1
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Wang Y, Broeks A, Giardiello D, Hauptmann M, Jóźwiak K, Koop EA, Opdam M, Siesling S, Sonke GS, Stathonikos N, Ter Hoeve ND, van der Wall E, van Deurzen CHM, van Diest PJ, Voogd AC, Vreuls W, Linn SC, Dackus GMHE, Schmidt MK. External validation and clinical utility assessment of PREDICT breast cancer prognostic model in young, systemic treatment-naïve women with node-negative breast cancer. Eur J Cancer 2023; 195:113401. [PMID: 37925965 DOI: 10.1016/j.ejca.2023.113401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/19/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its clinical utility in young women with node-negative breast cancer who did not receive systemic treatment. METHODS We selected all women from the Netherlands Cancer Registry who were diagnosed with node-negative breast cancer under age 40 between 1989 and 2000, a period when adjuvant systemic treatment was not standard practice for women with node-negative disease. We evaluated the calibration and discrimination of PREDICT using the observed/expected (O/E) mortality ratio, and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, we compared the potential clinical utility of PREDICT for selectively administering chemotherapy to the chemotherapy-to-all strategy using decision curve analysis at predefined thresholds. RESULTS A total of 2264 women with a median age at diagnosis of 36 years were included. Of them, 71.2% had estrogen receptor (ER)-positive tumors and 44.0% had grade 3 tumors. Median tumor size was 16 mm. PREDICT v2.2 underestimated 10-year all-cause mortality by 33% in all women (O/E ratio:1.33, 95%CI:1.22-1.43). Model discrimination was moderate overall (AUC10-year:0.65, 95%CI:0.62-0.68), and poor for women with ER-negative tumors (AUC10-year:0.56, 95%CI:0.51-0.62). Compared to the chemotherapy-to-all strategy, PREDICT only showed a slightly higher net benefit in women with ER-positive tumors, but not in women with ER-negative tumors. CONCLUSIONS PREDICT yields unreliable predictions for young women with node-negative breast cancer. Further model updates are needed before PREDICT can be routinely used in this patient subset.
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Affiliation(s)
- Yuwei Wang
- Department of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Annegien Broeks
- Core Facility Molecular Pathology and Biobanking, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Daniele Giardiello
- Department of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam, the Netherlands; Eurac Research, Institute of Biomedicine, Epidemiology and Biostatistics, Bolzano, Italy
| | - Michael Hauptmann
- Institute of Biostatistics and Registry Research, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Katarzyna Jóźwiak
- Institute of Biostatistics and Registry Research, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Esther A Koop
- Department of Pathology, Gelre Ziekenhuizen, Apeldoorn, the Netherlands
| | - Mark Opdam
- Department of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Sabine Siesling
- Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands; Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands
| | - Gabe S Sonke
- Department of Medical Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Nikolas Stathonikos
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Natalie D Ter Hoeve
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Elsken van der Wall
- Division of Internal Medicine and Dermatology, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Adri C Voogd
- Department of Epidemiology, Maastricht University, Maastricht, the Netherlands
| | - Willem Vreuls
- Department of Pathology, Canisius Wilhelmina Ziekenhuis, Nijmegen, the Netherlands
| | - Sabine C Linn
- Department of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Medical Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gwen M H E Dackus
- Department of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Marjanka K Schmidt
- Department of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands.
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Giardiello D, Melotti R, Barbieri G, Gögele M, Weichenberger CX, Foco L, Bottigliengo D, Barin L, Lundin R, Pramstaller PP, Pattaro C. Determinants of SARS-CoV-2 nasopharyngeal testing in a rural community sample susceptible of first infection: the CHRIS COVID-19 study. Pathog Glob Health 2023; 117:744-753. [PMID: 36992656 PMCID: PMC10614704 DOI: 10.1080/20477724.2023.2191232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023] Open
Abstract
To characterize COVID-19 epidemiology, numerous population-based studies have been undertaken to model the risk of SARS-CoV-2 infection. Less is known about what may drive the probability to undergo testing. Understanding how much testing is driven by contextual or individual conditions is important to delineate the role of individual behavior and to shape public health interventions and resource allocation. In the Val Venosta/Vinschgau district (South Tyrol, Italy), we conducted a population-representative longitudinal study on 697 individuals susceptible to first infection who completed 4,512 repeated online questionnaires at four-week intervals between September 2020 and May 2021. Mixed-effects logistic regression models were fitted to investigate associations of self-reported SARS-CoV-2 testing with individual characteristics (social, demographic, and biological) and contextual determinants. Testing was associated with month of reporting, reflecting the timing of both the pandemic intensity and public health interventions, COVID-19-related symptoms (odds ratio, OR:8.26; 95% confidence interval, CI:6.04-11.31), contacts with infected individuals within home (OR:7.47, 95%CI:3.81-14.62) or outside home (OR:9.87, 95%CI:5.78-16.85), and being retired (OR:0.50, 95%CI:0.34-0.73). Symptoms and next within- and outside-home contacts were the leading determinants of swab testing predisposition in the most acute phase of the pandemics. Testing was not associated with age, sex, education, comorbidities, or lifestyle factors. In the study area, contextual determinants reflecting the course of the pandemic were predominant compared to individual sociodemographic characteristics in explaining the SARS-CoV-2 probability of testing. Decision makers should evaluate whether the intended target groups were correctly prioritized by the testing campaign.
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Affiliation(s)
- Daniele Giardiello
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Roberto Melotti
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Giulia Barbieri
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Martin Gögele
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | | | - Luisa Foco
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Daniele Bottigliengo
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Laura Barin
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Rebecca Lundin
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Peter P. Pramstaller
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
- Department of Neurology, General Central Hospital, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
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Barbieri G, Pizzato M, Gögele M, Giardiello D, Weichenberger CX, Foco L, Bottigliengo D, Bertelli C, Barin L, Lundin R, Pramstaller PP, Pattaro C, Melotti R. Trends and symptoms of SARS-CoV-2 infection: a longitudinal study on an Alpine population representative sample. BMJ Open 2023; 13:e072650. [PMID: 37290944 PMCID: PMC10254957 DOI: 10.1136/bmjopen-2023-072650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/18/2023] [Indexed: 06/10/2023] Open
Abstract
OBJECTIVES The continuous monitoring of SARS-CoV-2 infection waves and the emergence of novel pathogens pose a challenge for effective public health surveillance strategies based on diagnostics. Longitudinal population representative studies on incident events and symptoms of SARS-CoV-2 infection are scarce. We aimed at describing the evolution of the COVID-19 pandemic during 2020 and 2021 through regular monitoring of self-reported symptoms in an Alpine community sample. DESIGN To this purpose, we designed a longitudinal population representative study, the Cooperative Health Research in South Tyrol COVID-19 study. PARTICIPANTS AND OUTCOME MEASURES A sample of 845 participants was retrospectively investigated for active and past infections with swab and blood tests, by August 2020, allowing adjusted cumulative incidence estimation. Of them, 700 participants without previous infection or vaccination were followed up monthly until July 2021 for first-time infection and symptom self-reporting: COVID-19 anamnesis, social contacts, lifestyle and sociodemographic data were assessed remotely through digital questionnaires. Temporal symptom trajectories and infection rates were modelled through longitudinal clustering and dynamic correlation analysis. Negative binomial regression and random forest analysis assessed the relative importance of symptoms. RESULTS At baseline, the cumulative incidence of SARS-CoV-2 infection was 1.10% (95% CI 0.51%, 2.10%). Symptom trajectories mimicked both self-reported and confirmed cases of incident infections. Cluster analysis identified two groups of high-frequency and low-frequency symptoms. Symptoms like fever and loss of smell fell in the low-frequency cluster. Symptoms most discriminative of test positivity (loss of smell, fatigue and joint-muscle aches) confirmed prior evidence. CONCLUSIONS Regular symptom tracking from population representative samples is an effective screening tool auxiliary to laboratory diagnostics for novel pathogens at critical times, as manifested in this study of COVID-19 patterns. Integrated surveillance systems might benefit from more direct involvement of citizens' active symptom tracking.
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Affiliation(s)
- Giulia Barbieri
- Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Massimo Pizzato
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Martin Gögele
- Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Daniele Giardiello
- Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
| | | | - Luisa Foco
- Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Daniele Bottigliengo
- Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Cinzia Bertelli
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Laura Barin
- Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Rebecca Lundin
- Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Peter P Pramstaller
- Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Cristian Pattaro
- Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Roberto Melotti
- Institute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Bolzano, Italy
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Austin PC, Giardiello D, van Buuren S. Impute-then-exclude versus exclude-then-impute: Lessons when imputing a variable used both in cohort creation and as an independent variable in the analysis model. Stat Med 2023; 42:1525-1541. [PMID: 36807923 DOI: 10.1002/sim.9685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 10/18/2022] [Accepted: 02/03/2023] [Indexed: 02/21/2023]
Abstract
We examined the setting in which a variable that is subject to missingness is used both as an inclusion/exclusion criterion for creating the analytic sample and subsequently as the primary exposure in the analysis model that is of scientific interest. An example is cancer stage, where patients with stage IV cancer are often excluded from the analytic sample, and cancer stage (I to III) is an exposure variable in the analysis model. We considered two analytic strategies. The first strategy, referred to as "exclude-then-impute," excludes subjects for whom the observed value of the target variable is equal to the specified value and then uses multiple imputation to complete the data in the resultant sample. The second strategy, referred to as "impute-then-exclude," first uses multiple imputation to complete the data and then excludes subjects based on the observed or filled-in values in the completed samples. Monte Carlo simulations were used to compare five methods (one based on "exclude-then-impute" and four based on "impute-then-exclude") along with the use of a complete case analysis. We considered both missing completely at random and missing at random missing data mechanisms. We found that an impute-then-exclude strategy using substantive model compatible fully conditional specification tended to have superior performance across 72 different scenarios. We illustrated the application of these methods using empirical data on patients hospitalized with heart failure when heart failure subtype was used for cohort creation (excluding subjects with heart failure with preserved ejection fraction) and was also an exposure in the analysis model.
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Affiliation(s)
- Peter C Austin
- ICES, Toronto, Ontario, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.,Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Daniele Giardiello
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Stef van Buuren
- University of Utrecht, Utrecht, The Netherlands.,Netherlands Organisation for Applied Scientific Research TNO, Leiden, The Netherlands
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5
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McLernon DJ, Giardiello D, Van Calster B, Wynants L, van Geloven N, van Smeden M, Therneau T, Steyerberg EW. Assessing Performance and Clinical Usefulness in Prediction Models With Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models. Ann Intern Med 2023; 176:105-114. [PMID: 36571841 DOI: 10.7326/m22-0844] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Risk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time horizon at which predictions can be made. This article describes measures to evaluate predictions and the potential improvement in decision making from survival models based on Cox proportional hazards regression. As a motivating case study, the authors consider the prediction of the composite outcome of recurrence or death (the "event") in patients with breast cancer after surgery. They developed a simple Cox regression model with 3 predictors, as in the Nottingham Prognostic Index, in 2982 women (1275 events over 5 years of follow-up) and externally validated this model in 686 women (285 events over 5 years). Improvement in performance was assessed after the addition of progesterone receptor as a prognostic biomarker. The model predictions can be evaluated across the full range of observed follow-up times or for the event occurring by the end of a fixed time horizon of interest. The authors first discuss recommended statistical measures that evaluate model performance in terms of discrimination, calibration, or overall performance. Further, they evaluate the potential clinical utility of the model to support clinical decision making according to a net benefit measure. They provide SAS and R code to illustrate internal and external validation. The authors recommend the proposed set of performance measures for transparent reporting of the validity of predictions from survival models.
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Affiliation(s)
- David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom (D.J.M.)
| | - Daniele Giardiello
- Netherlands Cancer Institute, Amsterdam, the Netherlands, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands, and Institute of Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy (D.G.)
| | - Ben Van Calster
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands, and Department of Development and Regeneration, Katholieke Universiteit Leuven, Leuven, Belgium (B.V.)
| | - Laure Wynants
- School for Public Health and Primary Care, Maastricht University, Maastricht, the Netherlands (L.W.)
| | - Nan van Geloven
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands (N.V., E.W.S.)
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands (M.V.)
| | - Terry Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota (T.T.)
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands (N.V., E.W.S.)
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Wang Y, Dackus G, Broeks A, Giardiello D, Hauptmann M, Jóźwiak K, Koop E, Opdam M, Siesling S, Sonke G, Stathonikos N, ter Hoeve N, van der Wall E, van Duerzen C, van Diest P, Voogd A, Vreuls W, Linn S, Schmidt M. External validation and clinical utility assessment of PREDICT v2.2 prognostic model in young, node-negative, systemic treatment-naïve breast cancer patients. Eur J Cancer 2022. [DOI: 10.1016/s0959-8049(22)01363-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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7
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Giardiello D, Hooning MJ, Hauptmann M, Keeman R, Heemskerk-Gerritsen BAM, Becher H, Blomqvist C, Bojesen SE, Bolla MK, Camp NJ, Czene K, Devilee P, Eccles DM, Fasching PA, Figueroa JD, Flyger H, García-Closas M, Haiman CA, Hamann U, Hopper JL, Jakubowska A, Leeuwen FE, Lindblom A, Lubiński J, Margolin S, Martinez ME, Nevanlinna H, Nevelsteen I, Pelders S, Pharoah PDP, Siesling S, Southey MC, van der Hout AH, van Hest LP, Chang-Claude J, Hall P, Easton DF, Steyerberg EW, Schmidt MK. PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients. Breast Cancer Res 2022; 24:69. [PMID: 36271417 PMCID: PMC9585761 DOI: 10.1186/s13058-022-01567-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/07/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. METHODS We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. RESULTS The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.
