A multigene circulating biomarker to predict the lack of FOLFIRINOX response after a single cycle in patients with pancreatic ductal adenocarcinoma (PDAC)

A multigene circulating biomarker to predict the lack of FOLFIRINOX response after a single cycle in patients with pancreatic ductal adenocarcinoma (PDAC) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A multigene circulating biomarker to predict the lack of FOLFIRINOX response after a single cycle in patients with pancreatic ductal adenocarcinoma (PDAC) Casper W.F. van Eijck, Willem de Koning, Fleur van der Sijde, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2008977/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: FOLFIRINOX chemotherapy showed promising results in treating patients with pancreatic ductal adenocarcinoma (PDAC). However, many patients and physicians are reluctant to start FOLFIRINOX due to its high toxicity and limited clinical response rates. In this study, we investigated the effect of a single cycle of FOLFIRINOX, in combination with a granulocyte colony-stimulating factor (G-CSF), on the blood immune transcriptome of PDAC patients. We aimed to identify an early circulating biomarker to predict the lack of FOLFIRINOX response. Methods Blood samples of 68 patients from all PDAC disease stages, who received at least four FOLFIRINOX cycles, were collected at baseline and after the first cycle. Patients were divided into “disease control” and “progressive disease” following the RECIST criteria 1.1. RNA was isolated and targeted immune-gene expression profiling was performed using the PanCancer Immune profiling panel of NanoString. The FOLFIRINOX delta Gene Expression Profiling (FFX-ΔGEP) score was calculated using the weight of eight genes following LASSO multivariate regression analysis. Results Comparing the immune gene expression profile of samples at baseline to after a single FOLFIRINOX cycle resulted in the identification of 395 differentially expressed genes (BH.P < 0.05), correlating to 30 significant alterations in relative immune cell abundancies and pathway activities (BH.P < 0.05). The patient cohort included 48 disease control and 10 progressive disease patients. The FFX-ΔGEP score, composed of eight genes ( BID , FOXP3 , KIR3DL1 , MAF , PDGFRB , RRAD , SIGLEC1 , and TGFB2) , could predict the lack of FOLFIRINOX response with a leave-one-out cross-validated AUC [95% CI] of 0.87 [0.60–0.98]. Our FFX-ΔGEP score outperformed the predictiveness of absolute and proportional ΔCA19-9 values with an AUC [95% CI] of 0.70 [0.27–1.0] and 0.52 [0.24–0.80], respectively. Notably, immune-gene expression profiles of baseline samples could not predict the lack of FOLFIRINOX response. Conclusions A single FOLFIRINOX cycle, combined with G-CSF, alters the peripheral immune transcriptome indisputably. We revealed a novel multigene FFX-ΔGEP score which is, to our knowledge, the first gene expression-based early circulating biomarker that predicts the lack of FOLFIRINOX response after only a single cycle. Validation in a larger independent cohort of samples is crucial before clinical implementation. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal and aggressive solid malignancies [ 1 ]. The prognosis is poor; the incidence (495,773) and the mortality rate (466,000) worldwide were comparable in 2020 [ 2 ], and the 5-year overall survival (OS) rate for all stages combined is approximately 9% [ 3 ]. The poor prognosis is, among other things, related to the lack of distinctive symptoms, the lack of reliable biomarkers for early diagnosis, progressive metastatic spread, and the complex tumor (immune) microenvironment (TME) [ 4 ]. The only curative treatment for early-stage PDAC is surgical resection in combination with chemotherapy, however, only 20% of tumors are resectable at the time of diagnosis, and more than 50% of patients present with metastatic disease [ 5 – 7 ]. The combined chemotherapeutic regimen of 5-fluorouracil, folinic acid, irinotecan, and oxaliplatin (FOLFIRINOX) is considered the best adjuvant and first-line treatment for patients with locally advanced (LAPC) and metastatic pancreatic cancer [ 8 ]. Various studies reported improved OS in FOLFIRINOX-treated compared to gemcitabine-treated patients for all disease stages [ 9 – 11 ]. A meta-analysis combining 11 studies reported improved OS in LAPC (24.2 months vs. 6–13 months) [ 9 ]; a multicenter, randomized, phase 2–3 trial reported improved OS in metastatic patients (11.1 months vs. 6.8 months) [ 10 ]; and a multicenter, randomized, phase 3 trial reported the most prolonged OS in patients with stage I-II or borderline resectable patients [ 11 ]. In addition, neoadjuvant FOLFIRINOX followed by surgical resection showed favorable median OS, resection rate, and R0-resection rate in resectable pancreatic cancer patients [ 12 ]. Nevertheless, 25% of PDAC patients treated with FOLFIRINOX show disease progression during treatment despite the generally improved FOLFIRINOX response rates [ 10 , 13 ]. In addition, FOLFIRINOX is closely associated with triggering toxicity-related events which were higher in FOLFIRINOX-treated patients compared to patients receiving gemcitabine [ 10 , 14 ]. Prophylactic treatment with granulocyte colony-stimulating factor (G-CSF), such as lipegfilgrastim, is commonly used to prevent FOLFIRINOX-induced neutropenia associated with poor survival [ 15 – 17 ]. G-CSF stimulates granulocyte production by the bone marrow, mainly targeting neutrophil generation and differentiation [ 18 ]. Treatment response is evaluated through computed tomography (CT) imaging, but not until after four cycles of FOLFIRINOX. Exposure to ineffective but toxic treatment reduces patients' quality of life, carries unnecessary costs, and withholds patients from potentially effective treatment. Hence, it is desirable to identify a biomarker that predicts the lack of response at an early stage. Carbohydrate Antigen 19 − 9 (CA19-9) is the only FDA-approved biomarker used in clinical practice for routine management of PDAC [ 19 ]. However, CA19-9 is not specific for PDAC, Lewis-A antigen-negative patients cannot synthesize CA19-9, and the decrease of CA19-9 levels may predict FOLFIRINOX response only after multiple cycles [ 20 ]. Several studies showed that oxaliplatin, 5-FU, and irinotecan enhanced tumor antigen presentation by increasing HLA-I and programmed death-ligand 1 (PD-L1) tumor expression in poor immunogenic cancer types such as PDAC [ 21 , 22 ]. This could synthesize the tumor for immune checkpoint (IC) inhibitory-based immunotherapy and increase the activation of CD8 + cytotoxic T lymphocytes (CTLs) [ 23 ]. In addition, oxaliplatin is a well-known inducer of immunogenic cancer cell death by evoking the presentation of damage-associated molecular patterns within cancer cells [ 24 , 25 ]. Oxaliplatin exerts immunomodulatory effects resulting in increased antigenicity, enhanced adaptive immune responses [ 26 ], and antitumor systemic immune response [ 27 ]. However, the peripheral immune alterations in following FOLFIRINOX are not measured. We hypothesized that the immunological effects of FOLFIRINOX may be visible in the peripheral blood after a single cycle of treatment which can be utilized to predict the lack of response to treatment. The aim of this study was to identify an early circulating biomarker predictive of the lack of FOLFIRINOX response in PDAC patients. To that aim, we investigated the effect of a single cycle of FOLFIRINOX, accompanied by prophylactic G-CSF, on the peripheral immune transcriptome of PDAC patients using targeted immune-gene expression profiling. 2. Methods 2.1 Patient population A total of 80 PDAC patients were included in this study. PDAC patients were hospitalized at the Erasmus University Medical Centre Rotterdam between February 2018 and February 2021. Twenty-three patients with (borderline) resectable PDAC participated in the randomized clinical trial PREOPANC-2 (Dutch trial register NL7094), and 57 patients with locally advanced or metastasized PDAC participated in the prospective cohort study iKnowIT (Dutch trial register NL7522). Exclusion criteria were < 18 years of age, previous treatment with FOLFIRINOX, or co-treatment with another chemotherapeutic. 2.2 Clinical procedure Following histological confirmation of the primary tumor or metastases, patients were treated with at least four cycles of FOLFIRINOX chemotherapy. All patients were prophylactically treated with the long-acting G-CSF lipegfilgrastim (Lonquex®; Teva Ltd, Petach Tikva, Israel), 24 hours after each cycle, to reduce FOLFIRINOX-induced neutropenia [ 17 , 28 ]. Two whole blood samples from each patient were collected: at baseline (immediately before the first cycle) and 14 days after the first but just before the second FOLFIRINOX cycle. As part of the standard clinical routine, serum CA19-9 concentrations were determined at the same time points using an enzyme-linked immunosorbent assay (ELISA). A patient’s response to FOLFIRINOX was assessed based on a CT scan made after four cycles, evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria (Fig. 1 ) [ 29 ]. 2.3 Clinicopathological groups To compare immune profiles, patients were grouped based on their clinicopathological characteristics. Disease stage at baseline included resectable, locally advanced pancreatic cancer (LAPC), and metastatic patients. Baseline CA19-9 values included patients with low (35–150 µmol/L) and high (> 1500 µmol/L) values. Patients who showed stable disease, partial response, or complete response were defined as “disease control”. Patients showing disease progress were defined as “progressive disease”. 2.3 Whole blood sample collection and RNA isolation Whole blood samples were collected in Tempus tubes (Applied Biosystems, Foster City, CA, USA) and stored at -80°C. Tempus tubes contain an RNA stabilizing reagent, which preserves the RNA quality and enables measuring gene expression profiles without isolating the peripheral blood mononuclear cells [ 30 ]. Total RNA was extracted from blood in Tempus tubes using the Tempus Spin RNA Isolation Kit of Thermo Fisher Scientific (Waltham, MA, USA) following the manufacturer's instructions. RNA quality control was done using the Agilent 2100 BioAnalyzer (Santa Clara, CA, USA). Samples with RNA concentrations less than 35 mg/mL were excluded. Corrected RNA concentrations were calculated based on the percentage of fragments of 300–4000 nucleotides to correct for RNA degradation. 2.4 Targeted multiplex gene expression Targeted gene expression profiling was performed using the nCounter® FLEX system and PanCancer Immune profiling panel, which includes 40 housekeeping genes and 730 immune-related genes [ 31 ]. A total of 200 ng RNA per sample in a maximum of 7 µL was used for hybridization, which was performed at 65°C for 17 hours using the SimpliAmp Thermal Cycler (Applied Biosystems). Gene expression was counted by scanning 490 Fields of View (FOV). 2.4.1 Data processing and analysis Data quality control, normalization, and analysis were performed using the nSolver™ software (version 4.0) and the Advanced Analysis module (version 2.0) of NanoString Technology Inc. [ 32 ]. A patient’s gene expression profile was included if all positive and negative control genes were within the expected values and if binding density values ranged between 0.5 and 3.0. Raw gene counts were normalized based on the most stable 34 housekeeping genes, identified by the geNorm algorithm [ 33 ] (Table S1), and all normalized data were log 2 transformed. Genes were included when they were higher than the limit of detection of 4.384 log 2 , calculated as the average of all eight negative control genes multiplied by two, in > 80% of the gene expression profiles. Differentially expressed genes (DEGs) were identified using simplified negative binomial models, mixture negative binomial models, or log-linear models based on the convergence of each gene. Genes with a P-value < 0.05 after correction for multiple testing with the Benjamin-Hochberg (BH) method were considered DEGs. 2.4.2 Immune cell type analysis with the NanoString nSolver module The peripheral abundance of various immune cell types was quantified using the nSolver Advanced Analysis module, which assigns relative immune cell type scores to each sample [ 34 ]. Marker genes, that identify specific immune cell types, were selected based on the pairwise similarities method tailored specifically for PDAC [ 35 ]. Marker genes were accepted to define an immune cell type when pairwise similarity was sufficient (R 2 ≥ 0.6). Accordingly, the relative abundance of immune cells was calculated between the tested groups (Table S2). 2.4.3 Pathway analysis with the NanoString nSolver module and the Cytoscape plug-in ClueGO Genes were clustered into predefined pathways (Table S3) using the nSolver Advanced Analysis module to examine immune-associated pathway alterations. We calculated the square root of the average squared t-statistic of all genes in the corresponding pathway [ 34 ], resulting in a pathway score for each sample. In addition, to explore the potential role of unique DEGs in disease control and progressive disease patients, we performed functional enrichment analysis using the Cytoscape plug-in ClueGO [ 36 ]. DEGs were included in the ClueGO analysis if they met two criteria: (1) a log 2 fold-of-change (FOC) > |0.5| after a single FOLFIRINOX cycle and (2) a log 2 FOC > |0.5| difference between disease control and progressive disease patients. 2.5 Statistical analysis Statistical testing and data visualization were performed with R Statistical Software (v.4.1.2) [ 37 ]. Data were tested for normality with Shapiro-Wilk tests. We used paired or unpaired two-sided student t-tests for parametrical data and paired Wilcoxon tests or unpaired Mann-Whitney U tests for non-parametrical data. All tests were corrected with the BH correction for multiple testing. We used the R packages ggplot2 [ 38 ] and EnhancedVolcano [ 39 ] for data visualization. 2.6 The FOLFIRINOX delta gene expression profiling (FFX-ΔGEP) score A gene signature representing an early predictive circulating biomarker of the lack of FOLFIRINOX response was identified (FFX-ΔGEP) score. Briefly, log 2 normalized gene expression counts of baseline samples were subtracted from the log 2 normalized gene expression counts of samples after a single FOLFIRINOX cycle, resulting in Δ expression counts for each gene. Genes that showed statistically significant differences (BH.P < 0.05) in Δ expression count between disease control and progressive disease patients were identified as candidate genes for the FFX-ΔGEP score. Patients were randomly split into training and test sets (75%/25%). To find the combination of candidate genes predicting the lack of FOLFIRINOX response most accurately, the least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was conducted on the training set with leave-one-out cross-validation. Weights (regression coefficient) were assigned to the candidate genes to improve model robustness and avoid overfitting. Genes weighted with a regression coefficient of 0 were excluded from the FFX-ΔGEP score. The fitted model was used in the corresponding test set to predict the lack of FOLFIRINOX response. The overall predictive performance was assessed by receiver operating characteristic (ROC) analysis depicting the area under the curve (AUC) value with a 95% confidence interval (CI). The absolute (µmol/L) and proportional (%) change in CA19-9 was calculated to compare the predictive performance to the FFX-ΔGEP score. CA19-9 values of baseline samples were subtracted from those after a single FOLFIRINOX cycle to obtain absolute Δ CA19-9 values. The proportional Δ CA19-9 values were calculated by dividing the absolute Δ CA19-9 values by their baseline Δ CA19-9 values. ROC analysis was performed for both absolute and proportional Δ CA19-9 values, and the AUC value was compared to the AUC value of the FFX-ΔGEP score. 