Introduction

The contribution of pancreatic cancer to global cancer-related mortality continues to increase, with an almost uniformly fatal outcome.1 2 Current chemotherapy regimens are minimally effective3 and immune checkpoint inhibitors that have shown promise in the treatment of other cancer types have generally failed to show patient benefit for pancreatic cancer.4 5 With pancreatic cancer set to become the second-leading cause of cancer mortality within the next decade,6 there is an urgent need to identify new therapeutic strategies for the treatment of this disease.

Effective T-cell responses require the presence of immunogenic tumour antigens. Mutations or gene rearrangements can give rise to tumour ‘neoantigens’ which are recognised by the immune system as non-self. In contrast, proteins differentially expressed in cancer can give rise to non-mutated tumour-associated antigens (TAAs), which despite being classed as self-antigens, can still be recognised by the immune system.4 Neoantigens have been identified as T-cell targets in rare long-term survivors of pancreatic ductal adenocarcinoma (PDAC)7 and are associated with increased expression of an antitumour immunity gene signature in human pancreatic cancer patients with double-strand break repair and mismatch repair signatures.8 However, outside of these rare cases, no clear association between neoantigen load, effector T-cell infiltration and pancreatic cancer patient survival has been identified.9 10 Studies using the Pdx-1 Cre; LSL-KrasG12D/+; LSL-Trp53R172H/+ (KPC) mouse model of PDAC have only identified a very low number of somatic mutations with even fewer predicted neoantigens.11 12 Despite this, some tumours can exhibit high T-cell infiltration12 and combination therapies can unlock effective antitumour CD8 T-cell responses.13–15 Such observations suggest that non-mutated TAAs may also be important in T-cell-mediated immunity against pancreatic tumours.

Interferon-γ (IFNγ) is an essential mediator of immunity, promoting T-cell tumour recognition through regulating multiple pathways, including those involved in antigen processing and presentation.16 Expression of Major Histocompatibility Complex class-I (MHC-I), which is induced in response to IFNγ, is often downregulated in tumours, resulting in immune evasion due to a lack of antigen presentation.17 The composition of the proteasome that degrades ubiquitinated proteins to generate peptide antigens is also regulated by IFNγ, with the catalytic β subunits of the constitutive proteasome, β1, β2 and β5, being replaced by Psmb8 (β5i), Psmb9 (β1i) and Psmb10 (β2i) to form the immunoproteasome.18 19 Immunoproteasome deficiency has been linked to a reduction in the diversity of the antigen repertoire and poor prognosis in patients with non-small cell lung cancer.20 Similar findings have been reported in the context of melanoma, where immunoproteasome expression has also been identified as a better predictor of response to immune checkpoint therapy than mutational burden.21 Therefore, deregulated IFNγ signalling is an important mechanism of immune evasion in cancer.

Here, we identify a novel kinase-independent, nuclear-dependent role for the non-receptor protein tyrosine kinase focal adhesion kinase (FAK) in suppressing antigen processing and presentation to promote immune evasion in PDAC. We show that FAK deletion in cancer cells derived from mouse KPC tumours reprogrammes the cellular response to IFNγ, increasing both antigen diversity through activation of the immunoproteasome and surface presentation through upregulation of MHC-I to promote immunosurveillance. Mechanistically, we find that expression of the immunoproteasome and MHC-I is dependent on interferon regulatory factor 1 (IRF-1) and that FAK loss leads to upregulation of the class-I transcriptional co-activator NLRC5. In addition, we also find that FAK stabilises the STAT1/STAT3 heterodimer and show that co-depletion of FAK and STAT3 leads to STAT1-dependent hyperactivation of these pathways, further amplifying the effector CD8 T-cell response. In human PDAC, proteomic analysis of 13 patient-derived PDAC cell lines and bioinformatic analysis of International Cancer Genome Consortium and The Cancer Genome Atlas (TCGA) transcriptomics data from PDAC tumours also identified FAK-dependent suppression of antigen processing/presentation and IFNγ signalling. Lastly, we identify a previously unappreciated role for PDAC molecular subtype in impacting FAK function with respect to regulation of antigen processing and presentation pathways. These findings highlight the need to develop a new generation of FAK targeted therapies aimed at protein degradation in order to fully harness the therapeutic potential of targeting FAK for the treatment of PDAC.

