Paul Mclellan

MD Julie Henriques

PhD Feryel Ksontini

MD Solène Doat

MD Pascal Hammel

PhD Jérome Desrame

PhD Isabelle Trouilloud

MD Christophe Louvet

MD Daniel Pietrasz

MD Dewi Vernerey

PhD Jean-Baptiste Bachet
email: jean-baptiste.bachet@aphp.fr

Prognostic value of early change in neutrophil to lymphocyte ratio in metastatic pancreatic adenocarcinoma Running title: Neutrophil-lymphocyte ratio in pancreatic cancer

Keywords: neutrophil to lymphocyte ratio, prognostic, metastatic pancreatic cancer, biomarker Highlights -

High neutrophil-lymphocyte (NLR) at diagnosis is a marker of poor prognosis in metastatic pancreatic adenocarcinoma. Prognosis role of baseline NLR and its early change and NLR evolution under first-line chemotherapy was determined. We conducted a retrospective study based on one prospective cohort from a single center and a randomized open-label multicenter randomized trial. Two hundred and twelve patients were analyzed. Baseline NLR >5 was an independent poor prognosis biomarker for overall survival (HR=2.01, 95%CI 1.33-3.05; P=0.001) and for progression-free survival (HR=1.80, 95%CI 1.23-2.65; P=0.0026). According to NLR dynamics (n=172), patients with NLR ≤5 on days 1 and 15 had a significantly better prognosis than those with NLR ≤5 on day 1 and >5 on day 15 (HR=2.23, 95%CI 1.18-4.21; P=0.013), NLR >5 on day 1 and ≤5 on day 15 (HR=3.25, 95%CI 1.86-5.68; P<0.001), and NLR >5 on days 1 and 15 (HR=3.37, 95%CI 1.93-5.90; P<0.001). Over time, "bad responder" (progression-free survival <6 months) had a significantly higher mean NLR than "good responder" (group effect P<0.0001). Seven in 8 patients with baseline NLR >5 had circulating tumor DNA. We confirm the independent prognostic value of baseline NLR >5 in metastatic pancreatic cancer. NLR evolution is also a prognosis indicator in patients with NLR ≤5.

Background

Pancreatic adenocarcinoma (PAC) is a severe disease and median overall survival (OS) in patients with metastatic disease is under 12 months. (1,[START_REF] Conroy | FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer[END_REF] The 1-year OS rate is about 30% and decreases at 7% after 5 years. Latest outcome figures confirm increase in the incident rate and prevalence of PAC in western countries.(3) PAC is the fourth cause of cancer deaths in the United States regardless of gender. [START_REF] Siegel | Cancer statistics, 2016: Cancer Statistics[END_REF] First-line treatment of patients with metastatic PAC is based on chemotherapy such as FOLFIRINOX (oxaliplatin, irinotecan, 5-fluorouracil, and leucovorin) [START_REF] Conroy | FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer[END_REF] or on the combination of gemcitabine and nab-paclitaxel in those with good Eastern Cooperative Oncology Group Performance Status (ECOG PS). [START_REF] Goldstein | nab-Paclitaxel plus gemcitabine for metastatic pancreatic cancer: long-term survival from a phase III trial[END_REF][START_REF] Bachet | A randomized phase II study of weekly nab-paclitaxel plus gemcitabine or simplified LV5FU2 as first-line therapy in patients with metastatic pancreatic cancer: the AFUGEM GERCOR trial[END_REF] In PAC frail patients (ECOG PS >2), unfit to support polychemotherapy, gemcitabine monotherapy or best supportive care are the standard. [START_REF] Burris | Improvements in survival and clinical benefit with gemcitabine as first-line therapy for patients with advanced pancreas cancer: a randomized trial[END_REF] Tumour markers such as ECOG PS 2, age > 65 years old, liver metastasis, [START_REF] Tabernero | Prognostic factors of survival in a randomized phase III trial (MPACT) of weekly nabpaclitaxel plus gemcitabine versus gemcitabine alone in patients with metastatic pancreatic cancer[END_REF] increase in lactate dehydrogenase (LDH), carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA 19-9) levels have been defined as poor prognostic factors. [START_REF] Tas | Serum levels of LDH, CEA, and CA19-9 have prognostic roles on survival in patients with metastatic pancreatic cancer receiving gemcitabine-based chemotherapy[END_REF] CA 19-9, is currently the only FDA-approved biomarker used in PAC, but it has several limitations including lack of specificity. This protein can be elevated in many situations such as cholestasis or others cancers, and can be normal in patients with Lewis negative genotype, representing about 5%-10% of the white population even in the advanced setting. [START_REF] Galli | CA 19-9: handle with care[END_REF][START_REF] Hamada | Investigation of unexpected serum CA19-9 elevation in Lewis-negative cancer patients[END_REF] Systemic inflammation is known to promote cancer and metastasis development. [START_REF] Wu | Inflammation: a driving force speeds cancer metastasis[END_REF][START_REF] Grivennikov | Immunity, inflammation, and cancer[END_REF] The role of inflammation and immune response within the tumour and its microenvironment is discussed. [START_REF] Feig | The pancreas cancer microenvironment[END_REF] The tumour stroma in PAC seems to play a key role in providing drug resistance to immune participation by antigenic tumoral presentation.

Neutrophils represent the majority of white blood cells and participate to anti-tumoral immunity and metastatic spreading. [START_REF] Casanova-Acebes | Neutrophils instruct homeostatic and pathological states in naive tissues[END_REF] Several tumour biomarkers for PAC have been evaluated such as neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio, fibrinogen, albumin, and C-reactive protein (CRP). [START_REF] Bhatti | Preoperative hematologic markers as independent predictors of prognosis in resected pancreatic ductal adenocarcinoma: neutrophil-lymphocyte versus platelet-lymphocyte ratio[END_REF][START_REF] Mroczko | Diagnostic usefulness of serum interleukin 6 (IL-6) and C-reactive protein (CRP) in the differentiation between pancreatic cancer and chronic pancreatitis[END_REF][START_REF] Wu | The C-reactive protein/albumin ratio predicts overall survival of patients with advanced pancreatic cancer[END_REF][START_REF] Oh | Prognostic Roles of Inflammatory Markers in Pancreatic Cancer: Comparison between the Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio[END_REF] A high NLR has been reported as a poor prognosis marker in PAC, [START_REF] Tabernero | Prognostic factors of survival in a randomized phase III trial (MPACT) of weekly nabpaclitaxel plus gemcitabine versus gemcitabine alone in patients with metastatic pancreatic cancer[END_REF] but also in various solid cancers. [START_REF] Zhao | Prognostic role of neutrophil to lymphocyte ratio in lung cancers: a meta-analysis including 7,054 patients[END_REF][START_REF] Liu | Prognostic role of pretreatment neutrophil to lymphocyte ratio in breast cancer patients: A meta-analysis[END_REF][START_REF] Chen | Neutrophil-to-Lymphocyte Ratio Is a Potential Prognostic Biomarker in Patients with Ovarian Cancer: A Meta-Analysis[END_REF] In resectable PAC, it is associated with a higher risk of recurrent metastatic disease and short OS. [START_REF] Bhatti | Preoperative hematologic markers as independent predictors of prognosis in resected pancreatic ductal adenocarcinoma: neutrophil-lymphocyte versus platelet-lymphocyte ratio[END_REF] In patients with metastatic PAC, the results of metaanalysis have demonstrated that high values of NLR at diagnosis, ranging from 2.5 to 5, predict poorer OS. [START_REF] Yang | Prognostic significance of neutrophil to lymphocyte ratio in pancreatic cancer: A meta-analysis[END_REF] NLR was shown to be a more accurate prognosis marker than the platelet-lymphocyte ratio in PAC resectable tumours. [START_REF] Bhatti | Preoperative hematologic markers as independent predictors of prognosis in resected pancreatic ductal adenocarcinoma: neutrophil-lymphocyte versus platelet-lymphocyte ratio[END_REF] To our knowledge little is known about NLR changes under chemotherapy. Our objective was to analyse a NLR evolution pattern in patients receiving first-line metastatic PAC treatment and to assess the impact of the NLR dynamic evolution on prognosis in this setting.

METHODS

Patients

We performed a retrospective analysis of patients derived from two different prospective cohorts. The first single-centre cohort consisted of consecutive patients who received firstline metastatic PAC treatment at Pitié-Salpêtrière Hospital (the GHPS cohort) from January 2010 to August 2016. Inclusion criteria were cytological or histologically confirmed PAC, metastatic disease, age ≥18 years, and signed consent for use of clinical and biological information. These patients have been included in a prospective translational study (approved by ethics committee) assessing the prognostic value of circulating tumour DNA (ctDNA). (24) The second cohort consisted of patients included in the French open-label, multicentre, randomized phase II AFUGEM trial (clinicaltrials.gov NCT01964534) comparing gemcitabine and nab-paclitaxel to LV5FU2 and nab-paclitaxel. [START_REF] Bachet | A randomized phase II study of weekly nab-paclitaxel plus gemcitabine or simplified LV5FU2 as first-line therapy in patients with metastatic pancreatic cancer: the AFUGEM GERCOR trial[END_REF] The study protocol was approved by the French ethics committee "Ile de France VI". All patients provided written informed consent before study enrolment. Studies protocol were conformed to the ethical guidelines of the 1975 Declaration of Helsinki (6th revision, 2008) as reflected in a priori approval by the institution's human research committee.

Data collection

For the GHPS cohort, all clinical, pathological and biological data were collected from patient medical records. These included the following: age at diagnosis, ECOG PS at diagnosis, tumour location, tumour differentiation grade, prior history of surgery, number of metastatic sites and location, dosages at diagnosis of albumin, platelets, CEA, CA 19-9 within 15 days before chemotherapy initiation, chemotherapy data (type of regimen, date of the first and last cycle, reasons for treatment interruption), the date of the last assessment and the date of death.

For the AFUGEM cohort, clinical, pathological, and biological data were prospectively collected in the electronic case report form as previously described. [START_REF] Bachet | Nabpaclitaxel plus either gemcitabine or simplified leucovorin and fluorouracil as first-line therapy for metastatic pancreatic adenocarcinoma (AFUGEM GERCOR): a noncomparative, multicentre, open-label, randomised phase 2 trial[END_REF] In the both cohorts, white blood count including neutrophil and lymphocyte count in units/mm 3 were collected every 15 days during the first 2 months of treatment (on days 1, 15, 30, 45, and 60) and on days 120 and 180, or at progression if it occurred before day 180. The laboratory tests were performed within 4 days before chemotherapy. Each patient went to the same laboratory during the follow-up. NLR was calculated by dividing the absolute neutrophils count by the absolute lymphocytes counts as previously described. [START_REF] Bhatti | Preoperative hematologic markers as independent predictors of prognosis in resected pancreatic ductal adenocarcinoma: neutrophil-lymphocyte versus platelet-lymphocyte ratio[END_REF] Progression was defined radiologically according to the RECIST 1.1 criteria or clinically if stopping treatment due to altered general status or death. Progression free-survival (PFS) was measured from the first chemotherapy administration to the date of progression or death from any cause, whichever occurred first. OS was defined as the time between first chemotherapy administration and death (all causes). Patients alive were censored at the last follow-up.

Statistical analysis

Patients' characteristics at baseline were compared between the two cohorts and between patients with and without NLR at baseline. Median with interquartile range (IQR) and frequencies with percentage were used to describe continuous and categorical variables, respectively that were compared by the Wilcoxon test and Chi-square tests. The final analysed study population consisted of patients who had NLR baseline data.

Patients were categorized into high NLR at baseline group and a low NLR at baseline group using the restricted cubic spline method to define the optimal cut-off value of baseline NLR.

