Early Detection of Multiple Cancer Types Using Multidimensional Cell-Free DNA Fragmentomics.

Hua Bao, Shanshan Yang，Xiaonan Wang,Yang Shao

Nature medicine（2025）

Geneseeq Research Institute

Cited 0|Views4

Abstract

The multicancer early detection (MCED) test has the potential to enhance current cancer-screening methods. We evaluated a new MCED test that analyzes plasma cell-free DNA using genetic- and fragmentomics-based features from whole-genome sequencing. The present study included an internal validation cohort of 3,021 patients with cancer and 3,370 noncancer controls, and an independent cohort of 677 patients with cancer and 687 noncancer individuals. The results demonstrated an overall sensitivity of 87.4%, specificity of 97.8% and tissue-of-origin prediction accuracy of 82.4% in the independent validation cohort. Preliminary results from a prospective study of 3,724 asymptomatic participants showed a sensitivity of 53.5% (predominantly early stage cancers) and specificity of 98.1%. These findings indicate that the MCED test has strong potential to improve early cancer detection and support clinical decision-making.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
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