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Tracing unknown tumor origins with a biological-pathway-based transformer model.
Xie, Jiajing; Chen, Ying; Luo, Shijie; Yang, Wenxian; Lin, Yuxiang; Wang, Liansheng; Ding, Xin; Tong, Mengsha; Yu, Rongshan.
Afiliación
  • Xie J; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China.
  • Chen Y; School of Informatics, Xiamen University, Xiamen, Fujian 361005, China.
  • Luo S; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China.
  • Yang W; Aginome Scientific, Xiamen, Fujian 361005, China.
  • Lin Y; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China.
  • Wang L; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China; School of Informatics, Xiamen University, Xiamen, Fujian 361005, China.
  • Ding X; Department of Pathology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian 361004, China. Electronic address: xinding2014@gmail.com.
  • Tong M; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China. Electronic address: mstong@xm
  • Yu R; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China; School of Informatics, Xiamen University, Xiamen, Fujian 361005, China; Aginome Scientific, Xiamen, Fujian 361005, China. Electronic address: rsyu@xmu.edu.cn.
Cell Rep Methods ; 4(6): 100797, 2024 Jun 17.
Article en En | MEDLINE | ID: mdl-38889685
ABSTRACT
Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods. Additionally, BPformer was validated in a retrospective study, demonstrating consistency with tumor sites diagnosed through immunohistochemistry and histopathology. Furthermore, BPformer was able to rank pathways based on their contribution to tumor origin identification, which helped to classify oncogenic signaling pathways into those that are highly conservative among different cancers versus those that are highly variable depending on their origins.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias Primarias Desconocidas Límite: Humans Idioma: En Revista: Cell Rep Methods Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias Primarias Desconocidas Límite: Humans Idioma: En Revista: Cell Rep Methods Año: 2024 Tipo del documento: Article País de afiliación: China