Molecular events activating the PI3K pathway are frequently detected in human tumors and the activation of PI3K signaling alters numerous cellular processes including tumor cell proliferation, survival, and motility. More recent studies have highlighted the impact of PI3K signaling on the cellular response to interferons and other immunologic processes relevant to antitumor immunity. Given the ability of IFNγ to regulate antigen processing and presentation and the pivotal role of MHC class I (MHCI) and II (MHCII) expression in T-cell–mediated antitumor immunity, we sought to determine the impact of PI3K signaling on MHCI and MHCII induction by IFNγ. We found that the induction of cell surface MHCI and MHCII molecules by IFNγ is enhanced by the clinical grade PI3K inhibitors dactolisib and pictilisib. We also found that PI3K inhibition increases STAT1 protein levels following IFNγ treatment and increases accessibility at genomic STAT1-binding motifs. Conversely, we found that pharmacologic activation of PI3K signaling can repress the induction of MHCI and MHCII molecules by IFNγ, and likewise, the loss of PTEN attenuates the induction of MHCI, MHCII, and STAT1 by IFNγ. Consistent with these in vitro studies, we found that within human head and neck squamous cell carcinomas, intratumoral regions with high phospho-AKT IHC staining had reduced MHCI IHC staining.

Implications:

Collectively, these findings demonstrate that MHC expression can be modulated by PI3K signaling and suggest that activation of PI3K signaling may promote immune escape via effects on antigen presentation.

The development of small-molecule kinase inhibitors to counter aberrant growth factor signaling within human tumors has been a major advancement in the treatment of cancer (1). In addition to targeting the intrinsic enzymatic activity of growth factor receptor tyrosine kinases (RTK) such as the EGFR and other members of the human EGFR (HER2-4) family, two RTK-downstream pathways that have received significant attention in this regard are the MAPK pathway and the PI3K pathway (2, 3). The major paradigm driving the development of these inhibitors has centered on the role of these pathways in tumor cell autonomous processes such as the regulation of cellular proliferation, apoptosis, and metastasis. However, a growing number of studies have highlighted the immunologic impact of oncogenic signaling and underscored the need to understand how these pathways and the medications designed to inhibit them influence processes governing cell-mediated antitumor immunity (4).

A key process regulating antitumor immunity and the response to immune-based therapy is the presentation of peptide antigens by MHC class I (MHCI) and class II (MHCII) molecules (5, 6). This notion is supported by the fact that tumors frequently downregulate the expression of MHCI and/or MHCII molecules and that generally speaking, a loss of MHC expression is associated with a poor prognosis and/or a worse response to immunotherapy (7). Conversely, high MHCI and/or MHCII expression is typically associated with an improved clinical response to immunotherapy (7, 8). Given the above paradigms, it is important to fully understand how oncogenic signaling and the small molecule kinase inhibitors used to inhibit this signaling alters the expression of MHCI and MHCII molecules. In contrast to prior studies linking EGFR/HER and MAPK signaling to MHC expression, the impact of PI3K signaling on MHC expression is largely undefined (9, 10). This is intriguing given the fact that the PI3K pathway has been implicated in mediating resistance to some of the effects of IFNγ, a potent inducer of both MHCI and MHCII expression (11). As such, we sought here to determine whether PI3K activation and/or inhibition could influence the ability of IFNγ to modulate MHC expression. We provide evidence that PI3K inhibition augments, whereas PI3K activation represses the induction of MHC molecules by IFNγ in some cellular contexts.

