What question did this study set out to answer?

Evaluate the efficacy of pucotenlimab across various solid tumors through integrated analytical frameworks.

April 29, 2026Cancer Medicine0 citationsOpen Access

Integration Analysis of Bayesian and Machine Learning for Heterogeneity, Biomarkers, and Optimal Combination Regimens of Pucotenlimab in Solid Tumors

YHYingge HeArmy Medical University CGChangqing GaoArmy Medical University SZShiyan ZhangChangchun University of Science and Technology

Puntos clave

Evaluate the efficacy of pucotenlimab across various solid tumors through integrated analytical frameworks.
Integrated data from 6 clinical trials across multiple tumor types.
Applied Bayesian meta-analysis and machine learning techniques to analyze efficacy and safety.
Utilized network meta-analysis to recommend treatment regimens and identify biomarkers.
Pucotenlimab significantly improved overall response rate (ORR) and progression-free survival (PFS) compared to control.
Identified combination therapies showed superior efficacy over monotherapy in specific cancer types.
Machine learning pinpointed critical biomarkers linked to treatment outcomes and risk factors for immune-related adverse events (irAEs).

Resumen

The efficacy of PD-1 inhibitor pucotenlimab (HX008) in solid tumors exhibits heterogeneity. This study integrated data from 6 clinical trials (covering gastric/gastroesophageal junction cancer, triple-negative breast cancer, melanoma, and dMMR/MSI-H solid tumors) using Bayesian meta-analysis, machine learning (optimal XGBoost AUC = 0.86), and network meta-analysis to construct an integrated "efficacy-prediction-safety" framework. Bayesian analysis showed pucotenlimab significantly improved outcomes versus control (ORR OR = 4.82, 95% CrI: 3.65-6.38; PFS HR = 0.41, 0.32-0.52; OS HR = 0.37, 0.26-0.51). Subgroups revealed TNBC patients with gemcitabine/cisplatin achieved highest ORR (80.6%, 62.5%-92.6%), while mucosal melanoma showed lowest response (8.7%, 1.1%-28.0%). Combination therapy demonstrated superior efficacy to monotherapy (ORR OR: 5.91 vs. 2.35). Machine learning identified 4 efficacy biomarkers (KMT2D mutation, post-treatment NLR decrease, PD-L1 CPS ≥ 1, high eotaxin) and 3 irAE risk factors (baseline NLR ≥ 4, irinotecan combination, high VEGF). Network analysis recommended regimens: gemcitabine/cisplatin for TNBC (SUCRA = 95.7%), oxaliplatin/capecitabine for G/GEJ cancer (ORR = 60.0% vs. irinotecan 27.6%, HR = 0.45). The integrated model classified high-benefit (≥ 3 points; ORR 78.2%) and low-benefit (≤ 0 points; ORR 28.3%) groups, plus high-risk (≤ -2 points; grade ≥ 3 irAEs 41.2%) and low-risk (≥ 1 point; irAEs 3.5%) groups, validated by decision curve analysis. This defines precise application scenarios and provides an extensible analytical paradigm.

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo