Jean-Luc Redelsperger

Chris D Thorncroft

Arona Diedhiou

Thierry Lebel

AND Douglas J Parker

Jan Polcher

Andreas Fink

Amadou Gaye

Jim Haywood

Paul Houser

Peter Lamb

Bob Molinari

Joe Prospero

Claire Reeves

Madeleine Thomson

We

Wgs

Sts Tts

Peter Brandt

Bernard Cappelaere

Lassine Dairra

Luc Descroix

Laurence Eymard

Michel Desbois

Cyrille Flamant

Laurence Fleury

Sylvie Galle

Serge Janicot

Greg Jenkins

Sarah Jones

Jean-Phlippe Lafore

Colin Lloyd

Celine Mari

Beatrice Marticorena

Andy Morse

Eric Mougin

Jacques Pelon

Alain Protat

Christophe Peugeot

Florence Rabier

Frank Roux

Paulo Ruti

Josiane Seghieri

Chris Taylor

Zoltan Toth

AFRICAN MONSOON MULTIDISCIPLINARY ANALYSIS An International Research Project and Field Campaign

) is an international project to improve our knowledge and understanding of the West African monsoon (WAM) and its variability with an emphasis on daily-to-interannual time scales. AMMA is motivated by an interest in fundamental scientific issues and by the societal need for improved prediction of the WAM and its impacts on West African nations.

A frican Monsoon Multidisciplinary Analysis (AMM

decades as demands on resources increase in association with one of the world's most rapidly growing populations. Vulnerability maybe further increased in association with the effects of climate change and other factors linked to the fast-growing population, such as land degradation and water pollution.

Recognizing the societal need to develop strategies that reduce the socioeconomic impacts of the variability of the WAM, AMMA will facilitate the multidisciplinary research required to provide improved predictions of the WAM and its impacts. The international AMMA project has three overarching aims:

1) To improve our understanding of the WAM and its influence on the physical, chemical and biological environment regionally and globally; 2) To provide the underpinning science that relates variability of the WAM to issues of health, water resources, food security and demography for West African nations and defining and implementing relevant monitoring and prediction strategies; and 3) To ensure that the multidisciplinary research carried out in AMMA is effectively integrated with prediction and decision making activity.

AMMA is endorsed by the World Climate Research Programme (WCRP) and continues to develop in association with the Climate Variability and Predictability (CLIVAR) and Global Energy and Water Cycle Experiment (GEWEX). AMMA has also been endorsed by two projects within the International Geosphere-Biosphere Programme (IGBP): International Global Atmospheric Chemistry (IGAC) and Integrated Land Ecosystem-Atmosphere Processes Study (ILEAPS). AMMA is working with other international projects and programs to achieve its aims, including the Global Climate Observing System (GCOS), Global Ocean Observing System (GOOS), and The Observing System Research and Predictability Experiment (THORPEX).

MOTIVATION AND MAJOR ISSUES. The in terannual and interdecadal variability of WAM is well documented and has motivated considerable research efforts (e.g., [START_REF] Nicholson | The nature of rainfall fluctuations in subtropical West Africa[END_REF][START_REF] Lamb | West African water variations between recent contrasting Subsaharan droughts[END_REF][START_REF] Folland | Sahel rainfall and worldwide sea temperature 1901-1985[END_REF][START_REF] Fontaine | Sea surface temperature fields associated with West African rainfall anomaly types[END_REF][START_REF] Barbe | Rainfall variability in West Africa during the years 1950-90[END_REF] sustainability, land degradation, and food and water security in the region.

We are currently hindered in providing skillful predictions of WAM variability and its impacts. There are still fundamental gaps in our knowledge of the coupled atmosphere-land-ocean system at least partly arising from a lack of appropriate observational datasets, but also because of the complex scale interactions between the atmosphere, biosphere, and hydrosphere, which ultimately determine the nature of the WAM. The monitoring system for WAM and its variability are inadequate, with many gaps in the standard routine network and a lack of routine monitoring of some key variables. While the next generation of satellites will undoubtedly help with routine monitoring and prediction efforts, more research is required to validate and exploit these data streams. Dynamical models used for prediction suffer from large systematic errors in the West African and tropical Atlantic regions (e.g., WCRP 2000; [START_REF] Davey | STOIC: A study of coupled model climatology and variability in tropical ocean regions[END_REF]. Finally, there is a lack of integrative science linking the work on WAM variability with work on food, water, and health impacts.

The WAM system provides an ideal framework for considering scale interactions in a monsoon system: it possesses pronounced zonal symmetry with characteristic jets and associated well-defined weather systems. Research on such scale interactions, and in particular those linking dynamics and convection with the land surface, will be relevant to other monsoon systems and is needed in order to improve the coupled atmosphere-ocean-land models used for weather and climate prediction.

In order to carry out this research extra observations are needed.

Further motivation for a research project concerned with WAM variability and predictability comes from recognizing the role West African weather and climate has on the rest of the world. Latent heat release in deep cumulonimbus clouds in the ITCZ over Africa represents one of the major heat sources on the planet. Its meridional migration and associated regional circulation impact other tropical and midlatitude regions, as exemplified in the known correlation between West African rainfall and Atlantic hurricane frequency (e.g., [START_REF] Landsea | The strong association between Western Sahel monsoon rainfall and intense Atlantic hurricanes[END_REF]. In addition to the large-scale interactions, we know that a majority of hurricanes that form in the Atlantic originate from weather systems over West Africa (e.g., [START_REF] Avila | Atlantic tropical systems of 1991[END_REF]; however, we know little about the processes that influence this and why only a small fraction of these "seedlings" actually become hurricanes. Tropical Africa is also the world s largest source of atmospheric dust. Both the fire aerosols and dust play a major role in radiative forcing and in cloud microphysics, and thus are an important part of the WAM system that require study.

Finally, West Africa is also an important source region for natural and anthropogenic emissions of precursors to key greenhouse forcing agents (e.g., ozone, aerosols). For example, Africa contributes around 20% to the global biomass-burning fires. Long-range transport of trace gases out of West Africa has important implications for the global oxidizing capacity of the atmosphere (which controls the level of many greenhouse gases), global climate change, and the transport of key constituents (e.g., water vapor, ozone-depleting substances) into the stratosphere.

A review of the present state of knowledge on the West African monsoon and the related scientific questions to be addressed by AMMA are described in the International Science Plan (ISP). The ISP also includes a description of the strategy proposed to tackle these questions. The ISP is available on the international AMMA Web site (online at www. amma-international.org/science/docs/AMMA_ISP_ May2005.pdf).

THE AMMA PROGRAMME.

A multiscale approach. To address the multiple scales that characterize WAM, the AMMA program is structured around the following four interacting spatial scales (Fig. 1): i) global scale: the scale at which WAM interacts with the rest of the globe (Emphasis is given to improving our understanding of the role of global SST patterns on WAM variability; seasonalto-decadal variability is the main time scale of interest.); ii) regional scale: the scale at which we consider monsoon processes and scale interactions [Emphasis is given to improving our understanding of the interactions between the atmosphere, land, and tropical Atlantic ocean (especially the Gulf of Guinea). It is important to study the role of landsurface feedbacks on variability of the WAM at this scale, including the key roles of vegetation and soil AMERICAN METEOROLOGICAL SOCIETY moisture. The annual cycle and seasonal-to-interannual variability are the main time scales of interest.]; iii) mesoscale: the scale of the typical rain-producing weather systems [This scale is central to the understanding of scale interactions in the WAM system (e.g., through interactions with synoptic easterly waves and the African easterly jet), and the coupling between hydrology and the atmosphere at the catchment scale.]; iv) local or submesoscale: from an atmospheric point of view, this is the convective rain scale (It is central to the hydrology of the Sahel and of small watersheds to the south; it is the main scale of interest for agriculture and for human impact studies in general).