Cell lines, chemical reagents, and cytokines

SqCCY/Y1 cells were a gift from Dong Shin (Winship Cancer Institute, Atlanta, GA) and HaCaT cells were purchased from AddexBio. FaDu and Detroit 562 cells were purchased from the ATCC and used for experiments immediately after thawing at less than 20 passages. The above cells were grown in DMEM with 1 g/L -glucose and l-glutamine (Thermo Fisher Scientific) with 10% FBS at 5% CO2 without antibiotics. SqCC/Y1 and HaCaT cells were validated by short tandem repeat analysis at the Emory Integrated Genomics Core, and Mycoplasma testing was performed multiple times using a commercially available PCR-based assay per manufacture instruction over the time of these experiments (Millipore Sigma). HCT116 colon carcinoma cells and HCT116 cells lacking PTEN were purchased from Horizon Discovery and were grown in RPMI1640 Medium with l-glutamine (Thermo Fisher Scientific) with 10% FBS at 5% CO2 without antibiotics. Dactolisib (NVP-Bez235) was purchased from LC Laboratories. Pictilisib (GDC-0941) was purchased from Selleck Chemical. SC79 was purchased from (Millipore Sigma). VO-OHPIC was purchased from Tocris. All small molecules were dissolved in DMSO and stored at −80°C until use. IFNγ purchased from PeproTech.

Antibodies and flow cytometry

MHC class I antibody clone G46-2.6 (Fig. 1A) was purchased from Becton Dickinson and clone W6/32 (used in all other flow cytometry figures) was purchased from BioLegend. MHC class II antibody clone L203 (Fig. 1A) was purchased from R&D Systems, and L243 (used in all other figures) purchased from BioLegend. Antibodies recognizing total STAT1, phospho-STAT1-Y701, phospho-STAT1-S727, total AKT, phospho-AKT-S473, phospho-AKT-T308, and PTEN were all purchased from Cell Signaling Technology. Antibodies against MHC class I (clone EMR8-5) used for IHC were purchased from MBL and that for phospho-AKT-S473 from Abcam.

Real-time RT-PCR

RNA isolation was carried out as described previously, and reverse transcription was carried out using AzuraQuant cDNA Synthesis Kit from Azura Genomics according to the manufacturer's instructions. Real-time PCR was performed on the Bio-Rad CFX96 platform using SYBR green and analyzed using Bio-Rad CFX Maestro software. All primers were purchased from RealTimePrimers.com.

Western blotting and Western blot quantification

Whole-cell protein lysates were prepared as described previously or using denaturing lysis buffer followed by column homogenization per the manufacturers' instructions (101BIO; ref. 12). SDS-PAGE was performed using Bio-Rad Criterion Stain Free Gels and after electrophoresis stain-free gels underwent activation using ultraviolet radiation on the ChemiDOC XRS+ platform to allow for visualization and quantification of protein loading. Total protein was quantified from the gel and/or the blot following transfer per manufacturer instructions. Chemiluminescence signals from bands of the expected sizes were detected and quantified on the ChemiDOC XRS+ platform and normalized to the corresponding total protein loaded from the corresponding sample.

Assay for transposase-accessible chromatin sequencing

The assay for transposase-accessible chromatin sequencing (ATAC-seq) assay was performed as detailed previously (13). Briefly, 50,000 cells were resuspended in 25 μL Transposition Reaction (1× TD Buffer, 2.5 μL Tn5, 0.02% Digitonin, 0.1% Tween-20) and incubated for 60 minutes at 37°C. DNA was purified and PCR amplified using Nextera Indexing Primers (Illumina) and HiFi Polymerase (Kapa Biosystems). Libraries were sequenced at the Yerkes Genomics Core at the Emory Vaccine Center (Druid Hills, GA). Raw sequencing reads were mapped to the hg19 version of the human genome using Bowtie with the default parameters (14). Enriched accessible loci were identified by MACS2 and differentially accessible regions (DAR) determined between sample groups with edgeR (15, 16). Reads mapping to peaks were annotated and reads per million (rpm) normalized using custom R/Bioconductor scripts. For PCA analysis, the vegan package in R/Bioconductor was used with Z-score normalized DAR as input. STAT1 motifs in DAR were identified using HOMER (17). All data processing scripts are available upon request. ATAC-seq data have been deposited in the NCBI GEO data base accession # GSE137719.

Mining of TCGA databases

The Cancer Genome Atlas (TCGA) genomic and RNA sequencing (RNA-seq) expression data were accessed using cBioportal (www.cbioportal.org; refs. 18, 19). Datasets used included the Head and Neck Squamous Cell Carcinoma (TCGA-HNSC, Provisional), Lung Squamous Cell Carcinoma (TCGA-LUSC, PanCancer), Pancreatic Adenocarcinoma (TCGA-PAAD, PanCancer), and Colon Adenocarcinoma (TCGA-COAD, PanCancer). Spearman and Pearson correlations between PTEN, PIK3CA, and HLA genes were assessed using coexpression queries at cBioportal.org. Mutation, copy number, and Z-score–normalized RNA-seq data for TCGA, LUSC, and PAAD were downloaded from cBioPortal and analyzed in R to generate violin plot distributions and compute P values of differential HLA gene expression between cases with PTEN or PIK3CA alteration, and cases with no alterations in either gene.

