An ultra-sensitive assay using cell-free DNA fragmentomics for multi-cancer early detection

被引：43

作者：

Bao, Hua ^{[1
]}

Wang, Zheng ^{[2
,3
,4
]}

Ma, Xiaoji ^{[5
,6
]}

Guo, Wei ^{[7
,8
]}

Zhang, Xiangyu ^{[2
,3
,4
]}

Tang, Wanxiangfu ^{[1
]}

Chen, Xin ^{[1
]}

Wang, Xinyu ^{[2
,3
,4
]}

Chen, Yikuan ^{[5
,6
]}

Mo, Shaobo ^{[5
,6
]}

Liang, Naixin ^{[9
]}

Ma, Qianli ^{[10
]}

Wu, Shuyu ^{[1
]}

Xu, Xiuxiu ^{[1
]}

Chang, Shuang ^{[1
]}

Wei, Yulin ^{[1
]}

Zhang, Xian ^{[1
]}

Bao, Hairong ^{[1
]}

Liu, Rui ^{[1
]}

Yang, Shanshan ^{[1
]}

Jiang, Ya ^{[1
]}

Wu, Xue ^{[1
]}

Li, Yaqi ^{[5
,6
]}

Zhang, Long ^{[5
,6
,11
]}

Tan, Fengwei ^{[7
,8
]}

Xue, Qi ^{[7
,8
]}

Liu, Fangqi ^{[5
,6
]}

Cai, Sanjun ^{[5
,6
,11
]}

Gao, Shugeng ^{[7
,8
]}

Peng, Junjie ^{[5
,6
]}

Zhou, Jian ^{[2
,3
,4
,12
,13
]}

Shao, Yang ^{[1
,14
]}

机构：

[1] Nanjing Geneseeq Technol Inc, Geneseeq Res Inst, Nanjing 210000, Jiangsu, Peoples R China

[2] Fudan Univ, Zhongshan Hosp, Liver Canc Inst, Dept Liver Surg & Transplantat, Shanghai 200032, Peoples R China

[3] Fudan Univ, Minist Educ, Key Lab Carcinogenesis & Canc Invas, Shanghai 200032, Peoples R China

[4] Fudan Univ, Zhongshan Hosp, Shanghai Key Lab Organ Transplantat, 130 Fenglin Rd, Shanghai 200032, Peoples R China

[5] Fudan Univ, Dept Colorectal Surg, Shanghai Canc Ctr, Shanghai 200032, Peoples R China

[6] Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai 200032, Peoples R China

[7] Chinese Acad Med Sci & Peking Union Med Coll, Dept Thorac Surg, Natl Canc Ctr, Natl Clin Res Ctr Canc,Canc Hosp, Beijing 100730, Peoples R China

[8] Chinese Acad Med Sci, Key Lab Minimally Invas Therapy Res Lung Canc, Beijing 100730, Peoples R China

[9] Chinese Acad Med Sci, Peking Union Med Coll Hosp, Dept Thorac Surg, Beijing 100730, Peoples R China

[10] China Japan Friendship Hosp, Dept Thorac Surg, Beijing 100029, Peoples R China

[11] Fudan Univ, Shanghai Canc Ctr, Dept Canc Inst, Shanghai 200032, Peoples R China

[12] Fudan Univ, Inst Biomed Sci, Shanghai 200032, Peoples R China

[13] Fudan Univ, State Key Lab Genet Engn, Shanghai 200032, Peoples R China

[14] Nanjing Med Univ, Sch Publ Hlth, Nanjing 210029, Jiangsu, Peoples R China

来源：

MOLECULAR CANCER | 2022年 / 21卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Multi-cancer early detection; Cell-free DNA; Fragmentomics; Machine learning;

D O I：

10.1186/s12943-022-01594-w

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Early detection can benefit cancer patients with more effective treatments and better prognosis, but existing early screening tests are limited, especially for multi-cancer detection. This study investigated the most prevalent and lethal cancer types, including primary liver cancer (PLC), colorectal adenocarcinoma (CRC), and lung adenocarcinoma (LUAD). Leveraging the emerging cell-free DNA (cfDNA) fragmentomics, we developed a robust machine learning model for multi-cancer early detection. 1,214 participants, including 381 PLC, 298 CRC, 292 LUAD patients, and 243 healthy volunteers, were enrolled. The majority of patients (N = 971) were at early stages (stage 0, N = 34; stage I, N = 799). The participants were randomly divided into a training cohort and a test cohort in a 1:1 ratio while maintaining the ratio for the major histology subtypes. An ensemble stacked machine learning approach was developed using multiple plasma cfDNA fragmentomic features. The model was trained solely in the training cohort and then evaluated in the test cohort. Our model showed an Area Under the Curve (AUC) of 0.983 for differentiating cancer patients from healthy individuals. At 95.0% specificity, the sensitivity of detecting all cancer reached 95.5%, while 100%, 94.6%, and 90.4% for PLC, CRC, and LUAD, individually. The cancer origin model demonstrated an overall 93.1% accuracy for predicting cancer origin in the test cohort (97.4%, 94.3%, and 85.6% for PLC, CRC, and LUAD, respectively). Our model sensitivity is consistently high for early-stage and small-size tumors. Furthermore, its detection and origin classification power remained superior when reducing sequencing depth to 1x (cancer detection: >= 91.5% sensitivity at 95.0% specificity; cancer origin: >= 91.6% accuracy). In conclusion, we have incorporated plasma cfDNA fragmentomics into the ensemble stacked model and established an ultrasensitive assay for multi-cancer early detection, shedding light on developing cancer early screening in clinical practice.

引用

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