Survival curves and follow-up were estimated using the Kaplan-Meier and reverse Kaplan-Meier methods, respectively, described with median and 95% confidence interval (95% CI) and compared with log-rank test. Hazard ratios (HRs) with 95% CIs were estimated with Cox proportional hazard models. Association between baseline characteristics including NLR at baseline and survivals was assessed with the univariate Cox model. Variables with P-value < 0.1 were investigated in a multivariate analysis with a stepwise selection. The proportional hazards assumption was checked graphically by plotting a log-minus log plot of the survival and the correlation between variables.

In order to assess the dynamic change of NLR under first-line chemotherapy, patients were categorized into four groups according to NLR at baseline (day 1) and on day 15: group 1/ NLR under the cut-off value on days 1 and 15, group 2/ NLR under the cut-off value on day 1 and above the cut-off value on day 15, group 3/ NLR above the cut-off value on day 1 and under the cut-off value on day 15, and group 4/ NLR above the cut-off value on days 1 and 15. Both OS and PFS were assessed in all four groups.

For long term NLR follow-up, two groups of patients were defined: a group of "good responders" with PFS 6 months and a group of "bad responders" with PFS <6 months.

Median NLR presented with IQR was compared at each date using the Wilcoxon test.

Evolution of NLR over time and across groups was estimated with a repeated measures mixed model. Interaction between groups and time was tested.

All analyses were replicated separately in both cohorts to assess robustness of the results.

The database of the AFUGEM trial and the prospective cohort were locked for analysis on September 2016 and December 2016, respectively.

Next-generation sequencing was used for ctDNA analysis as previously described. (24) An exploratory analysis of the correlation between the presence of ctDNA and NLR was performed. The association between the presence of ctDNA and survivals in patients with low NLR was assessed.

All analyses were performed using SAS version 9.3 software (SAS Institute, Cary NC) and R version 3.4.3 software (R Development Core Team, Vienna, Austria; https://www.rproject.org).

All tests were two-sided and P values below 0.05 were considered statistically significant

RESULTS

Population of interest

A total of 259 patients with proven metastatic PAC were selected from the GHPS cohort (n=145) and the AFUGEM trial (n=114; Figure 1). In the GHPS cohort, patients had significantly more ECOG PS scores of 2 or 3, had more tumours with head location, had more often presented with a prior history of surgery, had more frequently administered adjuvant chemotherapy, had fewer liver metastasis, and had the higher incidence of low CA 19-9 levels (Table A.1). OS and PFS were similar between the two cohorts (Figures A.1 and A.2). We then pooled together the data from two prospective cohorts into a single cohort study (n=259).

Of the 259 analysed patients, 212 (81%) had NLR at baseline; 127 (88%) in the GHPS cohort and 85 (75%) in the AFUGEM cohort. Characteristics and survival of patients whose NLR was missing were comparable to those whose NLR was available (Table A.2 and Figures A. 3 and A.4). The prognostic value of NLR analysis was performed on data from 212 patients excluding those whose NLR was missing at baseline. In GHPS cohort, ten patients with ECOG PS 3 due to symptoms relative to the disease and without comorbidity began a palliative chemotherapy. These patients were younger than patients with ECOG PS 0-2 (66.8 vs 69.4 years).

Determination of the NLR cut-off value

We used restricted cubic spline method to define the relation between NLR and OS. There was an increased risk of death until NLR was equal to 5 and then stabilization was observed (Figure A.5). We assumed that a baseline NLR value of 5 was a potential cut-off value for metastatic PAC patients. Therefore, this threshold was chosen for subsequent analyses.

Overall, 50 (24%) patients had NLR >5 at baseline.

Prognostic value of NLR at baseline

We compared patients with low NLR 5 (n=162) to those with high NLR >5 (n=50) at baseline. The two groups were comparable in terms of sex, tumour differentiation grade, and number of metastatic sites. Patients in the high NLR group had statistically poorer ECOG PS, presented less frequently a history of surgical resection, and had more often lower albumin and increased CEA levels (Table 1 A.9). Among patients with ECOG PS 3 at baseline, the 6 patients with a NLR >5 died before two months whereas 2 of the 4 patients with a NLR 5 were alive at 6 months.

Univariate and multivariate analysis for OS and PFS at baseline

In univariate analysis, poor prognosis factors for OS were ECOG PS 2-3 (P<0.0001), body and tail tumour location (P=0.0022), age ≥65 years (P=0.02), more than three metastatic sites (P=0.0066), CEA ≥8 (P=0.0018), CA 19-9 ≥1000 UI/ml (P<0.001), and NLR >5 (P<0.0001).

Factors associated with better prognosis were previous history of primary tumour resection (P=0.0012), well-differentiated tumour (P=0.001), and albumin level ≥40 g/L (P=0.0005; Table 2).

In multivariate analysis, NLR >5 at baseline was an independent poor prognosis biomarker for OS (HR=2.01, 95% CI 1.33-3.05; P=0.001; Table 2) and for PFS (HR=1.80, 95% CI 1.23-2.65; P=0.0026; Tables A.3 and A.4).

Prognostic value of early change of NLR

NLR data at baseline (day 1) and on day 15 were available for 171 patients. Patients with NLR ≤5 on day 1 and on day 15 (n=125) had significantly better prognosis compared to those with NLR >5 at one or two dates (Figure 2). Consistent results were found for PFS (Figure A.10). Patients with the worst prognostic were those with NLR >5 at baseline and on day 15.

Results were unchanged after exclusion of the 10 patients with ECOG PS 3 at baseline (Figures A.11 and A.12).

NLR evolution over time

NLR differences were analysed at each date in the group of "good responders" with PFS 6 months (n=82) and in the group of "bad responders" (n=121). "Bad responders" had a significantly higher median NLR at all dates than "good responders", except that on day 45. The evolution of NLR over time showed a significant group effect (P<0.0001), no significant time effect (P=0.1031), and nor interaction between both groups (P=0.1252; Table 3 andFigure 3).

Analyses in each study cohort

Analyses were replicated in both cohorts separately (Tables A. [START_REF] Goldstein | nab-Paclitaxel plus gemcitabine for metastatic pancreatic cancer: long-term survival from a phase III trial[END_REF] to A.8). Patients with NLR ≤5 at baseline and on day 15 had longer survival than other patients in both cohorts (Figure A.13 to A.17). Results were unchanged when analysed in the fluoropyrimidine plus nabpaclitaxel arm of the AFUGEM trial alone (data not shown).

NLR correlation with ctDNA

ctDNA data before first-line chemotherapy was available for 52 patients in the GHPS cohort.

The ctDNA was more often detected in patients with NLR>5 at baseline (7/8, 87%) than in those with NLR ≤5 (22/40, 55%). In patients with NLR ≤5, the presence of ctDNA was associated with shorter PFS and OS (Figure A.18 and A.19).

DISCUSSION

In this study, we confirmed that high NLR (>5) at baseline is an independent prognostic biomarker of OS and PFS in patients treated in first-line for metastatic PAC. Moreover, NLR dynamic during the first 15 days of treatment also appears to be a prognostic biomarker in patients with NLR ≤ 5. Depending on PFS, the mean NLR was higher at each date for patients with poor prognosis. Therefore, NLR increasing over time appears to be a prognostic biomarker.

High NLR is known to be associated with poor OS in various solid cancers such as lung, [START_REF] Zhao | Prognostic role of neutrophil to lymphocyte ratio in lung cancers: a meta-analysis including 7,054 patients[END_REF] breast cancer, [START_REF] Liu | Prognostic role of pretreatment neutrophil to lymphocyte ratio in breast cancer patients: A meta-analysis[END_REF] or ovarian. [START_REF] Chen | Neutrophil-to-Lymphocyte Ratio Is a Potential Prognostic Biomarker in Patients with Ovarian Cancer: A Meta-Analysis[END_REF] In 2014, a meta-analysis of 100 studies comprising more than 40 000 patients confirmed the prognosis role of high NLR in gastro-oesophageal cancers, cholangiocarcinoma, hepatocellular cancer, colorectal cancer, renal cell carcinoma, and non-small cell lung cancer. [START_REF] Templeton | Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis[END_REF] In PAC, NLR is also useful in patients with advanced PAC treated by chemoradiotherapy alone [START_REF] Lee | The Neutrophil-Lymphocyte Ratio and Platelet-Lymphocyte Ratio Are Prognostic Factors in Patients with Locally Advanced Pancreatic Cancer Treated with Chemoradiotherapy[END_REF] or in those receiving chemoradiotherapy before curative surgery.( 28)

The optimal NLR cut-off value of 5 in our study is consistent with that of previous studies. [START_REF] Goldstein | nab-Paclitaxel plus gemcitabine for metastatic pancreatic cancer: long-term survival from a phase III trial[END_REF] Nevertheless, in PAC, several NLR thresholds have been reported. In a recent meta-analysis the cut-off values for elevated NLR were not consistent and ranged from 2.5 to 4, so it did not provide the most optimal value to be used. [START_REF] Yang | Prognostic significance of neutrophil to lymphocyte ratio in pancreatic cancer: A meta-analysis[END_REF] In our study, low ECOG PS and albumin, and high CEA levels were correlated with high NLR. These factors are known to be associated with poor prognosis in metastatic PAC. The prognostic potential of the systemic inflammation-based markers in PAC is still unclear.

PAC is known for high inflammation not only in the tumour's stroma and microenvironment, but also on a systemic level. We may hypothesize that the severity of systemic response reflects aggressiveness of the tumour microenvironment. An elevated NLR may originate from raised neutrophil or decreased lymphocyte counts. PAC microenvironment was proven to induce tumour-associated neutrophils, which promotes metastatic invasion. [START_REF] Bekes | Tumor-recruited neutrophils and neutrophil TIMP-free MMP-9 regulate coordinately the levels of tumor angiogenesis and efficiency of malignant cell intravasation[END_REF] Neutrophils can induce angiogenesis and suppress anti-tumour activity as such allowing tumour growth. They also produce or release various chemokines (including VEGF), metalloprotease, and reactive oxygen species that play a key role in tumour vascular development and migration. [START_REF] El-Hag | Immunosuppression by activated human neutrophils. Dependence on the myeloperoxidase system[END_REF][START_REF] Scapini | CXCL1/macrophage inflammatory protein-2-induced angiogenesis in vivo is mediated by neutrophil-derived vascular endothelial growth factor-A[END_REF][START_REF] Carlo | Neutrophils in the antitumoral immune response[END_REF] The activation of the KRAS pathway, frequent in PAC, [START_REF] Bailey | Genomic analyses identify molecular subtypes of pancreatic cancer[END_REF] was shown to recruit and activate neutrophils. [START_REF] Zhang | Interleukin-6 is required for pancreatic cancer progression by promoting MAPK signaling activation and oxidative stress resistance[END_REF] Transforming growth factor-beta in the tumour stroma was shown to induce specific neutrophils with pro-tumour phenotype. [START_REF] Fridlender | Polarization of Tumor-Associated Neutrophil Phenotype by TGF-β: "N1" versus "N2[END_REF] Decreased lymphocyte counts resulting in raised NLR may explain weaker defences against the tumour and the cancer ability to escape chemotherapy response. Decreased lymphocyte counts has been reported to be associated with shorter survival in PAC. [START_REF] Fogar | Decreased total lymphocyte counts in pancreatic cancer: an index of adverse outcome[END_REF] High NLR at baseline has been reported as a strong independent prognostic biomarker, but its evolution over time could be also of interest. Chen et al. assessed NLR evolution between baseline and after 2 cycles of chemotherapy in 132 patients treated for advanced or metastatic PAC. [START_REF] Chen | Significance of baseline and change in neutrophil-to-lymphocyte ratio in predicting prognosis: a retrospective analysis in advanced pancreatic ductal adenocarcinoma[END_REF] The value of 2.78 was selected as the NLR cut-off. Patients with increased NLR at 1 month had a poorer prognostic than others. We performed the same analysis in our population, but did not find any difference (Table A.12). However, the four groups strategy used in our work emphasizes the poor prognosis associated with a high NLR level whatever the moment. Chen et al reported relatively similar data with their methodology, in particular by defining their four sub-groups based on delta. Others studies are necessary to define the best method to use in clinical practice (delta of NLR or threshold at 5). We hypothesized that patients with high NLR at baseline turning <5 on day 15 would have an intermediate

prognosis, but we did not observe it.