IHC

IHC was performed as reported previously, and slide scanning was performed at the Emory Winship Core Pathology Laboratory (12). Formalin-fixed paraffin-embedded deidentified archived tumor samples were stained using antibodies against MHC class I (clone EMR8-5) and phospho-AKT-S473 (Abcam, catalog number ab81283).

PI3K inhibitors enhance the induction of MHC class I and II molecules by IFNγ in epithelial cells

To understand the impact of PI3K inhibition on the induction of MHCI and MHCII by IFNγ, we used SqCC/Y1 oral carcinoma cells as a model for squamous cell carcinoma of the head and neck (SCCHN; ref. 20). To inhibit PI3K signaling, we used dactolisib, a dual PI3K/mammalian (also referred to as mechanistic) target of rapamycin (mTOR) inhibitor (also referred to as NVP-BEZ235; ref. 21). We found that 0.5 μmol/L dactolisib increased the average mean fluorescence intensity (MFI) for MHCI following IFNγ (Fig. 1A). In contrast, the impact of dactolisib on MHCII expression was not significantly different in these experiments and varied in other experiments depending on the cell line used and the conditions of the experiments (data not shown) (Fig. 1A; ref. 22). Because dactolisib inhibits mTOR in addition to being a pan-PI3K inhibitor, we also examined the impact of another PI3K inhibitor, pictilisib (also referred to GDC-0941), on the induction of MHCI and MHCII by IFNγ (23). We included treatments with rapamycin, a canonical mTOR inhibitor to further define the roles of PI3K and mTOR on the induction of MHCI and MHCII by IFNγ. We found that like dactolisib, pictilisib could enhance the induction of MHCI by IFNγ (Fig. 1A). Consistent with a prior study examining the effect of rapamycin on a murine model of SCCHN, in our model, rapamycin augmented the induction of MHCI by IFNγ, although to a lesser extent than either of the PI3K inhibitors (Fig. 1A; ref. 24). In addition to MHCI, pictilisib repeatedly augmented MHCII induction (Fig. 1B). In contrast to MHCI where rapamycin had a slight enhancing effect on pictilisib, rapamycin attenuated the ability of pictilisib to enhance MHCII induction to levels that approximated those of dactolisib. As expected for a PI3K inhibitor, dactolisib decreased levels of phosphorylated AKT-S473 as determined by Western blotting (Fig. 1B) as did pictilisib (Fig. 1E). These results suggest that PI3K activity and/or mTOR activity can modulate the induction of MHCI and MHCII by IFNγ in some cellular contexts.

To further define the impact of pictilisib on MHC induction, a time course was conducted over 96 hours. As shown in Fig. 1C, in the presence of pictilisib, there were sustained increases in cell surface levels of MHCI and MHCII over the entire course of the experiment. To better define the roles of mTOR in MHCI and MHCII induction, we repeated experiments using a pan mTOR inhibitor AZD8055 and a PDK1 inhibitor BX795. As shown in Fig. 1D and as we observed with rapamycin, AZD8055 by itself augmented MHCI levels following IFNγ and did not alter the effect of pictilisib on MHCI induction. In contrast and like rapamycin, AZD8055 attenuated the impact of pictilisib on MHCII levels. The PDK1 inhibitor BX795 had no effect on MHCI or II induction. As shown in Fig. 1E, dactolisib, pictilisib, and AZD8055 reduced levels of phospho-AKT-S473. In contrast to the changes seen in MHCI and/or MHCII, we observed no significant changes in EGFR expression with these treatments (Fig. 1F).

