Tertiary lymphoid structure-related RNA indicator as metastasis risk factor in nasopharyngeal carcinoma

Abstract

Background: Nasopharyngeal carcinoma (NPC) patients who develop distant metastasis have significantly reduced survival rates. Therefore, understanding of metastasis and identifying high-risk patients are important, and a robust predictive model for accurately assessing the distant-metastasis risk before treatment is needed for personalised treatment.

Methods: NPC patients diagnosed at four Hong Kong public hospitals and at Sun Yat-Sen University Cancer Center in Guangzhou were selected. Patients were divided into two training cohorts (n = 77 and 30, respectively) and one testing cohort (n = 70). Two independent NPC cohorts collected from Sun Yat-Sen University Cancer Center (n = 88), and a randomised phase III trial (NPC-0501) in Hong Kong (n = 81) were used for external validation of the model-based risk prediction.

Results: Our RNA-based risk score could stratify the patient groups into high and low risk of metastasis and disease progression in two independent external validation cohorts. In predicting NPC 3-year distant metastasis, the score significantly improved the area under the curve from 84.8% to 90.4% when combined with the known prognostic clinical parameters. This RNA-based risk score was highly associated with dysregulated functions of B cells and T helper 17 cells and reduced plasma B cells and tertiary lymphoid structure (TLS) formation. The analysis of biopsy samples revealed a significant enrichment of the TLS in non-metastatic NPC patients.

Conclusions: This study improved the accuracy of NPC metastasis prediction and highlight the potential association of TLS against metastatic NPC, encouraging future studies to understand how TLS interacts with NPC to prevent distant metastasis. Furthermore, the multi-cohort Pareto-optimisation-based feature selection approach offers a practical method to explicitly avoid model overfitting and achieve a more robust model.

Novelty and Impact: In this multicentre study, we established a new and robust predictive model for NPC distant metastasis using markers selected by a Pareto optimisation approach designed for multi-cohort data. When combined with clinical parameters, our RNA-based risk score significantly improved the area under the curve to 90.4%. This study revealed that reduced B-cell immunity, and TLS formation, may be associated with NPC metastasis, providing insights for the future studies in NPC metastasis.

Keywords

distant metastasis / nasopharyngeal carcinoma / Pareto optimisation / risk score / RNA sequencing

References

[1] Okafor S, Muzaffar J, Jang D, El Sayed I, Abi Hachem R. Nasopharyngeal carcinoma: case presentation and literature review of treatment innovation with immunotherapy. J Neurol Surg Rep. 2023; 84: e113-e115.

[2] Cao S-M, Yang Q, Guo L, et al. Neoadjuvant chemotherapy followed by concurrent chemoradiotherapy versus concurrent chemoradiotherapy alone in locoregionally advanced nasopharyngeal carcinoma: a phase III multicentre randomised controlled trial. Eur J Cancer. 2017; 75: 14-23.

[3] Dmytriw AA, Ortega C, Anconina R, et al. Nasopharyngeal carcinoma radiomic evaluation with serial PET/CT: exploring features predictive of survival in patients with long-term follow-up. Cancers. 2022; 14: 3105.

[4] Kong L, Zhang Y, Hu C, Guo Y, Lu JJ. Effects of induction docetaxel, platinum, and fluorouracil chemotherapy in patients with stage III or IVA/B nasopharyngeal cancer treated with concurrent chemoradiation therapy: final results of 2 parallel phase 2 clinical trials. Cancer. 2017; 123: 2258-2267.

[5] Mai H-Q, Chen Q-Y, Chen D, et al. Toripalimab plus chemotherapy for recurrent or metastatic nasopharyngeal carcinoma: the JUPITER-02 randomized clinical trial. JAMA. 2023; 330: 1961-1970.

[6] Chen Y-P, Yin J-H, Li W-F, et al. Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. Cell Res. 2020; 30: 1024-1042.

[7] Tang X-R, Li Y-Q, Liang S-B, et al. Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study. Lancet Oncol. 2018; 19: 382-393.