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Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.,Institute of Biomedicine, EURAC Research Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Michael Hauptmann
- Brandenburg Medical School, Institute of Biostatistics and Registry Research, Neuruppin, Germany
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | | | - Heiko Becher
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland.,Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.,Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manjeet K Bolla
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Nicola J Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Diana M Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Peter A Fasching
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.,Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen, Germany
| | - Jonine D Figueroa
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.,Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK.,Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - John L Hopper
- Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC, Australia
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland.,Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Floor E Leeuwen
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Sara Margolin
- Department of Oncology, Södersjukhuset, Stockholm, Sweden.,Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
| | - Maria Elena Martinez
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.,Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Ines Nevelsteen
- Department of Oncology, Leuven Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven, Louven, Belgium
| | - Saskia Pelders
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.,Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Sabine Siesling
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands.,Department of HealthTechnology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Annemieke H van der Hout
- Department of Genetics, University Medical Center Groningen, University Groningen, Groningen, The Netherlands
| | - Liselotte P van Hest
- Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.,Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.,Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. .,Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
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8
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Buisman FE, Giardiello D, Kemeny NE, Steyerberg EW, Höppener DJ, Galjart B, Nierop PMH, Balachandran VP, Cercek A, Drebin JA, Gönen M, Jarnagin WR, Kingham TP, Vermeulen PB, Wei AC, Grünhagen DJ, Verhoef C, D'Angelica MI, Koerkamp BG. Predicting 10-year survival after resection of colorectal liver metastases; an international study including biomarkers and perioperative treatment. Eur J Cancer 2022; 168:25-33. [PMID: 35430383 PMCID: PMC9117473 DOI: 10.1016/j.ejca.2022.01.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 12/18/2021] [Accepted: 01/05/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND The aim of this study was to develop a prediction model for 10-year overall survival (OS) after resection of colorectal liver metastasis (CRLM) based on patient, tumour and treatment characteristics. METHODS Consecutive patients after complete resection of CRLM were included from two centres (1992-2019). A prediction model providing 10-year OS probabilities was developed using Cox regression analysis, including KRAS, BRAF and histopathological growth patterns. Discrimination and calibration were assessed using cross-validation. A web-based calculator was built to predict individual 10-year OS probabilities. RESULTS A total of 4112 patients were included. The estimated 10-year OS was 30% (95% CI 29-32). Fifteen patient, tumour and treatment characteristics were independent prognostic factors for 10-year OS; age, gender, location and nodal status of the primary tumour, disease-free interval, number and diameter of CRLM, preoperative CEA, resection margin, extrahepatic disease, KRAS and BRAF mutation status, histopathological growth patterns, perioperative systemic chemotherapy and hepatic arterial infusion pump chemotherapy. The discrimination at 10-years was 0.73 for both centres. A simplified risk score identified four risk groups with a 10-year OS of 57%, 38%, 24%, and 12%. CONCLUSIONS Ten-year OS after resection of CRLM is best predicted with a model including 15 patient, tumour, and treatment characteristics. The web-based calculator can be used to inform patients. This model serves as a benchmark to determine the prognostic value of novel biomarkers.
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Affiliation(s)
- Florian E Buisman
- Department of Surgery, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, the Netherlands
| | - Daniele Giardiello
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands; Department of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Nancy E Kemeny
- Department Medical Oncology, Memorial Sloan Kettering Cancer Centre, New York, USA
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands; Department of Public Health, Erasmus MC, PO Box 20400, 3000 CA Rotterdam, the Netherlands
| | - Diederik J Höppener
- Department of Surgery, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, the Netherlands
| | - Boris Galjart
- Department of Surgery, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, the Netherlands
| | - Pieter M H Nierop
- Department of Surgery, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, the Netherlands
| | | | - Andrea Cercek
- Department Medical Oncology, Memorial Sloan Kettering Cancer Centre, New York, USA
| | - Jeffrey A Drebin
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Mithat Gönen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - William R Jarnagin
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, USA
| | - T P Kingham
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Peter B Vermeulen
- Translational Cancer Research Unit (GZA Hospitals and University of Antwerp), Antwerp, Belgium
| | - Alice C Wei
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Dirk J Grünhagen
- Department of Surgery, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, the Netherlands
| | - Cornelis Verhoef
- Department of Surgery, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, the Netherlands
| | | | - Bas Groot Koerkamp
- Department of Surgery, Erasmus MC Cancer Institute, Erasmus University, Rotterdam, the Netherlands.
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9
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van Geloven N, Giardiello D, Bonneville EF, Teece L, Ramspek CL, van Smeden M, Snell KIE, van Calster B, Pohar-Perme M, Riley RD, Putter H, Steyerberg E. Validation of prediction models in the presence of competing risks: a guide through modern methods. BMJ 2022; 377:e069249. [PMID: 35609902 DOI: 10.1136/bmj-2021-069249] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Nan van Geloven
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Daniele Giardiello
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Edouard F Bonneville
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Lucy Teece
- Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Keele, UK
| | - Chava L Ramspek
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Maarten van Smeden
- Department of Epidemiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Keele, UK
| | - Ben van Calster
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Maja Pohar-Perme
- Department of Biostatistics and Medical Informatics, University of Ljubljana, Ljubljana, Slovenia
| | - Richard D Riley
- Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Keele, UK
| | - Hein Putter
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Ewout Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
- Department of Public Health, Erasmus MC-University Medical Centre, Rotterdam, Netherlands
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10
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Austin PC, Putter H, Giardiello D, van Klaveren D. Graphical calibration curves and the integrated calibration index (ICI) for competing risk models. Diagn Progn Res 2022; 6:2. [PMID: 35039069 PMCID: PMC8762819 DOI: 10.1186/s41512-021-00114-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/06/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Assessing calibration-the agreement between estimated risk and observed proportions-is an important component of deriving and validating clinical prediction models. Methods for assessing the calibration of prognostic models for use with competing risk data have received little attention. METHODS We propose a method for graphically assessing the calibration of competing risk regression models. Our proposed method can be used to assess the calibration of any model for estimating incidence in the presence of competing risk (e.g., a Fine-Gray subdistribution hazard model; a combination of cause-specific hazard functions; or a random survival forest). Our method is based on using the Fine-Gray subdistribution hazard model to regress the cumulative incidence function of the cause-specific outcome of interest on the predicted outcome risk of the model whose calibration we want to assess. We provide modifications of the integrated calibration index (ICI), of E50 and of E90, which are numerical calibration metrics, for use with competing risk data. We conducted a series of Monte Carlo simulations to evaluate the performance of these calibration measures when the underlying model has been correctly specified and when the model was mis-specified and when the incidence of the cause-specific outcome differed between the derivation and validation samples. We illustrated the usefulness of calibration curves and the numerical calibration metrics by comparing the calibration of a Fine-Gray subdistribution hazards regression model with that of random survival forests for predicting cardiovascular mortality in patients hospitalized with heart failure. RESULTS The simulations indicated that the method for constructing graphical calibration curves and the associated calibration metrics performed as desired. We also demonstrated that the numerical calibration metrics can be used as optimization criteria when tuning machine learning methods for competing risk outcomes. CONCLUSIONS The calibration curves and numeric calibration metrics permit a comprehensive comparison of the calibration of different competing risk models.
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Affiliation(s)
- Peter C Austin
- ICES, G106, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada.
- Institute of Health Management, Policy and Evaluation, University of Toronto, Toronto, Ontario, Canada.
- Sunnybrook Research Institute, Toronto, Ontario, Canada.
| | - Hein Putter
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
| | - Daniele Giardiello
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - David van Klaveren
- Department of Public Health, Erasmus MC, Rotterdam, The Netherlands
- Predictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, USA
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11
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Giardiello D, Hooning MJ, Hauptmann M, Keeman R, Heemskerk-Gerritsen BAM, Becher H, Blomqvist C, Bojesen SE, Bolla MK, Camp NJ, Czene K, Devilee P, Eccles DM, Fasching PA, Figueroa JD, Flyger H, García-Closas M, Haiman CA, Hamann U, Hopper JL, Jakubowska A, Leeuwen FE, Lindblom A, Lubiński J, Margolin S, Martinez ME, Nevanlinna H, Nevelsteen I, Pelders S, Pharoah PDP, Siesling S, Southey MC, van der Hout AH, van Hest LP, Chang-Claude J, Hall P, Easton DF, Steyerberg EW, Schmidt MK. Correction: PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients. Breast Cancer Res 2022; 24:82. [PMID: 36419099 PMCID: PMC9682632 DOI: 10.1186/s13058-022-01579-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Daniele Giardiello
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands ,grid.10419.3d0000000089452978Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands ,grid.418908.c0000 0001 1089 6435Institute of Biomedicine, EURAC Research Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Maartje J. Hooning
- grid.508717.c0000 0004 0637 3764Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Michael Hauptmann
- grid.473452.3Brandenburg Medical School, Institute of Biostatistics and Registry Research, Neuruppin, Germany
| | - Renske Keeman
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - B. A. M. Heemskerk-Gerritsen
- grid.508717.c0000 0004 0637 3764Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Heiko Becher
- grid.13648.380000 0001 2180 3484Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carl Blomqvist
- grid.7737.40000 0004 0410 2071Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland ,grid.412367.50000 0001 0123 6208Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - Stig E. Bojesen
- grid.4973.90000 0004 0646 7373Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark ,grid.4973.90000 0004 0646 7373Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark ,grid.5254.60000 0001 0674 042XFaculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manjeet K. Bolla
- grid.5335.00000000121885934Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Nicola J. Camp
- grid.223827.e0000 0001 2193 0096Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA
| | - Kamila Czene
- grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter Devilee
- grid.10419.3d0000000089452978Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands ,grid.10419.3d0000000089452978Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Diana M. Eccles
- grid.5491.90000 0004 1936 9297Faculty of Medicine, University of Southampton, Southampton, UK
| | - Peter A. Fasching
- grid.19006.3e0000 0000 9632 6718Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA USA ,grid.5330.50000 0001 2107 3311Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen, Germany
| | - Jonine D. Figueroa
- grid.4305.20000 0004 1936 7988Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK ,grid.4305.20000 0004 1936 7988Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK ,grid.48336.3a0000 0004 1936 8075Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - Henrik Flyger
- grid.4973.90000 0004 0646 7373Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Montserrat García-Closas
- grid.48336.3a0000 0004 1936 8075Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - Christopher A. Haiman
- grid.42505.360000 0001 2156 6853Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Ute Hamann
- grid.7497.d0000 0004 0492 0584Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - John L. Hopper
- grid.1008.90000 0001 2179 088XMelbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC Australia
| | - Anna Jakubowska
- grid.107950.a0000 0001 1411 4349Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland ,grid.107950.a0000 0001 1411 4349Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Floor E. Leeuwen
- grid.430814.a0000 0001 0674 1393Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Annika Lindblom
- grid.4714.60000 0004 1937 0626Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden ,grid.24381.3c0000 0000 9241 5705Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Jan Lubiński
- grid.107950.a0000 0001 1411 4349Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Sara Margolin
- grid.416648.90000 0000 8986 2221Department of Oncology, Södersjukhuset, Stockholm, Sweden ,grid.416648.90000 0000 8986 2221Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
| | - Maria Elena Martinez
- grid.266100.30000 0001 2107 4242Moores Cancer Center, University of California San Diego, La Jolla, CA USA ,grid.266100.30000 0001 2107 4242Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA USA
| | - Heli Nevanlinna
- grid.7737.40000 0004 0410 2071Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Ines Nevelsteen
- grid.410569.f0000 0004 0626 3338Department of Oncology, Leuven Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven, Louven, Belgium
| | - Saskia Pelders
- grid.508717.c0000 0004 0637 3764Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Paul D. P. Pharoah
- grid.5335.00000000121885934Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK ,grid.5335.00000000121885934Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Sabine Siesling
- grid.470266.10000 0004 0501 9982Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands ,grid.6214.10000 0004 0399 8953Department of HealthTechnology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Melissa C. Southey
- grid.1002.30000 0004 1936 7857Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC Australia ,grid.1008.90000 0001 2179 088XDepartment of Clinical Pathology, The University of Melbourne, Melbourne, VIC Australia ,grid.3263.40000 0001 1482 3639Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC Australia
| | - Annemieke H. van der Hout
- grid.4494.d0000 0000 9558 4598Department of Genetics, University Medical Center Groningen, University Groningen, Groningen, The Netherlands
| | - Liselotte P. van Hest
- grid.12380.380000 0004 1754 9227Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jenny Chang-Claude
- grid.7497.d0000 0004 0492 0584Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.13648.380000 0001 2180 3484Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Per Hall
- grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden ,grid.416648.90000 0000 8986 2221Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Douglas F. Easton
- grid.5335.00000000121885934Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK ,grid.5335.00000000121885934Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Ewout W. Steyerberg
- grid.10419.3d0000000089452978Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands ,grid.508717.c0000 0004 0637 3764Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marjanka K. Schmidt
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands ,grid.430814.a0000 0001 0674 1393Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
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12
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van Seijen M, Lips EH, Fu L, Giardiello D, van Duijnhoven F, de Munck L, Elshof LE, Thompson A, Sawyer E, Ryser MD, Hwang ES, Schmidt MK, Elkhuizen PHM, Wesseling J, Schaapveld M. Long-term risk of subsequent ipsilateral lesions after surgery with or without radiotherapy for ductal carcinoma in situ of the breast. Br J Cancer 2021; 125:1443-1449. [PMID: 34408284 PMCID: PMC8575990 DOI: 10.1038/s41416-021-01496-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 05/20/2021] [Accepted: 07/09/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Radiotherapy (RT) following breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS) reduces ipsilateral breast event rates in clinical trials. This study assessed the impact of DCIS treatment on a 20-year risk of ipsilateral DCIS (iDCIS) and ipsilateral invasive breast cancer (iIBC) in a population-based cohort. METHODS The cohort comprised all women diagnosed with DCIS in the Netherlands during 1989-2004 with follow-up until 2017. Cumulative incidence of iDCIS and iIBC following BCS and BCS + RT were assessed. Associations of DCIS treatment with iDCIS and iIBC risk were estimated in multivariable Cox models. RESULTS The 20-year cumulative incidence of any ipsilateral breast event was 30.6% (95% confidence interval (CI): 28.9-32.6) after BCS compared to 18.2% (95% CI 16.3-20.3) following BCS + RT. Women treated with BCS compared to BCS + RT had higher risk of developing iDCIS and iIBC within 5 years after DCIS diagnosis (for iDCIS: hazard ratio (HR)age < 50 3.2 (95% CI 1.6-6.6); HRage ≥ 50 3.6 (95% CI 2.6-4.8) and for iIBC: HRage<50 2.1 (95% CI 1.4-3.2); HRage ≥ 50 4.3 (95% CI 3.0-6.0)). After 10 years, the risk of iDCIS and iIBC no longer differed for BCS versus BCS + RT (for iDCIS: HRage < 50 0.7 (95% CI 0.3-1.5); HRage ≥ 50 0.7 (95% CI 0.4-1.3) and for iIBC: HRage < 50 0.6 (95% CI 0.4-0.9); HRage ≥ 50 1.2 (95% CI 0.9-1.6)). CONCLUSION RT is associated with lower iDCIS and iIBC risk up to 10 years after BCS, but this effect wanes thereafter.