AMMA emphasizes the importance of an improved understanding of how these scales interact and combine to characterize the WAM and its variability, including how these interactions impact the sources and transport of water vapor, aerosol, and key chemical species (e.g., key greenhouse gases, ozone, and aerosol precursors) in the West African region and globally.

Integrative science. While it is convenient and appropriate to describe the research plans in terms of the different spatial scales for an improved understanding, it is essential to study the scale and process interactions. The implementation of AMMA is designed in this spirit (Fig. 2). The AMMA project integrates the scales at which the geophysical and human processes interact. Furthermore, the various disciplines involved in the study of WAM need to be integrated to achieve the three overarching aims. This approach has guided the structuring of the scientific objectives.

From the geophysical perspective, the fundamental science underpinning the AMMA project can be viewed as the various disciplines coming together within broader integrative science topics: i) the interactions between WAM and global climate from a physical as well as a chemical perspective, ii) the water cycle of WAM from the regional to the local scale, and iii) the coupled atmosphere-land-ocean system and its multiple scales. To feed these integrative topics with sound disciplinary knowledge of the processes and their scale dependence detailed studies of the following processes are needed: i) atmospheric processes, with a focus on the convective processes that are key to the rainfall production; ii) oceanic processes, because they contribute to and depend on WAM; iii) biophysical processes over the continent from the regional to the local scale; and iv) aerosol and chemical processes in the atmosphere.

the predictive skill gained with this knowledge.

The field program. AMMA is a multiyear project and involves three nested observation periods. The enhancement of observations during these periods will provide a unique opportunity to determine the future operational monitoring necessary to improve weather and climate forecasts over the West African region. More than this, a high priority for AMMA is to establish this operational network of observations, providing a visible legacy useful for society from the international AMMA program.

• The Long term Observing Period (LOP) is concerned with observations of two To study the human dimension of the variability and possible trends in the West African monsoon, AMMA aims to address the direct impact of the environmental conditions on the following three limiting conditions for African societies: i) land productivity, ii) water resources, and iii) health impacts. This activity will be coordinated to achieve a better understanding of how weather and climate variability impact food security and human processes in the region.

To achieve the AMMA scientific objectives and master the challenge of multiscale and multidisciplinary aspects, the following consistent set of tools and methods adapted to the problem of the West African monsoon will be used: i) models and data assimilation; ii) field campaigns; iii) satellite remote sensing and long-term atmosphere, and ocean data collection; and iv) database creation. These activities are key to transferring knowledge from the geophysical community in AMMA to the activities in the human dimension. AMMA will strive to use the above tools and activities to collect and consolidate knowledge, integrate the knowledge, and materialize AMMA implementation (see the AMMA Web site online at www.amma-international.org). Here we only briefly describe the different periods.

Central to the observing strategy for AMMA is the Coupling of the Tropical Atmosphere and the Hydrological Cycle (CATCH) hydrology project. CATCH has established enhanced observations in the "CATCH window" depicted in Fig. 3 to support the long-term monitoring of the surface component of the continental water cycle. It includes three mesosites that sample different environments along a climate transect. The observing strategy for the EOP is to enhance the observations of the atmosphere, land, and ocean along the "climate transect," which includes the CATCH hydrology project. These enhancements include i) extra radiosoundings, ii) new surface flux measurements along the climate transect, iii) ground-based remotely sensed observations (e.g., radars, profilers), iv) enhanced hydrological observations (underground water fluxes, soil moisture), and vegetation, aerosol, and trace gas monitoring, and v) ocean observations in the tropical Atlantic, including the Gulf of Guinea, to extend the climate transect into the ocean key for understanding the coupled WAM system.

Embedded within the multiyear framework provided by the long-term observing period (LOP)/ EOP is the SOP. Detailed studies of key processes, impractical to study in a multiyear framework, will be carried out within the SOP and are focused on enhancing the observations along the climate transect through the provision of additional ground-based instruments (e.g., radars, lidars, sodars, high-frequency radiosoundings, etc.), research aircraft, and other platforms (e.g., driftsonde, tethered balloons, etc.). A considerable effort was made to ensure that all relevant data (e.g., extra radiosondes, dropsondes) were broadcast to the Global Telecommunication System (GTS) to support operational forecasting and to facilitate future observing system experiments (see working group 5 below). The special measurements proposed during the SOP, combined with those established through the LOP/EOP, will provide the international community with an invaluable set of observations to investigate the multiple-scale interactions and processes that determine the nature of WAM and its variability.

Modeling studies in AMMA. AMMA will coordinate various modeling activities and will ensure that the benefits of this work are communicated to the operational prediction centers. The datasets obtained in AMMA provide a unique opportunity to assess the ability of models to predict WAM and its impacts at daily-tointerannual time scales, to evaluate and improve key parameterizations, and to recommend a sustained observing system necessary for further research and prediction at these time scales. The following are several foci for modeling studies in AMMA:

• State-of-the-art general circulation models (GCMs) used for weather and climate prediction have difficulty simulating the annual cycle of rainfall and associated regional circulations. AMMA observations along the climate transect, including the extension into the ocean, will be especially useful for exploring the interactions between the heat low and the ITCZ and investigating how well these are represented in dynamical models. • The zonal symmetry of the low-level thermodynamic contrasts, well-defined jets, and associated weather systems make this an ideal natural laboratory for investigating scale interaction issues in monsoon systems [START_REF] Redelsperger | Multiscale description of a Sahelian synoptic weather system representative of the West African monsoon[END_REF]. Together with analysis of the intense diurnal cycle (see [START_REF] Parker | The diurnal cycle of the west African monsoon circulation[END_REF], such investigations are central for improving the GCMs used for weather and climate prediction in West Africa and tropical Atlantic regions. • Recent modeling studies (e.g., [START_REF] Koster | Regions of strong coupling between soil moisture and precipitation[END_REF] suggest that land surface-atmosphere feedbacks are particularly strong in the West African region. However, observations are currently lacking in order to evaluate these studies. AMMA will provide new observations to explore land surface-atmosphere interactions and associated feedbacks, including how these are modeled at a range of spatial scales. • The large amounts of dust present in the vicinity of WAM makes this region ideal for investigating aerosol issues and assessing their impact on regional weather (including downstream hurricanes) and climate. AMMA is providing new observations and coordinating various modeling efforts to investigate the role of aerosol in influencing WAM and its variability. • AMMA is taking advantage of the observations made during the LOP/EOP and the SOP to carry out a suite of observing system experiments. These are needed to recommend the future sustained observing system for supporting weather and climate prediction.

IMPLEMENTATION OF AMMA.

National and pan-national projects. The international AMMA program benefits from several national and pan-national projects (see information online at www.amma-international.org/projects/index). AMMA-Africa brings together scientists from many African nations and coordinates their contribution to AMMA. International coordination. AMMA aims to strengthen the international framework needed to facilitate interactions between researchers working in the different national and pan-national projects and to ensure that the field campaigns are well coordinated to optimize the scientific impact of the observations. An international structure has been established to oversee and coordinate these efforts (see Fig. 4). The International Scientific Steering Committee (ISSC) will ensure the scientific integrity and coherency of the scientific objectives of AMMA and the fulfillment of the three overarching aims. The ISSC is under the control of the International Governing Board (IGB) to ensure that it fulfills its coordination role for AMMA. Implementation of the multiyear field campaign is the responsibility of the International Implementation and Coordination Group (ICIG).