The presence of ctDNA is a prognostic biomarker at baseline in PAC as in other solid tumours. (24) Our exploratory results suggest an association between high NLR and presence of ctDNA, though one patient with NLR >5 did not have detectable ctDNA. Moreover, the presence of ctDNA seemed to be a prognostic biomarker in patients with NLR <5 at baseline.

These two biomarkers may provide different information. NLR can reflect more the state of the inflammation and immunodepression associated to the disease whereas the ctDNA can be more correlated with the "aggressiveness" of the tumour cells or the tumour burden. In order to better understanding these points more data are necessary.

The retrospective design, the use of different chemotherapy regimens in first-line (Tables A.9

to A.11), the lack of data regarding corticosteroids use and granulocyte colony-stimulating factor administration, and the relative low number of patients in each subgroup for NLR dynamic analyses, especially in NLR >5 group, are limitations to our study. To explore potential biases, the NLR analyses were done for patients with and without NLR information at baseline and for each cohort in order to detect the subgroup effect. These analyses showed that NLR at baseline and its evolution under treatment are comparable between each cohort.

Therefore, the study patients are representative of the general population of patients with metastatic PAC receiving first-line chemotherapy in France. Given the heterogeneity of chemotherapy regimens, we did not analyse the relation between NLR and treatment toxicity.

ctDNA data were only available for a subgroup of patients and these results are of an exploratory nature.

The aim of assessing affordable, easily accessible, and performant biomarkers remains a key to treatment optimization, combined with clinical and imaging features. With these objectives, NLR appears as a promising dynamic and prognostic biomarker.

In conclusion, high NLR before or during chemotherapy was indicative of a poor prognosis in patients with metastatic PAC. These results suggest the potential interest of following NLR at each chemotherapy cycle. Further validation in prospective studies is required.

Table and

  Systemic inflammation, reflected by high NLR, could emphasizes patients' symptoms such as anorexia and asthenia, and, consecutively, be responsible of a poorer PS. NLR was lower in patients who had curative intent resection. These patients undergo regular follow-up visits after surgery. The tumor burden and the systemic inflammation associated are thus probably less important at relapse diagnosis than in patients with metastasis at diagnosis. The step-wise multivariate analysis strategy confirmed the independent poor prognosis value of baseline high NLR. Other systemic biomarkers such as CRP, albumin, platelet-lymphocyte ratio may also give indication about the immune response of the host. Among them, high NLR seems to be the most accurate.[START_REF] Oh | Prognostic Roles of Inflammatory Markers in Pancreatic Cancer: Comparison between the Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio[END_REF] NLR is affordable and easily accessible biological marker. Various cytokines like interferon or interleukine-6, and angiogenic factors (e.g. platelets derived growth factors) are the factors of interest in evaluating prognosis of patients with PAC,[START_REF] Mroczko | Diagnostic usefulness of serum interleukin 6 (IL-6) and C-reactive protein (CRP) in the differentiation between pancreatic cancer and chronic pancreatitis[END_REF][START_REF] Yako | Inflammatory cytokines and angiogenic factors as potential biomarkers in South African pancreatic ductal adenocarcinoma patients: A preliminary report[END_REF] though none of these is currently recommended in clinical practice. Other inflammatory markers such as the Glasgow prognostic score based on albumin and CRP or the NARCA prognosis score based on neutrophils-to-albumin ratio and CA 19-9 have been also proposed.[START_REF] Tingle | NARCA: A novel prognostic scoring system using neutrophil-albumin ratio and Ca19-9 to predict overall survival in palliative pancreatic cancer[END_REF] Based on the first international consensus on mandatory baseline and prognostic characteristics in future trials for the treatment of unresectable PAC reported by Ter Veer et al., CRP and NLR were defined as the compulsory measurements.[START_REF] Ter Veer | Consensus statement on mandatory measurements in pancreatic cancer trials (COMM-PACT) for systemic treatment of unresectable disease[END_REF]

Figure A. 1 .Figure A. 2 .Figure A. 3 .Figure A. 4 .Figure A. 5 .Figure A. 6 . 5 Figure A. 7 . 5 Figure A. 8 .Figure A. 9 .Figure A. 10 .Figure A11 .Figure A12 .Figure A13 .Figure A15 .Figure A17 .

 1234565758910A11A12A13A15A17 Figure A.1. Overall survival in the both study cohorts

Figure A7 .Figure A. 8 .Figure A10 . 0788 Figure A12 .B 86 Figure A18 .

 A78A100788A1286A18 Figure A7. Progression-free survival according to a NLR baseline cut-off of 5

Figure A19 .

 A19 Figure A19. Progression-free survival in patients with baseline NLR < 5 according to the presence of ctDNA in the GHPS cohort

Table 1 .

 1 Comparison of characteristics between patients according to NLR at baseline

Table 2 .

 2 Univariate and multivariate analyses of the association between baseline patient characteristics and overall survival

Table 3 .

 3 Description of NLR over time according to progression-free survival under first-line

	chemotherapy (6<months or >6 months) (A) and mixed model of repeated measures and time
	as categorical variable (from day 1 to day 180) (B)
	Abbreviations
	ECOG PS: Eastern Cooperative Oncology Group Performance Status
	CA 19-9: carbohydrate antigen 19-9
	CEA: carcinoembryonic antigen
	NLR: neutrophil to lymphocyte ratio
	OS: overall survival
	PFS: progression-free survival
	HR: hazard ratio
	CI: confidence interval
	IQR: interquartile range
	GHPS: Pitié-Salpêtrière Hospital Group

Figure 1. Population flow chart Figure 2. Overall survival curves according to NLR at baseline and on day 15

Figure 3. Evolution curves of NLR from day 1 to day 180 under first-line of chemotherapy in "good responders" (PFS >6 months) and "bad responders" (PFS <6 months) (n=212)

Table 1 .

 1 Comparison of characteristics between patients according to NLR at baseline 544 545

	546	Abbreviations: NLR=neutrophil to lymphocyte ratio; ECOG PS=Eastern Cooperative Oncology Group
	547	Performance Status; CA 19-9=carbohydrate antigen 19-9; CEA=carcinoembryonic antigen; IQR=interquartile
	548	range; OS=overall survival; PFS=progression-free survival

*Age at randomization for AFUGEM and age at first-line chemotherapy initiation for retrospective study 549 **no missing data

Table 2 .

 2 Univariate and multivariate analyses of the association between baseline patient characteristics and overall survival 551

				Univariate analysis		Multivariate analysis N = 186;
			N (events)	HR	95% CI	P -value	HR	95% CI	P -value
		< 65 years	90 (63)	1		0.0208
		≥ 65 years	113 (88)	1.47	1.06-2.04
	Gender	Male Female	131 (99) 81 (52)	1 0.77	0.55-1.08	0.1338
		0-1	150 (103)	1		< 0.0001	1	0.0002
		2-3	62 (48)	2.85	2-4.05		2.32	1.48-3.62
		Head/ head and body	122 (82)	1		0.0022	1	0.0086
		Other	90 (69)	1.66	1.20-2.30		1.66	1.14-2.43
		Poor and moderate	110 (86)	1		0.001
		Well	64 (39)	0.46	0.31-0.69
		No	153 (111)	1		0.0012	1	0.0133
		Yes	59 (40)	0.54	0.37-0.78		0.57	0.37-0.89
		1	131 (93)	1		0.0066	1	0.0353
		2	58 (41)	1.02	0.71-1.48		1.23	0.82-1.84
		≥ 3	21 (16)	2.37	1.38-4.06		2.19	1.20-4.01
		< 40	132 (101)	1		0.0005	1	0.0006
		≥ 40	69 (43)	0.53	0.37-0.76		0.48	0.31-0.73
		< 1000	120 (79)	1		< 0.0001	1	0.0206
		≥ 1000	77 (62)	2.16	1.53-3.06		1.57	1.07-2.30
		< 8	115 (84)	1		0.0118
		≥ 8	80 (57)	1.55	1.10-2.17
		≤ 5	162 (109)	1		< 0.0001	1	0.001
		> 5	50 (42)	3.22	2.23-4.64		2.01	1.33-3.05

N events = 133 Age Albumin (g/l) CEA (ng/ml) CA 19-9 (UI/ml) NLR at baseline ECOG PS Primary tumour location Differentiation grade Resection of primary tumour Number of metastatic sites 552

	553	Abbreviations: HR=hazard ratio; NLR=neutrophil to lymphocyte ratio; ECOG PS=Eastern Cooperative Oncology Group Performance Status; CA 19-9=carbohydrate antigen 19-9;
	554	CEA=carcinoembryonic antigen; IQR=interquartile range

Table 3 .

 3 Description of NLR over time according to progression-free survival under first-line chemotherapy (< 6 months or > 6 months) and results of mixed model of repeated measures and time as categorical variable (from day 1 to day 180)

Description of NLR over time according to progression-free survival

	Under first-line chemotherapy (<6 months or >6 months)
		Responder	N	Mean	SD	Median	IQR
	D1	bad	129	4.67	4.04	3.80	2.0-6.20
		good	83	3.00	2.02	2.69	1.75-3.49
	D15	bad	98	4.08	5.76	2.63	1.28-4.81
		good	74	2.36	1.82	1.82	1.22-2.88
	D30	bad	83	3.77	3.48	2.69	1.47-5.13
		good	75	2.45	1.81	1.89	1.45-3.17
	D45	bad	85	4.39	4.72	2.76	1.53-5.31
		good	79	3.02	2.74	2.29	1.31-3.32
	D60	bad	58	4.09	4.18	2.83	1.79-4.25
		good	74	3.16	3.42	2.11	1.18-3.28
	D120	bad	31	5.37	4.35	3.30	2.45-7.25
		good	66	2.45	1.92	2.05	1.28-2.95
	D180	bad	15	4.81	2.47	5.53	2.53-6.23
		good	59	2.83	2.40	1.98	1.32-3.29
	Results

of mixed model of repeated measures and time as categorical variable (from day 1 to day 180)

	Effect	N DF	Den DF F Value	Pr > F
	Good responder	1	210	34.69	< 0.0001
	Evaluation	6	785	1.77	0.1031
	Responder*evaluation	6	785	1.67	0.1252

Table A . 1 .

 A1 Comparison of baseline characteristics between the two cohorts of patients

Table A . 2 .

 A2 Comparison of characteristics between patients with and without NLR data at baseline

	Table A.3. Univariate Cox proportional hazards model for progression-free survival
	Table A.4. Multivariate Cox proportional hazards model for progression-free survival
	Table A.5. Comparison of characteristics between patients with and without NLR data at
	baseline in the AFUGEM cohort
	Table A.6. Comparison of characteristics between patients with and without NLR data at
	baseline in the GHPS cohort

Table A .

 A

7. Comparison of patient characteristics according to NLR at baseline in the AFUGEM cohort Table A.8. Comparison of patient characteristics according to NLR at baseline in the GHPS cohort Table A.9. Chemotherapy regimen in the GHPS cohort (n =145) Table A.10. Dosage of different chemotherapy regimens Table A.11. Inclusion and exclusion criteria in the AFUGEM phase II trial Table A12. Evaluation of prognostic value of NLR evolution between baseline and Day 15, and between baseline and Day 30.

Table A1 .

