Effects of Tobacco Smoking on the Tumor Immune Microenvironment in Head and Neck Squamous Cell Carcinoma

It is estimated that there are 1.2 billion smokers over the age of 15 years globally (1). Tobacco smoking is a major cause of head and neck squamous cell carcinoma (HNSCC) that remains a significant cause of morbidity worldwide with approximately 400,000 new cases per year (2). It is well established that patients with HNSCC with a significant tobacco smoking history have poorer prognosis compared with never smokers (3, 4). Furthermore, patients who actively smoke during treatment have worse survival compared with former smokers, suggesting smoking has acute effects beyond the cumulative effects of genomic alterations associated with extensive smoking history on molecular progression of HNSCC from premalignant lesions to invasive disease (5–8).

Increasing evidence suggests the tumor immune microenvironment in HNSCC is immunosuppressive, and the tumor-mediated immunosuppression may play a pivotal role in HNSCC progression and treatment resistance (9–11). In addition, several studies have indicated the numbers of infiltrating immune cells reflecting antitumor immune response in HNSCC are predictive of overall survival (12–14). The importance of understanding the interaction between smoking and tumor immune microenvironment (TIM) is further highlighted by emergence of immune checkpoint inhibitors as a promising therapeutic option in management of HNSCC, as smokers are less likely to benefit from the anti-PD-1 checkpoint inhibitors (11, 15–17). It is currently known that HNSCC evades immune surveillance by deregulating four key signaling steps for robust antitumor immunity: decreased TCR:HLA-peptide antigen interactions, increased immunosuppressive costimulatory (or coinhibitory) signals, increased immunosuppressive type 2 cytokines, and decreased chemokines to recruit cellular immune populations into the TIM and amplify antitumor immunity (18). Each of these deregulated signals contributes to localized immunosuppression and unrestrained tumor growth in the TIM.

We hypothesize the poor prognosis in current smokers with HNSCC is, at least in part, due to ongoing suppression of immune response by active tobacco use. Our study reports that active tobacco use is associated with an immunosuppressive signature as observed by lower tumor infiltration of cytotoxic T cells in concert with reduced expression levels of IFN response pathways. Our study highlights the importance of understanding the interaction between smoking and TIM in HNSCC.

Patients with HNSCC were identified through three Institutional review board (IRB)-approved studies: Total Cancer Care (MCC#14690) with a tissue diagnosis of HNSCC and obtained written consent, Epidemiology of Head and Neck Cancer Study (MCC#17041, written consent waived), and Evaluation of The Tumor and Its Microenvironment in Head and Neck Cancer Patients (MCC#18754, written consent waived). IRB approval was obtained in accordance with the Department of Health and Human Services Federal Policy for the Protection of Human Subjects (US Common Rule). The study was initiated after the IRB approvals. From these studies, a cohort of 177 cases with human papillomavirus (HPV)-negative squamous cell carcinoma of the oral cavity, pharynx, and larynx seen at Moffitt Cancer Center between 2006 and 2016 were included (Supplementary Tables S1 and S2). Clinical data were extracted from medical records and included patient demographics, smoking history, treatment history, and disease outcome. Specimens available for study were derived from primary tumors, second primary tumors, recurrent tumors, or recurrent tumors from second primary tumors (Supplementary Table S1). The second primary tumors were separated from the original primary tumors by time (>10 years), tumor subsite, or histology. Expert review (J.C. Hernandez-Prera and C.H. Chung) of clinical and pathology records was performed to assess the origin of each of the specimens. Smoking history was classified into three categories: never-smokers having consumed less than 100 lifetime cigarettes, former-smokers who smoked more than 100 lifetime cigarettes and quit at least 12 months prior to the date of diagnosis, and current-smokers who smoked more than 100 cigarettes within the last 12 months.

