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Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets
BACKGROUND: We established a radiogenomic model to predict pathological complete response (pCR) in triple-negative breast cancer (TNBC) and explored the association between high-frequency mutations and drug resistance. METHODS: From April 2018 to September 2019, 112 patients who had received neoadju...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9171937/ https://www.ncbi.nlm.nih.gov/pubmed/35672824 http://dx.doi.org/10.1186/s12967-022-03452-1 |
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author | Zhang, Ying You, Chao Pei, Yuchen Yang, Fan Li, Daqiang Jiang, Yi-zhou Shao, Zhimin |
author_facet | Zhang, Ying You, Chao Pei, Yuchen Yang, Fan Li, Daqiang Jiang, Yi-zhou Shao, Zhimin |
author_sort | Zhang, Ying |
collection | PubMed |
description | BACKGROUND: We established a radiogenomic model to predict pathological complete response (pCR) in triple-negative breast cancer (TNBC) and explored the association between high-frequency mutations and drug resistance. METHODS: From April 2018 to September 2019, 112 patients who had received neoadjuvant chemotherapy were included. We randomly split the study population into training and validation sets (2:1 ratio). Contrast-enhanced magnetic resonance imaging scans were obtained at baseline and after two cycles of treatment and were used to extract quantitative radiomic features and to construct two radiomics-only models using a light gradient boosting machine. By incorporating the variant allele frequency features obtained from baseline core tissues, a radiogenomic model was constructed to predict pCR. Additionally, we explored the association between recurrent mutations and drug resistance. RESULTS: The two radiomics-only models showed similar performance with AUCs of 0.71 and 0.73 (p = 0.55). The radiogenomic model had a higher predictive ability than the radiomics-only model in the validation set (p = 0.04), with a corresponding AUC of 0.87 (0.73–0.91). Two highly frequent mutations were selected after comparing the mutation sites of pCR and non-pCR populations. The MED23 mutation p.P394H caused epirubicin resistance in vitro (p < 0.01). The expression levels of γ-H2A.X, p-ATM and p-CHK2 in MED23 p.P394H cells were significantly lower than those in wild type cells (p < 0.01). In the HR repair system, the GFP positivity rate of MED23 p.P394H cells was higher than that in wild-type cells (p < 0.01). CONCLUSIONS: The proposed radiogenomic model has the potential to accurately predict pCR in TNBC patients. Epirubicin resistance after MED23 p.P394H mutation might be affected by HR repair through regulation of the p-ATM-γ-H2A.X-p-CHK2 pathway. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03452-1. |
format | Online Article Text |
id | pubmed-9171937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91719372022-06-08 Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets Zhang, Ying You, Chao Pei, Yuchen Yang, Fan Li, Daqiang Jiang, Yi-zhou Shao, Zhimin J Transl Med Research BACKGROUND: We established a radiogenomic model to predict pathological complete response (pCR) in triple-negative breast cancer (TNBC) and explored the association between high-frequency mutations and drug resistance. METHODS: From April 2018 to September 2019, 112 patients who had received neoadjuvant chemotherapy were included. We randomly split the study population into training and validation sets (2:1 ratio). Contrast-enhanced magnetic resonance imaging scans were obtained at baseline and after two cycles of treatment and were used to extract quantitative radiomic features and to construct two radiomics-only models using a light gradient boosting machine. By incorporating the variant allele frequency features obtained from baseline core tissues, a radiogenomic model was constructed to predict pCR. Additionally, we explored the association between recurrent mutations and drug resistance. RESULTS: The two radiomics-only models showed similar performance with AUCs of 0.71 and 0.73 (p = 0.55). The radiogenomic model had a higher predictive ability than the radiomics-only model in the validation set (p = 0.04), with a corresponding AUC of 0.87 (0.73–0.91). Two highly frequent mutations were selected after comparing the mutation sites of pCR and non-pCR populations. The MED23 mutation p.P394H caused epirubicin resistance in vitro (p < 0.01). The expression levels of γ-H2A.X, p-ATM and p-CHK2 in MED23 p.P394H cells were significantly lower than those in wild type cells (p < 0.01). In the HR repair system, the GFP positivity rate of MED23 p.P394H cells was higher than that in wild-type cells (p < 0.01). CONCLUSIONS: The proposed radiogenomic model has the potential to accurately predict pCR in TNBC patients. Epirubicin resistance after MED23 p.P394H mutation might be affected by HR repair through regulation of the p-ATM-γ-H2A.X-p-CHK2 pathway. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03452-1. BioMed Central 2022-06-07 /pmc/articles/PMC9171937/ /pubmed/35672824 http://dx.doi.org/10.1186/s12967-022-03452-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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. |
spellingShingle | Research Zhang, Ying You, Chao Pei, Yuchen Yang, Fan Li, Daqiang Jiang, Yi-zhou Shao, Zhimin Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets |
title | Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets |
title_full | Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets |
title_fullStr | Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets |
title_full_unstemmed | Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets |
title_short | Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets |
title_sort | integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9171937/ https://www.ncbi.nlm.nih.gov/pubmed/35672824 http://dx.doi.org/10.1186/s12967-022-03452-1 |
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