 A1 Comparison of baseline characteristics between the two cohorts of patients

		Well Missing	74 (35.8) 47	51 (38.1) 18	23 (31.5) 29
	Death	Moderate	103 (49.8) 182 (70.3)	62 (46.3) 103 (71.0)	41 (56.2) 79 (73.8)	0.7615
	Characteristics Progression OS in months Number of metastatic sites Age PFS in months Follow-up in months Liver metastases* Gender* *no missing data Resection of primary tumour* Tumour location Stage* Differentiation grade NLR at baseline CEA (ng/ml) CA 19-9 (UI/ml) Albumin (g/l) ECOG PS* Abbreviations: IQR=interquartile range; ECOG PS=Eastern Cooperative Oncology Group Performance Status; CA 19-9=carbohydrate antigen; Total N=259 n % GHPS N=145 n % Poor 30 (14.5) 21 (15.7) 9 (12.3) 145 (56.0) 83 (57.2) 62 (54.4) 0.6458 AFUGEM N=114 Missing 55 11 41 P-value 0.4714 Median 95% CI 10.32 (9.3-12.6) 10.32 (8.4-13.8) 10.02 (8.8-13.6) 0.8061 n % 1 156 (60.7) 84 (58.7) 72 (63.2) 0.904 Median 65.7 65.4 ≥ 2 101 (39.3) 59 (41.3) 42 (36.8) Median 95% CI 5.29 (4.4-6.1) 4.57 (3.3-5.6) 6.41 (4.8-7.7) 0.8121 66.1 IQR 60.6-72.8 60.4-72.9 Missing 2 2 0 61.5-72.7 Missing 16 16 0.0001 Median 95% CI 19.35 (17.3-23.6) 31.38 (24.4-61.6) 16.89 (15.5-17.9) 0.0119 0 162 (62.6) 76 (52.4) 86 (75.4) 0.8185 Male 157 (60.6) 87 (60.0) 0.0001 (61.4) Female 102 (39.4) 58 (40.0) (38.6) 0 78 (30.1) 41 (28.3) (32.5) 1 109 (42.1) 50 (34.5) 2 59 (22.8) 41 (28.3) 3 13 (5.0) 13 (9.0) Head 123 (47.7) 80 (55.2) Body 47 (18.2) 22 (15.2) Tail 53 (20.5) 29 (20) Head and body 21 (8.1) 14 (9.7) Body and tail 14 (5.4) 0 Missing 1 0 I/II 43 (16.6) 38 (26.2) III 7 (2.7) 5 (3.5) IV 209 (80.7) 102 (70.3) IQR 1.9-4.9 1.9-4.9 1.9-4.9 0.3942 Median 2.9 2.8 3.0 107 (93.9) 0.9845 2 (1.8) Missing 20 9 11 5 (4.4) IQR 2.3-19.6 2-18 2.5-23 < 0.0001 Median 5.4 4.5 6.0 1 0.1558 (12.4) Missing 18 9 9 7 (6.2) IQR 39.8-4413.0 29-2555.5 65-9205 (21.2) Median 496 355 891 (22.1) 0.0093 (38.1) Missing 15 15 0 < 0.0001 IQR 32-40.5 30-39 34-42 0 Median 37 36 38.2 (15.8) 0.0021 (51.8) Missing 3 0 3 0.0002 Yes 44 (17.2) 39 (26.9) 5 (4.5) 64 (24.7) 49 (33.8) 15 (13.2) Adjuvant chemotherapy < 0.0001 CEA=Carcinoembryonic antigen; NLR=neutrophil-to-lymphocyte ratio; OS=overall survival; PFS=progression-free survival

Table A2 .

 A2 Comparison of characteristics between patients with and without NLR data at baseline

		Moderate GHPS	103 (49.8) 145 (55.9)	18 (54.6) 18 (38.3)	85 (48.9) 127 (59.9)
			Poor	30 (14.5)	5 (15.2)	25 (14.4)
	Death		Missing	55 182 (70.3)	14 31 (66.0)	38 151 (71.2)	0.4746
	Characteristics Number of metastatic Progression	Total 145 (56.0)	no NLR data at baseline 25 (53.2)	NLR data at baseline 120 (56.6)	P-value 0.2436 0.6698
	sites OS in months
		N=259 156 (60.7) Median 95% CI 10.32 (9.3-12.6) 1	N=47 25 (53.2) 9.89 (6.7-15.3)	N=212 131 (62.4) 10.74 (8.9-13.3)	0.7894
	PFS in months	≥ 2	n % 101 (39.3)	n % 22 (46.8)	n % 79 (37.6)
	Age	Missing Median 95% CI	2 5.29 (4.4-6.1)	0 4.37 (2.3-7.1)	2 5.36 (4.4-6.2)	0.1042 0.7104
	Median Liver metastases* Follow-up in months	65.7 162 (62.6)	65.3 24 (51.1)	65.8 138 (65.1)	0.0722
	IQR Resection of primary Median 95% CI 19.35 (17.3-23.6) 60.6-72.8 64 (24.7)	63.4-70.3 5 (10.7) 17.28 (15.5-30.7)	60.3-73.0 59 (27.8) 19.35 (17.4-24.6)	0.0134 0.1845
	Missing *No missing data tumour*	16	7	9
	Gender* Adjuvant chemotherapy Abbreviations: IQR= interquartile range; ECOG PS=Eastern Cooperative Oncology Group Performance Status; CA 19-9=carbohydrate antigen; 0.4112 Male 157 (60.6) 26 (5.3) 131 (61.8) 0.0919 CEA=carcinoembryonic antigen; NLR=neutrophil-to-lymphocyte ratio; OS=overall survival; PFS=progression-free survival
			Female Yes	102 (39.4) 44 (17.2)	21 (44.7) 4 (8.7)	81 (38.2) 40 (19.1)
	ECOG PS*	Missing	3	1	2	0.1697
	Albumin (g/l)	0	78 (30.1)	11 (23.4)	67 (31.6)	0.1042
			1 Median	109 (42.1) 37	26 (55.3) 35	83 (39.2) 37
			2 IQR	59 (22.8) 32-40.5	7 (14.9) 30.4-39	52 (24.5) 32-41
			3 Missing	13 (5) 15	3 (6.4) 4	10 (4.7) 11
	Tumour location CA 19-9 (UI/ml)				0.0463 0.161
			Head Median	123 (47.7) 496	16 (34.8) 968.2	107 (50.5) 450
			Body IQR	47 (18.2) 39.8-4413.0	9 (19.6) 64.8-14000	38 (17.9) 37-3616
			Tail Missing	53 (20.1) 18	9 (19.6) 3	44 (20.8) 15
	Head and body CEA (ng/ml)	21 (8.1)	6 (13.0)	15 (7.1)	0.1421
		Body and tail Median	14 (5.4) 5.4	6 (13.0) 8.8	8 (3.8) 5
			Missing	1	1	0
	Stage*		IQR	2.3-19.6	2.7-45.3	2-19	0.0418
			I/II	43 (16.0)	3 (6.4)	40 (18.9)
			III Missing	7 (2.7) 20	0 (0) 3	7 (3.3) 17
	IV Differentiation grade Cohort* Well AFUGEM	209 (80.7) 74 (35.8) 114 (44.0)	44 (93.6) 10 (30.3) 29 (61.7)	165 (77.8) 64 (36.8) 85 (40.1)	0.0069 0.7717

Table A3 .

 A3 Univariate Cox proportional hazards model for progression-free survival

		≥ 1000	77 (67)	1.55	1.12-2.12
	NLR at baseline	≤ 5	162 (130)	1	< 0.0001
		> 5	50 (43)	2.38	1.67-3.38
	N (events) Abbreviations: ECOG PS=Eastern Cooperative Oncology Group Performance Status; CA 19-9=carbohydrate antigen; CEA=carcinoembryonic HR 95% CI P-value
	Gender antigen; NLR=neutrophil-to-lymphocyte ratio Male	131 (112)	1	0.1314
		Female	81 (61)	0.79	0.58-1.08
	ECOG PS	0-1	150 (123)	1	< 0.0001
		2-3	62 (50)	2.06	1.48-2.88
	Primary tumour	Head/Head and body	122 (95)	1	0.0024
	location	Other	90 (78)	1.6	1.18-2.16
	Age	< 65 years	90 (74)	1	0.0465
		≥ 65 years	113 (99)	1.36	1.00-1.85
	Stage	IV	45 (35)	1	0.0183
		I-III	158 (138)	0.63	0.43-0.93
	Differentiation grade	Poor and moderate	110 (95)	1	0.0072
		Well	64 (50)	0.62	0.44-0.88
	Number of metastatic	1	131 (107)	1	0.1152
	sites	2	58 (49)	1.16	0.82-1.62
		≥ 3	21 (16)	1.74	1.02-2.96
	Liver metastases	Yes	132 (116)	1	0.2177
		No	71 (57)	0.82	0.60-1.13
	Resection of primary	No	153 (129)	1	0.0127
	tumour	Yes	59 (44)	0.64	0.45-0.91
	Adjuvant	No	163 (141)	1	0.3235
	chemotherapy	Yes	38 (31)	0.82	0.56-1.21
	Albumin (g/l)	< 40	132 (111)	1	0.0012
		≥ 40	69 (53)	0.58	0.42-0.81
	CEA (ng/ml)	< 8	115 (96)	1	0.0585
		≥ 8	80 (64)	1.36	0.99-1.87
	CA19-9 (UI/ml)	< 1000	120 (94)	1	0.0073

Table A4 .

 A4 Multivariate Cox proportional hazards model for progression-free survival

		N (events) HR	95% CI	P-value
		188 (153)
	NLR at baseline	≤ 5	1		0.0026
		> 5	1.80	1.23-2.65
	Primary tumour	Head/Head
	location	and body	1		0.0019
		Other	1.70	1.22-2.37
	Albumin (g/l)	< 40	1		0.0003
		≥ 40	0.52	0.37-0.74
	CA19-9 (UI/ml)	< 1000	1		0.0205
		≥ 1000	1.49	1.06-2.08
	Abbreviations: CA 19-9=carbohydrate antigen; NLR=neutrophil-to-lymphocyte ratio

Table A5 .

 A5 Comparison of characteristics between patients with and without NLR data at baseline in the AFUGEM cohort

	Characteristics Stage Simplified leucovorin I/II III and fluorouracil plus nab-paclitaxel Age* Median IQR Min-max IV Differentiation grade Death Well Progression Moderate OS in months Poor Median 95% CI 10.02 (8.8-13.6) Total 5 (4.4) 2 (1.8) 75 (65.8) N = 114 n (%) 66.1 61.5-72.7 45.0-85.7 107 (93.9) 79 (69.30) 23 (31.5) 62 (54.4) 41 (56.2) 9 (12.3) Missing 41 PFS in months Metastatic site Median 95% CI 6.41 (4.8-7.7) 1 72 (63.2) Abbreviations: IQR= interquartile range; ECOG PS=Eastern Cooperative Oncology Group Performance Status; CA 19-9=carbohydrate antigen; no NLR data at baseline NLR P-value 1 1 (3.5) 4 (4.7) 0 21 (72.4) 54 (63.5) 2 (2.4) information at baseline N = 29 n (%) N = 85 n (%) 0.3488 64.4 66.4 62.9-70.2 61.3-73.1 45.0-85.0 46.6-85.7 28 (96.6) 79 (92.9) 21 (72.41) 58 (68.24) 0.6736 0.2433 2 (13.3) 18 (62.1) 44 (51.8) 0.336 21 (36.2) 11 (73.3) 30 (51.7) 2 (13.3) 9.49 (5.9-16.4) 10.81 (8.8-14.1) 0.7587 7 (12.1) 14 27 4.86 (1.9-8) 7.20 (4.9-8.1) 0.2727 0.7603 19 (65.5) 53 (62.4) ≥ 2 42 (36.9) 10 (34.5) 32 (37.7) CEA=carcinoembryonic antigen; NLR=neutrophil-to-lymphocyte ratio; OS=overall survival; PFS=progression-free survival
	Missing Liver metastases	0 86 (75.4)	0 21 (72.4)	0 65 (76.5)	0.6612
	Gender Resection of primary Male tumour	70 (61.4) 15 (13.2)	17 (58.6) 1 (3.5)	53 (62.3) 14 (16.5)	0.7215 0.0615
	Female Adjuvant chemotherapy	44 (38.6)	12 (41.4)	32 (37.7)	1
	ECOG PS	Yes	5 (4.5)	1 (3.6)	4 (4.8)	0.0075
	0 Missing	37 (32.5) 3	6 (20.7) 1	31 (36.5) 2
	Albumin (g/l)	1	59 (51.8)	22 (75.9)	37 (43.5)	0.1626
	2 Median	18 (15.8) 38.3	1 (3.5) 36	17 (20.0) 39.3
		3 IQR	0 34-42	0 33.2-40	0 34.7-42
	Tumour location CA 19-9 (UI/ml)					0.2311 0.3262
	Head Median	43 (38.1) 891	6 (21.4) 1375.5	37 (43.5) 812.4
		Body IQR	25 (22.1) 65-9205	7 (25.0) 186.3-9928.5	18 (21.2) 50.2-9205
	CEA (ng/ml)	Tail	24 (21.2)	7 (25.0)	17 (20.0)	0.454
	Head and body Median IQR	7 (6.2) 6 2.5-23	2 (7.1) 9.96 2.7-47.4	5.5 5 (5.9) 2.5-19.6
	Treatment arm Body and tail Missing Gemcitabine plus nab-paclitaxel	14 (12.4) 1 39 (34.2)	6 (21.4) 1 8 (27.6)	8 (9.4) 0 31 (36.5)	0.3839

Table A6 .

