Patterns of Care and Outcomes of Patients With METAstatic BONE Tumors in a Real-life Setting
Conditions: Metastatic Bone Tumor
Sponsor: Institut Bergonié

ID: IB2019-METABONE 
    
 
 
Patterns of Care and Outcomes in Patients With Metastatic Bone Tumors (METABONE)     
 
 
NCT03963531 
 
 
Jan 5, 2007 
 
 

INCLUSION 
 
- Patients age > 12 year old at initial diagnosis 
- Histology of Ewing's sarcoma type, osteosarcoma, chondrosarcoma 
- Diagnostic of primary event between 2008 and 2018 
- Diagnostic of unresectable locoregional disease where palliative treatment is indicated, or metastatic disease 
- Treatment and monitoring made in one of the GSF-GETO centers, with follow up data on treatment received 
available. 
 
 
OBJECTIFS 
 
I. 
OBJECTIVES AND ENDPOINTS 
 
The primary objective of the METABONE study is to give a retrospective descriptive analysis of clinico-biological 
profiles, patterns of care and modalities of treatment of patients with unresectable locally advanced and/or 
metastatic malignant bone tumors in a real-life national setting. 
 
The primary endpoint is description of initial and local/metastatic relapse events management of adult patients 
with the diagnosis of Ewing's sarcoma, osteosarcoma, chondrosarcoma, made in one of the centers of the RESOS 
network between 2008 and 2018, in terms of therapeutic strategies including chemotherapy, surgery, 
radiotherapy, interventional radiology. 
 
Secondary endpoints 
- Description of systemic treatments received in the palliative setting in terms of PFS (progression free survival) 
and TNT (Time to next treatment) 
- Overall survival from unresectable locally advanced and/or metastatic stage   
- Identification of factors associated with PFS and TNT for molecules used in the unresectable locally advanced 
and metastatic setting 
- Identification of prognostic factors for overall survival (OS) in the unresectable locally advanced and/ 
metastatic setting 
 
 
II. 
METHODS 
 
PATIENTS 
We estimate the population size in the database of about 2800 patient cases.  
We seek to include 800 patients to be screened in the present study.  
We aim to get about 400 to 500 informative patients with unresectable locally advanced or metastatic disease. 
 
VARIABLES 
Variables collected will be those of the CONTICABONE DICTIONNARY  
Descriptive data   
Centers, number of patients per center 
Initial patients characteristics at diagnostic: Age at diagnosis, gender, PS at diagnosis, previous history of specific 
disease (Li Fraumeni, Multiple Exostosis) 
 
Tumor characteristics at diagnostic : location, side, size, multifocality, LR spread and type, stage N M, histology, 
grade, previous radiation area, 
Initial treatment: surgery, type of surgery, margins, tumor spillage, residual cellularity, neo/adjuvant systemic 
treatment indication (start and stop date, type), radiation therapy, other treatment (cryotherapy, 
radiofrequency, embolisation), clinical trial inclusion 
 
Metastatic relapse/event characteristics: PS, location of first metastasis, number of sites 
 

Treatment of metastatic event: systemic treatment (PS molecule used, start and stop date, best RECIST response, 
date of progression, clinical trial), locoregional treatment (surgery, radiotherapy, cryotherapy, radiofrequency, 
embolisation), clinical trial.  
 
Dates: Birth, initial diagnostic, surgery of primary, local relapse, metastatic relapse, start of each chemo line, date 
of progression for each chemo line, last contact, death 
 
Samples: Molecular cytogenetics : method (RT PCR, FISH), transcript (FOR EWING SARCOMA ONNLY) 
 
Survival data 
 
Median follow up  
Progression free survival (PFS): PFS defined as the time from the systemic treatment onset to progression or 
death due to any cause, whichever comes first. When neither death nor progression is observed, PFS is censored 
at the date of last patient contact. 
 
Time to next treatment (TNT): TNT defined as the time from the systemic treatment onset to the next treatment 
or death due to any cause, whichever comes first. When neither death nor new systemic therapy is observed, 
TNT is censored at the date of last patient contact. 
 
Overall survival (OS): OS is defined as the interval between the diagnosis of first tumor event and the time of 
death. OS in the unresectable locally advanced setting is defined as the interval between the diagnosis of 
unresectable locally advanced setting (at diagnosis or relapse) and the time of death. OS in the metastatic setting 
is defined as the interval between the diagnosis of metastatic disease (at diagnosis or relapse) and the time of 
death. When death is not observed, OS is censored at the date of last patient contact. 
 
 
 
 
STATISTICS 
 
Statistical analysis of the baseline demographics and clinical outcomes will be based on all data available up to 
the cut-off date of August 31, 2021.  
Descriptive statistics will be used to show the distribution of variables in the population.  
The variables included in the univariate and multivariate analyses are the descriptive data including: 
- 
type of chemotherapy used (poly vs mono, class of chemo: TKI,  Ifosfamide, other)  
- 
locoregional treatment of metastasis,  
- 
inclusion in a clinical trial in first line,  
- 
at least one inclusion in a clinical trial during metastatic stage,  
- 
use of off-label drug at least once during metastatic stage 
Multivariate logistic regression models will be used to identify biological and clinical factors associated with the 
type of treatment received after 1rst line: in second, third, 4rth line, and with the probability of survival 5 years 
after the diagnosis of metastases. 
Follow-up times will be described as median values based on the inverse Kaplan–Meier estimator. 
Prognostic factors of TNT for each line (1rst, second, Third) and OS will be identified using Cox proportional 
hazard models.  
The correlation between TNT and OS will be evaluated at each of the four first-lines of metastatic chemotherapy 
by a Spearman rank correlation coefficient and expressed as a value between 0 (no association) and 1 (perfect 
association). We will use a reviewed copula-based approach that introduces an iterative multiple imputation 
method for the estimation of the correlation coefficient. The data will be analyzed using the SAS v9.3 and R v3.3 
software packages.