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Lissidini G, Nicosia L, Sargenti M, Cucchi MC, Fabi A, Falco G, Gardani M, Grilz G, Maugeri I, Murgo R, Neri A, Pellini F, Sensi C, Scomersi S, Taffurelli M, Bagnardi V, Oriecuia C, Pagan E, Sangalli C, Dessena M, Veronesi P, Galimberti V. Male breast cancer: a multicenter study to provide a guide for proper management. Breast Cancer Res Treat 2024:10.1007/s10549-024-07380-0. [PMID: 38896332 DOI: 10.1007/s10549-024-07380-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/14/2024] [Indexed: 06/21/2024]
Abstract
INTRODUCTION To offer an extensive retrospective experience on the management of male breast cancer. METHODS A multicenter retrospective observational cohort study was conducted, including male patients diagnosed with breast cancer (invasive or in situ) in 12 Italian breast units from January 1975 to December 2019. Patients aged 18 years or older were assessed for eligibility. Exclusion criteria were metastatic cancer at diagnosis, previous cancer(s), received neoadjuvant treatment, incomplete data on (neo) adjuvant treatment(s), and/or follow-up data. Data on radiological examinations, demographic characteristics, risk factors, histological features, receptor status, treatments, and follow-up were collected. RESULTS In a series of 671 male patients with breast cancer assessed for eligibility, 403 (28 in situ and 375 invasive neoplasms) were included in the study. All included patients underwent surgery. The median age at surgery was 63.8 years (IQR 56.1-72.1). In 68% of cases, patients underwent echography, and in 55.1%, a mammography. Most patients were ER and PR positive (63.8%), HER2 negative (80.4%), with high (≥ 20%) Ki67 values (61.3%), and luminal B subtype (51.1%). The 10-year overall survival was 73.6% (95% CI 67.0-79.1) for invasive breast cancer and 90% (95% CI 65.6-97.4) for in situ breast cancer. In patients with invasive breast cancer, at univariable analysis, having a G3 tumor (vs. G1), pT2/3/4 (vs. pT1), pN2/3 (vs. pN0), luminal B subtype with Ki67 ≥ 20% (vs. Luminal A), were significantly associated with a higher risk of death. In multivariable analyses, pT2/3/4 (vs. pT1) remained significantly associated with a higher risk of death (HR 3.14, 95% CI 1.83-5.39), and having a HER2 positive or a triple-negative subtype (vs. Luminal A) was also significantly associated with a higher risk of mortality (HR 4.76, 95% CI 1.26-18.1). CONCLUSION Male breast cancer is a rare disease, the better understanding of which is necessary for a more effective diagnostic and therapeutic approach.
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Affiliation(s)
- Germana Lissidini
- Division of Breast Surgery, IEO, European Institute of Oncology IRCCS, 20141, Milan, Italy
| | - Luca Nicosia
- Division of Breast Radiology, IRCSS, IEO European Institute of Oncology, 20141, Milan, Italy.
