OncoTrace‐TOO: Interpretable Machine Learning Framework for Cancer Tissue‐of‐Origin Identification Using Transcriptomic Signatures
ABSTRACT Background Cancer of unknown primary remains a formidable diagnostic challenge due to the inability to pinpoint the primary tumor site, which restricts the use of targeted therapeutics. Although machine‐learning methods that integrate transcriptomic approaches have provided valuable insight...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Cancer Reports |
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| Online Access: | https://doi.org/10.1002/cnr2.70311 |
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| _version_ | 1849222376326168576 |
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| author | Yang Hao Haochun Huang Daiyun Huang Jianwen Ruan Xin Liu Jianquan Zhang |
| author_facet | Yang Hao Haochun Huang Daiyun Huang Jianwen Ruan Xin Liu Jianquan Zhang |
| author_sort | Yang Hao |
| collection | DOAJ |
| description | ABSTRACT Background Cancer of unknown primary remains a formidable diagnostic challenge due to the inability to pinpoint the primary tumor site, which restricts the use of targeted therapeutics. Although machine‐learning methods that integrate transcriptomic approaches have provided valuable insights into tumor origins, they often face challenges in distinguishing biologically similar tumors and typically lack biological interpretability. Aims This study aims to develop a transparent and biologically interpretable machine learning framework to accurately classify tissue‐of‐origin across diverse cancer types, thereby facilitation clinical diagnosis. Methods We designed OncoTrace‐TOO, a novel tissue‐of‐origin classification model based on gene expression profiles. The model utilizes pan‐cancer discriminative molecular features identified through one‐vs‐rest differential expression analysis and applies logistic regression as the classification algorithm. Results OncoTrace‐TOO achieved an overall accuracy of 0.967, with perfect classification for seven cancer types (e.g., CHOL, DLBC, and LAML). The model demonstrated high predictive accuracy in both primary and metastatic cancers across TCGA and GEO validation datasets, with enhanced capability in resolving histologically related malignancies as well as classifying rare cancer subtypes. When applied to independent clinical tumor samples, the model achieved TOO prediction accuracies of 0.857, further validating its robustness. Importantly, the framework offers biologically interpretable predictions by revealing tumor‐specific molecular signatures, thus enhancing its clinical applicability. Conclusions OncoTrace‐TOO not only offers high predictive accuracy for tissue‐of‐origin classification, but also delivers biologically meaningful insights that support clinical decision‐making. This framework holds promise for improving diagnostic precision and guiding personalized treatment in challenging cancer cases. |
| format | Article |
| id | doaj-art-3d724b7c865347bb87083e443fe3cde1 |
| institution | Kabale University |
| issn | 2573-8348 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Reports |
| spelling | doaj-art-3d724b7c865347bb87083e443fe3cde12025-08-26T06:00:41ZengWileyCancer Reports2573-83482025-08-0188n/an/a10.1002/cnr2.70311OncoTrace‐TOO: Interpretable Machine Learning Framework for Cancer Tissue‐of‐Origin Identification Using Transcriptomic SignaturesYang Hao0Haochun Huang1Daiyun Huang2Jianwen Ruan3Xin Liu4Jianquan Zhang5Hepatobiliary and Pancreatic Surgery Central South University Xiangya School of Medicine Affiliated Haikou Hospital Haikou ChinaDepartment of Biosciences and Bioinformatics Xi'an Jiaotong‐Liverpool University Suzhou ChinaWisdom Lake Academy of Pharmacy Xi'an Jiaotong‐Liverpool University Suzhou ChinaHepatology Central South University Xiangya School of Medicine Affiliated Haikou Hospital Haikou ChinaWisdom Lake Academy of Pharmacy Xi'an Jiaotong‐Liverpool University Suzhou ChinaHepatobiliary and Pancreatic Surgery Central South University Xiangya School of Medicine Affiliated Haikou Hospital Haikou ChinaABSTRACT Background Cancer of unknown primary remains a formidable diagnostic challenge due to the inability to pinpoint the primary tumor site, which restricts the use of targeted therapeutics. Although machine‐learning methods that integrate transcriptomic approaches have provided valuable insights into tumor origins, they often face challenges in distinguishing biologically similar tumors and typically lack biological interpretability. Aims This study aims to develop a transparent and biologically interpretable machine learning framework to accurately classify tissue‐of‐origin across diverse cancer types, thereby facilitation clinical diagnosis. Methods We designed OncoTrace‐TOO, a novel tissue‐of‐origin classification model based on gene expression profiles. The model utilizes pan‐cancer discriminative molecular features identified through one‐vs‐rest differential expression analysis and applies logistic regression as the classification algorithm. Results OncoTrace‐TOO achieved an overall accuracy of 0.967, with perfect classification for seven cancer types (e.g., CHOL, DLBC, and LAML). The model demonstrated high predictive accuracy in both primary and metastatic cancers across TCGA and GEO validation datasets, with enhanced capability in resolving histologically related malignancies as well as classifying rare cancer subtypes. When applied to independent clinical tumor samples, the model achieved TOO prediction accuracies of 0.857, further validating its robustness. Importantly, the framework offers biologically interpretable predictions by revealing tumor‐specific molecular signatures, thus enhancing its clinical applicability. Conclusions OncoTrace‐TOO not only offers high predictive accuracy for tissue‐of‐origin classification, but also delivers biologically meaningful insights that support clinical decision‐making. This framework holds promise for improving diagnostic precision and guiding personalized treatment in challenging cancer cases.https://doi.org/10.1002/cnr2.70311cancer of unknown primarymachine learningmetastasistissue‐of‐origin identificationtranscriptomics |
| spellingShingle | Yang Hao Haochun Huang Daiyun Huang Jianwen Ruan Xin Liu Jianquan Zhang OncoTrace‐TOO: Interpretable Machine Learning Framework for Cancer Tissue‐of‐Origin Identification Using Transcriptomic Signatures Cancer Reports cancer of unknown primary machine learning metastasis tissue‐of‐origin identification transcriptomics |
| title | OncoTrace‐TOO: Interpretable Machine Learning Framework for Cancer Tissue‐of‐Origin Identification Using Transcriptomic Signatures |
| title_full | OncoTrace‐TOO: Interpretable Machine Learning Framework for Cancer Tissue‐of‐Origin Identification Using Transcriptomic Signatures |
| title_fullStr | OncoTrace‐TOO: Interpretable Machine Learning Framework for Cancer Tissue‐of‐Origin Identification Using Transcriptomic Signatures |
| title_full_unstemmed | OncoTrace‐TOO: Interpretable Machine Learning Framework for Cancer Tissue‐of‐Origin Identification Using Transcriptomic Signatures |
| title_short | OncoTrace‐TOO: Interpretable Machine Learning Framework for Cancer Tissue‐of‐Origin Identification Using Transcriptomic Signatures |
| title_sort | oncotrace too interpretable machine learning framework for cancer tissue of origin identification using transcriptomic signatures |
| topic | cancer of unknown primary machine learning metastasis tissue‐of‐origin identification transcriptomics |
| url | https://doi.org/10.1002/cnr2.70311 |
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