ResultsFAK regulates antigen processing and presentation pathways

FAK kinase activity is elevated in human PDAC14 and FAK inhibitors, either alone or in combination with immunotherapies, can impair tumour growth in mouse models of PDAC.14 22 23 Clinical trials are now testing FAK inhibitors in combination with immune checkpoint inhibitors in patients with advanced pancreatic cancer (ClinicalTrials.gov; NCT02546531, NCT02758587, NCT03727880). However, our understanding of FAK as an immune modulator in PDAC is limited to the effects of FAK inhibitors on the tumour stroma and immune microenvironment in murine models of pancreatic cancer.14 23 Therefore, we set out to better understand how FAK regulates the antitumour immune response with particular focus on cancer cell-intrinsic mechanisms of immune evasion. We first used CRISPR-Cas9 gene editing to delete ptk2 (FAK gene) expression in Panc47 cells, a syngeneic cell line isolated from PDAC arising in fully back-crossed C57BL/6 KPC mice, and reconstituted wild-type FAK (FAK-wt) expression into a clonal Panc47 FAK-/- (herein termed FAK-/-) cell line (figure 1A). No increase in Pyk2 expression or phosphorylation on tyrosine-402 were observed following FAK loss. 0.5×106 FAK-wt or FAK-/- cells were then implanted into the pancreas of C57BL/6 mice and tumours harvested and weighed after 2 weeks. FAK loss resulted in a tumour growth delay (figure 1B). We have previously shown using a murine model of skin squamous cell carcinoma that cancer cell-intrinsic deletion of FAK expression can promote an antitumour CD8 T-cell response via modulation of the effector CD8 T-cell: regulatory T-cell ratio in tumours.24 To determine whether the observed delay in FAK-/- tumour growth was also dependent on CD8 T-cells, C57BL/6 mice were treated with either isotype control or anti-CD8 T-cell-depleting antibodies and 0.5×106 FAK-wt or FAK-/- cells were implanted into the pancreas. Tumours were harvested and weighed 2 weeks postimplantation. CD8 T-cell depletion restored a significant proportion of the delay in FAK-/- tumour growth, but had no effect on the growth of FAK-wt tumours (figure 1C). Thus, FAK-loss was sufficient to promote an antitumour CD8 T-cell response that could restrain tumour growth.

Figure 1

FAK regulates antigen processing and presentation pathways in Kras+/G12Dp53+/R172HPDXcre pancreatic cancer cells. (A) Representative anti-FAK, FAK pY397, Pyk2 and Pyk2 pY402 western blot using whole cell lysates from parental Panc47 cells and FAK-wt and FAK-/- clonal cell lines. Anti-tubulin antibody used as a loading control. (B) Fold-change in tumour weight relative to FAK-wt tumours 2 weeks postimplantation into the pancreas of C57BL/6 mice. n=13–16 tumours per group. (C) Fold-change in tumour weight relative to FAK-wt tumours 2 weeks postimplantation into the pancreas of C57BL/6 mice. Mice were either treated with isotype control or anti-CD8 T-cell-depleting antibodies. n=4–6 mice per group. (D) Flow cytometry analysis of IFNγ expression in FAK-wt and FAK-/- tumours. Left, quantification of the frequency of CD45+IFNγ+ cells as a percentage of live cells; right, representative histograms of IFNγ staining in CD45-, CD45+ and CD45+CD3+ cells. FMO=fully stained sample minus IFNγ antibody. n=4. (E) Cluster analysis of NanoString nCounter gene expression data acquired using the mouse PanCancer Immune Profiling panel. Clusters 1–3 are annotated with the top 20 most over-expressed genes in IFNγ-stimulated FAK-/- cells compared with FAK-wt cells and further annotated with immunoproteasome and antigen processing and presentation components identified in (F, G). (F) KEGG pathway enrichment analysis of genes in clusters 1–3 in E. (G) Functional association network analysis of hits in the top two most enriched pathways in (F). Network edges (connecting lines) represent reported physical (dark orange) or predicted (light orange) interactions. Pathway membership is delineated in grey. Nodes (circles) representing immunoproteasome components have thick node borders. (H) Relative quantification of Psmb8, Psmb9 and Psmb10 gene expression using qRT-PCR following stimulation with IFNγ. n=3. (I) Flow cytometry quantification of MHC-I surface expression on FAK-wt and FAK-/- cells following IFNγ stimulation. n=3. (J) Fold-change in tumour weight relative to untreated FAK-wt tumours 2 weeks postimplantation into the pancreas of C57BL/6 mice. Mice were treated daily with PBS or IFNγ by intraperitoneal injection from day 8 until day 14. n=7–14 mice per group. (K) Flow cytometry quantification of the frequency of CD45-MHC-I+ cells in FAK-wt and FAK-/- tumours±IFNγ as a percentage of live cells. n=4–8 tumours per group. (L) Flow cytometry quantification of the median fluorescence intensity of MHC-I expression in CD45-MHC-I+ cells in FAK-wt and FAK-/- tumours±IFNγ. n=4–8 tumours per group. IFNγ treatment was 10 ng/mL for 24 hours (E–H) or 72 hours (I). Unless otherwise stated, all data are represented as mean±SEM. Statistical significance in B, D, H and I was calculated using an unpaired t-test. Statistical significance in (C, J, K, L) was calculated using a one-way ANOVA with Tukey’s multiple comparison test. *p≤0.05, **p≤0.01, ***p≤0.001, ****p≤0.0001. ANOVA, analysis of variance; FAK, focal adhesion kinase; MHC-I, Major Histocompatibility Complex class-I.

