Computational biology and chemistry · 2026
Construction of efficacy prediction model for T-DXd treatment of HER2-positive and HER2-low expression breast cancer based on hematological indicators: A real-world study.
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Su Yanfang, Sang Die, Zhang Guoyong, Liu Haiyan, Zhang Yurong, Fan Shanmin, Zhang Jintao, Li Shiyu, Zhang Yanyan, Feng Huachao, Zhao Meiqing, Zhao Longmei, Yuan Peng, He Binsheng, Li Man
Аннотация
BACKGROUND: This study explores hematological changes during trastuzumab deruxtecan (T-DXd) treatment in HER2-positive and HER2-low breast cancer patients, aiming to develop an efficacy prediction model for personalized clinical decisions.
MATERIALS: A retrospective analysis of 114 patients treated with T-DXd at Beijing Chaoyang District Sanhuan Cancer Hospital (March 2023-December 2024) was conducted. Data included demographics, treatment history, and laboratory results. Efficacy was assessed via RECIST 1.1, with analyses using the Wilcoxon test and Kaplan-Meier method.
RESULTS: HER2-positive patients had a progression-free survival (PFS) of 12.2 months vs. 10.2 months for HER2-low. Common adverse events included Nausea, primarily grade 1 or 2. T-DXd caused significant reductions in red blood cell count, hemoglobin, hematocrit, creatine kinase, and homocysteine (p < 0.05), along with increased red cell distribution width. Responders (CR+PR+SD) and progressors (PD) differed in platelets, plateletcrit, and homocysteine (p < 0.05). Stratification by estrogen receptor (ER) and progesterone receptor (PR) expression and Ki67 revealed distinct PFS outcomes. We aimed to predict the efficacy of T-DXd using pre-treatment hematological parameters and various machine learning models, including Random Forest, SVM, XGBoost, and LightGBM. The performance of the optimal models, as evaluated by leave-one-out cross-validation, yielded AUC values of 0.748 (XGBoost), 0.881 (SVM), and 0.830 (SVM) for models based on Complete Blood Count (CBC) parameters, biochemical markers, and their combination, respectively.
CONCLUSION: Hematological toxicity is a recognized concern with T-DXd therapy. Our predictive model demonstrates strong performance in identifying patients suitable for treatment, highlighting that rigorous monitoring of hematological indicators is critical for mitigating risk and upholding safety standards in clinical practice.
MATERIALS: A retrospective analysis of 114 patients treated with T-DXd at Beijing Chaoyang District Sanhuan Cancer Hospital (March 2023-December 2024) was conducted. Data included demographics, treatment history, and laboratory results. Efficacy was assessed via RECIST 1.1, with analyses using the Wilcoxon test and Kaplan-Meier method.
RESULTS: HER2-positive patients had a progression-free survival (PFS) of 12.2 months vs. 10.2 months for HER2-low. Common adverse events included Nausea, primarily grade 1 or 2. T-DXd caused significant reductions in red blood cell count, hemoglobin, hematocrit, creatine kinase, and homocysteine (p < 0.05), along with increased red cell distribution width. Responders (CR+PR+SD) and progressors (PD) differed in platelets, plateletcrit, and homocysteine (p < 0.05). Stratification by estrogen receptor (ER) and progesterone receptor (PR) expression and Ki67 revealed distinct PFS outcomes. We aimed to predict the efficacy of T-DXd using pre-treatment hematological parameters and various machine learning models, including Random Forest, SVM, XGBoost, and LightGBM. The performance of the optimal models, as evaluated by leave-one-out cross-validation, yielded AUC values of 0.748 (XGBoost), 0.881 (SVM), and 0.830 (SVM) for models based on Complete Blood Count (CBC) parameters, biochemical markers, and their combination, respectively.
CONCLUSION: Hematological toxicity is a recognized concern with T-DXd therapy. Our predictive model demonstrates strong performance in identifying patients suitable for treatment, highlighting that rigorous monitoring of hematological indicators is critical for mitigating risk and upholding safety standards in clinical practice.