To determine the impact of pictilisib on MHCI/MHCII induction in other cellular models, we performed similar studies on Fadu cells, which are derived from a tumor of the hypopharynx, HaCaT cells, an immortalized epidermal keratinocyte cell line, and Detroit 562 cells, which are derived from a pharyngeal metastatic tumor and harbor an activating mutation in PIK3CA (25–27). Pictilisib augmented the induction of MHCII in all cell lines as measured by average MFI and/or the percentage of cells expressing the MHCII gene HLA-DR (Fig. 1G). In contrast, the impact of pictilisib on MHCI induction was less robust though still significant in FaDu and HaCaT cells. These results support the notion that inhibition of PI3K using pictilisib can alter the ability of IFNγ to induce genes involved in antigen presentation.

STAT1 is a key regulator of both MHCI and MHCII expression and both STAT1 transcriptional activity and expression are increased by IFNγ (28–31). Interestingly, STAT1 expression has been reported to be repressed by signaling mediated by oncogenic RAS, which is upstream of PI3K signaling (32, 33). Therefore, we tested the impact of PI3K inhibition using pictilisib on STAT1 induction by IFNγ and found that pictilisib increased the induction of total STAT1 by IFNγ with concomitant increases in phospho-STAT1-Y701 and S727 (Fig. 2A and B). These results suggest that in some contexts, PI3K signaling can act as a negative regulator of STAT1 expression. This underscores the complex interplay between the PI3K pathway and STAT1 given that prior results have shown that the PI3K pathway phosphorylates STAT1 at serine 727, a modification that has been reported to be required for STAT1 to be fully active as a transcriptional regulator (34).

Pictilisib increases accessibility at genomic STAT1 consensus sequences

The cellular response to IFNγ involves changes in transcription that are associated with changes in chromatin structure (35, 36). To better define the impact of PI3K inhibition on this aspect of the cellular response to IFNγ, we performed ATAC-seq on SqCC/Y1 cells treated with vehicle (DMSO) and pictilisib alone or in the presence of IFNγ (37). ATAC-seq provides information about chromatin structure because the transposase used in this assay can access genomic DNA and place DNA linkers preferentially in regions of open chromatin (which are typically transcriptionally active), whereas this is less probable in regions with more compact chromatin. Principal component analysis of DAR highlights the impact of pictilisib on the response to IFNγ (Fig. 3A). Although there is small variation between cells treated with DMSO and pictilisib alone, in the presence of IFNγ, the variation is more pronounced. Importantly, and in line with our data showing that pictilisib increases STAT1 induction by IFNγ, pictilisib treatment leads to significantly increased chromatin accessibility at regions surrounding STAT1-binding consensus sequences as revealed by ATAC-seq footprinting (Fig. 3B and C). Thus, PI3K inhibition not only augments the induction of STAT1 by IFNγ but in addition alters the changes to chromatin structure induced by IFNγ including those at STAT1-binding sites.

Pharmacologic activation of PI3K signaling represses the induction of MHCI and MHCII by IFNγ

The above results whereby PI3K inhibitors increase MHC levels suggest that PI3K signaling has a repressive effect on the induction of MHCI and/or MHCII by IFNγ. To further explore this notion, we examined how the activation of PI3K signaling impacts the induction of MHC molecules by IFNγ. Because AKT is a key downstream effector of PI3K signaling, we employed a pan-AKT activator (named SC79) in our assay. SC79 binds to the pleckstrin homology domain of AKT and promotes its phosphorylation and activation by upstream kinases (38). We found that SC79 had the opposite effect of the PI3K inhibitors and repressed both MHCI and MHCII induction by IFNγ in SqCC/Y1 cells (Fig. 4A). As expected, SC79 promoted AKT phosphorylation at serine 473 and threonine 408 (Fig. 4B). To complement these studies and activate PI3K signaling via a different mechanism, we targeted the activity of PTEN, a pivotal negative regulator of PI3K signaling and a well-defined tumor suppressor (39). For these studies, we used a PTEN inhibitor, VO-OHpic, which is a 3-hydroxypicolinate vanadium complex that inhibits the phosphatase activity of PTEN (40). As we observed with the AKT activator SC79, the PTEN inhibitor VO-OHpic repressed the induction of MHCI and MHCII by IFNγ (Fig. 4C). As expected, the PTEN inhibitor VO-OHpic activated PI3K signaling and promoted the phosphorylation of AKT at serine 473 and threonine 308 (Fig. 4D).

