Jeremy Augustin

Caroline Gabignon

Aurélie Scriva

Laëtitia Menu

Claire Calmel

Olivier Scatton

François Paye

Jean-François Fléjou

Françoise Praz

Pascale Cervera
email: pascale.cervera@aphp.fr

Dominique Wendum

Testing for ROS1, ALK, MET, and HER2 rearrangements and amplifications in a large series of biliary tract adenocarcinomas

Keywords: Biliary tract adenocarcinoma, HER2, MET, ROS1, fluorescent in situ hybridization, immunohistochemistry

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Although biliary tract adenocarcinoma is a rare cancer (five cases per 100,000 inhabitants in industrialized countries) [START_REF] Koerkamp | Outcomes in biliary malignancy[END_REF], its incidence has been increasing during the last thirty years particularly for intrahepatic cholangiocarcinomas [START_REF] Shaib | The epidemiology of cholangiocarcinoma[END_REF]. Biliary tract adenocarcinomas can rarely be surgically resected and more than 50% of the patients will relapse [START_REF] Deoliveira | Cholangiocarcinoma: thirty-one-year experience with 564 patients at a single institution[END_REF]. The use of adjuvant treatments is still controversial with a median survival of only 9 to 15 months given the chemoresistance of this cancer [4]. In this context, other therapies are needed and characterization of molecular alterations that may be potential targets for therapy is crucial.

Several theranostic alterations have been targeted in clinical trials like ACSé (NCT02034981) evaluating crizotinib safety and efficacy in patients with locally advanced rearranged cancers with ALK, MET, and ROS1 rearrangements, or NCT02836847 and NCT03093870 trials that investigated trastuzumab and varlitinib efficacy in HER2 amplified biliary tract adenocarcinomas [START_REF] Rizvi | Emerging molecular therapeutic targets for cholangiocarcinoma[END_REF].

However, there are few data regarding these alterations in biliary tract adenocarcinomas and previous studies reported contradictory results (Table 1 of the supplementary data) [START_REF] Gu | Survey of tyrosine kinase signaling reveals ROS kinase fusions in human cholangiocarcinoma[END_REF][START_REF] Neia | Screening for the FIG-ROS1 fusion in biliary tract carcinomas by nested PCR[END_REF][START_REF] Lim | Rare Incidence of ROS1 Rearrangement in Cholangiocarcinoma[END_REF][START_REF] Chiang | Expression levels of ROS1/ALK/c-MET and therapeutic efficacy of cetuximab plus chemotherapy in advanced biliary tract cancer[END_REF][START_REF] Graham | Fibroblast growth factor receptor 2 translocations in intrahepatic cholangiocarcinoma[END_REF][START_REF] Voss | Molecular profiling of cholangiocarcinoma shows potential for targeted therapy treatment decisions[END_REF][START_REF] Albrecht | HER2 gene (ERBB2) amplification is a low-frequency driver with potential predictive value in gallbladder carcinoma[END_REF][START_REF] Albrecht | HER2 gene (ERBB2) amplification is a rare event in non-liver-fluke associated cholangiocarcinogenesis[END_REF][START_REF] Javle | Biliary cancer: Utility of next-generation sequencing for clinical management[END_REF][START_REF] Kim | Prevalence and Clinicopathological Significance of MET Overexpression and Gene Amplification in Patients with Gallbladder Carcinoma[END_REF][START_REF] Nakazawa | Amplification and overexpression of c-erbB-2, epidermal growth factor receptor, and c-met in biliary tract cancers[END_REF][START_REF] Shafizadeh | Epidermal growth factor receptor and HER-2/neu status by immunohistochemistry and fluorescence in situ hybridization in adenocarcinomas of the biliary tree and gallbladder[END_REF][START_REF] Yoshida | A significant subgroup of resectable gallbladder cancer patients has an HER2 positive status[END_REF][START_REF] Yoshikawa | Clinicopathological and prognostic significance of EGFR, VEGF, and HER2 expression in cholangiocarcinoma[END_REF]. FIG-ROS1 fusions were detected by PCR in 8.7% cholangiocarcinomas [START_REF] Gu | Survey of tyrosine kinase signaling reveals ROS kinase fusions in human cholangiocarcinoma[END_REF] and 9.2%, biliary tract carcinomas (exclusively in extrahepatic biliary cholangiocarcinomas and gallbladder carcinomas) [START_REF] Neia | Screening for the FIG-ROS1 fusion in biliary tract carcinomas by nested PCR[END_REF]. The frequency of ROS1 rearrangements was lower with 3 cases of ROS1 rearrangements out of 261 cholangiocarcinomas (1.1%) using fluorescent in situ hybridization (FISH), and exclusively in intrahepatic tumors [START_REF] Lim | Rare Incidence of ROS1 Rearrangement in Cholangiocarcinoma[END_REF]. A recent report mentioned the absence of ROS1 rearrangement out of 110 biliary tract adenocarcinomas assessed by FISH [START_REF] Chiang | Expression levels of ROS1/ALK/c-MET and therapeutic efficacy of cetuximab plus chemotherapy in advanced biliary tract cancer[END_REF]. In this study, one case with ALK rearrangement was detected using FISH and MET expression was explored using only immunohistochemistry (IHC). HER2 amplification has been described in biliary tract adenocarcinomas with a frequency around 3% in distal extrahepatic cholangiocarcinomas and 10% in gallbladder carcinomas [START_REF] Graham | Fibroblast growth factor receptor 2 translocations in intrahepatic cholangiocarcinoma[END_REF][START_REF] Albrecht | HER2 gene (ERBB2) amplification is a low-frequency driver with potential predictive value in gallbladder carcinoma[END_REF][START_REF] Albrecht | HER2 gene (ERBB2) amplification is a rare event in non-liver-fluke associated cholangiocarcinogenesis[END_REF][START_REF] Nakazawa | Amplification and overexpression of c-erbB-2, epidermal growth factor receptor, and c-met in biliary tract cancers[END_REF][START_REF] Yoshida | A significant subgroup of resectable gallbladder cancer patients has an HER2 positive status[END_REF][START_REF] Yoshikawa | Clinicopathological and prognostic significance of EGFR, VEGF, and HER2 expression in cholangiocarcinoma[END_REF][START_REF] Pellino | Precision medicine in cholangiocarcinoma[END_REF][START_REF] Wiggers | Differences in immunohistochemical biomarkers between intra-and extrahepatic cholangiocarcinoma: a systematic review and meta-analysis[END_REF].

Biliary tract adenocarcinomas are gathered together in studies although it is well known that they do not represent a single tumor type, as they arise from different biliary cells and display different mutational profiles [START_REF] Rizvi | Emerging molecular therapeutic targets for cholangiocarcinoma[END_REF][START_REF] Nakamura | Genomic spectra of biliary tract cancer[END_REF][START_REF] Banales | Expert consensus document: Cholangiocarcinoma: current knowledge and future perspectives consensus statement from the European Network for the Study of Cholangiocarcinoma (ENS-CCA)[END_REF][START_REF] Kendall | Anatomical, histomorphological and molecular classification of cholangiocarcinoma[END_REF].

They are classified according to their anatomical location into intrahepatic, extrahepatic cholangiocarcinomas (perihilar or distal) and gallbladder carcinomas. Morphomolecular subtypes (small and large duct type) are now integrated for intrahepatic cholangiocarcinomas in the fifth edition of the WHO classification of digestive system tumors, [START_REF] Liau | Morphological subclassification of intrahepatic cholangiocarcinoma: etiological, clinicopathological, and molecular features[END_REF][START_REF] Paradis | Tumours of the liver and intrahepatic bile ductsWHO classification of tumours : Digestive system tumours[END_REF]. Therefore, in order to have a precise view of some molecular targets in biliary tract adenocarcinomas, we assessed ROS1 and ALK rearrangements and amplifications, as well as MET and HER2 amplifications using FISH as gold standard in a large French series of biliary tract carcinomas that are well-characterized in terms of location, pathology and staging. We also performed immunohistochemical stainings in order to determine which method, IHC or FISH, was more effective for screening these tumors.

Patients and methods

Case selection

One hundred and ninety-four (194) cases of histologically-confirmed biliary tract adenocarcinomas diagnosed on surgically-resected specimens between 2005 and 2019 were retrieved from the Saint-Antoine Hospital database.

Only cases with invasive carcinomas were included. Informed consent was obtained for the study.

Age, gender, tumor location, associated risk factors of liver or biliary tract disease were recorded. Tumor stage, nodal status, vascular invasion, perineural invasion and status of the resection margins were retrieved from pathologic reports. Tumor stage was defined for all cases according the 7 th edition of the TNM classification.

Biliary tract adenocarcinoma specimens and tissue microarray construction

All specimens were formalin-fixed and paraffin-embedded. Based on radiologic and pathologic findings, biliary tract adenocarcinomas were subdivided into four subtypes according to their location: intrahepatic, perihilar bile duct, distal extrahepatic bile duct cholangiocarcinomas, and gallbladder adenocarcinomas. For each case, one representative tumor block was selected for tissue microarray construction. Two to five 0.6-mm tissue cores were used and hematoxylin and eosin (HE) staining was performed on each tissue microarray slide to confirm the presence of tumor areas. Whole tissue sections were used for the 36 distal extrahepatic bile duct cholangiocarcinomas.