The multiplex immunofluorescence (mIF) staining was performed using the autostainer BOND-RX (Leica Microsystems), with Opal 7-color kit (Perkin Elmer) and detected using the Bond Research Detection System 2 kit (Leica Biosystems). This kit uses individual tyramide signal amplification-conjugated fluorophores to detect the stained targets. Antigen retrieval was accomplished using Bond Epitope Retrieval 1 (pH 6.0) or 2 (pH 9.0) (Leica Biosystems). Archival formalin-fixed, paraffin-embedded (FFPE) tissue blocks were obtained from Moffitt Surgical Pathology and used to cut sequential 4-μm thick sections for mIF. Antibodies against the following were used for staining: CD3 (clone F7.2.38, dilution 1:100, Dako), PD-1 (clone D4W2J, dilution 1:200; Cell Signaling Technology), CD8 (clone C8/144B, dilution 1:100, Dako), PD-L1 (clone E1L3N, dilution 1:100; Cell Signaling Technology), FOXP3 (clone 236A/E7, dilution 1:200, Abcam), and pancytokeratin (clone AE1/AE3, dilution 1:600, Dako) and DAPI for nuclei. Optimal dilutions were chosen based on the specific cell expression, background, uniformity, and pattern of the staining by an expert head and neck pathologist (J.C. Hernandez-Prera). Following the manufacturer's instructions, Opal 7-colors kit (same order as antibodies) Opal 520 (green), Opal 540 (pink), Opal 570 (yellow), Opal 620 (Orange), Opal 650 (red), and Opal 690 (Aqua blue) was used at dilution 1:150. The Opal 7-colors kit (Opal 520, Opal 540, Opal 570, Opal 620, Opal 650, and Opal 690) was used according to the manufacturer's instructions (Perkin Elmer). A positive control was used for each protein as follows: human tonsil for CD3, CD8, PD-1, FOXP3, and pancytokeratin AE1/AE3 and human placenta for PD-L1. A negative control slide for autofluorescence was included and stained with primary and secondary antibody but omitting fluorochrome-tyramide.

Multiplex staining slides were imaged using the Vectra 3.0 spectral imaging system (Perkin Elmer). Fluorescence intensity information was extracted using the fluorescence protocol at 10 nm λ from 420 nm to 720 nm. A similar method was used to build the spectral library using the InForm 2.2.1 image analysis software (Perkin Elmer). For the multiplex immunofluorescence slides, each set of slides was scanned with the Vectra imaging system. After scanning the full image at low resolution, nine regions of interest (ROI) from each slide were chosen by a pathologist (J.C. Hernandez-Prera) using Phenochart 1.0.4 (Perkin Elmer), 3 in the tumor core (TC), 3 at the tumor margin (TM), and 3 outside in the adjacent stroma (S) area. The size of the ROIs was standardized at 1356 × 1012 pixels with a resolution of 0.5 μm/pixel for a total surface area of 0.343 mm². The TM area was chosen to cover the tumor/stroma interface such that approximately half of the image covered the tumor region and the other half covered the surrounding stromal region.

The data from Vectra were accessed by the InForm imaging software, and each individual staining channel was combined using the spectral library information to link each fluorochrome component with a mIF component. Immune cell populations were characterized and quantified using the cell segmentation and phenotype cell tool of the InForm image analysis software under pathologist supervision (J.C. Hernandez-Prera). The InForm cell recognition tool was trained on a tonsil sample for all markers, except for the use of placenta for PD-L1 staining. Cell-level data was collected in a single InForm batch run and summarized using the phenoptr package for R (19). Cells positive for CD8 or FOXP3 were also required to be positive for CD3 to be counted in the phenotyping process, but they will be referred to as CD8⁺ and FOXP3⁺ in the text. To reduce variability due to low cell counts, we summed cells for each phenotype across the three ROI replicates for each of the tissue classes (TC, TM, and S). To allow distinction between PD-L1⁺ lymphocytes and PD-L1⁺ tumor cells, we classified all PD-L1⁺ cells as either PCK⁺ or PCK⁻ using average intensity of PCK measurements. The number of marker-positive cells per ROI was used as our primary endpoint for all marker comparisons. All statistical analyses were performed using R Project for Statistical Computing (version 3.4.3) and P values less than 0.05 were considered statistically significant.

