Background: Chemotherapy combined with immunotherapy (CT-IO) has become the first-line treatment for de novo metastatic nasopharyngeal carcinoma (dmNPC). Locoregional radiotherapy (LRRT) following chemotherapy has been shown to significantly improve survival outcomes in patients with dmNPC. However, it remains unclear whether LRRT provides additional benefits in the context of CT-IO. Furthermore, there is no consensus on how to identify the optimal patient population for LRRT after first-line CT-IO. Methods: This study included patients with dmNPC who received platinum-based palliative chemotherapy and anti–PD-1 immunotherapy, with or without LRRT. Progression-free survival (PFS) was assessed in LRRT and non-LRRT groups using inverse probability of treatment weighting (IPTW) to mitigate selection bias. Median PFS (mPFS) at the 6-month landmark was estimated using Kaplan-Meier analyses. A novel prognostic nomogram was developed and validated to predict PFS and stratify patients by risk. Using prognostic scores from the nomogram, a model-based tree approach was employed to assess stratified treatment outcomes and identify the ideal candidates for LRRT. Results: A total of 500 patients were included, with 367 receiving LRRT and 133 not receiving it. At the 6-month conditional landmark, IPTW-adjusted Kaplan-Meier curves demonstrated significantly improved survival in the LRRT group compared with the non-LRRT group (mPFS, not reached vs 21.5 months; P<.001). Patients were randomized into training and validation cohorts in a 7:3 ratio. A prognostic model integrating serum lactate dehydrogenase (LDH) level, posttreatment Epstein-Barr virus DNA level, number of metastatic lesions, and liver metastases status was developed from the training cohort and graphically represented as a nomogram. The model demonstrated favorable discrimination (C-index, 0.721; 95% CI, 0.681–0.761) and predictive accuracy (1-year time-dependent area under the curve [tAUC]), 0.788), and its performance was validated in the internal cohort (C-index, 0.752; 95% CI, 0.698–0.806; 1-year tAUC, 0.778). A tree-based risk stratification derived from the model classified patients into 2 prognostic subgroups. Low-risk patients benefited from additional LRRT (mPFS, not reached vs 23.6 months; P<.001), whereas high-risk patients did not (mPFS, 18.3 vs 16.5 months; P=.210). Conclusions: In patients with dmNPC, additional LRRT following first-line CT-IO was associated with improved PFS, particularly among low-risk patients identified using a novel prognostic model.
Submitted July 19, 2024; final revision received October 22, 2024; accepted for publication November 1, 2024. Published online March 13, 2025.
D. Wen, J. Jin, J. Lin, and M. Luo contributed equally and are co-first authors.
S. Guo, Y. Liang, and Q. Chen contributed equally and are co-last authors.
Author contributions: Conceptualization: Wen, Guo, Liang, Chen. Data curation: Jin, Lin, Luo, R. Liu, S. Liu, Xiong. Formal analysis: Wen. Funding acquisition: L. Liu, Tang, Mai, Guo, Liang, Chen. Investigation: Wen, L. Liu, Tang, Mai. Methodology: Wen, Jin, Lin, Luo, R. Liu, S. Liu, Xiong, Guo, Liang, Chen. Project administration: L. Liu, Tang, Mai, Guo, Liang, Chen. Supervision: L. Liu, Tang, Mai, Guo, Liang, Chen. Validation: Lin. Visualization: Wen, Xiong. Writing—original draft: Wen, Guo, Liang, Chen. Writing—review & editing: Wen, Jin, Luo, Guo, Liang, Chen.
Data availability statement: The authenticity of this study has been verified by depositing the primary raw data on the Research Data Deposit public platform (www.researchdata.org.cn).
Disclosures: The authors have disclosed that they have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.
Funding: This work was supported by funding from the Natural Science Foundation of China (No. 82303967, 32200651, 82203776, 82203125, 82222050, 82272739, 82272882, 82173287, 82073003, 82003267, 82002852; Y. Liang, Q. Chen, L. Liu, H. Mai, L. Tang, S. Guo), National Key Research and Development Program of China (2022YFC2505800, 2022YFC2705005; L. Tang, H. Mai), Basic and Applied Basic Research Foundation of Guangdong Province (2021B1515230002), Science and Technology Program of Guangzhou (202201011561, 2023A04J2127), Sun Yat-sen University 5010 program (No. 201315, 2015021, 2017010, 2019023; Q. Chen, H. Mai, L. Tang), Innovative Research Team of High-level Local University in Shanghai (SSMUZLCX20180500), National Postdoctoral Program for Innovative Talents (BX20220361), Science and Technology Planning Project of Guangdong Province (2019B020230002), and Key Youth Teacher Cultivating Program of Sun Yat-sen University (20ykzd24).
Supplementary material: Supplementary material associated with this article is available online at https://doi.org/10.6004/jnccn.2024.7086. The supplementary material has been supplied by the author(s) and appears in its originally submitted form. It has not been edited or vetted by JNCCN. All contents and opinions are solely those of the author. Any comments or questions related to the supplementary materials should be directed to the corresponding author.