[8] Liu S-L, Sun X-S, Chen Q-Y, et al. Development and validation of a transcriptomics-based gene signature to predict distant metastasis and guide induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma. Eur J Cancer. 2022; 163: 26-34.

[9] Samaga D, Hornung R, Braselmann H, et al. Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study. Radiat Oncol. 2020; 15: 1-14.

[10] FitzGerald C, Hurst S. Implicit bias in healthcare professionals: a systematic review. BMC Med Ethics. 2017; 18: 1-18.

[11] Yu Y, Zhang N, Mai Y, et al. Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method. Genome Biol. 2023; 24: 201.

[12] Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43:e47.

[13] Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods. 2019; 16: 1289-1296.

[14] Da-Ano R, Masson I, Lucia F, et al. Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Sci Rep. 2020; 10:10248.

[15] Zhang Z, Mathew D, Lim TL, et al. Recovery of biological signals lost in single-cell batch integration with CellANOVA. Nat Biotechnol. 2025;43: 1861-1877.

[16] Kashef S, Nezamabadi-Pour H. A label-specific multi-label feature selection algorithm based on the Pareto dominance concept. Pattern Recognit. 2019; 88: 654-667.

[17] Habib M, Aljarah I, Faris H, Mirjalili S. Multi-objective particle swarm optimization: theory, literature review, and application in feature selection for medical diagnosis. Evolutionary Machine Learning Techniques: Algorithms and Applications. Springer; 2020: 175-201.

[18] Breiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat Sci. 2001; 16: 199-231.

[19] Chen S, Yang D, Liao X, et al. Failure patterns of recurrence and metastasis after intensity-modulated radiotherapy in patients with nasopharyngeal carcinoma: results of a multicentric clinical study. Front Oncol. 2022; 11:693199.

[20] Zhang L, MacIsaac KD, Zhou T, et al. Genomic analysis of nasopharyngeal carcinoma reveals TME-based subtypes. Mol Cancer Res. 2017; 15: 1722-1732.

[21] Chiang CL, Chan KSK, Li H, et al. Using the genomic adjusted radiation dose (GARD) to personalize the radiation dose in nasopharyngeal cancer. Radiother Oncol. 2024; 196:110287.

[22] Campbell I. Chi-squared and Fisher–Irwin tests of two-by-two tables with small sample recommendations. Stat Med. 2007; 26: 3661-3675.

[23] Kishore K, Jaswal V. Statistics corner: chi-squared test. J Postgrad Med Educ Res. 2023; 57: 40-44.

[24] Pettaway C, Srigley J, Brookland R, Choyke P, Amin M. AJCC Cancer Staging Manual. 8th ed. Springer-Verlag; 2017.

[25] Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012; 28: 2184-2185.

[26] Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29: 15-21.

[27] Frankish A, Diekhans M, Ferreira AM, et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 2019; 47: D766-D773.

[28] Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015; 31: 166-169.

[29] Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15: 550.

[30] Kamkar I, Gupta SK, Phung D, Venkatesh S. Stable feature selection for clinical prediction: exploiting ICD tree structure using Tree-Lasso. J Biomed Inform. 2015; 53: 277-290.

[31] Bagherzadeh-Khiabani F, Ramezankhani A, Azizi F, Hadaegh F, Steyerberg EW, Khalili D. A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results. J Clin Epidemiol. 2016; 71: 76-85.

[32] Remeseiro B, Bolon-Canedo V. A review of feature selection methods in medical applications. Comput Biol Med. 2019; 112:103375.

[33] Zhao H, Coston A, Adel T, Gordon GJ. Conditional learning of fair representations. arXiv preprint arXiv:191007162. 2019.

[34] Grosan C, Abraham A, Tigan S. Multicriteria programming in medical diagnosis and treatments. Appl Soft Comput. 2008; 8: 1407-1417.

[35] Patra SS, Mittal M, Jena OP. Multiobjective evolutionary algorithm based on decomposition for feature selection in medical diagnosis. Predictive Modeling in Biomedical Data Mining and Analysis. Elsevier; 2022: 253-293.