Loss of PTEN represses the induction of MHCI and MHCII by IFNγ

Although the above pharmacologic studies targeting PI3K, AKT, and PTEN suggest that PI3K signaling can modulate MHCI and MHCII induction, we next sought to further assess these interactions using a genetic model. To this end, we assessed how the loss of PTEN expression would influence MHCI and MHCII induction by IFNγ using commercially available parental HCT116 colon carcinoma cells and HCT116 cells that were rendered PTEN null by CRISPR/Cas9 gene editing (41). As expected, the HCT116 cells lacking PTEN showed evidence of increased PI3K signaling as measured by AKT phosphorylation at serine 473 and threonine 308 (Fig. 4E). We confirmed that parental HCT116 cells and those lacking PTEN expressed both chains of the IFNγ receptor complex (Fig. 4F). In addition, although both cell lines expressed the same level of the EGFR, we repeatedly observed lower surface MHCI levels in untreated HCT116 cells lacking PTEN (Fig. 4F). Consistent with our prior findings, the induction of MHCI and MHCII by IFNγ was attenuated in HCT116 cells lacking PTEN at three different IFNγ concentrations and at 24-, 48-, and 72-hour time points after IFNγ treatment (Fig. 4G). We also examined the impact of PTEN loss on SqCC/Y1 cells. Although some PTEN expression remains, the decrease in PTEN lead to an increase in AKT phosphorylation (Fig. 4H). In addition, the ability of IFNγ to induce MHC class II expression was significantly decreased in SqCC/Y1 cells with decreased levels of PTEN, although there were only small differences in MHCI induction (Fig. 4I). In addition, the ability of pictilisib to enhance MHCII induction was lost in SqCC/Y1 cells with reduced PTEN expression. Taken together, these results demonstrate that activation of PI3K signaling can repress the induction of MHCI and/or MHCII by IFNγ and alter the ability of PI3K inhibition to augment MHC induction.

Loss of PTEN predominantly impacts the expression of HLA-B

Although the loss of PTEN in HCT116 cells impacted basal MHCI expression and the induction of MHCI and MHCII by IFNγ, the effect was most pronounced on MHCII induction and more modest on the induction of MHCI. Although all three MHCI genes (HLA-A, B, and C) are upregulated by IFNγ, there are gene-specific differences in their transcriptional regulation and the expression status a single MHCI gene may have important clinical implications in the setting of immunotherapy or resistance to immunotherapy (7, 42, 43). To determine whether the loss of PTEN has MHCI gene-specific effects, we assessed the expression of HLA-A, B, and C protein separately by Western blot analysis in untreated and IFNγ treated HCT116 cells and those lacking PTEN. As shown in Fig. 5A and B, the loss of PTEN had the most marked impact on HLA-B protein expression and its induction by IFNγ.

Loss of PTEN represses the induction of STAT1 by IFNγ

Given the increase in STAT1 protein induction that we observed in the presence of the PI3K inhibitor pictilisib, we examined how the loss of PTEN would influence STAT1 phosphorylation and its induction by IFNγ in parental HCT116 cells and those lacking PTEN. As shown in Fig. 5C, there were no differences between parental HCT116 and HCT116 PTEN knockout cells with regards to STAT-1 tyrosine 701 phosphorylation 90 minutes following IFNγ treatment. In contrast, when we assessed STAT1 levels 48 hours after IFNγ treatment, STAT1 levels were reduced in HCT116 cells lacking PTEN (Fig. 5D). Interestingly, the lack of PTEN had a more prominent impact on the induction of the full length STAT1 protein (STAT1-α) as compared with the induction of STAT1-β, which lacks the carboxy-terminal transactivation domain of 38 amino acids.