Morphological analysis of the tumors

HE-stained sections were reviewed to specify histological type and grade according to the WHO classification [START_REF]WHO Classification of Tumours Edittorial Board[END_REF]. We also subclassified intrahepatic cholangiocarcinomas into cholangiolar and bile duct type using morphological criteria described in Liau et al. [START_REF] Liau | Morphological subclassification of intrahepatic cholangiocarcinoma: etiological, clinicopathological, and molecular features[END_REF], which are similar to the WHO criteria's defining the small and large duct types [START_REF] Paradis | Tumours of the liver and intrahepatic bile ductsWHO classification of tumours : Digestive system tumours[END_REF].

Fluorescent in situ hybridization for ROS1, ALK, MET and HER2,

Probes and technical details for each probe are listed in Table 2 of the supplementary data. FISH was performed on tissue microarrays using six probes to detect ROS1 rearrangement or amplification, cMET amplification, HER2 amplification, ALK rearrangement or amplification, according to the manufacturers' instructions. Briefly, whatever the probe used, 3-µm slides from tissue microarray blocks were deparaffinized using xylene washes. After rehydration, slides were microwaved preprocessed and after pepsine digestion, dried before co-denaturation and overnight hybridization using a hybridizer system (Dako, Agilent, CA, United States). Post-hybridization washes and counterstain were performed according to each of the probes that were used.

For ROS1 break-apart probes detection, FISH slides were scanned using PathScan® FISH (Excilone, EXCS-PS-F, Elancourt, France) at x60 objective, after delimitating regions of interest on HE slides. Scanned FISH slides were visualized on PathScan® Viewer (Excilone, Elancourt, France), selecting appropriate channels. FISH signal abnormalities were confirmed using a fluorescence microscope with appropriate channels. For all other probes, FISH slides were directly visualized on fluorescence microscope. Forty non-overlapping nuclei from three tumor areas were evaluated for each probe at x100 oil immersion objective. ROS1 translocation was defined as the presence of green and red signals separated by at least two signal diameters in the cell nucleus, with still a fused red and green signal. ROS1 monosomy was defined as a unique fused red and green signal with two HER2, MET and ALK signals, in most nuclei. HER2 amplification was defined by HER2/CEP17 ratio higher than 2, with no HER2 signals less than 6 [START_REF] Ruschoff | HER2 diagnostics in gastric cancer-guideline validation and development of standardized immunohistochemical testing[END_REF]. As proposed by some authors for lung adenocarcinoma, MET amplification was defined by the existence of 6 copies or more of MET per cell [START_REF] Koeppen | Biomarker analyses from a placebo-controlled phase II study evaluating erlotinib+/-onartuzumab in advanced nonsmall cell lung cancer: MET expression levels are predictive of patient benefit[END_REF], and because the probe used did not target centromere 7, MET/CEP7 ratio was not evaluated. Because there are no official guidelines to evaluate ROS1 amplification, we defined ROS1 amplification as for MET. ALK amplification was defined by an ALK/CEP2 ratio higher than 2 [START_REF] Peretti | ALK gene copy number gains in non-small-cell lung cancer: prognostic impact and clinico-pathological correlations[END_REF].

Aneuploidy was defined by an unbalanced number of chromosomes or loss of large portions of chromosomes; polysomy as a type of aneuploidy, with three or more copies of one or more chromosomes. Because we used dual probes, one locus-specific and one centromeric, one fused signal loss might suggest whole or segmental chromosomal aneuploidy or monosomy [START_REF] Zasadil | 2n or not 2n: Aneuploidy, polyploidy and chromosomal instability in primary and tumor cells[END_REF].

Unbalanced ratios of specific gene loci and centromeric alpha-satellite sequences were analyzed, by enumeration of the fluorescent signals in the tumor cells, considering cell-to-cell variability, a feature of dynamic chromosomal instability [START_REF] Geigl | Defining 'chromosomal instability[END_REF].

Centromeric alpha-satellite sequences over-representation was defined as a number of alpha-satellites signals greater than 2 with a number of specific genes loci signals equal to 2.

IHC detection of ROS1, MET, and HER2 proteins

Antibodies used for IHC procedures are summarized in Table 3 supplementary data. IHC procedures were performed on 3-µm deparaffinized tissue microarray sections. For antigen retrieval, we used a pH 8.0 EDTA solution (15 minutes of boiling water). IHC against ROS1 protein was performed manually whereas IHC against MET and HER2 was performed on Bond autostainer (Leica Biosystems, Wetzlar, Germany). For ROS1 IHC, HCC78 lung adenocarcinoma and U-138-MG glioblastoma cell lines were used as positive controls. These two cell lines express an SLC34A2-ROS1 fusion and a FIG-ROS1 fusion, respectively. Mz-ChA1, a gallbladder adenocarcinoma cell line, which is not ROS1 rearranged and does not express ROS1 protein was used as a negative control. RT-PCR evaluating levels of ROS1 5' and 3' gene regions and anti-ROS1 immunoblotting have been conducted in the laboratory to confirm these data (data not shown). A diffuse cytoplasmic staining was considered as positive. For HER2 IHC, a breast HER2 positive (3+) adenocarcinoma was used as a positive control. A strong complete, basolateral or lateral membranous HER2 staining in more than 10% of tumor cells was considered as strongly positive (equivalent to 3+ score). A weak to moderate complete, basolateral or lateral membranous staining in more than 10% of tumor cells was considered weakly positive (equivalent to 2+ score). Faint or barely perceptible membranous staining in more than 10% (equivalent to 1+ score) and no or staining in less than 10% of tumor cells were both considered negative [START_REF] Bartley | HER2 Testing and Clinical Decision Making in Gastroesophageal Adenocarcinoma: Guideline From the College of American Pathologists, American Society for Clinical Pathology, and the American Society of Clinical Oncology[END_REF]. For MET IHC, a MET strongly positive lung adenocarcinoma was used as control. Intensity of membranous and cytoplasmic immunostaining was scored according to a fourtier system: no staining (0); weak (1+); moderate (2+); and strong (3+) [START_REF] Xu | Expression and clinical significance of c-Met in advanced esophageal squamous cell carcinoma[END_REF]. Cases with no or weak staining (0 and 1+) were considered negative, and cases with moderate and strong staining (2+ and 3+) were considered positive.

Results

Population

Main clinical characteristics of our series are summarized in Table 1.

One hundred and ninety-four (194) cases of surgically resected biliary tract adenocarcinomas were included in this study. Sex ratio (men to women) was 1.3. Mean age was 65.5 years (SD= 10.9 years) and median age was 66 years (range 29 to 88 years).

Tumor characteristics

Characteristics of tumors according to their location and type are summarized in Table 1.

Fluorescent in situ hybridization results

The chromosomal alterations detected by FISH and the clinical and pathological features associated with those cases are specified in Table 2.

ROS1 chromosomal alterations

In the whole series of 194 biliary tract adenocarcinomas, we did not observe any case of ROS1 rearrangement or amplification. However, we detected 24 (12.4%) cases with chromosome 6 monosomy or large deletion. For each of these cases, most of tumor cells had only one fused signal of ROS1 with the ROS1 Dual Color Break Apart Probe and obvious single ROS1 and CEP6 signals with the ROS1/CEN 6 Dual Color Probe (Table 2), whereas they contained two HER2, MET and ALK signals. One example of ROS1 monosomy is shown in Figure 1.

MET amplification

Two of the 194 tumors harbored a MET amplification. These tumors were respectively a poorly differentiated intrahepatic cholangiocarcinoma and a well /moderately differentiated perihilar cholangiocarcinoma. Figures 2a, 2b and 2c illustrate the intrahepatic cholangiocarcinoma with MET amplification.

ALK alterations

Using the ALK corresponding probes, we did not detect any amplification or rearrangement in the whole series.

HER2 amplification

HER2 amplified tumors were more often located in gallbladder adenocarcinoma. Out of the 73 intrahepatic cholangiocarcinomas, using FISH with the HER2 IQFISH pharmDx™, we detected one case (1.4%) with HER2 amplification (Table 2). This case developed within a mucinous cystic neoplasm and was classified as bile duct type. Out of the 36 distal extrahepatic bile duct cholangiocarcinomas, we detected 2 cases (5.5%) with HER2 amplification, one developed in an intraductal papillary neoplasm. Out of the 45 gallbladder adenocarcinomas, we detected 5 cases (11.1%) with HER2 amplification (Table 2), of which 3 were poorly differentiated. None of the 40 perihilar bile duct cholangiocarcinomas harbored HER2 amplification. An example of a HER2 amplified tumor is shown in Figures 2d, 2e and2f.

Unbalanced HER2/CEP17 and ROS1/CEP6 signal ratios and polysomy

Using dual probes with specific probe spanning HER2 and ROS1 and their corresponding centromeric probes, we observed three cases (Table 2), with unbalanced ratio of HER2/CEP17 and ROS1/CEP6, with cell-to-cell variations within tumors. In addition, one of these three cases was also polysomic showing multiple copies of chromosome 6. At last, one case was polysomic with multiple copies of chromosome 17, with a cell-to-cell variation. The percentage of tumor cells with different ratios are summarized in Table 2.