IFNγ signalling plays an important role in promoting T-cell tumour recognition,16 and IFNγ is secreted by multiple cell types present within the tumour microenvironment (TME).17 We, therefore, sought to determine whether IFNγ was present within the TME of FAK-wt and FAK-/- tumours, and whether FAK deletion altered the response of PDAC cells to this important proinflammatory cytokine. 0.5×106 FAK-wt or FAK-/- cells were implanted into the pancreas of C57BL/6 mice and tumours harvested 2 weeks later for flow cytometry analysis. FAK-/- tumours exhibited a significant increase in the number of cells positive for IFNγ expression when compared with FAK-wt tumours (figure 1D, left), with almost all of the IFNγ being secreted by immune (CD45+) cells (figure 1D, right). Interestingly, while T-cells were a source of IFNγ, they did not appear to be the major source in these tumours. To investigate whether FAK expression had any impact on the cancer cell response to IFNγ, we treated FAK-wt and FAK-/- cells in vitro with 10 ng/mL IFNγ and measured (1) the secretion of chemokines and cytokines, and (2) the expression of 770 immune-related genes. Forward-phase array profiling of tumour cell-secreted proteins identified a number of IFNγ-induced chemokines and cytokines likely to mediate paracrine signalling with the TME, several of which were downregulated in response to FAK loss and are protumourigenic, such as interleukin-6 (IL-6),25 26 in the context of pancreatic cancer (online supplemental figure 1A). We performed NanoString nCounter digital gene expression analysis using the PanCancer Immune Profiling Panel, which identified subsets of IFNγ-induced genes that were upregulated in FAK-/- cells compared with FAK-wt cells (figure 1E and online supplemental table 1). Pathway enrichment analysis of these gene clusters identified the proteasome and antigen processing and presentation as the two most significantly enriched pathways (figure 1F). Network analysis of these pathway hits showed direct association of the immunoproteasome components Psmb8, 9 and 10 with the antigen presentation module and enrichment in IFNγ-treated FAK-/- cells of several key pathway components, including Psmb8, Psmb9, Tap1 and H2-K1 (figure 1G and online supplemental figure 1B). Similar findings were observed when FAK-wt and FAK-/- cells were cultured in conditioned media from activated CD8 T-cells (online supplemental figure 1C and D and online supplemental table 2). qRT-PCR confirmed the upregulation of IFNγ-induced Psmb8 and Psmb9 expression in FAK-/- cells when compared with FAK-wt cells (figure 1H). Expression of Psmb10 was not regulated by FAK. Flow cytometry analysis confirmed the upregulation of IFNγ-induced MHC-I (including H2-K) expression on FAK-/- cells when compared with FAK-wt cells, with both an increase in the proportion of cells positive for expression of MHC-I and the level of MHC-I expression (figure 1I). To determine whether similar modulation of the IFNγ response also occurred in vivo, 0.5×106 FAK-wt or FAK-/- cells were implanted into the pancreas of C57BL/6 mice and tumours allowed to develop for 7 days. On day 8, half of each cohort was dosed by intraperitoneal injection with vehicle (PBS) and the other half with IFNγ. Treatment was administered daily for a total of 7 days, and on day 14 tumours were harvested, weighed and analysed by flow cytometry. IFNγ treatment resulted in a small increase in the average weight of FAK-wt tumours; however, this was not statistically significant (figure 1J). In contrast, IFNγ treatment resulted in a significant decrease in the growth of FAK-/- tumours (figure 1J). Flow cytometry analysis of MHC-I expression showed a greater proportion of CD45- cells (including tumour cells) positive for expression of MHC-I in both FAK-wt and FAK-/- tumours in response to IFNγ treatment (figure 1K). However, the median fluorescence intensity (MFI) of MHC-I-positive staining was significantly lower in FAK-wt tumours when compared with FAK-/- tumours, irrespective of IFNγ treatment (figure 1L), supporting in vitro findings that FAK loss upregulates MHC-I expression on the cell surface. Overall, these findings imply that FAK deletion reprogrammes the response of PDAC cells to IFNγ, resulting in increased expression of pathways important for T-cell tumour recognition.