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Affiliation(s)
- Maartje van Seijen
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Esther H. Lips
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Liping Fu
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Daniele Giardiello
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Frederieke van Duijnhoven
- grid.430814.a0000 0001 0674 1393Department of Surgery, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Linda de Munck
- grid.470266.10000 0004 0501 9982Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands
| | - Lotte E. Elshof
- grid.414725.10000 0004 0368 8146Department of radiology, Meander Medical Centre, Amersfoort, The Netherlands
| | - Alastair Thompson
- grid.39382.330000 0001 2160 926XDan L Duncan Comprehensive Cancer Centre, Baylor College of Medicine, Houston, TX USA
| | - Elinor Sawyer
- grid.239826.40000 0004 0391 895XDivision of Cancer Studies, King’s College London, Comprehensive Cancer Centre, Guy’s Hospital, London, UK
| | - Marc D. Ryser
- grid.26009.3d0000 0004 1936 7961Department of Population Health Sciences, Duke University, Durham, NC USA ,grid.26009.3d0000 0004 1936 7961Department of Mathematics, Duke University, Durham, NC USA
| | - E. Shelley Hwang
- grid.26009.3d0000 0004 1936 7961Department of Surgery, Duke University, Durham, NC USA
| | - Marjanka K. Schmidt
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands ,grid.10419.3d0000000089452978Department of clinical genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Paula H. M. Elkhuizen
- grid.430814.a0000 0001 0674 1393Department of radiotherapy, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | | | - Jelle Wesseling
- grid.430814.a0000 0001 0674 1393Department of pathology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands ,grid.10419.3d0000000089452978Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Michael Schaapveld
- grid.430814.a0000 0001 0674 1393Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
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13
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van der Plas-Krijgsman WG, Giardiello D, Putter H, Steyerberg EW, Bastiaannet E, Stiggelbout AM, Mooijaart SP, Kroep JR, Portielje JEA, Liefers GJ, de Glas NA. Development and validation of the PORTRET tool to predict recurrence, overall survival, and other-cause mortality in older patients with breast cancer in the Netherlands: a population-based study. Lancet Healthy Longev 2021; 2:e704-e711. [PMID: 36098027 DOI: 10.1016/s2666-7568(21)00229-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Current prediction tools for breast cancer outcomes are not tailored to the older patient, in whom competing risk strongly influences treatment effects. We aimed to develop and validate a prediction tool for 5-year recurrence, overall mortality, and other-cause mortality for older patients (aged ≥65 years) with early invasive breast cancer and to estimate individualised expected benefits of adjuvant systemic treatment. METHODS We selected surgically treated patients with early invasive breast cancer (stage I-III) aged 65 years or older from the population-based FOCUS cohort in the Netherlands. We developed prediction models for 5-year recurrence, overall mortality, and other-cause mortality using cause-specific Cox proportional hazard models. External validation was performed in a Dutch Cancer registry cohort. Performance was evaluated with discrimination accuracy and calibration plots. FINDINGS We included 2744 female patients in the development cohort and 13631 female patients in the validation cohort. Median age was 74·8 years (range 65-98) in the development cohort and 76·0 years (70-101) in the validation cohort. 5-year follow-up was complete for more than 99% of all patients. We observed 343 and 1462 recurrences, and 831 and 3594 deaths, of which 586 and 2565 were without recurrence, in the development and validation cohort, respectively. The area under the receiver-operating-characteristic curve at 5 years in the external dataset was 0·76 (95% CI 0·75-0·76) for overall mortality, 0·76 (0·76-0·77) for recurrence, and 0·75 (0·74-0·75) for other-cause mortality. INTERPRETATION The PORTRET tool can accurately predict 5-year recurrence, overall mortality, and other-cause mortality in older patients with breast cancer. The tool can support shared decision making, especially since it provides individualised estimated benefits of adjuvant treatment. FUNDING Dutch Cancer Foundation and ZonMw.
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Affiliation(s)
| | - Daniele Giardiello
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands; Division of Molecular Pathology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands; Eurac Research, Institute for Biomedicine, Bolzano, Italy
| | - Hein Putter
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands; Department of Public Health, Erasmus MC, Rotterdam, Netherlands
| | - Esther Bastiaannet
- Department of Medical Oncology, Leiden University Medical Center, Leiden, Netherlands; Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - Anne M Stiggelbout
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Simon P Mooijaart
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Judith R Kroep
- Department of Medical Oncology, Leiden University Medical Center, Leiden, Netherlands
| | | | - Gerrit-Jan Liefers
- Department of Surgery, Leiden University Medical Center, Leiden, Netherlands.
| | - Nienke A de Glas
- Department of Medical Oncology, Leiden University Medical Center, Leiden, Netherlands
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van der Plas-Krijgsman W, Giardiello D, Putter H, Steyerberg EW, Bastiaannet E, Stiggelbout AM, Mooijaart SP, Portielje JEA, Liefers GJ, de Glas NA. Abstract PS6-08: The PORTRET-tool: A prediction tool for older patients with breast cancer that predicts recurrence, survival and other-cause mortality. Cancer Res 2021. [DOI: 10.1158/1538-7445.sabcs20-ps6-08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Introduction Previous studies have shown that available tools such as ‘Adjuvant Online!” are not able to accurately predict the prognosis of patients aged 65 years or older with breast cancer. Furthermore, all available tools predict prognosis in terms of recurrence-free survival or overall survival, whilst the risk of other-cause mortality is often high in the older patient with breast cancer. This is highly relevant as it may influence treatment decisions. Patient characteristics such as comorbidity and various geriatric variables have shown to be predictive for these outcomes and could enhance the precision of prognostic tools for this target population. The objective of this study was to develop a prediction tool for recurrence, survival and other-cause mortality for older patients with breast cancer who received locoregional treatment, with incorporation of patient-, tumor- and geriatric variables. The tool additionally predicts expected benefits of systemic treatment. Methods Data from the large population-based FOCUS cohort was used, consisting of consecutive breast cancer patients in the South-Western part of the Netherlands, diagnosed between 1997 and 2004, aged 65 years and older. It contains detailed information on tumor characteristics, treatment, comorbidity and geriatric parameters. We developed a risk prediction model using a Cox proportional hazards regression model for overall survival and cause-specific Cox proportional hazards models for recurrence and other-cause mortality (defined as mortality without recurrence). The included predictors were derived from the PREDICT tool (consisting of age and various tumor variables), since this tool was previously shown to have the best performance in older adults so far. Predictors were complemented with comorbidity and geriatric variables. Discrimination accuracy was evaluated using time-dependent area under the curve (AUC). The potential annual benefit of chemotherapy was calculated assuming a relative risk of chemotherapy on recurrence of 0.7, derived from data from the most recent updates of the Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Additional benefit of endocrine treatment will be included in further development of the tool. Results A total of 2,744 patients were included for the initial development. For all patients, 5-year follow-up was complete with a high event-rate including 343 recurrences and 831 total deaths of which 586 without recurrence. The strongest predictors for overall survival and non-recurrence mortality were age (HR = 2.14, 95% CI: 1.89 - 2.43 and HR = 2.87, 95% CI: 2.46 - 3.35, respectively) and dementia (HR = 1.52, 95% CI: 1.16 - 1.99 and HR = 1.9, 95% CI: 1.49 - 2.65, respectively), and for recurrence, nodal status (HR = 1.80, 95% CI: 1.45 - 2.24) and tumor grade (HR = 2.96, 95% CI: 1.88 - 4.66). The time-dependent AUC at 5 years for recurrence-specific and other-cause mortality were 0.78 (95% CI: 0.76 - 0.81), and 0.75 (95% CI: 0.72 - 0.78), respectively. The AUC for overall survival was 0.75 (95% CI: 0.72 - 0.78). External validation is currently being performed in a large dataset retrieved from the national cancer registry (N= 13,631). These results will be presented during the symposium. Conclusion We have developed a model for predicting 5-year recurrence, other-cause mortality and overall survival, including expected benefits of adjuvant treatment, for older patients with breast cancer, with a good discrimination performance within a large-population based cohort. To our knowledge, this is the first model specifically designed for the older population, including competing risk as a predicted outcome and with incorporation of geriatric variables.
Citation Format: Willeke van der Plas-Krijgsman, Daniele Giardiello, Hein Putter, Ewout W Steyerberg, Esther Bastiaannet, Anne M Stiggelbout, Simon P Mooijaart, Johanneke EA Portielje, Gerrit J Liefers, Nienke A de Glas. The PORTRET-tool: A prediction tool for older patients with breast cancer that predicts recurrence, survival and other-cause mortality [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS6-08.
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Affiliation(s)
| | - Daniele Giardiello
- 2Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Hein Putter
- 2Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Ewout W Steyerberg
- 2Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Esther Bastiaannet
- 3Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - Anne M Stiggelbout
- 4Department of Medical Decision Making, Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Simon P Mooijaart
- 5Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | | | - Gerrit J Liefers
- 3Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - Nienke A de Glas
- 1Department of Medical Oncology, Leiden University Medical Center, Leiden, Netherlands
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15
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Huber V, Di Guardo L, Lalli L, Giardiello D, Cova A, Squarcina P, Frati P, Di Giacomo AM, Pilla L, Tazzari M, Camisaschi C, Arienti F, Castelli C, Rodolfo M, Beretta V, Di Nicola M, Maio M, Del Vecchio M, de Braud F, Mariani L, Rivoltini L. Back to simplicity: a four-marker blood cell score to quantify prognostically relevant myeloid cells in melanoma patients. J Immunother Cancer 2021; 9:jitc-2020-001167. [PMID: 33589521 PMCID: PMC7887358 DOI: 10.1136/jitc-2020-001167] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2020] [Indexed: 12/25/2022] Open
Abstract
Background Myeloid-derived suppressor cells (MDSC), a cornerstone of cancer-related immunosuppression, influence response to therapy and disease outcomes in melanoma patients. Nevertheless, their quantification is far from being integrated into routine clinical practice mostly because of the complex and still evolving phenotypic signatures applied to define the cell subsets. Here, we used a multistep downsizing process to verify whether a core of few markers could be sufficient to capture the prognostic potential of myeloid cells in peripheral blood mononuclear cells (PBMC) of metastatic melanoma patients. Methods In baseline frozen PBMC from a total of 143 stage IIIc to IV melanoma patients, we first assessed the relevant or redundant expression of myeloid and MDSC-related markers by flow cytometry (screening set, n=23 patients). Subsequently, we applied the identified panel to the development set samples (n=59 patients undergoing first/second-line therapy) to obtain prognostic variables associated with overall survival (OS) and progression-free survival (PFS) by machine learning adaptive index modeling. Finally, the identified score was confirmed in a validation set (n=61) and compared with standard clinical prognostic factors to assess its additive value in patient prognostication. Results This selection process led to the identification of what we defined myeloid index score (MIS), which is composed by four cell subsets (CD14+, CD14+HLA-DRneg, CD14+PD-L1+ and CD15+ cells), whose frequencies above cut-offs stratified melanoma patients according to progressively worse prognosis. Patients with a MIS=0, showing no over-threshold value of MIS subsets, had the best clinical outcome, with a median survival of >33.6 months, while in patients with MIS 1→3, OS deteriorated from 10.9 to 6.8 and 6.0 months as the MIS increased (p<0.0001, c-index=0.745). MIS clustered patients into risk groups also according to PFS (p<0.0001). The inverse correlation between MIS and survival was confirmed in the validation set, was independent of the type of therapy and was not interfered by clinical prognostic factors. MIS HR was remarkably superior to that of lactate dehydrogenase, tumor burden and neutrophil-to-lymphocyte ratio. Conclusion The MIS >0 identifies melanoma patients with a more aggressive disease, thus acting as a simple blood biomarker that can help tailoring therapeutic choices in real-life oncology.
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Affiliation(s)
- Veronica Huber
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Lorenza Di Guardo
- Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Luca Lalli
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Daniele Giardiello
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.,Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Agata Cova
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Paola Squarcina
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Paola Frati
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Lorenzo Pilla
- Unit of Immuno-biotherapy of Melanoma and Solid Tumors, IRCCS San Raffaele Hospital, Milan, Italy.,Division of Medical Oncology, Ospedale San Gerardo, Monza, Italy
| | - Marcella Tazzari
- Immunotherapy-Cell Therapy and Biobank Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Chiara Camisaschi
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.,Biomarkers Unit, Department of Applied Research and Technical Development, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Flavio Arienti
- Immunohematology and Transfusion Medicine Service (SIMT), Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Chiara Castelli
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Monica Rodolfo
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Valeria Beretta
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.,Experimental Hematology Unit, IRCCS San Raffaele Hospital, Milan, Italy
| | - Massimo Di Nicola
- Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Michele Maio
- Center for Immuno-Oncology, University Hospital of Siena, Siena, Italy
| | - Michele Del Vecchio
- Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Filippo de Braud
- Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Luigi Mariani
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Licia Rivoltini
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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16
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Kramer I, Hooning MJ, Mavaddat N, Hauptmann M, Keeman R, Steyerberg EW, Giardiello D, Antoniou AC, Pharoah PDP, Canisius S, Abu-Ful Z, Andrulis IL, Anton-Culver H, Aronson KJ, Augustinsson A, Becher H, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bogdanova NV, Bojesen SE, Bolla MK, Bonanni B, Brauch H, Bremer M, Brucker SY, Burwinkel B, Castelao JE, Chan TL, Chang-Claude J, Chanock SJ, Chenevix-Trench G, Choi JY, Clarke CL, Collée JM, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dos-Santos-Silva I, Dunning AM, Dwek M, Eccles DM, Evans DG, Fasching PA, Flyger H, Gago-Dominguez M, García-Closas M, García-Sáenz JA, Giles GG, Goldgar DE, González-Neira A, Haiman CA, Håkansson N, Hamann U, Hartman M, Heemskerk-Gerritsen BAM, Hollestelle A, Hopper JL, Hou MF, Howell A, Ito H, Jakimovska M, Jakubowska A, Janni W, John EM, Jung A, Kang D, Kets CM, Khusnutdinova E, Ko YD, Kristensen VN, Kurian AW, Kwong A, Lambrechts D, Le Marchand L, Li J, Lindblom A, Lubiński J, Mannermaa A, Manoochehri M, Margolin S, Matsuo K, Mavroudis D, Meindl A, Milne RL, Mulligan AM, Muranen TA, Neuhausen SL, Nevanlinna H, Newman WG, Olshan AF, Olson JE, Olsson H, Park-Simon TW, Peto J, Petridis C, Plaseska-Karanfilska D, Presneau N, Pylkäs K, Radice P, Rennert G, Romero A, Roylance R, Saloustros E, Sawyer EJ, Schmutzler RK, Schwentner L, Scott C, See MH, Shah M, Shen CY, Shu XO, Siesling S, Slager S, Sohn C, Southey MC, Spinelli JJ, Stone J, Tapper WJ, Tengström M, Teo SH, Terry MB, Tollenaar RAEM, Tomlinson I, Troester MA, Vachon CM, van Ongeval C, van Veen EM, Winqvist R, Wolk A, Zheng W, Ziogas A, Easton DF, Hall P, Schmidt MK. Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk. Am J Hum Genet 2020; 107:837-848. [PMID: 33022221 PMCID: PMC7675034 DOI: 10.1016/j.ajhg.2020.09.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 09/02/2020] [Indexed: 12/18/2022] Open
Abstract
Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.
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Affiliation(s)
- Iris Kramer
- The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Division of Molecular Pathology, Amsterdam 1066 CX, the Netherlands
| | - Maartje J Hooning
- Erasmus MC Cancer Institute, Department of Medical Oncology, Rotterdam 3015 CN, the Netherlands
| | - Nasim Mavaddat
- University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Cambridge CB1 8RN, UK
| | - Michael Hauptmann
- The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Department of Epidemiology and Biostatistics, Amsterdam 1066 CX, the Netherlands; Brandenburg Medical School Theodor Fontane, Institute of Biostatistics and Registry Research, Neuruppin 16816, Germany
| | - Renske Keeman
- The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Division of Molecular Pathology, Amsterdam 1066 CX, the Netherlands
| | - Ewout W Steyerberg
- Leiden University Medical Center, Department of Biomedical Data Sciences, Leiden 2333 ZA, the Netherlands; Erasmus MC, Department of Public Health, Rotterdam 3015 GD, the Netherlands
| | - Daniele Giardiello
- The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Division of Molecular Pathology, Amsterdam 1066 CX, the Netherlands; Leiden University Medical Center, Department of Biomedical Data Sciences, Leiden 2333 ZA, the Netherlands
| | - Antonis C Antoniou
- University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Cambridge CB1 8RN, UK
| | - Paul D P Pharoah
- University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Cambridge CB1 8RN, UK; University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge CB1 8RN, UK
| | - Sander Canisius
- The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Division of Molecular Pathology, Amsterdam 1066 CX, the Netherlands; The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Division of Molecular Carcinogenesis, Amsterdam 1066 CX, the Netherlands
| | - Zumuruda Abu-Ful
- Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa 35254, Israel
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Fred A. Litwin Center for Cancer Genetics, Toronto, ON M5G 1X5, Canada; University of Toronto, Department of Molecular Genetics, Toronto, ON M5S 1A8, Canada
| | - Hoda Anton-Culver
- University of California Irvine, Department of Epidemiology, Genetic Epidemiology Research Institute, Irvine, CA 92617, USA
| | - Kristan J Aronson
- Queen's University, Department of Public Health Sciences, and Cancer Research Institute, Kingston, ON K7L 3N6, Canada
| | - Annelie Augustinsson
- Lund University, Department of Cancer Epidemiology, Clinical Sciences, Lund 222 42, Sweden
| | - Heiko Becher
- University Medical Center Hamburg-Eppendorf, Institute of Medical Biometry and Epidemiology, Hamburg 20246, Germany; Charité -Universitätsmedizin Berlin, Institute of Biometry and Clinical Epidemiology, Berlin 10117, Germany
| | - Matthias W Beckmann
- University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, Erlangen 91054, Germany
| | - Sabine Behrens
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg 69120, Germany
| | - Javier Benitez
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), Madrid 28029, Spain; Spanish National Cancer Research Centre (CNIO), Human Cancer Genetics Programme, Madrid 28029, Spain
| | - Marina Bermisheva
- Ufa Federal Research Centre of the Russian Academy of Sciences, Institute of Biochemistry and Genetics, Ufa 450054, Russia
| | - Natalia V Bogdanova
- Hannover Medical School, Department of Radiation Oncology, Hannover 30625, Germany; Hannover Medical School, Gynaecology Research Unit, Hannover 30625, Germany; N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk 223040, Belarus
| | - Stig E Bojesen
- Copenhagen University Hospital, Copenhagen General Population Study, Herlev and Gentofte Hospital, Herlev 2730, Denmark; Copenhagen University Hospital, Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Herlev 2730, Denmark; University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen 2200, Denmark
| | - Manjeet K Bolla
- University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Cambridge CB1 8RN, UK
| | - Bernardo Bonanni
- IEO, European Institute of Oncology IRCCS, Division of Cancer Prevention and Genetics, Milan 20141, Italy
| | - Hiltrud Brauch
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart 70376, Germany; University of Tübingen, iFIT-Cluster of Excellence, Tübingen 72074, Germany; German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Partner Site Tübingen, Tübingen 72074, Germany
| | - Michael Bremer
- Hannover Medical School, Department of Radiation Oncology, Hannover 30625, Germany
| | - Sara Y Brucker
- University of Tübingen, Department of Gynecology and Obstetrics, Tübingen 72076, Germany
| | - Barbara Burwinkel
- German Cancer Research Center (DKFZ), Molecular Epidemiology Group, C080, Heidelberg 69120, Germany; University of Heidelberg, Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, Heidelberg 69120, Germany
| | - Jose E Castelao
- Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Oncology and Genetics Unit, Vigo 36312, Spain
| | - Tsun L Chan
- Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong; Hong Kong Sanatorium and Hospital, Department of Pathology, Happy Valley, Hong Kong
| | - Jenny Chang-Claude
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg 69120, Germany; University Medical Center Hamburg-Eppendorf, Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), Hamburg 20246, Germany
| | - Stephen J Chanock
- National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, Bethesda, MD 20850, USA
| | - Georgia Chenevix-Trench
- QIMR Berghofer Medical Research Institute, Department of Genetics and Computational Biology, Brisbane, QLD 4006, Australia
| | - Ji-Yeob Choi
- Seoul National University Graduate School, Department of Biomedical Sciences, Seoul 03080, Korea; Seoul National University, Cancer Research Institute, Seoul 03080, Korea
| | - Christine L Clarke
- University of Sydney, Westmead Institute for Medical Research, Sydney, NSW 2145, Australia
| | - J Margriet Collée
- Erasmus University Medical Center, Department of Clinical Genetics, Rotterdam 3015 CN, the Netherlands
| | - Fergus J Couch
- Mayo Clinic, Department of Laboratory Medicine and Pathology, Rochester, MN 55905, USA
| | - Angela Cox
- University of Sheffield, Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, Sheffield S10 2TN, UK
| | - Simon S Cross
- University of Sheffield, Academic Unit of Pathology, Department of Neuroscience, Sheffield S10 2TN, UK
| | - Kamila Czene
- Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm 171 65, Sweden
| | - Mary B Daly
- Fox Chase Cancer Center, Department of Clinical Genetics, Philadelphia, PA 19111, USA
| | - Peter Devilee
- Leiden University Medical Center, Department of Pathology, Leiden 2333 ZA, the Netherlands; Leiden University Medical Center, Department of Human Genetics, Leiden 2333 ZA, the Netherlands
| | - Thilo Dörk
- Hannover Medical School, Gynaecology Research Unit, Hannover 30625, Germany
| | - Isabel Dos-Santos-Silva
- London School of Hygiene and Tropical Medicine, Department of Non-Communicable Disease Epidemiology, London WC1E 7HT, UK
| | - Alison M Dunning
- University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge CB1 8RN, UK
| | - Miriam Dwek
- University of Westminster, School of Life Sciences, London W1B 2HW, UK
| | - Diana M Eccles
- University of Southampton, Faculty of Medicine, Southampton SO17 1BJ, UK
| | - D Gareth Evans
- University of Manchester, Manchester Academic Health Science Centre, Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester M13 9WL, UK; St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester M13 9WL, UK
| | - Peter A Fasching
- University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, Erlangen 91054, Germany; University of California at Los Angeles, David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, Los Angeles, CA 90095, USA
| | - Henrik Flyger
- Copenhagen University Hospital, Department of Breast Surgery, Herlev and Gentofte Hospital, Herlev 2730, Denmark
| | - Manuela Gago-Dominguez
- Grupo de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Fundación Pública Galega de Medicina Xenómica, Santiago de Compostela 15706, Spain; University of California San Diego, Moores Cancer Center, La Jolla, CA 92037, USA
| | - Montserrat García-Closas
- National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, Bethesda, MD 20850, USA
| | - José A García-Sáenz
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Medical Oncology Department, Hospital Clínico San Carlos, Madrid 28040, Spain
| | - Graham G Giles
- Cancer Council Victoria, Cancer Epidemiology Division, Melbourne, VIC 3004, Australia; The University of Melbourne, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Melbourne, VIC 3010, Australia; Monash University, Precision Medicine, School of Clinical Sciences at Monash Health, Clayton, VIC 3168, Australia
| | - David E Goldgar
- Huntsman Cancer Institute, University of Utah School of Medicine, Department of Dermatology, Salt Lake City, UT 84112, USA
| | - Anna González-Neira
- Spanish National Cancer Research Centre (CNIO), Human Cancer Genetics Programme, Madrid 28029, Spain
| | - Christopher A Haiman
- University of Southern California, Department of Preventive Medicine, Keck School of Medicine, Los Angeles, CA 90033, USA
| | - Niclas Håkansson
- Karolinska Institutet, Institute of Environmental Medicine, Stockholm 171 77, Sweden
| | - Ute Hamann
- German Cancer Research Center (DKFZ), Molecular Genetics of Breast Cancer, Heidelberg 69120, Germany