In addition, a permanent Project Office (PO) assists the ISSC and ICIG.

SCIENTIFIC WORKING GROUPS.

For scientific coordination the work of the ISCC is structured by five integrative science working groups (WGs) (Fig. 4), which take up five topics central to the aims of AMMA. WG1, "West African monsoon and global climate," is concerned with the two-way interactions between WAM and the rest of the globe, especially as they relate to and influence the variability of WAM and its global impacts. It includes aerosol and chemistry aspects. WG2, "Water cycle," is concerned with the processes in-volved in the water budget occurring through all scales (i.e., regional scale, mesoscale, and local scale; see Fig. 1). WG3, "Surfaceatmosphere feedbacks," is concerned with providing increased knowledge and understanding of the coupling between atmosphere and continental surfaces at the regional scale and mesoscale and, separately, the coupling between the atmosphere and ocean (the coupled atmosphere-landocean system is addressed in WG1). WG4, "Prediction of climate impacts," is concerned with the second major aim of AMMA and Rg 4 |nternational will provide strong linkages between the work taking place on impacts and that taking place on observed variability and predictability of WAM in WG1. WG5, "High-impact weather prediction and predictability," is a joint WG with THORPEX and is concerned with improving our knowledge and understanding of high-impact weather over the West African continent, and its impacts on the tropical Atlantic and extratropics. Operational activities will be promoted, including the tailoring of forecast products for users, and data impact and targeting studies.

IMPLEMENTATION OF FIELD OBSERVATIONS. The implementation of the field program is carried out through the establishment of task teams (TTs) and support teams (STs). The TT responsibilities are i) to design an observational strategy for a given subset of scales/variables of interest that addresses the scientific objectives presented in the ISP, and ii) to monitor and have final responsibility for deployment of relevant instrumentation. The TTs are composed of the Principal Investigators (Pis) of the instruments planned to be deployed in the framework of the space/time scale covered by the TTs. The SST responsibilities are i) to act in support of TTs, ii) to look in more detail into operational matters, and iii) to propose a scheme of operations to be agreed upon by TT leaders and to be submitted to the ISSC to verify that these schemes satisfy the needs of AMMA.

The AMMA Operations Centre (managed by ST2) was particularly important for facilitating the organization of AMMA.

smooth running of the field program during the SOP. A crucial component of this was the forecasting activity to support in-the-field decision making. The AMMA forecasting team represented a successful collaboration between numerical weather prediction centers [the European Centre for Medium-Range Weather Forecasts (ECMWF), Meteo-France, National Centers for Environmental Prediction (NCEP), and the Met Office], together with the African Centre of Meteorological Application for Development (ACMAD) and African Weather Services. The AMMA forecasting team was responsible for the development of a comprehensive set of Web pages to support forecasting in the West African region (see information online at http://aoc.amma-international.org), which will be maintained as far as possible after the SOP to support future operational forecasting. AMMA is also establishing several training activities for African forecasters, including coordinating visits to ECMWF and NCEP (Africa desk) and also a series of training workshops in collaboration with ACMAD in Niamey, Niger. The aim of these workshops is to discuss forecasting strategies and to help familiarize forecasters with new forecasting products (before the SOP), and to evaluate these products and debrief the forecasting activity (after the SOP).

FINAL COMMENTS. AMMA has been carefully

conceived to improve our fundamental understanding of the West African monsoon and its societal impacts, and to make sustainable improvements to monitoring and predicting the West African environment. Our activities are embedded within a "long-term observing period" ("LOP") structure, which will ensure that our intensive activities are directed toward systematic improvements in monitoring and predicting over the coming decades. We will develop and upgrade two important land-based atmospheric monitoring systems (for the upper-air and surface fluxes), and over the LOP we will transfer responsibility for these networks to the local African agencies. In addition, ocean-monitoring systems in the tropical Atlantic, including the Gulf of Guinea, that have been shown to improve both weather and climate forecasts will continue to provide data to these groups. These networks of observations are of enormous value both to global prediction systems and to local forecasting systems based in Africa.

  FIG. I. Simplified schematic of key phenomena together with their associated space and time scales. The arrow is included to highlight the importance of scale interactions and transport processes in the WAM. SOP, EOP and LOP refer to the AMMA observing periods, Special Observing Period, Enhanced Observing Period and Long-term Observing Period respectively.

FIG. 2 .

 2 FIG. 2. Implementation of AMMA: (a) Integrative science for the geophysical types: (i) historical obserand (b) human dimension, (c) Integration from this knowledge through various vations to study interantools and for the exploitation by impact studies. nual-to-decadal variability of the WAM (including currently unarchived observations) and (ii) additional long term observations (2002-2010) to document and analyse the interannual variability of the WAM. • The Enhanced Observing Period (EOP) is designed to serve as a link between the LOP and the SOP (below). Its main objective is to document over a climatic transect the annual cycle of the surface conditions and atmosphere and to study the surface memory effects at the seasonal scale. The EOP will be 2-3 year duration (2005-2007). • The Special Observing Period (SOP) focused on detailed observations of specific processes and weather systems at various key stages of the rainy season during three periods in the summer of 2006: (i) the dry season (Jan-Feb), (ii) Monsoon onset (15 May-30 June), (iii) Peak monsoon (1 July-14 August) and (iv) Late monsoon (15 August-15 September).

FIG. 3 .

 3 FIG. 3. Field implementation of AMMA observations based on nested networks. Circles indicated the atmospheric sounding network activated during the SOP.

  It is based onproposals made by teams in universities and operational and research centers. Compared to the other components of AMMA, the emphasis in AMMA-Africa is more on impact studies. AMMA-EU is an integrated project funded by the European Union. It federates most of the national activities in Europe and brings together the geophysical and socioeconomic communities working in Africa. AMMA-France, which initiated

	AMMA, is a project supported by all French research
	organizations involved in environmental sciences and
	covers most aspects of AMMA. AMM A-UK is the sum
	of various projects associated with a Natural Environ-
	mental Research Council (NERC)-funded consortium,
	bringing together the British AMMA community.
	AMMA-US gathered a number of American scientists
	to prepare a U.S. proposal for AMMA. While this was
	not supported as a unified program, the Department
	of Energy (DOE), National Aeronautics and Space
	Administration (NASA), and National Oceanic and
	Atmospheric Administration (NOAA) are supporting
	several projects that incorporate some subprojects that
	arose from this proposal.

Unauthenticated | Downloaded 01/13/21 08:02 AM UTC

ACKNOWLEDGMENTS.

Based on an French initiative, AMMA was built by an international scientific group and is currently funded by a large number of agencies, especially from France, the United Kingdom, the United States, and Africa. It has been the beneficiary of a major financial contribution from the European Community's Sixth Framework Research Programme. Detailed information on scientific coordination and funding is available on the AMMA International Web site at www.amma-international.org. AMMA relies on the inputs and help of many people. We would also like to give special thanks to the members of the AMMA Project Office: Elisabeth van der Akker, Isabelle Genau, Karine Ginoux, and Cheikh Kane. We would like to acknowledge all other members of the International Scientific Steering Committee: Ernest Afiesimama, Abel Afouda, Abou Amani, Anton Beljaars, Benrard Bourles,


Brice Amadeo
email: brice.amadeo@u-bordeaux.fr

Nicolas Penel

Jean-Michel M Coindre

Isabelle Ray-Coquard

Karine Ligier

Patricia Delafosse

Anne-Marie M Bouvier

Sandrine Plouvier

Justine Gallet

Aude Lacourt

Gaëlle Coureau

Alain Monnereau

Simone Mathoulin-Pélissier

Emmanuel Desandes

Incidence and time trends of sarcoma (2000-2013): results from the French network of cancer registries (FRANCIM)

Keywords: Sarcoma, Incidence, Trends in incidence, France, Cancer registry

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Background

Sarcomas are a heterogeneous group of rare malignant tumors derived from primitive mesenchymal cells. These tumors arise from muscle, connective tissue, supportive tissue and vascular tissue, and more than 80 histologic subtypes are included in the 2013 World Health Organization (WHO) Classification of Tumors of Soft Tissue and Bone [START_REF] Fletcher | WHO classification of Tumours of soft tissue and bone[END_REF]. In addition to having a multiple and complex histology, these tumors can occur in almost any anatomic site. In spite of these facts, sarcomas account for less than 1% of all adult cancers and for about 20% of all malignant solid tumors in children, adolescents and young adults [START_REF] Burningham | The epidemiology of sarcoma[END_REF].

