Predicting Pathological Complete Response in Neoadjuvant Dual Blockade With Trastuzumab and Pertuzumab in HER2 Gene Amplified Breast Cancer
BackgroundDual-targeted therapy is the standard treatment for human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and effective biomarkers to predict the response to neoadjuvant trastuzumab and pertuzumab treatment need further investigation. Here, we developed a predictive model...
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| Format: | Article |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.877825/full |
| _version_ | 1818252809928179712 |
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| author | Yi Xiao Yi Xiao Jiahan Ding Dachang Ma Sheng Chen Xun Li Keda Yu |
| author_facet | Yi Xiao Yi Xiao Jiahan Ding Dachang Ma Sheng Chen Xun Li Keda Yu |
| author_sort | Yi Xiao |
| collection | DOAJ |
| description | BackgroundDual-targeted therapy is the standard treatment for human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and effective biomarkers to predict the response to neoadjuvant trastuzumab and pertuzumab treatment need further investigation. Here, we developed a predictive model to evaluate the dual-targeted neoadjuvant treatment efficacy in HER2 gene-amplified breast cancer.MethodThis retrospective study included 159 HER2-amplified patients with locally advanced breast cancer who received neoadjuvant trastuzumab, pertuzumab, and chemotherapy. The correlation between clinicopathological factors and pathological complete response (pCR, in the breast and axilla) was evaluated. Patients were randomly assigned into the training set (n=110) and the testing set (n=49). We used an independent cohort (n=65) for external validation. We constructed our predictive nomogram model with the results of risk variables associated with pCR identified in the multivariate logistic analysis. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration curves were employed to assess the nomogram’s performance.ResultsWe revealed that the HER2/CEP17 ratio (p=0.001), CD8 levels (p=0.005), and histological grade (p=0.007) were independent indicators for pCR in dual-targeted neoadjuvant treatment after multivariate adjustment. The combined prediction efficacy of the three indicators was significantly higher than that of each single indicator alone. The AUCs were 0.819, 0.773, and 0.744 in the training, testing, and external validation sets, respectively.ConclusionsThe HER2/CEP17 ratio, CD8 levels, and histological grade were significantly correlated with pCR in dual-targeted neoadjuvant treatment. The combined model using these three markers provided a better predictive value for pCR than the HER2/CEP17 ratio, CD8 levels, and the histological grade alone, which showed that an immunological effect partially mediates the predictive impact of neoadjuvant treatment. |
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| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-12-12T16:30:05Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj.art-0b179d85a1ae4b668672b8bbbda055b22022-12-22T00:18:48ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-05-011310.3389/fimmu.2022.877825877825Predicting Pathological Complete Response in Neoadjuvant Dual Blockade With Trastuzumab and Pertuzumab in HER2 Gene Amplified Breast CancerYi Xiao0Yi Xiao1Jiahan Ding2Dachang Ma3Sheng Chen4Xun Li5Keda Yu6Department of Breast Surgery, The First Hospital of Lanzhou University, Lanzhou, ChinaDepartment of General Surgery, The First Hospital of Lanzhou University, Lanzhou, ChinaDepartment of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of Breast Surgery, The First Hospital of Lanzhou University, Lanzhou, ChinaDepartment of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, ChinaDepartment of General Surgery, The First Hospital of Lanzhou University, Lanzhou, ChinaDepartment of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, ChinaBackgroundDual-targeted therapy is the standard treatment for human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and effective biomarkers to predict the response to neoadjuvant trastuzumab and pertuzumab treatment need further investigation. Here, we developed a predictive model to evaluate the dual-targeted neoadjuvant treatment efficacy in HER2 gene-amplified breast cancer.MethodThis retrospective study included 159 HER2-amplified patients with locally advanced breast cancer who received neoadjuvant trastuzumab, pertuzumab, and chemotherapy. The correlation between clinicopathological factors and pathological complete response (pCR, in the breast and axilla) was evaluated. Patients were randomly assigned into the training set (n=110) and the testing set (n=49). We used an independent cohort (n=65) for external validation. We constructed our predictive nomogram model with the results of risk variables associated with pCR identified in the multivariate logistic analysis. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration curves were employed to assess the nomogram’s performance.ResultsWe revealed that the HER2/CEP17 ratio (p=0.001), CD8 levels (p=0.005), and histological grade (p=0.007) were independent indicators for pCR in dual-targeted neoadjuvant treatment after multivariate adjustment. The combined prediction efficacy of the three indicators was significantly higher than that of each single indicator alone. The AUCs were 0.819, 0.773, and 0.744 in the training, testing, and external validation sets, respectively.ConclusionsThe HER2/CEP17 ratio, CD8 levels, and histological grade were significantly correlated with pCR in dual-targeted neoadjuvant treatment. The combined model using these three markers provided a better predictive value for pCR than the HER2/CEP17 ratio, CD8 levels, and the histological grade alone, which showed that an immunological effect partially mediates the predictive impact of neoadjuvant treatment.https://www.frontiersin.org/articles/10.3389/fimmu.2022.877825/fulltrastuzumabpertuzumabpredictive modelneoadjuvant treatmentHER2-amplified breast cancer |
| spellingShingle | Yi Xiao Yi Xiao Jiahan Ding Dachang Ma Sheng Chen Xun Li Keda Yu Predicting Pathological Complete Response in Neoadjuvant Dual Blockade With Trastuzumab and Pertuzumab in HER2 Gene Amplified Breast Cancer Frontiers in Immunology trastuzumab pertuzumab predictive model neoadjuvant treatment HER2-amplified breast cancer |
| title | Predicting Pathological Complete Response in Neoadjuvant Dual Blockade With Trastuzumab and Pertuzumab in HER2 Gene Amplified Breast Cancer |
| title_full | Predicting Pathological Complete Response in Neoadjuvant Dual Blockade With Trastuzumab and Pertuzumab in HER2 Gene Amplified Breast Cancer |
| title_fullStr | Predicting Pathological Complete Response in Neoadjuvant Dual Blockade With Trastuzumab and Pertuzumab in HER2 Gene Amplified Breast Cancer |
| title_full_unstemmed | Predicting Pathological Complete Response in Neoadjuvant Dual Blockade With Trastuzumab and Pertuzumab in HER2 Gene Amplified Breast Cancer |
| title_short | Predicting Pathological Complete Response in Neoadjuvant Dual Blockade With Trastuzumab and Pertuzumab in HER2 Gene Amplified Breast Cancer |
| title_sort | predicting pathological complete response in neoadjuvant dual blockade with trastuzumab and pertuzumab in her2 gene amplified breast cancer |
| topic | trastuzumab pertuzumab predictive model neoadjuvant treatment HER2-amplified breast cancer |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.877825/full |
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