| | - Mikael Hartman
- National University of Singapore and National University Health System, Saw Swee Hock School of Public Health, Singapore 119077, Singapore; National University Health System, Department of Surgery, Singapore 119228, Singapore
| | | | - Antoinette Hollestelle
- Erasmus MC Cancer Institute, Department of Medical Oncology, Rotterdam 3015 CN, the Netherlands
| | - John L Hopper
- The University of Melbourne, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Melbourne, VIC 3010, Australia
| | - Ming-Feng Hou
- Kaohsiung Medical University, Chung-Ho Memorial Hospital, Kaohsiung 807, Taiwan
| | - Anthony Howell
- University of Manchester, Division of Cancer Sciences, Manchester M13 9PL, UK
| | - Hidemi Ito
- Aichi Cancer Center Research Institute, Division of Cancer Epidemiology and Prevention, Nagoya 464-8681, Japan; Nagoya University Graduate School of Medicine, Division of Cancer Epidemiology, Nagoya 466-8550, Japan
| | - Milena Jakimovska
- MASA, Research Centre for Genetic Engineering and Biotechnology 'Georgi D. Efremov', Skopje 1000, Republic of North Macedonia
| | - Anna Jakubowska
- Pomeranian Medical University, Department of Genetics and Pathology, Szczecin 71-252, Poland; Pomeranian Medical University, Independent Laboratory of Molecular Biology and Genetic Diagnostics, Szczecin 71-252, Poland
| | - Wolfgang Janni
- University Hospital Ulm, Department of Gynaecology and Obstetrics, Ulm 89075, Germany
| | - Esther M John
- Stanford Cancer Institute, Stanford University School of Medicine, Department of Epidemiology & Population Health, Stanford, CA 94304, USA
| | - Audrey Jung
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg 69120, Germany
| | - Daehee Kang
- Seoul National University Graduate School, Department of Biomedical Sciences, Seoul 03080, Korea; Seoul National University, Cancer Research Institute, Seoul 03080, Korea; Seoul National University College of Medicine, Department of Preventive Medicine, Seoul 03080, Korea
| | - C Marleen Kets
- the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Department of Clinical Genetics, Amsterdam 1066 CX, the Netherlands
| | - Elza Khusnutdinova
- Ufa Federal Research Centre of the Russian Academy of Sciences, Institute of Biochemistry and Genetics, Ufa 450054, Russia; Bashkir State University, Department of Genetics and Fundamental Medicine, Ufa 450000, Russia
| | - Yon-Dschun Ko
- Johanniter Krankenhaus, Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Bonn 53177, Germany
| | - Vessela N Kristensen
- Oslo University Hospital-Radiumhospitalet, Department of Cancer Genetics, Institute for Cancer Research, Oslo 0379, Norway; Oslo University Hospital and University of Olso, Department of Medical Genetics, Oslo 0379, Norway
| | - Allison W Kurian
- Stanford Cancer Institute, Stanford University School of Medicine, Department of Epidemiology & Population Health, Stanford, CA 94304, USA; Stanford University School of Medicine, Department of Health Research and Policy, Stanford, CA 94305, USA
| | - Ava Kwong
- Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong; The University of Hong Kong, Department of Surgery, Pok Fu Lam, Hong Kong; Hong Kong Sanatorium and Hospital, Cancer Genetics Center and Department of Surgery, Happy Valley, Hong Kong
| | - Diether Lambrechts
- VIB Center for Cancer Biology, Leuven 3001, Belgium; University of Leuven, Laboratory for Translational Genetics, Department of Human Genetics, Leuven 3000, Belgium
| | - Loic Le Marchand
- University of Hawaii Cancer Center, Epidemiology Program, Honolulu, HI 96813, USA
| | - Jingmei Li
- Genome Institute of Singapore, Human Genetics Division, Singapore 138672, Singapore
| | - Annika Lindblom
- Karolinska Institutet, Department of Molecular Medicine and Surgery, Stockholm 171 76, Sweden; Karolinska University Hospital, Department of Clinical Genetics, Stockholm 171 76, Sweden
| | - Jan Lubiński
- Pomeranian Medical University, Department of Genetics and Pathology, Szczecin 71-252, Poland
| | - Arto Mannermaa
- University of Eastern Finland, Translational Cancer Research Area, Kuopio 70210, Finland; University of Eastern Finland, Institute of Clinical Medicine, Pathology and Forensic Medicine, Kuopio 70210, Finland; Kuopio University Hospital, Biobank of Eastern Finland, Kuopio 70210, Finland
| | - Mehdi Manoochehri
- German Cancer Research Center (DKFZ), Molecular Genetics of Breast Cancer, Heidelberg 69120, Germany
| | - Sara Margolin
- Södersjukhuset, Department of Oncology, Stockholm 118 83, Sweden; Karolinska Institutet, Department of Clinical Science and Education, Södersjukhuset, Stockholm 118 83, Sweden
| | - Keitaro Matsuo
- Aichi Cancer Center Research Institute, Division of Cancer Epidemiology and Prevention, Nagoya 464-8681, Japan; Nagoya University Graduate School of Medicine, Division of Cancer Epidemiology, Nagoya 466-8550, Japan
| | - Dimitrios Mavroudis
- University Hospital of Heraklion, Department of Medical Oncology, Heraklion 711 10, Greece
| | - Alfons Meindl
- University of Munich, Campus Großhadern, Department of Gynecology and Obstetrics, Munich 81377, Germany
| | - Roger L Milne
- Cancer Council Victoria, Cancer Epidemiology Division, Melbourne, VIC 3004, Australia; The University of Melbourne, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Melbourne, VIC 3010, Australia; Monash University, Precision Medicine, School of Clinical Sciences at Monash Health, Clayton, VIC 3168, Australia
| | - Anna Marie Mulligan
- University of Toronto, Department of Laboratory Medicine and Pathobiology, Toronto, ON M5S 1A8, Canada; University Health Network, Laboratory Medicine Program, Toronto, ON M5G 2C4, Canada
| | - Taru A Muranen
- Helsinki University Hospital, Department of Obstetrics and Gynecology, University of Helsinki, Helsinki 00290, Finland
| | - Susan L Neuhausen
- Beckman Research Institute of City of Hope, Department of Population Sciences, Duarte, CA 91010, USA
| | - Heli Nevanlinna
- Helsinki University Hospital, Department of Obstetrics and Gynecology, University of Helsinki, Helsinki 00290, Finland
| | - William G Newman
- University of Manchester, Manchester Academic Health Science Centre, Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester M13 9WL, UK; St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester M13 9WL, UK
| | - Andrew F Olshan
- University of North Carolina at Chapel Hill, Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC 27599, USA
| | - Janet E Olson
- Mayo Clinic, Department of Health Sciences Research, Rochester, MN 55905, USA
| | - Håkan Olsson
- Lund University, Department of Cancer Epidemiology, Clinical Sciences, Lund 222 42, Sweden
| | | | - Julian Peto
- London School of Hygiene and Tropical Medicine, Department of Non-Communicable Disease Epidemiology, London WC1E 7HT, UK
| | - Christos Petridis
- King's College London, Research Oncology, Guy's Hospital, London SE1 9RT, UK
| | - Dijana Plaseska-Karanfilska
- MASA, Research Centre for Genetic Engineering and Biotechnology 'Georgi D. Efremov', Skopje 1000, Republic of North Macedonia
| | - Nadege Presneau
- University of Westminster, School of Life Sciences, London W1B 2HW, UK
| | - Katri Pylkäs
- University of Oulu, Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, Oulu 90220, Finland; Northern Finland Laboratory Centre Oulu, Laboratory of Cancer Genetics and Tumor Biology, Oulu 90220, Finland
| | - Paolo Radice
- Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Milan 20133, Italy
| | - Gad Rennert
- Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa 35254, Israel
| | - Atocha Romero
- Hospital Universitario Puerta de Hierro, Medical Oncology Department, Madrid 28222, Spain
| | - Rebecca Roylance
- UCLH Foundation Trust, Department of Oncology, London NW1 2PG, UK
| | | | - Elinor J Sawyer
- King's College London, School of Cancer & Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy's Campus, London SE1 1UL, UK
| | - Rita K Schmutzler
- Faculty of Medicine and University Hospital Cologne, University of Cologne, Center for Familial Breast and Ovarian Cancer, Cologne 50937, Germany; Faculty of Medicine and University Hospital Cologne, University of Cologne, Center for Integrated Oncology (CIO), Cologne 50937, Germany; Faculty of Medicine and University Hospital Cologne, University of Cologne, Center for Molecular Medicine Cologne (CMMC), Cologne 50931, Germany
| | - Lukas Schwentner
- University Hospital Ulm, Department of Gynaecology and Obstetrics, Ulm 89075, Germany
| | - Christopher Scott
- Mayo Clinic, Department of Health Sciences Research, Rochester, MN 55905, USA
| | - Mee-Hoong See
- University of Malaya, Breast Cancer Research Unit, University Malaya Cancer Research Institute, Faculty of Medicine, Kuala Lumpur 50603, Malaysia
| | - Mitul Shah
- University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge CB1 8RN, UK
| | - Chen-Yang Shen
- Academia Sinica, Institute of Biomedical Sciences, Taipei 115, Taiwan; China Medical University, School of Public Health, Taichung 40402, Taiwan
| | - Xiao-Ou Shu
- Vanderbilt University School of Medicine, Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Nashville, TN 37232, USA
| | - Sabine Siesling
- Netherlands Comprehensive Cancer Organisation (IKNL), Department of Research, Utrecht 3511 DT, the Netherlands; University of Twente, Department of Health Technology and Service Research, Technical Medical Center, Enschede 7522 NB, the Netherlands
| | - Susan Slager
- Mayo Clinic, Department of Health Sciences Research, Rochester, MN 55905, USA
| | - Christof Sohn
- University Hospital and German Cancer Research Center, National Center for Tumor Diseases, Heidelberg 69120, Germany
| | - Melissa C Southey
- Cancer Council Victoria, Cancer Epidemiology Division, Melbourne, VIC 3004, Australia; Monash University, Precision Medicine, School of Clinical Sciences at Monash Health, Clayton, VIC 3168, Australia; The University of Melbourne, Department of Clinical Pathology, Melbourne, VIC 3010, Australia
| | - John J Spinelli
- BC Cancer, Population Oncology, Vancouver, BC V5Z 1G1, Canada; University of British Columbia, School of Population and Public Health, Vancouver, BC V6T 1Z4, Canada
| | - Jennifer Stone
- The University of Melbourne, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Melbourne, VIC 3010, Australia; Curtin University and University of Western Australia, The Curtin UWA Centre for Genetic Origins of Health and Disease, Perth, WA 6000, Australia
| | - William J Tapper
- University of Southampton, Faculty of Medicine, Southampton SO17 1BJ, UK
| | - Maria Tengström
- University of Eastern Finland, Translational Cancer Research Area, Kuopio 70210, Finland; Kuopio University Hospital, Department of Oncology, Cancer Center, Kuopio 70210, Finland; University of Eastern Finland, Institute of Clinical Medicine, Oncology, Kuopio 70210, Finland
| | - Soo Hwang Teo
- Cancer Research Malaysia, Breast Cancer Research Programme, Subang Jaya, Selangor 47500, Malaysia; University of Malaya, Department of Surgery, Faculty of Medicine, Kuala Lumpur 50603, Malaysia
| | - Mary Beth Terry
- Columbia University, Department of Epidemiology, Mailman School of Public Health, New York, NY 10032, USA
| | - Rob A E M Tollenaar
- Leiden University Medical Center, Department of Surgery, Leiden 2333 ZA, the Netherlands
| | - Ian Tomlinson
- University of Birmingham, Institute of Cancer and Genomic Sciences, Birmingham B15 2TT, UK; University of Oxford, Wellcome Trust Centre for Human Genetics and Oxford NIHR Biomedical Research Centre, Oxford OX3 7BN, UK
| | - Melissa A Troester
- University of North Carolina at Chapel Hill, Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC 27599, USA
| | - Celine M Vachon
- Mayo Clinic, Department of Health Science Research, Division of Epidemiology, Rochester, MN 55905, USA
| | - Chantal van Ongeval
- Leuven Cancer Institute, University Hospitals Leuven, Leuven Multidisciplinary Breast Center, Department of Radiology, Leuven 3000, Belgium
| | - Elke M van Veen
- University of Manchester, Manchester Academic Health Science Centre, Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester M13 9WL, UK; St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester M13 9WL, UK
| | - Robert Winqvist
- University of Oulu, Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, Oulu 90220, Finland; Northern Finland Laboratory Centre Oulu, Laboratory of Cancer Genetics and Tumor Biology, Oulu 90220, Finland
| | - Alicja Wolk
- Karolinska Institutet, Institute of Environmental Medicine, Stockholm 171 77, Sweden; Uppsala University, Department of Surgical Sciences, Uppsala 751 05, Sweden
| | - Wei Zheng
- Vanderbilt University School of Medicine, Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Nashville, TN 37232, USA
| | - Argyrios Ziogas
- University of California Irvine, Department of Epidemiology, Genetic Epidemiology Research Institute, Irvine, CA 92617, USA
| | - Douglas F Easton
- University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Cambridge CB1 8RN, UK; University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge CB1 8RN, UK
| | - Per Hall
- Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm 171 65, Sweden; Södersjukhuset, Department of Oncology, Stockholm 118 83, Sweden
| | - Marjanka K Schmidt
- The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Division of Molecular Pathology, Amsterdam 1066 CX, the Netherlands; The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Division of Psychosocial Research and Epidemiology, Amsterdam 1066 CX, the Netherlands.
