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Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer
Breast cancer prognosis and management for both men and women are reliant upon estrogen receptor alpha (ERα) and progesterone receptor (PR) expression to inform therapy. Previous studies have shown that there are sex-specific binding characteristics of ERα and PR in breast cancer and, counterintuiti...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632426/ https://www.ncbi.nlm.nih.gov/pubmed/37940649 http://dx.doi.org/10.1038/s41523-023-00599-y |
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author | Chatterji, Subarnarekha Niehues, Jan Moritz van Treeck, Marko Loeffler, Chiara Maria Lavinia Saldanha, Oliver Lester Veldhuizen, Gregory Patrick Cifci, Didem Carrero, Zunamys Itzell Abu-Eid, Rasha Speirs, Valerie Kather, Jakob Nikolas |
author_facet | Chatterji, Subarnarekha Niehues, Jan Moritz van Treeck, Marko Loeffler, Chiara Maria Lavinia Saldanha, Oliver Lester Veldhuizen, Gregory Patrick Cifci, Didem Carrero, Zunamys Itzell Abu-Eid, Rasha Speirs, Valerie Kather, Jakob Nikolas |
author_sort | Chatterji, Subarnarekha |
collection | PubMed |
description | Breast cancer prognosis and management for both men and women are reliant upon estrogen receptor alpha (ERα) and progesterone receptor (PR) expression to inform therapy. Previous studies have shown that there are sex-specific binding characteristics of ERα and PR in breast cancer and, counterintuitively, ERα expression is more common in male than female breast cancer. We hypothesized that these differences could have morphological manifestations that are undetectable to human observers but could be elucidated computationally. To investigate this, we trained attention-based multiple instance learning prediction models for ERα and PR using H&E-stained images of female breast cancer from the Cancer Genome Atlas (TCGA) (n = 1085) and deployed them on external female (n = 192) and male breast cancer images (n = 245). Both targets were predicted in the internal (AUROC for ERα prediction: 0.86 ± 0.02, p < 0.001; AUROC for PR prediction = 0.76 ± 0.03, p < 0.001) and external female cohorts (AUROC for ERα prediction: 0.78 ± 0.03, p < 0.001; AUROC for PR prediction = 0.80 ± 0.04, p < 0.001) but not the male cohort (AUROC for ERα prediction: 0.66 ± 0.14, p = 0.43; AUROC for PR prediction = 0.63 ± 0.04, p = 0.05). This suggests that subtle morphological differences invisible upon visual inspection may exist between the sexes, supporting previous immunohistochemical, genomic, and transcriptomic analyses. |
format | Online Article Text |
id | pubmed-10632426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106324262023-11-10 Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer Chatterji, Subarnarekha Niehues, Jan Moritz van Treeck, Marko Loeffler, Chiara Maria Lavinia Saldanha, Oliver Lester Veldhuizen, Gregory Patrick Cifci, Didem Carrero, Zunamys Itzell Abu-Eid, Rasha Speirs, Valerie Kather, Jakob Nikolas NPJ Breast Cancer Article Breast cancer prognosis and management for both men and women are reliant upon estrogen receptor alpha (ERα) and progesterone receptor (PR) expression to inform therapy. Previous studies have shown that there are sex-specific binding characteristics of ERα and PR in breast cancer and, counterintuitively, ERα expression is more common in male than female breast cancer. We hypothesized that these differences could have morphological manifestations that are undetectable to human observers but could be elucidated computationally. To investigate this, we trained attention-based multiple instance learning prediction models for ERα and PR using H&E-stained images of female breast cancer from the Cancer Genome Atlas (TCGA) (n = 1085) and deployed them on external female (n = 192) and male breast cancer images (n = 245). Both targets were predicted in the internal (AUROC for ERα prediction: 0.86 ± 0.02, p < 0.001; AUROC for PR prediction = 0.76 ± 0.03, p < 0.001) and external female cohorts (AUROC for ERα prediction: 0.78 ± 0.03, p < 0.001; AUROC for PR prediction = 0.80 ± 0.04, p < 0.001) but not the male cohort (AUROC for ERα prediction: 0.66 ± 0.14, p = 0.43; AUROC for PR prediction = 0.63 ± 0.04, p = 0.05). This suggests that subtle morphological differences invisible upon visual inspection may exist between the sexes, supporting previous immunohistochemical, genomic, and transcriptomic analyses. Nature Publishing Group UK 2023-11-08 /pmc/articles/PMC10632426/ /pubmed/37940649 http://dx.doi.org/10.1038/s41523-023-00599-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Chatterji, Subarnarekha Niehues, Jan Moritz van Treeck, Marko Loeffler, Chiara Maria Lavinia Saldanha, Oliver Lester Veldhuizen, Gregory Patrick Cifci, Didem Carrero, Zunamys Itzell Abu-Eid, Rasha Speirs, Valerie Kather, Jakob Nikolas Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer |
title | Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer |
title_full | Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer |
title_fullStr | Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer |
title_full_unstemmed | Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer |
title_short | Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer |
title_sort | prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632426/ https://www.ncbi.nlm.nih.gov/pubmed/37940649 http://dx.doi.org/10.1038/s41523-023-00599-y |
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