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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2374-4677 |