Correlation between circulating tumor DNA quantity assessed by methylated markers and tumor volume in patients with metastatic pancreatic adenocarcinoma.

Correlation between circulating tumor DNA quantity assessed by methylated markers and tumor volume in patients with metastatic pancreatic adenocarcinoma. | 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 Article Correlation between circulating tumor DNA quantity assessed by methylated markers and tumor volume in patients with metastatic pancreatic adenocarcinoma. Olivier Caliez, Mathilde Wagner, Valérie Taly, Léo Mas, Solène Doat, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6422905/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Circulating tumor DNA (ctDNA) is a promising marker with a strong prognostic value in metastatic pancreatic ductal adenocarcinoma (mPDAC). However, ctDNA is not detected in about a third of patients with mPDAC. The objective of this study was to assess the correlation between ctDNA and tumor volume (TV). In this monocentric cohort of mPDAC patients naïve for chemotherapy, ctDNA quantity at baseline was assessed by droplet-based digital PCR targeting two methylated markers (HOXD8 and POU4F1). TV was measured in 3D, for the primary lesion and the metastatic sites, from baseline thoraco-abdomino-pelvic computed tomography (CT) scan. Among the 71 included patients, ctDNA was detected in 47 patients (66.2%). There was a significant correlation of total and liver TV with ctDNA quantity, with Spearman's ρ of 0.353 (p = 0.01) and 0.500 (p < 0.001), respectively. Total and liver TV were higher in patients with detected ctDNA (129.5 vs 31.8 mL, p = 0.002 and 18 vs 1 mL, p < 0.001, respectively). Total and liver TV thresholds of 90.1 and 3.7 mL were associated with ctDNA detection, respectively. This study demonstrates the correlation between ctDNA and TV in mPDAC, especially for liver metastases. It supports the data that ctDNA could be a surrogate marker of TV in patients with mPDAC and liver metastases. Health sciences/Oncology/Cancer/Gastrointestinal cancer/Pancreatic cancer Health sciences/Oncology/Cancer/Tumour biomarkers Biological sciences/Cancer/Cancer imaging Metastatic pancreatic adenocarcinoma circulating tumor DNA tumor volume tumor biomarkers cancer imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Pancreatic ductal adenocarcinoma (PDAC) is about to become the third leading cause of cancer death in the European Union 1 . GLOBOCAN predicts a worldwide increasing trend in PDAC incidence (+ 77.7%, with 356,358 new cases) and mortality (+ 79.9%, with 345,181 deaths) between 2018 and 2040 2 . Despite recent improvements with new chemotherapy protocols, FOLFIRINOX 3 or Gemcitabine plus Nab-Paclitaxel 4 , patients’ prognosis remains poor. In the era of personalized medicine for cancer patients, tools to predict prognosis, personalize treatment, and monitor response are needed. Carbohydrate antigen 19–9 (CA 19–9) is currently the only biomarker used for PDAC and has several limitations. It can be elevated in many situations (cholestasis, other cancers) and is constitutively normal in patients with a negative Lewis genotype (5–10% of the Caucasian population) 5 . Search of KRAS mutations or other frequent mutations in PDAC such as TP53 , CDKN2A , or SMAD4 is used to identify circulating tumor DNA (ctDNA) from circulating cell-free DNA (ccfDNA). It has demonstrated both high specificity 6 and prognostic value 7 – 9 . Other methods have been developed for the detection of ctDNA based on epigenetic modifications using the methylation profile of gene promoters. We previously identified and validated two specific methylated markers of PDACs, HOXD8 and POU4F1. The level of ctDNA detected by the presence of HOXD8 and/or POUAF1 in patients with metastatic PDAC (mPDAC) had a strong prognostic value in a prospective cohort and two randomized phase 2 trials 10 . The ctDNA quantity has been validated as a pertinent prognostic marker 9 , 11 – 13 . Recent studies suggest that ctDNA could be a surrogate for tumor volume (TV) 14 , 15 . However, the dense desmoplastic stroma