma were predicted to fail based on early treatment response dynamics (Glazar et al. 2022). Interestingly, EGFR amplification was not associated with drug responsiveness (Cohen et al. 2005). The possible reasons for such decline in the activity of EGFR inhibitor is because of EGFR protein interacting partners and the presence of multiple phosphorylated proteins due to post-translational modifications, that directly affect EGFR signaling and trafficking (Negrón-Vega et al. 2022).

Resistance to EGFR inhibitor is known to correlate to phosphorylation of EGFR and its interactome. It has been reported that in addition to tyrosine, more than 70 predicted EGFR phosphorylation sites might be available in serine, threonine, and tyrosine (Malik et al. 2019). Post-translational modifications also affect PPIs, which affect the functionality of EGFR and ability of Cetuximab to inhibit EGFR (Malik et al. 2019).

Thus, understanding of PPIs of key proliferation and apoptosis related proteins in oral cancer has become an emerging topic of interest among head and neck cancer researchers across the globe. Here, we present a simplified method pipeline (that includes co-immunoprecipitation mass spectrometry—proteome profiler—immune activation flow cytometry) to understand the protein interacting partners responsible for lowering the anti-tumour reactivity of immune cells in 3D cancer—immune co-culture model.

Experimental design

Design of proteomics and protein-based assay setup

Proteins were extracted from the cancer and immune cells in each co-culture model. Rather than being discarded, the medium should be used for proteomic analysis to determine the paracrine effect of immune and cancer cells during indirect cancer-immune cell interactions. Proteomic samples were retrieved from: (i) cancer cells in the 2D culture, (ii) cancer cells in 3D co-culture model, (iii) immune cells in the 2D culture on a semi-permeable membrane, (iv) immune cells in 3D co-culture model, (v) medium from cancer cells in 2D culture, (vi) medium from cancer cells in 3D co-culture model, (vii) medium from immune cells in the 2D culture on a semi-permeable membrane, (viii) medium from immune cells in 3D co-culture model, (ix) complete growth media of all head and neck cancer cells and normal oral cells and (x) complete growth media of all immune cells used during the project.

For the phosphokinase array, capture and control antibodies were spotted in duplicate on a nitrocellulose membrane. Cell lysates were diluted and incubated overnight at 4 °C with the phosphokinase array. Unbound proteins were removed by washing, followed by incubation with detection antibodies. After the recommended incubation period, chemiluminescent reagents were applied, and signals recorded. Details of sites of protein phosphorylation in nitrocellulose membrane array is provided in Supplementary Table 1.

Design of immune cell activation flow cytometry-based assay setup

Immune cells were collected from the 3D cancer-immune co-culture models. Jurkat E6.1 clone cells were used for the regulatory T-cell assay, which was based on FoxP3 and ILR2α/CD25 receptor staining to determine immunosuppressive cells. NK cells were stained with interferon gamma and NKp30 (interferon gamma and NKp30 antibodies added to NK cells). Both markers helped to identify the activated immune cells in the co-culture models.

All experiments had positive, negative, experimental, and internal controls. Further details of controls are provided in the quality section of the paper. Apart from these four controls, OKF6, the normal oral cell line, was used in each experiment to act as a non-cancer cell control (Fig. 1).

Fig. 1
figure 1

Workflow showing a simplified method pipeline. The pipeline helps to identify the protein-interaction partners of proliferation mechanism proteins coupled with immune activation and proteome profiler assay

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Sample preparation and storage

The proteomics and protein-based assays were based on protein extraction using sodium dodecyl sulphate. Following extraction of the proteins (cell lysates), the samples were sonicated and heat denatured (placing lysate in a heat block) at 95 °C for 5 min. The amount of protein in each sample was determined using a Pierce bicinchoninic acid assay before the final sample preparation. The protein samples were further reduced using dithiothreitol, alkylated using iodoacetamide and digested using sequencing-grade trypsin. Pierce detergent removal columns were used to remove the detergents when the protein samples were prepared for co-immunoprecipitation MS-based proteomics assay. The dried peptides were further dissolved in 0.1% formic acid (maintaining the pH of the analyte) and stored at − 80 °C until further use.

For the immune cell activation flow cytometry assay, the collected immune cells were first Fc blocked using blocking IgG. Different markers were chosen based on the different fluorescent spectra, with minimal overlap to avoid overcompensation during data analysis. The immune activation flow cytometry samples required intracellular (e.g. interferon gamma antibody) and/or extracellular staining (e.g. NKp30 antibody) of cells. The reagents were chosen as per the desired objective of the immune activation assay. Intracellular staining was conducted by incubating the cells in a permeabilization buffer. Further multiple washes using the permeabilization buffer were done before they were finally resuspended in the flow cytometry staining buffer.