Centromeric alpha-satellite sequences over-representation

We detected five cases with centromeric alpha-satellite sequences over-representation (Table 2). For each of these cases, we observed two specific signals corresponding to HER2 or ROS1, whereas more than 2 centromeric signals were observed in tumor cells. All these cases were intrahepatic cholangiocarcinomas (cholangiolar type n=3, bile duct type n=2).

Correlation between fluorescent in situ hybridization and immunohistochemistry

All cases that carried amplification of MET (n=2) or HER2 (n=8) were positive by IHC. Examples of MET and HER2 positive immunostainings are respectively shown in Figures 2c and2f.

All non-amplified HER2 cases were negative by IHC. On the contrary, IHC was positive in 14 (7, 4%) nonamplified MET cases. IHC against ROS1 protein did not show any positivity in the whole series (n=194). Cases with unbalanced ratios of HER2/CEP17 and ROS1/CEP6 chromosomal and case with polysomy showed a negative immunostaining using anti-ROS1, anti-HER2 and anti-MET antibodies.

DISCUSSION

In this series of 194 biliary tract adenocarcinomas, using FISH assays with probes against ROS1, MET, ALK and HER2 loci, we detected targetable alterations in 5.1% of cases (n=10). From these, 8 cases were HER2 amplified, while the remaining 2 cases displayed MET amplification. The HER2 amplified cases were mostly gallbladder adenocarcinomas (11.1%). The three other cases were distal extrahepatic and intrahepatic (bile duct type) cholangiocarcinomas, in accordance with other studies [START_REF] Albrecht | HER2 gene (ERBB2) amplification is a low-frequency driver with potential predictive value in gallbladder carcinoma[END_REF][START_REF] Albrecht | HER2 gene (ERBB2) amplification is a rare event in non-liver-fluke associated cholangiocarcinogenesis[END_REF][START_REF] Roa | Overexpression of the HER2/neu Gene: A New Therapeutic Possibility for Patients With Advanced Gallbladder Cancer[END_REF][START_REF] Yan | HER2 expression status in diverse cancers: review of results from 37,992 patients[END_REF].

The two cases displaying MET amplification were an intrahepatic (bile duct type) and an extrahepatic perihilar cholangiocarcinoma, in accordance with the recent literature suggesting that MET amplification is a rare event in biliary tract adenocarcinomas. Indeed, using hybrid capture sequencing, in a large series, Javle et al. detected MET amplifications in 2% of intrahepatic cholangiocarcinomas and in 1% of gallbladder adenocarcinomas, whereas none of the 57 extrahepatic bile duct cholangiocarcinomas displayed MET amplification [START_REF] Javle | Biliary cancer: Utility of next-generation sequencing for clinical management[END_REF].

We found no ROS1 rearrangement in our series of 194 biliary tract adenocarcinomas. This result is in contradiction with the initial studies using a PCR method that reported 8.7 and 9.2% of ROS1 rearrangements in 23 cholangiocarcinomas and 65 biliary adenocarcinomas respectively [START_REF] Gu | Survey of tyrosine kinase signaling reveals ROS kinase fusions in human cholangiocarcinoma[END_REF][START_REF] Neia | Screening for the FIG-ROS1 fusion in biliary tract carcinomas by nested PCR[END_REF]. In large series (1.4% of 208 intahepatic cholangiocarcinomas, 1% of 100 cholangiocarcinomas without anatomical or subtype specification), using FISH, with or without an IHC pre-screening, no or very few ROS1 rearrangement was evidenced, which is in accordance with our findings [START_REF] Lim | Rare Incidence of ROS1 Rearrangement in Cholangiocarcinoma[END_REF][START_REF] Chiang | Expression levels of ROS1/ALK/c-MET and therapeutic efficacy of cetuximab plus chemotherapy in advanced biliary tract cancer[END_REF][START_REF] Graham | Fibroblast growth factor receptor 2 translocations in intrahepatic cholangiocarcinoma[END_REF]. Therefore, ROS1 rearrangement is probably a very rare targetable alteration in biliary tract adenocarcinomas.

We did not detect any case of ALK rearrangement in our series, which is in accordance with previous studies [START_REF] Chiang | Expression levels of ROS1/ALK/c-MET and therapeutic efficacy of cetuximab plus chemotherapy in advanced biliary tract cancer[END_REF].

We detected ROS1 monosomy in 13.7% of intrahepatic cholangiocarcinomas, 10% of perihilar cholangiocarcinomas, 16.7% of distal cholangiocarcinomas and 8.9% of gallbladder carcinomas in our series. As we detected one copy of ROS1 gene and one copy of chromosome 6 centromere, complete loss or extensive deletion of chromosome 6, likely occurred. Partial losses of chromosome 6 have already been described in the literature, but we did not find reports of complete loss [START_REF] Sia | Integrative molecular analysis of intrahepatic cholangiocarcinoma reveals 2 classes that have different outcomes[END_REF][START_REF] Rijken | Genomic alterations in distal bile duct carcinoma by comparative genomic hybridization and karyotype analysis[END_REF].

Using FISH and focusing on a subset of target genes, we evidenced four cases with chromosomal alterations with a cell-to-cell variability suggestive of dynamic chromosomal instability. Among these cases, which were mostly gallbladder carcinomas (3/4) two showed unbalanced ratios of ROS1/CEP6 and HER2/CEP17; one showed both unbalanced ROS1/CEP6 and HER2/CEP17 ratios and polysomy with multiple copies of chromosome 6 centromere; one showed polysomy with multiple copies of chromosome 17 centromere, without HER2 overrepresentation: this last occurrence may also be a pattern of numerical chromosomal instability [START_REF] Potapova | Aneuploidy and chromosomal instability: a vicious cycle driving cellular evolution and cancer genome chaos[END_REF].

Only intrahepatic cholangiocarcinomas (n=5, three cholangiolar and two bile duct type), showed centromeric alpha-satellite sequences over-representation. These sequences are essential elements that stabilize interactions with DNA binding proteins, maintain heterochromatin architecture [START_REF] Garrido-Ramos | Satellite DNA: An Evolving Topic[END_REF] and over-representation of these sequences seems to be characteristic of some tumors [START_REF] Eymery | A transcriptomic analysis of human centromeric and pericentric sequences in normal and tumor cells[END_REF]. Such repeats appear to be involved in the development of breast adenocarcinoma [START_REF] Ichida | Overexpression of satellite alpha transcripts leads to chromosomal instability via segregation errors at specific chromosomes[END_REF] and associated with aneuploidy [START_REF] Mcnulty | Alpha satellite DNA biology: finding function in the recesses of the genome[END_REF], but they have not been reported in biliary tract adenocarcinoma.

Our observations confirm the complexity of genetic abnormalities involved in the process of biliary tract neoplasia, which may underlie the mechanisms of resistance to EGFR inhibitors in cholangiocarcinoma [START_REF] Vaquero | Unveiling resistance mechanisms to EGFR inhibitors in cholangiocarcinoma[END_REF][START_REF] Fouassier | Signalling networks in cholangiocarcinoma: Molecular pathogenesis, targeted therapies and drug resistance[END_REF]. The balanced rearrangements are probably not the primary or major event, initiating tumor emergence, in cholangiocarcinoma.

The design of the study allowed us to conclude that screening of HER2 and MET amplifications may reliably be performed by immunohistochemistry. IHC seems to be a sensitive and specific method to detect HER2 amplifications in biliary tract adenocarcinomas. Conversely, MET IHC is a sensitive though not specific method to detect MET amplification because 7.3% of the non-amplified MET cases were positive by IHC. Therefore, additional FISH technique is mandatory in MET immunostaining positive cases. The value of IHC to detect ROS1 or ALK rearrangement or amplification, could not be assessed in this study.

The main strength of this study is its large number of cases and the precise anatomical distribution according to WHO and TNM classifications in addition to the main characteristics of the tumors, including their morphological pattern [START_REF] Liau | Morphological subclassification of intrahepatic cholangiocarcinoma: etiological, clinicopathological, and molecular features[END_REF][START_REF] Paradis | Tumours of the liver and intrahepatic bile ductsWHO classification of tumours : Digestive system tumours[END_REF][START_REF] Klimstra | Tumours of the gallbladder and extrahepatic bile ductsWHO classification of tumours : Digestive system tumours[END_REF]. Indeed, the four types of biliary tract adenocarcinomas have different biological behavior with specific molecular alterations [START_REF] Nakamura | Genomic spectra of biliary tract cancer[END_REF][START_REF] Kendall | Anatomical, histomorphological and molecular classification of cholangiocarcinoma[END_REF]. Therefore, we separated these types in order to obtain accurate data for each type of carcinoma. When analyzing our results by tumor type, we confirm the predominance of HER2 amplification in gallbladder carcinomas and distal extrahepatic cholangiocarciomas. Centromeric alpha-satellite sequences over-representation seems to concern only intrahepatic cholangiocarcinomas and the frequency of ROS1 monosomy seems to be quite comparable between the different types of tumor types (8.9% to 16,7%).