FAK-dependent regulation of antigen processing and presentation is kinase-independent but requires FAK nuclear translocation

A number of FAK kinase inhibitors are currently in clinical development and are being tested in combination with immunotherapies for the treatment of PDAC.27 Therefore, to determine whether regulation of antigen processing and presentation pathways was dependent on FAK kinase activity we used three different FAK kinase inhibitors: GSK2256098, VS4718 and Defactinib. To identify the lowest dose required to achieve maximum inhibition of FAK phosphorylation on tyrosine-397, the autophosphorylation site used as a surrogate readout of FAK kinase activity, we first treated FAK-wt cells with a range of drug concentrations. This identified 100 nM of GSK2256098 and 500 nM of both VS4718 and Defactinib as optimal concentrations (figure 2A). We next treated FAK-wt cells with these concentrations of each inhibitor for either 2 days or 14 days and then stimulated cells with IFNγ in the presence of inhibitor. qRT-PCR showed that neither regulation of Psmb8, Psmb9 nor H2-Kb transcription was dependent on FAK kinase activity (figure 2B,C). Flow cytometry analysis also confirmed that regulation of MHC-I surface expression on FAK-wt cells was unchanged following treatment with FAK kinase inhibitors (figure 2D). To further support these findings, we also re-expressed a FAK kinase-deficient mutant, FAK-G431,28 into FAK-/- cells at similar levels to FAK-wt cells (figure 2E—left). FAK-wt, FAK-/- and FAK-G431 cells were stimulated with IFNγ and qRT-PCR used to quantify Psmb8, Psmb9 and H2-Kb gene expression (figure 2E—right). FAK-G431 cells expressed comparable levels of Psmb8, Psmb9 and H2-Kb to FAK-wt cells. Thus, FAK-dependent regulation of Psmb8, Psmb9 and H2-Kb is independent of FAK kinase activity.

Figure 2

FAK-dependent suppression of antigen processing and presentation is independent of kinase activity but requires nuclear translocation. (A) Anti-FAK, FAK pY397, Pyk2 and Pyk2 pY402 western blots of FAK-wt cells treated with a range of concentrations of GSK2256098, VS4718 and Defactinib. Anti-tubulin used as a loading control. (B, C) Relative quantification of Psmb8, Psmb9 and H2-Kb expression in IFNγ stimulated FAK-wt cells using qRT-PCR following either 2 days or 14 days of treatment with FAK kinase inhibitors. n=3. (D) Relative quantification of the percentage of cells positive for MHC-I expression and the median fluorescence intensity (MFI) of MHC-I expression using flow cytometry. FAK-wt cells were treated with FAK kinase inhibitors for 2 days prior to IFNγ stimulation in the presence of inhibitor. n=3. (E) Left—Representative anti-FAK and anti-FAK pY397 western blot using whole cell lysates isolated from FAK-wt, FAK-/- and FAK-G431 cells. Anti-tubulin used as a loading control. Right—Relative quantification of Psmb8, Psmb9 and H2-Kb expression using qRT-PCR following IFNγ stimulation. n=3. (F) Left—Representative anti-FAK and anti-FAK pY397 western blot from whole cell and nuclear lysates isolated from FAK-wt, FAK-/- and FAK-NLS cells. Right—Relative quantification of Psmb8, Psmb9 and H2-Kb expression using qRT-PCR following IFNγ stimulation. n=3. IFNγ treatment was 10 ng/mL for 24 hours (B, C, E, F) or 72 hours (D). Data in (B–F) represented as mean±SEM. Statistical significance in (B–F) was calculated using a one-way ANOVA with Tukey’s multiple comparison test. **p≤0.01, ***p≤0.001, ****p≤0.0001. ANOVA, analysis of variance; FAK, focal adhesion kinase.