From an epidemiological point of view, the lack of a unified method of reporting sarcomas has led to considerable variations in the reported incidence and time trends Sarcomas are sometimes mistaken for carcinomas of the same organ, and can involve a variety of localizations. As a consequence, 30 % of sarcomas are misclassified at initial diagnosis [START_REF] Lurkin | Epidemiological evaluation of concordance between initial diagnosis and central pathology review in a comprehensive and prospective series of sarcoma patients in the Rhone-Alpes region[END_REF]. In addition, sarcomas encompass a wide variety of histological and molecular subtypes and are categorized in rapidly evolving phenotypic and molecular subgroup classification schemas now used for sarcoma diagnosis, which has a growing impact on the management of patients [START_REF] Antonescu | The role of genetic testing in soft tissue sarcoma[END_REF]. Furthermore, innovation in immune-histochemistry and molecular biology techniques in the last three decades has led to major changes in the diagnosis and classification of sarcoma subtypes.

Currently, data for sarcomas in the French population are provided by the reference networks for sarcomas that collect and manage cases of soft tissue, bone and visceral sarcomas. Reference networks propose a systematic second histologic review by expert pathologists [START_REF] Honoré | Soft tissue sarcoma in France in 2015: epidemiology, classification and organization of clinical care[END_REF][6][START_REF] Blay | The value of research collaborations and consortia in rare cancers[END_REF]. A few French studies carried out by these reference networks provided world age-standardized incidence rates of 4.8 and 3.3 per 100,000 inhabitants per year for all sarcomas and soft-tissue sarcomas (STS) respectively [START_REF] Ducimetiere | Incidence of sarcoma histotypes and molecular subtypes in a prospective epidemiological study with central pathology review and molecular testing[END_REF][START_REF] Mathoulin-Pelissier | Adherence to consensusbased diagnosis and treatment guidelines in adult soft-tissue sarcoma patients: a French prospective population-based study[END_REF]. However, data from these reference networks based on the voluntary participation are not totally exhaustive.

Besides reference networks, cancer surveillance information is coming from the French Network of populationbased cancer registries that exhaustively collects all newly diagnosed and confirmed cancer cases within geographical areas in France [START_REF]Francim Network[END_REF]. The exhaustive collection of sarcoma cases from population-based cancer registries and the systematic second review of diagnosis from reference centers offer an optimal framework to study the incidence and time trends of sarcomas in France. The incidence trends have never been studied in France and the results from other countries are divergent [START_REF] Wibmer | Increasing incidence rates of soft tissue sarcomas? A population-based epidemiologic study and literature review[END_REF]. We undertook this study to describe sarcoma entity according to anatomic sites, histologic subtypes and genetic groups based on guidelines developed by sarcoma specialists.

Methods

Data sources

Cases included in this study were children and adults with sarcoma diagnosed between January 1, 2000 and December 31, 2013, and living in one of the administrative areas covered by a population-based cancer registry of the French Network (details in online supplementary material). The French sarcoma pathological reference network (RRePS) and the French reference Network for bone sarcoma and rare bone tumors (RESOS) propose a systematic second histologic review and confirmation for all diagnoses of sarcomas across France [6].

Data collection and classification

The following data were collected for each case: general demographic characteristics of the patients (age, sex, and residence area), the date of diagnosis, the anatomical site, and the histology of the tumor according to the International Classification of Diseases for Oncology, third edition (ICD-O-3) [START_REF] Stiller | Descriptive epidemiology of sarcomas in Europe: report from the RARECARE project[END_REF].

This study included intermediate (only with a "/3" behavior) and malignant sarcomas presenting morphologic criteria described in the 2013 WHO Classification of Tumors of Soft Tissue and Bone (fourth edition), regardless of the anatomic site [START_REF] Fletcher | WHO classification of Tumours of soft tissue and bone[END_REF]. This recent classification includes histologic updates not defined in ICD-O-3 and new terms, synonyms, morphology and behavior codes. For this reason, and whenever possible, cases were reclassified according to the updated version. The alignments from ICD-O-3 to the 2013 WHO standard classification of tumors have been validated by a panel of sarcoma specialists (clinical and pathological experts) from sarcoma Networks (NP, JMC and IRC).

Certain alignments could not be performed: ten morphological terms not described in this updated classification (e.g. sarcoma NOS, periosteal fibrosarcoma, fascial fibrosarcoma …) have been maintained for analyses. Conversely, well differentiated liposarcoma and chondroblastoma have been changed from malignant to borderline diseases. In the same way, behaviors for dermatofibrosarcoma protuberans and pigmented dermatofibrosarcoma protuberans have been also changed from malignant to borderline with henceforth, only fibrosarcomatous dermatofibrosarcoma protuberans which is coded as malignant behavior. In our analyses, we have made the choice to keep all dermatofibrosarcomas. Indeed, we do not have the possibility to differentiate if this is a dermatofibrosarcoma borderline or malignant. Besides, endometrial stromal sarcoma NOS (89303), low grade endometrial stromal sarcoma (89313) and stromal sarcoma (89353) not described in the WHO 2013 have been also included. Additional details on the list and choice of classification systems are provided in the online supporting material (see Additional File 1).

This classification also provides new genetic and molecular data for each histologic entity allowing a better characterization of sarcomas. The same group of experts were consulted with the aim of proposing the optimal classification system for sarcomas based on the genetic profile. Two main distinct genetic groups were defined: (i) sarcomas defined with simple genetics based on recurrent translocations (e.g. Ewing sarcoma, myxoïd liposarcoma), activating or inactivating mutations (e.g epithelioid sarcoma, gastrointestinal stromal tumor), MDM2 amplification (e.g. dedifferentiated liposarcoma, low-grade central osteosarcoma); and (ii) sarcomas with complex genomic profiles (e.g. angiosarcoma, leiomyosarcoma). Another group was defined for miscellaneous and undefined alterations. The list of histology codes according to their genetic groups is presented in the supplementary material.

This study is based on data from cancer registries gathered in the French network of cancer registries and a representative of each registry was involved in the study and approved the use of its data All French registries received an authorization to collect patient data from the data protection authority (Commission Nationale de l'Informatique et des Libertés). Ethics approval and consent to participate were not required for this study which is an observational research without direct contact with patient.

Statistical analyses

Two datasets were used: i) the first one was used to estimate the incidence of patients diagnosed during the 2010-13 period and that included data from 19 registries; and ii) the second one was used to examine trends in the incidence from 2000 to 2013 in only 11 registries for which data were available over the entire studied period. Incidence rates were presented per 100,000 person-years.

The incidence of sarcomas was described according to 1) the anatomic group (i.e. soft-tissue, bone, gastrointestinal, skin, female genital organs, other viscera and other sites), and to 2) histologic and 3) genetic groups based on guidelines developed by sarcoma specialists (see Additional File 1).