Whole-exome (WES) and RNA sequencing (RNA-seq) on all tumor and matched normal samples were performed at the HudsonAlpha Institute for Biotechnology. DNA was extracted from germline samples and fresh frozen tissues using the Qiagen QIASymphony method. Total RNA from tumor tissues was extracted using Qiagen RNAeasy Plus Mini Kit. DNA and RNA from all FFPE samples were extracted using the Qiagen All Prep FFPE DNA/RNA Kit. Each exome was captured using Roche SeqCap EZ Exome v3.0 kit and sequenced on an Illumina HiSeq 4000 platform to a depth of approximately 100× for germline and approximately 300× for tumor tissues. RNA libraries were prepared using the Illumina TruSeq RNA Exome protocol and kit reagents and further sequenced on an Illumina HiSeq 4000 with 100 million total reads per sample (50 million paired reads). For DNA exome sequencing analysis, the trimmed and filtered reads were aligned to human genome (hs37d5) using BWA (0.7.7; ref. 20) and further refined with Picard/GATK tool (21). Somatic mutation detection was then performed using software MuTect (1.1.4; ref. 22) and Strelka (1.0.13; ref. 23). The detected variants were annotated by software ANNOVAR (24). For RNA-sequencing (RNA-seq) data processing, Cutadapt (1.16) was used to trim off adaptor containment sequences and low-quality bases at the ends. Trimmed and filtered reads were then aligned to the reference transcriptome (hs37d5) using STAR (2.5.3a; ref. 25). Gene-level quantification was conducted using HTSeq-count (26). Raw count data was normalized and further analyzed using DESeq2 package (27). Gene expression values were log₂(1+x) transformed for downstream analysis.

Tumor mutation burden (TMB) was calculated by counting mutations in each sample meeting the following criteria: observed as PASS in Strelka or in both Strelka and MuTect, located within the targeted regions, and absent in 1000 Genomes. Mutation counts were divided by target region size (63.6 Mb) giving mutations per megabase targeted. Tumor heterogeneity was quantified using MATH (Mutant-Allele Tumor Heterogeneity) score (28), which was calculated with R package Maftools (29) based on the mutation annotation format (MAF) files processed from the WES analysis pipeline. Gene-set enrichment analysis (GSEA) was performed using the GSEA toolkit version 4.0 (30) against Hallmark of Cancer, Oncogenic Signatures, and Immune Signatures. False discovery rate (FDR) and P value for the enriched pathways were estimated by performing 1,000 gene-set permutations, and gene sets with FDR < 5% were considered as significant in this study. We also performed single-sample GSEA (ssGSEA) analysis on the samples using the GenePattern workflow. CIBERSORT (http://cibersort.stanford.edu/) was used to estimate the tumor-infiltrating immune cell populations of the samples (31). RNA-seq data were submitted to the CIBERSORT web server as mixture file, and 22 immune cell type (LM22) is selected as the signature gene file. Quantile normalization option was disabled as recommended by CIBERSORT for RNA-seq, and 1000 permutations were performed for estimating the immune cell populations (CIBERSORT Results for individual samples were provided in Supplementary Table S8).

To study the effects of smoking on the TIM, we selected a cohort consisting of 177 HNSCC cases with primary tumors and clinical information (Table 1). We elected to analyze only HPV-negative HNSCC because HPV-positive HNSCC is a biologically distinct disease with potentially different mechanisms of immune escape (32). One-hundred (57%) of 177 patients were current smokers compared with 22 (12%) never or 55 (31%) former smokers. There was a difference in average age at diagnosis and smoking status (P < 0.001, Kruskal–Wallis test), which was attributed to a higher age for former smokers than for current smokers (P < 0.001, Dunn post test; Supplementary Fig. S1). There was no difference of smoking status with gender, Eastern Cooperative Oncology Group (ECOG) status, race, or disease stage (Table 1). The smoking distribution was unequal by primary tumor site (P = 0.038, Fisher exact test), which was mainly driven by a larger proportion of the larynx tumors in the current smoker group. We observed differences in overall survival (OS) based on smoking status, although these did not reach statistical significance (Supplementary Fig. S2). The observed features and parameters in our cohort are largely in accordance with expectations from a retrospectively collected HNSCC cohort (3, 33)