 A6 Comparison of characteristics between patients with and without NLR data at baseline in the GHPS cohort

	grade
				Well	(38.1)	8 (44.4)	43 (37.1)
		Moderate Poor	Total (46.3) (15.7)	no NLR information at 7 (38.9) 3 (16.7)	55 (47.4) NLR information at 18 (15.5)	P-value
	Characteristics Metastatic site				baseline	baseline	0.0192
				1	(58.7)	6 (33.3)	78 (62.4)
	≥ 2 Median Liver metastases Age* Resection of primary tumour	N = 145 n % (41.3) (52.4) 65.4 (33.8)	N = 18 n % 12 (66.7) 3 (16.7) 67.2 4 (22.2)	N = 127 47 (37.6) n % 73 (57.5) 65.3 45 (35.4)	0.0012 0.2957 0.2674
				IQR	60.4-72.9	64.5-73.2	60.2-72.9	0.3999
	Gender		Yes	(26.9)	3 (16.7)	36 (28.4)	0.3548
	Albumin (g/l)	Male	(60.0)	9 (50.0)	78 (61.4)	0.0414
		Female Median	(40.0) 36	9 (50.0) 31.5	49 (38.6) 36.5
	ECOG PS		IQR	30-39	27-37	31-40	0.4857
	CA 19-9 (UI/ml)	0	(28.3)	5 (27.8)	36 (28.4)	0.9327
		1 Median	(34.5) 355	4 (22.2) 213.5	46 (36.2) 369
				2 IQR	(28.3) 29-2555.5	6 (33.3) 10-18765	35 (27.6) 31.5-2092
	CEA (ng/ml)		3	13 (9.0)	3 (16.7)	10 (7.9)	0.2671
	Tumour location Head Median	(55.2) 4.5	10 (55.6) 6.5	70 (55.1) 4	0.2591
			Body IQR	(15.2) 2-18	2 (11.1) 3-43.5	20 (15.8) 2-17
	Death	Tail Head and body	(20.0) 14 (9.7) 1103 (71.03)	2 (11.1) 4 (22.2) 10 (55.6)	27 (21.2) 10 (7.3) 93 (73.2)	0.1219
	Body and tail Progression	0 (57.2)	0 7 (38.9)	0 76 (59.9)	0.0926
	Stage OS			I/II	(26.2)	2 (11.1)	36 (28.4)	0.2532
		III Median 95%CI 10.32 (8.4-13.8) 11.70 (2.9-24.2) 10.32 (8.3-13.8) 0.9578 5 (3.5) 0 5 (3.9)
	PFS			IV	102 (70.3)	16 (88.9)	86 (67.7)
	Differentiation Median 95%CI 4.57 (3.3-5.6)	3.29 (2.4-19.3)	4.60 (3.3-5.6)	0.7864 0.5935

Table A7 .

 A7 Comparison of patient characteristics according to NLR at baseline in the AFUGEM cohort

	Well Simplified leucovorin	21 (36.2)	15 (32.6)	6 (50.0)
	Moderate and fluorouracil plus	30 (51.7) 54 (63.5)	27 (58.7) 40 (62.5)	3 (25.0) 14 (66.7)
	Characteristics	Poor nab-paclitaxel	NLR data at baseline 7 (12.1)	NLR ≤ 5 4 (8.7)	NLR > 5 3 (25.0)	P-value
	Missing 1 ≥ 2 Median 95%CI Median Missing IQR Liver metastases* Death Metastatic site Progression OS in months Age* PFS in months Median 95%CI Gender* Male Resection of primary tumour* *No missing data Abbreviations: IQR= interquartile range; ECOG PS=Eastern Cooperative Oncology Group Performance Status; CA 19-9=carbohydrate antigen; 27 18 9 58 (68.2) 38 (69.4) 20 (95.2) 0.0022 N = 85 n % N = 64 n % 0.6382 44 (51.7) 30 (46.9) 14 (66.7) 0.1153 N = 21 53 (62.3) 39 (60.9) 14 (66.7) n % 32 (37.6) 25 (39.1) 7 (33.3) 10.81 (8.77-14.09) 13.77 (9.5-17.6) 4.63 (2.3-6.34) < 0.0001 0.3949 66.3 66.1 0 0 0 68.6 61.3-73.1 59.7-73.0 65 (76.5) 50 (78.1) 15 (71.4) 0.5605 7.20 (4.93-8.12) 8.21 (7.2-10.3) 2.33 (1.6-4.0) < 0.0001 62.54-73.11 0.2771 53 (62.3) 42 (65.6) 14 (16.5) 13 (20.3) 1 (4.8) 0.1721 11 (52.9) Female 32 (37.6) 22 (34.9) ECOG PS* 0 31 (36.5) 26 (40.6) 1 37 (43.5) 29 (45.3) 2 17 (20.0) 9 (14.1) 3 0 0 Tumour location Head 37 (43.5) 29 (45.3) Body 18 (21.2) 11 (17.2) Tail 17 (20.0) 13 (20.3) Head and body 5 (5.9) 5 (7.8) Body and tail 8 (9.4) 6 (9.9) Missing 0 0 Stage* I/II 4 (4.7) 4 (6.2) III 2 (2.3) 2 (3.1) 0 IV 79 (92.9) 58 (90.6) 21 (100) nab-paclitaxel 31 (36.5) 24 (37.5) 7 (33.3) Gemcitabine plus 0 Treatment* 0.7307 0.7559 Missing 10 6 4 0 IQR 2.5-19.6 2.5-19.10 3-112.2 2 (9.5) Median 5.5 5.36 8.6 0 CEA (ng/ml) 0.2627 4 (19.0) Missing 8 5 3 7 (33.3) IQR 50.20-9205 43-7477 53.35-10453 8 (38.1) Median 812.4 561.7 2320.5 0.511 CA 19-9 (UI/ml) 0.263 0 IQR 34.68-42 34.95-42.05 31.72-40 8 (38.1) Median 39.3 40 37 8 (38.1) Albumin (g/L)* 0.1735 5 (23.8) Missing 2 2 0 0.0503 Yes 4 (4.8) 4 (6.4) 0 1 0(47.6) Adjuvant chemotherapy 0.5678 CEA=carcinoembryonic antigen; NLR=neutrophil-to-lymphocyte ratio; OS=overall survival; PFS=progression-free survival
	Differentiation grade				0.0834

Table A8 .

 A8 Comparison of patient characteristics according to NLR at baseline in the GHPS cohort

	1 2	IV Table A9. Chemotherapy regimen in GHPS cohort (n=145) 86 (67.72) 63 (64.29) Differentiation grade Progression 76 (59.8) 68 (69.4)	23 (79.31) 8 (27.6)	0.3723 < 0.0001
		OS in months Chemotherapy	Well Frequency 43 (37.1)	37 (40.2) Percent	6 (25.0) Cumulative Cumulative
		Characteristics Median 95% CI Moderate	NLR information at 55 (47.4) 10.32 (8.4-13.8)	NLR ≤ 5 41 (44.6) 13.47 (10.1-18.3) (%) frequency NLR > 5 14 (58.3) 2.53 (1.2-5.2) percentage P-value < 0.0001 (%)
		PFS in months	Poor	baseline 18 (15.5)	14 (15.2)		4 (16.7)
	3 4 5	N = 127 n (%) 65.25 IQR 60.15-72.88 Median Missing 9 Male 78 (61.4) Female 49 (38.6) 0 36 (28.3) 1 46 (36.2) 2 35 (27.6) 3 10 (7.9) Head 70 (55.1) Missing 11 Median 95% CI 4.57 (3.3-5.6) 5.36 (4.07-7.39) 1.61 (1.18-3.91) 0.0124 N = 98 n (%) N = 29 n (%) 0.0953 64.8 67.79 58.88-72.22 61.98-76.52 4 5 0.7247 61 (62.2) 17(58.6) 37 (37.8) 12 (41.4) 0.0014 33 (33.7) 3 (10.3) 38 (38.8) 8 (27.6) 23 (23.5) 12 (41.4) 4 (4.1) 6 (20.7) Tumour location* Age Gender* ECOG PS* 0.2148 57 (58.2) 13 (44.8) 6 5 Metastatic site *No missing data None 9 6.4 9 6.2 0.0258 1 78 (62.40) 65 (67.71) Abbreviations: IQR= interquartile range; ECOG PS=Eastern Cooperative Oncology Group Performance Status; CA 19-9=carbohydrate antigen; Gemcitabine 29 20.7 38 26.2 13 (44.83) ≥ 2 47 (37.60) 31 (32.29) CEA=carcinoembryonic antigen; NLR=neutrophil-to-lymphocyte ratio; OS=overall survival; PFS=progression-free survival Gemcitabine-Oxaliplatin 2 1.4 40 27.6 16 (55.17) Missing 2 2 FOLFIRINOX 47 33.6 87 60.0 0 Liver metastases* 73 (57.5) 53 (54.1) 20 (69.0) FOLFOX 30 21.4 117 80.7 0.1544 FOLFIRI 6 4.3 123 84.8 Resection of primary 45 (35.4) 39 (39.8) 6 (20.7) 0.0588 Gemcitabine-Abraxane 2 1.4 125 86.2 tumour* Adjuvant chemotherapy 36 (28.3) 31 (31.6) 5 (17.2) 5-FU-Abraxane 5 3.6 130 89.7 0.1309 Albumin (g/L) Gemcitabine-Erlotinib 8 5.7 138 95.2 0.0574 Median 36.5 37 Erlotinib 1 0.7 139 95.9 32 IQR 31-40 33-40 Maestro Trial 1 0.7 140 96.6 27-39 Missing 11 8 Missing 5 3.6 145 100 3 CA 19-9 (UI/ml) 0.2087 Median 369 341 1672 IQR 31.5-2092 36-1200 14-10000
			Body Missing	20 (15.7) 7	15 (15.3) 5		5 (17.2) 2
		CEA (ng/ml)	Tail	27 (21.3)	17 (17.3)		10 (34.5)	0.0922
		Head and body Median	10 (7.9) 4	9 (9.2) 4		1 (3.4) 9
		Body and tail IQR	0 2-17	0 2-15		0 3-29
		Stage*	Missing	7	5		2	0.0848
		Death	I/II	36 (28.35) 93 (73.2)	32 (32.65) 71 (72.4)	4 (13.79) 22 (75.9)	0.7154
			III	5 (3.94)	3 (3.06)		2 (6.90)
							56

Table A10 .