3. Results 3.1 Samples and patient characteristics Blood samples of 80 patients treated with at least four cycles of FOLFIRINOX, in combination with prophylactic G-CSF, were collected at baseline and after the first cycle (Figure 1). RNA isolation was performed for a total of 160 blood samples, of which eight were excluded due to poor RNA concentration (< 35 mg/mL), and four were excluded due to poor binding density ( 3.0). After removing corresponding pairs, 68 patients (136 samples) were included in the data analysis. The response to four FOLFIRINOX cycles was assessed by CT scan evaluation in 58 out of 68 patients, which resulted in 48 disease control and 10 progressive disease patients (Table 1). The overall survival [95% CI] for the disease control and the progressive disease patients was 40.2 [32.7 – 46.5] and 13.8 [11.2 – 15.5] months. All clinicopathological characteristics are summarized in Table 1. Table 1 Clinicopathological characteristics of patients in the study All patients Treatment response (n = 68) Disease control (n = 48) Progressive disease (n = 10) Age (y), mean (range) 65 (47 – 81) 65 (49 – 78) 60 (47 – 69) Gender, no (%) Male 35 (51%) 25 (52%) 5 (50%) Female 33 (49%) 23 (48%) 5 (50%) Alcohol, no (%) Yes 35 (51%) 22 (46%) 3 (30%) No 33 (49%) 26 (54%) 7 (70%) Smoking, no (%) Yes 40 (59%) 28 (58%) 6 (60%) No 28 (41%) 20 (42%) 4 (40%) Diabetes Mellitus (DM), no (%) Yes 14 (11%) 11 (33%) 2 (20%) No 54 (79%) 37 (77%) 8 (80%) Disease stage, no (%) Resectable disease 21 (31%) 17 (35%) 2 (20%) LAPC 28 (41%) 19 (40%) 5 (50%) Metastatic disease 19 (28%) 12 (25%) 3 (30%) Baseline CA19-9 (U/mL), no (%) Mean (± SD) 2919 (± 10893) 1352 (± 4182) 10503 (± 25859) No expression ( 1500) 15 (22%) 7 (14.5%) 4 (40%) CA19-9 difference after a single cycle, compared to baseline Mean (U/mL) (± SD) 225.5 (± 1883.4) 128.1 (± 1776.8) 242.5 (± 2041.9) Mean (%) (range) 15 (-62 – 304) 20% (-53 – 304) 5% (-62 – 49) Baseline clinical parameters, mean (± SD) CEA (µg/L) 19.09 (± 49.04) 11.86 (± 24.41) 32.70 (± 69.34) Bilirubin (µmol/L) 13 (± 12) 13 (± 8) 20 (± 23) CRP (mg/L) 16 (± 24) 17 (± 25) 17 (± 24) SII 1182 (± 1151) 1116 (± 1067) 1189 (± 713) NLR 4.0 (± 2.8) 4.0 (± 3.0) 3.8 (± 1.8) Total cycles of FOLFIRINOX, mean (± SD) 7 (± 3) 8 (± 2) 4 (± 2) Median OS (months), median [95% CI] 32.0 [27.8 – 42.8] 40.2 [32.7 – 46.5] 13.8 [11.2 – 15.5] Abbreviations LAPC: Locally Advanced Pancreatic Cancer; CA19-9: Carbohydrate Antigen 19-9; CEA: Carcinoembryonic Antigen; CRP: C-Reactive Protein; SII: Systemic Immune-inflammation Index; NLR: Neutrophil-to-Lymphocyte Ratio; SD: Standard Deviation; OS: Overall Survival. 3.2 PDAC patients with different disease stages or different baseline CA19-19 values show comparable immune profiles Immune profiles based on the three disease stages (resectable, LAPC, metastatic) and based on the two baseline CA19-9 values (low and high) were compared at baseline and after a single FOLFIRINOX cycle. Baseline immune profiles revealed eight DEGs between the three disease stages and no DEGs between low and high baseline CA19-9 values (Figure S1). The activity of the pathways in baseline samples was not altered in any of the comparisons (BH.P > 0.05; Figure S2). Two immune cell types were relatively different between the three disease stages (BH.P < 0.05). Resectable patients showed relatively lower NK cells compared to LAPC and metastatic patients and relatively lower conventional dendritic cells type 2 (cDC2s) compared to metastatic patients (BH.P 0.05). A single FOLFIRINOX cycle induced multiple DEGs amongst the three disease stages and the two baseline CA19-9 values (Figure S3). However, unique DEGs between groups were scarce resulting in six statistically significant differences (BH.P < 0.05) in altered pathways and immune cell type abundances (Figure S4). The cytotoxicity pathway was less activated in metastatic compared to resectable patients (BH.P < 0.05). The relative cytotoxic cell abundance was higher in metastatic compared to resectable and LAPC patients (BH.P < 0.05). Also, patients with high baseline CA19-9 values showed relatively higher neutrophils and NK CD56 dim cells and relatively lower monocytes compared to patients with low CA19-9 values (BH.P < 0.05; Figures S3 and S4). 3.3 A single FOLFIRINOX cycle altered the peripheral immune transcriptome of PDAC patients Data analysis revealed 395 DEGs (BH.P < 0.01) in samples after a single FOLFIRINOX cycle compared to baseline samples (Figure 2A). Filtering the DEGs based on a log 2 FOC ≥ |1.0| revealed 36 upregulated genes and three downregulated genes after a single FOLFIRINOX cycle (Figure 2B, Table S4). Pathway analysis revealed alterations among all immune-associated pathways (BH.P < 0.001; Figure 3A-3C). Pathway-specific genes with log 2 FOC ≥ |1.0| were considered key pathway drivers (Table S4). The pathways of adhesion, chemokines, cytokines, interleukins, macrophage function, pathogen defense, toll-like receptor (TLR), and tumor necrosis factor (TNF) superfamily were enhanced after a single FOLFIRINOX cycle while the immune-associated pathways of antigen processing, B cell, NK cell, and T cell functions, and cytotoxicity were diminished. Immune cell type analysis revealed alterations among all peripheral immune cells (BH.P < 0.05; Figure 3D). The relative peripheral abundance of the total immune cells ( PTPRC , CD45 + ), cDC2, monocytes, NK cells, and neutrophils increased while the B cells, cytotoxic cells, NK CD56 dim cells, total T cells, T regulatory (Treg) cells, and CD8 + T cells decreased after a single FOLFIRINOX cycle (Figure 3D). 3.4 A single FOLFIRINOX cycle altered the expression of IC regulatory genes Th e expression of the IC inhibitory genes PDCD1 (PD-1), CD274 (PD-L1), and PDCD1LG2 (PD-L2) were upregulated after a single FOLFIRINOX cycle compared to baseline with an average log 2 FOC of 1.1 (BH.P < 0.001; Figure 4). In contrast, the IC inhibitory genes BTLA, CTLA4 and its ligand CD86 (B7-2), HAVCR2 (TIM-3), and TIGIT were statistically significant downregulated (BH.P < 0.001; Figure 4). The IC inhibitory gene LAG3 was not altered (Figure S5). 3.5 Subtle differences in the immune transcriptome of disease control and progressive disease patients after a single FOLFIRINOX cycle Immune profiles of the disease control and progressive disease patients were compared at baseline and after a single FOLFIRINOX cycle. Baseline immune profiles revealed no differences in pathway activities between the two groups. However, a relatively high abundance in the total immune cells and Treg cells were observed in progressive disease patients (Figure 5). Immune profiles after a single FOLFIRINOX cycle revealed 400 DEGs in disease control and 256 DEGs in progressive disease patients (Figure S6), which were used in the ClueGo analysis based on the criteria in the materials and method section. Two key genes involved in the negative regulation of type-I interferon-mediated (IFN-I) signaling pathway were downregulated in disease control but not in progressive disease patients (BH.P < 0.01; Figures 6A and 6B). The change in IC regulatory gene expression, pathway activity, and immune cell type abundance was comparable in both groups (Figure S7), with one immune cell type exception. Driven by its solitary marker KIR3DL1 , the relative abundance of NK CD56 dim cells was decreased in disease control but increased in progressive disease patients (BH.P < 0.05; Figure 6C). 3.6 An eight-gene FFX- GEP score predicted the lack of response after a single FOLFIRINOX cycle To identify an early circulating biomarker that predicts the lack of FOLFIRINOX response, we developed an FFX-ΔGEP score. The Δ gene expression count, which results from subtracting the log 2 normalized gene expression counts of baseline samples from samples after a single FOLFIRINOX cycle, revealed fourteen candidate genes that differed significantly between disease control and progressive disease patients (BH.P < 0.05; Table 2). LASSO multivariate regression analysis, which constructed the most optimal combination of candidate genes by assigning a regression coefficient (weight) to all candidate genes, was conducted. Six candidate genes were assigned a weight of zero and the FFX-ΔGEP score was composed of the remaining eight genes (Figure 7): The eight-gene FFX-ΔGEP score ranged from 3.82 to -1.76 among all patients, and the performance to predict lack of FOLFIRINOX response after a single cycle was assessed by ROC analysis (Figure 8A). The leave-one-out cross-validated AUC [95% CI] was 0.87 [0.60 – 0.98], indicating that the FFX-ΔGEP score could distinguish between disease control and progressive disease patients. The predictive performance of the currently used absolute and proportional Δ CA19-9 values [95% CI] were 0.70 [0.27 – 1.0] and 0.52 [0.24 – 0.80]. Importantly, the FFX-ΔGEP score outperformed Δ CA19-9 values with less overlap in the designation of disease control and progressive disease patients (Figures 8B-8D). Table 2 The f ourteen candidate genes selected for the FFX-ΔGEP score Gene Disease control mean (± SD) Progressive disease Mean (± SD) BH.P value Weights BID 0.030 (± 0.48) -0.237 (± 0.42) 0.030 -1.63 FOXP3 0.012 (± 0.67) -0.518 (± 0.43) 0.012 -0.10 KIR3DL1 0.018 (± 0.59) 0.255 (± 0.78) 0.018 0.26 KLRC1 0.035 (± 0.56) 0.075 (± 0.71) 0.035 0 KLRD1 0.044 (± 0.46) -0.293 (± 0.53) 0.044 0 KLRG1 0.041 (± 0.46) -0.204 (± 0.47) 0.041 0 MAF 0.023 (± 0.44) -0.111 (± 0.47) 0.023 0.54 NFATC2 0.024 (± 0.43) -0.136 (± 0.37) 0.024 0 PDGFRB 0.038 (± 0.60) 0.011 (± 0.49) 0.038 0.31 PLAU 0.043 (± 0.99) 0.611 (± 0.71) 0.043 0 REL 0.028 (± 0.32) 0.278 (± 0.37) 0.028 0 RRAD 0.008 (± 0.46) 1.412 (± 0.70) 0.008 0.97 SIGLEC1 0.020 (± 0.84) 0.403 (± 1.24) 0.020 0.21 TGFB2 0.020 (± 0.64) 0.790 (± 0.60) 0.020 0.81 The mean values of the Δ gene expression counts (± SD) per response group. The BH.P value between disease control and progressive disease patients. The assigned weights are calculated using LASSO multivariate regression analysis. Abbreviations : SD: Standard Deviation; BH.P: Benjamin-Hochberg P value; LASSO: Least Absolute Shrinkage and Selection Operator. 4. Discussion In this study, we used paired blood samples of 68 PDAC patients to investigate the effect of a single FOLFIRINOX cycle on the immune profile. We aimed to identify an early circulating biomarker to predict the lack of response to FOLFIRINOX. We revealed an eight-gene FFX-ΔGEP score that predicted the lack of FOLFIRINOX response only after the first cycle, independent of disease stage or change in CA19-9. This novel multigene FFX-ΔGEP score is, to our knowledge, the first gene expression-based early circulating biomarker predicting the lack of FOLFIRINOX response in PDAC patients from all disease stages. The FFX-ΔGEP score is composed of eight immune-related genes in which FOXP3 , KIR3DL1 , MAF , and SIGLEC1 [40-43] are associated with immune cell types, and BID , PDGFRB , RRAD , and TGFB2 are associated with the tumor or chemotherapeutic efficacy [44-47]. FOXP3 is a marker for Tregs, associated with poor PDAC prognosis [40, 48]. Neoadjuvant FOLFIRINOX reduced the peripheral and intra-tumoral abundance of Tregs in PDAC [49, 50]. KIR3DL1 inhibits NK cell activity [41] and was associated with PDAC progression [51]. This fits well with our observation that KIR3DL1 expression did not change in progressive disease but decreased in disease control patients after a single FOLFIRINOX cycle. MAF is a transcription factor known to induce CD8 T cell dysfunction [42]. In addition, MAF is known to be highly expressed in M2 macrophages [52], associated with poor prognosis in PDAC [53]. MAF expression was not changed in progressive disease, but it was downregulated in disease control patients after a single cycle. SIGLEC1 is a sialic acid-binding cell-surface protein that mediates pathogenic phagocytosis and endocytosis [54]. In the blood, SIGLEC1 is exclusively expressed by activated CD14 + monocytes, mostly in reaction to IFN-I [43, 55], which stimulate CD8 + cells through tumor antigen presentation [56]. However, our results showed a downregulation of SIGLEC1 expression but an increased IFN-I pathway activity in disease control patients. In addition, monocytes were not found to be associated with the increased expression of SIGLEC1. Suggesting that SIGLEC1 might have a different function in PDAC patients. BID encodes pro-apoptotic intracellular proteins that belong to the BCL-2 family [44]. The deregulated expression of the BCL-2 family is associated with apoptotic resistance in PDAC [57]. In accordance, our results showed downregulation in BID expression in progressive disease patients only. PDGFRB encodes the platelet-derived growth factor receptor β associated with poor disease-free survival, cancer cell invasion, and metastasis in PDAC [45, 46]. This is in line with our results showing no change in PDGFRB expression in progressive disease but downregulation in disease control patients. In gastric and colorectal cancer, 5-FU and oxaliplatin, two chemotherapeutic agents of FOLFIRINOX, displayed increased efficacy when combined with RRAD inhibition [58]. We observed a smaller upregulation of RRAD expression in disease control patients. Transforming growth factor-β2 ( TGFB2 ) plays a dual and complicated role in PDAC [59]. Improved clinical outcome of LAPC patients treated with the combination of FOLFIRINOX and the TGF-β antagonist (Losartan) followed by individualized chemo-radiotherapy, was observed previously [60]. This fits well with the greater upregulated TGFB2 expression in progressive disease compared to disease control patients. Our FFX-ΔGEP score was identified based on 48 disease control and 10 progressive disease patients. The score needs to be validated using an external larger cohort of samples. In addition, PDAC patients included in our study were treated with G-CSF within 24 hours after each FOLFIRINOX cycle. We have not yet examined the FFX-ΔGEP score in patients who did not receive G-CSF. Moreover, longitudinal blood sample collection is needed to explore the applicability of the FFX-ΔGEP score in patient follow-up. In our study, we treatment response using CT scan evaluation. However, some patients experience prolonged OS without showing imaging response. Therefore, it must be examined if the FFX-ΔGEP score can predict FOLFIRINOX-induced prolonged OS. Based on our result, we could not calculate a cut-off value for the FFX-ΔGEP score indicating response or lack of response to FOLFIRINOX treatment. A higher number of samples is needed to calculate an accurate cut-off value. To our knowledge, this study is the first to describe the effect of a single FOLFIRINOX cycle, accompanied by prophylactic G-CSF, on the immune transcriptome of PDAC patients. We discovered that a single cycle of FOLFIRINOX changed the expression of 395 immune-related genes significantly, even after two weeks of recovery. Our results showed that the relative peripheral abundance of total immune cells (CD45 + ), B cells, cDC2, cytotoxic cells, monocytes, NK CD56 dim cells, and all T cell subsets (total, CD8 + , and Treg) were reduced while the relative neutrophil abundance was increased after a single cycle of treatment. The increase in granulocyte-derived cells can be explained as an effect of G-CSF that was injected into all patients in our cohort, which affects the relative abundance of the other immune cells. In line with this discovery, previous studies described a rapid recovery of total lymphocytes, cDCs, and monocytes after two weeks of chemotherapy [47, 61] and increased cDC2s after G-CSF treatment [62]. Importantly, the immune transcriptome in patients with different disease stages or different baseline CA19-9 values was similar. This suggests that the progression of PDAC does not stimulate the systemic immune response. Additionally, we could not predict the lack of FOLFIRINOX response using baseline samples only. This highlights the challenges we phase in applying precision medicine protocols or stratifying PDAC patients to receive their most suitable treatment. Based on our results, at least one cycle of FOLFIRINOX is needed to predict the lack of response in PDAC patients. 