Age-standardized incidence rates (ASR) were estimated using direct standardization and were calculated using the population data for each age group and year supplied by the National Institute of Statistics and Economic Studies (www.insee.fr) and the European (ASR-E), Segi World (ASR-W), and the US (ASR-US) standard populations. The analyses presented here describe the overall ASR and the ASR by sex. Age-specific incidence rates are provided by age groups (0-14; 15-24; 25-39; 40-64; 65-74 and 75 and more) and by sex and presented in figures. Time trends were calculated using Joinpoint Trend Analysis Software setting a maximum of a single Joinpoint (details in online supplementary material). The annual percent change (APC) with the 95% confidence interval (CI) was estimated according to topographic and histologic groups.

Results

Over the 2010-13 period, sarcomas accounted for 1.3% (3942/307,862) of all malignant tumors diagnosed over the French registry area. The male/female ratio for overall sarcomas was 1.0 but ranged from 0.5 for angiosarcomas to 6.2 for Kaposi sarcomas (KS) (Table 1). The median age was 63 years (range: 0-106) with large intergroup variations. About 9% of subjects were under 24 years and 27% were older than 75 years. Almost half of the cases were soft tissue sarcomas (45%). The most frequent histological subtypes were undifferentiated or unclassified sarcomas (16%), leiomyosarcoma (14%) and GIST (13%). Sarcomas with complex genomics accounted for the most frequent molecular profile (40%).

The crude incidence rate and ASR-W of sarcomas were 7.4 and 5.0, respectively (Table 2). The ASR-W of soft tissue, bone and gastro-intestinal sarcomas were 2.1, 1.0 and 0.6, respectively. For the five most frequent histological subtypes, the ASR-W ranged from 0.3 to 0.7 with gender variations. For the two most frequent genomic profiles (over 60% of all sarcoma cases) the ASR-W was 1.9 for complex genomic and 1.3 for recurrent translocation events.

The overall sarcoma incidence peaked at 22 in patients aged 75 or over (data not shown). Age-specific rates for soft tissue, viscera and skin sarcomas were relatively stable among patients aged between 0 and 40 years, and then increased with age (Fig. 1). This increase was less pronounced in women. In men, bone sarcomas presented a biphasic profile with a first peak in young people between 15 and 25 years of age and a second peak in adults aged between 65 and 74 years of age. With respect to histological subtypes, age-specific incidence rates had various profiles (see Additional File 2). According to the genomic profile, the incidence increased steadily with age, except for tumors harboring recurrent translocations and MDM2 amplification among women (see Additional File 3).

The ASR-W for all sarcomas increased between 2000 and 2005 (APC = 3.6%), and remained stable since 2005 (non-significant APC, Table 3). According to the anatomic site, the ASR-W decreased for skin sarcomas (APC = -2.0%) and female genital tumors between 2005 and 2013 (APC = -2.2%). Stratifying by major histological subtypes, the ASR-W increased for GIST (APC = 3.7%), chondrosarcoma (APC = 4.1%), myxofibrosarcoma (8.2%) and solitary fibrous tumors

Discussion

In this study, we precisely described the incidence of sarcomas according to different classifications (anatomic, histologic and genetic) using data from population-based cancer registries. To our knowledge, this is one of the first reports on sarcomas based on a systematic pathological review of these cancers while taking into account the updated sarcoma classifications. In this study, sarcomas accounted for 1.3% of all malignant tumors (1.1% for soft tissue -including skin and viscera-and 0.2% for bone) and had an ASR-E of 6.1 per 100,000 person-years over the 2010-2013 period (European population standard). The ASR-E was slightly higher than that reported in Europe [START_REF] Stiller | Descriptive epidemiology of sarcomas in Europe: report from the RARECARE project[END_REF]. Data comparison between countries is difficult due to the heterogeneity of sarcoma definition used as inclusion criteria. This heterogeneity is mainly related to some analysis characteristics: i) certain specific histological subtypes are not consistently included in analyses (e.g. Kaposi sarcoma or dermatofibroma sarcoma); ii) some studies consider adults and children separately, while others mix them; and iii) anatomic sites may be limited to specific sites such as STS. The current approach to describe sarcomas using registry data based on expert recommendations are expected to better follow epidemiological indicators and to carry out reliable comparisons between countries.

With respect to the anatomic site, ASR-E for STS (2.7) in our study was below most published international incidence rates. This may be explained by the exclusion of visceral sarcomas of soft tissue and the different description of well-differentiated liposarcoma compared to the WHO 2013 classification. In the current study, ASR-Ws for bone sarcomas among males and females (1.1 and 0.9 respectively) were close to those recently reported in five continents (2010-13 period, ASR-W 0.8-1.2 in males and 0.5-1.0 in females) [START_REF] Valery | Bone cancer incidence by morphological subtype: a global assessment[END_REF]. For visceral sarcomas, the comparison between studies with inclusion periods close to that in the present study showed ASR-E similar to ours [START_REF] Ducimetiere | Incidence of sarcoma histotypes and molecular subtypes in a prospective epidemiological study with central pathology review and molecular testing[END_REF][START_REF] Mastrangelo | Incidence of soft tissue sarcoma and beyond: a population-based prospective study in 3 European regions[END_REF]. In contrast, the ASR was greater than that reported in the RARECARE project, which may be due to differences in the definition of visceral sarcomas (GIST not included) [START_REF] Mastrangelo | Incidence of soft tissue sarcoma and beyond: a population-based prospective study in 3 European regions[END_REF].

The comparison of ASRs for main histologic groups between studies with a shorter inclusion period showed that the ASR-E for leiomyosarcoma (0.8; 0.6 for males and 1.0 for females) was greater than that reported in France (0.6) and was similar to that reported in three European regions (0.5 for males and 1.0 for females) [START_REF] Ducimetiere | Incidence of sarcoma histotypes and molecular subtypes in a prospective epidemiological study with central pathology review and molecular testing[END_REF][START_REF] Mastrangelo | Incidence of soft tissue sarcoma and beyond: a population-based prospective study in 3 European regions[END_REF]. ASR-E for liposarcoma in our study (0.5; 0.7 for males and 0.4 for females), was lower than that reported in France (0.8) and in three European regions (1.06 for males and 0.59 for females), which may be attributed to differences in the definition of liposarcoma as inclusion criteria [START_REF] Ducimetiere | Incidence of sarcoma histotypes and molecular subtypes in a prospective epidemiological study with central pathology review and molecular testing[END_REF][START_REF] Mastrangelo | Incidence of soft tissue sarcoma and beyond: a population-based prospective study in 3 European regions[END_REF]. In our study, we found an ASR-W for osteosarcoma slightly lower than that of chondrosarcoma (0.28 versus 0.34). For male, ASR-W was equivalent (0.34 versus 0.32). A recent population-based study from Swiss cancer registries showed similar results [START_REF] Kollár | Incidence, mortality, and survival trends of soft tissue and bone sarcoma in Switzerland between 1996 and 2015[END_REF]. In contrast, others studies based on older inclusion period of sarcoma diagnosis found an ASR-W slightly higher for osteosarcoma [START_REF] Ducimetiere | Incidence of sarcoma histotypes and molecular subtypes in a prospective epidemiological study with central pathology review and molecular testing[END_REF][START_REF] Anfinsen | Age-period-cohort analysis of primary bone cancer incidence rates in the United States (1976-2005)[END_REF]. However, looking at the trend in our study (Additional File 5), we can notice that the ASR-W of osteosarcoma was actually higher over the period 2000-2005 than the ASR-W of chondrosarcoma in accordance with these studies. The increasing trend in the ASR of chondrosarcoma and the stabilization of the ASR of osteosarcomas may logically explain why the incidence of chondrosarcomas has been higher than that of osteosarcomas in recent years. Molecular biology of sarcomas, available for diagnosis in France since 2010 is a complementary approach and has led to a molecular classification for sarcomas [START_REF] Coindre | Illustration de partenariat public/privé. Prises en charge des sarcomes des tissus mous en France-une analyse rétrospective de la « base clinico-biologique sarcomes[END_REF].