Of the full cohort of 177 cases, 107 had sufficient FFPE tumors for analysis by mIF (Supplementary Fig. S3; Supplementary Table S2 and S3) for expression of CD3, CD8, FOXP3, PD-1, PD-L1, and pancytokeratin (PCK). An overview of the 107 cases for mIF with associated clinical and molecular profiles is shown in Fig. 1. The mIF data were obtained from three separate regions of interests (ROI) selected within the TC, TM, and S. These separate regions were chosen because several studies have demonstrated that host immune response is often concentrated at the TMs and that large differences in infiltrating immune cells may exist between TC, TM, and S areas of a tumor specimen (34, 35). Of the cohort of 107 mIF cases, 80 had a sufficiently large specimen to allow the selection of all three regions of interest (ROI) for each of the three compartments. TC was available for the complete three ROIs in 100 cases with the remaining 7 cases consisting of two TC ROIs. A full three ROIs were available for TM in 94 cases and for S in 93 cases (Supplementary Table S3). We distinguished the following phenotype classes: PCK⁺ (tumor cells), CD3⁺, CD3⁺CD8⁺, CD3⁺FOXP3⁺, PD-1⁺, and PD-L1⁺ cells. Examples of representative staining patterns are shown in Fig. 2. The PD-L1⁺ cells were further separated into tumor (PCK⁺PD-L1⁺) and nontumor (PCK⁻PD-L1⁺) cells based on PCK staining intensity using median as a cutoff. There were only 2 cases with detectable PD-1–positive cells in any compartment, which was too limited to incorporate in subsequent analyses. This result is similar to those obtained by Schneider and colleagues using a different antibody for PD-1 where PD-1 staining was not observed in tumor cells from 124 cases of HNSCC, while strong staining of infiltrating immune cells were mostly confined to oropharyngeal carcinomas (36). A separate study by Balermpas and colleagues also found that PD-1 expression is significantly more common in pharyngeal (∼50%) than in oral cavity carcinoma (<20%) (37). Of the 107 cases with mIF measurements, only 8 tumors are from the oropharynx or hypopharynx that may explain the low number of cases with PD-1 staining in our cohort.

To determine possible differences in the TIM between newly diagnosed and independent recurrent tumors, we compared each of the markers in TC, TM, and S for these groups. We found evidence of lower immune activity in TM and S where the numbers of CD3⁺ cells were significantly lower in recurrent tumors than in newly diagnosed primary tumors (P = 0.005 and P = 0.010, Wilcoxon rank test, respectively) suggesting recurrent tumors may have been selected for immune evasion allowing the recurrence (Supplementary Figs. S4A and S4B). In TC, the numbers of PD-L1⁺ cells were generally lower in recurrent tumors than in newly diagnosed primary tumors (Supplementary Fig. S4C). For the subclass of PCK⁻PD-L1⁺ cells, the difference was statistically significant (P = 0.034, Wilcoxon rank test) while the difference for PCK⁺PD-L1⁺ cells were borderline significant (P = 0.065, Wilcoxon rank test; Supplementary Fig. S4D and S4E). The cell numbers for the other markers in the three ROI types were not different between newly diagnosed and recurrent tumors (Supplementary Figs. S4F–S4Q). Similar analyses were performed for the newly diagnosed primary versus second primary tumors, but no significant differences were observed. Although it is difficult to make a firm conclusion because our newly diagnosed and recurrent tumors were not matched pairs, we chose to use the full group of 134 primary tumors for subsequent analyses of the TIM excluding the 43 recurrent tumors based on our current data.

