Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
Epigenetic Instability-Based Metrics in Cell-Free DNA for Early Cancer Detection.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Thursby Sara-Jayne, Jin Zhicheng, Blum Jacob, Gurau Andrei, Noë Michaël, Scharpf Robert B, Velculescu Victor E, Cope Leslie, Brock Malcolm, Baylin Stephen, Pisanic Thomas, Easwaran Hariharan
Аннотация
PURPOSE: Cancers present significant DNA methylation changes, which arise in a stochastic manner, marked by extensive epigenetic variation, indicative of high epigenetic instability. We aimed to evaluate the utility of epigenetic instability for cell-free DNA (cfDNA)-based cancer detection.
EXPERIMENTAL DESIGN: Through analysis of cancer DNA methylation datasets (n = 2,084), we identified a set of 269 CpG island regions that robustly captures this instability in a cancer-specific manner. We developed metrics to measure this epigenetic instability, termed the epigenetic instability index (EII), for cancer screening via cfDNA methylation.
RESULTS: Machine learning classifiers using the EII of these 269 regions efficiently identified breast and lung cancers from cfDNA, differentiating even stage IA lung adenocarcinoma with ∼81% sensitivity and early-stage breast cancer with ∼68% sensitivity, both at 95% specificity.
CONCLUSIONS: Our studies demonstrate that quantifying epigenetic instability is a novel, capable approach to distinguishing cancer from normal cases using cfDNA, performing better than standard approaches using absolute methylation changes. The epigenetic instability-based approaches for cancer detection developed here, along with their validation in independent datasets, support further development and the potential for future clinical application of these strategies in cancer screening.
EXPERIMENTAL DESIGN: Through analysis of cancer DNA methylation datasets (n = 2,084), we identified a set of 269 CpG island regions that robustly captures this instability in a cancer-specific manner. We developed metrics to measure this epigenetic instability, termed the epigenetic instability index (EII), for cancer screening via cfDNA methylation.
RESULTS: Machine learning classifiers using the EII of these 269 regions efficiently identified breast and lung cancers from cfDNA, differentiating even stage IA lung adenocarcinoma with ∼81% sensitivity and early-stage breast cancer with ∼68% sensitivity, both at 95% specificity.
CONCLUSIONS: Our studies demonstrate that quantifying epigenetic instability is a novel, capable approach to distinguishing cancer from normal cases using cfDNA, performing better than standard approaches using absolute methylation changes. The epigenetic instability-based approaches for cancer detection developed here, along with their validation in independent datasets, support further development and the potential for future clinical application of these strategies in cancer screening.