 A10 Dosages of different chemotherapy regimens Leucovorin, 400 mg/m 2 and 5-FU, 400 mg/m 2 given as a bolus followed by 2400 mg/m 2 given as a 46-hour continuous infusion  Leucovorin, 400 mg/m 2 and 5-FU, 400 mg/m 2 given as a bolus followed by 2400 mg/m 2 given as a 46-hour continuous infusion  Leucovorin, 400 mg/m 2 and 5-FU, 400 mg/m 2 given as a bolus followed by 2400 mg/m 2 given as a 46-hour continuous infusion One cycle every 2 weeks in case of liver metastases), total bilirubin ≤ 1.5 x ULN, albumin ≥ 25 g/L, 12.Baseline evaluations performed before randomization: clinical and blood evaluations no more than 14 days prior to randomization, tumour assessment (CTscan or MRI, evaluation of nonmeasurable lesions) no more than 21 days prior to randomization, 13.Female patients must be surgically sterile, or be postmenopausal, or must commit to using reliable and appropriate methods of contraception during the study and during at least 6 months after the end of study treatment (when applicable). All female patients with reproductive potential must have a negative pregnancy test (β HCG) within 72 h prior to starting nab-paclitaxel treatment. Breastfeeding is not allowed. Male patients must agree to use effective contraception in addition to having their partner use a contraceptive method as well during the trial and during at least 6 months after the end of the study treatment, 14.Registration with the French National Health Care System. Medical history or evidence of CNS metastasis upon physical examination, unless adequately treated (e.g., non-irradiated CNS metastasis, seizure not controlled with Table A12. Evaluation of prognostic value of NLR evolution between baseline and Day 15, and between baseline and Day 30.

	Chemotherapy regimens Gemcitabine monotherapy Gemcitabine and nab-paclitaxel 5-FU and nab-paclitaxel 5-FU, irinotecan, and oxaliplatin criteria 5-FU and irinotecan Exclusion OS D15≤D0 Dosage  Gemcitabine: 1000 mg/m 2 , days 1, 8, 15 One cycle every 4 weeks  Gemcitabine: 1000 mg/m 2 , days 1, 8, 15  Nab-paclitaxel: 125 mg/m 2 , days 1, 15, One cycle every 4 weeks  Nab-paclitaxel: 125 mg/m 2 One cycle every 2 weeks  Oxaliplatin, 85 mg/m 2  Irinotecan, 180 mg/m 2 One cycle every 2 weeks  Irinotecan, 180 mg/m 2 HR 95%CI 106 (77) 1 D15>D0 65 (48) 0.85 0.59-1.22 PFS D15≤D0 106 (90) 1 D15>J0 65 (55) 1.01 0.72-1.41 n(events) HR 95%CI OS D30≤D0 97 (66) 1 D30>D0 60 (46) 1.08 0.74-1.58 PFS D30≤D0 97 (82) 1 1. n(events) D30>D0 60 (50) 0.98 0.69-1.40	pvalue 0.3724 0.9603 pvalue 0.6875 0.9127

Acknowledgements

We thank Magdalena Benetkiewicz for reviewing and editing assistance.

Funding

None. The AFUGEM trial was supported by Celgene through grants to GERCOR, but had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Conflict of interest

JBB is a consultant/advisory board member for Amgen, Celgene, and Merck Serono, has received personal fees from Amgen, Bayer, Celgene, Merck Serono, Roche, Sanofi, Servier and non-financial support from Amgen, Merck Serono, and Roche. PH has received grants from Celgene and Roche; personal fees from Baxalta, Celgene, Ipsen, Lilly, Merck Serono, Novartis, and Pfizer and non-financial support from Celgene, Ipsen, Merck Serono, Novartis, and Pfizer. .All other authors declare no conflict of interest.

Ethics approval and consent to participate

All experiments utilizing human samples were approved by the Ethical Committee of Medical Research, Pitié-Salpêtrière Hospital, Sorbonne University. The study protocol was approved by the French ethics committee "Ile de France VI". All patients provided written informed consent before study enrolment.

This study was performed in accordance with the Declaration of Helsinki.

APPENDICES


Baussaron Loïc

Julcour-Lebigue Carine

Wilhelm Anne-Marie
email: annemarie.wilhelm@ensiacet.fr

Delmas Henri

Wetting Topology in Trickle Bed Reactors

Keywords: wetting efficiency, trickle-bed reactor, PIV, image processing, dye-adsorption, wettability, contact angle

Local and average partial wetting efficiencies in trickle-beds have been directly measured by image processing and PIV analysis. Two different set-ups have been implemented: a vertical monolayer of pellets where the wetting structure and stability and also local velocity gradient are characterized by PIV, and a fixed bed to analyze (by photography) the axial evolution of wetting over bed cross section after a transient injection of dye. Porous alumina spheres and different liquids have been used to examine the effect of liquid-solid affinity; two liquid distributors and bed pre-wetting were investigated at low liquid superficial velocities (0.5•10 -3 -10 -2 m/s). Contrary to usual techniques (excepting MRI), not only average wetting efficiencies are derived from the colorimetric tracing, but also local features: axial evolution of wetting, size and locations of dry zones, distributions of wetting efficiency at a particle scale. All those local data are important to improve reactor models assuming uniform pellet wetting. The effect of liquid-solid affinity is predominant in the case of the monolayer of beads, and, contrary to usual assumption, is still significant for the real trickle bed in the low range of wetting efficiency, corresponding to liquid superficial velocity lower than 2•10 -3 m/s.

Introduction

Trickle-bed reactors are packed beds of catalyst particles with co-current downflow of liquid and gas reactants. Their use is widespread in petroleum industry particularly in hydrotreatment processes such as hydrodesulfurization and hydrocracking. Bench-scale reactors usually operate at the same liquid hourly space velocities as the commercial reactors, leading to very low liquid velocities and partial wetting of the catalyst. Partial wetting can result in a poor utilization of the catalyst and in hot spots formation.

According to the value of the gas and liquid flow rates encountered in both industrial and pilot plants, liquid flows over the solid in the form of films, rivulets or droplets: this flow texture has been investigated by Charpentier et al. 1,2 . These authors showed that the liquid flow on the packing of absorption columns was heterogeneous with isotropic structures as films, and anisotropic ones as rivulets and drops. In trickle-bed reactors films, quasi stagnant pockets of liquid and rivulets may exist, but probably no drops. But very few results can be found on the effect of static wettability, quantifying the affinity between the solid and the liquid, on the wetting efficiency f in fixed-bed, i.e. the fraction of the catalyst external surface which is wetted by flowing liquid. Ortiz-Arroyo et al. 3 showed theoretically, by solving the equation of Young-Laplace on two identical spheres in contact on a vertical line, that the contact angle had an important influence on the meniscus which is formed between the two particles. Khanna and Nigam 4 developed a conceptual approach for a better understanding of the relation between wettability and wetting efficiency on a porous support. Contrary to the intuition, they considered that wetting efficiency does not increase regularly with wettability. The authors highlight an improving effect of solid particles porosity on the spreading out of the liquid. They confirmed this effect with a perforated plate modelling a porous substrate and showed that internal wetting improves external wetting by the formation of liquid bridges. The pores of catalyst also drive the liquid rivulets which flow from pores to pores 5 . Let us notice however that the "pores" of these experiments are holes of several millimetres, very far from the usual size of catalyst pores.

But no studies are able to discriminate the effects of physico-chemistry and hydrodynamics on the spreading out of the liquid in the case of a complex geometry, as a bed of particles.

However, several methods have been proposed to measure the wetting efficiency f in trickle bed reactors: dynamic tracer technique [START_REF] Colombo | Solid-liquid contacting effectiveness in trickle bed reactors[END_REF][START_REF] Al-Dahhan | Catalyst wetting efficiency in trickle-bed reactors at high pressure[END_REF] , chemical reaction method [START_REF] Llano | Determination of wetting efficiency in trickle-bed reactors by a reaction method[END_REF] , and more recently, hydrodynamic technique [START_REF] Pironti | Liquid-solid wetting factor in trickle bed reactors: its determination by a physical method[END_REF] and Magnetic Resonance Imaging technique, MRI [START_REF] Gladden | Magnetic resonance imaging of single and two-phase flow in fixed-bed reactors[END_REF] . Except MRI, all those approaches are overall, indirect and require a model of reactor involving hydrodynamic, transfer and/or kinetic parameters.

Ravindra et al. [START_REF] Ravindra | Liquid flow texture in trickle-bed reactors: an experimental study[END_REF] proposed a dye-adsorption technique to investigate qualitatively features of liquid flow texture inside a trickle bed at different depths. They completed these bed-scale visualisations by measuring exit liquid distributions and pressure drop. Very recently, van Houwelingen et al. [START_REF] Van Houwelingen | Particle wetting distribution in trickle-bed reactors[END_REF] used a similar technique to derive wetting efficiencies for different modes of prewetting. When investigating catalyst wetting in a trickle-bed reactor, the question always arises about how the liquid is locally distributed inside the bed: is a pellet-scale wetting or a bed-scale wetting to be considered, what is the evolution of the dry/wetted features along the bed height? For simplicity and as usual measuring techniques give only an overall value of the wetting efficiency, usual reactor models assume that all pellets are equally wetted whatever the gas and liquid flow rates, the liquid distribution, or the prewetting. In this study two methods have been developed to directly measure wetting efficiencies in order to understand qualitative trends, but also to quantify particle-scale wetting, distributions of wetting in a column, as well as liquid film velocities. These techniques allow to answer the previous questions and to quantify the extent of error when assuming a uniform wetting. These two methods are based on image processing and PIV analysis of a coloured tracer injected in the liquid phase, and are carried out in two different set-ups:

-a bidimensional monolayer of pellets where the liquid phase is seeded with micronic rhodamine/PMMA particles illuminated by an expanded laser beam, -a fixed bed consisting of two concentric columns, where the inner one is coupled to a piston, able to push the bed upward once the experiment is completed and to unload the bed by 11 sections to investigate the axial evolution of wetting after circulating a liquid dye solution through the bed. The use of porous alumina spheres and different liquids (ethanol, water, heptane) allowed to investigate the effect of liquid-solid affinity on the wetting efficiency. During this study, the effects of liquid distribution and of the bed prewetting were first investigated. Then, a local study was carried out to determine a wetting efficiency per pellet, a distribution of wetting efficiency in a column cross-section and a characteristic scale of dry zones. Finally, the effect of physicochemistry was put forward on the average wetting efficiency of the column.

Experimental section

Two-dimensional monolayer of pellets

Description of the set-up

A schematic diagram of the two-dimensional monolayer is presented in Figure 1. Pellets of 6•10 -3 m in diameter are pierced along a diameter and stringed on 10 vertical metal stems fixed at the top and bottom so that to avoid any contact between the transparent PVC backwall of the set-up and the particles. The liquid distributor is a tube connected to ten needles which distribute the liquid uniformly on each row of pellets. The distributor can be moved (from left to right and from back to front) in order to sprinkle the axis of the beads or their periphery. The liquid flow rate, measured by a Coriolis mass flow-meter, ranges between 1.4•10 -7 and 1.4•10 -6 m 3 /s ('superficial' linear velocities from 0.4•10 -3 to 4•10 -3 m/s were calculated by dividing the flow rate by the area of the rectangular cross-section of the flow: one pellet diameter versus 10 pellets diameters) and allows to cover a broad range of wetting efficiencies.