| | - Manuela Sargenti
- Division of Breast Surgery, IEO, European Institute of Oncology IRCCS, 20141, Milan, Italy
| | | | - Alessandra Fabi
- Precision Medicine Unit in Senology, Fondazione Policlinico Universitario A. Gemelli IRCCS Rome, Rome, Italy
| | | | - Marco Gardani
- Department of Surgery, Breast Unit Piacenza, Guglielmo da Saliceto Hospital, Piacenza, Italy
| | - Greta Grilz
- Breast Surgery Unit Chirurgia Senologica Breast Unit Ospedale Cottolengo, Turin, Italy
| | - Ilaria Maugeri
- Breast Surgery Unit, Fondazione IRCCS Istituto Nazionale Tumori Di Milano, Milan, Italy
| | - Roberto Murgo
- Breast Surgery Unit, IRCCS Ospedale Casa Sollievo Della Sofferenza, San Giovanni Rotondo, Foggia, Italy
| | - Alessandro Neri
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Francesca Pellini
- Complex Operative Unit (UOC) Breast Surgery, Breast Unit, Oncology Department Azienda Ospedaliera, Universitaria Integrata (AOUI) Verona, Verona, Italy
| | | | - Serena Scomersi
- Division of General Surgery, Breast Unit, Hospital of Cattinara, Azienda Sanitaria Universitaria Giuliano Isontina, Trieste, Italy
| | - Mario Taffurelli
- Breast and General Surgery Unit, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, Bologna, Italy
| | - Vincenzo Bagnardi
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Chiara Oriecuia
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Eleonora Pagan
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Claudia Sangalli
- Clinical Trial Office, European Institute of Oncology IRCCS, 20141, Milan, Italy
| | - Massimo Dessena
- Breast Surgery Unit, Azienda Ospedaliero-Universitaria Di Cagliari, Cagliari, Italy
| | - Paolo Veronesi
- Division of Breast Surgery, IEO, European Institute of Oncology IRCCS, 20141, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Viviana Galimberti
- Division of Breast Surgery, IEO, European Institute of Oncology IRCCS, 20141, Milan, Italy
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Fan Z, Kernan KF, Sriram A, Benos PV, Canna SW, Carcillo JA, Kim S, Park HJ. Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems. Gigascience 2022; 12:giad044. [PMID: 37395630 PMCID: PMC10316696 DOI: 10.1093/gigascience/giad044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/31/2023] [Accepted: 05/29/2023] [Indexed: 07/04/2023] Open
Abstract
BACKGROUND Learning the causal structure helps identify risk factors, disease mechanisms, and candidate therapeutics for complex diseases. However, although complex biological systems are characterized by nonlinear associations, existing bioinformatic methods of causal inference cannot identify the nonlinear relationships and estimate their effect size. RESULTS To overcome these limitations, we developed the first computational method that explicitly learns nonlinear causal relations and estimates the effect size using a deep neural network approach coupled with the knockoff framework, named causal directed acyclic graphs using deep learning variable selection (DAG-deepVASE). Using simulation data of diverse scenarios and identifying known and novel causal relations in molecular and clinical data of various diseases, we demonstrated that DAG-deepVASE consistently outperforms existing methods in identifying true and known causal relations. In the analyses, we also illustrate how identifying nonlinear causal relations and estimating their effect size help understand the complex disease pathobiology, which is not possible using other methods. CONCLUSIONS With these advantages, the application of DAG-deepVASE can help identify driver genes and therapeutic agents in biomedical studies and clinical trials.
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Affiliation(s)
- Zhenjiang Fan
- Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Kate F Kernan
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, Children's Hospital of Pittsburgh, Center for Critical Care Nephrology and Clinical Research Investigation and Systems Modeling of Acute Illness Center, University of Pittsburgh, Pittsburgh, PA 15260,USA
| | - Aditya Sriram
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Panayiotis V Benos
- Department of Epidemiology, University of Florida, Gainesville, FL 32610, USA
| | - Scott W Canna
- Pediatric Rheumatology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Joseph A Carcillo
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, Children's Hospital of Pittsburgh, Center for Critical Care Nephrology and Clinical Research Investigation and Systems Modeling of Acute Illness Center, University of Pittsburgh, Pittsburgh, PA 15260,USA
| | - Soyeon Kim
- Division of Pediatric Pulmonary Medicine, Children's Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Hyun Jung Park
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Garutti M, Griguolo G, Botticelli A, Buzzatti G, De Angelis C, Gerratana L, Molinelli C, Adamo V, Bianchini G, Biganzoli L, Curigliano G, De Laurentiis M, Fabi A, Frassoldati A, Gennari A, Marchiò C, Perrone F, Viale G, Zamagni C, Zambelli A, Del Mastro L, De Placido S, Guarneri V, Marchetti P, Puglisi F. Definition of High-Risk Early Hormone-Positive HER2−Negative Breast Cancer: A Consensus Review. Cancers (Basel) 2022; 14:cancers14081898. [PMID: 35454806 PMCID: PMC9029479 DOI: 10.3390/cancers14081898] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/06/2022] [Accepted: 04/06/2022] [Indexed: 11/16/2022] Open
Abstract
Breast cancer is one of the major causes of cancer-related morbidity and mortality in women worldwide. During the past three decades, several improvements in the adjuvant treatment of hormone receptor-positive/HER2−negative breast cancer have been achieved with the introduction of optimized adjuvant chemotherapy and endocrine treatment. However, estimating the risk of relapse of breast cancer on an individual basis is still challenging. The IRIDE (hIGh Risk DEfinition in breast cancer) working group was established with the aim of reviewing evidence from the literature to synthesize the current relevant features that predict hormone-positive/HER2−negative early breast cancer relapse. A panel of experts in breast cancer was involved in identifying clinical, pathological, morphological, and genetic factors. A RAND consensus method was used to define the relevance of each risk factor. Among the 21 features included, 12 were considered relevant risk factors for relapse. For each of these, we provided a consensus statement and relevant comments on the supporting scientific evidence. This work may guide clinicians in the practical management of hormone-positive/HER2−negative early breast cancers.
