Deep transfer learning enables lesion tracing of circulating tumor cells

Xiaoxu Guo, Fanghe Lin, Chuanyou Yi, Juan Song, Di Sun, Li Lin, Zhixing Zhong, Zhaorun Wu, Xiaoyu Wang, Yingkun Zhang, Jin Li, Huimin Zhang (), Feng Liu (), Chaoyong Yang () and Jia Song ()
Additional contact information
Xiaoxu Guo: College of Chemistry and Chemical Engineering, Xiamen University
Fanghe Lin: College of Chemistry and Chemical Engineering, Xiamen University
Chuanyou Yi: Fudan University
Juan Song: College of Chemistry and Chemical Engineering, Xiamen University
Di Sun: Shanghai Jiao Tong University
Li Lin: College of Chemistry and Chemical Engineering, Xiamen University
Zhixing Zhong: College of Chemistry and Chemical Engineering, Xiamen University
Zhaorun Wu: College of Chemistry and Chemical Engineering, Xiamen University
Xiaoyu Wang: College of Chemistry and Chemical Engineering, Xiamen University
Yingkun Zhang: College of Chemistry and Chemical Engineering, Xiamen University
Jin Li: Fudan University
Huimin Zhang: Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM)
Feng Liu: The University of Melbourne, Parkville, Melbourne
Chaoyong Yang: College of Chemistry and Chemical Engineering, Xiamen University
Jia Song: Shanghai Jiao Tong University

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract Liquid biopsy offers great promise for noninvasive cancer diagnostics, while the lack of adequate target characterization and analysis hinders its wide application. Single-cell RNA sequencing (scRNA-seq) is a powerful technology for cell characterization. Integrating scRNA-seq into a CTC-focused liquid biopsy study can perhaps classify CTCs by their original lesions. However, the lack of CTC scRNA-seq data accumulation and prior knowledge hinders further development. Therefore, we design CTC-Tracer, a transfer learning-based algorithm, to correct the distributional shift between primary cancer cells and CTCs to transfer lesion labels from the primary cancer cell atlas to CTCs. The robustness and accuracy of CTC-Tracer are validated by 8 individual standard datasets. We apply CTC-Tracer on a complex dataset consisting of RNA-seq profiles of single CTCs, CTC clusters from a BRCA patient, and two xenografts, and demonstrate that CTC-Tracer has potential in knowledge transfer between different types of RNA-seq data of lesions and CTCs.

Date: 2022
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DOI: 10.1038/s41467-022-35296-0

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