5. Conclusion Using targeted immune-gene expression profiling, we discovered a novel multigene FFX-ΔGEP score predictive of the lack of FOLFIRINOX response only after the first cycle. In our cohort, the FFX-ΔGEP score predicted the lack of FOLFIRINOX response with more accuracy than the absolute or proportional change in CA19-9 levels. Our FFX-ΔGEP score must be validated in a larger independent cohort of samples, which includes PDAC patients without G-CSF treatment, and it should be examined if the score can predict the lack of FOLFIRINOX response based on OS. Additionally, we are the first to describe the pronounced effect of a single FOLFIRINOX cycle on the immune transcriptome in the blood of PDAC patients from all disease stages. Abbreviations AUC Area Under the Curve BH.P Benjamin-Hochberg corrected P value BID BH3-Interacting Domain Death Agonist BTLA B and T Lymphocyte Associated CA19-9 Cancer antigen 19-9 CD Cluster of Differentiation CD86 (B7-2) CD86 Antigen (CD28 Antigen Ligand 2, B7-2 Antigen) CD274 (PD-L1) Programmed Cell Death Ligand 1 cDC2s conventional Dendritic Cells type 2 CI Confidence Interval CT Computed Tomography CTLs Cytotoxic T lymphocytes CTLA4 Cytotoxic T-Lymphocyte Associated protein 4 DEGs Differentially Expressed Genes ELISA Enzyme-Linked Immunosorbent Assay FFX-ΔGEP score FOLFIRINOX delta Gene Expression Profiling score FOC Fold-Of-Change FOLFIRINOX Regimen of 5-fluorouracil, folinic acid, irinotecan, and oxaliplatin FOV Fields Of View FOXP3 Forkhead Box P3 G-CSF Granulocyte-colony stimulating factor HAVCR2 (TIM-3) Hepatitis A Virus Cellular Receptor 2 IC Immune Checkpoint IFN-I Interferon-I ISG15 Interferon-Stimulated Gene 15 KIR3DL1 Killer Cell Immunoglobulin Like Receptor 3DL1 LAG3 Lymphocyte Activating Gene 3 LAPC Locally Advanced Pancreatic Cancer LASSO Least Absolute Shrinkage and Selection Operator Lipegfilgrastim glycoPEGylated human N-methionyl granulocyte-colony stimulating factor MAF Avian Musculoaponeurotic Fibrosarcoma OAS3 2'-5'-Oligoadenylate Synthetase 3 OS Overall survival PDAC Pancreatic Ductal Adenocarcinoma PDCD1 (PD-1) Programmed Cell Death 1 PDCD1LG2 (PD-L2) Programmed Cell Death Ligand 2 PDGFRB Platelet-Derived Growth Factor Receptor β PTPRC Protein Tyrosine Phosphatase Receptor Type C RRAD Ras-Related Associated with Diabetes RECIST Response Evaluation Criteria In Solid Tumors ROC Receiver Operating Curve SIGLEC1 (CD196) Sialic Acid Binding Ig Like Lectin 1 TGFB2 Transforming growth factor β 2 TIGIT T cell Immunoreceptor with Ig and ITIM domains TLR Toll-Like Receptor TME Tumor Microenvironment TNF Tumor Necrosis Factor Treg T regulatory cell Declarations Ethics approval and consent to participate Participating patients in this study were included in two trials conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committees of Erasmus MC (ethics committee reference number MEC-2018-087 and MEC-2018-004). Written informed consent was obtained from all patients. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available, with permission of the Erasmus Medical Center Rotterdam, from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding This work was financially supported by the Survival with Pancreatic Cancer Foundation (www.supportcasper.nl) Authors' contributions CWFvE, WdK, and DM concepted and designed the study. FvdS, MM, BGK, MH, CHJvE, and DM were responsible for all resources. FvdS and MM, BGK, and MH collected and provided clinical data and samples. CWFvE and WdK performed the formal (statistical) analyses and visualization. CWFvE, SB, and DM wrote the manuscript. CHJvE and DM supervised this work. All authors have reviewed and agreed to the final version of the manuscript. Acknowledgments The authors would like to give special thanks to all students involved in the blood collection, to Disha S. Vadgama and Jasper Dumas for their help in processing the blood samples, and to Jie Ju for her assistance with the LASSO multivariate regression analysis. References Sarantis, P., et al., Pancreatic ductal adenocarcinoma: Treatment hurdles, tumor microenvironment and immunotherapy . World J Gastrointest Oncol, 2020. 12 (2): p. 173–181. Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries . CA Cancer J Clin, 2021. 71 (3): p. 209–249. Rangarajan, K., et al., Systemic neoadjuvant chemotherapy in modern pancreatic cancer treatment: a systematic review and meta-analysis . Ann R Coll Surg Engl, 2019. 101 (7): p. 453–462. Kikuyama, M., et al., Early Diagnosis to Improve the Poor Prognosis of Pancreatic Cancer . Cancers (Basel), 2018. 10 (2). Adamska, A., A. Domenichini, and M. Falasca, Pancreatic Ductal Adenocarcinoma: Current and Evolving Therapies . Int J Mol Sci, 2017. 18 (7). Fan, J.Q., et al., Current advances and outlooks in immunotherapy for pancreatic ductal adenocarcinoma . Mol Cancer, 2020. 19 (1): p. 32. Wolfgang, C.L., et al., Recent progress in pancreatic cancer . CA Cancer J Clin, 2013. 63 (5): p. 318–48. van der Sijde, F., et al., Treatment Response and Conditional Survival in Advanced Pancreatic Cancer Patients Treated with FOLFIRINOX: A Multicenter Cohort Study. J Oncol, 2022. 2022 : p. 8549487. Suker, M., et al., FOLFIRINOX for locally advanced pancreatic cancer: a systematic review and patient-level meta-analysis . Lancet Oncol, 2016. 17 (6): p. 801–810. Conroy, T., et al., FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer . N Engl J Med, 2011. 364 (19): p. 1817–25. Conroy, T., et al., FOLFIRINOX or Gemcitabine as Adjuvant Therapy for Pancreatic Cancer . N Engl J Med, 2018. 379 (25): p. 2395–2406. Janssen, Q.P., et al., Neoadjuvant FOLFIRINOX in Patients With Borderline Resectable Pancreatic Cancer: A Systematic Review and Patient-Level Meta-Analysis . J Natl Cancer Inst, 2019. 111 (8): p. 782–794. Perri, G., et al., Response and Survival Associated With First-line FOLFIRINOX vs Gemcitabine and nab-Paclitaxel Chemotherapy for Localized Pancreatic Ductal Adenocarcinoma . JAMA Surg, 2020. 155 (9): p. 832–839. Thibodeau, S. and I.A. Voutsadakis, FOLFIRINOX Chemotherapy in Metastatic Pancreatic Cancer: A Systematic Review and Meta-Analysis of Retrospective and Phase II Studies . J Clin Med, 2018. 7 (1). Steger, G., et al., 1697P - Use of lipegfilgrastim for the prophylaxis of chemotherapy-induced neutropenia: Pan-European non-interventional study . Annals of Oncology, 2018. 29 : p. viii607-viii608. Terazawa, T., et al., Efficacy of Prophylactic G-CSF in Patients Receiving FOLFIRINOX: A Preliminary Retrospective Study . Intern Med, 2015. 54 (23): p. 2969–73. Timmer-Bonte, J.N.H., et al., Lipegfilgrastim for prophylaxis of chemotherapy-induced neutropenia in Dutch patients . Neth J Med, 2020. 78 (5): p. 270–276. Richards, M.K., et al., Pivotal role of granulocyte colony-stimulating factor in the development of progenitors in the common myeloid pathway . Blood, 2003. 102 (10): p. 3562–8. Winter, J.M., C.J. Yeo, and J.R. Brody, Diagnostic, prognostic, and predictive biomarkers in pancreatic cancer . J Surg Oncol, 2013. 107 (1): p. 15–22. van der Sijde, F., et al., Circulating Biomarkers for Prediction of Objective Response to Chemotherapy in Pancreatic Cancer Patients . Cancers (Basel), 2019. 11 (1). Principe, D.R., et al., Long-Term Gemcitabine Treatment Reshapes the Pancreatic Tumor Microenvironment and Sensitizes Murine Carcinoma to Combination Immunotherapy . Cancer Res, 2020. 80 (15): p. 3101–3115. Zhou, C., et al., Camrelizumab plus carboplatin and pemetrexed versus chemotherapy alone in chemotherapy-naive patients with advanced non-squamous non-small-cell lung cancer (CameL): a randomised, open-label, multicentre, phase 3 trial . Lancet Respir Med, 2021. 9 (3): p. 305–314. Park, S.J., et al., Cisplatin and oxaliplatin induce similar immunogenic changes in preclinical models of head and neck cancer . Oral Oncol, 2019. 95 : p. 127–135. Bezu, L., et al., Combinatorial strategies for the induction of immunogenic cell death . Front Immunol, 2015. 6 : p. 187. Tesniere, A., et al., Immunogenic death of colon cancer cells treated with oxaliplatin . Oncogene, 2010. 29 (4): p. 482–91. Liu, W.M., et al., Pre-treatment with chemotherapy can enhance the antigenicity and immunogenicity of tumours by promoting adaptive immune responses . Br J Cancer, 2010. 102 (1): p. 115–23. Stojanovska, V., et al., Oxaliplatin Treatment Alters Systemic Immune Responses. Biomed Res Int, 2019. 2019 : p. 4650695. Lyman, G.H., et al., Overall survival and risk of second malignancies with cancer chemotherapy and G-CSF support . Ann Oncol, 2018. 29 (9): p. 1903–1910. Eisenhauer, E.A., et al., New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1) . Eur J Cancer, 2009. 45 (2): p. 228–47. van der Sijde, F., et al., RNA from stabilized whole blood enables more comprehensive immune gene expression profiling compared to RNA from peripheral blood mononuclear cells . PLoS One, 2020. 15 (6): p. e0235413. Cesano, A., nCounter((R)) PanCancer Immune Profiling Panel (NanoString Technologies, Inc., Seattle, WA) . J Immunother Cancer, 2015. 3 : p. 42. Geiss, G.K., et al., Direct multiplexed measurement of gene expression with color-coded probe pairs . Nat Biotechnol, 2008. 26 (3): p. 317–25. Vandesompele, J., et al., Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes . Genome Biology, 2002. 3 (7): p. research0034.1. NanoString Technologies®, I., nCounter Advanced Analysis 2.0 User Manual . 2018: Seattle, Washington. de Koning, W., et al., Identification, Validation, and Utilization of Immune Cells in Pancreatic Ductal Adenocarcinoma Based on Marker Genes . Front Immunol, 2021. 12 : p. 649061. Bindea, G., et al., ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks . Bioinformatics, 2009. 25 (8): p. 1091–3. Team, R.C., R: A Language and Environment for Statistical Computing . 2021, R Foundation for Statistical Computing: Vienna, Austria. Wickham, H., ggplot2: Elegant Graphics for Data Analysis . 2016, Springer-Verlag New York. Blighe, K., S. Rana, and M. Lewis, EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling . 2022. Hu, L., et al., The prognostic value of intratumoral and peritumoral tumor-infiltrating FoxP3 + Treg cells in of pancreatic adenocarcinoma: a meta-analysis . World J Surg Oncol, 2021. 19 (1): p. 300. Peng, Y.P., et al., Comprehensive analysis of the percentage of surface receptors and cytotoxic granules positive natural killer cells in patients with pancreatic cancer, gastric cancer, and colorectal cancer . J Transl Med, 2013. 11 : p. 262. Speiser, D.E., P.C. Ho, and G. Verdeil, Regulatory circuits of T cell function in cancer . Nat Rev Immunol, 2016. 16 (10): p. 599–611. Oliveira, J.J., et al., The plasma biomarker soluble SIGLEC-1 is associated with the type I interferon transcriptional signature, ethnic background and renal disease in systemic lupus erythematosus . Arthritis Res Ther, 2018. 20 (1): p. 152. Adams, J.M. and S. Cory, Bcl-2-regulated apoptosis: mechanism and therapeutic potential . Curr Opin Immunol, 2007. 19 (5): p. 488–96. Singh, P.K., et al., Platelet-derived growth factor receptor beta-mediated phosphorylation of MUC1 enhances invasiveness in pancreatic adenocarcinoma cells . Cancer Res, 2007. 67 (11): p. 5201–10. Weissmueller, S., et al., Mutant p53 drives pancreatic cancer metastasis through cell-autonomous PDGF receptor beta signaling . Cell, 2014. 157 (2): p. 382–394. Bang, S., et al., Differences in immune cells engaged in cell-mediated immunity after chemotherapy for far advanced pancreatic cancer . Pancreas, 2006. 32 (1): p. 29–36. Ino, Y., et al., Immune cell infiltration as an indicator of the immune microenvironment of pancreatic cancer . Br J Cancer, 2013. 108 (4): p. 914–23. Peng, H., et al., Neoadjuvant FOLFIRINOX Therapy Is Associated with Increased Effector T Cells and Reduced Suppressor Cells in Patients with Pancreatic Cancer . Clin Cancer Res, 2021. 27 (24): p. 6761–6771. Michelakos, T., et al., Tumor Microenvironment Immune Response in Pancreatic Ductal Adenocarcinoma Patients Treated With Neoadjuvant Therapy . J Natl Cancer Inst, 2021. 113 (2): p. 182–191. Lee, H.S., et al., Peripheral natural killer cell activity is associated with poor clinical outcomes in pancreatic ductal adenocarcinoma . J Gastroenterol Hepatol, 2021. 36 (2): p. 516–522. Kang, K., et al., Interferon-gamma Represses M2 Gene Expression in Human Macrophages by Disassembling Enhancers Bound by the Transcription Factor MAF . Immunity, 2017. 47 (2): p. 235–250 e4. Yang, S., Q. Liu, and Q. Liao, Tumor-Associated Macrophages in Pancreatic Ductal Adenocarcinoma: Origin, Polarization, Function, and Reprogramming . Front Cell Dev Biol, 2020. 8 : p. 607209. O'Neill, A.S., T.K. van den Berg, and G.E. Mullen, Sialoadhesin - a macrophage-restricted marker of immunoregulation and inflammation . Immunology, 2013. 138 (3): p. 198–207. Xiong, Y.S., et al., Increased expression of Siglec-1 on peripheral blood monocytes and its role in mononuclear cell reactivity to autoantigen in rheumatoid arthritis . Rheumatology (Oxford), 2014. 