Measuring technique

The PIV system, provided by LaVision Company, involves a camera recording images of seed particles illuminated by a front laser beam at successive instants. The liquid phase is seeded with fluorescent particles (rhodamine/PPMA) of diameters ranging between 1 and 20 microns, sufficiently large to be visible with the camera, but small enough to enable the flow through the needles. The laser source used is a pulsed laser delivering two pulses of several tens of millijoules, the time interval being adjustable according to the range of liquid velocities. The front of the set-up is illuminated by the laser and this illuminated zone is relatively broad, 0.02-0.03 m on each side. The magnitude of the depth of focus is one particle radius, contrary to the traditional PIV which uses a fine laser sheet perpendicular to the camera. The movement of the particles is recorded by a CCD camera (12 bits, 1376×1040 resolution). Rhodamine re-emitting the laser light at a known wavelength (580 nm), an interferential filter centered on the rhodamine wavelength allowed to get rid of reflections and re-emission by the beads. A record involves 30 couples of images and lasts approximately 10 seconds. The wetting efficiency is calculated after processing the rough images, and the velocity fields were evaluated in order to differentiate the stagnant and dynamic zones.

Image processing

Various treatments, carried out on Matlab, were applied to the rough images of flow (Figure 2a: water-alumina) to calculate the wetting efficiency f. The first step consisted in traditional image processing:

-binarisation (Figure 2b) to determine the liquid fraction represented in white, -multiplication by the binarised blank image (image without flow showing the solid fraction, Figure 3a) to keep only the fraction of liquid on the pellets, -morphological operation (closing) in order to make continuous the wet zones (Figure 3b). This morphological operation is essential for a good evaluation of the wet zones which appear "discontinuous", because of detection of the rhodamine particles present in the liquid on the particles. Each step was performed using existing functions of the Image Processing toolbox from Matlab. The second step of the image treatment was to take account of the sphericity of the particles. The real surface of the half-spheres is rebuilt (Figure 5) from the surface projected on the 2D image by multiplying the surface of successive crowns by its 'weight' α k(i,j) , increasing from the centre to the outside of the pellet. α k(i,j) is calculated as the ratio between the real surface of the crown and its projected surface. The 3D image of the dry bed (accessible to the camera) is thus completely rebuilt. This method of rebuilding, which must be carried out sphere by sphere, was validated by calculating the surface of a half-sphere starting from a disc of known diameter. With 30 crowns, the difference between the rebuilt surface and the real surface is only 2 %.

A semi-automatic treatment for the recognition of objects was also developed in order to extract each particle as a separate object and measure its 3D wetted fraction. The first step of this procedure consists in segmenting the particles on the black and white image as illustrated in Figure 4. A chart of the Euclidean distances from the complementary image is carried out to differentiate in levels of grey the successive zones from the centre towards the periphery of the objects. The objects are then separated by the 'watershed algorithm'. The second step is the detection of the objects and the comparison with discs filling the square in which the object is inscribed (Matlab function by John McCarthy). Each disc is then divided into crowns to form the 2D/3D mask of projection which is initially multiplied by the basic image without flow (Figure 3a) to be used as reference (3D image without flow). Then, the binarized image with flow (on pellets only, Figure 3b) is multiplied by this 3D/2D mask as presented in Figure 5. The 'true' wetting efficiency (f) is finally calculated by dividing the wet surfaces area by the solid surface of the particle; the wet surface is the sum of α k(i,j) .P i,j of the 3D image with flow (P i,j being equal to 1 if the pixel i,j is filled with liquid and to 0 elsewhere), and the solid surface is given by the sum of the α k(i,j) .P' i,j of the 3D image without flow (P i,j ' being equal to 1 if the pixel i,j is filled with solid and to 0 elsewhere).

( ) ( )

∑ ∑ ⋅ ⋅ = D D mask j i k w withoutflo image j i D D mask j i k withflow image j i P P f 2 / 3 ), , ( , 2 / 3 ), , ( , ' α α

The fixed bed for dye-adsorption

Description of the set-up

The second set-up, used for the dye adsorption tests, is presented in Figures 6 and7. Two tanks respectively contain pure liquid (3) and coloured liquid (3'). A three way valve (7) makes it possible to rock quickly from one feed to the other. The liquid, moved by a gear pump, is introduced at the top of the column and distributed independently of the gas. The geometry of the distributor (Figure 7b), involving 49 capillaries of 10 -3 m internal diameter and 0.1 m length (9), allows a good distribution of the liquid and of the gas on the column cross-section. A space hollowed in an altuglas part (8) ensures a minimum level of liquid above the capillaries to homogenize its distribution. The gas, supplied by a cylinder, is injected into the intermediate space of the distributor (10) and is distributed, independently of the liquid, by 24 openings of 2•10 -3 m in diameter (11). The column consists in two concentric tubes, the inner one being divided into 11 slices, 0.035 m in height and 0.057 m in internal diameter, which allows the investigation of the axial evolution of wetting efficiencies.

A piston is placed at the bottom of the column. At the end of the test, the fixed bed can thus be pushed upwards and cut slice by slice to determine the axial evolution of the wetting efficiency by image analysis. The loading of the bed was carried out according to a precise protocol. A 0.5 m length sleeve is added at the top of column to ensure a sufficient falling speed to the particles. The amount of loaded particles is weighed for each test to check the reproducibility of the fixed bed voidage.

Measuring techniques

The colorimetric tracing is used to chart the bed. The bed is flooded with single liquid flow during 5 minutes, drained and then it is fed with gas and pure liquid during 30 minutes to make sure that steady state is reached. The three way valve is then commutated for the coloured liquid injection. The dyes used are: eosin in water and ethanol, Sudan red in heptane. The concentrations were selected to obtain an important contrast between the wet and dry zones, without modifying γ L. The coloured liquid circulates during 5 to 30 minutes, according to the studied flow-rate, then the supply of tracer is cut off and a step of rinsing with pure liquid is carried out. The bed is drained, the distributor is withdrawn and the plate for the particles collection installed. The hinged jib supporting the camera ( 13) is placed at the top of the bed and the taking of photographs of bed cross section is carried out. The piston is pushed upwards and the first section of tube withdrawn, the pellets contained in the section are evacuated and a new photograph is taken. A second series of photos is obtained by spreading out a sample of the pellets contained in each slices on a perforated plate to get wetting efficiency following random orientations. For this method, the time duration of the dye solution injection is optimized so that all active zones of the bed can be irrigated by the solution without any colour spreading across the particle by diffusion.

Image processing

The image processing associated to the colorimetric measurements is slightly different from the one developed previously. Figure 8 presents an example of photographs (Figure 8a) and of the treatment applied (Figure 8b to d).

Firstly, the processed area is defined by locating a circle on the rough image, and removing at the column wall the equivalent of a diameter of particles so that the edge of the column does not appear on the treated image. Then the image is divided into three zones: wet zones (in red on the Figure 8b), dry zones (in white on the Figure 8c), and interparticle spaces of dark colour for which no distinction can be made between wet and dry. The algorithm a*b*L of the toolbox Image Processing of Matlab makes it possible to separate these three colours thanks to two criteria: the levels of colour a (from red to green) and b (from blue to yellow), and the luminosity L. The luminosity is necessary to differentiate black and white when the only criterion of colour does not allow it. For each colour, a group of pixels is selected on the image and for each area, the values of a, b, and L are calculated.

The coloured markers of each zone have thus an identified value of a, b and L for each processed image. They are used to classify each pixel by calculating the Euclidean distance between the pixel and each marker:

-The pixel is initially regarded as "red" or "not red" according to the Euclidean distance to the markers, calculated with the 2 criteria of colour, a and b. -For the zones which are regarded as not red, according to the criterion of luminosity, the pixel is viewed as "white" or "black". At the end of this first treatment, an average value of the wetting efficiency on the section can be calculated as the surface ratio of the red zones to the sum of the white and red zones. The second step separates the objects (Figure 8d) thanks to a recognition software and an automatic differentiation, based on the watershed algorithm (as described previously). These "section views" obtained along the column by taking photographs of the cross-section on the top of each module are very useful to locate some typical flow structures (large dry zones or regular patterns of liquid distribution) and thus to chart the bed axially. These pictures have two drawbacks: they are taken from the top of the column and only show the top half of the beads (facing the flow) and they characterize only a 2D cross-section and not a 3D volume of bed. Therefore "volume views" were carried out by taking a representative sample of the pellets included in the slice volume and spreading it randomly on a perforated plate. Average wetting efficiencies were calculated for a section volume, and the wetting efficiency per particle could be estimated, independently of the orientation of the particle in the bed.

Operating conditions

The operating conditions ranges are summarized in Table 1 for the two experimental set-ups. Three liquids and several sizes of alumina particles are studied. The tests are carried out at ambient temperature and atmospheric pressure, with no gas flow.

Various types of alumina, with different structural densities, internal porosities ε i and specific area a s have been used for these experiments. Two particles diameters were studied, 2.5•10 -3 and 6•10 -3 m in diameter.

Results and discussion

Monolayer of pellets

Effects of the liquid flow rate and liquid-solid affinity

Two qualitative observations could be noticed concerning the film flow structure over the pellet surface: -The position of the rivulets proved to be very stable, which validates the dye-adsorption method that integrates the wetting history in the final result.

-If the liquid distribution is intermittent (drop by drop), the liquid front moves along the particles surface in the form of drops or of film with "waves", according to the feed frequency (the time-evolution of the wetting efficiency is periodic). Then, Figure 9 shows the evolution of the wetting efficiency with the liquid flow rate: f increases in a linear but very moderate way with liquid flow rate. This result is in agreement with the literature where the wetting efficiency increases moderately with the liquid flow rate, generally as Q L β with 0.1<β<0.4 [START_REF] Al-Dahhan | Catalyst wetting efficiency in trickle-bed reactors at high pressure[END_REF][START_REF] Ring | Trickle-bed reactors: Tracer study of liquid holdup and wetting efficiency at high temperature and pressure[END_REF] . Our experimental results are compared with the correlations of El-Hisnawi [START_REF] El-Hisnawi | Trickle-bed reactors: dynamic tracer tests, reaction studies, and modelling of reactor performance[END_REF] and Burghardt [START_REF] Burghardt | Experimental studies of liquid-solid wetting efficiency in trickle-bed cocurrent reactors[END_REF] for water flow over porous particles and appear consistent with Burghardt's correlation results.

Liquid-solid affinity has been quantified by the contact angle, θ c , measured by the Washburn method, based on the dynamics of the capillary impregnation of a dry fixed bed of particles in contact with a static liquid. Liquid-solid affinity seems a key factor on the wetting efficiency. For the pellets A, the wetting efficiency increases by 30 to 50% when changing from water (liquid-solid contact angle θ c =65°) to ethanol (θ c =39°). Wetting efficiency appears to be strongly dependent on liquid/solid affinity variations. Indeed, when the contact angle increases, the liquid flows in thicker but narrower films, which results in a decrease of the wetting efficiency.

Dynamics of the flow

The velocity fields were estimated with PIV measurements only for the water-alumina system. It can be noticed that there are strong velocities gradients between the centre and the edge of the liquid vein. Figure 10a shows that there is no completely motionless liquid.

In Figure 10b, a chart of the velocity vectors modules is proposed, revealing three zones (when considering only the liquid flowing on the particles) according to their colour: red: V L <0.5•10 -3 m/s (zone B), light blue: 0.5•10 -3 m/s <V L <3•10 -3 m/s, green: 3•10 -3 m/s <V L (zone A). Table 3 shows that liquid flow rate has only very little effect on the distribution of these velocity zones. In all cases, the velocities between the rows of particles and on the surface of particles (zones A, 3•10 -3 to 5•10 -3 m/s) are 10 to 20 times higher that at the contact points between two spheres placed one at the top of the other or on the frontline dry-wet zones (zones B, 0 to 0.5•10 -3 m/s).

