Surgical or Medical Treatment of Breast Cancer Metastasis: A Multicentre Observational Study
Conditions: Breast Cancer, Liver Metastases
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS

 
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Study Protocol 
APRIL 2023 

 
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STUDY PROTOCOL 
 
Surgical or Medical Treatment of Breast Cancer Liver Metastasis:  
a Multicenter Observational Study 
 
ACRONYM: SurMed_BLCM 
 
Principal Investigator (PI): Dr. Francesco Giovinazzo, U.O.C. General and Liver Transplant 
Surgery, Department of Medical and Surgical sciences, Fondazione Policlinico Universitario 
Agostino Gemelli IRCCS, Largo Francesco Vito 1, 00168, Rome (Italy) 
 
Sub-Investigators: Dr. Amelia Mattia, U.O.C. General and Liver Transplant Surgery, 
Department of Medical and Surgical sciences, Fondazione Policlinico Universitario 
Agostino Gemelli IRCCS, Largo Francesco Vito 1, 00168, Rome (Italy) 
 
 
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo Francesco 
Vito 1, 00168, Rome (Italy) 
 
 
 

 
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Satellite centers 
1. Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, 
Houston, TX (USA); 
2. Department of Surgical and Perioperative Sciences/Surgery, Umeå University (Sweden); 
3. Ospedale San Raffaele, Milano (Italy)  
4. Institute of Liver Studies, King's College Hospital, London, United Kingdom; 
5. Department of Surgery, Memorial Sloan Kettering Cancer Center 1275 Avenue, New 
York NY10065, (USA); 
6. Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, 
Houston, Texas - (USA); 
7. Department of Surgery, Naval Hospital of Varna, Varna; 1Clinic of Surgery, Specialized 
Hospital for Oncologic Diseases of Varna, Varna (Bulgaria). 
 
Funding: No Profit  
 
Version: 1.0 
 
Date: February 21, 2023

 
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Background and rationale 
The liver represents the third most common site of breast cancer (BC) metastases behind the 
lymphatics and bone [1]. Breast cancer that spreads to the liver can include cancers with 
different cell characteristics, including hormone receptor-positive, HER2 -positive, or triple-
negative breast cancers [2]. People of any background or age may develop liver metastasis. 
The process of liver metastasis consists of multiple steps and involves various factors from 
breast cancer cells and the liver microenvironment [3].  
About 50% of people diagnosed with metastatic breast cancer develop liver metastases 
(BCLM), and 5% to 12% of these people develop liver metastases as the main site of breast 
cancer recurrence [4,5]. Survival in breast cancer with liver metastases (BCLM) typically 
does not exceed 8 months if left untreated.  
The primary treatment for BCLM remains chemo-therapy and, more recently, targeted 
immunotherapy. Liver metastases from other primary tumors, such as colorectal cancers, 
are routinely resected as part of standard management. Breast cancer rarely develops 
isolated liver metastases because neo-plastic cells at this stage have often already reached 
systemic circulation with the possibility of further localization, unlike colon cancer, where 
the liver, through the portal system, is the first organ to be colonized. Therefore, the role of 
liver resection in BCLM remains controversial [6-8].  
 
 

 
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Hepatic resection and/or ablation was not associated with a survival advantage in some 
studies [9], whilst in other disclosed a remarkable improvement as compared to systemic 
treatment alone [10,11], thus indicating surgical intervention as suggested in highly selected 
patients with the goal of providing time off of systemic chemotherapy.  
Our study thus aims to provide further insights in this complex and still unclear field of 
research.

 
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OBJECTIVES 
 
Primary Objective 
Primary aim of the study is to compare the efficacy of liver resection vs. medical therapy 
alone in Breast Cancer Liver Metastasis (BCLM) patients. 
 
Secondary Objectives 
• To assess the recurrence rate after liver resection or medical treatment alone; 
• To assess Recurrence free survival (RFS); 
• To assess potential factors involved in both overall survival and RFS. 
 

 
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METHODS 
Study design 
Multicenter observational ambispective study. 
 
Population 
We will enroll all patients with BCLM underwent liver resection or medical treatment alone 
afferent to the Fondazione Policlinico Gemelli IRCSS of Rome (Italy), and other 8 
International centers (as detailed in the attached document), following the inclusion and 
exclusion criteria hereafter detailed. 
 
Study duration 
The study will overall last 68 months since the approval of the present protocol by the 
sponsor center institutional review board (IRB), i.e. Fondazione Policlinico Gemelli IRCSS 
of Rome (Italy), and of all the satellite centers. The prospective enrolment of patients will 
last till the December 31, 2023 and follow-up phase will last for other 5 years, till December 
31, 2028. 