When we compared immune cell numbers across the three ROI types, higher immune activity was observed in TM compared with TC or S. For instance, TM had the highest numbers of CD3⁺ cells compared with TC and S (both P < 0.001, Dunn posttest, Fig. 3A). While absolute numbers were lower, a similar pattern was observed for both CD8⁺ and FOXP3⁺ cells (Fig. 3B and C). Again, the difference could be attributed to higher numbers of CD8⁺ and FOXP3⁺ cells in TM compared with TC and S (both P < 0.001, Dunn posttest). Using PCK as a marker for tumor cells, PCK⁺PD-L1⁺ cell counts were not different between TC and TM, but nontumor PCK⁻PDL1⁺ counts were higher in TM than in TC (P = 0.031, Wilcoxon rank test; Fig. 3D and E). These findings suggest that there is significant intratumor heterogeneity in immune cell infiltration.

To determine the association between tobacco smoke exposure and the composition of immune cells in the TIM, we summarized cells classified as CD3⁺, CD8⁺, FOXP3⁺, PD-L1⁺ (both PCK⁺ tumor and PCK⁻ non-tumor cells) in TC, TM, and S from the newly diagnosed primary tumors and compared their numbers between current, former, and never smokers. The numbers of CD8⁺ cells were significantly different in TM among the different smoking classes (P = 0.038, Kruskal–Wallis test), but not for TC (P = 0.235, Kruskal–Wallis test; Fig. 4A and B). Pairwise comparisons showed that TM had significantly lower numbers of CD8⁺ cells in current smokers compared with never smokers (P = 0.009, Dunn posttest) while the difference with former smokers almost reached significance (P = 0.051, Dunn posttest). The numbers of PCK⁺PD-L1⁺ tumor cells in TM were also dependent on smoking status: current smokers had significantly lower PCK⁺PD-L1⁺ tumor cells in TM (P = 0.001, Kruskal–Wallis test) compared with former or never smokers, Fig. 4C). PCK⁺PD-L1⁺ results were similar for TC, although they did not reach statistical significance (P = 0.059, Kruskal–Wallis test; Fig. 4D). The numbers of PCK⁻PD-L1⁺ cells were also significantly different with respect to smoking classes measured in TC and TM but did not reach significance in stroma (P = 0.045, P = 0.025, and P = 0.097, Kruskal–Wallis test, respectively; Fig. 4E–G). There was no significant difference between the smoking groups for CD3⁺ or FOXP3⁺ cells in any of the TC, TM, or S ROI types. Taken together, the absence of differences between former and never smokers suggests smoking might actively modulate the tumor and immune cell interaction at the tumor margin.

High tumor mutation burden (TMB) is often used as a surrogate marker of immune response to tumors harboring neoantigens due to high levels of somatic mutations (38). Furthermore, carcinogens in tobacco smoke are expected to cause permanent DNA damage that is reflected in TMB (39). Within the data set of 104 cases with available exome sequencing data, we observed a median TMB of 6.4 mutations/Mb (range: 1.5–41 Mb), which is similar to values reported in the literature (40). However, in this cohort, we did not find evidence for an association between smoking status and TMB (Supplementary Fig. S5A). Another measure related to treatment resistance is the level of intratumor heterogeneity, which can be estimated on the basis of differences among mutated loci as summarized by the mutant allele tumor heterogeneity (MATH score; ref. 28). This measure also did not show any association with smoking status (Supplementary Fig. S5B). On the basis of these findings, the presence of higher number of immune cells in the TM is suggested to be, at least in part, a result of active TIM modulation by smoking rather than simply driven smoking-related genomic changes.

