Single-cell transcriptome dissecting the microenvironment remodeled by PD1 blockade combined with photodynamic therapy in a mouse model of oral carcinogenesis

Abstract

Oral squamous cell carcinoma (OSCC) stands as a predominant and perilous malignant neoplasm globally, with the majority of cases originating from oral potential malignant disorders (OPMDs). Despite this, effective strategies to impede the progression of OPMDs to OSCC remain elusive. In this study, we established mouse models of oral carcinogenesis via 4-nitroquinoline 1-oxide induction, mirroring the sequential transformation from normal oral mucosa to OPMDs, culminating in OSCC development. By intervening during the OPMDs stage, we observed that combining PD1 blockade with photodynamic therapy (PDT) significantly mitigated oral carcinogenesis progression. Single-cell transcriptomic sequencing unveiled microenvironmental dysregulation occurring predominantly from OPMDs to OSCC stages, fostering a tumor-promoting milieu characterized by increased Treg proportion, heightened S100A8 expression, and decreased Fib_Igfbp5 (a specific fibroblast subtype) proportion, among others. Notably, intervening with PD1 blockade and PDT during the OPMDs stage hindered the formation of the tumor-promoting microenvironment, resulting in decreased Treg proportion, reduced S100A8 expression, and increased Fib_Igfbp5 proportion. Moreover, combination therapy elicited a more robust treatment-associated immune response compared with monotherapy. In essence, our findings present a novel strategy for curtailing the progression of oral carcinogenesis.

Keywords

immune checkpoint blockade / multiomics / oral carcinogenesis / photodynamic therapy / single-cell transcriptome sequencing

