Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative Transformer
Abstract Homologous recombination deficiency (HRD) has been recognized as a key biomarker for poly-ADP ribose polymerase inhibitors (PARPi) and platinum-based chemotherapy in breast cancer (BC). HRD prediction typically relies on molecular biology assays, which have a high turnaround time, and cost....
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00950-5 |
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| _version_ | 1849688433021157376 |
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| author | Haijing Luan Taiyuan Hu Jifang Hu Weier Liu Kaixing Yang Yue Pei Ruilin Li Jiayin He Yajun Gao Dawei Sun Xiaohong Duan Rui Yan S. Kevin Zhou Beifang Niu |
| author_facet | Haijing Luan Taiyuan Hu Jifang Hu Weier Liu Kaixing Yang Yue Pei Ruilin Li Jiayin He Yajun Gao Dawei Sun Xiaohong Duan Rui Yan S. Kevin Zhou Beifang Niu |
| author_sort | Haijing Luan |
| collection | DOAJ |
| description | Abstract Homologous recombination deficiency (HRD) has been recognized as a key biomarker for poly-ADP ribose polymerase inhibitors (PARPi) and platinum-based chemotherapy in breast cancer (BC). HRD prediction typically relies on molecular biology assays, which have a high turnaround time, and cost. In contrast, tissue sections stained with hematoxylin and eosin (H&E) are ubiquitously available. However, current HRD prediction methods that utilize pathological images are usually based on attention-based multiple instance learning, which is ineffective for modeling the global context of whole slide images (WSIs). To address this challenge, we propose a Sufficient and Representative Transformer (SuRe-Transformer) for WSI-based prediction of HRD. Experimental results demonstrate the superior performance of SuRe-Transformer in predicting HRD status compared to state-of-the-art methods, achieving an AUROC of 0.887 ± 0.034. Furthermore, SuRe-Transformer demonstrates generalizability across multiple external patient cohorts and achieves state-of-the-art performance in predicting several gene mutation biomarkers from BC WSIs. |
| format | Article |
| id | doaj-art-2b5a0ff6a97d458f8a2dcc5d79bf642e |
| institution | DOAJ |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-2b5a0ff6a97d458f8a2dcc5d79bf642e2025-08-20T03:22:00ZengNature Portfolionpj Precision Oncology2397-768X2025-05-019111410.1038/s41698-025-00950-5Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative TransformerHaijing Luan0Taiyuan Hu1Jifang Hu2Weier Liu3Kaixing Yang4Yue Pei5Ruilin Li6Jiayin He7Yajun Gao8Dawei Sun9Xiaohong Duan10Rui Yan11S. Kevin Zhou12Beifang Niu13Computer Network Information Center, Chinese Academy of SciencesComputer Network Information Center, Chinese Academy of SciencesComputer Network Information Center, Chinese Academy of SciencesComputer Network Information Center, Chinese Academy of SciencesUniversity of Chinese Academy of SciencesComputer Network Information Center, Chinese Academy of SciencesComputer Network Information Center, Chinese Academy of SciencesComputer Network Information Center, Chinese Academy of SciencesBeijing ChosenMed Clinical Laboratory Co. Ltd.Beijing ChosenMed Clinical Laboratory Co. Ltd.School of Disaster and Emergency Medicine, Faculty of Medicine, Tianjin UniversitySchool of Biomedical Engineering, University of Science and Technology of ChinaSchool of Biomedical Engineering, University of Science and Technology of ChinaComputer Network Information Center, Chinese Academy of SciencesAbstract Homologous recombination deficiency (HRD) has been recognized as a key biomarker for poly-ADP ribose polymerase inhibitors (PARPi) and platinum-based chemotherapy in breast cancer (BC). HRD prediction typically relies on molecular biology assays, which have a high turnaround time, and cost. In contrast, tissue sections stained with hematoxylin and eosin (H&E) are ubiquitously available. However, current HRD prediction methods that utilize pathological images are usually based on attention-based multiple instance learning, which is ineffective for modeling the global context of whole slide images (WSIs). To address this challenge, we propose a Sufficient and Representative Transformer (SuRe-Transformer) for WSI-based prediction of HRD. Experimental results demonstrate the superior performance of SuRe-Transformer in predicting HRD status compared to state-of-the-art methods, achieving an AUROC of 0.887 ± 0.034. Furthermore, SuRe-Transformer demonstrates generalizability across multiple external patient cohorts and achieves state-of-the-art performance in predicting several gene mutation biomarkers from BC WSIs.https://doi.org/10.1038/s41698-025-00950-5 |
| spellingShingle | Haijing Luan Taiyuan Hu Jifang Hu Weier Liu Kaixing Yang Yue Pei Ruilin Li Jiayin He Yajun Gao Dawei Sun Xiaohong Duan Rui Yan S. Kevin Zhou Beifang Niu Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative Transformer npj Precision Oncology |
| title | Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative Transformer |
| title_full | Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative Transformer |
| title_fullStr | Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative Transformer |
| title_full_unstemmed | Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative Transformer |
| title_short | Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative Transformer |
| title_sort | breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative transformer |
| url | https://doi.org/10.1038/s41698-025-00950-5 |
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