We have previously shown that FAK can localise to the nucleus where it can interact with transcription factors and transcriptional regulators to control chemokine and cytokine expression.24 We therefore re-expressed a FAK mutant deficient in nuclear translocation (FAK-NLS) into FAK-/- cells at comparable levels to FAK-wt cells (figure 2F—left) and stimulated FAK-wt, FAK-/- and FAK-NLS cells with IFNγ. qRT-PCR showed that FAK-dependent suppression of Psmb8, Psmb9 and H2-Kb transcription required nuclear FAK (figure 2F—right). Thus, FAK-dependent regulation of immunoproteasome and MHC-I genes is independent of FAK kinase activity but requires FAK nuclear translocation.

Psmb8 deletion restores FAK-/- tumour growth

Psmb8 is a key member of the immunoproteasome and is essential for maturation of the preproteasome containing Psmb9 and Psmb10, and subsequent acquisition of catalytic activity.29 30 Little is known about the immunoproteasome in the context of pancreatic cancer and whether it can contribute to the induction of antitumour T-cell responses. We, therefore, used CRISPR-Cas9 gene editing to delete Psmb8 expression in FAK-/- cells, generating two independent Psmb8 knockout clones termed C23 and C34 (figure 3A). Western blotting of whole cell lysates from IFNγ-stimulated FAK-wt, FAK-/-, FAK-/-Psmb8-/-C23 and FAK-/-Psmb8-/-C34 cells identified that Psmb8 knockout also resulted in loss of Psmb9 expression, but had no effect on Psmb10 (figure 3B). qRT-PCR further confirmed these results (figure 3C,D), suggesting that loss of Psmb9 expression was due to transcriptional downregulation. Psmb8 knockout also resulted in downregulation of MHC-I expression (figure 3E,F), implying that the activity of Psmb8 was important for sustaining the elevated MHC-I expression observed in FAK-/- cells. To determine whether Psmb8 upregulation contributed to the growth defect of FAK-/- tumours, 0.5×106 FAK-wt, FAK-/-, FAK-/-Psmb8-/-C23 or FAK-/-Psmb8-/-C34 cells were implanted into the pancreas of C57BL/6 mice and tumours harvested and weighed after 2 weeks. Psmb8 knockout was sufficient to rescue the FAK-/- tumour growth delay (figure 3G), suggesting that Psmb8 upregulation was critical in restraining FAK-/- tumour growth. These findings were further validated using two independent shRNAs to deplete Psmb8 expression in FAK-/- cells (online supplemental figure 2A), both of which promoted the growth of FAK-/- tumours (online supplemental figure 2B).

Figure 3

Psmb8 deletion restores FAK-/- tumour growth. (A) Relative quantification of Psmb8 expression using qRT-PCR following IFNγ stimulation. n=3. (B) Representative western blot showing expression of Psmb9, Psmb10, FAK pY397 and FAK in FAK-wt, FAK-/-, FAK-/-Psmb8-/-C23 and FAK-/-Psmb8-/-C34 cells following IFNγ stimulation. Anti-tubulin was used as a loading control. (C) Relative quantification of Psmb9 expression using qRT-PCR following IFNγ stimulation. n=3. (D) Relative quantification of Psmb10 expression using qRT-PCR following IFNγ stimulation. n=3. (E) Flow cytometry quantification of the frequency of cells positive for MHC-I expression as a percentage of live cells following IFNγ stimulation. n=3. (F) Flow cytometry quantification of the median fluorescence intensity of MHC-I expression following IFNγ stimulation. n=3. (G) Fold change in tumour weight relative to FAK-wt tumours 2 weeks postimplantation into the pancreas of C57BL/6 mice. n=6–9 tumours per group. IFNγ treatment was 10 ng/mL for 24 hours (A–D) or 72 hours (E and F). All data represented as mean±SEM. Statistical significance was calculated using a one-way ANOVA with Tukey’s multiple comparison test. *p≤0.05, **p≤0.01, ***p≤0.001, ****p≤0.0001. ANOVA, analysis of variance; FAK, focal adhesion kinase; MHC-I, Major Histocompatibility Complex class-I.