Moreover, the design of the study was uniform and systematic: we performed FISH for all targeted chromosomal alterations (HER2 and MET amplifications, ROS1 and ALK rearrangements and amplifications), in our whole series with IHC in parallel. Assessment of IHC was performed blinded to FISH results at the time of evaluation. Because there are currently no official guidelines defining how to assess HER2, MET, ROS1 and ALK status using FISH and IHC in biliary tract adenocarcinomas, positivity was defined based on well-established criteria, well-known by pathologists, and applied for other organs. Several studies looked for targetable chromosomal alterations in biliary tract adenocarcinomas, but authors often used IHC, RT-PCR, or NGS based methods rather than FISH which is the standard for detecting chromosome rearrangements in pathology laboratories [START_REF] Chiang | Expression levels of ROS1/ALK/c-MET and therapeutic efficacy of cetuximab plus chemotherapy in advanced biliary tract cancer[END_REF][START_REF] Javle | Biliary cancer: Utility of next-generation sequencing for clinical management[END_REF][START_REF] Nakamura | Genomic spectra of biliary tract cancer[END_REF][START_REF] Churi | Mutation profiling in cholangiocarcinoma: prognostic and therapeutic implications[END_REF]. In some studies, FISH was used, but not systematically which hinders the comparison between the diagnostic tools [START_REF] Chiang | Expression levels of ROS1/ALK/c-MET and therapeutic efficacy of cetuximab plus chemotherapy in advanced biliary tract cancer[END_REF][START_REF] Graham | Fibroblast growth factor receptor 2 translocations in intrahepatic cholangiocarcinoma[END_REF][START_REF] Yoshida | A significant subgroup of resectable gallbladder cancer patients has an HER2 positive status[END_REF]. In Chiang et al. study, ROS1 and ALK rearrangements were examined using FISH only for tumors overexpressing ROS1 or ALK whereas these authors used IHC only to detect MET amplifications, with possible nonamplified IHC positive cases [START_REF] Chiang | Expression levels of ROS1/ALK/c-MET and therapeutic efficacy of cetuximab plus chemotherapy in advanced biliary tract cancer[END_REF].

However, our study has some limitations. First, the small number of tumors with MET alterations found (n=2) does not allow a comparison between the tumor types. Second, there is also a low number (n=9) of bile duct type intrahepatic cholangiocarcinomas in our study which leads to the same limitations.

Moreover, our study is monocentric. Therefore, pre-analytical phases, known to influence IHC and FISH assays were comparable for all the samples. On the one hand, this allowed us to investigate these alterations in welldefined experimental conditions. On the other hand, it did not allow us to evaluate the robustness of these techniques between laboratories. The value of IHC for MET and HER2 amplification screening should be confirmed by other studies. Finally, rearrangements have only been studied by FISH which is less informative than RNA sequencing.

To conclude, by studying a subset of target genes in a large series of well-defined biliary tract adenocarcinomas, we confirm the existence of a spectrum of molecular and chromosomal alterations varying with the tumor location, the relatively high rate of HER2 amplification in gallbladder adenocarcinomas and the efficacy of IHC to screen these cases as to screen MET amplification, although very rare in biliary tract adenocarcinomas. ROS1 rearrangements cannot be regarded as a molecular hallmark of biliary tract adenocarcinomas and, contrary to the initial published data, it seems to be a very rare event. Our data confirm the frequent existence of aneuploidy and chromosomal instability in biliary tract adenocarcinomas and reveal the existence of centromeric alpha-satellite sequences over-representation in intrahepatic cholangiocarcinomas.

Declarations

Captions

Table S1: overview of studies on ROS1, ALK, MET and HER2 alterations in biliary tract adenocarcinomas.

Table S2: probes and protocols used for fluorescent in situ hybridization analysis.

Table S3: characteristics of antibodies for the immunohistochemistry analyses.

0 0 0 (0) 0 0 0 (0) 0 1 (2.8) (11.1) 0 0 0 (0) 1 (2.5) 0 0 (0) 0 1 (2.8) 0 (0) 0 0 0 (0) 0 0 0 (0) 0 0 (11.1) 0 0 0 (0) 0 1 (2.

Fig. 1 .

 1 Fig. 1. Example of ROS1 monosomy. Well to moderately differentiated intrahepatic cholangiocarcinoma (1a) with only one red green fused signal per cell (1b).

Fig. 2 a

 2 Fig. 2 a-c. Example of MET amplification. Poorly differentiated intrahepatic cholangiocarcinoma (2a); MET amplification with more than 6 red signals per cell (2b); MET immunohistochemistry showing a positive cell membrane and cytoplasmic staining (2c). d-e Example of HER2 amplification. Well to moderately differentiated gallbladder adenocarcinoma (2d); HER2 amplification with more than 15 red HER2 signals for 2 green centromere signals per cell (2e); HER2 immunohistochemistry showing a positive cell membrane staining (2f).

Table 1 :

 1 clinical and pathological characteristics of study groups.

Table 2 :

 2 clinicopathologic details of cases with chromosomal abnormalities and corresponding gene copies number, highlighted by fluorescent in situ hybridization.

Table 1 : clinica

 1

	cholangiolar (n=64)
	N (%)

Table S2 :

