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

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.

• Authors: 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, Xiangfeng Duan
• Journal: Nature communications (Nat Commun) Vol. 13 Issue 1 Pg. 617 (02 01 2022) ISSN: 2041-1723 [Electronic] England
• PMID: 35105875 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: © 2022. The Author(s).

Chemical References

• Antigens, Neoplasm
• Biomarkers, Tumor

Topics

• Antigens, Neoplasm (blood)
• Biomarkers, Tumor (blood)
• China
• Cohort Studies
• Early Detection of Cancer (methods)
• Female
• Humans
• Lasers
• Machine Learning
• Male
• Nanostructures (chemistry)
• Neoplasms (classification, diagnosis)
• Sensitivity and Specificity