Effects of PTEN loss on MHC steady state mRNA levels

To assess whether the loss of PTEN impacts MHC and STAT1 expression at the mRNA level, real-time RT-PCR was performed on untreated and IFNγ-treated parental HCT116 cells and those lacking PTEN. We also examined the expression of the key transcriptional coactivators of MHCI and MHCII known as NLRC5 and CIITA, respectively (44, 45). With respect to MHCI genes, we found that the lack of PTEN had the largest impact on steady state mRNA levels of HLA-B, a smaller impact on HLA-A and no significant impacts on HLA-C or NLRC5 (Fig. 5E). This pattern is similar to what we observed at the protein level where the loss of PTEN had the greatest impact on HLA-B expression and less of an effect on HLA-A and HLA-C. The lack of PTEN also had a significant impact on the induction of STAT1 and the MHCII gene HLA-DR as well as its co-activator CIITA, which drives MHCII expression (46). These findings suggest that some of the effects of PTEN loss on the response to IFNγ are due to changes in steady state mRNA levels of IFNγ-regulated genes.

To further define the interplay between PI3K/PTEN and MHC expression, we examined public databases containing genomic and RNA-seq data in SCCHN. As expected from the data outlined above, there were positive correlations between MHC class I and II expression with PTEN expression and negative correlations between MHC and PIK3CA expression (Fig. 5F). However, we found no differences in MHC levels between SCCHN tumors with genetic alterations in PTEN or PIK3CA and tumors lacking alterations in these genes. In contrast, when we extended our analysis to include other cancers, we found that in both pancreatic cancer and squamous cell carcinoma (SCC) of the lung tumors with alteration in PTEN or PIK3CA had statistically significant lower expression levels of HLA-A, B, and C (Fig. 5G). With regards to colon carcinoma and consistent with our data using HCT116 cells, we found a positive correlation between PTEN expression and the expression of the MHCII gene HLA-DRA (Fig. 5H).

Intratumoral regions with high levels of phospho-AKT have decreased MHCI staining

The above studies provide evidence that there's an inverse relationship between PI3K signaling and MHC expression. To further explore these interactions in vivo, we used IHC to asses MHCI protein expression and the expression of phosphorylated AKT-S473 (p-AKT) as a marker of activated PI3K signaling in SCCHN. As shown in the panels within Fig. 6, although MHCI expression is readily seen, there is regional variation of MHCI staining intensity within tumor nodules with regions of high MHCI staining intensity as well as areas with less MHCI staining intensity. Importantly, in tumors from different patients, we repeatedly observed that the staining pattern of phospho-AKT appeared as an inverse image of that for MHC class I. Namely, regions with the highest MHCI staining had low to no detectable phospho-AKT staining and conversely, regions with high phospho-AKT staining had conspicuously lower levels of MHCI staining (compared to other regions within the tumor). Thus, intratumoral regions with increased PI3K signaling are associated with a decrease in MHCI protein expression.

In this study, we examined the impact of inhibition and activation of the PI3K pathway on the expression of MHC class I and II molecules and their induction by IFNγ, a cytokine that plays a pivotal role in immunosurveillance, antitumor immunity, and the response to immunotherapy (47, 48). Using human cell lines and archived SCCHN, we demonstrate that activation of PI3K signaling is associated with a repression of MHC expression/induction. Importantly, during the time our manuscript was under review, two groups (Marijt and colleagues and Sivaram and colleagues) published studies that support our findings. In these studies, murine model systems were used demonstrating that: (i) PI3K activation (through low oxygen and/or glucose) can repress STAT1 signaling and MHCI induction/expression and (ii) PI3K-mediated changes in MHCI levels can functionally impact T-cell infiltration into tumors and antitumor immunity (49, 50). Collectively, our work and the work of these authors support a broader model of the PI3K pathway that links PI3K signaling to MHC expression (Fig. 7). Given the frequency of PI3K signaling alterations in human cancer and the pivotal role of MHC expression in anti-tumor immunity, moving forward it will be critical to define PI3K-MHC interactions and develop approaches to reverse the repressive effects of PI3K signaling on MHC expression (2, 51).