 S2 probes and protocols used for fulorescent in situ hybridization analysis

	V, hepatitis C virus; CASOR, centromeric alpha-satellite sequences over-representation; NA, data no Gallbladder (n=45) N (%) 67 66.1 (11.1) 5 (11.1) 1 (2.2) 41 (91.1) 1 (2.2) ot available ROS1 translocation ROS1 amplification Manufacturer Zytovision Zytovision ZytoLight ® SPEC ROS1 Dual Color Break Apart Probe ZytoLight ® SPEC ROS1/CEN 6 Dual Color Probe Reference Z-2144-200 Z-2162-200 cMET amplification HER2 amplification Vysis DAKO Vysis MET Spectrum Red FISH Probe HER2 IQFISH pharmDx™ 06N05-020 K5731 ALK translocation ALK amplification Zytovision Zytovision ZytoLight ® SPEC ALK Dual Color Break Apart Probe ZytoLight ® SPEC ALK/2q11 Dual Color Probe Z-2124-200 Z-2161-200 Molecular alteration Meta Analysis in biliary tract adenocarcinomas: PCR, immunohistochemistry, In situ hybridization Cancer type and localisation HER2 MET ALK ROS Case number intrahepatic cholangiocarcinoma NR 58% (15) NR NR 26 intrahepatic cholangiocarcinoma 0-NR 21,4%-0% NR NR 28 Nakazawa K, Dobashi Y, Suzuki S, Fujii H, Takeda Y, Ooi A (2005) Authors Terada T, Nakanuma Y, Sirica AE (1994) extrahepatic cholangiocarcinoma 11,5-8,5% 0-NR NR NR 78 Gallbladder adenocarcinoma 15,7-21,6% 0-NR NR NR 89 intrahepatic cholangiocarcinoma 0,009 NR NR 106 Yoshikawa D, Ojima H, Iwasaki M, Hiraoka N, Kosuge T, Kasai S, Hirohashi S, Shibata T (2008) extrahepatic cholangiocarcinoma 8.5% NR NR NR 130 adenocarcinomas of the biliary tree (45) and gallbladder (6) 2 (4%)-polysomy 16; Amplification 1/51 (2%) NR NR NR 51 Shafizadeh N, Grenert JP, Sahai V, Kakar S. (2010) adenocarcinomas of the extrahepatic biliary tree 0-0 NR NR NR 10 Pignochino Y, Sarotto I, Peraldo-Neia C, Penachioni JY, Cavalloni G, et al. (2010) adenocarcinomas of the intrahepatic biliary tree 5 (26,3%)-1 with amplification (5%) and 4 with polysomy (21%) 19 gallbladder 1(10%)-1 (10%) NR NR NR 10 NR NR NR 2 FIG-ROS1 fusion 23 Gu T-L, Deng X, Huang F, et al (2011) Gallbladder adenocarcinoma 12,80% NR NR NR 187 Roa I, de Toro G, Schalper K, et al (2014) biliary tract carcinomas NR NR NR FIG-ROS1 fusion : 6 (9%) 65 Peraldo Neia C, Cavalloni G, Balsamo A, et al (2014) intrahepatic cholangiocarcinoma NR 80/110 Chiang N-J, Hsu C, Chen J-S, et al (2016) Gallbladder adenocarcinoma 16,6-11,8% NR NR NR 211 Yoshida H, Shimada K, Kosuge T, Hiraoka N.(2016) Cholangiocarcinoma NR NR NR 38 (19,1%)-3 (1,1%) intrahépatic 261 Lim SM, Yoo JE, Lim KH, Meng Tai DW, Cho BC, Park YN (2017) intrahepatic cholangiocarcinoma 4.8 %-17,9 % NR NR NR 27 with elegible criteria/440 Galdy S, Lamarca A, McNamara MG, et al (2017) Review extrahepatic cholangiocarcinoma 19.9 %-57.6 % NR NR NR Pellino A, Loupakis F, Cadamuro M, et al (2018) review Location ROS1 ALK MET (methods / results) (methods / results) (methods / results) Advanced Gallbladder Cancer. Gastrointest Cancer Res journal Hum Pathol;29:175-180. J Pathol. ;206:356-365 Br J Cancer;98:418-425 Hum Pathol.; 41(4):485-92. BMC Cancer 18;10:631 PLoS ONE 6:e15640 Genes Chromosomes Cancer Sci Rep. 3;6:25369. Virchows Arch;468(4):431-9 Cancer Res Treat.;49(1):185-192 Cancer and Metastasis Reviews 36:141-157 Translational Gastroenterology and Hepatology Gu et al (2011) [6] PLoS ONE 6 (1):e15640 Not specified (Cholangiocarcinomas) (n=23) PCR (after immunoaffinity phosphoproteomic screening) HER2 IHC (overexpression) extrahepatic cholangiocarcinoma (n=78) 0 intrahepatic cholangiocarcinoma 0 Kim et al. (2019) Cancer Res Treatment doi: https://doi.org/10.4143/crt.2019.370 39,8% (methods / results) 4% gallbladder carcinoma (n= 93) FISH (amplification) ROS1 translocation ROS1 amplification 9.7 to 18;3% according to the interpretation rules Manufacturer Zytovision Zytovision 0 (n=28) FIG-ROS1 fusion 8,7% Peraldo Neia et al (2014) [7] Genes Chromosomes Cancer 53 (12):1033-1040 gallbladder carcinoma (n=14) Yoshikawa et al (2008) extrahepatic cholangiocarcinoma Javle et al (2016) [32] Cancer 122 (24) : 3838-47 gallbladder carcinoma (n=85) Next-generation sequencing ZytoLight ® SPEC ZytoLight ® SPEC 1% IHC 16% ROS1 Dual Color Break Apart Probe ROS1/CEN 6 Dual Color Probe PCR FIG-ROS1 fusion 14,3% British Journal of cancer 98; 418-425 (n=130) extrahepatic cholangiocarcinoma (n=57) 0 8.5% 11% Z-2144-200 Z-2162-200 Reference 1 (2.2) 0 0 1 (2.2) 1 (2.2) Digestion 7 minutes 7 minutes Denaturation 10 minutes; 75°C 10 minutes; 75°C Hybridization 16 hours; 37°C 16 hours; 37°C Green spot 6q22.1 proximal 6q22.1 Red spot 6q22.1 distal alpha-satellites seq on Chr6 7 minutes 7 minutes 5 minutes; 73°C 5 minutes; 85°C 16 hours; 37°C 16 hours; 45°C -Centromeric region of chromosome 17 (accorgind to manufacturer) spectrum red 7q31.2 218kb region encompassing HER2 gene (according to manufacturer) 7 minutes 7 minutes 10 minutes; 75°C 10 minutes; 75°C 16 hours; 37°C 16 hours; 37°C 2p23.1 et p23.2 2p23.2 2p23.2 distale 2q11.2 gallbladder adenocarcinoma NR NR NR 2 14 extrahepatic cholangiocarcinoma NR NR NR 4 25 intrahepatic cholangiocarcinoma NR NR NR 0 26 Gallbladder adenocarcinoma NR 37 (39,8%)-17 (18,3%) NR NR 113 Y. Kim, S. Bang, S. Jee, S. Park, S. Shin, S. S. Paik (2019) Cancer Res Treat.2019.370 Gallbladder adenocarcinoma 10-10 (5,4%) NR NR NR 186 Albrecht T, Rausch M, Roessler S, et al. (2019) Virchows Arch. 14 decembre intrahepatic cholangiocarcinoma 1-1 (micopapillary morphology) NR NR NR 155 Albrecht T, Rausch M, Roessler S, et al. (2019) BMC Cancer 19:1191 proximal extrahepatic cholangiocarcinoma 2-2 NR NR NR 155 distal extrahepatic cholangiocarcinoma 3-3 (1,4%) NR NR NR 126 extrahepatic cholangiocarcinoma (n=25) PCR FIG-ROS fusion 16% intrahepatic cholangiocarcinoma (n=26) intrahepatic cholangiocarcinoma (n=106) intrahepatic cholangiocarcinoma 2% IHC 3% Digestion 7 minutes 7 minutes (n=412) 0.9% Denaturation 10 minutes; 75°C 10 minutes; 75°C PCR FIG-ROS fusion 0 Lim et al (2017) [8] Cancer research and treatment 49 (1):185-192 extrahepatic cholangiocarcinoma (n=53) Shafizadeh et al ((2010) Human Pathology 41: 485-492 IHC Hybridization 16 hours; 37°C 16 hours; 37°C gallbladder carcinoma (n=6) 0 Green spot 6q22.1 proximal 6q22.1 FISH (break apart) 0 (n=53) Red spot 6q22.1 distal alpha-satellites seq on Chr6
	10,5%	18 (40) 27 (60) 26 (57.8)	intrahepatic cholangiocarcinoma (n=208) extrahepatic cholangiocarcinoma (n=19)	FISH (break apart) 1,4%
	28 (62.2) 4 (8.9) 0 18 (40) Chiang et al (2016) [9] 0 1 (2.2) 19 (42.2) Scientific reports 6:25369. Yoshida et al (2016) Virchows Archivs;468(4):431-9 FISH (pre-screening by IHC) 16,6%	gallbladder carcinoma + extrahepatic intrahepatic cholangiocarcinoma (n=26) cholangiocarcinoma (n=30) gallbladder carcinoma (n=211)	FISH (pre-screening by IHC) 0	FISH (pre-screening by IHC) 0	IHC 0
	0 13 (28.9) 0 0 0 26 (57.8) 24 (53.3) 21 (46.7) Graham et al (2014) [10] Human Patholology 45 (8):1630-1638 Albrecht et al (2019) BMC Cancer 19:1191 FISH (pre-screening by IHC) 2,4% FISH (pre-screening by IHC) (2 extrahepatic, 1 intrahepatic) 3% 1,30%	intrahepatic cholangiocarcinoma (n=80) distal extrahepatic cholangiocarcinoma (n=126) Cholangiocarcinoma Anatomical location not systematically specified (n=100) perihilar extrahepatic (n=155) cholangiocarcinoma	FISH (pre-screening by IHC) 0 FISH (break apart) 1%	FISH (pre-screening by IHC) 1,2%	IHC 7,5%
	(66.7) (33.3) 0 (0) 0 0 0 0 11 (24.4) Voss et al (2013) Human Pathology 44: 1216-1222 0,60% 18 (40) 16 (35.6) Albrecht et al (2019) FISH (pre-screening by IHC) Virchows archiv doi: 10.1007/s00428-019-02706-6. 5,4%	22 (55) 16 (40) 2 (5) intrahepatic cholangiocarcinoma (n=67) intrahepatic cholangiocarcinoma (n=155) extrahepatic cholangiocarcinoma (n=27) gallbladder carcinoma (n=186)	14 (38.9) 22 (61.1) 0 (0)		MassARRAY (mutation) 4,5% 3,70%
	(44.4) (55.6) 0 (0) 0 NA 24 (53.3) 18 (40) 3 (6.6) Nakazawa et al (2005) J Pathol. ;206:356-365 FISH (pre-screening by IHC) 10%	25 (62.5) 15 (37.5) 0 (0) gallbladder carcinoma (n=89)	24 (66.7) 11 (30.6) 1 (2.8)		FISH (pre-screening by IHC) 0
		NA
		(55.6) NA 33 (73.3)	35 (87.5)	28 (77.8)
		(11.1) NA 5 (11.1)	0	2 (5.6)
		0 NA 0	0	0
		0 0	0	0
		0 NA 0	0	0
		0 0	0	0
		0 35.3 (19) 0 (0)	1 (2.5)	0 (0)
		(22.2) 15 0	0 (0)	0
		(11.1) 7 (15.6)	4 (10)	6 (16.7)
		(11.1) 45 (100) 4 (8.9)	4 (10)	6 (16.7)
		0 0 3 (6.7)	0 (0)	0 (0)
		5 (11.1)
		14 (31.1)
		20 (44.4)

Table S2 :

 S2 probes and protocols used for fulorescent in situ hybridization analysis

	cMET amplification	HER2 amplification
	Vysis	DAKO
	Vysis MET Spectrum Red FISH Probe	HER2 IQFISH pharmDx™
	06N05-020	K5731
	7 minutes	7 minutes
	5 minutes; 73°C	5 minutes; 85°C
	16 hours; 37°C	16 hours; 45°C
	-	Centromeric region of chromosome 17 (accorgind to manufacturer)
	spectrum red 7q31.2	218kb region encompassing HER2 gene (according to manufacturer)

Acknowledgements

This work was financially supported by AFEF (Association Française pour l'Etude du Foie).

We thank Sylvie Dumont and Fatiha Merabtene ("Plateforme d'Histomorphologie St-Antoine", Sorbonne Université, UMS30 LUMIC) for technical assistance.

Funding: This study was funded by the academic grant from AFEF (AAP 2014-R14192DD).

Conflict of Interest:

The authors declare that they have no conflict of interest.

Availability of data and material (data transparency): on supplementary data. Material is available in AP-HP, Hôpital Saint Antoine, Service d'Anatomie et Cytologie Pathologiques, 75012 Paris, France.