Quality control of the experiments

Each experiment had positive, negative, experimental, and internal controls. It is worth mentioning that for the immune cell activation flow cytometry assay, isotype controls for each antibody were used in addition to other controls. Isotype control used for T regulatory cell flow cytometry was Rabbit-IgG control-PE (R&D Systems, Cat No. 968117). Isotype control used for NK cell flow cytometry was mIgG1 Alexa Fluor 405 antibody (R&D Systems, Cat No.968460), rbIgG Alexa Fluor 647 antibody (R&D Systems, Cat No. 968462), mIgG2A Alexa Fluor 488 antibody (R&D Systems, Cat No. 968464), mIgG1 Alexa Fluor 700 antibody (R&D Systems, Cat No. 968466), mIgG2B PE antibody (R&D Systems, Cat No. 968471), and mIgG2A PerCP antibody (R&D Systems, Cat No. 968469). For the proteome profilers, GAPDH-positive reference spots and PBS -stained negative spots acted as the positive and negative controls, respectively. For co-immunoprecipitation MS experiments, samples without target antibodies acted as the negative controls. For each experiment, all controls were in triplicate. Negative controls were included in the experiment and the data set generated were a result of subtracting from the list of proteins identified in the experimental sample a list of contaminant proteins obtained from the negative control. Proteins were ranked based on `normalised intensity (Chi2) and excluded on the basis of number of spectra (protein fragments) and cross referenced by their molecular weights. Details of the list of materials used during the project is provided in Supplementary Table 2.

Timing

Day 1: Plate cancer cells on top of extracellular matrix coated bottom surface of the well plates; Day 2: Insert immune cells to complete cancer-immune co-culture model and allow exchange of medium; Day 3: Dismantle the co-culture set up and collect samples for further assay.

Procedure

Co-IP – LC–MS/MS

Dynabeads protocol


Day 1

  • Step 1: Protein extraction to be done using either (a) lysis buffer + protease inhibitor cocktail + phosphatase inhibitor cocktail or (b) co-immunoprecipitation buffer

  • Critical step: Check whether co-immunoprecipitation buffer contains protease inhibitors.

  • Step 2: Resuspend Dynabeads and transfer 50 µl Dynabeads to a tube

  • Step 3: Place the tube on a magnetic rack to separate magnets and supernatant. Discard supernatant.

  • Step 4: Add antibody diluted in 200 µl Ab binding and washing buffer

  • Critical step: Always use fresh antibody.

  • Step 5: Incubate at room temperature for 10 min

  • Critical step: The incubation time and temperature can be optimised depending on the type of antibody and target.

  • Step 6: Place the tube on a magnetic rack to separate magnets and supernatant. Discard supernatant.

  • Step 7: Wash once with 200 µl Ab binding and washing buffer. Repeat Step 5.

  • Step 8: Add protein lysate (100 µg) and incubate at room temperature for 10 min

  • Critical step: Same protein amount should be used across all experimental groups, positive and negative controls. The incubation time and temperature can be optimised.

  • Step 9: Repeat Step 5. Wash magnetic bead-Ab-Ag complex three times with wash buffer.

  • Critical step: Wash buffer is harsh. It can dislodge Ab-Ag binding.

  • Step 10: Add 20 µl elution buffer and incubate with rotation at room temperature for 2 min

  • Step 11: Place the tube on a magnetic rack to separate magnets and supernatant. Collect supernatant.

  • Alternate step: Do not separate magnet and supernatant. Let it be a one pot mixture.

  • Step 12: Dissolve the sample in 100 µl 50 mM ammonium bicarbonate

  • Step 13: Add 5 µl 200 mM DTT in 50 mM ammonium bicarbonate and incubate at room temperature for 60 min

  • Step 14: Add 10 µl 200 mM iodoacetamide in 50 mM ammonium bicarbonate and incubate at room temperature for 60 min in dark

  • Step 15: Add 0.1 µg/µl trypsin in 50 mM ammonium bicarbonate to the sample and incubate overnight at 37 °C

  • Critical step: Trypsin quantities can be adjusted to provide an approximately 1:25 trypsin:protein ratio.


Day 2

  • Step 16: Collect the supernatant and proceed towards detergent removal

  • Critical step: Separate magnet and supernatant using a magnetic rack. Do not discard the Dynabeads magnet. Store them in -80 ̊C until further use (if required).

  • Step 17: Use Pierce Detergent removal spin columns to collect detergent free sample

  • Critical step: Follow the manufacturers’ instruction carefully to avoid losing peptides during detergent removal procedure.