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Affiliation(s)
- Mattia Garutti
- CRO Aviano, National Cancer Institute, IRCCS, 33081 Aviano, Italy; (L.G.); (F.P.)
- Correspondence: ; Tel.: +39-04-3465-9092
| | - Gaia Griguolo
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35100 Padova, Italy; (G.G.); (V.G.)
- Division of Oncology 2, Istituto Oncologico Veneto IRCCS, 35100 Padova, Italy
| | - Andrea Botticelli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Policlinico Umberto I, 00100 Rome, Italy;
| | - Giulia Buzzatti
- Department of Medical Oncology, IRCCS Ospedale Policlinico San Martino, 16100 Genova, Italy; (G.B.); (C.M.); (L.D.M.)
| | - Carmine De Angelis
- Department of Clinical Medicine and Surgery, University of Naples Federico II, 80100 Naples, Italy; (C.D.A.); (S.D.P.)
| | - Lorenzo Gerratana
- CRO Aviano, National Cancer Institute, IRCCS, 33081 Aviano, Italy; (L.G.); (F.P.)
| | - Chiara Molinelli
- Department of Medical Oncology, IRCCS Ospedale Policlinico San Martino, 16100 Genova, Italy; (G.B.); (C.M.); (L.D.M.)
| | - Vincenzo Adamo
- Department of Human Pathology, Papardo Hospital, University of Messina, 89121 Messina, Italy;
| | - Giampaolo Bianchini
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, 20132 Milan, Italy;
- School of Medicine and Surgery, Università Vita-Salute San Raffaele, 20020 Milan, Italy
| | - Laura Biganzoli
- Ospedale Santo Stefano, Prato Sandro Pitigliani Medical Oncology Division, Hospital of Prato, 59100 Prato, Italy;
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development, European Institute of Oncology IRCCS, 20100 Milan, Italy;
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy;
| | - Michelino De Laurentiis
- Department of Breast and Thoracic Oncology, IRCCS INT Fondazione G. Pascale, 80144 Napoli, Italy;
| | - Alessandra Fabi
- Precision Medicine in Breast Cancer Unit, Department of Woman and Child Health and Public Health, IRCCS, Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli, 00168 Rome, Italy;
| | - Antonio Frassoldati
- Department of Traslational Medicine and for Romagna, Clinical Oncology, S Anna University Hospital, Università degli Studi di Ferrara, 44121 Ferrara, Italy;
| | - Alessandra Gennari
- Department of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, Italy;
- Azienda Ospedaliero-Universitaria Maggiore della Carità, 28100 Novara, Italy
| | - Caterina Marchiò
- Candiolo Cancer Institute, FPO IRCCS, 10060 Candiolo, Italy;
- Department of Medical Sciences, University of Turin, 10126 Turin, Italy
| | - Francesco Perrone
- Clinical Trials Unit, Istituto Nazionale Tumori di Napoli, IRCCS Fondazione Pascale, 80144 Naples, Italy;
| | - Giuseppe Viale
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy;
- Department of Pathology, European Institute of Oncology IRCCS, 20122 Milan, Italy
| | - Claudio Zamagni
- Medical Oncology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Azienda Ospedaliero-Universitaria di Bologna, 40100 Bologna, Italy;
| | - Alberto Zambelli
- Breast Cancer Section Department of Biomedical Sciences, IRCCS Humanitas Research Hospital, Humanitas University, Rozzano, 20089 Milan, Italy;
| | - Lucia Del Mastro
- Department of Medical Oncology, IRCCS Ospedale Policlinico San Martino, 16100 Genova, Italy; (G.B.); (C.M.); (L.D.M.)
