利用基于生物通路的转化器模型追踪未知肿瘤起源

IF 4.3 Q1 BIOCHEMICAL RESEARCH METHODS
Jiajing Xie, Ying Chen, Shijie Luo, Wenxian Yang, Yuxiang Lin, Liansheng Wang, Xin Ding, Mengsha Tong, Rongshan Yu
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引用次数: 0

摘要

原发灶不明癌症(CUP)是指尽管采用了标准诊断程序,但仍无法确定原发灶的转移性癌症。为了确定这种情况下的肿瘤来源,我们开发了一种深度学习方法 BPformer,它将变压器模型与生物通路的先验知识整合在一起。在来自 32 种癌症类型的 10,410 个原发肿瘤的转录组上进行训练后,BPformer 在原发肿瘤以及转移性肿瘤的原发和转移部位的准确率分别达到了 94%、92% 和 89%,超过了现有方法。此外,BPformer 还在一项回顾性研究中得到验证,证明与免疫组化和组织病理学诊断的肿瘤部位一致。此外,BPformer 还能根据对肿瘤来源识别的贡献对通路进行排序,这有助于将致癌信号通路分为在不同癌症中高度保守的通路和因肿瘤来源而高度多变的通路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tracing unknown tumor origins with a biological-pathway-based transformer model.

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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来源期刊
Cell Reports Methods
Cell Reports Methods Chemistry (General), Biochemistry, Genetics and Molecular Biology (General), Immunology and Microbiology (General)
CiteScore
3.80
自引率
0.00%
发文量
0
审稿时长
111 days
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