{Reference Type}: Journal Article
{Title}: Tracing unknown tumor origins with a biological-pathway-based transformer model.
{Author}: Xie J;Chen Y;Luo S;Yang W;Lin Y;Wang L;Ding X;Tong M;Yu R;
{Journal}: Cell Rep Methods
{Volume}: 4
{Issue}: 6
{Year}: 2024 Jun 17
暂无{DOI}: 10.1016/j.crmeth.2024.100797
{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.