- Dipartimento di Medicina Interna e Specialità Mediche, University of Genova, 16159 Genova, Italy
| | - Sabino De Placido
- Department of Clinical Medicine and Surgery, University of Naples Federico II, 80100 Naples, Italy; (C.D.A.); (S.D.P.)
| | - Valentina Guarneri
- Department of Surgery, Oncology and Gastroenterology, University of Padova, 35100 Padova, Italy; (G.G.); (V.G.)
- Division of Oncology 2, Istituto Oncologico Veneto IRCCS, 35100 Padova, Italy
| | - Paolo Marchetti
- IRCCS Istituto Dermopatico dell’Immacolata (IDI-IRCCS), 00167 Rome, Italy;
| | - Fabio Puglisi
- CRO Aviano, National Cancer Institute, IRCCS, 33081 Aviano, Italy; (L.G.); (F.P.)
- Department of Medicine, University of Udine, 33100 Udine, Italy
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Plichta JK, Thomas SM, Sergesketter AR, Greenup RA, Rosenberger LH, Fayanju OM, Kimmick G, Force J, Hyslop T, Hwang ES. A Novel Staging System for De Novo Metastatic Breast Cancer Refines Prognostic Estimates. Ann Surg 2022; 275:784-792. [PMID: 32657941 PMCID: PMC7794098 DOI: 10.1097/sla.0000000000004231] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE We aim to identify prognostic groups within a de novo metastatic cohort, incorporating both anatomic and biologic factors. BACKGROUND Staging for breast cancer now includes anatomic and biologic factors, although the guidelines for stage IV disease do not account for how these factors may influence outcomes. METHODS Adults with de novo metastatic breast cancer were selected from the National Cancer DataBase (2010-2013). Recursive partitioning analysis was used to group patients with similar overall survival (OS) based on clinical T/N stage, tumor grade, ER, PR, HER2, number of metastatic sites, and presence of bone-only metastases. Categories were created by amalgamating homogeneous groups based on 3-year OS rates (stage IVA: >50%, stage IVB: 30%-50%, stage IVC: <30%). RESULTS 16,187 patients were identified; median follow-up was 32 months. 65.2% had 1 site of distant metastasis, and 42.9% had bone-only metastases. Recursive partitioning analysis identified the number of metastatic sites (1 vs >1) as the first stratification point, and ER status as the second stratification point for both resulting groups. Additional divisions were made based on HER2 status, PR status, cT stage, tumor grade, and presence of bone-only metastases. After bootstrapping, significant differences in 3-year OS were noted between the 3 groups [stage IVB vs IVA: HR 1.58 (95% confidence interval 1.50-1.67), stage IVC vs IVA: HR 3.54 (95% confidence interval 3.33-3.77)]. CONCLUSIONS Both anatomic and biologic factors yielded reliable and reproducible prognostic estimates among patients with metastatic disease. These findings support formal stratification of de novo stage IV breast cancer into 3 distinct prognosis groups.