Because of heterogeneity in the cohort and the quantitative nature of the immune marker measurements, we chose to evaluate effects on survival using Cox proportional hazards modeling. Starting with the 134 primary and second primary tumors in the cohort, we evaluated all possible clinical prognostic factors in a single Cox model that included age, sex, ECOG status (4 classes), smoking (3 classes), disease stage, and node status (3 classes). The effects of age and nodal status were highly significant in this model (Supplementary Table S4A) while stage was not independently significant in this model. Thus, we chose age and nodal status as confounders in subsequent models for the immune markers. Because there was no significant difference in OS between N0 and N1 disease, we chose to collapse the nodal status to 2 classes for these models (N0 and N1 vs. N2). We evaluated OS according to immune markers in the 3 ROI types in the subset of 76 cases with primary tumors, full mIF data, and known nodal status. Using a Cox proportional hazards model that included age and node status, we found that higher numbers of CD8⁺ cells were associated with significantly better OS, especially in the TM (P < 0.002, Supplementary Table S4B). The numbers of cells positive for CD3, FoxP3, and PD-L1 were not associated with differences in OS in any of the 3 ROIs, although higher numbers of CD3⁺ cells in TM were associated with borderline significant better OS (P = 0.053; Supplementary Table S4C). These results confirm prior reports that a stronger immune response is associated with better survival (12–14).

We also obtained RNA-sequencing data from 75 tumors to determine the differential gene expression. RNA-sequencing data were available from 39 current, 22 former, and 14 never smokers (Supplementary Table S5). After removal of the 17 recurrent tumors, we performed GSEA on the remaining 58 cases for current/never, current/former, and former/never smokers using Hallmark of Cancer, Oncogenic Signature, and Immune Signature (Supplementary Table S6; Supplementary Fig. S6; ref. 30). As shown in Fig. 5A, current versus never smokers showed several differential pathways mapped to Hallmark of Cancer, including lower IFNα and IFNγ responses in current smokers compared with never smokers. Several smoking-associated pathways were higher in current smokers: oxidative phosphorylation and reactive oxygen species reflecting effects of active smoking. Similar comparisons using Oncogenic Signature and Immune Signature yielded similar results as the Hallmarks gene sets where the inflammation and immune-related pathways were depleted in current smokers. The comparison between current and former smokers for Cancer Hallmarks yielded similar results with higher expression of oxidative phosphorylation, xenobiotic metabolism, and reactive oxygen species gene sets in current smokers, and lower expression of genes in the IFNα and IFNγ response pathways (Fig. 5B).

These results prompted further gene expression analysis of chemokines and their receptors in current versus former/never smokers. Former and never smokers were grouped together because they were indistinguishable in the mIF and RNA-seq comparisons. All 58 primary tumor and second primary tumor cases with RNA-seq data were used to test all expressed cytokines and cytokine receptors with a minimum of 50% differential expression as shown in Fig. 5C. Chemokines are chemotactic cytokines that regulate immune cell trafficking with importance for the TIM. The most differentially expressed chemokines included the canonical IFN response CXCR3 chemokines, CXCL9, CXCL10, and CXCL11 that were expressed in lower levels in the current smokers compared with the former smokers (41). This was associated with a concurrent decrease in the expression levels of CCL5 that, along with CXCR3 chemokines, is produced in response to the STING/TBK1/IRF3 innate immune pathway (42, 43) and is a known lymphocyte-recruiting chemokine (44). It was recently shown that tumor-derived CCL5 and immune cell–expressed CXCL9 are involved in recruitment of cytotoxic T cells in response to IFNγ (45). To further study the association of CXCR3 chemokines and CCL5 with immune cell infiltration, we performed correlation analyses of these markers in the 42 cases for which these data were available. A clear pattern emerged whereby higher expression of chemokines CXCL9, CXCL10, CXCL11, CCL5, and IFNγ was correlated with higher CD3⁺ cell counts in TC and higher CD8⁺ cell counts in TM (Supplementary Table S7).

In previous studies, IFNγ response genes have been related to a T-cell–inflamed gene expression profile indicative of an inflamed TIM (46) that is predictive of response to PD-1 checkpoint inhibitor treatment across the KEYNOTE clinical datasets (47). We further investigated the 18-gene signature between current smokers (n = 27) and former or never smokers (N = 31) from primary or second primary tumors for which RNA-seq data was available (Fig. 5D). This analysis showed that all, except one, of the 18 genes had higher expression levels in tumors from non-current smokers compared with tumors from current smokers, again confirming higher activity of the IFNγ signaling pathway in tumors from individuals who are not currently smoking. Taken together, our data is consistent with an immune-suppressive effect of active smoking and suggests involvement of the IFNα and IFNγ response pathways, altering the balance between antitumor T cells and tumor-promoting effects of CXCR2.