For the first time, we provided ASR at national level and showed molecular profiles by age groups.

This study provides the first time trend analysis of sarcomas in France and shows that ASR-W for sarcomas increased between 2000 and 2005 (APC = 3.6%) and stabilized from 2005. The current study has not shown an increase in ASR-W for soft-tissue sarcomas. This is in contrast to reports in others countries covering different periods: in the United States APC was 1.2% for males and 0.8% for females between 1978 and 2001, in Japan APC was 0.6% between 1978 and 2007 and in Serbia APC was 0.77% between 1985 and 2009 [START_REF] Toro | Incidence patterns of soft tissue sarcomas, regardless of primary site, in the surveillance, epidemiology and end results program, 1978-2001. An analysis of 26,758 cases[END_REF][START_REF] Nomura | Incidence of soft tissue sarcoma focusing on gastrointestinal stromal sarcoma in Osaka, Japan, during 1978-2007[END_REF][START_REF] Dugandzija | Increasing frequency of soft tissue sarcomas in Vojvodina -comparison with the literature[END_REF]. We report a significant decrease in incidence for skin sarcomas over the study period and for female genital sarcomas since 2005. Some histological subtypes have shown a significant decrease over the study period: leiomyosarcoma, KS and fibrosarcoma. The decline for KS has also been described in the population from the United States over the same period [START_REF] Royse | Disparities in Kaposi sarcoma incidence and survival in the United States: 2000-2013[END_REF]. These changes are consistent with the improvement in access for antiretroviral therapy among HIV-infected patients and the declining AIDS incidence in developed countries. The decrease in incidence of leiomyosarcoma and fibrosarcoma could be explained by a histological classification published by the WHO in 2002 that includes new data of immunohistochemistry and new histological subtypes. Similarly, we report an increase in incidence of GIST, likely related to the introduction in the early 2000s of an immunohistochemical diagnostic test specific to GIST tumors (KIT-activating mutations). Further, the increase in GIST was more noticeable before 2005 and stabilised after 2005.

The time trend analysis also revealed a significant increase for chondrogenic sarcomas (APC = 4.4%). Such increase has been reported in a study from the United States including only women (1976-2005) [START_REF] Dugandzija | Increasing frequency of soft tissue sarcomas in Vojvodina -comparison with the literature[END_REF], whereas a study from the United Kingdom showed the same trend in incidence for both sexes (1988-2007) [START_REF] Valery | Bone cancer incidence by morphological subtype: a global assessment[END_REF]. The strongest hypothesis to explain the increased risk of chondrogenic sarcoma in women is the introduction of exogenous estrogen Indicates that the APC is significantly different from 0 at the alpha = 0.05 level exposures (oral contraceptives, hormone therapy), whereas other factors has to be identified in men [START_REF] Valery | Bone cancer incidence by morphological subtype: a global assessment[END_REF][START_REF] Anfinsen | Age-period-cohort analysis of primary bone cancer incidence rates in the United States (1976-2005)[END_REF]. The different incidence trends for sarcomas reported over the world may partly be explained by variations in diagnosis practices and the classification used. The impact of environmental factors in the etiology of these cancers may also be a point at issue. However, the large heterogeneity of histological subtypes and the rarity of sarcomas prevent examining this association and drawing conclusions from existing environmental epidemiological studies. A national French study on the etiology of sarcomas (Etiosarc) has been launched to study the possible effect of environmental factors [START_REF] Lacourt | ETIOSARC study : environmental aetiology of sarcomas from a French prospective multicentric populationbased case-control study-study protocol[END_REF].

A major strength in this study is that the incidence of sarcomas was estimated using the 2013 WHO classification [START_REF] Fletcher | WHO classification of Tumours of soft tissue and bone[END_REF]. Whenever possible, registry data was converted to the latest classification to take into account changes and evolutions between different classifications (e.g. new morphological terms, obsolete morphological codes and terms).

Moreover, this study is the first to describe sarcomas in a geographic area where an expert sarcoma pathologist reviews the pathologic diagnosis. Contrary to imperfectly estimated sarcoma incidence rates, this review allows to provide a consistent incidence of sarcomas. A French study, confirmed these results and indicated that 45% of sarcomas are misclassified at initial diagnosis and that 19% have complete discordance [START_REF] Lurkin | Epidemiological evaluation of concordance between initial diagnosis and central pathology review in a comprehensive and prospective series of sarcoma patients in the Rhone-Alpes region[END_REF]. For this reason, the review for sarcoma diagnosis is necessary to estimate a consistent incidence and more so for the different subgroups. In France, the second review was based on voluntary participation before the year 2010. Thereby, we cannot be certain that the review was obtained for all sarcomas in the period 2000-2010, even if significant efforts were made by French sarcoma network in order that pathologists systematically send slides of any newly diagnosed of sarcomas. For this reason, the estimated incidence over the 2010-2013 seems to be most relevant and reliable.

Conclusion

This study provided the opportunity to precisely describe the incidence of sarcomas according to three different groups (anatomic, histologic and genetic) defined by sarcoma specialists using data from population-based cancer registries. To our knowledge, this study is the first to report sarcoma incidence based on a systematic pathological review of these cancers and taking into account the updated sarcoma classifications. Due to literature paucity on sarcomas, future studies using data from population-based cancer registries will have to consider a strict inclusion criterion presented in our study to better describe and compare data between countries. The molecular classification will be useful for etiological studies as incidence studies.

  Abreviations: GIST Gastro-Intestinal Stromal Tumors, CIR Crude Incidence Rate per 100,000 persons-years, ASR-W, ASR-E and ASR-US Age-Standardized incidence Rate from three reference populations (W, World Segi; E, European; US, United-States) a Unclassified sarcomas include: Sarcoma not otherwise specified (ICDO-88003), undifferentiated spindle cell sarcoma (ICDO-88013), undifferentiated pleomorphic sarcoma (ICDO-88023), undifferentiated round cell sarcoma (ICDO-88033), epithelioid sarcoma (ICDO-88043), undifferentiated sarcoma NOS (ICDO-88053)

Fig. 1

 1 Fig.1Age-specific incidence rates of sarcomas per 100,000 person-years according to topographic groups. FRANCIM network data 2010-2013(19 registries)

Table 1

 1 Gender distribution of sarcoma patients according to age and topographic, genomic and histologic groups. FRANCIM network data 2010-2013(19 registries)