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Affiliation(s)
- Jennifer K. Plichta
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
- Duke Cancer Institute, Durham, North Carolina
| | - Samantha M. Thomas
- Duke Cancer Institute, Durham, North Carolina
- Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina
| | | | - Rachel A. Greenup
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
- Duke Cancer Institute, Durham, North Carolina
| | - Laura H. Rosenberger
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
- Duke Cancer Institute, Durham, North Carolina
| | - Oluwadamilola M. Fayanju
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
- Duke Cancer Institute, Durham, North Carolina
- Durham VA Medical Center, Durham, North Carolina
| | - Gretchen Kimmick
- Duke Cancer Institute, Durham, North Carolina
- Department of Medical Oncology, Duke University Medical Center, Durham, North Carolina
| | - Jeremy Force
- Duke Cancer Institute, Durham, North Carolina
- Department of Medical Oncology, Duke University Medical Center, Durham, North Carolina
| | - Terry Hyslop
- Duke Cancer Institute, Durham, North Carolina
- Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina
| | - E. Shelley Hwang
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
- Duke Cancer Institute, Durham, North Carolina
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Li GQ, Yu Y, Zhang WW, Zhou P, Lian CL, He ZY, Wu SG. OUP accepted manuscript. BJS Open 2022; 6:6573397. [PMID: 35466375 PMCID: PMC9035436 DOI: 10.1093/bjsopen/zrac025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 11/29/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
Background Methods Results Conclusion
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Affiliation(s)
- Guan-Qiao Li
- Department of Breast Surgery, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, People’s Republic of China
| | - Yang Yu
- Department of Breast Surgery, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, People’s Republic of China
| | - Wen-Wen Zhang
- Department of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, People’s Republic of China
| | - Ping Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of Xiamen University, Xiamen, People’s Republic of China
| | - Chen-Lu Lian
- Department of Radiation Oncology, The First Affiliated Hospital of Xiamen University, Xiamen, People’s Republic of China
| | - Zhen-Yu He
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, People’s Republic of China
- Correspondence to: Zhen-Yu He, Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, 651 Dongfeng Road East, Guangzhou 510060, People’s Republic of China (e-mail: ); San-Gang Wu Department of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen 361003, People’s Republic of China (e-mail: )
| | - San-Gang Wu
- Department of Radiation Oncology, The First Affiliated Hospital of Xiamen University, Xiamen, People’s Republic of China
- Correspondence to: Zhen-Yu He, Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, 651 Dongfeng Road East, Guangzhou 510060, People’s Republic of China (e-mail: ); San-Gang Wu Department of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen 361003, People’s Republic of China (e-mail: )
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Caselli E, Pelliccia C, Teti V, Bellezza G, Mandarano M, Ferri I, Hartmann K, Laible M, Sahin U, Varga Z, Lupi C, Stracci F, Sidoni A. Looking for more reliable biomarkers in breast cancer: Comparison between routine methods and RT-qPCR. PLoS One 2021; 16:e0255580. [PMID: 34555047 PMCID: PMC8460001 DOI: 10.1371/journal.pone.0255580] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 07/19/2021] [Indexed: 01/01/2023] Open
Abstract
PURPOSE Decades of quality control efforts have raised the standards of immunohistochemistry (IHC), the principle method used for biomarker testing in breast cancer; however, computational pathology and reverse transcription quantitative PCR (RT-qPCR) may also hold promise for additional substantial improvements. METHODS Herein, we investigated discrepancies in the assessment of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and marker of proliferation Ki67 comparing routinely obtained IHC (and FISH) data (ORI) with the results of manual (REV) and semi-automated (DIA) re-evaluation of the original IHC slides and then with RNA expression data from the same tissue block using the MammaTyper® (MT) gene expression assay. RESULTS Correlation for ER and PR was high between ORI IHC and the other three study methods (REV, DIA and RT-qPCR). For HER2, 10 out of 96 discrepant cases can be detected between ORI and REV that involved at least one call in the equivocal category (except for one case). For Ki67, 22 (29.1%) cases were categorized differently by either REV alone (n = 17), DIA alone (n = 15) or both (n = 10) and 28 cases (29.2%) for RT-qPCR. Most of the discrepant Ki67 cases changed from low to high between the original and following assessment and belonged to the intermediate Ki67 expression range (between 9 and 30%). CONCLUSIONS Determination of the breast cancer biomarkers ER, PR, HER2 and Ki67 at the mRNA level shows high degree of correlation with IHC and compares well with correlations between original with subsequent independent manual or semi-automated IHC assessments. The use of methods with wider dynamic range and higher reproducibility such as RT-qPCR may offer more precise assessment of endocrine responsiveness, improve Ki67 standardization and help resolve HER2 cases that remain equivocal or ambiguous by IHC/FISH. In summary, our findings seem to configure RT-qPCR as a complementary method to be used in cases of either equivocal results or presenting, at the traditional determination assays, biomarkers expressions close to the cut-off values.