Tobacco smoking has been clearly established as a cause of many cancers including HNSCC (48). However, the proinflammatory and immunosuppressive effects of tobacco smoking within the disease site–specific TIM has not been clearly delineated. In this study, we characterize the TIM in current, former, and never smokers with HNSCC and observed ongoing immunosuppressive state that may result in differential response to immune-modulating therapies and importance of smoking cessation during cancer treatment.

To date, most of the preclinical and clinical studies delineating the effects of tobacco smoking are conducted in chronic obstructive pulmonary disease and lung cancer models in context of chronic airway inflammation (48). Evaluation of the different cell types and immune microenvironment in the head and neck region is sorely lacking. These disease site–specific data are particularly important in context of the cancer–immune set point, the threshold needed to mount effective cancer immunity (49). The cancer–immune set point is defined as the summation of all immune regulators that are inherent to each individual. In addition to the obvious immune regulators such as the TCR signaling in CD8⁺ T cells, presence of neoantigens, host genetics, etc., there is mounting data that the TIM-driven factors may be important in mounting the effective immune response (50). It is clearly established that smoking can modulate the TIM by affecting the oral microbiome, oxygen deprivation, and biofilm formation (51). Furthermore, the degree of exposure and concentration of carcinogens contained in the tobacco smoking would also vary depending on the disease site (48). For example, Desrichard and colleagues evaluated the mutational signatures associated with tobacco smoking in HNSCC and lung SCC (LSCC) from TCGA and correlated with expression profiles of immune infiltration, cytolytic activity, and IFNγ pathway signaling (11). The differential gene expression analyses indicated immunosuppression in smoking-high HNSCC involving lower expression of MHCII molecules, TCRs, immunoregulatory molecules, cytotoxic effectors, and cytokines. In addition, proinflammatory profiles were also observed in smoking-high LSCC. Notably, the mutation and expression profiles of tobacco-related HNSCC and LSCC resemble each other very closely, suggesting immunosuppression in HNSCC is driven by interactions within the TIM (32). Therefore, comprehensive evaluation of the TIM specifically in HNSCC is required and extrapolating the data from other disease sites warrants caution.

We also found a temporal relationship to smoking exposure. Current smokers had the lowest number of the immune cells compared with the former and never smokers. Considering we did not find any significant differences in the mutation profiles based on smoking status, this difference may be driven by differential gene or protein expression due to the recent exposure to the tobacco smoking. In addition, we found that the IFNα and IFNγ response pathways were significantly downregulated in current smokers compared with never and former smokers. Further analyses of chemokine profiles revealed decreased expression of CXCL9, 10, and 11 in current smokers. These chemokines are known to regulate immune cell migration, differentiation, and activation through recruitment of cytotoxic lymphocytes and natural killer cells in response to IFNγ expression (52). Activation of the CXCL9, 10, 11/CXCR3 axis leads to migration of immune cells to their focal sites. Current data suggest that tumors with the IFNγ signature and inflamed phenotype with a high number of TILs have higher response and survival benefits given anti- PD-1 checkpoint inhibitors (46, 47). We observed that current smokers had significantly lower numbers of PD-L1⁺ cells in the TC and TM compared with never and former smokers, which is consistent with data that patients with smoking-high HNSCC have a lower response rate given anti-PD-1 checkpoint inhibitors compared with smoking-low HNSCC (11). Using an in vivo model, Chheda and colleagues demonstrated abrogation of anti-PD-1 inhibitor effects in CXCR3 knockout mice suggesting homing of T cells to the tumor through the CXCL9, 10, 11/CXCR3 axis may be critical for the anti-PD-1 inhibitor efficacy (53). However, activation of CXCL9, 10, 11 signaling in tumor cells has been shown to increase cell proliferation, angiogenesis, and metastasis in colon and breast cancer models (54, 55). Understanding the interaction between the tumor and immune cells and therapeutic strategies to selectively inhibit the CXCL9, 10, 11/CXCR3 signaling in the tumors without dampening the antitumor immune cell migration to the tumor site will be a challenge in immunotherapy development for cancer treatments.