		Male		Female		Overall		Sex
		n	%	n	%	n	%	ratio M/F
	Age group (in years)
	0-14	81	4.1	81	4.1		4.1	1.0
	15-24	99	5.0	79	4.0		4.5	1.3
	25-39	197	10.0	160	8.1		9.1	1.2
	40-64	671	34.1	738	37.4		35.7	0.9
	65-74	376	19.1	379	19.2		19.2	1.0
	75 and more	546	27.7	535	27.1		27.4	1.0
	Sarcoma topographic groups
	Soft tissue	972	49.3	812	41.2		45.3	1.2
	Bone	310	15.7	259	13.1		14.4	1.2
	Skin	262	13.3	167	8.5		10.9	1.6
	Viscera
	Gastro-intestinal organs	291	14.8	287	14.6		14.7	1.0
	Female genital organs	-	-	282	14.3		7.2	-
	Others visceral organs	102	5.2	129	6.5		5.9	0.8
	Other anatomic sites	33	1.7	36	1.8		1.8	0.9
	Sarcoma genomic groups
	Complex genomic alterations	723	36.6	847	43.0		39.8	0.9
	MDM2 amplification	135	6.9	81	4.1		5.5	1.7
	Mutations	274	13.9	276	14.0		14.0	1.0
	Recurrent translocations	340	17.3	438	22.2		19.7	0.8
	Undefined/Miscellaneous alterations	498	25.3	330	16.7		21.0	1.5
	Sarcoma histologic groups
	Unclassified sarcoma a	327	16.6	308	15.6		16.1	1.1
	Leiomyosarcoma	205	10.4	346	17.5		14.0	0.6
	GIST	246	12.5	250	12.7		12.6	1.0
	Liposarcoma	228	11.6	130	6.6		9.1	1.8
	Dedifferentiated liposarcoma	125	6.3	69	3.5		4.9	1.8
	Round cell \ Myxoid liposarcoma	42	2.1	29	1.5		1.8	1.4
	Pleomorphic liposarcoma	18	0.9	7	0.4		0.6	2.6
	Liposarcoma NOS	43	2.2	25	1.3		1.7	1.7
	Chondrosarcoma	123	6.2	118	6.0		6.1	1.0
	Dermatofibrosarcoma	101	5.1	124	6.3		5.7	0.8
	Kaposi sarcoma	156	7.9	25	1.3		4.6	6.2
	Angiosarcoma	54	2.7	115	5.8		4.3	0.5
	Osteosarcoma	84	4.3	71	3.6		3.9	1.2
	Ewing sarcoma	72	3.7	65	3.3		3.5	1.1
	Myxofibrosarcoma	75	3.8	49	2.5		3.1	1.5
	Rhabdomyosarcoma	66	3.4	51	2.6		3.0	1.3
	Embryonal rhabdomyosarcoma	27	1.4	16	0.8		1.1	1.7
	Alveolar rhabdomyosarcoma	10	0.5	12	0.6		0.6	0.8
	Pleomorphic rhabdomyosarcoma	14	0.7	7	0.4		0.5	2.0

Table 1

 1 Gender distribution of sarcoma patients according to age and topographic, genomic and histologic groups. FRANCIM network data 2010-2013 (19 registries) (Continued)

		Male		Female		Overall		Sex
		n	%	n	%	n	%	ratio M/F
	Spindle cell rhabdomyosarcoma	7	0.4	7	0.4	14	0.4	1.0
	Rhabdomyosarcoma NOS	8	0.4	9	0.5	17	0.4	0.9
	Nerve Sheath Tumors	38	1.9	44	2.2	82	2.1	0.9
	Endometrial stromal sarcoma	-	-	81	4.1	81	2.1	-
	Synovial sarcoma	37	1.9	40	2.0	77	2.0	0.9
	Spindle cell synovial sarcoma	19	1.0	20	1.0	39	1.0	1.0
	Biphasic synovial sarcoma	3	0.1	7	0.3	10	0.2	0.4
	Synovial sarcoma NOS	15	0.8	13	0.7	28	0.7	1.2
	Chordoma	40	2.0	27	1.4	67	1.7	1.5
	Solitary fibrous tumor, malignant	33	1.7	33	1.7	66	1.7	1.0
	Fibrosarcoma	15	0.8	16	0.8	31	0.8	0.9
	Malignant myoepithelioma	12	0.6	11	0.6	23	0.6	1.1
	Epithelioid haemangioendothelioma	9	0.5	11	0.6	20	0.5	0.8
	Other (with fewer than 20 cases)	49	2.5	57	2.9	106	2.7	0.7
	Overall	1970	100.0	1972	100.0	3942	100.0	1.0

a Unclassified sarcomas include: Sarcoma NOS (ICDO-88003), undifferentiated spindle cell sarcoma (ICDO-88013), undifferentiated pleomorphic sarcoma (ICDO-88023), undifferentiated round cell sarcoma (ICDO-88033), epithelioid sarcoma (ICDO-88043), undifferentiated sarcoma NOS (ICDO-88053)

Table 2

 2 Sarcoma crude and age-standardized incidence rate per 100,000 person-years according to topographic, genomic and histological groups by sex. FRANCIM network

		ASR-US	2.78	1.01	0.73		0.84	0.81	0.35	0.10		2.45	0.33	0.83	1.43	1.26		0.94	0.81	0.72	0.53	0.26	0.12	0.02	0.08	0.41	0.41	0.28	0.25	0.27	0.28	0.19
		ASR-E	2.68	1.01	0.72		0.81	0.82	0.34	0.10		2.38	0.31	0.80	1.44	1.22		0.90	0.79	0.70	0.51	0.25	0.12	0.02	0.08	0.41	0.42	0.27	0.23	0.27	0.28	0.19
		ASR-W (segi)	2.15	0.96	0.59		0.58	0.62	0.26	0.10		1.87	0.23	0.62	1.34	0.97		0.65	0.58	0.50	0.38	0.17	0.10	0.01	0.05	0.34	0.37	0.20	0.16	0.28	0.33	0.14
	Overall	CIR	3.30	1.10	0.80		1.10	1.00	0.40	0.10		2.90	0.40	1.00	1.50	1.60		1.20	1.00	0.90	0.70	0.40	0.10	0.00	0.10	0.50	0.40	0.30	0.30	0.30	0.30	0.20
		ASR-US	2.38	0.88	0.54		0.79	0.81	0.37	0.10		2.47	0.23	0.77	1.54	0.94		0.85	0.99	0.67	0.37	0.16	0.11	0.00	0.06	0.36	0.44	0.06	0.27	0.25	0.27	0.14
		ASR-E	2.34	0.87	0.54		0.77	0.82	0.36	0.10		2.44	0.23	0.76	1.56	0.90		0.82	0.99	0.65	0.37	0.16	0.11	0.00	0.05	0.36	0.45	0.06	0.25	0.24	0.27	0.14
		ASR-W (segi)	1.91	0.85	0.46		0.57	0.62	0.28	0.10		1.93	0.18	0.58	1.44	0.73		0.62	0.74	0.47	0.28	0.11	0.09	0.00	0.04	0.30	0.40	0.04	0.17	0.25	0.32	0.10
	Female	CIR	2.90	0.90	0.60		1.00	1.00	0.50	0.10		3.10	0.30	1.00	1.60	1.20		1.10	1.30	0.90	0.50	0.30	0.10	0.00	0.10	0.40	0.50	0.10	0.40	0.30	0.20	0.20
		ASR-US	3.31	1.17	0.91		0.95	-	0.33	0.13		2.43	0.45	0.90	1.30	1.70		1.08	0.68	0.78	0.72	0.38	0.16	0.05	0.14	0.40	0.35	0.54	0.16	0.33	0.30	0.22
		ASR-E	3.12	1.16	0.88		0.91	-	0.33	0.13		2.30	0.42	0.87	1.30	1.62		1.00	0.62	0.75	0.68	0.35	0.16	0.04	0.13	0.41	0.35	0.52	0.15	0.33	0.30	0.22
		ASR-W (segi)	2.43	1.09	0.70		0.65	-	0.26	0.15		1.78	0.30	0.66	1.23	1.27		0.71	0.43	0.52	0.48	0.24	0.13	0.03	0.09	0.32	0.31	0.40	0.10	0.34	0.35	0.16
	Male	CIR	3.70	1.20	1.00		1.10	-	0.40	0.10		2.80	0.50	1.10	1.30	1.90		1.30	0.80	0.90	0.90	0.50	0.20	0.10	0.20	0.50	0.40	0.60	0.20	0.30	0.30	0.30
	Median	Age	65	47	54		69	62	65	55		65	68	68	46	65		69	66	69	67	69	51	73	67	55	44	63	73	34	19	66
	data 2010-2013 (19 registries)	Sarcomas by topographic groups	Soft tissue	Bone	Skin	Viscera	Gastro-intestinal organs	Female genital organs	Other visceral organs	Others anatomic sites	Sarcomas by genomic groups	Complex genomic alterations	MDM2 amplification	Mutations	Recurrent translocations	Undefined/Miscellaneous alterations	Sarcomas by histologic groups	Unclassified sarcoma a	Leiomyosarcoma	GIST	Liposarcoma	Dedifferentiated liposarcoma	Round cell \ Myxoid liposarcoma	Pleomorphic liposarcoma	Liposarcoma, NOS	Chondrosarcoma	Dermatofibrosarcoma	Kaposi sarcoma	Angiosarcoma	Osteosarcoma	Ewing sarcoma	Myxofibrosarcoma