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Affiliation(s)
- Emanuele Caselli
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Cristina Pelliccia
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Valeria Teti
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Guido Bellezza
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Martina Mandarano
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Ivana Ferri
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | | | | | - Ugur Sahin
- BioNTech Diagnostics GmbH, Mainz, Germany
| | - Zsuzsanna Varga
- Institute for Pathology and Molecular Pathology, Universitätsspital Zürich, Zürich, Switzerland
| | | | - Fabrizio Stracci
- Umbria Cancer Registry, Perugia, Italy
- Department of Medicine and Surgery, Section of Public Health, University of Perugia, Perugia, Italy
| | - Angelo Sidoni
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
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Kantor O, Laws A, Pastorello RG, King C, Wong S, Dey T, Schnitt S, King TA, Mittendorf EA. Comparison of Breast Cancer Staging Systems After Neoadjuvant Chemotherapy. Ann Surg Oncol 2021; 28:7347-7355. [PMID: 33956276 DOI: 10.1245/s10434-021-09951-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/18/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND No consensus exists for optimal staging following neoadjuvant chemotherapy (NAC). We compared the performance of the American Joint Committee on Cancer (AJCC) pathologic prognostic staging system, Residual Cancer Burden (RCB) Index, and the Neo-Bioscore in breast cancer patients after NAC. METHODS Patients with stage I-III breast cancer who received NAC at Dana-Farber Cancer Institute from 2004 to 2014 were identified. Kaplan-Meier curves were used to estimate disease-free survival (DFS) and overall survival (OS), and model fits were compared by receiver operator characteristic (ROC) curve using the c-statistic and DeLong's test. RESULTS Overall, 802 patients with a median age of 48 years received NAC. Most patients presented with cT2 (n = 470, 58.6%) and cN1 (n = 422, 52.6%) disease. The subtype was estrogen receptor (ER)- and/or progesterone receptor (PR)-positive/human epidermal growth factor receptor 2 (HER2)-negative in 296 (36.9%) patients, HER2-positive in 261 (32.5%) patients, and triple-negative in 245 (30.5%) patients. Median follow-up was 79.5 months. There were 174 recurrences (30 local, 25 regional, 145 distant), with 676 (76.8%) patients alive at last follow-up. AJCC pathologic prognostic staging and RCB had better discrimination for estimated 7-year DFS and OS compared with the Neo-Bioscore. The ROC c-statistics for DFS model fit were similar for AJCC pathologic prognostic stage (0.72) and RCB (0.71, p = non-significant); both had improved model fit versus the Neo-Bioscore (0.65, p < 0.01). The c-statistics for OS were 0.74, 0.71, and 0.70 for AJCC pathologic prognostic stage, RCB, and Neo-Bioscore, respectively (p = non-significant). CONCLUSIONS These results validate the ability of these staging systems to stratify survival outcomes in NAC patients, with best discrimination achieved using AJCC pathologic prognostic stage or RCB.
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Affiliation(s)
- Olga Kantor
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.,Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA
| | - Alison Laws
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.,Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA
| | - Ricardo G Pastorello
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.,Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA
| | - Claire King
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Stephanie Wong
- Department of Surgery, McGill University Medical School, Montreal, QC, Canada
| | - Tanujit Dey
- Center for Surgery and Public Health, Brigham and Women's Hospital, Boston, MA, USA
| | - Stuart Schnitt
- Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA.,Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Tari A King
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.,Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA
| | - Elizabeth A Mittendorf
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA. .,Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA.
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8
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Yu X, Cao S, Zhou Y, Yu Z, Xu Y. Co-expression based cancer staging and application. Sci Rep 2020; 10:10624. [PMID: 32606385 PMCID: PMC7327081 DOI: 10.1038/s41598-020-67476-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 06/01/2020] [Indexed: 11/09/2022] Open
Abstract
A novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample. The R source code used in this study is available at GitHub (https://github.com/yxchspring/CECS).