In summary, our results indicate that active tobacco use in HNSCC may have a significant immunosuppressive effect through suppression of T-cell chemotaxis. The importance of understanding the interaction between smoking and TIM is further highlighted by emergence of immune checkpoint inhibitors as a promising therapeutic option in management of HNSCC and smokers are less likely to benefit from the anti-PD-1 checkpoint inhibitors (11, 15–17). Our findings add to the importance of further research that will allow a more granular examination of the TIM and changes due to tobacco smoke exposure in HNSCC.

R.J.C. Slebos is an employee/paid consultant for Bristol-Myers Squibb and CUE Biopharma. L. Martin-Gomez is an employee/paid consultant for GSK-Spain. J.K. Teer holds ownership interest (including patents) in Interpares Biomedicine. J. Conejo-Garcia is an employee/paid consultant for and reports receiving commercial research grants from Compass Therapeutics and Anixa Biosciences, holds ownership interest (including patents) in Anixa Biosciences and is an advisory board member/unpaid consultant for KSQ Therapeutics. C.H. Chung is an employee/paid consultant for Bristol-Myers Squibb and CUE Biopharma. No potential conflicts of interest were disclosed by the other authors.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the Grant Sponsor or the H. Lee Moffitt Cancer Center & Research Institute.

Conception and design: L. Harrison, C.H. Chung

Development of methodology: J.V. de la Iglesia, R.J.C. Slebos, M. Fournier, E.M. Siegel, J. Conejo-Garcia, J.C. Hernandez-Prera, C.H. Chung

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J.V. de la Iglesia, L. Martin-Gomez, T. van Veen, J. Masannat, E.M. Siegel, M.B. Schabath, J. Caudell, J.C. Hernandez-Prera, C.H. Chung

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J.V. de la Iglesia, R.J.C. Slebos, X. Wang, J.K. Teer, A.-C. Tan, G. Aden-Buie, R. Chaudhary, L. Harrison, C.H. Chung

Writing, review, and/or revision of the manuscript: J.V. de la Iglesia, R.J.C. Slebos, L. Martin-Gomez, J.K. Teer, A.-C. Tan, T.A. Gerke, J. Masannat, R. Chaudhary, F. Song, E.M. Siegel, M.B. Schabath, J.T. Wadsworth, J. Caudell, L. Harrison, B.M. Wenig, J. Conejo-Garcia, J.C. Hernandez-Prera, C.H. Chung

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J.V. de la Iglesia, R.J.C. Slebos, L. Martin-Gomez, T.A. Gerke, G. Aden-Buie, T. van Veen, J. Masannat, F. Song, E.M. Siegel, C.H. Chung

Study supervision: J.C. Hernandez-Prera, C.H. Chung

Other (operational development and implementation of study methods): M. Fournier

We appreciate the staffs at the Moffitt Tissue Core, Total Cancer Care, and M2GEN for their contribution. This work has been supported by the James and Esther King Biomedical Research Grant (7JK02) and Moffitt Merit Society Award (to C.H. Chung). It is also supported in part by the Moffitt's Total Cancer Care Initiative, Collaborative Data Services, Biostatistics and Bioinformatics, and Tissue Core Facilities at the H. Lee Moffitt Cancer Center & Research Institute, an NCI-designated Comprehensive Cancer Center (P30-CA076292). The WES and RNA-seq included in this work was obtained through the Oncology Research Information Exchange Network (ORIEN) Avatar Project initiated under the Total Cancer Care protocol at the Moffitt Cancer Center.

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.