 
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Study phases 
The study foresees two distinct phases: 
• I phase (only retrospective phase) → it will cover all patients underwent to either 
medical treatment or surgery for which all data are available, with a follow up of 5 years 
at March 31, 2023. We will retrieve all patients data from January 1994 till March 31, 2018. 
At the end of “phase I”, we foresee an “ad interim” analysis. 
• II phase (ambispective) → we will further include all patients retrieved from the archives 
between April 1, 2018 and the April 30, 2023, as well as all prospectively enrolled patients 
till December 31 2023, so that the last patient enrolled will end the 5-years follow-up 
phase within December 31, 2028.  

 
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Inclusion criteria 
● Age >18 years old; 
● Presence of BCLM; 
● BCLM and extra-hepatic disease; 
● Any treatment;  
● Ability to understand and sign the informed consent. 
 
Exclusion criteria 
● Inability to provide the informed consent; 
● Patients who underwent Best Supportive Care or palliation.

 
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Variables and procedures  
The following data will be retrieved from medical archives of each participating center for 
all retrospectively enrolled patient. 
Prospectively enrolled patients’ data will instead be collected in the related medical record 
and reported, alongside all other patients’ data, in an electronic dataset protected by 
password and accessible only to the PI and the co-investigators, and to any other person 
authorized by PI. 
In depth, the following data will be included: 
• Anonymized ID; 
• Demographic and anthropometric data (e.g., age, sex, BMI, etc..); 
• Name of the participating center; 
• ER, PR and HER2 status, 
• Presence of BRCA1-2 mutation,  
• Neoadjuvant-radiotherapy,  
• Neoadjuvant endocrine therapy,  
• Neoadjuvant-chemotherapy,  
• Neoadjuvant-anti-HER2,  
• Date of breast surgery,  
• Breast tumor T and N,  
• Adjuvant radiotherapy,  

 
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• Ad-endocrine therapy, Ad-chemotherapy, and Ad-anti-HER2,  
• Date of liver metastasis diagnosis,  
• Date of liver surgery,  
• Liver surgery,  
• Liver resection major/minor (Segmental or wedge resection or Left Lateral),  
• R status,  
• Number of liver metastasis,  
• Size of the bigger lesion (mm),  
• Distribution (uni or bilobar),  
• Postoperative morbidity,  
• Hormone receptor expression in the liver metasasis (ER+, PR+),  
• Extra-hepatic disease,  
• Site of Extra-hepatic disease,  
• Mortality and cause; 
• Date of death or last FU observation,  
• Recurrences,  
• Site of recurences,  
• Date of recurrences. 
 
 
 

 
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ENDPOINTS  
 
Primary endpoint  
The primary endpoint is to analyze the efficacy of liver surgery as compared to medical 
therapy alone in terms of 5-years overall survival in patients with breast cancer who 
underwent to liver resection or medical therapy alone. 
 
Secondary endpoints 
● 
Recurrence rate; 
● 
Post-operative mortality (in patients who underwent to surgical treatment). 
● 
Post-operative complication (in patients who underwent to surgical treatment). 
● 
To assess potential predictive factors of 5-years overall survival in patients with breast 
cancer who underwent to either liver resection or medical therapy alone. 
● 
To assess recurrence free survival (RFS) of liver surgery as compared to medical therapy 
alone in terms of 5-years overall survival in patients with breast cancer who underwent 
to liver resection or medical therapy alone. 
● 
To assess potential predictive factors of 5-years RFS in patients with breast cancer who 
underwent to either liver resection or medical therapy alone. 

 
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STATISTICAL ANALYSIS PLAN 
 
Sample size calculation 
The sample size was computed based on the assessment of an improvement in the OS of 
patients surgically treated as compared to systemic therapy alone. 
Based on the evidence from Ruiz and colleagues [10], which reported a 5-years OS of 24% 
among women treated only with medical therapy and of 69% among those surgically 
treated, we hypothesize to obtain in our study sample an OS of 60% in the treated group 
and of 25% in the systemic therapy group. 
As such, considering a treatment/control ratio of 0.15/0.85, a two-sided log-rank test with an 
overall sample size of 117 subjects (99 in the systemic therapy group and 18 in the surgical 
treatment group) achieves 90% power at a 0,05 significance level to detect a hazard ratio of 
0,37 when the proportion of 5-year OS in the control group is 25%. The study will consider 
a time-window of 30 years of total enrolment (from 1994 to 2023) and a follow-up period of 
5 years, for a total time of 35 years. The accrual pattern across time periods is uniform (all 
periods equal). We expect no subjects drop out both in the control group and treatment 
groups. The expect no. of events is of 96,5 in the control group and 14,3 in the treatment 
group. The sample size calculation was performed with PASS2022 v22.0.3 [12,13]. Such 
sample size estimation is consistent with the disease specific metastasis prevalence and on 
the no. of involved centers. In fact, we overall expect to include in both retrospective and 

 
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prospective phase 75-80 patients/center, for overall sample of 675-720 patients. Particularly, 
the retrospective phase, which will include patients till 2017 will consist of about 500 
patients, already consistent with the sample size estimate provided, whilst the other will 
pertain to the period 2018-2023. 
 