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Affiliation(s)
- Xiangchun Yu
- College of Computer Science and Technology, Jilin University, Changchun, China
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, USA
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
| | - Sha Cao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, USA
| | - Yi Zhou
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, USA
| | - Zhezhou Yu
- College of Computer Science and Technology, Jilin University, Changchun, China.
| | - Ying Xu
- Cancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun, China.
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, USA.
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9
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Dieci MV, Bisagni G, Brandes AA, Frassoldati A, Cavanna L, Giotta F, Aieta M, Gebbia V, Musolino A, Garrone O, Donadio M, Rimanti A, Beano A, Zamagni C, Soto Parra H, Piacentini F, Danese S, Ferro A, Cagossi K, Sarti S, Gambaro AR, Romito S, Bazan V, Amaducci L, Moretti G, Foschini MP, Balduzzi S, Vicini R, D'Amico R, Griguolo G, Guarneri V, Conte PF. Validation of the AJCC prognostic stage for HER2-positive breast cancer in the ShortHER trial. BMC Med 2019; 17:207. [PMID: 31747948 PMCID: PMC6868696 DOI: 10.1186/s12916-019-1445-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 10/04/2019] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND The 8th edition of the American Joint Committee on Cancer (AJCC) staging has introduced prognostic stage based on anatomic stage combined with biologic factors. We aimed to validate the prognostic stage in HER2-positive breast cancer patients enrolled in the ShortHER trial. METHODS The ShortHER trial randomized 1253 HER2-positive patients to 9 weeks or 1 year of adjuvant trastuzumab combined with chemotherapy. Patients were classified according to the anatomic and the prognostic stage. Distant disease-free survival (DDFS) was calculated from randomization to distant relapse or death. RESULTS A total of 1244 patients were included. Compared to anatomic stage, the prognostic stage downstaged 41.6% (n = 517) of patients to a more favorable stage category. Five-year DDFS based on anatomic stage was as follows: IA 96.6%, IB 94.1%, IIA 92.4%, IIB 87.3%, IIIA 81.3%, IIIC 70.5% (P < 0.001). Five-year DDFS according to prognostic stage was as follows: IA 95.7%, IB 91.4%, IIA 86.9%, IIB 85.0%, IIIA 77.6%, IIIC 67.7% (P < 0.001). The C index was similar (0.69209 and 0.69249, P = 0.975). Within anatomic stage I, the outcome was similar for patients treated with 9 weeks or 1 year trastuzumab (5-year DDFS 96.2% and 96.6%, P = 0.856). Within prognostic stage I, the outcome was numerically worse for patients treated with 9 weeks trastuzumab (5-year DDFS 93.7% and 96.3%, P = 0.080). CONCLUSIONS The prognostic stage downstaged 41.6% of patients, while maintaining a similar prognostic performance as the anatomic stage. The prognostic stage is valuable in counseling patients and may serve as reference for a clinical trial design. Our data do not support prognostic stage as guidance to de-escalate treatment. TRIAL REGISTRATION EUDRACT number: 2007-004326-25; NCI ClinicalTrials.gov number: NCT00629278.