53 (2): p. 250–9. Affandi, A.J., et al., CD169 Defines Activated CD14(+) Monocytes With Enhanced CD8(+) T Cell Activation Capacity . Front Immunol, 2021. 12 : p. 697840. Campani, D., et al., Bcl-2 expression in pancreas development and pancreatic cancer progression . J Pathol, 2001. 194 (4): p. 444–50. Kim, H.K., et al., RRAD expression in gastric and colorectal cancer with peritoneal carcinomatosis . Sci Rep, 2019. 9 (1): p. 19439. Chen, J., et al., Targeting transforming growth factor-beta signaling for enhanced cancer chemotherapy . Theranostics, 2021. 11 (3): p. 1345–1363. Murphy, J.E., et al., Total Neoadjuvant Therapy With FOLFIRINOX in Combination With Losartan Followed by Chemoradiotherapy for Locally Advanced Pancreatic Cancer: A Phase 2 Clinical Trial . JAMA Oncol, 2019. 5 (7): p. 1020–1027. Markowicz, S., et al., Recovery of dendritic cell counts and function in peripheral blood of cancer patients after chemotherapy . Cytokines Cell Mol Ther, 2002. 7 (1): p. 15–24. Bonanno, G., et al., Effects of pegylated G-CSF on immune cell number and function in patients with gynecological malignancies . J Transl Med, 2010. 8 : p. 114. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1SupplementaryTables.pdf Additionalfile2SupplementaryFigures.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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15:32:45","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":224456,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1SupplementaryTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2008977/v1/0a9bca61116f71a3a04e7f59.pdf"},{"id":25989857,"identity":"7ff337e9-ba57-4a8f-b076-6aadd295f261","added_by":"auto","created_at":"2022-09-02 15:32:45","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":2042044,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2008977/v1/313addbb58e4e1dae1c977fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A multigene circulating biomarker to predict the lack of FOLFIRINOX response after a single cycle in patients with pancreatic ductal adenocarcinoma (PDAC)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) is one of the most lethal and aggressive solid malignancies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prognosis is poor; the incidence (495,773) and the mortality rate (466,000) worldwide were comparable in 2020 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and the 5-year overall survival (OS) rate for all stages combined is approximately 9% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The poor prognosis is, among other things, related to the lack of distinctive symptoms, the lack of reliable biomarkers for early diagnosis, progressive metastatic spread, and the complex tumor (immune) microenvironment (TME) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The only curative treatment for early-stage PDAC is surgical resection in combination with chemotherapy, however, only 20% of tumors are resectable at the time of diagnosis, and more than 50% of patients present with metastatic disease [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe combined chemotherapeutic regimen of 5-fluorouracil, folinic acid, irinotecan, and oxaliplatin (FOLFIRINOX) is considered the best adjuvant and first-line treatment for patients with locally advanced (LAPC) and metastatic pancreatic cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Various studies reported improved OS in FOLFIRINOX-treated compared to gemcitabine-treated patients for all disease stages [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A meta-analysis combining 11 studies reported improved OS in LAPC (24.2 months vs. 6\u0026ndash;13 months) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; a multicenter, randomized, phase 2\u0026ndash;3 trial reported improved OS in metastatic patients (11.1 months vs. 6.8 months) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; and a multicenter, randomized, phase 3 trial reported the most prolonged OS in patients with stage I-II or borderline resectable patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, neoadjuvant FOLFIRINOX followed by surgical resection showed favorable median OS, resection rate, and R0-resection rate in resectable pancreatic cancer patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nevertheless, 25% of PDAC patients treated with FOLFIRINOX show disease progression during treatment despite the generally improved FOLFIRINOX response rates [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, FOLFIRINOX is closely associated with triggering toxicity-related events which were higher in FOLFIRINOX-treated patients compared to patients receiving gemcitabine [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Prophylactic treatment with granulocyte colony-stimulating factor (G-CSF), such as lipegfilgrastim, is commonly used to prevent FOLFIRINOX-induced neutropenia associated with poor survival [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. G-CSF stimulates granulocyte production by the bone marrow, mainly targeting neutrophil generation and differentiation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Treatment response is evaluated through computed tomography (CT) imaging, but not until after four cycles of FOLFIRINOX. Exposure to ineffective but toxic treatment reduces patients' quality of life, carries unnecessary costs, and withholds patients from potentially effective treatment. Hence, it is desirable to identify a biomarker that predicts the lack of response at an early stage. Carbohydrate Antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9) is the only FDA-approved biomarker used in clinical practice for routine management of PDAC [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, CA19-9 is not specific for PDAC, Lewis-A antigen-negative patients cannot synthesize CA19-9, and the decrease of CA19-9 levels may predict FOLFIRINOX response only after multiple cycles [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies showed that oxaliplatin, 5-FU, and irinotecan enhanced tumor antigen presentation by increasing HLA-I and programmed death-ligand 1 (PD-L1) tumor expression in poor immunogenic cancer types such as PDAC [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This could synthesize the tumor for immune checkpoint (IC) inhibitory-based immunotherapy and increase the activation of CD8\u003csup\u003e+\u003c/sup\u003e cytotoxic T lymphocytes (CTLs) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, oxaliplatin is a well-known inducer of immunogenic cancer cell death by evoking the presentation of damage-associated molecular patterns within cancer cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Oxaliplatin exerts immunomodulatory effects resulting in increased antigenicity, enhanced adaptive immune responses [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and antitumor systemic immune response [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the peripheral immune alterations in following FOLFIRINOX are not measured.\u003c/p\u003e \u003cp\u003eWe hypothesized that the immunological effects of FOLFIRINOX may be visible in the peripheral blood after a single cycle of treatment which can be utilized to predict the lack of response to treatment. The aim of this study was to identify an early circulating biomarker predictive of the lack of FOLFIRINOX response in PDAC patients. To that aim, we investigated the effect of a single cycle of FOLFIRINOX, accompanied by prophylactic G-CSF, on the peripheral immune transcriptome of PDAC patients using targeted immune-gene expression profiling.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patient population\u003c/h2\u003e \u003cp\u003eA total of 80 PDAC patients were included in this study. PDAC patients were hospitalized at the Erasmus University Medical Centre Rotterdam between February 2018 and February 2021. Twenty-three patients with (borderline) resectable PDAC participated in the randomized clinical trial PREOPANC-2 (Dutch trial register NL7094), and 57 patients with locally advanced or metastasized PDAC participated in the prospective cohort study iKnowIT (Dutch trial register NL7522). Exclusion criteria were \u0026lt;\u0026thinsp;18 years of age, previous treatment with FOLFIRINOX, or co-treatment with another chemotherapeutic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical procedure\u003c/h2\u003e \u003cp\u003eFollowing histological confirmation of the primary tumor or metastases, patients were treated with at least four cycles of FOLFIRINOX chemotherapy. All patients were prophylactically treated with the long-acting G-CSF lipegfilgrastim (Lonquex\u0026reg;; Teva Ltd, Petach Tikva, Israel), 24 hours after each cycle, to reduce FOLFIRINOX-induced neutropenia [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Two whole blood samples from each patient were collected: at baseline (immediately before the first cycle) and 14 days after the first but just before the second FOLFIRINOX cycle. As part of the standard clinical routine, serum CA19-9 concentrations were determined at the same time points using an enzyme-linked immunosorbent assay (ELISA). A patient\u0026rsquo;s response to FOLFIRINOX was assessed based on a CT scan made after four cycles, evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Clinicopathological groups\u003c/h2\u003e \u003cp\u003eTo compare immune profiles, patients were grouped based on their clinicopathological characteristics. Disease stage at baseline included resectable, locally advanced pancreatic cancer (LAPC), and metastatic patients. Baseline CA19-9 values included patients with low (35\u0026ndash;150 \u0026micro;mol/L) and high (\u0026gt;\u0026thinsp;1500 \u0026micro;mol/L) values. Patients who showed stable disease, partial response, or complete response were defined as \u0026ldquo;disease control\u0026rdquo;. Patients showing disease progress were defined as \u0026ldquo;progressive disease\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Whole blood sample collection and RNA isolation\u003c/h2\u003e \u003cp\u003eWhole blood samples were collected in Tempus tubes (Applied Biosystems, Foster City, CA, USA) and stored at -80\u0026deg;C. Tempus tubes contain an RNA stabilizing reagent, which preserves the RNA quality and enables measuring gene expression profiles without isolating the peripheral blood mononuclear cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Total RNA was extracted from blood in Tempus tubes using the Tempus Spin RNA Isolation Kit of Thermo Fisher Scientific (Waltham, MA, USA) following the manufacturer's instructions. RNA quality control was done using the Agilent 2100 BioAnalyzer (Santa Clara, CA, USA). Samples with RNA concentrations less than 35 mg/mL were excluded. Corrected RNA concentrations were calculated based on the percentage of fragments of 300\u0026ndash;4000 nucleotides to correct for RNA degradation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Targeted multiplex gene expression\u003c/h2\u003e \u003cp\u003eTargeted gene expression profiling was performed using the nCounter\u0026reg; FLEX system and PanCancer Immune profiling panel, which includes 40 housekeeping genes and 730 immune-related genes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A total of 200 ng RNA per sample in a maximum of 7 \u0026micro;L was used for hybridization, which was performed at 65\u0026deg;C for 17 hours using the SimpliAmp Thermal Cycler (Applied Biosystems). Gene expression was counted by scanning 490 Fields of View (FOV).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Data processing and analysis\u003c/h2\u003e \u003cp\u003eData quality control, normalization, and analysis were performed using the nSolver\u0026trade; software (version 4.0) and the Advanced Analysis module (version 2.0) of NanoString Technology Inc. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A patient\u0026rsquo;s gene expression profile was included if all positive and negative control genes were within the expected values and if binding density values ranged between 0.5 and 3.0. Raw gene counts were normalized based on the most stable 34 housekeeping genes, identified by the geNorm algorithm [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (Table S1), and all normalized data were log\u003csub\u003e2\u003c/sub\u003e transformed. Genes were included when they were higher than the limit of detection of 4.384 log\u003csub\u003e2\u003c/sub\u003e, calculated as the average of all eight negative control genes multiplied by two, in \u0026gt;\u0026thinsp;80% of the gene expression profiles. Differentially expressed genes (DEGs) were identified using simplified negative binomial models, mixture negative binomial models, or log-linear models based on the convergence of each gene. Genes with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after correction for multiple testing with the Benjamin-Hochberg (BH) method were considered DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Immune cell type analysis with the NanoString nSolver module\u003c/h2\u003e \u003cp\u003eThe peripheral abundance of various immune cell types was quantified using the nSolver Advanced Analysis module, which assigns relative immune cell type scores to each sample [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Marker genes, that identify specific immune cell types, were selected based on the pairwise similarities method tailored specifically for PDAC [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Marker genes were accepted to define an immune cell type when pairwise similarity was sufficient (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.6). Accordingly, the relative abundance of immune cells was calculated between the tested groups (Table S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Pathway analysis with the NanoString nSolver module and the Cytoscape plug-in ClueGO\u003c/h2\u003e \u003cp\u003eGenes were clustered into predefined pathways (Table S3) using the nSolver Advanced Analysis module to examine immune-associated pathway alterations. We calculated the square root of the average squared t-statistic of all genes in the corresponding pathway [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], resulting in a pathway score for each sample. In addition, to explore the potential role of unique DEGs in disease control and progressive disease patients, we performed functional enrichment analysis using the Cytoscape plug-in ClueGO [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. DEGs were included in the ClueGO analysis if they met two criteria: (1) a log\u003csub\u003e2\u003c/sub\u003e fold-of-change (FOC) \u0026gt; |0.5| after a single FOLFIRINOX cycle and (2) a log\u003csub\u003e2\u003c/sub\u003e FOC \u0026gt; |0.5| difference between disease control and progressive disease patients.