Table 2

 2 Sarcoma crude and age-standardized incidence rate per 100,000 person-years according to topographic, genomic and histological groups by sex. FRANCIM network

	data 2010-2013 (19 registries)

Table 3

 3 Annual percentage change of world age-standardized incidence rate by topographic groups, histologic types. FRANCIM network data 2000-2013 (11 registries)

		n	Joinpoint	APC	95% CI
	Sarcomas by topographic groups
	Soft Tissue	3766		0.8	(-0.4; 2.0)
	Bone	1193		1.2	(-0.4; 2.9)
	Skin	1062		-2.0 a	(-3.5; -0.4)
	Viscera tumors organs
	Gastro-intestinal organs	1053		1.5	(-0.3; 3.3)
	Female genital organs	297	2000-2005	4.2	(-4.2; 13.2)
		376	2005-2013	-6.7 a	(-10.4; -2.7)
	Other visceral organs	540		-1.7	(-5.1; 1.8)
	Other anatomic sites	171		1.0	(-3.4; 5.6)
	Sarcomas by histologic groups
	Unclassified sarcoma	1513		-1.6	(-3.6; 0.3)
	Leiomyosarcoma	1281		-2.6 a	(-4.6; -0.6)
	GIST	822		3.7 a	(0.8; 6.8)
	Liposarcoma	713		1.3	(-1.1; 3.7)
	Dermatofibrosarcoma	496		0.6	(-1.4; 2.7)
	Chondrosarcoma	454		4.1 a	(1.6; 6.6)
	Kaposi sarcoma	419		-4.1 a	(-6.8; -1.4)
	Osteosarcoma	359		-0.6	(-3.7; 2.6)
	Angiosarcoma	335		2.2	(-1.2; 5.7)
	Ewing sarcoma	330		-0.2	(-4.1; 3.8)
	Rhabdomyosarcoma	286		-1.1	(-6.2; 4.4)
	Others (with fewer than 20 cases)	226		4.1	(-1.3; 9.8)
	Synovial sarcoma	219		1.2	(-4.2; 6.9)
	Nerve Sheath Tumors	191		-0.1	(-4.3; 4.4)
	Myxofibrosarcoma	183		8.2 a	(0.4; 16.6)
	Endometrial stromal sarcoma	173		-3.7	(-7.4; 0.1)
	Fibrosarcoma	151		-9.2 a	(-15.7; -2.3)
	Chordoma	126		0.8	(-4.8; 6.6)
	Solitary fibrous tumor. Malignant	102		12.2 a	(6.2; 18.5)
	Epithelioid haemangioendothelioma	55		-	-
	Myoepithelial carcinoma	24		-	-
	Total	3359	2000-2005	3.6 a	(0.2; 7.1)
		5099	2005-2013	-1.4	(-2.9; 0.1)
	Note. Joinpoint = years when statistically significant changes in incidence trend occurred
	APC Annual Percent Change, CI Confidence Interval
	a

Acknowledgments

We thank Vianney Jouhet for advice about classification alignements and Marie Poiseuil for datamanagement. Thanks to Jone Iriondo-Alberdi for proofreading and comments. We thank the Francim Network for their collaboration in the study: J Jégu, M Velten (Bas-Rhin General Cancer Registry); E Cornet, X Troussard (Registre Régional des Hémopathies Malignes de Basse Normandie); A M Bouvier (Registre Bourguignon des Cancers Digestifs); A V Guizard (Registre Général des Tumeurs du Calvados); V Bouvier, G Launoy (Registre des Tumeurs Digestives du Calvados); P Arveux (Breast cancers registry of Côte-d'Or France); M Maynadié, M Mounier (Hémopathies Malignes de Côte d'Or); A S Woronoff (Doubs and Belfort Territory General Cancer Registry); M Daoulas, M Robaszkiewicz (Finistère Cancer Registry); J Clavel, S Goujon (French National Registry of Childhood Hematopoietic Malignancies); B Lacour (National Registry of Childhood Solid Tumors); I Baldi, C Pouchieu (Gironde Registry of Primary Central Nervous System Tumors); B Amadeo, G Coureau (General Cancer Registry of Gironde Department); S Leguyader, A Monnereau, S Orazio (Registre des Hémopathies Malignes de la Gironde); P M Preux, F Rharbaoui (Registre Général des Cancers de Haute-Vienne); E Marrer (Haut-Rhin Cancer Registry); B Trétarre (Registre des Tumeurs de l'Hérault); M Colonna, P Delafosse (Registre du Cancer du Département de l'Isère); K Ligier, S Plouvier (Registre Général des Cancers de Lille et de sa Region); A Cowppli-Bony, F Molinié (Loire-Atlantique-Vendée Cancer Registry); S Bara (Manche Cancer Registry); O Ganry, B Lapôtre-Ledoux (Registre du Cancer de la Somme); P Grosclaude (Tarn Cancer Registry); N Bossard, Z Uhry (Hospices Civils de Lyon). We thank all pathologists, clinicians, and clinical research assistants of French sarcoma networks (RRePS, NetSarc and ReSos).

Funding

This work was supported by the French National Cancer Institute (in the framework of INCa-BCB 2012 grant for constitution of multicentre clinical and biological databases nationwide in cancer. Funding bodies had no role in the design of the study, collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

The datasets analyzed during the current study are not publicly available due to national regulations. Permission to use French cancer registry data was provided by the National Cancer Institute after consultation with the data protection authority.

Supplementary information

Supplementary information accompanies this paper at https://doi.org/10. 1186/s12885-020-6683-0.

Additional file 1. Complementary information on data collection and statistical analyses. Authors' contributions BA performed the statistical analyses and wrote the original draft. ED, SMP and NP conceived of the study and contributed to revising the manuscript for intellectual content. JMC, IRC, NP (sarcoma specialists) validated ICD-O3 codes to include in the study. Francim network participated in the data acquisition. JG contributed to manuscript preparation and writing review. KL, PD, AMB, SP, AL, GC and AM contributed to manuscript validation and writing-review. All authors read and approval the final manuscript.

Ethics approval and consent to participate

This study is based on data from cancer registries gathered in the French network of cancer registries and a representative of each registry was involved in the study and approved the use of its data. All French registries received an authorization to collect patient data from the data protection authority (Commission Nationale de l'Informatique et des Libertés). Ethics approval and consent to participate were not required for this study which is an observational research without direct contact with patient.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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