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Affiliation(s)
- Maria Vittoria Dieci
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy.,Medical Oncology 2, Istituto Oncologico Veneto - IRCCS, Via Gattamelata 64, 35128, Padova, Italy
| | - Giancarlo Bisagni
- Department of Oncology and Advanced Technologies, Oncology Unit, Azienda USL-IRCCS, Reggio Emilia, Italy
| | - Alba A Brandes
- Medical Oncology, Azienda Unità Sanitaria Locale di Bologna-IRCCS Istituto delle Scienze Neurologiche, Bologna, Italy
| | - Antonio Frassoldati
- Clinical Oncology, Department of Morphology, Surgery and Experimental Medicine, S Anna University Hospital, Ferrara, Italy
| | - Luigi Cavanna
- Department of Oncology-Hematology, G. da Saliceto Hospital, Piacenza, Italy
| | | | - Michele Aieta
- Division of Medical Oncology, IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero Vulture, Italy
| | - Vittorio Gebbia
- Medical Oncology, Casa di Cura La Maddalena, University of Palermo, Palermo, Italy
| | | | - Ornella Garrone
- Medical Oncology, A.O. S. Croce and Carle Teaching Hospital, Cuneo, Italy
| | - Michela Donadio
- Department of Medical Oncology 1, Città della Salute e della Scienza Hospital, Turin, Italy
| | - Anita Rimanti
- Medical Oncology, Azienda Ospedaliera di Mantova, Mantova, Italy
| | - Alessandra Beano
- Department of Medical Oncology 1, Città della Salute e della Scienza Hospital, Turin, Italy
| | - Claudio Zamagni
- Policlinico S.Orsola-Malpighi, SSD Oncologia Medica Addarii, Bologna, Italy
| | - Hector Soto Parra
- Medical Oncology Unit, AOU Policlinico Vittorio Emanuele, Catania, Italy
| | - Federico Piacentini
- Division of Medical Oncology Department of Medical and Surgical Sciences for Children & Adults, University Hospital of Modena, Modena, Italy
| | - Saverio Danese
- Department of Gynecology and Obstetrics, Ospedale S. Anna, Turin, Italy
| | - Antonella Ferro
- Rete clinica senologica - Oncologia medica S. Chiara, Trento, Italy
| | - Katia Cagossi
- Breast Unit Ausl Modena, Ramazzini Hospital, Carpi, Italy
| | - Samanta Sarti
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | | | - Sante Romito
- Medical Oncology, A.O.U. "Ospedali Riuniti", Foggia, Italy
| | - Viviana Bazan
- Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Laura Amaducci
- Medical Oncology Unit, Ospedale degli Infermi Faenza, Faenza, Italy
| | - Gabriella Moretti
- Department of Oncology and Advanced Technologies, Oncology Unit, Azienda USL-IRCCS, Reggio Emilia, Italy
| | - Maria Pia Foschini
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Unit of Anatomic Pathology at Bellaria Hospital, Bologna, Italy
| | - Sara Balduzzi
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena, Modena, Italy
| | - Roberto Vicini
- Azienda Ospedaliero-Universitaria di Modena, Modena, Italy
| | - Roberto D'Amico
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena, Modena, Italy.,Azienda Ospedaliero-Universitaria di Modena, Modena, Italy
| | - Gaia Griguolo
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy.,Medical Oncology 2, Istituto Oncologico Veneto - IRCCS, Via Gattamelata 64, 35128, Padova, Italy
| | - Valentina Guarneri
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy.,Medical Oncology 2, Istituto Oncologico Veneto - IRCCS, Via Gattamelata 64, 35128, Padova, Italy
| | - Pier Franco Conte
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy. .,Medical Oncology 2, Istituto Oncologico Veneto - IRCCS, Via Gattamelata 64, 35128, Padova, Italy.
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10
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Insights on CTC Biology and Clinical Impact Emerging from Advances in Capture Technology. Cells 2019; 8:cells8060553. [PMID: 31174404 PMCID: PMC6627072 DOI: 10.3390/cells8060553] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 05/31/2019] [Accepted: 06/03/2019] [Indexed: 01/01/2023] Open
Abstract
Circulating tumor cells (CTCs) and circulating tumor microemboli (CTM) have been shown to correlate negatively with patient survival. Actual CTC counts before and after treatment can be used to aid in the prognosis of patient outcomes. The presence of circulating tumor materials (CTMat) can advertise the presence of metastasis before clinical presentation, enabling the early detection of relapse. Importantly, emerging evidence is indicating that cancer treatments can actually increase the incidence of CTCs and metastasis in pre-clinical models. Subsequently, the study of CTCs, their biology and function are of vital importance. Emerging technologies for the capture of CTC/CTMs and CTMat are elucidating vitally important biological and functional information that can lead to important alterations in how therapies are administered. This paves the way for the development of a "liquid biopsy" where treatment decisions can be informed by information gleaned from tumor cells and tumor cell debris in the blood.
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