Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers
Abstract Background Pathological complete response (pCR) is an established surrogate marker for prognosis in patients with breast cancer (BC) after neoadjuvant chemotherapy. Individualized pCR prediction based on clinical information available at biopsy, particularly immunohistochemical (IHC) marker...
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2025-01-01
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| Series: | Breast Cancer Research |
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| Online Access: | https://doi.org/10.1186/s13058-025-01960-8 |
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| author | Lothar Häberle Ramona Erber Paul Gass Alexander Hein Melitta Niklos Bernhard Volz Carolin C. Hack Rüdiger Schulz-Wendtland Hanna Huebner Chloë Goossens Matthias Christgen Thilo Dörk Tjoung-Won Park-Simon Andreas Schneeweiss Michael Untch Valentina Nekljudova Sibylle Loibl Arndt Hartmann Matthias W. Beckmann Peter A. Fasching |
| author_facet | Lothar Häberle Ramona Erber Paul Gass Alexander Hein Melitta Niklos Bernhard Volz Carolin C. Hack Rüdiger Schulz-Wendtland Hanna Huebner Chloë Goossens Matthias Christgen Thilo Dörk Tjoung-Won Park-Simon Andreas Schneeweiss Michael Untch Valentina Nekljudova Sibylle Loibl Arndt Hartmann Matthias W. Beckmann Peter A. Fasching |
| author_sort | Lothar Häberle |
| collection | DOAJ |
| description | Abstract Background Pathological complete response (pCR) is an established surrogate marker for prognosis in patients with breast cancer (BC) after neoadjuvant chemotherapy. Individualized pCR prediction based on clinical information available at biopsy, particularly immunohistochemical (IHC) markers, may help identify patients who could benefit from preoperative chemotherapy. Methods Data from patients with HER2-negative BC who underwent neoadjuvant chemotherapy from 2002 to 2020 (n = 1166) were used to develop multivariable prediction models to estimate the probability of pCR (pCR-prob). The most precise model identified using cross-validation was implemented in an online calculator and a nomogram. Associations among pCR-prob, prognostic IHC3 distant recurrence and disease-free survival were studied using Cox regression and Kaplan–Meier analyses. The model’s utility was further evaluated in independent external validation cohorts. Results 273 patients (23.4%) achieved a pCR. The most precise model had across-validated area under the curve (AUC) of 0.84, sensitivity of 0.82, and specificity of 0.71. External validation yielded AUCs between 0.75 (95% CI, 0.70–0.81) and 0.83 (95% CI, 0.78–0.87). The higher the pCR-prob, the greater the prognostic impact of pCR status (presence/absence): hazard ratios decreased from 0.55 (95% central range, 0.07–1.77) at 0% to 0.20 (0.11–0.31) at 50% pCR-prob. Combining pCR-prob and IHC3 score further improved the precision of disease-free survival prognosis. Conclusions A pCR prediction model for neoadjuvant therapy decision-making was established. Combining pCR and recurrence prediction allows identification of not only patients who benefit most from neoadjuvant chemotherapy, but also patients with a very unfavorable prognosis for whom alternative treatment strategies should be considered. |
| format | Article |
| id | doaj-art-9fd0277ce8484dd4a5efb35306fd83e0 |
| institution | Kabale University |
| issn | 1465-542X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | Breast Cancer Research |
| spelling | doaj-art-9fd0277ce8484dd4a5efb35306fd83e02025-01-26T12:58:49ZengBMCBreast Cancer Research1465-542X2025-01-0127111310.1186/s13058-025-01960-8Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markersLothar Häberle0Ramona Erber1Paul Gass2Alexander Hein3Melitta Niklos4Bernhard Volz5Carolin C. Hack6Rüdiger Schulz-Wendtland7Hanna Huebner8Chloë Goossens9Matthias Christgen10Thilo Dörk11Tjoung-Won Park-Simon12Andreas Schneeweiss13Michael Untch14Valentina Nekljudova15Sibylle Loibl16Arndt Hartmann17Matthias W. Beckmann18Peter A. Fasching19Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergInstitute of Pathology, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergInstitute of Diagnostic Radiology, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergInstitute of Pathology, Hannover Medical SchoolGynecology Research Unit, Hannover Medical SchoolDepartment of Gynecology and Obstetrics, Hannover Medical SchoolNational Center for Tumor Diseases, University Hospital and German Cancer Research CenterDepartment of Gynecology and Obstetrics, Helios Clinic Berlin-BuchGerman Breast GroupGerman Breast GroupInstitute of Pathology, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–NurembergAbstract Background Pathological complete response (pCR) is an established surrogate marker for prognosis in patients with breast cancer (BC) after neoadjuvant chemotherapy. Individualized pCR prediction based on clinical information available at biopsy, particularly immunohistochemical (IHC) markers, may help identify patients who could benefit from preoperative chemotherapy. Methods Data from patients with HER2-negative BC who underwent neoadjuvant chemotherapy from 2002 to 2020 (n = 1166) were used to develop multivariable prediction models to estimate the probability of pCR (pCR-prob). The most precise model identified using cross-validation was implemented in an online calculator and a nomogram. Associations among pCR-prob, prognostic IHC3 distant recurrence and disease-free survival were studied using Cox regression and Kaplan–Meier analyses. The model’s utility was further evaluated in independent external validation cohorts. Results 273 patients (23.4%) achieved a pCR. The most precise model had across-validated area under the curve (AUC) of 0.84, sensitivity of 0.82, and specificity of 0.71. External validation yielded AUCs between 0.75 (95% CI, 0.70–0.81) and 0.83 (95% CI, 0.78–0.87). The higher the pCR-prob, the greater the prognostic impact of pCR status (presence/absence): hazard ratios decreased from 0.55 (95% central range, 0.07–1.77) at 0% to 0.20 (0.11–0.31) at 50% pCR-prob. Combining pCR-prob and IHC3 score further improved the precision of disease-free survival prognosis. Conclusions A pCR prediction model for neoadjuvant therapy decision-making was established. Combining pCR and recurrence prediction allows identification of not only patients who benefit most from neoadjuvant chemotherapy, but also patients with a very unfavorable prognosis for whom alternative treatment strategies should be considered.https://doi.org/10.1186/s13058-025-01960-8pCRPrediction modelBreast cancerNeoadjuvant chemotherapyImmunohistochemistryClinical data |
| spellingShingle | Lothar Häberle Ramona Erber Paul Gass Alexander Hein Melitta Niklos Bernhard Volz Carolin C. Hack Rüdiger Schulz-Wendtland Hanna Huebner Chloë Goossens Matthias Christgen Thilo Dörk Tjoung-Won Park-Simon Andreas Schneeweiss Michael Untch Valentina Nekljudova Sibylle Loibl Arndt Hartmann Matthias W. Beckmann Peter A. Fasching Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers Breast Cancer Research pCR Prediction model Breast cancer Neoadjuvant chemotherapy Immunohistochemistry Clinical data |
| title | Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers |
| title_full | Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers |
| title_fullStr | Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers |
| title_full_unstemmed | Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers |
| title_short | Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers |
| title_sort | prediction of pathological complete response after neoadjuvant chemotherapy for her2 negative breast cancer patients with routine immunohistochemical markers |
| topic | pCR Prediction model Breast cancer Neoadjuvant chemotherapy Immunohistochemistry Clinical data |
| url | https://doi.org/10.1186/s13058-025-01960-8 |
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