  • Step 18: Centrifuge at 10,000 g for 5 min

  • Step 19: Collect the supernatant and discard the precipitate

  • Step 20: Vacuum centrifuge for 2 h

  • Step 21: Add 50 µl of 0.1% formic acid in preparation for LC–MS/MS analysis

  • Critical step: To ensure that sample is injected for LC–MS/MS analysis, confirm that mass spectrometry sample vials do not contain air bubbles.

Collection of LC–MS/MS data on a Q-Exactive HF-X (Thermo Fisher Scientific) interfaced with an UltiMate 3000 HPLC and autosampler system (Dionex)

  • Step 1: Transfer mass spectrometry vials containing 10 µl (1 µg) of sample to the autosampler

    (loading buffer used: 2% acetonitrile (ACN) (LiChrosolv, 1.00030.4000, Merc) and 0.1% formic acid (Thermo Fisher Scientific, Cat 28905) in Milli-Q water and UltiMate 3000 HPLC dual gradient pump was used).

  • Step 2: Queue samples for data acquisition.

  • Open Xcalibur on the desktop

  • Once open, click on the Sequence Setup view button (green test tubes icon)

  • Click on File > New

  • In the New Sequence Template window, select:

    • Base file Name; path: instrument method; number of samples

    • Then click OK

  • Enter sample names, the vial positions and injection volumes

  • Critical step: Maximum sensitivity is achieved when injecting between 100 ng and 1000 ng protein, and injection volumes should be selected to reflect this.

    • Click Action > Run Sequence, then click OK

    • Clock icon – real-time view of data collection

Analysis using proteome discoverer

  • Step 1: Create a study for the Mascot analysis of raw files via Proteome Discoverer 2.5

    • Click New Study/Analysis

    • Type in a study name and select a root folder for the study

    • Select a generic processing workflow for a Mascot sequence database search

    • Select a generic consensus workflow for a broad-scale peptide identification experiment

  • Step 2: Set up and run the Mascot analysis from the study.

    • Click Add Files and select the Raw files to be analysed

    • Under the Input Files tab, drag and drop the Raw files to the Files for Analysis window

    • Under the Workflows tab, click on Files for Analysis and then the Mascot node

    • Enter the following search parameters:

      • Enzyme = trypsin; maximum missed cleavages = 2; fixed modifications = none; variable modifications = Carbamidomethyl (C), Oxidation (M); peptide mass tolerance = 5 ppm; fragment mass tolerance = 0.02 Da; instrument type = default. Select an appropriate protein database and taxonomy for your samples.

    • Under the Analysis window, click the By File checkbox

    • Click Run

To remove IP contaminants, Proteome Discoverer 2.5 is having in-built workflow that uses PSM match grouper.

  • Pass through peptide validator

  • Pass through protein filter

  • Pass through peptide filter

  • Pass through protein scorer

  • Pass through protein grouping

  • Pass through protein FDR validator

  • Pass through spectrum selector

  • Use SEQUEST HT

Proteome profiler assay

  • Step 1: After collecting protein lysates bring them to the room temperature

  • Step 2: Pipette 1 mL of array buffer 1 (Cat ARY003C, Lot P274286, Part No. 895477, R&D Systems) into each well.

  • Step 3: Incubate 1 h on a rocking platform

  • Step 4: Prepare samples by diluting the lysates to a final volume of 2 mL with array buffer 1

  • Critical step: The suggested range of cell lysates is 200–600 µg per array set.

  • Step 5: Remove the membranes and put it in individual plastic containers (same containers for membrane A and membrane B) (Cat ARY003C, Lot P311749, R&D Systems) with 20 mL 1X wash buffer for 10 min (3 times)

  • Step 6: Add 20 µl of reconstituted detection antibody A (Cat ARY003C, Lot 1643534, Part No. 899188) to 1 ml (1X Array buffer 2/3) (Cat ARY003C, Lot P291148 & P291152, Part No. 895478 & 895008, R&D Systems) to each part A

  • Step 7: Add 20 µl of reconstituted detection antibody B (Cat ARY003C, Lot 1643535, Part No. 899189) to 1 mL (1X Array buffer 2/3) to each part B

  • Step 8: Incubate for 2 h on a rocking platform

  • Step 9: Repeat step 5 but in different containers for part A and part B

  • Critical step: A thorough and consistent wash of the individual arrays is essential to minimize background.

  • Step 10: Dilute Streptavidine-HRP (Cat ARY003C, Lot 1615938, Part No. 893019, R&D Systems) in 1X Array buffer 2/3. Add 1 mL into each well and incubate for 30 min at room temperature on a rocking platform

  • Step 11: Repeat step 5

  • Critical step: Do not allow membranes to dry out to avoid high background.

