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.