Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026
Development of a Classifier for Metabolic Subtypes of Nasopharyngeal Carcinoma to Guide Personalized Immunotherapy Strategies: Biomarker Analysis of the Phase III CONTINUUM and DIPPER Trials.
СтатьяКлиническое исследование III фазыНазвание и аннотация: Перевод пока не подготовлен
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Huang Sai-Wei, Liang Ye-Lin, Tao Ya-Lan, Li Wen-Fei, Zhou Guan-Qun, Mao Yan-Ping, Guo Rui, Chen Lei, Xu Sha, Liu Xu, Zhang Ning, Liu Fang, Shen Liang-Fang, Zhou Yang-Ying, Yuan Ya-Wei, Zou Guo-Rong, Jin Feng, Tang Ling-Long, Sun Ying, Li Ying-Qin, Ma Jun, Liu Na
Аннотация
PURPOSE: Personalized immunotherapy strategies are urgently needed for patients with locoregionally advanced nasopharyngeal carcinoma (NPC). We aim to identify biomarkers predictive of immunotherapy benefits, using data from the phase III CONTINUUM (ClinicalTrials.gov identifier: NCT03700476) and DIPPER (ClinicalTrials.gov identifier: NCT03427827) randomized clinical trials.
PATIENTS AND METHODS: Tumor samples from 407 patients in the CONTINUUM (discovery cohort) and DIPPER (validation cohort) trials were subjected to RNA sequencing. In the discovery cohort, metabolic gene-based consensus clustering was performed to determine subtypes. A machine learning-based classifier was subsequently developed in the discovery cohort and then applied to the validation cohort to assign metabolic subtypes. Gene set enrichment analyses were used to characterize the biological features of each metabolic subtype. The clinical end point was event-free survival (EFS).
RESULTS: In the discovery cohort, three metabolic subtypes were identified with distinct tumor-intrinsic and immune features as well as differential EFS benefits from adding anti-PD-1 to chemoradiotherapy (CRT). Specifically, the MS1 subtype exhibited a significant improvement in 3-year EFS in the anti-PD-1 plus CRT arm compared with the CRT-alone arm (3-year EFS, 90.2% v 69.6%; hazard ratio, 0.27 [95% CI, 0.11 to 0.67]), whereas MS2 (3-year EFS, 94.1% v 93.8%) and MS3 subtypes (3-year EFS, 75.0% v 75.0%) derived no significant survival benefit. The subtype features were preserved in the validation cohort, with consistent prognostic and predictive value. A pooled analysis of both cohorts demonstrated the significant interaction between metabolic subtypes and the treatment effect (Pinteraction = 0.0074).
CONCLUSION: In this biomarker study, we defined metabolic subtypes of NPC that predicted the EFS benefit from immunotherapy. This novel molecular classification provides a promising predictive biomarker for personalized treatment decision for patients with locoregionally advanced NPC.
PATIENTS AND METHODS: Tumor samples from 407 patients in the CONTINUUM (discovery cohort) and DIPPER (validation cohort) trials were subjected to RNA sequencing. In the discovery cohort, metabolic gene-based consensus clustering was performed to determine subtypes. A machine learning-based classifier was subsequently developed in the discovery cohort and then applied to the validation cohort to assign metabolic subtypes. Gene set enrichment analyses were used to characterize the biological features of each metabolic subtype. The clinical end point was event-free survival (EFS).
RESULTS: In the discovery cohort, three metabolic subtypes were identified with distinct tumor-intrinsic and immune features as well as differential EFS benefits from adding anti-PD-1 to chemoradiotherapy (CRT). Specifically, the MS1 subtype exhibited a significant improvement in 3-year EFS in the anti-PD-1 plus CRT arm compared with the CRT-alone arm (3-year EFS, 90.2% v 69.6%; hazard ratio, 0.27 [95% CI, 0.11 to 0.67]), whereas MS2 (3-year EFS, 94.1% v 93.8%) and MS3 subtypes (3-year EFS, 75.0% v 75.0%) derived no significant survival benefit. The subtype features were preserved in the validation cohort, with consistent prognostic and predictive value. A pooled analysis of both cohorts demonstrated the significant interaction between metabolic subtypes and the treatment effect (Pinteraction = 0.0074).
CONCLUSION: In this biomarker study, we defined metabolic subtypes of NPC that predicted the EFS benefit from immunotherapy. This novel molecular classification provides a promising predictive biomarker for personalized treatment decision for patients with locoregionally advanced NPC.