Statistical analysis 
The sample will be described in its clinical and demographic characteristics by descriptive 
statistics techniques. In depth, qualitative data will be expressed as absolute and relative 
percentage frequency, whilst quantitative variables either by mean and standard deviation 
(SD) or median and interquartile range (IQR), as appropriate. To verify the Gaussian 
distribution of quantitative variables, the Shapiro-Wilk test will be applied. 
Missing values, as <5% in all cases, were treated by imputeR package, using multiple 
imputation with Lasso Regression methods centered on the mean as for quantitative data, 
whilst classification trees for imputation by “rpartC” function, centered on the mode, i.e. 
most represented class object, were applied on qualitative data [14]. 
Between groups differences were analyzed by either the Chi squared test or the Fisher-
Freeman-Halton test for qualitative data, whilst either the Student’s t test or non-parametric 
Mann-Withney U test will be applied on quantitative data, as appropriate. 
Differences between groups will be further exemplified by clustered violin plots or plots 
describing pre-post variation in individual groups. Such plots will be produced with the R 
packages “ggplot2”, “ggstatsplot”, “ggpubr”, “ggprism”, and “ggsignif” [15-19]. 

 
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OS will be calculated from the date of treatment initiation through the date of death from 
any cause; subjects alive at the last follow-up will be censored. Overall survival and RFS 
probabilities at 5 years will be estimated using the Kaplan Meier method and  compared by 
the log-rank test. The KM curve will also be reported. The analysis will be performed with 
the R package “ggplot2” and “survminer” R packages [15,20]. 
To assess potential prognostic factors of 5-year RFS and OS, both univariable and 
multivariable Cox proportional hazards regression models will be fitted, and hazard ratios 
(HR) and 95% confidence intervals (CI) consequently reported. The proportionality of the 
hazards will be assessed by visual inspection of the hazards and Schoenfeld residuals. In 
case of doubtful proportionality, Cox weighted regression models will be fitted. "survival" 
and "coxphw" R packages will be used for the whole analysis [21-24]. The predictors to be 
included in the multivariable model will be selected on the basis of univariable analysis 
(p<0.05 or suggestive, i.e. 0.05≤p<0.10) and expert opinion, also considering the possibility 
of overcoming the rule of 10 events per variable (EPV) [25,26]. Statistical significance will be 
set at a p-value <0.05. Suggestive p-values will be further reported (0.05 ≤ p < 0.10). All 
analyses will be performed with R software version 4.2.0 (CRAN ®, R Core 2022) [27]. 
 
 

 
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References 
[1] Diamond JR, Finlayson CA, Borges VF. Hepatic complications of breast cancer. Lancet 
Oncol. 2009;10(6):615-621. doi:10.1016/S1470-2045(09)70029-4 
[2] Arciero CA, Guo Y, Jiang R, et al. ER+/HER2+ Breast Cancer Has Different Metastatic 
Patterns and Better Survival Than ER-/HER2+ Breast Cancer. Clin Breast Cancer. 
2019;19(4):236-245. doi:10.1016/j.clbc.2019.02.001 
[3] Ma R, Feng Y, Lin S, et al. Mechanisms involved in breast cancer liver metastasis. J 
Transl Med. 2015;13:64. Published 2015 Feb 15. doi:10.1186/s12967-015-0425-0 
[4] DeSantis CE, Ma J, Gaudet MM, et al. Breast cancer statistics, 2019. CA Cancer J Clin. 
2019;69(6):438-451. doi:10.3322/caac.21583  
[5] Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021 [published correction 
appears in CA Cancer J Clin. 2021 Jul;71(4):359]. CA Cancer J Clin. 2021;71(1):7-33. 
doi:10.3322/caac.21654 
[6] Howlader M, Heaton N, Rela M. Resection of liver metastases from breast cancer: 
towards 
a 
management 
guideline. Int 
J 
Surg. 
2011;9(4):285-291. 
doi:10.1016/j.ijsu.2011.01.009  
[7] Golse N, Adam R. Liver Metastases From Breast Cancer: What Role for Surgery? 
Indications 
and 
Results. Clin 
Breast 
Cancer. 
2017;17(4):256-265. 
doi:10.1016/j.clbc.2016.12.012  