ALK amplification


Karima Khadir
email: kkhadir@laas.fr

Nawal Guermouche

Thierry Monteil
email: monteil@laas.fr

Amal Guittoum
email: aguittoum@laas.fr

Towards avatar-based discovery for IoT services using social networking and clustering mechanisms

Keywords: Avatars, Social Web of Things, Fuzzy c-means, IoT Services, Distributed discovery, Semantic Social Metrics Topology Scalability Semantic End- UDDI architecture WS interaction degree Centralized Undefined No Yes [10] UDDI architecture QoS Centralized No No No [11] Server/client architecture C-WOR, SOR, C-LOR, OOR, POR Centralized Undefined Yes No [12] Undefined Node centrality degree Decentralized Yes No Yes [13] [14] Server/ objects architecture personality, proactivity, locality, trust Centralized Undefined Yes No [15] [16] RESTful architecture C-WOR, SOR, C-LOR, OOR, POR Centralized Undefined No No [17] PaaS architecture C-WOR, SOR, C-LOR, OOR, POR Decentralized Undefined Cloud architecture C-WOR, C-LOR, OOR

The Internet of Things (IoT) paradigm is defined as a complex large scale and distributed, and dynamic infrastructure composed of a huge number of heterogeneous devices. Identifying particular services provided by a massive number of IoT devices remains a challenging problem. The classical centralized discovery approaches are no more suitable. In our previous work, we have proposed an avatar-based Fog-Cloud architecture to support IoT object management. The avatars are defined as virtual entities of heterogeneous IoT objects. They are endowed with reasoning capabilities that make them able to coordinate with each other to accomplish an IoT application. Through this paper, we propose to extend our previous work by a new distributed mechanism for efficient discovery of IoT services relying on Social Networking (SN) and clustering methods. This is particularly interesting in large scale IoT systems since it allows to reduce the search space so that only the neighboring social avatars most apt to participate in the collaboration to accomplish an IoT application are considered. The proposed solution has been evaluated in connected vehicles context.

I. INTRODUCTION

The Internet of Things (IoT) infrastructure interconnects a multitude of heterogeneous objects, manages their interactions, generates huge amounts of data, and provides billions of services. Over time, the IoT has suffered from a vertical fragmentation of approaches used to cover the needs of different application areas. To overcome this problem, an abstract layer, called Web of Things (WoT) [START_REF] Raggett | The web of things: Challenges and opportunities[END_REF], has been designed on top of IoT. This layer aims to hide IoT heterogeneity problems, and therefore reduces the complexity of the IoT applications development process. In this context, we have already proposed an Avatar-based Fog-Cloud architecture [START_REF] Khadir | Autonomous avatar-based architecture for value-added services provision[END_REF]. An avatar is an artifact entity that represents a heterogeneous IoT device in a uniform description on the Web. It gives to its corresponding IoT device the ability to reason on its capabilities and to make intelligent decisions using semantic Web features, called Semantic Web of Things SWoT [START_REF] Seydoux | Towards cooperative semantic computing: a distributed reasoning approach for fog-enabled swot[END_REF].

With the proliferation of IoT objects, new opportunities have been provided to build new innovative applications and scenarios. However, the IoT services discovery remains one of the challenging problems. Indeed, identifying particular IoT service among a huge number of IoT objects, required to achieve complex applications can suffer from scalability issues. In this context, using centralized approaches where the discovery is fulfilled by one entity is limited [START_REF] Jia | A centralized service discovery algorithm via multi-stage semantic service matching in internet of things[END_REF].

In this paper, we propose a new distributed IoT services discovery approach that relies on Social Networking (SN) and clustering mechanisms. Indeed, the integration of SN in the WoT domain, which gives rise to the Social Web of Things (SoWoT) [START_REF] Atzori | The social internet of things (siot)-when social networks meet the internet of things: Concept, architecture and network characterization[END_REF], seems promising. The goal is to transform smart IoT objects into smart social IoT objects and to build a set of intelligent avatars able to organize themselves to form social communities. The social avatars are linked to each other according to commitments and similarity criteria. Social Networking allows to bring out the collective intelligence of an avatars group that can provide much more interesting and precise answers to complex problems than a single avatar [START_REF] Mennis | The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations[END_REF]. More specifically, in the proposed work, when a given avatar fails to achieve a complex objective by itself, based on its cooperative intelligence capabilities, it can proceed to autonomously build its social network and requests its relations to find the underlying needed IoT services. The use of SN concepts improve the effectiveness and the scalability of the discovery process. It makes it possible to accelerate and facilitate navigability in a dynamic IoT network made up of a large number of objects. To make the discovery more efficient and to reduce the search space, a Fuzzy C-means based clustering process [START_REF] Wang | The global fuzzy c-means clustering algorithm[END_REF] is proposed to classify avatars in the social network according to their functionalities.

This article is organized as follows. Section II analyses the related works around the existing discovery methods based mainly on SNs. Then, an overtaking scenario in the context of connected vehicles is introduced in Section III. Section IV presents the proposed SN-based discovery approach. Then, the implementation and experimental results are provided in Section V. Finally, Section VI concludes the paper.

II. RELATED WORKS

The discovery problem remains one of the challenging problems of IoT based systems. If traditional discovery approaches include a large panel of scientific researches such as [START_REF] Grigori | Ranking bpel processes for service discovery[END_REF] and [START_REF] Dong | Architecting semantics-based publish/subscribe applications for the internet of things[END_REF], the social-based approaches are relatively new.

One of the first main contributions in this field is the work of Kranz and al. [START_REF] Kranz | Things that twitter: social networks and the internet of things[END_REF]. The authors propose to use human social networks as an infrastructure to allow users to share the services provided by their objects with their friends. However, this solution implies a strong human intervention and opposes the connectivity principle on which the IoT paradigm is based. In [START_REF] Maamar | Linkedws: A novel web services discovery model based on the metaphor of "social networks[END_REF], the social networking metaphor in the Web services discovery is explored. The authors were inspired by conventional human social networks such as Facebook to propose a new framework allowing the distributed discovery of Web services called LinkedWS (Linked Web Services). This model relies on capturing the different interactions that can occur between Web services to create social relationships between them accordingly. Two types of relationships are defined: collaboration or substitution. This method is interesting, however, the social relationships created between Web services are based only on the interaction degree that does not reflect the social aspects as location or interests for example. The integration of social networking in Web service discovery has been considered in [START_REF] Chen | Constructing a global social service network for better quality of web service discovery[END_REF]. This work focused only on QoS attributes and does not take into account some important social features such as the collaboration relationship between services. Atzori and al. have contributed to the use of social networking in IoT [START_REF] Atzori | Siot: Giving a social structure to the internet of things[END_REF]. They have proposed five types of social relationships which are widely used in several works such as [START_REF] Alam | tnote: A social network of vehicles under internet of things[END_REF]. These relationships include: Co-Work Object Relationship (C-WOR), Co-Location Objects Relationship (C-LOR), Ownership Object Relationship (OOR), Parental Object Relationship (POR), and Social Object Relationship (SOR). Similarly in [START_REF] Nitti | Friendship selection in the social internet of things: challenges and possible strategies[END_REF], a decentralized system based on IoT node centrality degree (number of object connections) is proposed. This method makes it possible to reduce the search space but using only the centrality degree criterion. However, using only this criterion is limited to build interesting social relations. In [START_REF] Hussein | Dynamic social structure of things: A contextual approach in cpss[END_REF], the authors have proposed a new system called DSSoT (Dynamic Social Structure of Things). DSSoT is designed to strengthen sociability and to reduce the complexity of the context detection process of a given situation for heterogeneous systems. A dynamic service discovery approach, which integrates spatio-temporal contextual information and social aspects using a cognitive reasoning mechanism, is proposed in [START_REF] Hussein | Towards a dynamic discovery of smart services in the social internet of things[END_REF]. This work illustrates the implementation of the dynamic discovery of IoT services in a real-life scenario, taking as an example an instance of an airport terminal. However, their discovery approach are carried out via a single manager which makes the approach limited, non-scalable and suffers from the single failure point problem.

Several SWoT platforms have been proposed on this issue, such as ThingSpeak and Lysis. ThingSpeak platform [15] [START_REF] Girau | Implementation of an experimental platform for the social internet of things[END_REF] has been used in several domains. It provides methods for creating and managing social relationships between IoT objects embedded in a centralized server based on object virtualization. However, the use of virtualization is limited to remote server registrations and does not provide any autonomous capabilities in terms of creating and managing social relationships that are managed at the central server level. Concerning Lysis, it provides a SWoT platform based on the Cloud infrastructure and it is deployed as a Platform-as-a-Service (PaaS) [START_REF] Girau | Lysis: A platform for iot distributed applications over socially connected objects[END_REF]. However, the end-to-end mechanisms for IoT services discovery are not addressed at all.

Table I summarizes the studied works and compares them according to six properties: Architecture: defines the system deployment architecture, Social Metrics: designates the social used metrics, Topology: indicates the nature of the systems management, Scalability: indicates whether the approach is applicable for large-scale systems, Semantic: refers to the use of rich semantic features, End-to-end process: specifies if the social network building process and the services discovery methods are supported.

