Biological correlates associated with high-risk breast cancer patients identified using a computational method
Abstract Using a novel unsupervised method to integrate multi-omic data, we previously identified a breast cancer group with a poor prognosis. In the current study, we characterize the biological features of this subgroup, defined as the high-risk group, using various data sources. Assessment of thr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Breast Cancer |
| Online Access: | https://doi.org/10.1038/s41523-025-00725-y |
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| _version_ | 1832571426520956928 |
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| author | Jung Hun Oh Fresia Pareja Rena Elkin Kaiming Xu Larry Norton Joseph O. Deasy |
| author_facet | Jung Hun Oh Fresia Pareja Rena Elkin Kaiming Xu Larry Norton Joseph O. Deasy |
| author_sort | Jung Hun Oh |
| collection | DOAJ |
| description | Abstract Using a novel unsupervised method to integrate multi-omic data, we previously identified a breast cancer group with a poor prognosis. In the current study, we characterize the biological features of this subgroup, defined as the high-risk group, using various data sources. Assessment of three published hypoxia signatures showed that the high-risk group exhibited higher hypoxia scores (p < 0.0001 in all three signatures), compared to the low-risk group. Our analysis of the immune cell composition using CIBERSORT and leukocyte fraction showed significant differences between the high and low-risk groups across the entire cohort, as well as within PAM50 subtypes. Within the basal subtype, the low-risk group had a statistically significantly higher spatial fraction of tumor-infiltrating lymphocytes (TILs) compared to the high-risk group (p = 0.0362). Our findings indicate that this subgroup with poor prognosis is driven by a distinct biological signature with high activation of hypoxia-related genes as well as a low number of TILs. |
| format | Article |
| id | doaj-art-1d3296ffcb6b4edb8e3e9a2330d13b20 |
| institution | Kabale University |
| issn | 2374-4677 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Breast Cancer |
| spelling | doaj-art-1d3296ffcb6b4edb8e3e9a2330d13b202025-02-02T12:35:30ZengNature Portfolionpj Breast Cancer2374-46772025-01-011111810.1038/s41523-025-00725-yBiological correlates associated with high-risk breast cancer patients identified using a computational methodJung Hun Oh0Fresia Pareja1Rena Elkin2Kaiming Xu3Larry Norton4Joseph O. Deasy5Department of Medical Physics, Memorial Sloan Kettering Cancer CenterDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer CenterDepartment of Medical Physics, Memorial Sloan Kettering Cancer CenterDepartment of Applied Mathematics and Statistics, Stony Brook UniversityDepartment of Medicine, Memorial Sloan Kettering Cancer CenterDepartment of Medical Physics, Memorial Sloan Kettering Cancer CenterAbstract Using a novel unsupervised method to integrate multi-omic data, we previously identified a breast cancer group with a poor prognosis. In the current study, we characterize the biological features of this subgroup, defined as the high-risk group, using various data sources. Assessment of three published hypoxia signatures showed that the high-risk group exhibited higher hypoxia scores (p < 0.0001 in all three signatures), compared to the low-risk group. Our analysis of the immune cell composition using CIBERSORT and leukocyte fraction showed significant differences between the high and low-risk groups across the entire cohort, as well as within PAM50 subtypes. Within the basal subtype, the low-risk group had a statistically significantly higher spatial fraction of tumor-infiltrating lymphocytes (TILs) compared to the high-risk group (p = 0.0362). Our findings indicate that this subgroup with poor prognosis is driven by a distinct biological signature with high activation of hypoxia-related genes as well as a low number of TILs.https://doi.org/10.1038/s41523-025-00725-y |
| spellingShingle | Jung Hun Oh Fresia Pareja Rena Elkin Kaiming Xu Larry Norton Joseph O. Deasy Biological correlates associated with high-risk breast cancer patients identified using a computational method npj Breast Cancer |
| title | Biological correlates associated with high-risk breast cancer patients identified using a computational method |
| title_full | Biological correlates associated with high-risk breast cancer patients identified using a computational method |
| title_fullStr | Biological correlates associated with high-risk breast cancer patients identified using a computational method |
| title_full_unstemmed | Biological correlates associated with high-risk breast cancer patients identified using a computational method |
| title_short | Biological correlates associated with high-risk breast cancer patients identified using a computational method |
| title_sort | biological correlates associated with high risk breast cancer patients identified using a computational method |
| url | https://doi.org/10.1038/s41523-025-00725-y |
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