 
Protocol SurMed_BCLM V.1.0 of February 21, 2023  
 
 
     Pag. 16 | 18 
 
[8] Rashid NS, Grible JM, Clevenger CV, Harrell JC. Breast cancer liver metastasis: current 
and 
future 
treatment 
approaches. Clin 
Exp 
Metastasis. 
2021;38(3):263-277. 
doi:10.1007/s10585-021-10080-4 
[9] Sadot E, Lee SY, Sofocleous CT, et al. Hepatic Resection or Ablation for Isolated Breast 
Cancer Liver Metastasis: A Case-control Study With Comparison to Medically Treated 
Patients. Ann Surg. 2016;264(1):147-154. doi:10.1097/SLA.0000000000001371 
[10] Ruiz A, van Hillegersberg R, Siesling S, et al. Surgical resection versus systemic therapy 
for breast cancer liver metastases: Results of a European case matched comparison. Eur 
J Cancer. 2018;95:1-10. doi:10.1016/j.ejca.2018.02.024  
[11] Mariani P, Servois V, De Rycke Y, et al. Liver metastases from breast cancer: Surgical 
resection or not? A case-matched control study in highly selected patients. Eur J Surg 
Oncol. 2013;39(12):1377-1383. doi:10.1016/j.ejso.2013.09.021 
[12] Lakatos E. Designing complex group sequential survival trials. Stat Med. 
2002;21(14):1969-1989. doi:10.1002/sim.1193  
[13] PASS 2022 Power Analysis and Sample Size Software (2022). NCSS, LLC. Kaysville, 
Utah, USA, ncss.com/software/pass. PASS 2022, Version 22.0.3 
[14]  Feng L, Moritz S, Nowak G, Welsh AH, O'Neill TJ (2020). _imputeR: A General 
Multivariate Imputation Framework_. R package version 2.2, <https://CRAN.R-
project.org/package=imputeR>. 
[15] H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 
2016. 

 
Protocol SurMed_BCLM V.1.0 of February 21, 2023  
 
 
     Pag. 17 | 18 
 
[16] Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach. Journal 
of Open Source Software, 6(61), 3167, doi: 10.21105/joss.03167 
[17] Kassambara A (2020). _ggpubr: 'ggplot2' Based Publication Ready Plots_. R package 
version 0.4.0, <https://CRAN.R-project.org/package=ggpubr>. 
[18] Dawson C (2021). _ggprism: A 'ggplot2' Extension Inspired by 'GraphPad Prism'_. R 
package version 1.0.3, <https://CRAN.R-project.org/package=ggprism>. 
[19] Ahlmann-Eltze, C., & Patil, I. (2021). ggsignif: R Package for Displaying Significance 
Brackets for 'ggplot2'. PsyArxiv. doi:10.31234/osf.io/7awm6 
[20] Kassambara A, Kosinski M, Biecek P (2021). _survminer: Drawing Survival Curves 
using 
'ggplot2'_. 
R 
package 
version 
0.4.9, 
<https://CRAN.R-
project.org/package=survminer>. 
[21] Therneau T (2022). _A Package for Survival Analysis in R_. R package version 3.4-0,  
<https://CRAN.R-project.org/package=survival>. 
[22] Terry M. Therneau, Patricia M. Grambsch (2000). _Modeling Survival Data: Extending 
the Cox Model_. Springer, New York. ISBN 0-387-98784-3. 
[23] Schemper M. Cox Analysis of Survival Data with Non-Proportional Hazard Functions. 
Journal of the Royal Statistical Society. Series D (The Statistician) 1192; 41(4): 455-65. doi: 
10.2307/2349009.  
[24] Schemper M, Wakounig S, Heinze G. The estimation of average hazard ratios by 
weighted Cox regression. Stat Med. 2009;28(19):2473-2489. doi:10.1002/sim.3623  

 
Protocol SurMed_BCLM V.1.0 of February 21, 2023  
 
 
     Pag. 18 | 18 
 
[25] van Smeden M, Moons KG, de Groot JA, et al. Sample size for binary logistic prediction 
models: Beyond events per variable criteria. Stat Methods Med Res. 2019;28(8):2455-
2474. doi:10.1177/0962280218784726 
[26] Moons KG, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable 
prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and 
elaboration. Ann Intern Med. 2015;162(1):W1-W73. doi:10.7326/M14-0698 
[27] R Core Team (2022). R: A language and environment for statistical computing. R 
Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-
project.org/.