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical testing and data visualization were performed with R Statistical Software (v.4.1.2) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Data were tested for normality with Shapiro-Wilk tests. We used paired or unpaired two-sided student t-tests for parametrical data and paired Wilcoxon tests or unpaired Mann-Whitney U tests for non-parametrical data. All tests were corrected with the BH correction for multiple testing. We used the R packages ggplot2 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and EnhancedVolcano [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] for data visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6 The FOLFIRINOX delta gene expression profiling (FFX-ΔGEP) score\u003c/h2\u003e \u003cp\u003eA gene signature representing an early predictive circulating biomarker of the lack of FOLFIRINOX response was identified (FFX-ΔGEP) score. Briefly, log\u003csub\u003e2\u003c/sub\u003e normalized gene expression counts of baseline samples were subtracted from the log\u003csub\u003e2\u003c/sub\u003e normalized gene expression counts of samples after a single FOLFIRINOX cycle, resulting in Δ expression counts for each gene. Genes that showed statistically significant differences (BH.P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in Δ expression count between disease control and progressive disease patients were identified as candidate genes for the FFX-ΔGEP score. Patients were randomly split into training and test sets (75%/25%). To find the combination of candidate genes predicting the lack of FOLFIRINOX response most accurately, the least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was conducted on the training set with leave-one-out cross-validation. Weights (regression coefficient) were assigned to the candidate genes to improve model robustness and avoid overfitting. Genes weighted with a regression coefficient of 0 were excluded from the FFX-ΔGEP score. The fitted model was used in the corresponding test set to predict the lack of FOLFIRINOX response. The overall predictive performance was assessed by receiver operating characteristic (ROC) analysis depicting the area under the curve (AUC) value with a 95% confidence interval (CI).\u003c/p\u003e \u003cp\u003eThe absolute (\u0026micro;mol/L) and proportional (%) change in CA19-9 was calculated to compare the predictive performance to the FFX-ΔGEP score. CA19-9 values of baseline samples were subtracted from those after a single FOLFIRINOX cycle to obtain absolute Δ CA19-9 values. The proportional Δ CA19-9 values were calculated by dividing the absolute Δ CA19-9 values by their baseline Δ CA19-9 values. ROC analysis was performed for both absolute and proportional Δ CA19-9 values, and the AUC value was compared to the AUC value of the FFX-ΔGEP score.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Samples and patient characteristics\u003c/h2\u003e\n\u003cp\u003eBlood samples of 80 patients treated with at least four cycles of FOLFIRINOX, in combination with prophylactic G-CSF, were collected at baseline and after the first cycle (Figure 1). RNA isolation was performed for a total of 160 blood samples, of which eight were excluded due to poor RNA concentration (\u0026lt; 35 mg/mL), and four were excluded due to poor binding density (\u0026lt; 0.5 or \u0026gt; 3.0). After removing corresponding pairs, 68 patients (136 samples) were included in the data analysis. The response to four FOLFIRINOX cycles was assessed by CT scan evaluation in 58 out of 68 patients, which resulted in 48 disease control and 10 progressive disease patients (Table 1). The overall survival [95% CI] for the disease control and the progressive disease patients was 40.2 [32.7 \u0026ndash; 46.5] and 13.8 [11.2 \u0026ndash; 15.5] months. All clinicopathological characteristics are summarized in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eClinicopathological characteristics of patients in the study\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.12396694214876%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.35537190082645%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.84297520661157%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"38.67768595041322%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment response\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.81188118811881%\"\u003e\n \u003cp\u003e(n = 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.636963696369637%\"\u003e\n \u003cp\u003eDisease control\u0026nbsp;\u003cbr\u003e\u0026nbsp;(n = 48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.141914191419144%\"\u003e\n \u003cp\u003eProgressive disease\u003cbr\u003e\u0026nbsp;(n = 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (y),\u0026nbsp;\u003c/strong\u003emean (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e65 (47 \u0026ndash; 81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e65 (49 \u0026ndash; 78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e60 (47 \u0026ndash; 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender,\u0026nbsp;\u003c/strong\u003eno (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e35 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e25 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e5 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e33 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e23 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e5 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol,\u0026nbsp;\u003c/strong\u003eno (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e35 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e22 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e3 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e33 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e26 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e7 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking,\u0026nbsp;\u003c/strong\u003eno (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e40 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e28 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e6 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e28 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e20 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e4 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes Mellitus (DM),\u0026nbsp;\u003c/strong\u003eno (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e14 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e11 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e2 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e54 (79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e37 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e8 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease stage, no (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eResectable disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e21 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e17 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e2 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eLAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e28 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e19 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e5 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eMetastatic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e19 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e12 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e3 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline CA19-9 (U/mL),\u0026nbsp;\u003c/strong\u003eno (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eMean (\u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e2919 (\u0026plusmn; 10893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e1352 (\u0026plusmn; 4182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e10503 (\u0026plusmn; 25859)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eNo expression (\u0026lt; 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e13 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e9 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e3 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eLow expression (35-150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e15 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e13 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eModerate expression (150-1500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e25 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e19 (39.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e3 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eHigh expression (\u0026gt; 1500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e15 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e7 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e4 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCA19-9 difference\u003c/strong\u003e \u003cstrong\u003eafter a single cycle, compared to baseline\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eMean (U/mL) (\u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e225.5 (\u0026plusmn; 1883.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e128.1 (\u0026plusmn; 1776.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e242.5 (\u0026plusmn; 2041.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.115511551155116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eMean (%) (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e15 (-62 \u0026ndash; 304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e20% (-53 \u0026ndash; 304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e5% (-62 \u0026ndash; 49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline clinical parameters,\u0026nbsp;\u003c/strong\u003emean (\u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eCEA (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e19.09 (\u0026plusmn; 49.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e11.86 (\u0026plusmn; 24.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e32.70 (\u0026plusmn; 69.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eBilirubin (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e13 (\u0026plusmn; 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e13 (\u0026plusmn; 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e20 (\u0026plusmn; 23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e16 (\u0026plusmn; 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e17 (\u0026plusmn; 25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e17 (\u0026plusmn; 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e1182 (\u0026plusmn; 1151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e1116 (\u0026plusmn; 1067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e1189 (\u0026plusmn; 713)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"5.115511551155116%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.29372937293729%\"\u003e\n \u003cp\u003eNLR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e4.0 (\u0026plusmn; 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e4.0 (\u0026plusmn; 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e3.8 (\u0026plusmn; 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cycles of FOLFIRINOX,\u0026nbsp;\u003c/strong\u003emean (\u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e7 (\u0026plusmn; 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e8 (\u0026plusmn; 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e4 (\u0026plusmn; 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.40924092409241%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian OS (months),\u0026nbsp;\u003c/strong\u003emedian [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.81188118811881%\"\u003e\n \u003cp\u003e32.0 [27.8 \u0026ndash; 42.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.636963696369637%\"\u003e\n \u003cp\u003e40.2 [32.7 \u0026ndash; 46.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.141914191419144%\"\u003e\n \u003cp\u003e13.8 [11.2 \u0026ndash; 15.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eLAPC:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eLocally Advanced Pancreatic Cancer;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCA19-9: Carbohydrate Antigen 19-9; CEA: Carcinoembryonic Antigen; CRP: C-Reactive Protein; SII: Systemic Immune-inflammation Index; NLR: Neutrophil-to-Lymphocyte Ratio; SD: Standard Deviation; OS: Overall Survival.\u003c/p\u003e\n\u003ch2\u003e3.2 PDAC patients with different disease stages or different \u003cem\u003ebaseline CA19-19 values show comparable\u0026nbsp;\u003c/em\u003eimmune profiles\u003c/h2\u003e\n\u003cp\u003eImmune profiles based on the three disease stages (resectable, LAPC, metastatic) and based on the two baseline CA19-9 values (low and high) were compared at baseline and after a single FOLFIRINOX cycle. Baseline \u003cem\u003eimmune profiles revealed eight DEGs between the three disease stages and no DEGs between low and high baseline CA19-9 values (Figure S1). The activity of the\u003c/em\u003e pathways in baseline samples was not altered in any of the comparisons (BH.P \u0026gt; 0.05; Figure S2). Two immune cell types were relatively different between the three disease stages (BH.P \u0026lt; 0.05). Resectable patients showed relatively lower NK cells compared to LAPC and metastatic patients and relatively lower conventional dendritic cells type 2 (cDC2s) compared to metastatic patients (BH.P \u0026lt; 0.05; Figure S2). No immune cell types were relatively different between the two baseline CA19-9 values (BH.P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eA single FOLFIRINOX cycle induced multiple DEGs amongst the three disease stages and the two baseline CA19-9 values (Figure S3). However, unique DEGs between groups were scarce resulting in six statistically significant differences (BH.P \u0026lt; 0.05) in altered pathways and immune cell type abundances (Figure S4). The cytotoxicity pathway was less activated in metastatic compared to resectable patients (BH.P \u0026lt; 0.05). The relative cytotoxic cell abundance was higher in metastatic compared to resectable and LAPC patients (BH.P \u0026lt; 0.05). Also, patients with high baseline CA19-9 values showed relatively higher neutrophils and NK CD56\u003csup\u003edim\u003c/sup\u003e cells and relatively lower monocytes compared to patients with low CA19-9 values (BH.P \u0026lt; 0.05; Figures S3 and S4).