In contrast to the PI3K pathway, several prior studies have demonstrated that EGFR/HER activity and the activity of enzymes in the MAPK pathway (such as BRAFV600E and MEK1/2) can influence MHC expression. For example, expression of HER2 and BRAFV600E have been reported to downregulate MHCI levels in breast cancer and melanoma, respectively, and with regards to gastric cancer, an inverse correlation between MAPK activation (as measured by levels of phosphorylated ERK) and MHCI has been reported (10, 22, 52–54). These studies are supported by a recent RNAi screen for HLA-regulating kinases where MAP2K1 (MEK), the EGFR and RET were validated as negative regulators of MHC expression (55). Conversely, EGFR, HER2, and MAPK inhibitors have been shown to increase levels of MHCI and/or MHCII expression (56). Our data combined with that of Marijt and colleagues and Sivaram and colleagues add to these studies and now demonstrate that in some contexts, PI3K signaling can also influence MHCI and/or MHCII induction.

Tumor cell MHC expression is pivotal to host antitumor immunity and the response to immune-based therapies such as immune checkpoint blockade (57). At present, it is unclear whether changes in MHCI and/or MHCII expression/induction mediated by PI3K signaling are functionally relevant to antitumor immunity in humans. However, our data support other observations whereby activating events in PI3K signaling are associated with mechanisms of immune escape and/or resistance to the effects of IFNγ (48, 58, 59). For example, prior studies have shown that the loss of PTEN can alter the antiproliferative and cytotoxic effects of IFNγ in lung and gastric carcinoma models (11, 59). Our work supports these observations and suggests that the loss of PTEN also represses the ability of IFNγ to induce MHCI and/or MHCII expression. Moreover, our study suggests that in some contexts, there are MHCI gene-specific effects of PTEN loss because in HCT116 cells, we observed that the induction of HLA-B was the most sensitive (vs. HLA-A and HLA-C) to PTEN loss. In addition, the impact of PTEN loss may not be apparent in the absence of IFNγ. Indeed, the two MHC genes impacted the most by PTEN loss (HLA-B and HLA-DR) in our study were the ones whose expression was most induced by IFNγ.

STAT1 is downstream of IFNγ and plays a complex yet critical role in antitumor immunity through its ability to regulate the expression of genes with immunologic function including those within the MHC (60). STAT1 control of MHCII was previously shown to be mediated by direct binding to the CIITA promoter and CIITA levels correlate with the levels of all MHCII genes (61). In a similar manner, STAT1 also regulates NLRC5 expression, which is a key regulator of MHCI genes (45). IFNγ regulates STAT1 function through the action of Jak kinases, which phosphorylate STAT1, thereby promoting dimerization and nuclear translocation. In addition, IFNγ induces the expression of STAT1 (and other members of the STAT family such as STAT2) via regulatory regions within the STAT1 promoter and more distal elements (31, 62). Our studies implicate the PI3K pathway as a negative regulator of STAT1 expression because inhibition of PI3K increased, whereas activation of PI3K signaling repressed STAT1 levels following IFNγ treatment. This is consistent with prior studies demonstrating that RAS, which is upstream of PI3K, can repress STAT1 expression (32). Moreover, other studies have shown that oncogenic signaling can influence the phosphorylation and functions of STAT1 via effects on phosphatases such as SHP2 (63). Another intriguing aspect of STAT1 biology centers on the role of unphosphorylated STAT1, which has been shown to have effects on gene expression that are distinct from those of phosphorylated STAT1 (64). Thus, through their ability to alter STAT1 expression and/or phosphorylation, PI3K signaling may disrupt the cellular response to cytokines and in doing so cell-mediated antitumor immunity.

It is worth noting that multiple groups, including our own, have shown that the effects of kinase inhibitors are variable and not universal with some cells responding to kinase inhibitors with increases in MHC expression, whereas in other cells, the same inhibitors have no effect on MHC expression/induction (10, 22). Indeed, in a previous study, none of the 10 melanoma cell lines tested responded to dactolisib with increases in MHC expression (22). Similar findings were found by Sivaram and colleagues where an AKT inhibitor increased MHCI levels in only three of eight human pancreatic carcinoma cell lines (49). These differences may reflect differences in cell lineage, signaling, and/or the status of driver mutations. Likewise, the study by Peng and colleagues failed to detect any impact of PTEN loss on MHC expression in human melanoma. Interestingly, the recent study by Marijt and colleagues demonstrates that in B16 melanoma cells, PI3K inhibitors can reverse MHCI repression mediated in response to low oxygen and glucose. Thus, metabolic state may influence the impact of PI3K signaling on MHCI expression.

