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. 2022 Jul 28;5(1):757.
doi: 10.1038/s42003-022-03675-4.

Mutant p53 drives an immune cold tumor immune microenvironment in oral squamous cell carcinoma

Yewen Shi  1   2 , Tongxin Xie  1 , Bingbing Wang  1 , Rong Wang  3 , Yu Cai  4 , Bo Yuan  5 , Frederico O Gleber-Netto  1 , Xiangjun Tian  6 , Alanis E Rodriguez-Rosario  1   7 , Abdullah A Osman  1 , Jing Wang  6 , Curtis R Pickering  1 , Xiaoyong Ren  2 , Andrew G Sikora  1 , Jeffrey N Myers  8 , Roberto Rangel  9
Affiliations

Mutant p53 drives an immune cold tumor immune microenvironment in oral squamous cell carcinoma

Yewen Shi et al. Commun Biol. .

Abstract

The critical role of the tumor immune microenvironment (TIME) in determining response to immune checkpoint inhibitor (ICI) therapy underscores the importance of understanding cancer cell-intrinsic mechanisms driving immune-excluded ("cold") TIMEs. One such cold tumor is oral cavity squamous cell carcinoma (OSCC), a tobacco-associated cancer with mutations in the TP53 gene which responds poorly to ICI therapy. Because altered TP53 function promotes tumor progression and plays a potential role in TIME modulation, here we developed a syngeneic OSCC models with defined Trp53 (p53) mutations and characterized their TIMEs and degree of ICI responsiveness. We observed that a carcinogen-induced p53 mutation promoted a cold TIME enriched with immunosuppressive M2 macrophages highly resistant to ICI therapy. p53-mutated cold tumors failed to respond to combination ICI treatment; however, the combination of a programmed cell death protein 1 (PD-1) inhibitor and stimulator of interferon genes (STING) agonist restored responsiveness. These syngeneic OSCC models can be used to gain insights into tumor cell-intrinsic drivers of immune resistance and to develop effective immunotherapeutic approaches for OSCC and other ICI-resistant solid tumors.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Mutational landscape and tumorigenesis of syngeneic ROC cell lines derived from a murine model of carcinogen-induced oral cancer.
a Strategy for generating syngeneic ROC mouse oral cancer cell lines. b Frequency of somatic substitutions in ROC cell lines. c Frequency of functional INDELs in ROC cell lines. d Comparative analysis among ROC cell lines of the genes most frequently mutated in the TCGA-HNSCC cohort. *p53 nonsense mutation; **p53 deletion. e Tumor growth curves of different numbers of injected ROC1–3 cells in the orthotopic tongue model. Mean tumor volume are represented in the graph (n = 5, error bars = standard error mean). The right panels show the primary tumor in the tongue (top) and a representative hematoxylin and eosin–stained section of the primary tumor (bottom). Scale bars, 50 μm.
Fig. 2
Fig. 2. ROC1–3 TIMEs have distinct patterns of immune infiltration and exclusion.
IHC quantification analysis shows that T-lymphocyte markers (a), macrophage markers (b), and immune checkpoints (c) are differentially expressed among ROC1–3 tumors. The images were scan using Vectra Polaris imaging system and images were process using Phenochart software (version 1.0.12). Representative antibody-stained images are shown and include scale bars 50 μm (bar representative of n = 3 stained tumors, error bars= standard deviations). One-way ANOVA with Tukey’s post hoc test p-values shown, *p < 0.05, ***p < 0.005, ****p < 0.0001, for all comparisons of the ROC tumors (error bars = standard deviations).
Fig. 3
Fig. 3. Immune-excluded mutant p53–mediated ROC1 tumors do not respond to immune checkpoint inhibition.
a Process diagrams of tumor cells orthotopically injected and treated with anti–PD-1 and/or anti-TIGIT antibodies (50,000 cells implanted; n = 10 mice). b ROC1-tumor-bearing mice that received the combination therapy had a slight robust tumor response. Left and right graphs represent individual and mean tumor growth (error bars = standard deviation). c Tumor-free survival with not significant overall survival (Long-Rank/Mantel–Cox test).
Fig. 4