\u003c/p\u003e\n\u003ch2\u003e3.3 A single FOLFIRINOX cycle altered the peripheral immune transcriptome of PDAC patients\u003c/h2\u003e\n\u003cp\u003eData analysis revealed 395 DEGs (BH.P \u0026lt; 0.01) in samples after a single FOLFIRINOX cycle compared to baseline samples (Figure 2A). Filtering the DEGs based on a log\u003csub\u003e2\u003c/sub\u003e FOC \u0026ge; |1.0| revealed 36 upregulated genes and three downregulated genes after a single FOLFIRINOX cycle (Figure 2B, Table S4). Pathway analysis revealed alterations among all immune-associated pathways (BH.P \u0026lt; 0.001; Figure 3A-3C). Pathway-specific genes with log\u003csub\u003e2\u003c/sub\u003e FOC \u0026ge; |1.0| were considered key pathway drivers (Table S4). The pathways of adhesion, chemokines, cytokines, interleukins, macrophage function, pathogen defense, toll-like receptor (TLR), and tumor necrosis factor (TNF) superfamily were enhanced after a single FOLFIRINOX cycle while the immune-associated pathways of antigen processing, B cell, NK cell, and T cell functions, and cytotoxicity were diminished. Immune cell type analysis revealed alterations among all peripheral immune cells (BH.P \u0026lt; 0.05; Figure 3D). The relative peripheral abundance of the total immune cells (\u003cem\u003ePTPRC\u003c/em\u003e, CD45\u003csup\u003e+\u003c/sup\u003e), cDC2, monocytes, NK cells, and neutrophils increased while the B cells, cytotoxic cells, NK CD56\u003csup\u003edim\u003c/sup\u003e cells, total T cells, T regulatory (Treg) cells, and CD8\u003csup\u003e+\u003c/sup\u003e T cells decreased after a single FOLFIRINOX cycle (Figure 3D).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.4\u0026nbsp;\u003c/em\u003eA single FOLFIRINOX cycle altered the expression of \u003cem\u003eIC regulatory genes\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eTh\u003c/em\u003ee expression of the IC inhibitory genes \u003cem\u003ePDCD1\u003c/em\u003e (PD-1), \u003cem\u003eCD274 (PD-L1), and PDCD1LG2 (PD-L2)\u0026nbsp;\u003c/em\u003ewere upregulated after a single FOLFIRINOX cycle compared to baseline with an average log\u003csub\u003e2\u003c/sub\u003e FOC of 1.1 (BH.P \u0026lt; 0.001; Figure 4). In contrast, the IC inhibitory genes \u003cem\u003eBTLA,\u003c/em\u003e \u003cem\u003eCTLA4\u003c/em\u003e \u003cem\u003eand its ligand CD86 (B7-2), HAVCR2 (TIM-3), and TIGIT were\u0026nbsp;\u003c/em\u003estatistically significant \u003cem\u003edownregulated (BH.P \u0026lt; 0.001; Figure 4). The\u0026nbsp;\u003c/em\u003eIC inhibitory \u003cem\u003egene LAG3 was not altered (Figure S5).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e3.5 Subtle differences in the immune transcriptome of disease control and progressive disease\u003cstrong\u003e\u0026nbsp;patients after a single FOLFIRINOX cycle\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune profiles of the disease control and progressive disease patients were compared at baseline and after a single FOLFIRINOX cycle. Baseline immune profiles revealed no differences in pathway activities between the two groups. However, a relatively high abundance in the total immune cells and Treg cells were observed in progressive disease patients (Figure 5). Immune profiles after a single FOLFIRINOX cycle revealed 400 DEGs in disease control and 256 DEGs in progressive disease patients (Figure S6), which were used in the ClueGo analysis based on the criteria in the materials and method section. Two key genes involved in the negative regulation of type-I interferon-mediated (IFN-I) signaling pathway were downregulated in disease control but not in progressive disease patients (BH.P \u0026lt; 0.01; Figures 6A and 6B). The change in IC regulatory gene expression, pathway activity, and immune cell type abundance was comparable in both groups (Figure S7), with one immune cell type exception. Driven by its solitary marker \u003cem\u003eKIR3DL1\u003c/em\u003e, the relative abundance of NK CD56\u003csup\u003edim\u003c/sup\u003e cells was decreased in disease control but increased in progressive disease patients (BH.P \u0026lt; 0.05; Figure 6C).\u003c/p\u003e\n\u003cp\u003e3.6\u003cem\u003e\u0026nbsp;An eight-gene\u0026nbsp;\u003c/em\u003e\u003cem\u003eFFX-\u003c/em\u003eGEP score predicted the lack of response after a single FOLFIRINOX cycle\u003c/p\u003e\n\u003cp\u003eTo identify an early circulating biomarker that predicts the lack of FOLFIRINOX response, we developed an FFX-\u0026Delta;GEP\u0026nbsp;score. The\u0026nbsp;\u0026Delta; gene expression count, which results from subtracting the log\u003csub\u003e2\u003c/sub\u003e normalized gene expression counts of baseline samples from samples after a single FOLFIRINOX cycle, revealed fourteen candidate genes that differed significantly between disease control and progressive disease patients (BH.P \u0026lt; 0.05; Table 2). LASSO multivariate regression analysis, which constructed the most optimal combination of candidate genes by assigning a regression coefficient (weight) to all candidate genes, was conducted. Six candidate genes were assigned a weight of zero and the FFX-\u0026Delta;GEP score was composed of the remaining eight genes (Figure 7):\u003c/p\u003e\n\u003cp\u003eThe eight-gene\u0026nbsp;FFX-\u0026Delta;GEP\u0026nbsp;score ranged from 3.82 to -1.76 among all patients, and the performance to predict\u0026nbsp;lack of FOLFIRINOX response\u0026nbsp;after a single cycle was assessed by ROC analysis (Figure 8A). The leave-one-out cross-validated AUC [95% CI] was 0.87 [0.60 \u0026ndash; 0.98], indicating that the\u0026nbsp;FFX-\u0026Delta;GEP score could distinguish between\u0026nbsp;disease control and progressive disease patients. The predictive performance of the\u0026nbsp;currently used\u0026nbsp;absolute and proportional\u0026nbsp;\u0026Delta;\u0026nbsp;CA19-9 values [95% CI] were 0.70 [0.27 \u0026ndash; 1.0] and 0.52 [0.24 \u0026ndash; 0.80]. Importantly, the\u0026nbsp;FFX-\u0026Delta;GEP score outperformed\u0026nbsp;\u0026Delta;\u0026nbsp;CA19-9 values with less overlap in the designation of\u0026nbsp;disease control and progressive disease patients\u0026nbsp;(Figures 8B-8D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 The f\u003c/strong\u003e\u003cstrong\u003eourteen candidate genes selected for the\u0026nbsp;FFX-\u0026Delta;GEP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;score\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.75544388609715%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"26.298157453936348%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease control\u0026nbsp;\u003cbr\u003e\u0026nbsp;mean (\u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"26.298157453936348%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProgressive disease\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (\u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.23785594639866%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBH.P value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.410385259631491%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeights\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eBID\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e-0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e-1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eFOXP3\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e-0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eKIR3DL1\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eKLRC1\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eKLRD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e-0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eKLRG1\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e-0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eMAF\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eNFATC2\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003ePDGFRB\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003ePLAU\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eREL\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eRRAD\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e1.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eSIGLEC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.725752508361204%\"\u003e\n \u003cp\u003e\u003cem\u003eTGFB2\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.20401337792642%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.050167224080267%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.040133779264215%\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.381270903010034%\"\u003e\n \u003cp\u003e(\u0026plusmn; 0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214046822742475%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe mean values of the \u0026Delta; gene expression counts (\u0026plusmn; SD) per response group. The BH.P value between disease control and progressive disease patients. The assigned weights are calculated using LASSO multivariate regression analysis. \u003cem\u003eAbbreviations\u003c/em\u003e: SD: Standard Deviation; BH.P: Benjamin-Hochberg P value; LASSO: Least Absolute Shrinkage and Selection Operator.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we used paired blood samples of 68 PDAC patients to investigate the effect of a single FOLFIRINOX cycle on the immune profile. We aimed to identify an early circulating biomarker to predict the lack of response to FOLFIRINOX. We revealed an eight-gene FFX-\u0026Delta;GEP score that predicted the lack of FOLFIRINOX response only after the first cycle, independent of disease stage or change in CA19-9. This novel multigene FFX-\u0026Delta;GEP score is, to our knowledge, the first gene expression-based early circulating biomarker predicting the lack of FOLFIRINOX response in PDAC patients from all disease stages. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe FFX-\u0026Delta;GEP score is composed of eight immune-related genes in which \u003cem\u003eFOXP3\u003c/em\u003e, \u003cem\u003eKIR3DL1\u003c/em\u003e, \u003cem\u003eMAF\u003c/em\u003e, and \u003cem\u003eSIGLEC1\u0026nbsp;\u003c/em\u003e[40-43] are associated with immune cell types, and \u003cem\u003eBID\u003c/em\u003e, \u003cem\u003ePDGFRB\u003c/em\u003e, \u003cem\u003eRRAD\u003c/em\u003e, and \u003cem\u003eTGFB2\u003c/em\u003e are associated with the tumor or chemotherapeutic efficacy [44-47]. \u003cem\u003eFOXP3\u003c/em\u003e is a marker for Tregs, associated with poor PDAC prognosis [40, 48]. Neoadjuvant FOLFIRINOX reduced the peripheral and intra-tumoral abundance of Tregs in PDAC [49, 50]. \u003cem\u003eKIR3DL1\u003c/em\u003e inhibits NK cell activity [41] and was associated with PDAC progression [51]. This fits well with our observation that \u003cem\u003eKIR3DL1\u003c/em\u003e expression did not change in progressive disease but decreased in disease control patients after a single FOLFIRINOX cycle. \u003cem\u003eMAF\u003c/em\u003e is a transcription factor known to induce CD8 T cell dysfunction [42]. In addition, \u003cem\u003eMAF\u003c/em\u003e is known to be highly expressed in M2 macrophages [52], associated with poor prognosis in PDAC [53]. \u003cem\u003eMAF\u003c/em\u003e expression was not changed in progressive disease, but it was downregulated in disease control patients after a single cycle. \u003cem\u003eSIGLEC1\u003c/em\u003e is a sialic acid-binding cell-surface protein that mediates pathogenic phagocytosis and endocytosis [54]. In the blood, \u003cem\u003eSIGLEC1\u003c/em\u003e is exclusively expressed by activated CD14\u003csup\u003e+\u003c/sup\u003e monocytes, mostly in reaction to IFN-I [43, 55], which stimulate CD8\u003csup\u003e+\u003c/sup\u003e cells through tumor antigen presentation [56]. However, our results showed a downregulation of \u003cem\u003eSIGLEC1\u003c/em\u003e expression but an increased IFN-I pathway activity in disease control patients. In addition, monocytes were not found to be associated with the increased expression of \u003cem\u003eSIGLEC1.\u0026nbsp;\u003c/em\u003eSuggesting that \u003cem\u003eSIGLEC1\u003c/em\u003e might have a different function in PDAC patients. \u003cem\u003eBID\u003c/em\u003e encodes pro-apoptotic intracellular proteins that belong to the BCL-2 family [44]. The deregulated expression of the BCL-2 family is associated with apoptotic resistance in PDAC [57]. In accordance, our results showed downregulation in \u003cem\u003eBID\u003c/em\u003e expression in progressive disease patients only. \u003cem\u003ePDGFRB\u003c/em\u003e encodes the platelet-derived growth factor receptor \u0026beta; associated with poor disease-free survival, cancer cell invasion, and metastasis in PDAC [45, 46]. This is in line with our results showing no change in \u003cem\u003ePDGFRB\u0026nbsp;\u003c/em\u003eexpression in progressive disease but downregulation in disease control patients. In gastric and colorectal cancer, 5-FU and oxaliplatin, two chemotherapeutic agents of FOLFIRINOX, displayed increased efficacy when combined with \u003cem\u003eRRAD\u003c/em\u003e inhibition [58]. We observed a smaller upregulation of \u003cem\u003eRRAD\u003c/em\u003e expression in disease control patients. Transforming growth factor-\u0026beta;2 (\u003cem\u003eTGFB2\u003c/em\u003e) plays a dual and complicated role in PDAC [59]. Improved clinical outcome of LAPC patients treated with the combination of FOLFIRINOX and the TGF-\u0026beta; antagonist (Losartan) followed by individualized chemo-radiotherapy, was observed previously [60]. This fits well with the greater upregulated \u003cem\u003eTGFB2\u0026nbsp;\u003c/em\u003eexpression in progressive disease compared to disease control patients.\u003c/p\u003e\n\u003cp\u003eOur FFX-\u0026Delta;GEP score was identified based on 48 disease control and 10 progressive disease patients. The score needs to be validated using an external larger cohort of samples. In addition, PDAC patients included in our study were treated with G-CSF within 24 hours after each FOLFIRINOX cycle. We have not yet examined the FFX-\u0026Delta;GEP score in patients who did not receive G-CSF. Moreover, longitudinal blood sample collection is needed to explore the applicability of the FFX-\u0026Delta;GEP score in patient follow-up. In our study, we treatment response using CT scan evaluation. However, some patients experience prolonged OS without showing imaging response. Therefore, it must be examined if the FFX-\u0026Delta;GEP score can predict FOLFIRINOX-induced prolonged OS. Based on our result, we could not calculate a cut-off value for the FFX-\u0026Delta;GEP score indicating response or lack of response to FOLFIRINOX treatment. A higher number of samples is needed to calculate an accurate cut-off value. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo our knowledge, this study is the first to describe the effect of a single FOLFIRINOX cycle, accompanied by prophylactic G-CSF, on the immune transcriptome of PDAC patients. We discovered that a single cycle of FOLFIRINOX changed the expression of 395 immune-related genes significantly, even after two weeks of recovery. Our results showed that the relative peripheral abundance of total immune cells (CD45\u003csup\u003e+\u003c/sup\u003e), B cells, cDC2, cytotoxic cells, monocytes, NK CD56\u003csup\u003edim\u003c/sup\u003e cells, and all T cell subsets (total, CD8\u003csup\u003e+\u003c/sup\u003e, and Treg) were reduced while the relative neutrophil abundance was increased after a single cycle of treatment. The increase in granulocyte-derived cells can be explained as an effect of G-CSF that was injected into all patients in our cohort, which affects the relative abundance of the other immune cells. In line with this discovery, previous studies described a rapid recovery of total lymphocytes, cDCs, and monocytes after two weeks of chemotherapy [47, 61] and increased cDC2s after G-CSF treatment [62].