In contrast to the variable effects on MHCI induction observed using different cell lines, the impact of PI3K inhibition and activation on MHCII induction was more consistent in our study. Indeed, in all the malignant cell lines used, PI3K inhibition doubled the number of MHCII-positive cells and/or the levels of MHCII on the cell surface. Conversely, using several pharmacologic and genetic approaches/models, PI3K activation decreased the induction of MHCII by 50% or more. To our knowledge these effects of PI3K signaling on MHCII induction in human cancer cells have not been previously reported despite the fact that tumor cell MHCII expression is being increasingly recognized for its relevance to anti-tumor immunity (57, 65, 66). Along these lines, in the study by Sivaram and colleagues, the authors found that antitumor immunity against pancreatic tumors lacking PIK3CA required CD4+ T cells (in addition to CD8+ T cells), further implicating potential roles for tumor cell MHCII expression (49). These findings with MHCII may be due to the fact that in contrast to MHCI expression, which is regulated by multiple signaling pathways, in most epithelial cells, MHCII expression is regulated/induced almost exclusively by IFNγ signaling (67). Moving forward, it will be important to determine what governs PI3K–MHC interactions and the response to PI3K inhibition within human cell lines, tumors, and tissues.

In summary, we provide additional evidence that the PI3K pathway can regulate MHC expression and/or induction by IFNγ within human tumor cells and that within human SCCHN, there is an inverse relationship between phospho-AKT and MHCI expression. These findings extend the mechanisms through which the activation of PI3K signaling within tumor cells may influence T-cell–mediated antitumor immunity and adds to the list of ways that the PI3K pathway can alter how cells respond to IFNγ (39). Given their key roles in mediating responses to immunotherapy, it will be important to more precisely define how alterations within the PI3K pathway impact cellular responses to IFNs and the induction and/or expression of MHC molecules.

D.A. Gutman is a scientific advisory board member at HistoWiz Inc. G.B. Lesinski is a consultant at ProDa Biotech, LLC. No potential conflicts of interest were disclosed by the other authors.

Conception and design: S. Chandrasekaran, J.T. Seykora, D.A. Gutman, E.K. Waller, R.A. Swerlick, B.P. Pollack

Development of methodology: D.A. Gutman, L.A. Cooper, B.P. Pollack

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S. Chandrasekaran, D.G. Patterson, K.R. Magliocca, B. Sapkota, D.A. Gutman, B.P. Pollack

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S. Chandrasekaran, C.D. Scharer, H.T. Kissick, K.R. Magliocca, J.T. Seykora, D.A. Gutman, L.A. Cooper, E.K. Waller, S.V. Kotenko, J.M. Boss, C.S. Moreno, R.A. Swerlick, B.P. Pollack

Writing, review, and/or revision of the manuscript: S. Chandrasekaran, C.D. Scharer, D.A. Gutman, G.B. Lesinski, E.K. Waller, S.N. Thomas, J.M. Boss, C.S. Moreno, R.A. Swerlick, B.P. Pollack

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S. Chandrasekaran, M. Sasaki, K.R. Magliocca, B. Sapkota, B.P. Pollack

Study supervision: B.P. Pollack

This work was supported by Merit Review Award (#1I01BX001922-01A1), by NIH/NCI grant U54 CA119338-04, P50 CA128613 (Head and Neck Cancer SPORE), the Melanoma and Skin Cancer Fund from the Winship Cancer Institute, and the Melanoma Innovation Fund from the Winship Cancer Institiute (to B.P. Pollack). Additional support was provided by NIH NRSA T32 CA160040 and the Nell W. and William Simpson Elkin Fellowship (to S. Chandrasekaran) and NIH Award 5R01CA207619-03 (to S.N. Thomas). This study was supported in part by the Emory Integrated Genomics Core (EIGC), which is subsidized by the Emory University School of Medicine and is one of the Emory Integrated Core Facilities. Additional support was provided by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR000454 and P30CA138292. The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. Research reported in this publication was supported in part by the Cancer Tissue and Pathology shared resource of Winship Cancer Institute of Emory University and NIH/NCI under award number P30CA138292. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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