In addition to the constitutive and immunoproteasomes, two further proteasomes have been identified and termed intermediate proteasomes.19 These contain either Psmb8 or Psmb8 and Psmb9 from the immunoproteasome, with the remaining catalytic subunits coming from the constitutive proteasome. Therefore, we also investigated the requirement for Psmb9 in regulating the growth of FAK-/- tumours. CRISPR-Cas9 gene editing was used to delete Psmb9 expression, generating two Psmb9 knockout clones termed C2 and C6 (online supplemental figure 3A). Psmb9 deletion resulted in a reduction in Psmb8 expression. However, Psmb8 expression was still significantly higher in FAK-/-Psmb9-/-C2 and C6 cells when compared with FAK-wt cells (online supplemental figure 3B). The percentage of cells positive for expression of MHC-I was variable between Psmb9-/- clones but remained significantly higher than FAK-wt cells (online supplemental figure 3C). A small decrease in the MFI of MHC-I expression was observed in FAK-/-Psmb9-/-C2 and C6 cells when compared with FAK-/- cells; however, MHC-I expression was still significantly higher than in FAK-wt cells (online supplemental figure 3D). To determine whether Psmb9 upregulation in FAK-/- cells contributed to the defect in tumour growth, 0.5×106 FAK-wt, FAK-/-, FAK-/-Psmb9C2 or FAK-/-Psmb9C6 cells were implanted into the pancreas of C57BL/6 mice and tumours harvested and weighed after 2 weeks (online supplemental figure 3E). Psmb9 depletion did not rescue FAK-/- tumour growth, implying that an intermediate proteasome containing Psmb8 is sufficient to restrain FAK-/- tumour growth.

FAK regulates the antigen repertoire in a Psmb8-dependent manner

To better understand how FAK and Psmb8 might act to restrain tumour growth, we next profiled the antigen repertoire using mass spectrometry (MS)-based immunopeptidomics. 1×108 FAK-wt, FAK-/-, FAK-/-Psmb8-/-C23 and FAK-/-Psmb8-/-C34 cells were stimulated with IFNγ for 24 hours, then lysed as detailed in materials and methods. MHC-I (specifically, H2-Kb) was immunoprecipitated using 20 mg of protein lysate and bound peptides eluted for analysis by MS. A total of 144 peptide antigens were identified from FAK-wt cells, 613 from FAK-/- cells, 116 from FAK-/-Psmb8-/-C23 cells and 68 from FAK-/-Psmb8-/-C34 cells. Comparison of peptide antigens from FAK-wt and FAK-/- cells identified 62 peptides specific to FAK-wt cells, 82 common to both FAK-wt and FAK-/- cells and 531 peptides specific to FAK-/- cells (figure 4A and online supplemental table 3). Further comparisons between FAK-/- and FAK-/-Psmb8-/-C23 and C34 cells suggested that a substantial proportion of the unique peptides presented by FAK-/- cells were dependent on Psmb8 expression (figure 4B and online supplemental table 3). Thus, FAK deletion increases the diversity of the antigen repertoire presented by Panc47 cells in a Psmb8-dependent manner.

Figure 4

FAK regulates the antigen repertoire in a Psmb8-dependent manner. (A) Venn diagram showing common and unique antigen peptides identified from IFNγ treated FAK-wt and FAK-/- cells using MS. (B) Venn diagram showing common and unique peptides from IFNγ treated FAK-/- vs FAK-/-Psmb8-/-C23 cells (left) and FAK-/- vs FAK-/-Psmb8-/-C34 cells (right). (C) The percentage of peptide antigens predicted to bind strongly to MHC-I molecules normalised to sample size. (D) Analysis of amino acid frequency and hydrophobicity of the C-terminal residue of each peptide antigen identified from FAK-wt, FAK-/-, FAK-/-Psmb8-/-C23 and FAK-/-Psmb8-/-C34 cells. (E) Bar chart showing the frequency of peptides containing a hydrophobic C-terminal residue as a proportion of the total peptide number. (F) Weighted average hydrophobicity score representing all hydrophobic residues in each sample. (G) Log2 fold change in the expression of antigen peptides common between FAK-wt and FAK-/- cells grouped by protein of origin. Blue, enriched in FAK-wt cells; red, enriched in FAK-/- cells. IFNγ treatment was 10 ng/mL for 24 hours. FAK, focal adhesion kinase.

