Tracing unknown tumor origins with a biological-pathway-based transformer model
Open accessAbstract
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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PathTIGR consistently outperformed seven state-of-the-art deep learning frameworks (BPformer, DeepCC, HyperTMO, IRnet, MOGAT, MOGDx, and MOGONET) across all evaluated metrics on multiple immunotherapy datasets.
“Despite their sophisticated computational frameworks, independent evaluation across multiple immunotherapy datasets revealed consistent underperformance relative to PathTIGR across all evaluated metrics (Fig. 5, A and B, and fig. S3).”
From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [43]
The Results (Performance evaluation of BPformer) compare BPformer with five methods for predicting tumor origins, not PathTIGR with seven frameworks on immunotherapy datasets. Searches of our copy for PathTIGR and immunotherapy comparisons found no passage addressing the claimed result. (LLM, conf=0.76)
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“Given the rapid advancement of deep learning methodologies in biomedical applications, we conducted rigorous comparison with seven state- of- the-art deep learning frameworks: BPformer ( 43 ), DeepCC ( 44 ), HyperTMO ( 45 ), IRnet ( 26 ), MOGAT ( 46 ), MOGDx ( 47 ), and MOGONET ( 48 ).”
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Self-citation
- Authors
- Xie, J., Chen, Y., Luo, S., Yang, W., Lin, Y., Wang, L., Ding, X., Tong, M., & Yu, R.
- Affiliations
- Xiamen University (CN)
- Corresponding author
- Ding, Xin
- Tong, Mengsha
- Yu, Rongshan
- Venue
- Cell Reports Methods
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 4
- Issue
- 6
- Pages
- 100797
- ISSN
- 2667-2375 (ISSN-L)
- PMC
- PMC11228371
- OpenAlex
- W4399735300
- Cited by
- 9
- References
- 73
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 2
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports Methods (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (8200 2529)
- Xiamen University
- Topics
- Health Sciences › Medicine › Oncology › Cancer Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- cancer of unknown primary, tumor origin identification, metastatic cancer, transformer models, transcriptome, oncogenic signaling
- MeSH terms
- Deep Learning, Humans, Neoplasms, Unknown Primary, Retrospective Studies, Signal Transduction, Transcriptome
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