[36] Liang S, Tian S, Kang X, Zhang D, Wu W, Yu L. Skin lesion classification base on multi-hierarchy contrastive learning with pareto optimality. Biomed Signal Process Control. 2023; 86:105187.

[37] Nyamundanda G, Poudel P, Patil Y, Sadanandam A. A novel statistical method to diagnose, quantify and correct batch effects in genomic studies. Sci Rep. 2017; 7:10849.

[38] Ross JP, van Dijk S, Phang M, Skilton MR, Molloy PL, Oytam Y. Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data. Clin Epigenetics. 2022; 14: 58.

[39] Toğan V, Daloğlu AT. An improved genetic algorithm with initial population strategy and self-adaptive member grouping. Comput Struct. 2008; 86: 1204-1218.

[40] Liu Y, He S, Wang X-L, et al. Tumour heterogeneity and intercellular networks of nasopharyngeal carcinoma at single cell resolution. Nat Commun. 2021; 12: 741.

[41] Gong L, Kwong DL-W, Dai W, et al. Comprehensive single-cell sequencing reveals the stromal dynamics and tumor-specific characteristics in the microenvironment of nasopharyngeal carcinoma. Nat Commun. 2021; 12: 1540.

[42] Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015; 12: 453-457.

[43] Jiménez-Sánchez A, Cast O, Miller ML. Comprehensive benchmarking and integration of tumor microenvironment cell estimation methods. Cancer Res. 2019; 79: 6238-6246.

[44] Wang J, Liang Y, Xue A, et al. Intratumoral CXCL13+ CD160+ CD8+ T cells promote the formation of tertiary lymphoid structures to enhance the efficacy of immunotherapy in advanced gastric cancer. J Immunother Cancer. 2024; 12(9):e009603.

[45] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004; 13: 600-612.

[46] Bar-Joseph Z, Gifford DK, Jaakkola TS. Fast optimal leaf ordering for hierarchical clustering. Bioinformatics. 2001; 17: S22-S29.

[47] Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993; 39: 561-577.

[48] Briggs WM, Zaretzki R. The skill plot: a graphical technique for evaluating continuous diagnostic tests. Biometrics. 2008; 64: 250-256.

[49] Rundo L, Ledda RE, di Noia C, et al. A low-dose CT-based radiomic model to improve characterization and screening recall intervals of indeterminate prevalent pulmonary nodules. Diagnostics. 2021; 11: 1610.

[50] Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010; 33: 1.

[51] Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations Trends Mach Learn. 2011; 3: 1-122.

[52] Fruchterman TM, Reingold EM. Graph drawing by force-directed placement. Softw Practice Experience. 1991; 21: 1129-1164.

[53] Phipson B, Lee S, Majewski IJ, Alexander WS, Smyth GK. Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Ann Appl Stat. 2016; 10: 946.

[54] Ritchie ME, Silver J, Oshlack A, et al. A comparison of background correction methods for two-colour microarrays. Bioinformatics. 2007; 23: 2700-2707.

[55] Kim KY, Le Q-T, Yom SS, et al. Clinical utility of Epstein‒Barr virus DNA testing in the treatment of nasopharyngeal carcinoma patients. Int J Radiat Oncol Biol Phys. 2017; 98: 996-1001.

[56] Liu Y, Ye S-Y, He S, et al. Single-cell and spatial transcriptome analyses reveal tertiary lymphoid structures linked to tumour progression and immunotherapy response in nasopharyngeal carcinoma. Nat Commun. 2024; 15: 7713.

[57] Lee J, Mani A, Shin M-J, Krauss RM. Leveraging altered lipid metabolism in treating B cell malignancies. Prog Lipid Res. 2024;95:101288.

[58] Magi A, Masselli M, Sala C, et al. The ion channels and transporters gene expression profile indicates a shift in excitability and metabolisms during malignant progression of follicular lymphoma. Sci Rep. 2019; 9: 8586.