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Giardiello D, Kramer I, Hooning MJ, Hauptmann M, Lips EH, Sawyer E, Thompson AM, de Munck L, Siesling S, Wesseling J, Steyerberg EW, Schmidt MK. Contralateral breast cancer risk in patients with ductal carcinoma in situ and invasive breast cancer. NPJ Breast Cancer 2020; 6:60. [PMID: 33298933 PMCID: PMC7609533 DOI: 10.1038/s41523-020-00202-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/01/2020] [Indexed: 12/11/2022] Open
Abstract
We aimed to assess contralateral breast cancer (CBC) risk in patients with ductal carcinoma in situ (DCIS) compared with invasive breast cancer (BC). Women diagnosed with DCIS (N = 28,003) or stage I-III BC (N = 275,836) between 1989 and 2017 were identified from the nationwide Netherlands Cancer Registry. Cumulative incidences were estimated, accounting for competing risks, and hazard ratios (HRs) for metachronous invasive CBC. To evaluate effects of adjuvant systemic therapy and screening, separate analyses were performed for stage I BC without adjuvant systemic therapy and by mode of first BC detection. Multivariable models including clinico-pathological and treatment data were created to assess CBC risk prediction performance in DCIS patients. The 10-year cumulative incidence of invasive CBC was 4.8% for DCIS patients (CBC = 1334). Invasive CBC risk was higher in DCIS patients compared with invasive BC overall (HR = 1.10, 95% confidence interval (CI) = 1.04-1.17), and lower compared with stage I BC without adjuvant systemic therapy (HR = 0.87; 95% CI = 0.82-0.92). In patients diagnosed ≥2011, the HR for invasive CBC was 1.38 (95% CI = 1.35-1.68) after screen-detected DCIS compared with screen-detected invasive BC, and was 2.14 (95% CI = 1.46-3.13) when not screen-detected. The C-index was 0.52 (95% CI = 0.50-0.54) for invasive CBC prediction in DCIS patients. In conclusion, CBC risks are low overall. DCIS patients had a slightly higher risk of invasive CBC compared with invasive BC, likely explained by the risk-reducing effect of (neo)adjuvant systemic therapy among BC patients. For support of clinical decision making more information is needed to differentiate CBC risks among DCIS patients.
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Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Iris Kramer
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Maartje J Hooning
- Department of Medical Oncology-Cancer Epidemiology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Michael Hauptmann
- Institute of Biostatistics and Registry Research, Brandenburg Medical School, Neuruppin, Germany
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Esther H Lips
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Elinor Sawyer
- School of Cancer & Pharmaceutical Sciences, Kings College London, London, UK
| | - Alastair M Thompson
- Department of Surgery, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, USA
| | - Linda de Munck
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, the Netherlands
| | - Sabine Siesling
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, the Netherlands
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands
| | - Jelle Wesseling
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health, Erasmus MC, Rotterdam, the Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
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18
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Giardiello D, Kramer I, Hooning M, Hauptmann M, Lips E, Sawley E, Thompson A, de Munck L, Siesling S, Wesseling J, Steyerberg E, Schmidt M. Contralateral breast cancer in patients with ductal carcinoma in situ and invasive breast cancer in the Netherlands. Eur J Cancer 2020. [DOI: 10.1016/s0959-8049(20)30553-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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19
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Giardiello D, Hauptmann M, Steyerberg EW, Adank MA, Akdeniz D, Blom JC, Blomqvist C, Bojesen SE, Bolla MK, Brinkhuis M, Chang-Claude J, Czene K, Devilee P, Dunning AM, Easton DF, Eccles DM, Fasching PA, Figueroa J, Flyger H, García-Closas M, Haeberle L, Haiman CA, Hall P, Hamann U, Hopper JL, Jager A, Jakubowska A, Jung A, Keeman R, Koppert LB, Kramer I, Lambrechts D, Le Marchand L, Lindblom A, Lubiński J, Manoochehri M, Mariani L, Nevanlinna H, Oldenburg HSA, Pelders S, Pharoah PDP, Shah M, Siesling S, Smit VTHBM, Southey MC, Tapper WJ, Tollenaar RAEM, van den Broek AJ, van Deurzen CHM, van Leeuwen FE, van Ongeval C, Van't Veer LJ, Wang Q, Wendt C, Westenend PJ, Hooning MJ, Schmidt MK. Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts. Breast Cancer Res Treat 2020; 181:423-434. [PMID: 32279280 PMCID: PMC8380991 DOI: 10.1007/s10549-020-05611-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 03/21/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). METHODS We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. RESULTS The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula. CONCLUSIONS Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.
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Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Michael Hauptmann
- Brandenburg Medical School, Institute of Biostatistics and Registry Research, Neuruppin, Germany
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Muriel A Adank
- Family Cancer Clinic, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Delal Akdeniz
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Jannet C Blom
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Mariël Brinkhuis
- Laboratory for Pathology, East-Netherlands, Hengelo, The Netherlands
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Center Hamburg-Eppendorf, Cancer Epidemiology, University Cancer Center Hamburg (UCCH), Hamburg, Germany
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Diana M Eccles
- Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Peter A Fasching
- David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California At Los Angeles, Los Angeles, CA, USA
- University Hospital Erlangen, Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Jonine Figueroa
- The University of Edinburgh Medical School, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Edinburgh, UK
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Montserrat García-Closas
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Lothar Haeberle
- University Hospital Erlangen, Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Agnes Jager
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Linetta B Koppert
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Iris Kramer
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Diether Lambrechts
- VIB Center for Cancer Biology, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Mehdi Manoochehri
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Luigi Mariani
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Hester S A Oldenburg
- Department of Surgical Oncology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Saskia Pelders
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Sabine Siesling
- Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands
| | - Vincent T H B M Smit
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia
| | | | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Alexandra J van den Broek
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | | | - Flora E van Leeuwen
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Chantal van Ongeval
- Leuven Cancer Institute, Leuven Multidisciplinary Breast Center, Department of Oncology, University Hospitals Leuven, Leuven, Belgium
| | - Laura J Van't Veer
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Camilla Wendt
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
| | | | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
- Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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20
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Giardiello D, Steyerberg EW, Hauptmann M, Adank MA, Jager A, Oldenburg HOA, Hooning MJ, Schmidt MK. Abstract P5-07-12: Prediction and clinical utility of a contralateral breast cancer risk model. Cancer Res 2020. [DOI: 10.1158/1538-7445.sabcs19-p5-07-12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model, and evaluate its applicability for clinical decision-making.
Methods: We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4,682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics, and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B). Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility.
Results: In the multivariable model, BRCA1/2 germline mutation status, family history and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years: 0.52–0.74; at 10 years: 0.53–0.72). Calibration in-the-large was -0.13 (95%PI: -1.62–1.37) and the calibration slope was 0.90 (95%PI: 0.73–1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52–0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.
Conclusions: We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent, however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision regarding contralateral preventive mastectomy, especially in the general breast cancer population, remains challenging.
Citation Format: Daniele Giardiello, Ewout W Steyerberg, Michael Hauptmann, Muriel A Adank, Agnes Jager, Hester OA Oldenburg, Maartje J Hooning, Marjanka K Schmidt, Breast Cancer Association Consortium. Prediction and clinical utility of a contralateral breast cancer risk model [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-07-12.
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Affiliation(s)
| | | | | | - Muriel A Adank
- 1The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Agnes Jager
- 4Erasmus MC Cancer Institute, Rotterdam, Netherlands
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Giardiello D, Antoniou AC, Mariani L, Easton DF, Steyerberg EW. Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction. Breast Cancer Res 2020; 22:17. [PMID: 32041655 PMCID: PMC7011440 DOI: 10.1186/s13058-020-1255-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Accepted: 01/26/2020] [Indexed: 12/23/2022] Open
Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
| | - Antonis C Antoniou
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Luigi Mariani
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
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Giardiello D, Steyerberg EW, Hauptmann M, Adank MA, Akdeniz D, Blomqvist C, Bojesen SE, Bolla MK, Brinkhuis M, Chang-Claude J, Czene K, Devilee P, Dunning AM, Easton DF, Eccles DM, Fasching PA, Figueroa J, Flyger H, García-Closas M, Haeberle L, Haiman CA, Hall P, Hamann U, Hopper JL, Jager A, Jakubowska A, Jung A, Keeman R, Kramer I, Lambrechts D, Le Marchand L, Lindblom A, Lubiński J, Manoochehri M, Mariani L, Nevanlinna H, Oldenburg HSA, Pelders S, Pharoah PDP, Shah M, Siesling S, Smit VTHBM, Southey MC, Tapper WJ, Tollenaar RAEM, van den Broek AJ, van Deurzen CHM, van Leeuwen FE, van Ongeval C, Van't Veer LJ, Wang Q, Wendt C, Westenend PJ, Hooning MJ, Schmidt MK. Prediction and clinical utility of a contralateral breast cancer risk model. Breast Cancer Res 2019; 21:144. [PMID: 31847907 PMCID: PMC6918633 DOI: 10.1186/s13058-019-1221-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 10/29/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. METHODS We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. RESULTS In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.
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Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Michael Hauptmann
- Institute of Biometry and Registry Research, Brandenburg Medical School, Neuruppin, Germany
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Muriel A Adank
- The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Family Cancer Clinic, Amsterdam, The Netherlands
| | - Delal Akdeniz
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Mariël Brinkhuis
- East-Netherlands, Laboratory for Pathology, Hengelo, The Netherlands
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Diana M Eccles
- Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Peter A Fasching
- Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Jonine Figueroa
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Edinburgh, UK
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Montserrat García-Closas
- Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Lothar Haeberle
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Agnes Jager
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Iris Kramer
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Diether Lambrechts
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Loic Le Marchand
- University of Hawaii Cancer Center, Epidemiology Program, Honolulu, HI, USA
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Mehdi Manoochehri
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Luigi Mariani
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Hester S A Oldenburg
- Department of Surgical Oncology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Saskia Pelders
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Sabine Siesling
- Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands
| | - Vincent T H B M Smit
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Alexandra J van den Broek
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | | | - Flora E van Leeuwen
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands
| | - Chantal van Ongeval
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Laura J Van't Veer
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Camilla Wendt
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | | | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands.
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Derks MGM, van de Velde CJH, Giardiello D, Seynaeve C, Putter H, Nortier JWR, Dirix LY, Bastiaannet E, Portielje JEA, Liefers GJ. Impact of Comorbidities and Age on Cause-Specific Mortality in Postmenopausal Patients with Breast Cancer. Oncologist 2019; 24:e467-e474. [PMID: 30606886 DOI: 10.1634/theoncologist.2018-0010] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 11/15/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The aim was to study the impact of comorbidities and age on breast cancer mortality, taking into account competing causes of death. SUBJECTS, MATERIALS, AND METHODS Cohort analysis of Dutch and Belgian patients with postmenopausal, early hormone receptor-positive breast cancer included in the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial between 2001 and 2006. This is a randomized controlled trial of patients who had completed local treatment with curative intent and were randomized to receive exemestane for 5 years, or sequential treatment of tamoxifen followed by exemestane for a duration of 5 years. Patients were categorized by number of comorbidities (no comorbidities, 1-2 comorbidities, and >2 comorbidities) and age (<70 years and ≥70 years). Main outcome was breast cancer mortality considering other-cause mortality as competing event; cumulative incidences were calculated using the Cumulative Incidence Competing Risk Methods, and the Fine and Gray model was used to calculate the effect of age and comorbidities for the cause-specific incidences of breast cancer death, taking into account the effect of competing causes of death. RESULTS Overall, 3,159 patients were included, of which 2,203 (69.7%) were aged <70 years and 956 (30.3%) were aged ≥70 years at diagnosis. Cumulative incidence of breast cancer mortality was higher among patients ≥70 without comorbidities (22.2%, 95% CI, 17.5-26.9) compared with patients <70 without comorbidities (15.6%, 95% CI, 13.6-17.7, reference group), multivariable subdistribution hazard ratio (sHR) 1.49 (95% CI, 1.12-1.97, p = .005) after a median follow-up of 10 years. Use of chemotherapy was lower in older patients (1%, irrespective of the number of comorbidities) compared with younger patients (50%, 44%, and 38% for patients with no, 1-2, or >2 comorbidities, p < .001). CONCLUSION Older patients without comorbidities have a higher risk of dying due to breast cancer than younger counterparts, even when taking into account higher competing mortality, while use of chemotherapy in this group was low. These findings underline the need to take into account comorbidities, age, and competing mortality in the prognosis of breast cancer for accurate decision making. IMPLICATIONS FOR PRACTICE Older patients without comorbidity are at increased risk of dying from breast cancer, despite a higher other-cause mortality. This study shows that including age and comorbidity for the assessment of breast cancer mortality and other-cause mortality is indispensable for treatment decision making in older patients. Future prognostic tools for breast cancer prognosis should incorporate these items as well as risk of toxicity of adjuvant chemotherapy to adequately predict outcomes to optimize personalized treatment for older patients with early breast cancer.
