未知原发癌(CUP)代表转移性癌症,尽管有标准的诊断程序,原发部位仍未被识别。为了确定这种情况下的肿瘤起源,我们开发了BPformer,一种深度学习方法,将变压器模型与生物路径的先验知识相结合。对来自32种癌症类型的10,410种原发性肿瘤的转录组进行了培训,BPformer取得了94%的显著准确率,92%,89%在原发肿瘤和转移性肿瘤的原发和转移部位,分别,超越现有方法。此外,BPformer在一项回顾性研究中得到了验证,与通过免疫组织化学和组织病理学诊断的肿瘤部位一致。此外,BPformer能够根据它们对肿瘤起源鉴定的贡献对通路进行排序,这有助于将致癌信号传导途径分类为在不同癌症中高度保守的那些,而不是根据其起源高度可变的那些。
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.