Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy
Abstract Hormone receptor-positive/HER2-negative breast cancer (BC) is the most common subtype of BC and typically occurs as an early, operable disease. In patients receiving neoadjuvant chemotherapy (NACT), pathological complete response (pCR) is rare and multiple efforts have been made to predict...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | npj Breast Cancer |
| Online Access: | https://doi.org/10.1038/s41523-025-00727-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823861696192577536 |
|---|---|
| author | Luca Mastrantoni Giovanna Garufi Giulia Giordano Noemi Maliziola Elena Di Monte Giorgia Arcuri Valentina Frescura Angelachiara Rotondi Armando Orlandi Luisa Carbognin Antonella Palazzo Federica Miglietta Letizia Pontolillo Alessandra Fabi Lorenzo Gerratana Sergio Pannunzio Ida Paris Sara Pilotto Fabio Marazzi Antonio Franco Gianluca Franceschini Maria Vittoria Dieci Roberta Mazzeo Fabio Puglisi Valentina Guarneri Michele Milella Giovanni Scambia Diana Giannarelli Giampaolo Tortora Emilio Bria |
| author_facet | Luca Mastrantoni Giovanna Garufi Giulia Giordano Noemi Maliziola Elena Di Monte Giorgia Arcuri Valentina Frescura Angelachiara Rotondi Armando Orlandi Luisa Carbognin Antonella Palazzo Federica Miglietta Letizia Pontolillo Alessandra Fabi Lorenzo Gerratana Sergio Pannunzio Ida Paris Sara Pilotto Fabio Marazzi Antonio Franco Gianluca Franceschini Maria Vittoria Dieci Roberta Mazzeo Fabio Puglisi Valentina Guarneri Michele Milella Giovanni Scambia Diana Giannarelli Giampaolo Tortora Emilio Bria |
| author_sort | Luca Mastrantoni |
| collection | DOAJ |
| description | Abstract Hormone receptor-positive/HER2-negative breast cancer (BC) is the most common subtype of BC and typically occurs as an early, operable disease. In patients receiving neoadjuvant chemotherapy (NACT), pathological complete response (pCR) is rare and multiple efforts have been made to predict disease recurrence. We developed a framework to predict pCR using clinicopathological characteristics widely available at diagnosis. The machine learning (ML) models were trained to predict pCR (n = 463), evaluated in an internal validation cohort (n = 109) and validated in an external validation cohort (n = 151). The best model was an Elastic Net, which achieved an area under the curve (AUC) of respectively 0.86 and 0.81. Our results highlight how simpler models using few input variables can be as valuable as more complex ML architectures. Our model is freely available and can be used to enhance the stratification of BC patients receiving NACT, providing a framework for the development of risk-adapted clinical trials. |
| format | Article |
| id | doaj-art-071d2ee3b6c24d3ca0260f479dba6d52 |
| institution | Kabale University |
| issn | 2374-4677 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Breast Cancer |
| spelling | doaj-art-071d2ee3b6c24d3ca0260f479dba6d522025-02-09T12:48:28ZengNature Portfolionpj Breast Cancer2374-46772025-02-0111111310.1038/s41523-025-00727-wAccessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapyLuca Mastrantoni0Giovanna Garufi1Giulia Giordano2Noemi Maliziola3Elena Di Monte4Giorgia Arcuri5Valentina Frescura6Angelachiara Rotondi7Armando Orlandi8Luisa Carbognin9Antonella Palazzo10Federica Miglietta11Letizia Pontolillo12Alessandra Fabi13Lorenzo Gerratana14Sergio Pannunzio15Ida Paris16Sara Pilotto17Fabio Marazzi18Antonio Franco19Gianluca Franceschini20Maria Vittoria Dieci21Roberta Mazzeo22Fabio Puglisi23Valentina Guarneri24Michele Milella25Giovanni Scambia26Diana Giannarelli27Giampaolo Tortora28Emilio Bria29Medical Oncology, Università Cattolica del Sacro CuoreMedical Oncology, Università Cattolica del Sacro CuoreDepartment of Geriatrics, Orthopedics and Rheumatological Sciences, Fondazione Policlinico Universitario Agostino Gemelli, IRCCSMedical Oncology, Università Cattolica del Sacro CuoreMedical Oncology, Università Cattolica del Sacro CuoreMedical Oncology, Università Cattolica del Sacro CuoreMedical Oncology, Università Cattolica del Sacro CuoreMedical Oncology, Università Cattolica del Sacro CuoreMedical Oncology, Università Cattolica del Sacro CuorePrecision Medicine Breast Unit, Scientific Directorate, Department of Women, Children and Public Health Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCSMedical Oncology, Università Cattolica del Sacro CuoreMedical Oncology 2, Istituto Oncologico Veneto IOV-IRCCSMedical Oncology, Università Cattolica del Sacro CuorePrecision Medicine Breast Unit, Scientific Directorate, Department of Women, Children and Public Health Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCSOncologia Medica, Centro di Riferimento Oncologico (CRO), IRCCS, Aviano (PN), Italy University of UdineMedical Oncology, Università Cattolica del Sacro CuorePrecision Medicine Breast Unit, Scientific Directorate, Department of Women, Children and Public Health Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCSSection of Oncology, Department of Medicine, University of Verona School of Medicine and Verona University Hospital TrustUOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCSBreast Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro CuoreBreast Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro CuoreMedical Oncology 2, Istituto Oncologico Veneto IOV-IRCCSOncologia Medica, Centro di Riferimento Oncologico (CRO), IRCCS, Aviano (PN), Italy University of UdineOncologia Medica, Centro di Riferimento Oncologico (CRO), IRCCS, Aviano (PN), Italy University of UdineMedical Oncology 2, Istituto Oncologico Veneto IOV-IRCCSSection of Oncology, Department of Medicine, University of Verona School of Medicine and Verona University Hospital TrustDepartment of Woman, Child, and Public Health, Fondazione Policlinico Universitario A Gemelli IRCCSBiostatistic, Fondazione Policlinico Universitario Agostino Gemelli IRCCSMedical Oncology, Università Cattolica del Sacro CuoreMedical Oncology, Università Cattolica del Sacro CuoreAbstract Hormone receptor-positive/HER2-negative breast cancer (BC) is the most common subtype of BC and typically occurs as an early, operable disease. In patients receiving neoadjuvant chemotherapy (NACT), pathological complete response (pCR) is rare and multiple efforts have been made to predict disease recurrence. We developed a framework to predict pCR using clinicopathological characteristics widely available at diagnosis. The machine learning (ML) models were trained to predict pCR (n = 463), evaluated in an internal validation cohort (n = 109) and validated in an external validation cohort (n = 151). The best model was an Elastic Net, which achieved an area under the curve (AUC) of respectively 0.86 and 0.81. Our results highlight how simpler models using few input variables can be as valuable as more complex ML architectures. Our model is freely available and can be used to enhance the stratification of BC patients receiving NACT, providing a framework for the development of risk-adapted clinical trials.https://doi.org/10.1038/s41523-025-00727-w |
| spellingShingle | Luca Mastrantoni Giovanna Garufi Giulia Giordano Noemi Maliziola Elena Di Monte Giorgia Arcuri Valentina Frescura Angelachiara Rotondi Armando Orlandi Luisa Carbognin Antonella Palazzo Federica Miglietta Letizia Pontolillo Alessandra Fabi Lorenzo Gerratana Sergio Pannunzio Ida Paris Sara Pilotto Fabio Marazzi Antonio Franco Gianluca Franceschini Maria Vittoria Dieci Roberta Mazzeo Fabio Puglisi Valentina Guarneri Michele Milella Giovanni Scambia Diana Giannarelli Giampaolo Tortora Emilio Bria Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy npj Breast Cancer |
| title | Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy |
| title_full | Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy |
| title_fullStr | Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy |
| title_full_unstemmed | Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy |
| title_short | Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy |
| title_sort | accessible model predicts response in hormone receptor positive her2 negative breast cancer receiving neoadjuvant chemotherapy |
| url | https://doi.org/10.1038/s41523-025-00727-w |
| work_keys_str_mv | AT lucamastrantoni accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT giovannagarufi accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT giuliagiordano accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT noemimaliziola accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT elenadimonte accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT giorgiaarcuri accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT valentinafrescura accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT angelachiararotondi accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT armandoorlandi accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT luisacarbognin accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT antonellapalazzo accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT federicamiglietta accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT letiziapontolillo accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT alessandrafabi accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT lorenzogerratana accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT sergiopannunzio accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT idaparis accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT sarapilotto accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT fabiomarazzi accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT antoniofranco accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT gianlucafranceschini accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT mariavittoriadieci accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT robertamazzeo accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT fabiopuglisi accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT valentinaguarneri accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT michelemilella accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT giovanniscambia accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT dianagiannarelli accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT giampaolotortora accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy AT emiliobria accessiblemodelpredictsresponseinhormonereceptorpositiveher2negativebreastcancerreceivingneoadjuvantchemotherapy |