[59] Tang H, Liu X, Li H, et al. Comprehensive Analysis Reveals RANBP17 as a Potential Biomarker for Prognosis and Immunotherapy in Glioblastoma. 2023.

[60] Fan JJ, Huang X. Ion channels in cancer: orchestrators of electrical signaling and cellular crosstalk. Targets of Cancer Diagnosis and Treatment: Ion Transport in Tumor Biology. 2020: 103-133.

[61] Eil R, Vodnala SK, Clever D, et al. Ionic immune suppression within the tumour microenvironment limits T cell effector function. Nature. 2016; 537: 539-543.

[62] Li X, Guo Y, Xiao M, Zhang W. The immune escape mechanism of nasopharyngeal carcinoma. FASEB J. 2023; 37:e23055.

[63] Netzer C, von Arps-Aubert V, Mačinković I, et al. Association between spatial distribution of leukocyte subsets and clinical presentation of head and neck squamous cell carcinoma. Front Immunol. 2024; 14:1240394.

[64] Wang Y, Sun Q, Ye Y, et al. FGF-2 signaling in nasopharyngeal carcinoma modulates pericyte-macrophage crosstalk and metastasis. JCI Insight. 2022; 7(10):e157874.

[65] Liu X, Hogg GD, Zuo C, et al. Context-dependent activation of STING-interferon signaling by CD11b agonists enhances anti-tumor immunity. Cancer Cell. 2023; 41: 1073-1090.e12.

[66] Zhou M, Zhang P, Zhao Y, Liu R, Zhang Y. Overexpressed circRANBP17 acts as an oncogene to facilitate nasopharyngeal carcinoma via the miR-635/RUNX2 axis. J Cancer. 2021; 12: 4322.

[67] Peng W-S, Zhou X, Yan W-B, et al. Dissecting the heterogeneity of the microenvironment in primary and recurrent nasopharyngeal carcinomas using single-cell RNA sequencing. Oncoimmunology. 2022; 11:2026583.

[68] Sautès-Fridman C, Petitprez F, Calderaro J, Fridman WH. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat Rev Cancer. 2019; 19: 307-325.

[69] Meylan M, Petitprez F, Becht E, et al. Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer. Immunity. 2022; 55: 527-541.e5.

[70] Fridman WH, Meylan M, Petitprez F, Sun C-M, Italiano A, Sautès-Fridman C. B cells and tertiary lymphoid structures as determinants of tumour immune contexture and clinical outcome. Nat Rev Clin Oncol. 2022; 19: 441-457.

[71] Siliņa K, Soltermann A, Attar FM, et al. Germinal centers determine the prognostic relevance of tertiary lymphoid structures and are impaired by corticosteroids in lung squamous cell carcinoma. Cancer Res. 2018; 78: 1308-1320.

[72] Hiraoka N, Ino Y, Yamazaki-Itoh R, Kanai Y, Kosuge T, Shimada K. Intratumoral tertiary lymphoid organ is a favourable prognosticator in patients with pancreatic cancer. Br J Cancer. 2015; 112: 1782-1790.

[73] Peters A, Pitcher LA, Sullivan JM, et al. Th17 cells induce ectopic lymphoid follicles in central nervous system tissue inflammation. Immunity. 2011; 35: 986-996.

[74] Yamaguchi K, Ito M, Ohmura H, et al. Helper T cell-dominant tertiary lymphoid structures are associated with disease relapse of advanced colorectal cancer. Oncoimmunology. 2020; 9:1724763.

[75] Mitsdoerffer M, Lee Y, Jäger A, et al. Proinflammatory T helper type 17 cells are effective B-cell helpers. Proc Natl Acad Sci U S A. 2010; 107: 14292-14297.

[76] Chen T, Chen X, Zhang S, et al. The genome sequence archive family: toward explosive data growth and diverse data types. Genom Proteom Bioinform. 2021; 19: 578-583.

[77] Xue Y, Bao Y, Zhang Z, et al. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022. Nucleic Acids Res. 2022;50:D27-D38.

RIGHTS & PERMISSIONS

2025 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.