Multiplexed nanomaterial-assisted laser desorption/ionization for pan-cancer diagnosis and classification

Hua Zhang, Lin Zhao, Jingjing Jiang, Jie Zheng, Li Yang, Yanyan Li, Jian Zhou, Tianshu Liu, Jianmin Xu, Wenhui Lou, Weige Yang, Lijie Tan, Weiren Liu, Yiyi Yu, Meiling Ji, Yaolin Xu, Yan Lu, Xiaomu Li, Zhen Liu, Rong Tian, Cheng Hu, Shumang Zhang, Qinsheng Hu, Yangdong Deng, Hao Ying, Sheng Zhong, Xingdong Zhang, Yunbing Wang (), Hua Wang (), Jingwei Bai (), Xiaoying Li () and Xiangfeng Duan
Additional contact information
Hua Zhang: National Engineering Research Center for Biomaterials, Sichuan University
Lin Zhao: Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University
Jingjing Jiang: Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University
Jie Zheng: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
Li Yang: National Engineering Research Center for Biomaterials, Sichuan University
Yanyan Li: National Engineering Research Center for Biomaterials, Sichuan University
Jian Zhou: Liver Cancer Institute, Zhongshan Hospital, Fudan University
Tianshu Liu: Zhongshan Hospital, Fudan University
Jianmin Xu: Zhongshan Hospital, Fudan University
Wenhui Lou: Zhongshan Hospital, Fudan University
Weige Yang: Zhongshan Hospital, Fudan University
Lijie Tan: Zhongshan Hospital, Fudan University
Weiren Liu: Liver Cancer Institute, Zhongshan Hospital, Fudan University
Yiyi Yu: Zhongshan Hospital, Fudan University
Meiling Ji: Zhongshan Hospital, Fudan University
Yaolin Xu: Zhongshan Hospital, Fudan University
Yan Lu: Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University
Xiaomu Li: Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University
Zhen Liu: School of Pharmaceutical Sciences, Tsinghua University
Rong Tian: School of Pharmaceutical Sciences, Tsinghua University
Cheng Hu: National Engineering Research Center for Biomaterials, Sichuan University
Shumang Zhang: National Engineering Research Center for Biomaterials, Sichuan University
Qinsheng Hu: National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University
Yangdong Deng: School of Software, Tsinghua University
Hao Ying: CAS Key Laboratory of Nutrition, Metabolism and Food safety, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences
Sheng Zhong: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
Xingdong Zhang: National Engineering Research Center for Biomaterials, Sichuan University
Yunbing Wang: National Engineering Research Center for Biomaterials, Sichuan University
Hua Wang: the First Affiliated Hospital, Institute for Liver Diseases of Anhui Medical University
Jingwei Bai: School of Pharmaceutical Sciences, Tsinghua University
Xiaoying Li: Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University
Xiangfeng Duan: University of California

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract As cancer is increasingly considered a metabolic disorder, it is postulated that serum metabolite profiling can be a viable approach for detecting the presence of cancer. By multiplexing mass spectrometry fingerprints from two independent nanostructured matrixes through machine learning for highly sensitive detection and high throughput analysis, we report a laser desorption/ionization (LDI) mass spectrometry-based liquid biopsy for pan-cancer screening and classification. The Multiplexed Nanomaterial-Assisted LDI for Cancer Identification (MNALCI) is applied in 1,183 individuals that include 233 healthy controls and 950 patients with liver, lung, pancreatic, colorectal, gastric, thyroid cancers from two independent cohorts. MNALCI demonstrates 93% sensitivity at 91% specificity for distinguishing cancers from healthy controls in the internal validation cohort, and 84% sensitivity at 84% specificity in the external validation cohort, with up to eight metabolite biomarkers identified. In addition, across those six different cancers, the overall accuracy for identifying the tumor tissue of origin is 92% in the internal validation cohort and 85% in the external validation cohort. The excellent accuracy and minimum sample consumption make the high throughput assay a promising solution for non-invasive cancer diagnosis.

Date: 2022
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DOI: 10.1038/s41467-021-26642-9

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