Fig. 4. Mutant p53 in ROC1 tumors modulates tumor cell-intrinsic factors required for immune escape in immunocompetent mice.
a Western blot analysis of the parental, Control (Non-Targeting Control-shRNA), and p53-KD (p53-shRNA) clones. β-actin was used as a loading control. b, c ROC1 cells (50,000) were implanted into the tongues of C57BL/6J mice (n = 5) and immunodeficient beige mice (n = 5) respectively, KD of mutant p53 impairs tumor growth in immune competent mice (b) compared with immune deficient Beige mice (c) (error bars = standard deviation). d Differential chemokine and cytokine gene expression analysis of ROC1 p53-KD cells relative to control cells, showing genes with significant Q values. e Validation of cytokine and chemokine RNA expression by qPCR (Bar = mean of triplicate experiment, error bars = standard error mean). Unpaired two-tailed Student’s t test p-values shown, ***P < 0.0005, ****P < 0.0001, for comparison of control vs p53-KD ROC1-tumor cells. f Fold changes in mouse cytokine protein expression in p53-KD tumor cells relative to control tumor cells. Antibody array assay was used to measure cytokine levels from an equal amount of supernatant of 48-h culture (details in Material and methods section). The bars represent the average of duplicate independent experiment for comparison of control vs p53-KD ROC1 cell supernatants. g Proposed molecular model by which mutant p53 regulates the TIME. ROC1 cells acquire a carcinogen-induced mutation in p53, yielding cytokines that promote M2 macrophage polarization and the infiltration of Tregs, thereby generating an immunosuppressive TIME by excluding effector immune cells. Created with BioRender.com.
Fig. 5
Fig. 5. Flow cytometry of immune populations regulated by ROC1 control tumor- and ROC1 p53-KD tumor-conditioned medium.
Comparison of the frequencies of IFNγ+CD8+ T cells (a); IFNγ+CD4+ T cells (Th1 cells; b); and FOXP3+CD4+ (Tregs; c) in splenocytes cultured in the presence of ROC1 control tumor- or ROC1 p53-KD tumor-conditioned media. d Comparison of the frequencies of MHCII+CD206− (M1-type) and MHCII-CD206+(M2-type) macrophages in bone marrow precursor cells cultured in the presence of control tumor- or p53-KD tumor-conditioned medium. Dots represent individual sample (n = 4–5, error bars = standard deviation). Unpaired two-tailed Student’s t test p-values shown, *p < 0.05, **p < 0.001, ****p < 0.0001.
Fig. 6
Fig. 6. Combination immunotherapy targeting innate and adaptive immunity overcomes p53-driven tumor-mediated immune suppression.
a Process diagram of STING agonist treatment of the orthotopic ROC1-tumor model. b Process diagram of combined STING agonist and anti–PD-1 antibody treatment of ROC1 tumors. c, d Immune-excluded ROC1 tumors respond poorly to STING agonist monotherapy (c) and have reduced tumor-free survival (d). ***P < 0.0002, by Mantel–Cox test. e, f ROC1 tumors treated with STING and anti-PD-1 antibody have significantly improved tumor response (e) and survival (f). ****P < 0.0001, by Mantel–Cox test. Mice were injected orthotopically with 50,000 ROC1 cells control group n = 5, therapy group n = 10. The orange arrows indicate the time point selected for multiplex IHC in Fig. 7.
Fig. 7
Fig. 7. STING agonist c-di-GMP influences the TIME of ROC1 cold tumors.
Fluorescent multiplex IHC quantification analysis shows that STING (a), T-lymphocyte markers (CD8a, CD4, granzyme B, and Foxp3; (b)), macrophage markers (CD68 and CD206; (c)), and PD-1 (d) are differentially expressed between STING agonist (c-di-GMP) and PBS control groups. Representative images were processed using phenochart 1.0.12 software (scale bars, 20 μm). Multiplex IHC quantification was defined as the average optical density per view and density of cells per view (×20 magnification) and cellular density per field was quantified using Image J software (n = 3 slides and three random fields from each slide were quantified). Unpaired two-tailed Student’s t test showed the p-value = ****p < 0.0001.
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