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Affiliation(s)
- Marloes G M Derks
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Daniele Giardiello
- Department of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Caroline Seynaeve
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Hein Putter
- Department of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan W R Nortier
- Department of Medical Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Luc Y Dirix
- Oncology Center, Sint-Augustinus, Wilrijk-Antwerp, Belgium
| | - Esther Bastiaannet
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
- Department of Medical Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Gerrit-Jan Liefers
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
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Akdeniz D, Schmidt MK, Seynaeve CM, McCool D, Giardiello D, van den Broek AJ, Hauptmann M, Steyerberg EW, Hooning MJ. Risk factors for metachronous contralateral breast cancer: A systematic review and meta-analysis. Breast 2018; 44:1-14. [PMID: 30580169 DOI: 10.1016/j.breast.2018.11.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/12/2018] [Accepted: 11/16/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The risk of developing metachronous contralateral breast cancer (CBC) is a recurrent topic at the outpatient clinic. We aimed to provide CBC risk estimates of published patient, pathological, and primary breast cancer (PBC) treatment-related factors. METHODS PubMed was searched for publications on factors associated with CBC risk. Meta-analyses were performed with grouping of studies by mutation status (i.e., BRCA1, BRCA2, CHEK2 c.1100delC), familial cohorts, and general population-based cohorts. RESULTS Sixty-eight papers satisfied our inclusion criteria. Strong associations with CBC were found for carrying a BRCA1 (RR = 3.7; 95%CI:2.8-4.9), BRCA2 (RR = 2.8; 95%CI:1.8-4.3) or CHEK2 c.1100delC (RR = 2.7; 95%CI:2.0-3.7) mutation. In population-based cohorts, PBC family history (RR = 1.8; 95%CI:1.2-2.6), body mass index (BMI) ≥30 kg/m2 (RR = 1.5; 95%CI:1.3-1.9), lobular PBC (RR = 1.4; 95%CI:1.1-1.8), estrogen receptor-negative PBC (RR = 1.5; 95%CI:1.0-2.3) and treatment with radiotherapy <40 years (RR = 1.4; 95%CI:1.1-1.7) was associated with increased CBC risk. Older age at PBC diagnosis (RR per decade = 0.93; 95%CI:0.88-0.98), and treatment with chemotherapy (RR = 0.7; 95%CI:0.6-0.8) or endocrine therapy (RR = 0.6; 95%CI:0.5-0.7) were associated with decreased CBC risk. CONCLUSIONS Mutation status, family history, and PBC treatment are key factors for CBC risk. Age at PBC diagnosis, BMI, lobular histology and hormone receptor status have weaker associations and should be considered in combination with key factors to accurately predict CBC risk.
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Affiliation(s)
- Delal Akdeniz
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, Netherlands; Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands; Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Marjanka K Schmidt
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands; Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Caroline M Seynaeve
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Danielle McCool
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Daniele Giardiello
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands; Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, Netherlands
| | - Alexandra J van den Broek
- Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Michael Hauptmann
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Ewout W Steyerberg
- Department of Public Health, Erasmus MC, Rotterdam, Netherlands; Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, Netherlands
| | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, Netherlands.
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Giardiello D, Hooning M, Hauptmann M, Oldenburg H, Adank M, Jager A, Steyerberg E, Schmidt M. Prediction of contralateral breast cancer risk using individual patient data. Eur J Cancer 2018. [DOI: 10.1016/s0959-8049(18)30359-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Necchi A, Giardiello D, Mariani L. Methodological Considerations for Early-phase Development of Immune Checkpoint Inhibitors in Urothelial Bladder Cancer. Eur Urol 2016; 71:840-841. [PMID: 27720533 DOI: 10.1016/j.eururo.2016.09.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 09/28/2016] [Indexed: 11/24/2022]
Affiliation(s)
- Andrea Necchi
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
| | | | - Luigi Mariani
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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Necchi A, Sonpavde G, Lo Vullo S, Giardiello D, Bamias A, Crabb SJ, Harshman LC, Bellmunt J, De Giorgi U, Sternberg CN, Cerbone L, Ladoire S, Wong YN, Yu EY, Chowdhury S, Niegisch G, Srinivas S, Vaishampayan UN, Pal SK, Agarwal N, Alva A, Baniel J, Golshayan AR, Morales-Barrera R, Bowles DW, Milowsky MI, Theodore C, Berthold DR, Daugaard G, Sridhar SS, Powles T, Rosenberg JE, Galsky MD, Mariani L. Nomogram-based Prediction of Overall Survival in Patients with Metastatic Urothelial Carcinoma Receiving First-line Platinum-based Chemotherapy: Retrospective International Study of Invasive/Advanced Cancer of the Urothelium (RISC). Eur Urol 2016; 71:281-289. [PMID: 27726966 DOI: 10.1016/j.eururo.2016.09.042] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 09/28/2016] [Indexed: 12/19/2022]
Abstract
BACKGROUND The available prognostic models for overall survival (OS) in patients with metastatic urothelial carcinoma (UC) have been derived from clinical trial populations of cisplatin-treated patients. OBJECTIVE To develop a new model based on real-world patients. DESIGN, SETTING, AND PARTICIPANTS Individual patient-level data from 29 centers were collected, including metastatic UC and first-line cisplatin- or carboplatin-based chemotherapy administered between January 2006 and January 2011. INTERVENTION First-line, platinum-based, combination chemotherapy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The population was randomly split into a development and a validation cohort. Generalized boosted regression modelling was used to screen out irrelevant variables and address multivariable analyses. Two nomograms were built to estimate OS probability, the first based on baseline factors and platinum agent, the second incorporating objective response (OR). The performance of the above nomograms and that of other available models was assessed. We plotted decision curves to evaluate the clinical usefulness of the two nomograms. RESULTS AND LIMITATIONS A total of 1020 patients were analyzed (development: 687, validation: 333). In a platinum-stratified Cox model, significant variables for OS were performance status (p<0.001), white blood cell count (p=0.013), body mass index (p=0.003), ethnicity (p=0.012), lung, liver, or bone metastases (p<0.001), and prior perioperative chemotherapy (p=0.012). The c-index was 0.660. The distribution of the nomogram scores was associated with OR (p<0.001), and incorporating OR into the model further improved the c-index in the validation cohort (0.670). CONCLUSIONS We developed and validated two nomograms for OS to be used before and after completion of first-line chemotherapy for metastatic UC. PATIENT SUMMARY We proposed two models for estimating overall survival of patients with metastatic urothelial carcinoma receiving first-line, platinum-based chemotherapy. These nomograms have been developed on real-world patients who were treated outside of clinical trials and may be used irrespective of the chemotherapeutic platinum agent used.
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Affiliation(s)
- Andrea Necchi
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy.
| | - Guru Sonpavde
- UAB Comprehensive Cancer Center, Birmingham, AL, USA
| | | | | | | | | | | | | | - Ugo De Giorgi
- IRCCS Istituto Scientifico Romagnolo per lo studio e la Cura dei Tumori, Meldola, Italy
| | | | | | | | | | - Evan Y Yu
- University of Washington, Seattle, WA, USA
| | | | - Gunter Niegisch
- Heinrich-Heine-University, Medical Faculty, Department of Urology, Düsseldorf, Germany
| | - Sandy Srinivas
- Stanford University School of Medicine, Stanford, CA, USA
| | | | - Sumanta K Pal
- City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | | | - Ajjai Alva
- University of Michigan, Ann Arbor, MI, USA
| | | | | | - Rafael Morales-Barrera
- Vall d'Hebron Institute of Oncology, Vall d'Hebron University Hospital, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Daniel W Bowles
- Denver Veterans Affairs Medical Center, Eastern Colorado Health Care System, Denver, CO, USA
| | - Matthew I Milowsky
- University of North Carolina at Chapel Hill, Lineberger Comprehensive Cancer Center, NC, USA
| | | | | | - Gedske Daugaard
- Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Srikala S Sridhar
- Princess Margaret Hospital, University Health Network, Toronto, Canada
| | - Thomas Powles
- Barts Health and the Royal Free NHS Trust, Queen Mary University of London, London, UK
| | | | - Matthew D Galsky
- Mount Sinai School of Medicine, Tisch Cancer Institute, New York, NY, USA
| | - Luigi Mariani
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
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Necchi A, Giardiello D, Raggi D, Giannatempo P, Nicolai N, Catanzaro M, Torelli T, Biasoni D, Piva L, Stagni S, Calareso G, Togliardi E, Mariani L, Salvioni R. Dacomitinib as first-line treatment of locally-advanced (LA) or metastatic penile squamous cell carcinoma (PSCC): Interim analysis of an open-label, single-group, phase 2 trial. Ann Oncol 2016. [DOI: 10.1093/annonc/mdw373.67] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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29
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Tuccitto A, Tazzari M, Beretta V, Rini F, Miranda C, Greco A, Santinami M, Patuzzo R, Vergani B, Villa A, Manenti G, Cleris L, Giardiello D, Alison M, Rivoltini L, Castelli C, Perego M. Immunomodulatory Factors Control the Fate of Melanoma Tumor Initiating Cells. Stem Cells 2016; 34:2449-2460. [PMID: 27301067 DOI: 10.1002/stem.2413] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Accepted: 04/29/2016] [Indexed: 12/19/2022]
Abstract
Melanoma is a highly heterogeneous tumor for which recent evidence supports a model of dynamic stemness. Melanoma cells might temporally acquire tumor-initiating properties or switch from a status of tumor-initiating cells (TICs) to a more differentiated one depending on the tumor context. However, factors driving these functional changes are still unknown. We focused on the role of cyto/chemokines in shaping TICs isolated directly from tumor specimens of two melanoma patients, namely Me14346S and Me15888S. We analyzed the secretion profile of TICs and of their corresponding melanoma differentiated cells and we tested the ability of cyto/chemokines to influence TIC self-renewal and differentiation. We found that TICs, grown in vitro as melanospheres, had a complex secretory profile as compared to their differentiated counterparts. Some factors, such as CCL-2 and IL-8, also produced by adherent melanoma cells and melanocytes did not influence TIC properties. Conversely, IL-6, released by differentiated cells, reduced TIC self-renewal and induced TIC differentiation while IL-10, produced by Me15888S, strongly promoted TIC self-renewal through paracrine/autocrine actions. Complete neutralization of IL-10 activity by gene silencing and antibody-mediated blocking of the IL-10Rα was required to sensitize Me15888S to IL-6-induced differentiation. For the first time these results show that functional heterogeneity of melanoma could be directly influenced by inflammatory and suppressive soluble factors, with IL-6 favoring TIC differentiation, and IL-10 supporting TIC self-renewal. Thus, understanding the tumor microenvironment (TME) role in modulating melanoma TIC phenotype is fundamental to identifying novel therapeutic targets to achieve long-lasting regression of metastatic melanoma. Stem Cells 2016;34:2449-2460.
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Affiliation(s)
- Alessandra Tuccitto
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.,Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Marcella Tazzari
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.,Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Valeria Beretta
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.,Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Francesca Rini
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.,Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Claudia Miranda
- Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.,Molecular Mechanism Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Angela Greco
- Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.,Molecular Mechanism Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Mario Santinami
- Melanoma and Sarcoma Unit, Department of Surgery, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Roberto Patuzzo
- Melanoma and Sarcoma Unit, Department of Surgery, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Barbara Vergani
- Consorzio MIA (Microscopy and Image Analysis), University of Milano-Bicocca, Milano, Italy
| | - Antonello Villa
- Consorzio MIA (Microscopy and Image Analysis), University of Milano-Bicocca, Milano, Italy
| | - Giacomo Manenti
- Department of Predictive and Preventive Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Loredana Cleris
- Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Daniele Giardiello
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Malcolm Alison
- Centre for Tumour Biology, Barts Cancer Institute, Charterhouse Square, London, EC1M 6BQ, United Kingdom
| | - Licia Rivoltini
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.,Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Chiara Castelli
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy. .,Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.
| | - Michela Perego
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.,Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
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30
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Duca M, Paolini B, Antelmi E, Carcangiu ML, Mariani L, Giardiello D, Capri G, Mariani G, Mariani P, De Braud FG, Bianchi GV. Prognostic and predictive role of tumor-infiltrating lymphocytes in luminal b subtype breast cancer treated with neoadjuvant chemotherapy: A retrospective mono-institutional case series. J Clin Oncol 2016. [DOI: 10.1200/jco.2016.34.15_suppl.e12053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Matteo Duca
- Istituto Naz. Tumori-Fond. IRCCS- Oncologia Medica, Milan, Italy
| | - Biagio Paolini
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Ester Antelmi
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | | | - Luigi Mariani
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Giuseppe Capri
- Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Gabriella Mariani
- Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Paola Mariani
- Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
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31
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Giacomini E, Ferrari N, Pitozzi A, Remistani M, Giardiello D, Maes D, Alborali GL. Dynamics of Mycoplasma hyopneumoniae seroconversion and infection in pigs in the three main production systems. Vet Res Commun 2016; 40:81-8. [PMID: 27142053 DOI: 10.1007/s11259-016-9657-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Accepted: 04/25/2016] [Indexed: 10/21/2022]
Abstract
In this study, we investigated the dynamics of Mycoplasma hyopneumoniae infections in 66 pig farms, with different production systems (one-, two-, and three-site systems), and considered different risk factors. Serological assay was used to detect serum antibodies against M. hyopneumoniae and real time polymerase chain reaction (RT-PCR) was performed to detect M. hyopneumoniae DNA in tracheobronchial swabs. Results demonstrated that M. hyopneumoniae infection status was predominantly influenced by the age of the animals and the type of production system. Infection rates were higher in older animals and the prevalence was higher in the one- and two-site systems than in the three-site systems. Dynamics of infection by RT-PCR showed that earlier M. hyopneumoniae infection on one-site farms occurs earlier, while on two- and three-site farms occurs later but spreads faster, suggesting that contact between animals of different age favors the transmission.