Assembly of the immunoproteasome results in increased tryptic and chymotryptic activity, with a concomitant decrease in caspase-like activity. As a consequence, there is a preference for C-terminal cleavage at basic and hydrophobic residues. MHC-I preferentially binds peptides with basic or hydrophobic C-termini, suggesting that the immunoproteasome likely yields peptides with a higher affinity for binding MHC-I.19 Our data identifying an important role for Psmb8 but not Psmb9 in regulating FAK-/- tumour growth implied that an intermediate proteasome containing β1, β2 and Psmb8 may be sufficient to increase antigen diversity. However, this intermediate proteasome retains caspase-like activity via the β1 subunit, rendering it difficult to predict whether Psmb8 expression could influence the MHC-I binding affinity of the antigen repertoire presented by FAK-/- cells. To understand the binding affinity landscape in this model, we used NetMHCpan-4.0 to predict the binding affinity of each peptide in the immunopeptidomics profiles gathered. Peptides were classified based on their predicted percentage rank,31 which ranks stronger binders in lower percentiles. Strong binders are those peptides in the top 0.5% percentile, while weak binders are in the top 2%. The number of strong binders was then normalised to the number of peptides in the sample. Forty percent of all peptides identified from FAK-/- cells were predicted to bind H2-Kb strongly, compared with fewer than 10% of all peptides identified from FAK-wt, FAK-/-Psmb8-/-C23 and FAK-/-Psmb8-/-C34 (figure 4C and online supplemental table 4). Hence, Psmb8 expression in response to FAK loss resulted in an antigen repertoire predicted to bind H2-Kb with higher affinity.

To better understand why peptide antigens presented by FAK-/- cells might have higher affinity binding to H2-Kb, we next looked at important physicochemical properties of the peptides. Typical peptide binding lengths for MHC-I are 8–9 amino acids.32 Peptides identified from FAK-/- cells were consistently shorter than those from FAK-wt, FAK-/-Psmb8-/-C23 and FAK-/-Psmb8-/-C34 cells when comparing their length distributions (pairwise permutation test, 95% CI, p<0.05). Indeed, 53.5% of all peptides identified from FAK-/- cells were 8 amino acids long and 22.5% were 9 amino acids long (online supplemental figure 4 and online supplemental table 3). In contrast, only 37.5% of all peptides identified from FAK-wt cells were 8 amino acids long and 15.3% were 9 amino acids long. An even more pronounced reduction in the frequency of 8 and 9mer peptides was evident in FAK-/-Psmb8-/-C23 and C34 cells, suggesting that Psmb8 expression was important in the generation of peptide antigens with optimal length for binding MHC-I.

MHC-I preferentially binds peptides with either hydrophobic or basic C-termini.19 We, therefore, calculated the hydrophobicity score of the C-terminal amino acid for each peptide according to the Kyte-Doolittle scale.33 The C-terminal hydrophobicity of peptides differed significantly between samples (pairwise permutation test, 95% CI, p<0.05). Peptides identified from FAK-/- cells displayed a greater proportion of hydrophobic residues at their C-termini than those identified from any of the other samples, with leucine and valine residues dominating (53.3% and 10.3%, respectively) (figure 4D–F and online supplemental table 5). Notably, these are among the most hydrophobic of amino acids according to the assigned hydrophobicity score. While leucine was the most common C-terminal residue for peptides in all samples, the proportion of peptides containing a C-terminal leucine residue was lower in all other samples when compared with FAK-/-. Furthermore, the second most common hydrophobic residue in the FAK-/- sample was valine with a hydrophobicity score of 4.2, while in all other samples it was either methionine or alanine which have notably lower hydrophobicity scores than valine (4.2 vs 1.9 for methionine and 1.8 for alanine). These samples also showed an increase in peptides containing hydrophilic/polar C-terminal residues when compared with FAK-/- (figure 4D and online supplemental table 5). Hydrophobic C-terminal residues are generated through chymotryptic activity, a function of Psmb8.19 These data suggest that increased expression of Psmb8 as a consequence of FAK loss results in a preference for cleavage at hydrophobic C-terminal residues, further optimising peptide antigens for high affinity binding to MHC-I.