Through this study, we have concluded that in the most of existing contributions that integrate social concepts into IoT, few details about architectures and social models are provided. Besides, very few approaches have focused on the methods of discovery and composition of services provided by IoT objects. Mainly, the proposed approaches ignore important features when building social networks such as interests and collaboration capabilities. Moreover, the discovery is not distributed over a set of social networks which can be highly useful in large scale infrastructures. In this context, we propose through this paper a distributed approach that integrates social networking and clustering mechanism for efficient discovery of IoT services based on autonomous avatars.

III. MOTIVATING USE CASE

In this section, a use case of vehicles overtaking assistance system is provided. The aim is to find the appropriate IoT objects which can collaborate with the target vehicle that is not equipped with all the required devices that allow it to perform the overtaking alone in an efficient manner. The corresponding process is shown in Fig. 1. The overtaking process starts when the driver puts on the left-blinking of his vehicle. The first task to do is to check if the rear vehicle (T1) or the front vehicle are overtaking (T2). Then, and if those two conditions are checked, the distance from the front vehicle is measured (T3). If this distance is greater than 30m, the overtaking is possible, therefore, the speed of the front vehicle is also measured (T4). The speed difference with the front vehicle is calculated (T5). If this difference is less than 20km/h, accelerated overtaking is performed. For that, the possible and available distance to accomplish the overtaking is measured (T6) and it is compared (T9) to the minimum safety distance required are calculated according to [START_REF] Mo | Simulation and analysis on overtaking safety assistance system based on vehicle-to-vehicle communication[END_REF] (T8). To calculate this distance, the speed of the incoming vehicle is measured (T7). We suppose that this process is carried out in the situation shown in Fig. 2: In this scenario, we assume that the target vehicle A, which wants to overtake the tractor B which rolling at low speed, doesn't have the perception mechanisms as cameras to perceive its environment. So, to perform the overtaking safely, vehicle A needs to collaborate with the devices of its environment. In such a scenario, we suppose that there are: a radar and a camera installed by the roadside, a bicycle rolling beside, the pedestrian who uses a smartphone, a traffic light a few meters from host vehicle, the drivers of A, B and C have smartphones. A Global Navigation Satellite System (GNSS) is also available for vehicles tracking and an RSU (Road-Side Unit) which covers the region in which the host vehicle is located. We suppose also that the travel speed of the host, front, rear and oncoming vehicles is constant.

IV. AVATARS DISCOVERY PROCESS

The avatars discovery problem handled is described as follows: given an abstract process which models an IoT application that an avatar, which we call initiator, cannot perform alone, and a set of available avatars, the goal is to enable the initiator to discover in a distributed way a coalition of avatars that can collaborate with it to resolve the process. This approach is summarized in five steps (Fig. 3): 1) handling the abstract definition of avatars and the process to achieve, 2) building the social network of avatars, 3) social network clustering to classify its avatars according to their functionalities, 4) propagating and managing requests, and 5) composition plan construction. Hereafter, we detail each step.

A. Avatar and process definition

This section gives the definition of an avatar and the process that characterizes the objectives to realize.

1) Avatar definition: an avatar is modeled by a set of functionalities

f i = (f 1 i , f 2 i , .
., f p i ) and services S i = (s 1 i , s 2 i , .., s q i ) that it provides. These functionalities represent a generic description of what the services can achieve. It also characterized by a social context which is described by a set of semantic metadata published in its corresponding regional repository to allow it to build its social links. The social descriptions of an avatar A i include: 1) a set of interest quantified with an interest degrees that are modeled with a vector I i = (I 1 i , I 2 i , .., I m i ) of m dimensions (0 < I p < 1, p in [0,m]), 2) a location defined with the longitude and latitude of the represented object (LongA i ,LatA i ), and 3) the identifier of the owner.

2) Process definition: the process to be resolved is made up of several composite or/and atomic tasks. It is designed as a workflow. It is modeled by a vector T req = {T 1 , T 2 , ..., T n }, such as T k is its k th task. The tasks are interconnected with the sequential, parallel, loop, and Conditional operators.

Example: Table II gives an overview of the set of candidate avatars A i , their services S ij for each abstract task of the overtaking process T j , such as for each service S ij , the avatar A i can perform the abstract task T j . The set of interests depicted in the table are: I 1 is Transport, I 2 is Weather, I 3 is Perception, I 4 is Obstacle, I 5 Communication, I 6 is Luminosity, I 7 is Geolocalization and I 8 is Circulation. The set of functionalities are: f 1 is the environment perception, f 2 is the distance measure, f 3 is the speed measure, f 4 is the computing capacity, f 5 is the moving and f 6 represents the communication.

B. Social network building

A tree of connected regional repositories in which the avatars publish their semantic descriptions allows to manage the system scalability. So, the avatar begins by recovering the avatars that have at least one common interest by executing the following SPARQL query on the corresponding regional repository to which it is connected.

SELECT * WHERE {

? r e s o u r c e a v a t a r O n t : h a s I n t e r e s t ? I n t e r e s t FILTER ( ? I n t e r e s t IN ( a v a t a r O n t : I1 , . . . , a v a t a r O n t : Im ) ) } Once this list is recovered, the avatar calculates social relationships with each avatar on this list using three relationships: 1) Co-work Object Relationship (C-WOR): it binds the avatars that can collaborate with each other to accomplish a given work. The C-WOR between two avatars A i and A j , denoted CS(A i ,A j ), is calculated using their respective interests vectors I i and I j and based on cosine similarity metric [START_REF] Mei | Social-aware stateless forwarding in pocket switched networks[END_REF] as given in Equation 1:

CS(A i , A j ) = cos(I i , I j ) = I i .I j | I i | . | I j | (1)
Where | I i | and | I j | are the norms of I i and I j respectively and 0 < CS(A i , A j ) < 1 such that the value 0 denotes that A i and A j are independent and 1 indicates that they are similar.

2) Co-Location Objects Relationship (C-LOR): it groups the avatars whose physical objects are located in the same location. The C-LOR between two avatars A i and A j , denoted LS(A i ,A j ), is calculated relying on their GPS (Global Positioning System) coordinates using the distance as-the-crow-flies as given in Equation 2:

LS(A i , A j ) = 1 D(A i , A j ) + 1
(2)

D(A i , A j ) = R * acos[cos(LatA i ) * cos(LatA j ) * cos(LongA j -LongA i )sin(LatA i ) * sin(LatA j )] (3)
Where: R = 6371Km is the Earth radius, (LatA i ) and (LatA j ) are the decimal latitudes of A i and A j , and (LongA i ) and (LongA j ) their longitudes.

3) Ownership Object Relationship (OOR): it groups the avatars that belong to the same owners. It is calculated between A i and A j as given in Equation 4:

OS(A i , A j ) =
1 if A i and A j have the same owner 0 else (4)

4) Synthesis: based on the above relationships, the social distance between two avatars A i and A j is modeled by a function SD(A i , A j ) in [0,1] as given in Equation 5:

SD(A i , A j ) = α * CS(A i , A j ) + β * LS(A i , A j )+ γ * OS(A i , A j ) (5)
Weighting coefficients α, β, γ ∈ [0, 1] with α+β+γ=1 represent the preferences and the Weight given for each distance. They are defined by the experts for each type of application.

C. Neighboring avatars clustering

After the construction of its social network, the initiator avatar proceeds to the clustering of its neighboring avatars according to their functionalities to make the discovery more efficient by transmitting the requests to the appropriate avatars as well as possible. The Clustering [START_REF] Grira | Unsupervised and semisupervised clustering: a brief survey[END_REF] is defined as a process of partitioning a set of data points into homogeneous clusters. In our work, we rely on the Fuzzy C-means algorithm because an avatar can provide various functionalities and therefore it can belong to several clusters. The main idea of this algorithm is to use membership coefficients that designate the belonging degree of each avatar to each cluster. It is carried out using an iterative optimization of an objective function, with the updating of the membership coefficients and cluster centroids which are closely related. Algorithm 1 presents the Fuzzy C-means clustering procedure where N is the number of avatars and X is the data matrix. Each line of X represents the functionality vector of an avatar in a binary form: the component is worth 1 if the avatar can perform the corresponding functionality and 0 otherwise. m represents the fuzzification parameter of the algorithm and Dist is the distance to use to measure the similarity between centroids and avatars as the euclidean, standard Euclidean, manhattan, and camberra distance.

Algorithm 1 Avatars Fuzzy C-means Algorithm

Inputs: number of clusters C, avatars data matrix X=[x ij ] 1. Initialize randomly the membership coefficients matrix U=[u ij ], U (0) Such as:

C j=1 u ij = 1 2. Calculate the centroid C j of each cluster j C j = N i=1 [u ij ] m x ij N i=1 [u ij ] m (6) 3. Update the current membership matrix U (k) , U (k+1) u ij = 1 C z=1 ( Dist(Xi,Cj ) Dist(Xi,Cz) ) 2 m-1 (7)
If ( N i=1 C j=1 [u ij ] m Dist(X i , C j ) 2 < ε) Then Stop Else Return to step 2.
After obtaining the final membership matrix, a membership threshold is used to define the final clusters, i.e. an avatar belongs to a given cluster only if its membership degree is greater than this threshold which is generally between 0.4 and 0.7 to obtain good results according to our experiments.

