We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Deep transfer learning enables lesion tracing of circulating tumor cells.
- Authors
Guo, Xiaoxu; Lin, Fanghe; Yi, Chuanyou; Song, Juan; Sun, Di; Lin, Li; Zhong, Zhixing; Wu, Zhaorun; Wang, Xiaoyu; Zhang, Yingkun; Li, Jin; Zhang, Huimin; Liu, Feng; Yang, Chaoyong; Song, Jia
- 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. Liquid biopsy offers great promise for noninvasive cancer diagnostics, while the lack of adequate target characterization and analysis hinders its wide application. Here, the authors design a transfer learning-based algorithm to transfer lesion labels from the primary cancer cell atlas to circulating tumor cells.
- Subjects
DEEP learning; RNA sequencing; CANCER cells; KNOWLEDGE transfer; BRCA genes
- Publication
Nature Communications, 2022, Vol 13, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-022-35296-0