Having assessed potential differences in the physicochemical properties of the antigen peptides present in each sample, we next evaluated differences in the expression of those peptides that were commonly expressed between FAK-/- and FAK-wt cells. The number of unique spectra mapping onto each peptide was calculated and normalised as described in the methods section (online supplemental figure 5 and online supplemental table 6). There were significant differences in the expression of peptides common to the two samples (Welch’s two-sample t-test, 95% CI, p<0.05), with 58 out of 82 peptides being upregulated in the FAK-/- sample, 6 downregulated and 18 remaining unchanged in expression, taking 1.5-fold (log2 fold change=0.58) as the cut-off for the upregulated peptides and 0.5-fold (log2 fold change = −1) as the cut-off for downregulated peptides. In some cases, multiple peptides were found to have originated from the same protein, resulting in a total of 61 proteins being represented by the peptides common to both samples. We, therefore, also grouped peptides based on the protein from which they originated, and summed the unique spectra mapping onto each peptide as a total measure of protein expression (figure 4G and online supplemental table 7). In total, 45 proteins were upregulated in the FAK-/- sample, 4 were downregulated and 12 were unchanged using the same cut-offs as those applied to the peptide analysis.

In order to determine whether any of the peptides detected were neoantigens, whole genome sequencing datasets derived from FAK-/- cells were converted to protein FASTA files (online supplemental table 8) and used to search for the presence of mutated peptides in the immunopeptidomics datasets. No mutated peptides were identified.

FAK and STAT3 impair STAT1-dependent expression of Psmb8 and MHC-I

To explore the mechanism underpinning FAK-dependent regulation of these pathways, we first used flow cytometry to quantify IFNγ receptor 1 and 2 (IFNγR1 and IFNγR2) cell-surface expression on FAK-wt and FAK-/- cells untreated or treated with 10 ng/mL IFNγ for 24 hours (online supplemental figure 6). No difference in the expression of IFNγR1 or IFNγR2 was observed, suggesting that regulation of IFNγ receptor expression was not contributing to the observed phenotype following FAK loss. Downstream of IFNγ receptor activation, the Signal Transducer and Activation of Transcription family member STAT1 plays an important role in driving gene expression. Reciprocal regulation of STAT1 by STAT3 can impair STAT1 activity, suggesting that STAT3 controls the balance between activation of these transcription factors and subsequent downstream signalling.34 Tyrosine phosphorylation of STATs is crucial for IFN-mediated signalling and translocation to the nucleus.35 We, therefore, asked whether FAK could regulate STAT1/3 expression or tyrosine phosphorylation as a mechanism of controlling MHC-I and Psmb8 expression. Western blotting using whole cell lysates from FAK-wt and FAK-/- cells either untreated or treated with 10 ng/mL IFNγ identified that STAT1 was not constitutively expressed in either cell type, but that expression and phosphorylation on tyrosine-701 was induced in response to IFNγ. Little to no difference was observed in either the expression or phosphorylation of STAT1 when comparing FAK-wt and FAK-/- cells (figure 5A). In contrast, STAT3 was constitutively expressed and phosphorylated on tyrosine-705 in both FAK-wt and FAK-/- cells, with both expression and phosphorylation increasing in response to IFNγ. No difference in either expression or phosphorylation of STAT3 was observed between FAK-wt and FAK-/- cells. Thus, FAK does not regulate STAT1/3 expression or phosphorylation in these cells. To investigate whether FAK might regulate STAT1 or STAT3 subcellular localisation we performed confocal immunofluorescence studies in FAK-wt and FAK-/- cells treated with IFNγ (figure 5B). STAT3 localisation was restricted to the nucleus whereas STAT1 showed both nuclear and cytoplasmic staining. Quantification of STAT1 nuclear staining did not identify any difference between FAK-wt and FAK-/- cells (figure 5C). These findings show that FAK does not regulate the subcellular localisation of either STAT1 or STAT3.