\u003c/p\u003e\n\u003cp\u003eImportantly, the immune transcriptome in patients with different disease stages or different baseline CA19-9 values was similar. This suggests that the progression of PDAC does not stimulate the systemic immune response. Additionally, we could not predict the lack of FOLFIRINOX response using baseline samples only. This highlights the challenges we phase in applying precision medicine protocols or stratifying PDAC patients to receive their most suitable treatment. Based on our results, at least one cycle of FOLFIRINOX is needed to predict the lack of response in PDAC patients.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eUsing targeted immune-gene expression profiling, we discovered a novel multigene FFX-\u0026Delta;GEP score predictive of the lack of FOLFIRINOX response only after the first cycle. In our cohort, the FFX-\u0026Delta;GEP score predicted the lack of FOLFIRINOX response with more accuracy than the absolute or proportional change in CA19-9 levels. Our FFX-\u0026Delta;GEP score must be validated in a larger independent cohort of samples, which includes PDAC patients without G-CSF treatment, and it should be examined if the score can predict the lack of FOLFIRINOX response based on OS. Additionally, we are the first to describe the pronounced effect of a single FOLFIRINOX cycle on the immune transcriptome in the blood of PDAC patients from all disease stages.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBH.P\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eBenjamin-Hochberg corrected P value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBID\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eBH3-Interacting Domain Death Agonist\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBTLA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eB and T Lymphocyte Associated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCA19-9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eCancer antigen 19-9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eCluster of Differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCD86\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(B7-2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eCD86 Antigen (CD28 Antigen Ligand 2, B7-2 Antigen)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCD274\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(PD-L1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eProgrammed Cell Death Ligand 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003ecDC2s\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003econventional Dendritic Cells type 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eComputed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTLs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eCytotoxic T lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCTLA4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eCytotoxic T-Lymphocyte Associated protein 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDEGs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eELISA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eEnzyme-Linked Immunosorbent Assay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFFX-\u0026Delta;GEP score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eFOLFIRINOX delta Gene Expression Profiling score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eFold-Of-Change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOLFIRINOX\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eRegimen of 5-fluorouracil, folinic acid, irinotecan, and oxaliplatin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOV\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eFields Of View\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFOXP3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eForkhead Box P3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eG-CSF\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eGranulocyte-colony stimulating factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHAVCR2\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(TIM-3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eHepatitis A Virus Cellular Receptor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eImmune Checkpoint\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFN-I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eInterferon-I\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eISG15\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eInterferon-Stimulated Gene 15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eKIR3DL1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eKiller Cell Immunoglobulin Like Receptor 3DL1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLAG3\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eLymphocyte Activating Gene 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAPC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eLocally Advanced Pancreatic Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLASSO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipegfilgrastim\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eglycoPEGylated human N-methionyl granulocyte-colony stimulating factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMAF\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eAvian Musculoaponeurotic Fibrosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOAS3\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003e2\u0026apos;-5\u0026apos;-Oligoadenylate Synthetase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eOverall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003ePancreatic Ductal Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePDCD1\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(PD-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eProgrammed Cell Death 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePDCD1LG2\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(PD-L2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eProgrammed Cell Death Ligand 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePDGFRB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003ePlatelet-Derived Growth Factor Receptor \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePTPRC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eProtein Tyrosine Phosphatase Receptor Type C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eRRAD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eRas-Related Associated with Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRECIST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eResponse Evaluation Criteria In Solid Tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eReceiver Operating Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSIGLEC1 (CD196)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eSialic Acid Binding Ig Like Lectin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTGFB2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eTransforming growth factor \u0026beta; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTIGIT\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eT cell Immunoreceptor with Ig and ITIM domains\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eToll-Like Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTME\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eTumor Microenvironment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eTumor Necrosis Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.090121317157713%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreg\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"75.90987868284229%\"\u003e\n \u003cp\u003eT regulatory cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eParticipating patients in this study were included in two trials conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committees of Erasmus MC (ethics committee reference number MEC-2018-087 and MEC-2018-004). Written informed consent was obtained from all patients.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available, with permission of the Erasmus Medical Center Rotterdam, from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was financially supported by the Survival with Pancreatic Cancer Foundation (www.supportcasper.nl)\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eCWFvE, WdK, and DM concepted and designed the study. FvdS, MM, BGK, MH, CHJvE, and DM were responsible for all resources. FvdS and MM, BGK, and MH collected and provided clinical data and samples. CWFvE and WdK performed the formal (statistical) analyses and visualization. CWFvE, SB, and DM wrote the manuscript. CHJvE and DM supervised this work. All authors have reviewed and agreed to the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors would like to give special thanks to all students involved in the blood collection, to Disha S. Vadgama and Jasper Dumas for their help in processing the blood samples, and to Jie Ju for her assistance with the LASSO multivariate regression analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSarantis, P., et al., \u003cem\u003ePancreatic ductal adenocarcinoma: Treatment hurdles, tumor microenvironment and immunotherapy\u003c/em\u003e. World J Gastrointest Oncol, 2020. \u003cb\u003e12\u003c/b\u003e(2): p.\u0026nbsp;173\u0026ndash;181.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung, H., et al., \u003cem\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/em\u003e. CA Cancer J Clin, 2021. \u003cb\u003e71\u003c/b\u003e(3): p.\u0026nbsp;209\u0026ndash;249.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRangarajan, K., et al., \u003cem\u003eSystemic neoadjuvant chemotherapy in modern pancreatic cancer treatment: a systematic review and meta-analysis\u003c/em\u003e. Ann R Coll Surg Engl, 2019. \u003cb\u003e101\u003c/b\u003e(7): p.\u0026nbsp;453\u0026ndash;462.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKikuyama, M., et al., \u003cem\u003eEarly Diagnosis to Improve the Poor Prognosis of Pancreatic Cancer\u003c/em\u003e. Cancers (Basel), 2018. \u003cb\u003e10\u003c/b\u003e(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdamska, A., A. Domenichini, and M. Falasca, \u003cem\u003ePancreatic Ductal Adenocarcinoma: Current and Evolving Therapies\u003c/em\u003e. Int J Mol Sci, 2017. \u003cb\u003e18\u003c/b\u003e(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan, J.Q., et al., \u003cem\u003eCurrent advances and outlooks in immunotherapy for pancreatic ductal adenocarcinoma\u003c/em\u003e. Mol Cancer, 2020. \u003cb\u003e19\u003c/b\u003e(1): p.\u0026nbsp;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolfgang, C.L., et al., \u003cem\u003eRecent progress in pancreatic cancer\u003c/em\u003e. CA Cancer J Clin, 2013. \u003cb\u003e63\u003c/b\u003e(5): p.\u0026nbsp;318\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Sijde, F., et al., \u003cem\u003eTreatment Response and Conditional Survival in Advanced Pancreatic Cancer Patients Treated with FOLFIRINOX: A Multicenter Cohort Study.\u003c/em\u003e J Oncol, 2022. \u003cb\u003e2022\u003c/b\u003e: p.\u0026nbsp;8549487.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuker, M., et al., \u003cem\u003eFOLFIRINOX for locally advanced pancreatic cancer: a systematic review and patient-level meta-analysis\u003c/em\u003e. Lancet Oncol, 2016. \u003cb\u003e17\u003c/b\u003e(6): p.\u0026nbsp;801\u0026ndash;810.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConroy, T., et al., \u003cem\u003eFOLFIRINOX versus gemcitabine for metastatic pancreatic cancer\u003c/em\u003e. N Engl J Med, 2011. \u003cb\u003e364\u003c/b\u003e(19): p.\u0026nbsp;1817\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConroy, T., et al., \u003cem\u003eFOLFIRINOX or Gemcitabine as Adjuvant Therapy for Pancreatic Cancer\u003c/em\u003e. N Engl J Med, 2018. \u003cb\u003e379\u003c/b\u003e(25): p.\u0026nbsp;2395\u0026ndash;2406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanssen, Q.P., et al., \u003cem\u003eNeoadjuvant FOLFIRINOX in Patients With Borderline Resectable Pancreatic Cancer: A Systematic Review and Patient-Level Meta-Analysis\u003c/em\u003e. J Natl Cancer Inst, 2019. \u003cb\u003e111\u003c/b\u003e(8): p.\u0026nbsp;782\u0026ndash;794.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerri, G., et al., \u003cem\u003eResponse and Survival Associated With First-line FOLFIRINOX vs Gemcitabine and nab-Paclitaxel Chemotherapy for Localized Pancreatic Ductal Adenocarcinoma\u003c/em\u003e. JAMA Surg, 2020. \u003cb\u003e155\u003c/b\u003e(9): p.\u0026nbsp;832\u0026ndash;839.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThibodeau, S. and I.A. Voutsadakis, \u003cem\u003eFOLFIRINOX Chemotherapy in Metastatic Pancreatic Cancer: A Systematic Review and Meta-Analysis of Retrospective and Phase II Studies\u003c/em\u003e. J Clin Med, 2018. \u003cb\u003e7\u003c/b\u003e(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteger, G., et al., \u003cem\u003e1697P - Use of lipegfilgrastim for the prophylaxis of chemotherapy-induced neutropenia: Pan-European non-interventional study\u003c/em\u003e. Annals of Oncology, 2018. \u003cb\u003e29\u003c/b\u003e: p.\u0026nbsp;viii607-viii608.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerazawa, T., et al., \u003cem\u003eEfficacy of Prophylactic G-CSF in Patients Receiving FOLFIRINOX: A Preliminary Retrospective Study\u003c/em\u003e. Intern Med, 2015. \u003cb\u003e54\u003c/b\u003e(23): p.\u0026nbsp;2969\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimmer-Bonte, J.N.H., et al., \u003cem\u003eLipegfilgrastim for prophylaxis of chemotherapy-induced neutropenia in Dutch patients\u003c/em\u003e. Neth J Med, 2020. \u003cb\u003e78\u003c/b\u003e(5): p.\u0026nbsp;270\u0026ndash;276.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards, M.K., et al., \u003cem\u003ePivotal role of granulocyte colony-stimulating factor in the development of progenitors in the common myeloid pathway\u003c/em\u003e. Blood, 2003. \u003cb\u003e102\u003c/b\u003e(10): p.\u0026nbsp;3562\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinter, J.M., C.J. Yeo, and J.R. Brody, \u003cem\u003eDiagnostic, prognostic, and predictive biomarkers in pancreatic cancer\u003c/em\u003e. J Surg Oncol, 2013. \u003cb\u003e107\u003c/b\u003e(1): p.\u0026nbsp;15\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Sijde, F., et al., \u003cem\u003eCirculating Biomarkers for Prediction of Objective Response to Chemotherapy in Pancreatic Cancer Patients\u003c/em\u003e. Cancers (Basel), 2019. \u003cb\u003e11\u003c/b\u003e(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrincipe, D.R., et al., \u003cem\u003eLong-Term Gemcitabine Treatment Reshapes the Pancreatic Tumor Microenvironment and Sensitizes Murine Carcinoma to Combination Immunotherapy\u003c/em\u003e. Cancer Res, 2020. \u003cb\u003e80\u003c/b\u003e(15): p.