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Affiliation(s)
- Enrico Giacomini
- Istituto Zooprofilattico Sperimentale Lombardia Emilia Romagna, Brescia, Italy
| | - Nicola Ferrari
- Istituto Zooprofilattico Sperimentale Lombardia Emilia Romagna, Brescia, Italy.,Department of Veterinary Science and Public Health, Università degli Studi di Milano, Milan, Italy
| | - Alessandra Pitozzi
- Istituto Zooprofilattico Sperimentale Lombardia Emilia Romagna, Brescia, Italy
| | - Michela Remistani
- Istituto Zooprofilattico Sperimentale Lombardia Emilia Romagna, Brescia, Italy
| | - Daniele Giardiello
- Istituto Zooprofilattico Sperimentale Lombardia Emilia Romagna, Brescia, Italy
| | - Dominiek Maes
- Unit of Porcine Health Management, Faculty of Veterinary Medicine, Ghent University, Ghent, Belgium
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32
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Ferrari A, Lo Vullo S, Giardiello D, Veneroni L, Magni C, Clerici CA, Chiaravalli S, Casanova M, Luksch R, Terenziani M, Spreafico F, Meazza C, Catania S, Schiavello E, Biassoni V, Podda M, Bergamaschi L, Puma N, Massimino M, Mariani L. The Sooner the Better? How Symptom Interval Correlates With Outcome in Children and Adolescents With Solid Tumors: Regression Tree Analysis of the Findings of a Prospective Study. Pediatr Blood Cancer 2016; 63:479-85. [PMID: 26797893 DOI: 10.1002/pbc.25833] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Accepted: 10/09/2015] [Indexed: 11/07/2022]
Abstract
BACKGROUND The potential impact of diagnostic delays on patients' outcomes is a debated issue in pediatric oncology and discordant results have been published so far. We attempted to tackle this issue by analyzing a prospective series of 351 consecutive children and adolescents with solid malignancies using innovative statistical tools. METHODS To address the nonlinear complexity of the association between symptom interval and overall survival (OS), a regression tree algorithm was constructed with sequential binary splitting rules and used to identify homogeneous patient groups vis-à-vis functional relationship between diagnostic delay and OS. RESULTS Three different groups were identified: group A, with localized disease and good prognosis (5-year OS 85.4%); group B, with locally or regionally advanced, or metastatic disease and intermediate prognosis (5-year OS 72.9%), including neuroblastoma, Wilms tumor, non-rhabdomyosarcoma soft tissue sarcoma, and germ cell tumor; and group C, with locally or regionally advanced, or metastatic disease and poor prognosis (5-year OS 45%), including brain tumors, rhabdomyosarcoma, and bone sarcoma. The functional relationship between symptom interval and mortality risk differed between the three subgroups, there being no association in group A (hazard ratio [HR]: 0.96), a positive linear association in group B (HR: 1.48), and a negative linear association in group C (HR: 0.61). CONCLUSIONS Our analysis suggests that at least a subset of patients can benefit from an earlier diagnosis in terms of survival. For others, intrinsic aggressiveness may mask the potential effect of diagnostic delays. Based on these findings, early diagnosis should remain a goal for pediatric cancer patients.
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Affiliation(s)
- Andrea Ferrari
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Salvatore Lo Vullo
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Daniele Giardiello
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Laura Veneroni
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Chiara Magni
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Carlo Alfredo Clerici
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
- Section of Psychology, Department of Biomolecular Sciences and Biotechnology, Faculty of School of Medicine, University of Milan, Italy
| | - Stefano Chiaravalli
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Michela Casanova
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Roberto Luksch
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Monica Terenziani
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Filippo Spreafico
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Cristina Meazza
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Serena Catania
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | | | - Veronica Biassoni
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Marta Podda
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Luca Bergamaschi
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Nadia Puma
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Maura Massimino
- Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Luigi Mariani
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
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33
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Lavazza A, Chiari M, Nassuato C, Giardiello D, Tittarelli C, Grilli G. Serological Investigation on Encephalitozoon cuniculi in pet Rabbits in North-Central Italy. J Exot Pet Med 2016. [DOI: 10.1053/j.jepm.2015.12.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Bozzetti F, Cotogni P, Lo Vullo S, Pironi L, Giardiello D, Mariani L. Development and validation of a nomogram to predict survival in incurable cachectic cancer patients on home parenteral nutrition. Ann Oncol 2015; 26:2335-40. [DOI: 10.1093/annonc/mdv365] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 08/25/2015] [Indexed: 02/06/2023] Open
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35
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Raggi D, Mariani L, Giannatempo P, Lo Vullo S, Giardiello D, Nicolai N, Piva L, Biasoni D, Catanzaro M, Torelli T, Stagni S, Maffezzini M, Calareso G, Magni M, Di Nicola M, Verzoni E, Grassi P, Procopio G, De Braud F, Pizzocaro G, Salvioni R, Necchi A. Prognostic reclassification of patients with intermediate-risk metastatic germ cell tumors: Implications for clinical practice, trial design, and molecular interrogation. Urol Oncol 2015; 33:332.e19-24. [DOI: 10.1016/j.urolonc.2015.04.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Revised: 04/09/2015] [Accepted: 04/15/2015] [Indexed: 11/26/2022]
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36
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Raggi D, Mariani L, Giannatempo P, Lo Vullo S, Giardiello D, Nicolai N, Piva L, Biasoni D, Catanzaro M, Torelli T, Stagni S, Maffezzini M, Calareso G, Verzoni E, Grassi P, Procopio G, De Braud FG, Pizzocaro G, Salvioni R, Necchi A. Prognostic reclassification of patients with intermediate risk metastatic germ cell tumors (IRGCT): implications for clinical practice, trial design, and molecular interrogation. J Clin Oncol 2015. [DOI: 10.1200/jco.2015.33.15_suppl.e15572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Daniele Raggi
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Luigi Mariani
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | | | | | - Nicola Nicolai
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Luigi Piva
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Davide Biasoni
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | | | - Tullio Torelli
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Silvia Stagni
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | | | | | - Elena Verzoni
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Paolo Grassi
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Giuseppe Procopio
- Oncology Unit I, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | | | | | | | - Andrea Necchi
- Istituto Nazionale Tumori of Milan, Milano, MI, Italy
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37
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Raggi D, Lo Vullo S, Giannatempo P, Giardiello D, Nicolai N, Piva L, Biasoni D, Catanzaro M, Torelli T, Stagni S, Maffezzini M, Mariani L, Salvioni R, Necchi A. Clinical outcomes of intermediate risk metastatic germ cell tumors (IRGCT): Results from a single-institution series. J Clin Oncol 2015. [DOI: 10.1200/jco.2015.33.7_suppl.380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
380 Background: IRGCT comprises a consistent category of metastatic patients (pts), and information on the recommended management of these pts should be updated. Usually they enter clinical trials for poor prognosis GCT. We aimed to address the heterogeneity of this category and to identify clinical prognostic factors for sub-stratification of pts. Methods: Data on consecutive pts with IRGCT and who received treatment at Fondazione INT Milano in the time-frame 02/1980-03/2014 were collected. Cox regression analyses were done evaluating potential prognostic factors for overall survival (OS, primary endpoint) to first-line therapy. Each factor was evaluated in a multivariable model. An exploratory OS comparison between outlier groups was undertaken with Kaplan Meier curves and logrank test. Results: Data on 181 pts were collected. Median age was 27 yrs (IQR 22-32), 10 pts had a retroperitoneal (RP) primary, 6 had pure seminoma. 72 (39.8%) had lung metastases and 54 (32.3%) bulky (i.e. ≥10cm) RP lymph-nodes (LN). Pts received cisplatin, bleomycin and etoposide (PEB, n=156) or vinblastine (PVB, n=23), 2 other treatments. Median follow up was 173 months (IQR: 87-237). Globally, 5-y PFS and OS were 66.8% (95%CI: 60.1-74.2) and 83.3% (77.8-89.2). However, 5-y OS for pts with AFP 5,000-10,000 IU/ml (N=19) was 61.8% (95%CI: 43.0-88.7) while it was 89.1% (95%CI: 81.2-97.7) for nonseminomas with elevated LDH only (N=57) and similar for elevated HCG only (N=22); overall p<0.001. Multivariable analysis for OS is shown in the table (c-index= 0.63). Distribution of variables over time: bulky RP LN and elevated LDH were more frequent in earlier series (p=0.003 and 0.011). Conclusions: The prognostic heterogeneity of IRGCT category is a matter of fact and should be addressed by clinical trials. Pts with highly elevated AFP have an OS similar to poor prognostic category, while those categorized by elevated HCG or LDH only are close to good risk ones. [Table: see text]
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Affiliation(s)
- Daniele Raggi
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | | | | | | | - Nicola Nicolai
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Luigi Piva
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Davide Biasoni
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | | | - Tullio Torelli
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Silvia Stagni
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | | | - Luigi Mariani
- Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Andrea Necchi
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
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Chiari M, Ferrari N, Giardiello D, Avisani D, Pacciarini ML, Alborali L, Zanoni M, Boniotti MB. Spatiotemporal and Ecological Patterns of Mycobacterium microti Infection in Wild Boar (Sus scrofa). Transbound Emerg Dis 2015; 63:e381-8. [PMID: 25580561 DOI: 10.1111/tbed.12313] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Indexed: 12/01/2022]
Abstract
Mycobacterium microti has recently been described as the causative agent of tuberculosis-like lesions in wild boar (Sus scrofa), a reservoir specie of Mycobacterium tuberculosis complex (MTBC) in some European Mediterranean ecosystem. Through a five-year survey on tuberculosis in free-living wild boars, the epidemiological trend of M. microti infections and the host and population risk factors linked with its occurrence were described. Retropharyngeal and mandibular lymph nodes of 3041 hunted wild boars from six different districts were macroscopically inspected. The sex and age of each animal were registered, as well as the animal abundance in each district. Lesions compatible with tuberculosis (190) were collected and analysed using a gyrB PCR-RFLP assay. M. microti was identified directly in 99 tissue samples (Prev = 3.26%; 95% CI: 2.67-3.97%), while neither Mycobacterium bovis, nor other members of the MTBC were detected. The probability of being M. microti positive showed spatio-temporal variability, with 26% of increase of risk of being infected for each year. Moreover, a positive effect of wild boar abundance and age on the prevalence was detected. The generalized increase in the European wild boar population, coupled with its sensitivity to M. microti infection, poses a future concern for the identification and management of MTBC members in wild boar.
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Affiliation(s)
- M Chiari
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna (IZSLER), Brescia, Italy
| | - N Ferrari
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna (IZSLER), Brescia, Italy.,Department of Veterinary Sciences and Public Health, Università degli Studi di Milano, Milan, Italy
| | - D Giardiello
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna (IZSLER), Brescia, Italy
| | - D Avisani
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna (IZSLER), Brescia, Italy
| | - M L Pacciarini
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna (IZSLER), Brescia, Italy
| | - L Alborali
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna (IZSLER), Brescia, Italy
| | - M Zanoni
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna (IZSLER), Brescia, Italy
| | - M B Boniotti
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna (IZSLER), Brescia, Italy
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39
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Chiari M, Ferrari N, Giardiello D, Lanfranchi P, Zanoni M, Lavazza A, Alborali LG. Isolation and identification of Salmonella spp. from red foxes (Vulpes vulpes) and badgers (Meles meles) in northern Italy. Acta Vet Scand 2014; 56:86. [PMID: 25492524 PMCID: PMC4266207 DOI: 10.1186/s13028-014-0086-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 12/03/2014] [Indexed: 11/19/2022] Open
Abstract
Background Salmonella spp. have been isolated from a wide range of wild animals. Opportunistic wild carnivores such as red foxes (Vulpes vulpes) and badgers (Meles meles) may act as environmental indicators or as potential sources of salmonellosis in humans. The present study characterizes Salmonella spp. isolated from the intestinal contents of hunted or dead red foxes (n = 509) and badgers (n = 17) in northern Italy. Findings Thirty-one strains of Salmonella belonging to 3 Salmonella enterica subspecies were isolated. Fourteen different serovars of S. enterica subsp. enterica were identified, among which were serovars often associated with human illness. Conclusions Wild opportunistic predators can influence the probability of infection of both domestic animals and humans through active shedding of the pathogen to the environment. The epidemiological role of wild carnivores in the spread of salmonellosis needs to be further studied.
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Chiari M, Ferrari N, Giardiello D, Avisani D, Zanoni M, Alborali GL, Lanfranchi P, Guberti V, Lorenzo C, Antonio L. Temporal dynamics of European brown hare syndrome infection in Northern Italian brown hares (Lepus europaeus). EUR J WILDLIFE RES 2014. [DOI: 10.1007/s10344-014-0856-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Vrenna L, Castaldo AM, Castaldo P, Giardiello D, Di Giacomo C, Esposito LP, Romano F. Comparison between nephelometric and RIA methods for serum myoglobin, and efficiency of myoglobin assay for early diagnosis of myocardial infarction. Clin Chem 1992; 38:789-90. [PMID: 1582046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Vrenna L, Castaldo AM, Castaldo P, Giardiello D, Di Giacomo C, Esposito LP, Romano F. Comparison between Nephelometric and RIA Methods for Serum Myoglobin, and Efficiency of Myoglobin Assay for Early Diagnosis of Myocardial Infarction. Clin Chem 1992. [DOI: 10.1093/clinchem/38.5.789] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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