1023 - Treatment Response-Adapted Risk Index Model for Survival Prediction and Adjuvant Chemotherapy Selection in Nonmetastatic Nasopharyngeal Carcinoma

Y. Liu; National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing/China, China

Purpose/Objective(s): Dynamic response to therapy is strongly associated with outcomes for various malignancies. This study aims to develop and validate the model response-adapted individualized risk index (RAIRI) as an individual prognostic approach and predictive biomarker for adjuvant chemotherapy (AC) benefit in nonmetastatic nasopharyngeal carcinoma (NPC) based on pretreatment clinical characteristics, longitudinal plasma cell-free Epstein–Barr virus DNA (cfEBV-DNA), and tumor regression measurements collected during treatment. Materials/

Methods: RAIRI was tested in 2,148 patients across training, internal validation, external validation, and randomized controlled trial (RCT) cohorts from three academic cancer centers. Bayesian joint model was employed for integrative prediction. Prognostic accuracy was evaluated using calibration, concordance indices (C-indices), and areas under the curve (AUCs). RAIRI’s performances of predicting AC benefit were examined in patients from two RCTs designed to assess AC’s benefit in high-risk stage III/IVA NPC.
Results: RAIRI incorporates six pretreatment characteristics (age, T-stage, N-stage, cfEBV-DNA, lactate dehydrogenase, and central-nodal-necrosis), longitudinal cfEBV-DNA, and tumor regression measurements. In the training cohort, RAIRI demonstrated accurate calibration and high prognostic accuracy (5-year: C-index 0.849, AUC 0.895), significantly superior to the conventional models. The internal validation, external validation, and RCT cohorts confirmed RAIR’s prognostic accuracy with 5-year C-indices and AUCs all over 0.8. In the RCT cohort, RAIRI identified approximately 70% of low-risk patients that did not benefit from AC; however, the high-risk patients experiencing substantial benefits from AC versus observation (5-year PFS, 58.7% vs. 22.8%; HR=0.40; 95% CI, 0.22–0.73). Predictive performance of RAIRI for AC benefit was consistent across various clinical subsets and beyond post-radiotherapy cfEBV-DNA.
Conclusion: By integrating longitudinal treatment response measurements, we constructed and extensively validated RAIRI, providing real-time updated quantitative risk estimates for individuals, significantly superior to conventional risk models. Beyond the prognostic model, RAIRI could be a predictive biomarker facilitating personalized AC selection.