References

1	RL Siegel, KD Miller, NS Wagle, A Jemal. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48.
2	KJ Harrington, RL Ferris, G Blumenschein, et al. Nivolumab versus standard, single-agent therapy of investigator's choice in recurrent or metastatic squamous cell carcinoma of the head and neck (CheckMate 141): health-related quality-of-life results from a randomised, phase 3 trial. Lancet Oncol. 2017;18(8):1104-1115.
3	TM Wise-Draper, S Gulati, S Palackdharry, et al. Phase II clinical trial of neoadjuvant and adjuvant pembrolizumab in resectable local–regionally advanced head and neck squamous cell carcinoma. Clin Cancer Res. 2022;28(7):1345-1352.
4	AC Pinto, J Caramês, H Francisco, et al. Malignant transformation rate of oral leukoplakia—systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;129(6):600-611. e2.
5	Y Dong, Z Wang, F Mao, et al. PD-1 blockade prevents the progression of oral carcinogenesis. Carcinogenesis. 2021;42(6):891-902.
6	Y Dong, C Zhang, F Mao, et al. Mass cytometry and transcriptomic profiling reveal PD1 blockade induced alterations in oral carcinogenesis. Mol Carcinog. 2024;63(4):563-576.
7	GJ Hanna, A Villa, SP Nandi, et al. Nivolumab for patients with high-risk oral leukoplakia: a nonrandomized controlled trial. JAMA Oncol. 2023;10(1):32-41.
8	Q Chen, H Dan, F Tang, et al. Photodynamic therapy guidelines for the management of oral leucoplakia. Int J Oral Sci. 2019;11(2):14.
9	Y Li, B Wang, S Zheng, Y He. Photodynamic therapy in the treatment of oral leukoplakia: a systematic review. Photodiagnosis Photodyn Ther. 2019;25:17-22.
10	L Shen, T Zhou, Y Fan, et al. Recent progress in tumor photodynamic immunotherapy. Chin Chem Lett. 2020;31(7):1709-1716.
11	D Kanojia, MM Vaidya. 4-Nitroquinoline-1-oxide induced experimental oral carcinogenesis. Oral Oncol. 2006;42(7):655-667.
12	I Evren, ER Brouns, JB Poell, et al. Associations between clinical and histopathological characteristics in oral leukoplakia. Oral Dis. 2023;29(2):696-706.
13	S Tovaru, M Costache, P Perlea, et al. Oral leukoplakia: a clinicopathological study and malignant transformation. Oral Dis. 2023;29(4):1454-1463.
14	TN Schumacher, RD Schreiber. Neoantigens in cancer immunotherapy. Science. 2015;348(6230):69-74.
15	ZR Chalmers, CF Connelly, D Fabrizio, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017;9(1):34.
16	J Goveia, K Rohlenova, F Taverna, et al. An integrated gene expression landscape profiling approach to identify lung tumor endothelial cell heterogeneity and angiogenic candidates. Cancer Cell. 2020;37(1):21-36. e13.
17	J Kalucka, LPMH de Rooij, J Goveia, et al. Single-cell transcriptome atlas of murine endothelial cells. Cell. 2020;180(4):764-779. e20.
18	C Kessel, D Holzinger, D Foell. Phagocyte-derived S100 proteins in autoinflammation: putative role in pathogenesis and usefulness as biomarkers. Clin Immunol. 2013;147(3):229-241.
19	R Bill, P Wirapati, M Messemaker, et al. CXCL9:sPP1 macrophage polarity identifies a network of cellular programs that control human cancers. Science. 2023;381(6657):515-524.
20	Z Wang, VH Wu, MM Allevato, et al. Syngeneic animal models of tobacco-associated oral cancer reveal the activity of in situ anti-CTLA-4. Nat Commun. 2019;10(1):5546.
21	T Wang, S Sun, X Zeng, J Li. ICI-based therapies: a new strategy for oral potentially malignant disorders. Oral Oncol. 2023;140:106388.
22	SR Gordon, RL Maute, BW Dulken, et al. PD-1 expression by tumour-associated macrophages inhibits phagocytosis and tumour immunity. Nature. 2017;545(7655):495-499.
23	Y Liu, J Zugazagoitia, FS Ahmed, et al. Immune cell PD-L1 colocalizes with macrophages and is associated with outcome in PD-1 pathway blockade therapy. Clin Cancer Res. 2020;26(4):970-977.
24	H Zhao, L Wu, G Yan, et al. Inflammation and tumor progression: signaling pathways and targeted intervention. Signal Transduct Target Ther. 2021;6(1):263.
25	G Morad, BA Helmink, P Sharma, JA Wargo. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell. 2021;184(21):5309-5337.
26	MJ O'Shaughnessy, KS Murray, SP La Rosa, et al. Systemic antitumor immunity by PD-1/PD-L1 inhibition is potentiated by vascular-targeted photodynamic therapy of primary tumors. Clin Cancer Res. 2018;24(3):592-599.
27	R Reschke, TF Gajewski. CXCL9 and CXCL10 bring the heat to tumors. Sci Immunol. 2022;7(73):eabq6509.
28	R Reschke, TF Gajewski, CXCL9 and CXCL10 initiate a chemokine network and hot tumor microenvironment. Sci Immunol. 2022;7(73):eabq6509.
29	CHL Kürten, A Kulkarni, AR Cillo, et al. Investigating immune and non-immune cell interactions in head and neck tumors by single-cell RNA sequencing. Nat Commun. 2021;12(1):7338.