\u0026nbsp;3101\u0026ndash;3115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, C., et al., \u003cem\u003eCamrelizumab plus carboplatin and pemetrexed versus chemotherapy alone in chemotherapy-naive patients with advanced non-squamous non-small-cell lung cancer (CameL): a randomised, open-label, multicentre, phase 3 trial\u003c/em\u003e. Lancet Respir Med, 2021. \u003cb\u003e9\u003c/b\u003e(3): p.\u0026nbsp;305\u0026ndash;314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark, S.J., et al., \u003cem\u003eCisplatin and oxaliplatin induce similar immunogenic changes in preclinical models of head and neck cancer\u003c/em\u003e. Oral Oncol, 2019. \u003cb\u003e95\u003c/b\u003e: p.\u0026nbsp;127\u0026ndash;135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBezu, L., et al., \u003cem\u003eCombinatorial strategies for the induction of immunogenic cell death\u003c/em\u003e. Front Immunol, 2015. \u003cb\u003e6\u003c/b\u003e: p.\u0026nbsp;187.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesniere, A., et al., \u003cem\u003eImmunogenic death of colon cancer cells treated with oxaliplatin\u003c/em\u003e. Oncogene, 2010. \u003cb\u003e29\u003c/b\u003e(4): p.\u0026nbsp;482\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, W.M., et al., \u003cem\u003ePre-treatment with chemotherapy can enhance the antigenicity and immunogenicity of tumours by promoting adaptive immune responses\u003c/em\u003e. Br J Cancer, 2010. \u003cb\u003e102\u003c/b\u003e(1): p.\u0026nbsp;115\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStojanovska, V., et al., \u003cem\u003eOxaliplatin Treatment Alters Systemic Immune Responses.\u003c/em\u003e Biomed Res Int, 2019. \u003cb\u003e2019\u003c/b\u003e: p.\u0026nbsp;4650695.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLyman, G.H., et al., \u003cem\u003eOverall survival and risk of second malignancies with cancer chemotherapy and G-CSF support\u003c/em\u003e. Ann Oncol, 2018. \u003cb\u003e29\u003c/b\u003e(9): p.\u0026nbsp;1903\u0026ndash;1910.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisenhauer, E.A., et al., \u003cem\u003eNew response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)\u003c/em\u003e. Eur J Cancer, 2009. \u003cb\u003e45\u003c/b\u003e(2): p.\u0026nbsp;228\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Sijde, F., et al., \u003cem\u003eRNA from stabilized whole blood enables more comprehensive immune gene expression profiling compared to RNA from peripheral blood mononuclear cells\u003c/em\u003e. PLoS One, 2020. \u003cb\u003e15\u003c/b\u003e(6): p.\u0026nbsp;e0235413.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCesano, A., \u003cem\u003enCounter((R)) PanCancer Immune Profiling Panel (NanoString Technologies, Inc., Seattle, WA)\u003c/em\u003e. J Immunother Cancer, 2015. \u003cb\u003e3\u003c/b\u003e: p.\u0026nbsp;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeiss, G.K., et al., \u003cem\u003eDirect multiplexed measurement of gene expression with color-coded probe pairs\u003c/em\u003e. Nat Biotechnol, 2008. \u003cb\u003e26\u003c/b\u003e(3): p.\u0026nbsp;317\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVandesompele, J., et al., \u003cem\u003eAccurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes\u003c/em\u003e. Genome Biology, 2002. \u003cb\u003e3\u003c/b\u003e(7): p. research0034.1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNanoString Technologies\u0026reg;, I., \u003cem\u003enCounter Advanced Analysis 2.0 User Manual\u003c/em\u003e. 2018: Seattle, Washington.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Koning, W., et al., \u003cem\u003eIdentification, Validation, and Utilization of Immune Cells in Pancreatic Ductal Adenocarcinoma Based on Marker Genes\u003c/em\u003e. Front Immunol, 2021. \u003cb\u003e12\u003c/b\u003e: p.\u0026nbsp;649061.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBindea, G., et al., \u003cem\u003eClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks\u003c/em\u003e. Bioinformatics, 2009. \u003cb\u003e25\u003c/b\u003e(8): p.\u0026nbsp;1091\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeam, R.C., \u003cem\u003eR: A Language and Environment for Statistical Computing\u003c/em\u003e. 2021, R Foundation for Statistical Computing: Vienna, Austria.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham, H., \u003cem\u003eggplot2: Elegant Graphics for Data Analysis\u003c/em\u003e. 2016, Springer-Verlag New York.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlighe, K., S. Rana, and M. Lewis, \u003cem\u003eEnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling\u003c/em\u003e. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, L., et al., \u003cem\u003eThe prognostic value of intratumoral and peritumoral tumor-infiltrating FoxP3 + Treg cells in of pancreatic adenocarcinoma: a meta-analysis\u003c/em\u003e. World J Surg Oncol, 2021. \u003cb\u003e19\u003c/b\u003e(1): p.\u0026nbsp;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, Y.P., et al., \u003cem\u003eComprehensive analysis of the percentage of surface receptors and cytotoxic granules positive natural killer cells in patients with pancreatic cancer, gastric cancer, and colorectal cancer\u003c/em\u003e. J Transl Med, 2013. \u003cb\u003e11\u003c/b\u003e: p.\u0026nbsp;262.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpeiser, D.E., P.C. Ho, and G. Verdeil, \u003cem\u003eRegulatory circuits of T cell function in cancer\u003c/em\u003e. Nat Rev Immunol, 2016. \u003cb\u003e16\u003c/b\u003e(10): p.\u0026nbsp;599\u0026ndash;611.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveira, J.J., et al., \u003cem\u003eThe plasma biomarker soluble SIGLEC-1 is associated with the type I interferon transcriptional signature, ethnic background and renal disease in systemic lupus erythematosus\u003c/em\u003e. Arthritis Res Ther, 2018. \u003cb\u003e20\u003c/b\u003e(1): p.\u0026nbsp;152.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams, J.M. and S. Cory, \u003cem\u003eBcl-2-regulated apoptosis: mechanism and therapeutic potential\u003c/em\u003e. Curr Opin Immunol, 2007. \u003cb\u003e19\u003c/b\u003e(5): p.\u0026nbsp;488\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh, P.K., et al., \u003cem\u003ePlatelet-derived growth factor receptor beta-mediated phosphorylation of MUC1 enhances invasiveness in pancreatic adenocarcinoma cells\u003c/em\u003e. Cancer Res, 2007. \u003cb\u003e67\u003c/b\u003e(11): p.\u0026nbsp;5201\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeissmueller, S., et al., \u003cem\u003eMutant p53 drives pancreatic cancer metastasis through cell-autonomous PDGF receptor beta signaling\u003c/em\u003e. Cell, 2014. \u003cb\u003e157\u003c/b\u003e(2): p.\u0026nbsp;382\u0026ndash;394.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBang, S., et al., \u003cem\u003eDifferences in immune cells engaged in cell-mediated immunity after chemotherapy for far advanced pancreatic cancer\u003c/em\u003e. Pancreas, 2006. \u003cb\u003e32\u003c/b\u003e(1): p.\u0026nbsp;29\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIno, Y., et al., \u003cem\u003eImmune cell infiltration as an indicator of the immune microenvironment of pancreatic cancer\u003c/em\u003e. Br J Cancer, 2013. \u003cb\u003e108\u003c/b\u003e(4): p.\u0026nbsp;914\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, H., et al., \u003cem\u003eNeoadjuvant FOLFIRINOX Therapy Is Associated with Increased Effector T Cells and Reduced Suppressor Cells in Patients with Pancreatic Cancer\u003c/em\u003e. Clin Cancer Res, 2021. \u003cb\u003e27\u003c/b\u003e(24): p.\u0026nbsp;6761\u0026ndash;6771.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichelakos, T., et al., \u003cem\u003eTumor Microenvironment Immune Response in Pancreatic Ductal Adenocarcinoma Patients Treated With Neoadjuvant Therapy\u003c/em\u003e. J Natl Cancer Inst, 2021. \u003cb\u003e113\u003c/b\u003e(2): p.\u0026nbsp;182\u0026ndash;191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, H.S., et al., \u003cem\u003ePeripheral natural killer cell activity is associated with poor clinical outcomes in pancreatic ductal adenocarcinoma\u003c/em\u003e. J Gastroenterol Hepatol, 2021. \u003cb\u003e36\u003c/b\u003e(2): p.\u0026nbsp;516\u0026ndash;522.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang, K., et al., \u003cem\u003eInterferon-gamma Represses M2 Gene Expression in Human Macrophages by Disassembling Enhancers Bound by the Transcription Factor MAF\u003c/em\u003e. Immunity, 2017. \u003cb\u003e47\u003c/b\u003e(2): p.\u0026nbsp;235\u0026ndash;250 e4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, S., Q. Liu, and Q. Liao, \u003cem\u003eTumor-Associated Macrophages in Pancreatic Ductal Adenocarcinoma: Origin, Polarization, Function, and Reprogramming\u003c/em\u003e. Front Cell Dev Biol, 2020. \u003cb\u003e8\u003c/b\u003e: p.\u0026nbsp;607209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Neill, A.S., T.K. van den Berg, and G.E. Mullen, \u003cem\u003eSialoadhesin - a macrophage-restricted marker of immunoregulation and inflammation\u003c/em\u003e. Immunology, 2013. \u003cb\u003e138\u003c/b\u003e(3): p.\u0026nbsp;198\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong, Y.S., et al., \u003cem\u003eIncreased expression of Siglec-1 on peripheral blood monocytes and its role in mononuclear cell reactivity to autoantigen in rheumatoid arthritis\u003c/em\u003e. Rheumatology (Oxford), 2014. \u003cb\u003e53\u003c/b\u003e(2): p.\u0026nbsp;250\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAffandi, A.J., et al., \u003cem\u003eCD169 Defines Activated CD14(+) Monocytes With Enhanced CD8(+) T Cell Activation Capacity\u003c/em\u003e. Front Immunol, 2021. \u003cb\u003e12\u003c/b\u003e: p.\u0026nbsp;697840.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampani, D., et al., \u003cem\u003eBcl-2 expression in pancreas development and pancreatic cancer progression\u003c/em\u003e. J Pathol, 2001. \u003cb\u003e194\u003c/b\u003e(4): p.\u0026nbsp;444\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, H.K., et al., \u003cem\u003eRRAD expression in gastric and colorectal cancer with peritoneal carcinomatosis\u003c/em\u003e. Sci Rep, 2019. \u003cb\u003e9\u003c/b\u003e(1): p.\u0026nbsp;19439.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, J., et al., \u003cem\u003eTargeting transforming growth factor-beta signaling for enhanced cancer chemotherapy\u003c/em\u003e. Theranostics, 2021. \u003cb\u003e11\u003c/b\u003e(3): p.\u0026nbsp;1345\u0026ndash;1363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy, J.E., et al., \u003cem\u003eTotal Neoadjuvant Therapy With FOLFIRINOX in Combination With Losartan Followed by Chemoradiotherapy for Locally Advanced Pancreatic Cancer: A Phase 2 Clinical Trial\u003c/em\u003e. JAMA Oncol, 2019. \u003cb\u003e5\u003c/b\u003e(7): p.\u0026nbsp;1020\u0026ndash;1027.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkowicz, S., et al., \u003cem\u003eRecovery of dendritic cell counts and function in peripheral blood of cancer patients after chemotherapy\u003c/em\u003e. Cytokines Cell Mol Ther, 2002. \u003cb\u003e7\u003c/b\u003e(1): p.\u0026nbsp;15\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonanno, G., et al., \u003cem\u003eEffects of pegylated G-CSF on immune cell number and function in patients with gynecological malignancies\u003c/em\u003e. J Transl Med, 2010. \u003cb\u003e8\u003c/b\u003e: p.\u0026nbsp;114.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2008977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2008977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eFOLFIRINOX chemotherapy showed promising results in treating patients with pancreatic ductal adenocarcinoma (PDAC). However, many patients and physicians are reluctant to start FOLFIRINOX due to its high toxicity and limited clinical response rates. In this study, we investigated the effect of a single cycle of FOLFIRINOX, in combination with a granulocyte colony-stimulating factor (G-CSF), on the blood immune transcriptome of PDAC patients. We aimed to identify an early circulating biomarker to predict the lack of FOLFIRINOX response.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBlood samples of 68 patients from all PDAC disease stages, who received at least four FOLFIRINOX cycles, were collected at baseline and after the first cycle. Patients were divided into \u0026ldquo;disease control\u0026rdquo; and \u0026ldquo;progressive disease\u0026rdquo; following the RECIST criteria 1.1. RNA was isolated and targeted immune-gene expression profiling was performed using the PanCancer Immune profiling panel of NanoString. The FOLFIRINOX delta Gene Expression Profiling (FFX-ΔGEP) score was calculated using the weight of eight genes following LASSO multivariate regression analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eComparing the immune gene expression profile of samples at baseline to after a single FOLFIRINOX cycle resulted in the identification of 395 differentially expressed genes (BH.P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), correlating to 30 significant alterations in relative immune cell abundancies and pathway activities (BH.P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The patient cohort included 48 disease control and 10 progressive disease patients. The FFX-ΔGEP score, composed of eight genes (\u003cem\u003eBID\u003c/em\u003e, \u003cem\u003eFOXP3\u003c/em\u003e, \u003cem\u003eKIR3DL1\u003c/em\u003e, \u003cem\u003eMAF\u003c/em\u003e, \u003cem\u003ePDGFRB\u003c/em\u003e, \u003cem\u003eRRAD\u003c/em\u003e, \u003cem\u003eSIGLEC1\u003c/em\u003e, and \u003cem\u003eTGFB2)\u003c/em\u003e, could predict the lack of FOLFIRINOX response with a leave-one-out cross-validated AUC [95% CI] of 0.87 [0.60\u0026ndash;0.98]. Our FFX-ΔGEP score outperformed the predictiveness of absolute and proportional ΔCA19-9 values with an AUC [95% CI] of 0.70 [0.27\u0026ndash;1.0] and 0.52 [0.24\u0026ndash;0.80], respectively. Notably, immune-gene expression profiles of baseline samples could not predict the lack of FOLFIRINOX response.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA single FOLFIRINOX cycle, combined with G-CSF, alters the peripheral immune transcriptome indisputably. We revealed a novel multigene FFX-ΔGEP score which is, to our knowledge, the first gene expression-based early circulating biomarker that predicts the lack of FOLFIRINOX response after only a single cycle. Validation in a larger independent cohort of samples is crucial before clinical implementation.\u003c/p\u003e","manuscriptTitle":"A multigene circulating biomarker to predict the lack of FOLFIRINOX response after a single cycle in patients with pancreatic ductal adenocarcinoma (PDAC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-02 15:32:42","doi":"10.21203/rs.3.rs-2008977/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d919b7e6-3423-4991-83f1-52d8086b8446","owner":[],"postedDate":"September 2nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-09-03T12:44:12+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-02 15:32:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2008977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2008977","identity":"rs-2008977","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}