D. Discovery, selection and propagation

Once the social network clusters are built, an elected avatar is designated in each cluster. The delegate allows to handle requests sent to its cluster to discover the appropriate avatars. The aim is to propagate requests over different networks to enable distributed discovery while minimizing the redundancy of messages exchanged between avatars. After that, the initiator proceeds to the discovery of the required services for each task of the abstract process. For this, it sends a request to the elected avatar responsible for the functionality expressed in the task. The elected will take charge of searching for the requested service in its cluster. As soon as a service is found, it will be selected to accelerate the discovery. However, if no service is found at the cluster level, the elected avatar propagates the request by creating its own social network and designates an elected avatar in the new social network to perform the research. The propagation can continue up to sixth levels, inspiring from the effect of the small world of social networks in sociology [START_REF] Travers | An experimental study of the small world problem[END_REF].

E. Execution plan generation

Whenever a service is selected, two new triplets are added to the process description: a triplet to indicate the chosen avatar for the task [task:T k ] [isRealizedBy] [Avatar:A i ], and another to indicate the service to invoke [task:T k ][isExecutedBy][Service:S k ] to build the final composition execution plan. This plan will be shared by the initiator with the avatars involved in the composition. Example: For the example, the vehicles, the RSU, the GNSS, and the different devices are associated with their avatars in the regional-edge server. Once the left-blinking is put ON, the avatar A 1 starts by reasoning on its knowledge base. It finds that it can perform the tasks: T 3 ,T 4 ,T 5 ,T 8 and T 9 , but it can't perform the tasks T 1 ,T 2 ,T 6 ,T 7 (Perception, Distance Measure, and Speed Measure). Consequently, A 1 starts with discovering the avatars that have at least one interest in common with it from the server. After that, it builds its social network. For this, we set the application preferences to α=0.4, β=0.5, and γ=0.1 given the importance of interest and localization in such a scenario of connected vehicles. Taking a social network membership threshold equal to 0.6 and after calculating social relationships, we find that the avatars who have relationships with A 1 are: A 2 ,A 3 ,A 4 ,A 5 ,A 6 ,A 7 ,A 8 ,A 9 and A 12 . The result of clustering of these avatars according to their functionalities is: Cluster 1 that corresponds to Perception contains A 4 ,A 2 ,A 8 ,A 5 , Cluster 2 that represents Distance Measure contains A 7 ,A 12 ,A 9 ,A 6 and Cluster 3 that represents Speed Measure contains A 3 ,A 7 ,A 4 ,A 2 . The avatar which has the closest distance to the centroid of its cluster is elected. Therefore, A 4 is the elected of Cluster 1, A 7 is the elected of Cluster 2 and A 3 represents the elected of Cluster 3. So, for T 1 and T 2 (which implies the perception functionality), A 1 sends a request to the elected A 4 which takes care of finding the services responding to T 1 and T 2 in Cluster 1. It finds that T 1 can be performed by itself and T 2 by A 2 . Likewise, A 1 sends a request for T 6 , T 7 to the elected A 7 , and A 3 respectively. For T 6 no avatar of Cluster 2 can perform it and for T 7 , it can be performed directly by the elected A 3 . Therefore, the only task that cannot be solved at the first social level is T 6 . A 7 is responsible for researching this task by creating its social network which will contain A 6 , A 10 , A 8 , and A 3 . A 6 will be the elected of this social network to seek to resolve T 6 . This avatar finds that A 10 can perform this task. From our simulations, the response time for this scenario is 30ms.

V. APPROACH EVALUATION

In this section, we first describe the setup environment and the main technical choices. After that, the performance and the scalability of the proposed approach are evaluated. The avatars implementation is done using microservices components with Spring boot, Apache HTTP Components for communication, and XML format for transporting exchanged messages. The semantic descriptions is based on OWL, SPARQL and Apache JENA.

B. Experimental results

A study for the three principals stages of our approach is performed. Then a global performance study based on two criteria: response time and success rate is presented. The test execution environment is a Ubuntu Server with 251GB RAM and AMD Opteron multiprocessor.

1) Avatar Semantic data generation: the graph in Fig. 4 represents the variation of the generation step time which includes semantic data distribution, semantic file creation, and metadata publication. This step takes a considerable response time given the use of rich semantic descriptions but it is done only once at the startup time of the system.

2) Social network building: the graph in Fig. 5 represents the variation of social network building time. The results show that this stage, in addition to its impact on reducing the discovery search space, has a reduced response time even for a large scale of avatars (about 3s for 7000 avatars). According to graphs in Fig. 6, Fig. 7 and Fig. 8, we can confirm that the distance which can offer good clustering quality, as well as optimal clustering time, is Standard Euclidean distance, the adequate value of the fuzzifier is 2 and the good epsilon value is 0.1 . In Fig. 9, a performance evaluation of the chosen configuration using previous fixed Fuzzy C-means parameters shows that the clustering method has a reduced response time even with a large number of avatars (5s for 7000 avatars).

4) Discovery process: The graphs in Fig. 10 and Fig. 11 present a comparative study between our approach and the centralized approach which excludes clustering and social network construction. The considered parameters are: tasks number setting to 12, social network size setting to 40 avatars, α=0.4,β=0.4,δ=0.2 and cluster size to 10 avatars. 10 shows that the two methods give approximately the same results for small avatars networks (less than 500) while guaranteeing the same success rate as depicted in Fig. 11. But for big size-network (greater than 500 avatars), our approach outperforms the centralized approach while guaranteeing a high success rate.

VI. CONCLUSION

In this paper, we have proposed a new efficient distributed approach based on avatars for IoT services discovery. This approach relies on social networking to reduce the research space and clustering mechanisms to enable an efficient distributed request discovery. The proposed solution is particularly interesting in the context of complex systems with a huge number of heterogeneous objects. The proposed work has been implemented into a microservice architecture to support a generic framework for interoperability and modularity. Using this architecture, several experimental studies have been carried out on a large scale of avatars. The results show that our approach outperforms the traditional centralized approach, particularly when the number of devices is huge.

We plan to extend the proposed approach by integrating QoS (Quality of Service) parameters for distributed and parallel selection of the IoT services discovered in this stage.

Fig. 1 .

 1 Fig. 1. The process of overtaking use case

Fig. 2 .

 2 Fig. 2. Overtaking use case situation

Fig. 3 .

 3 Fig. 3. An overview of the proposed approach

Fig. 4 .

 4 Fig. 4. Semantic data generation time according to the avatars number

Fig. 6 .

 6 Fig. 6. Clustering quality variation as a function of both distance and fuzzifier

Fig. 8 .Fig. 9 .

 89 Fig. 8. Variation of clustering quality and time according to epsilon values

Fig. 10 .

 10 Fig. 10. Discovery time variation according to avatars number

TABLE II CANDIDATE

 II AVATARS SERVICES OF EACH ABSTRACT TASK

	Avatar		Services	Interests	Location	Owner Features
	A1: Vehicle A	S13,S14,	I1(0.6),I2(0.2),	43.57834	User1	f2,f3, f4,
			S15,S18,	I4(0.6)	1.441185		f5,f6
			S19
	A2: Vehicle B	S22,S23,	I1(0.9),I2(0.3),	43.578291	User2	f2,f3, f4,
			S24	I3(0.5),I4(0.6)	1.441146		f5,f6
	A3: Vehicle C	S37	I1(0.9),I2(0.3),	43.578074	User3	f2,f3, f4,
				I3(0.4),I4(0.5)	1.441052		f5
	A4: Vehicle D	S41	I1(0.9),I2(0.3),	43.578074	User4	f2,f3, f4,
				I3(0.4),I4(0.5)	1.441052		f5,f6
	A5: Bicycle	/	I1(0.9)	43.578315	User5	f5,f6
					1.441277
	A6: RSU	/	I1(0.7),I5(0.7),	43.577163	User6	f5,f6
				I7(0.5)	1.440768
	A7: Radar	S77	I1(0.7),I4(0.9) 43.57827	User6	f2,f3, f5
					1.4412062
	A8: Camera	S81,S82	I1(0.6),I2(0.4),	43.578405	User6	f1,f2, f5
				I3(0.9),I4(0.7)	1.441300
	A9:	Traffic	/	I1(0.7),I8(0.9) 43.577899	User6	f5
	light				1.440873
	A10: GNSS	S10,1,S10,2	I1(0.8),I4(0.7)	Server	User7	f1,f2, f3,
			S10,3,S10,4	I7(0.9)			f5
			S10,6,S10,7
	A11:	Road	/	I1(0.2),I4(0.3)	43.57822	User6	f5
	lighting			I6(0.9),I8(0.4)	1.441251
	A12:		/	I1(0.4),I5(0.9)	43.57834	User1	f6
	Smartphone A		I7(0.7)	1.441185
	A13:		/	I1(0.4),I5(0.9)	43.578291	User2	f6
	Smartphone B		I7(0.7)	1.441146
	A14:		/	I1(0.4),I5(0.9)	43.578074	User3	f6
	Smartphone C		I7(0.7)	1.441052
	A15:		/	I1(0.4),I5(0.9)	43.578332	User8	f6
	Smartphone X		I7(0.7)	1.441082

ACKNOWLEDGMENT

This work is funded by Continental Digital Service France (CDSF) in the framework of the eHorizon project.