30	S Hu, H Lu, W Xie, et al. TDO2+ myofibroblasts mediate immune suppression in malignant transformation of squamous cell carcinoma. J Clin Invest. 2022;132(19):e157649.
31	S Davidson, M Coles, T Thomas, et al. Fibroblasts as immune regulators in infection, inflammation and cancer. Nat Rev Immunol. 2021;21(11):704-717.
32	H Su, F Yang, R Fu, et al. Collagenolysis-dependent DDR1 signalling dictates pancreatic cancer outcome. Nature. 2022;610(7931):366-372.
33	MA Lakins, E Ghorani, H Munir, CP Martins, JD Shields. Cancer-associated fibroblasts induce antigen-specific deletion of CD8 + T Cells to protect tumour cells. Nat Commun. 2018;9(1):948.
34	DH Peng, BL Rodriguez, L Diao, et al. Collagen promotes anti-PD-1/PD-L1 resistance in cancer through LAIR1-dependent CD8+ T cell exhaustion. Nat Commun. 2020;11(1):4520.
35	Y Chen, KM McAndrews, R Kalluri. Clinical and therapeutic relevance of cancer-associated fibroblasts. Nat Rev Clin Oncol. 2021;18(12):792-804.
36	M Ding, RK Bruick, Y Yu. Secreted IGFBP5 mediates mTORC1-dependent feedback inhibition of IGF-1 signalling. Nat Cell Biol. 2016;18(3):319-327.
37	SC Lin, CP Wang, YM Chen, et al. Regulation of IGFBP-5 expression during tumourigenesis and differentiation of oral keratinocytes. J Pathol. 2002;198(3):317-325.
38	C Gebhardt, J Németh, P Angel, J Hess. S100A8 and S100A9 in inflammation and cancer. Biochem Pharmacol. 2006;72(11):1622-1631.
39	S Wang, R Song, Z Wang, et al. S100A8/A9 in inflammation. Front Immunol. 2018;9:1298.
40	G Srikrishna. S100A8 and S100A9: new insights into their roles in malignancy. J Innate Immun. 2011;4(1):31-40.
41	SY Lim, AE Yuzhalin, AN Gordon-Weeks, RJ Muschel. Tumor-infiltrating monocytes/macrophages promote tumor invasion and migration by upregulating S100A8 and S100A9 expression in cancer cells. Oncogene. 2016;35(44):5735-5745.
42	S Li, J Zhang, S Qian, et al. S100A8 promotes epithelial-mesenchymal transition and metastasis under TGF-β/USF2 axis in colorectal cancer. Cancer Commun (Lond). 2021;41(2):154-170.
43	Z Wu, D Jiang, X Huang, et al. S100A8 as a promising biomarker and oncogenic immune protein in the tumor microenvironment: an integrative pancancer analysis. J Oncol. 2022;2022:1-15.
44	R Core Team. R: A Language and Environment for Statistical Computing, Vienna, Austria. 2022.
45	H Li, R Durbin. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754-1760.
46	H Li, B Handsaker, A Wysoker, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078-2079.
47	íF Do Valle, E Giampieri, G Simonetti, et al. Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data. BMC Bioinformatics. 2016;17(S12):341.
48	CT Saunders, WSW Wong, S Swamy, et al. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics. 2012;28(14):1811-1817.
49	V Boeva, T Popova, K Bleakley, et al. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics. 2012;28(3):423-425.
50	K Wang, M Li, H Hakonarson. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164-e164.
51	S Wang, Z Tao, H Li, T Wu, X Liu, sigminer: Extract, Analyze and Visualize Mutational Signatures for Genomic Variations. 2022.
52	A Mayakonda, D Lin, Y Assenov, C Plass, PH Koeffler. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28(11):1747-1756.
53	A Mayakonda, TCGAmutations: Pre-Compiled Somatic Mutations from TCGA Cohorts. 2022.
54	Y Hao, S Hao, E Andersen-Nissen, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587.
55	C Wang, D Sun, T Liu et al,. MAESTRO: Model-based Analyses of Single-cell Transcriptome and Regulome. 2021.
56	J Guinney, R Castelo, GSVA: Gene Set Variation Analysis for microarray and RNA-seq data. 2022.
57	I Dolgalev, msigdbr: MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format. 2022.
58	R Kolde, pheatmap: Pretty Heatmaps. 2019.
59	C Trapnell, D Cacchiarelli, J Grimsby, et al. The dynamics and regulators of cell fate decisions are revealed by pseudo-temporal ordering of single cells. Nat Biotechnol. 2014;32(4):381-386.
60	G Yu, clusterProfiler: A Universal Enrichment Tool for Interpreting Omics Data. 2022.
61	S Jin, CellChat: Inference and Analysis of Cell-Cell Communication from Single-Cell Transcriptomics Data. 2022.
62	A Kassambara, M Kosinski, P Biecek, survminer: Drawing Survival Curves using ggplot2. 2021.
63	T Wei, V Simko, corrplot: Visualization of a Correlation Matrix. 2021.
64	T Wei, V Simko, R package ‘corrplot’: Visualization of a Correlation Matrix. 2021.
65	H Wickham, W Chang, L Henry et al,. ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. 2022.
66	A Kassambara, ggpubr: ggplot2 Based Publication Ready Plots. 2020.