-In zones B, which account for 15 to 20 % of total wetting efficiency (Figure 10b, in red), the film tends to thicken because of liquid accumulation. -In zones A, there is an acceleration of the liquid film with reduction of the film thickness.

The stability of these percentages of slow and fast liquid with liquid flow rates suggests that the difference between these flows comes mainly from the interstitial liquid. The streaming flow thus proceeds by very important accelerations and decelerations between the quasistagnant zones localised at the poles of the particles. Figure 11 is an example of a photograph taken in the column (section-view). It can be noticed that the borders between the dry and wet zones are very clear.

Trickle bed set-up

Effect of prewetting

Experiments were carried out with and without bed prewetting, in order to investigate the influence of the start-up procedure on the wetting efficiency. Prewetting consists in filling the column with only liquid, the exit valve being closed and the liquid flow rate being very low to avoid the trapping of air bubbles. Then the liquid is flushed out before the beginning of the trickling flow. Photographs presented Figure 12 (V LS =2•10 -3 m/s) illustrate the clear effect of prewetting. Indeed, two large dry zones appear along the bed axis when no prewetting is performed. As shown in Figure 12c the dry zones can disappear from an axial position to another, in particular for high liquid velocities.

The values of the wetting efficiency calculated from the Figure 12 images confirm the qualitative results obtained from image analysis and the large effect of prewetting on the average wetting efficiency (Figure 13a), especially at low liquid flow rates. Without prewetting, the average wetting efficiency is at least 10% lower than the value obtained with prewetting, and this difference increases when liquid velocity decreases. At low liquid velocities, completely dry zones are formed in the absence of prewetting due to severe channel flow, which will limit the performances of the reactor. This channelling is important as long as the liquid flow is not high enough to force new paths inside the bed. As alumina particles are porous, liquid fills the pores under the effect of the capillary forces during the bed prewetting. Then bridges are formed between intra and extra particle liquid and facilitate the spreading out of films on the particle by surface tension effect, liquid attracting liquid. These conclusions join those presented by Maiti et al. 5 . These authors used a tilting perforated plate, to represent a porous particle. They highlighted that the liquid drops in contact with "the pores" of the particle filled by liquid required a higher gravity force effect (i.e. a higher inclination angle of the plate) to be taken down. The absence of prewetting makes the space distribution of the liquid very random and also probably non-reproducible. Indeed, the average wetting efficiency for V LS =10 -3 m/s is slightly higher than for V LS =2•10 -3 m/s because of the presence of the two dry zones at V LS =2•10 -3 m/s (Figure 12b). Without prewetting the liquid distribution appears to be mainly influenced by the packing local properties. On the other hand, bed prewetting assures a good reproducibility of the wetting efficiencies. Figure 13b presents the axial evolution of the wetting efficiency with and without prewetting. When the bed is prewetted, the length needed for the wetting efficiency to be established, lies between 0.03 and 0.05 m for V LS >2•10 -3 m/s (i.e. approximately 15 particle diameters). For very low liquid flow rates, this axial establishment length can be a little longer. On the other hand, without prewetting, 0.2 m are necessary to get stable cross sectional averaged values of f (for V LS =2•10 -3 m/s). To conclude this study, prewetting has been confirmed -to yield a homogeneous liquid distribution on the cross-section of the reactor within a lower distance, -to allow more reproducible wetting structures, -to get average higher wetting efficiencies (of about 10 %) than without prewetting .

Effect of liquid distribution

The influence of the distributor on the wetting efficiency has been evaluated by comparing a monocapillary distributor (Figure 14a), provided with a single central hole of 4•10 -3 m in diameter located on the reactor axis, to the multicapillary one (described in experimental section). In Figure 14b, a "cross-section view" at Z=0 demonstrates the point distribution induced by the single point distributor, the irrigated zone accounting for only 25% of the cross-section.

Figure 15 presents the evolution of average wetting efficiency in the column with the superficial liquid velocity, for the prewetted pellets B and the two distributors: monocapillary and multicapillary. For V LS >2•10 -3 m/s, the average wetting efficiency is similar for both distribution modes (difference less than 5%). The fixed bed compensates for the very poor distribution due to the monocapillary distributor and allows a homogeneous distribution on the bottom of the reactor. On the other hand, the difference between the two modes of injection increases when V LS =2•10 -3 m/s, to become very large at 10 -3 and 0.5•10 -3 m/s, the liquid flow concentrating in the axial part of the reactor. The variation on f can reach 40% and very broad dry zone appear in the column.

A homogeneous distribution of the liquid flow at the top of the column guarantees a still better liquid distribution in the reactor and reduces the radial heterogeneities of wetting efficiency, even at low superficial liquid velocity.

Size and locations of the dry zones

For a prewetted bed with the multicapillary distributor, the evolution of the size and the position of the dry regions has been investigated (V LS =2•10 -3 m/s and 6•10 -3 m/s). The crosssection of the column is divided into two zones (Figure 16) of same area: the central and the surrounding zones. Then, the surface area and the position of the gravity centre of each dry structure have been determined, and the surface area is compared to the area of one particle (2.5•10 -3 m in diameter for pellets B). A histogram is plotted where the dry structures are classified by their surface areas (in particles numbers), and differentiated along with the location of their gravity centre (in the central or surrounding zone). This stacked column chart presents the ratio of area of these dry structures on the overall dry projected surface (in percents) and their size in X-coordinate.

Figure 17 presents the axial evolution of the size and the position of these dry structures. Two superficial liquid velocities are studied: 6•10 -3 and 2•10 -3 m/s, leading to average wetting efficiencies of respectively ~0.8 and ~0.55.

For these two velocities, the largely majority class is that of the small structures: smaller than one pellet. These dry zones are very homogeneously distributed between the centre and the periphery of the column. Moreover, almost no axial evolution was observed. Nevertheless, the largest structures (obtained at low velocity), equivalent to ten particles, are met near the wall of the column rather than in the central zone of the bed.

Wetting efficiency distribution

Figure 18 presents the distribution of wetting efficiency at a particle scale for two values of V LS . In both cases the majority classes are the extreme ones and a minimum appears at an intermediate class. At V LS =8•10 -3 m/s, for a total wetting efficiency of 0.75, approximately 45 % of pellets are almost completely wet, the other classes gathering only 5 to 10% of particles.

When superficial liquid velocity decreases, the fraction of pellets in the intermediate classes is not as strongly modified as in the extreme classes (dry and totally wetted particles). This is due to a balance in between the adjacent classes which does not occur for extreme classes. For the lowest velocity (V LS =10 -3 m/s), only 18% of the particles are completely wet whereas nearly 20% of the pellets are almost dry.

Effect of liquid-solid affinity on wetting efficiency

The results obtained on the two-dimensional set-up proved that the nature of the phases held a key-role on the wetting efficiency. Therefore, the average wetting efficiency in the trickle bed column was measured for different liquids: water, ethanol and heptane. Figure 19 presents the evolution of the wetting efficiency with the liquid velocity for these three liquids. These liquids have very different affinities for alumina. The contact angles measured by the Washburn technique are: 65°, 39° for water and ethanol respectively, and 0° for heptane since it has been selected as the reference totally wetting liquid for the Washburn method. The smaller the contact angle is, the stronger the affinity of the liquid for the solid is, and the thinner and wider the liquid film. However, for V LS >2•10 -3 m/s, Figure 19 shows that the wetting efficiencies measured with heptane are only 5% higher than those measured with water. Indeed, contrary to monolayer configuration, due to bed confinement, the liquid holdup is increased inducing a high number of bridges between liquid films, and the effect of the liquid-solid affinity is erased. On the other hand, at low liquid velocities, the difference in wetting efficiencies for the two fluids is more significant, up to 30% for V LS =0.5•10 -3 m/s. For this velocity, the amount of liquid is not sufficient to cover the major part of the particles and the liquid-solid affinity is the keyfactor for the geometry of the liquid film. For a wetting liquid, the film is less thick and wider, the wetting efficiency is thus higher. In addition, the less wetting the liquid is, the larger the impact of V LS is at these low V LS values. To conclude, two operating zones are thus to distinguish, based on liquid superficial velocity range:

-in zone 1 (V LS >2•10 -3 m/s), the wetting efficiency is mainly conditioned by hydrodynamics, -in zone 2 (V LS <2•10 -3 m/s), liquid-solid affinity controls wetting efficiency f. These results moderate the results obtained on the bidimensional set-up. This set of data of averaged wetting efficiency has been completed and used to develop a new correlation for wetting efficiency prediction, including not only the effect of hydrodynamics, but also that of physicochemistry (through capillary numbers based on gas/liquid and liquid/solid surface tensions) [START_REF] Baussaron | Etude du mouillage partiel et du transfert de matière liquide-solide en réacteur à lit fixe arrosé[END_REF] .

Conclusion

Different set-ups, experimental methods and image processing have been implemented and validated to detect wetting structures inside a catalytic bed exposed to a gas-liquid trickling flow. The results obtained, put forward the great interest of these direct techniques of image acquisition and processing for the direct measurement of local wetting efficiencies. The local study, carried out in a bidimensional monolayer of pellets, yielded a direct access to the dynamic wetting efficiency, highlighting the dominating effect played by liquid-solid affinity. A second study, carried out in a real trickle bed with dye-adsorption method, showed that in a correctly prewetted bed and so far as the liquid phase is homogeneously distributed, there is no structuring of the local wetting: the characteristic size of dry zones is not larger than one particle diameter. In those conditions, it is fully justified to consider a pellet-scale wetting when modelling a trickle-bed reactor with a homogeneous liquid distribution, but it is more controversial to assume that all pellets are equally wetted when only 15 to 30% of the particles exhibit a wetting efficiency equal to the mean value within +/-20% range. This is no more the case in a non-prewetted bed, where large dry zones are created over the entire length of the bed. The effects of liquid-solid affinity observed on the local model were a little bit moderated by the results of the trickle bed: the very important containment which exists in a stacking of particles in a real bed improves liquid film damping and reduces the effect of liquid-solid affinity for V LS >2•10 -3 m/s. On the other hand, for low liquid velocity, wettability always controls the wetting efficiency and should be accounted for in its modelling. b) The contribution of each pixel of coordinates (i,j) is weighted (factor α k(i,j) ) according to its location in one of these crowns (i.e. the 3D area it represents).

Notations

Figure 1 :Figure 4 :

 14 Figure 1: Schema of the bidimensional monolayer of pellets

Figure 5 :

 5 Figure 5: 3D treatments a) The pellet projection is divided in successive crowns: 1, 2, …, k.b) The contribution of each pixel of coordinates (i,j) is weighted (factor α k(i,j) ) according to its location in one of these crowns (i.e. the 3D area it represents).

Figure 6 :Figure 9 :Figure 10 :

 6910 Figure 6: Photograph of the fixed bed reactor for dye-adsorption 1: column, 2: gas-liquid distributor, 3: liquid storage tank, 4: gear pump, 5: Coriolis mass flow meter, 6: gas flow meters, 7: three way valve

Figure 11 :Figure 12 :Figure 15 :Figure 16 :Figure 18 :Figure 19 :

 111215161819 Figure 11: Picture of a column cross-section (pellets A; V LS =2•10 -3 m/s; water)

Table 2

 2

		d p [m]	ρ ρ ρ ρ s [kg/m 3 ]	ε ε ε ε i [-]	a s [m 2 /g]
	Pellet A	6•10 -3	2886	0.517	277
	Pellet B	2.5•10 -3	3804	0.75	192
	Pellet C	2.5•10 -3	3195	0.591	83

Table 3

 3

Acknowledgements

The authors wish to thank Martine Auriol and Nourédine Chateur from SAP (LGC Toulouse) for physical characterization of the solid particles, Christine Rouch for PIV analysis, Jacques Labadie, Alain Muller, and Jean-Louis Nadalin (LGC Toulouse) for the design and implementation of the experimental set-ups.

LIST OF TABLES