  • Step 12: Place each membrane on the bottom sheet of the plastic sheet protector. Place corresponding Part A and Part B end-to-end.

  • Step 13: Add 1 mL of the chemi-reagent mix (Cat ARY003C, Lot P291142, Part No. 894287, R&D Systems) on each membrane and cover with the top sheet of the plastic sheet protector. Incubate for 1 min

  • Step 14: Expose the membranes to X-ray film for 1–10 min. Recommended multiple exposures.

    • Precision exposure time

    • Manual selection

    • Standard sensitivity

    • Method-tray position

    • Chemiluminescent

    • Focus

    • Adjust

    • Start

    • View – positive grey

    • Save – 16-bit linear TIFF format

    • Use ImageJ https://imagej.nih.gov/ij/

    • Microarray Plugin download

    • Measure RT

    • Subtract background by subtracting negative control RT

    • Acquire protein expressions of all samples_all markers

    • Acquire all positive control RT of each sample and average

    • Normalise using positive control RT

    • Final acquisition of relative protein expression of each markers of each sample

  • Step 15: Upon acquiring list of proteins after conducting proteomics and proteome profiler, protein-interaction network and biological function analysis done.

  • Step 16: Protein-interaction network analysis using STRING: functional protein annotation networks (Szklarczyk et al. 2019) https://string-db.org/

  • Step 17: STRINGinteraction_short_tsv files transferred to Cytoscape 3.9.1 www.cytoscape.org, for construction of protein-interaction network (Doncheva et al. 2019).

  • Step 18: Use database for annotation, visualization, and integrated discovery (DAVID) https://david.ncifcrf.gov/ for protein annotation clustering and biological process analysis (Dennis et al. 2003).

Immune activation flow cytometry

  • Step 1: Collect and wash cells (Jurkat E6.1 clone cells or NK cells) with 1 mL flow cytometry buffer

  • Step 2: Add 100 µl of flow cytometry staining buffer to the pellet (acquired after centrifugation)

  • Step 3: Add desired volume of antibody and follow manufacturer instruction regarding incubation time and temperature

  • Step 4: Advised to incubate for 45 min in ice (minimum)

  • Critical step: Isotype control is required.

  • Step 5: Wash cells with 1X PBS

  • Step 6: Hundred µl transcription factor fixation concentrate (4X) + 300 µl transcription factor fixation diluent

  • Step 7: Add 400 µl transcription factor fixation buffer and incubate at 2–8 ̊C for 30 min

  • Optional steps:

  • Step (a) Keep 100 µl of transcription factor permeabilization + 900 µl deionized water at 2–8 °C.

  • Step (b) Wash two times with permeabilization solution.

  • Step (c) Add desired volume of antibody and follow manufacturer instruction regarding incubation time and temperature

  • Step (d) Wash with cold transcription factor permeabilization buffer

  • Critical step: Isotype control is required.

  • Step 8: Resuspend the cells in flow cytometry staining buffer

  • Critical step: Live/dead cell staining should be done to remove dead cell debris to avoid auto-fluorescence. For live/dead cell assessment, we used Annexin V flow cytometry kit (R&D Systems TACS Annexin V-FITC Apoptosis Detection Kit Cat RDS483001K).

  • Step 9: Run the unstained control, experimental control, positive control, negative control, isotype control, live/dead cell control, and the samples in the flow cytometer to acquire 10,000 events

  • Critical step: For multi-colour flow cytometry, check if any spectral overlap. If spectral spill over found, then compensate in flow cytometry viewing and analysing software.

Anticipated results

SWATH (Data Independent Acquisition), 2 h TOP 10 MS/MS used the following parameters: MS1: 350–1250 m/z, 250 ms accumulation, MS2: 2 + to 4 + precursors 100–1800, 0/1 s accumulation time, 30 s dynamic exclusion. SWATH data of all oral cancer cell line showed overexpression of EGFR. Based on the SWATH data, EGFR target protein was chosen. An appreciable abundance of proteins was seen in the co-immunoprecipitation MS protein–protein-interaction assay. A positive EGFR target protein was acquired, leading to the identification of EGFR binding partners in SCC25 in the cancer-immune cell co-culture model (Figs. 2, 3). Protein-interaction model shows EGFR closely interacting with Akt, STAT3, p53, BCL2, and JUN (Fig. 3). Functional enrichment analysis provided vital information regarding the EGFR binding partners affecting immune mechanisms (Fig. 4). Concomitantly, flow cytometry-based immune activation assay shows immune cells presenting both immunosuppressive and activated state of immune cells in 3D cancer—immune co-culture model (Fig. 5).

