Serum protein profiling reveals an inflammation signature as a predictor of early breast cancer survival
Karihtala, Peeter; Leivonen, Suvi-Katri; Puistola, Ulla; Urpilainen, Elina; Jääskeläinen, Anniina; Leppä, Sirpa; Jukkola, Arja (2024-04-09)
Biomed central
09.04.2024
Karihtala, P., Leivonen, SK., Puistola, U. et al. Serum protein profiling reveals an inflammation signature as a predictor of early breast cancer survival. Breast Cancer Res 26, 61 (2024). https://doi.org/10.1186/s13058-024-01812-x
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https://creativecommons.org/licenses/by/4.0/
© The Author(s) 2024. 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202404122685
https://urn.fi/URN:NBN:fi:oulu-202404122685
Tiivistelmä
Abstract
Background:
Breast cancers exhibit considerable heterogeneity in their biology, immunology, and prognosis. Currently, no validated, serum protein-based tools are available to evaluate the prognosis of patients with early breast cancer.
Methods:
The study population consisted of 521 early-stage breast cancer patients with a median follow-up of 8.9 years. Additionally, 61 patients with breast fibroadenoma or atypical ductal hyperplasia were included as controls. We used a proximity extension assay to measure the preoperative serum levels of 92 proteins associated with inflammatory and immune response processes. The invasive cancers were randomly split into discovery (n = 413) and validation (n = 108) cohorts for the statistical analyses.
Results:
Using LASSO regression, we identified a nine-protein signature (CCL8, CCL23, CCL28, CSCL10, S100A12, IL10, IL10RB, STAMPB2, and TNFβ) that predicted various survival endpoints more accurately than traditional prognostic factors. In the time-dependent analyses, the prognostic power of the model remained rather stable over time. We also developed and validated a 17-protein model with the potential to differentiate benign breast lesions from malignant lesions (Wilcoxon p < 2.2*10− 16; AUC 0.94).
Conclusions:
Inflammation and immunity-related serum proteins have the potential to rise above the classical prognostic factors of early-stage breast cancer. They may also help to distinguish benign from malignant breast lesions.
Background:
Breast cancers exhibit considerable heterogeneity in their biology, immunology, and prognosis. Currently, no validated, serum protein-based tools are available to evaluate the prognosis of patients with early breast cancer.
Methods:
The study population consisted of 521 early-stage breast cancer patients with a median follow-up of 8.9 years. Additionally, 61 patients with breast fibroadenoma or atypical ductal hyperplasia were included as controls. We used a proximity extension assay to measure the preoperative serum levels of 92 proteins associated with inflammatory and immune response processes. The invasive cancers were randomly split into discovery (n = 413) and validation (n = 108) cohorts for the statistical analyses.
Results:
Using LASSO regression, we identified a nine-protein signature (CCL8, CCL23, CCL28, CSCL10, S100A12, IL10, IL10RB, STAMPB2, and TNFβ) that predicted various survival endpoints more accurately than traditional prognostic factors. In the time-dependent analyses, the prognostic power of the model remained rather stable over time. We also developed and validated a 17-protein model with the potential to differentiate benign breast lesions from malignant lesions (Wilcoxon p < 2.2*10− 16; AUC 0.94).
Conclusions:
Inflammation and immunity-related serum proteins have the potential to rise above the classical prognostic factors of early-stage breast cancer. They may also help to distinguish benign from malignant breast lesions.
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