Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2025
Development and Validation of a Cell-Free DNA Fragmentomics-Based Model for Early Detection of Pancreatic Cancer.
СтатьяВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Yin Lingdi, Cao Cheng, Lin Jianzhen, Wang Zheng, Peng Yunpeng, Zhang Kai, Xu Cheng, Yang Ruowei, Zhu Dongqin, Wang Fufeng, Chang Shuang, Bao Hua, Yang Shanshan, Li Ningyou, Wu Xue, Shao Yang, Wu Zheng, Wu Shuai, Pu Ning, Xu Zhihang, Guo Feng, Feng Xu, Chen Jianmin, Xiao Bin, Tu Min, Li Qiang, Wei Jishu, Wu Junli, Gao Wentao, Miao Yi, Liu Liang, Lu Zipeng, Jiang Kuirong
Аннотация
PURPOSE: Pancreatic ductal adenocarcinoma (PDAC), known for its high fatality rate, is often diagnosed in its advanced stages where surgical options are not viable. This highlights the critical need for innovative and effective early detection techniques. This study focuses on the potential of cell-free DNA (cfDNA) fragmentomics integrating advanced machine learning to identify early-stage PDAC with high accuracy.
METHODS: Our study included a broad cohort of 1,167 participants, from which plasma was collected and subjected to shallow whole-genome sequencing. After rigorous quality assessments, 166 individuals diagnosed with PDAC and 167 healthy participants were in the training cohort, whereas the validation cohort consisted of 112 patients with PDAC and 111 healthy individuals. A separate group of 67 individuals with nonmalignant pancreatic cysts was also included to validate the model's accuracy. Finally, two additional external validation cohorts and one additional independent early-stage data set were included to evaluate the robustness of model. Our analysis used fragmentomic profiling, integrating copy-number variations, fragment size, mutational signatures, and methylation patterns analyzed using machine learning.
RESULTS: The model demonstrated remarkable accuracy in distinguishing patients with PDAC from controls, with an AUC of 0.992 in the training data set and 0.987 in the validation data set. At a cutoff of 0.52, the training set reached a sensitivity of 93.4% and a specificity of 95.2%. In the validation data set, the sensitivity was 97.3% with a specificity of 92.8%, while the external data set demonstrated a sensitivity of 90.91% and a specificity of 94.5%.
CONCLUSION: This study underscores the effectiveness of using cfDNA fragmentomics and machine learning for early detection of PDAC. Our approach promises significant potential in reducing PDAC mortalities through early intervention and could serve as a breakthrough in oncologic diagnostics.
METHODS: Our study included a broad cohort of 1,167 participants, from which plasma was collected and subjected to shallow whole-genome sequencing. After rigorous quality assessments, 166 individuals diagnosed with PDAC and 167 healthy participants were in the training cohort, whereas the validation cohort consisted of 112 patients with PDAC and 111 healthy individuals. A separate group of 67 individuals with nonmalignant pancreatic cysts was also included to validate the model's accuracy. Finally, two additional external validation cohorts and one additional independent early-stage data set were included to evaluate the robustness of model. Our analysis used fragmentomic profiling, integrating copy-number variations, fragment size, mutational signatures, and methylation patterns analyzed using machine learning.
RESULTS: The model demonstrated remarkable accuracy in distinguishing patients with PDAC from controls, with an AUC of 0.992 in the training data set and 0.987 in the validation data set. At a cutoff of 0.52, the training set reached a sensitivity of 93.4% and a specificity of 95.2%. In the validation data set, the sensitivity was 97.3% with a specificity of 92.8%, while the external data set demonstrated a sensitivity of 90.91% and a specificity of 94.5%.
CONCLUSION: This study underscores the effectiveness of using cfDNA fragmentomics and machine learning for early detection of PDAC. Our approach promises significant potential in reducing PDAC mortalities through early intervention and could serve as a breakthrough in oncologic diagnostics.