Fig. 2
figure 2

Protein-interaction network representation of EGFR interacting partners in OSCC cells. STRING protein-interaction network generated using STRING functional protein association networks https://string-db.org/

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Fig. 3
figure 3

Cytoscape protein-interaction network representation of EGFR interacting partners in OSCC cells. a Tab-separated values filtered based on score ≥ 0.4; exported to Cytoscape; model construction and style modification was based on degree and displayed first-stage nodes, expanded subnetwork, and the shortest path, b visualization of the top 25 ranks (closest interacting) achieved after ranking shortest path based on degree using CytoHubba control panel in Cytoscape 3.9.1 www.cytoscape.org. The degree of closeness of each node (represents each protein) is displayed as colours ranging from blue (low) to red (high)

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Fig. 4
figure 4

GO enrichment analysis of EGFR interacting partners in OSCC cells. a Dot plot analysis of overrepresented EGFR interacting partners in OSCC related protein pathways. The size of the dot represents the fold enrichment, while its colour represents the FDR (p adjusted) value for each enriched Reactome pathway (high: red, low: blue). The x-axis represents the percentage of overexpressed genes in the selected pathway that is presented in the y-axis, b this interactive static plot showing overexpressed proteins, identified by DIA, generated with the enrichment map application using KEGG database, (FDR cut off 0.05, edge cut off 0.3, and pathway size minimum 2 and maximum 2000) shows the relationship between enriched pathways. Two pathways are connected if they share 20% or more genes. Each node represents several proteins with the same GO or pathway terms. Darker nodes are more significantly enriched gene sets. Bigger nodes represent larger gene sets. Edges represents the gene overlaps, or cross talks between the pathways, and the width of the line indicates the strength of the gene overlap. Thicker edges represent more overlapped genes. The enrichment analysis was done using database for annotation, visualization, and integrated discovery (DAVID) https://david.ncifcrf.gov/

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Fig. 5
figure 5

T regulatory cell activation and NK cell activation in 3D SCC25—immune co-culture model. a Jurkat cells showing expressions of FoxP3+/IL-2Rα/CD25+ (T regulatory cells) and b NK cells showing NKp30+/IFN-γ+ (activated natural killer cells)

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Conclusion

According to Hanahan and Weinberg, the hallmarks of cancer constitute a logical organizing principle for rationalizing the diversity of cancer cases (Hanahan and Weinberg 2011). The original hallmarks of cancer proposed by Hanahan and Weinberg are sustaining proliferative signaling, evading growth suppressors, activating invasion and metastasis, enabling replicative immortality, inducing angiogenesis and resisting cell death (Hanahan and Weinberg 2011). In 2011, four new hallmarks of cancer were added: tumour-promoting inflammation, genome instability and mutation, deregulating cellular energetics, and evading immune destruction. Incipient cancer cells must undergo this multistep pathogenesis to acquire the traits that enable them to progress towards malignancy (Hanahan and Weinberg 2011).

Evading immune destruction is an emerging hallmark of cancer that has been under the spotlight of cancer research in the last decade. The role of dysregulated immune mechanisms in cancer progression was first validated following the observation of increased tumour burden in immunocompromised individuals (Vajdic and Leeuwen 2009). The concept was further promoted when immunodeficient mice with poor development or function of cytotoxic T-cells, T-helper cells and natural killer (NK) cells showed an observable increase in tumour burden compared with immunocompetent mice (Teng et al. 2008). Clinical epidemiological studies have also increasingly supported the notion of immune evasion, leading to poor prognosis in human cancers (Bindea et al. 2010). Despite FDA approval of cetuximab (EGFR inhibitor) for the treatment of oral squamous cell carcinoma, the treatment outcome in aggressive oral cancer using Cetuximab was not appreciable. The possible reason might be EGFR protein interacting partners that inhibit the anti-tumour reactivity of immune cells.

The role of EGFR protein interaction in cancer cells in anti-EGFR treatment response and resistance is contentious. It has been previously found that EGFR amplifications was not associated with drug responsiveness but EGFR binding partners can also deter treatment response. We propose an intriguing concept that protein interacting partners in cancer and immune cells play crucial role during immune evasion, and it also help cancer cells hijack immune cell energy that result in T-cell exhaustion. The above simplified approach will enable mapping oral cancer-immune cell interactome, enriching PPI databases and analysis tools, and discovering therapeutic targets for metastatic OSCC, capitalizing on clinically relevant 3D cancer—immune co-culture model that closely resembles an in vivo state without the need for animal models.