Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires
Abstract The immune system’s defense abilities rely on the diversity of T and B lymphocytes. T Cell Receptors (TCRs) are generated through V(D)J recombination, where distinct genetic elements combine and undergo modifications, creating extensive variability. In breast cancer, the most frequently dia...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-025-00573-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849234482571247616 |
|---|---|
| author | Miriam Zuckerbrot-Schuldenfrei Ari Raphael Alona Zilberberg Sol Efroni |
| author_facet | Miriam Zuckerbrot-Schuldenfrei Ari Raphael Alona Zilberberg Sol Efroni |
| author_sort | Miriam Zuckerbrot-Schuldenfrei |
| collection | DOAJ |
| description | Abstract The immune system’s defense abilities rely on the diversity of T and B lymphocytes. T Cell Receptors (TCRs) are generated through V(D)J recombination, where distinct genetic elements combine and undergo modifications, creating extensive variability. In breast cancer, the most frequently diagnosed cancer in women, early detection sometimes helps with highly effective and potentially curative treatment. The TCR repertoire may provide information about tumor status. To test this, we investigated the peripheral blood TCR repertoire and its association with tumor status. We collected blood samples from 98 women, including patients and healthy donors. Following TCR profiling, machine learning of these data was able to show an association between TCR profiles and breast cancer presence or absence with high accuracy (average AUC of 0.96). Our findings imply the immune system retains tumor-relevant, TCR-related, signals detectable in blood. This information could potentially benefit future derivatives from this knowledge, either in the field of detection or treatment. |
| format | Article |
| id | doaj-art-be7224952fd14f9fb5a8ce21d9a69f7e |
| institution | Kabale University |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-be7224952fd14f9fb5a8ce21d9a69f7e2025-08-20T04:03:07ZengNature Portfolionpj Systems Biology and Applications2056-71892025-08-011111810.1038/s41540-025-00573-3Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoiresMiriam Zuckerbrot-Schuldenfrei0Ari Raphael1Alona Zilberberg2Sol Efroni3The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan UniversityDavidoff cancer center, Rabin medical center. Faculty of Medical and Health sciences, Tel-Aviv UniversityThe Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan UniversityThe Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan UniversityAbstract The immune system’s defense abilities rely on the diversity of T and B lymphocytes. T Cell Receptors (TCRs) are generated through V(D)J recombination, where distinct genetic elements combine and undergo modifications, creating extensive variability. In breast cancer, the most frequently diagnosed cancer in women, early detection sometimes helps with highly effective and potentially curative treatment. The TCR repertoire may provide information about tumor status. To test this, we investigated the peripheral blood TCR repertoire and its association with tumor status. We collected blood samples from 98 women, including patients and healthy donors. Following TCR profiling, machine learning of these data was able to show an association between TCR profiles and breast cancer presence or absence with high accuracy (average AUC of 0.96). Our findings imply the immune system retains tumor-relevant, TCR-related, signals detectable in blood. This information could potentially benefit future derivatives from this knowledge, either in the field of detection or treatment.https://doi.org/10.1038/s41540-025-00573-3 |
| spellingShingle | Miriam Zuckerbrot-Schuldenfrei Ari Raphael Alona Zilberberg Sol Efroni Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires npj Systems Biology and Applications |
| title | Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires |
| title_full | Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires |
| title_fullStr | Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires |
| title_full_unstemmed | Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires |
| title_short | Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires |
| title_sort | breast cancer is detectable from peripheral blood using machine learning over t cell receptor repertoires |
| url | https://doi.org/10.1038/s41540-025-00573-3 |
| work_keys_str_mv | AT miriamzuckerbrotschuldenfrei breastcancerisdetectablefromperipheralbloodusingmachinelearningovertcellreceptorrepertoires AT ariraphael breastcancerisdetectablefromperipheralbloodusingmachinelearningovertcellreceptorrepertoires AT alonazilberberg breastcancerisdetectablefromperipheralbloodusingmachinelearningovertcellreceptorrepertoires AT solefroni breastcancerisdetectablefromperipheralbloodusingmachinelearningovertcellreceptorrepertoires |