characteristic of PDAC with relatively few tumor cells could impact the correlation between tumor volume (TV) and ctDNA quantity 16 . In this study, we evaluated in patients with mPDAC, the correlation between ctDNA quantity (assessed by methylated markers) and the TV (3D measured). The secondary objectives of this study were to assess TV threshold for ctDNA detection, analyze the correlation between ctDNA quantity and TV for different sites, particularly the primary tumor and liver metastases, examine the correlation between ccfDNA quantity and TV, and investigate the evolution of ctDNA and TV after the initiation of first-line chemotherapy. 2 Results Population Our cohort prospectively and consecutively included 129 patients with mPDAC. After verification, six patients with advanced non-metastatic tumor and three patients with a histological type other than PDAC (desmoid tumor, intestinal adenocarcinoma, and cholangiocarcinoma) were excluded. Five patients with missing plasma sample and 44 patients without an interpretable baseline CT were excluded. Details are shown in the flowchart (Fig. 2 ). Seventy-one patients were therefore included in the analysis. Their clinical, tumoral, biological, and therapeutic characteristics are presented in Table 1 . The median time between the CT scan and the ctDNA collection was 10 days [Interquartile (IQ) 6–21]. The median ccfDNA quantity was 11.2 ng/mL [IQ 5.8–19.5]. ctDNA was detected in 47 patients (66.2%). The median ctDNA quantity in patients with detectable ctDNA was 0.9 ng/mL [IQ 0.2–3.1]. Patients with detectable ctDNA had significantly more often a tumor with a lower degree of differentiation (p = 0.031). Median MAF in patients with detectable ctDNA was 8.3% [IQ 1.2–33.6]. Table 1 Patients’ characteristics Patients’ characteristics Overall population Negative ctDNA Positive ctDNA p n 71 24 47 Age, years (median [IQ]) 66.3 [60.1, 72.3] 61.2 [57.7, 74.5] 68.0 [61.9, 71.2] 0.356 Gender = Male (%) 47 (66.2) 17 (70.8) 30 (63.8) 0.745 BMI, kg/m2 (median [IQ]) 23.1 [20.2, 25.6] 22.1 [19.3, 25.4] 23.4 [21.0, 25.6] 0.451 ECOG PS = 2 (%) (vs 0–1) 13 (21.0) 2 (10.5) 11 (25.6) 0.315 Tumor location (%) 0.591 Head and isthmus 35 (49.3) 13 (54.2) 22 (46.8) Body 17 (23.9) 7 (29.2) 10 (21.3) Tail 16 (22.5) 4 (16.7) 12 (25.5) Differentiation grade (%) 0.031 Well 6 (8.5) 5 (20.8) 1 (2.1) Moderate 19 (26.8) 5 (20.8) 14 (29.8) Poor 17 (23.9) 5 (20.8) 12 (25.5) Not available 29 (40.8) 9 (37.6) 20 (42.6) CA 19 − 9, U/ml (median [IQ]) 1218.5 [142.3, 4827.7] 167.3 [61.0, 3716.0] 1795.0 [213.0, 9598.0] 0.122 CA 19 − 9 > 1000U/mL (%) 30 (51.7) 8 (38.1) 22 (59.5) 0.197 CEA, µg/L (median [IQ]) 6.3 [3.0, 35.0] 5.2 [2.3, 50.3] 11.0 [3.7, 20.6] 0.874 Total bilirubin, µmol/L (median [IQ]) 11.5 [8.0, 26.8] 10.9 [7.2, 39.5] 12.0 [8.8, 25.0] 0.785 Platelet, Giga/L (median [IQ]) 257.0 [210.0, 317.0] 254.0 [215.5, 337.5] 260.1 [209.5, 309.5] 0.678 Leucocytes, Giga/L (median [IQ]) 7.1 [5.3, 11.5] 5.8 [5.1, 7.7] 9.2 [5.5, 14.5] 0.061 Albumin, g/dL (median [IQ]) 38.0 [34.0, 40.5] 39.9 [38.5, 42.5] 37.0 [31.0, 39.75] 0.060 CcfDNA quantity (ng/mL) 11.2 [5.8, 19.5] 9.2 [5.9, 12.3] 13.4 [5.6, 23.2] 0.235 CtDNA quantity (ng/mL) - 0 0.9 [0.2, 3.1] NC MAF (%) - 0 8.3 [1.2, 33.6] NC First line chemotherapy protocol (%) 0.458 FOLFIRINOX 23 (32.4) 11 (47.8) 12 (25.5) GEMCITABINE-NAB PACLITAXEL 18 (25.4) 3 (13.0) 15 (31.9) Other 30 (42.2) 10 (41.6) 20 (42.6) Total tumor volume (median [IQ]) 69.6 [22.9, 193.2] 31.8 [14.8, 80.1] 129.5 [34.9, 243.3] 0.002 Primary lesion tumor volume (mL) (median [IQR]) 18.9 [7.4, 56.9] 18.0 [5.7, 57.8] 18.9 [8.4, 55.2] 0.519 Number of liver lesions (median [IQ]) 7.0 [2.0, 30.0] 1.0 [0.0, 5.3] 18.0 [6.0, 51.5] < 0.001 Liver tumor volume (mL) (median [IQ]) 11.2 [1.2, 100.4] 0.4 [0.0, 2.6] 28.3 [7.9, 141.9] < 0.001 TV and number of metastases The median total TV was 69.6 mL [IQ 22.9-193.2]. It was significantly higher in patients with detectable ctDNA with 129.5 mL [IQ 34.9-243.3] vs 31.8 mL [IQ 14.8–80.1] (p = 0.002). The TV of the primary lesion was not significantly different in patients with detectable or undetectable ctDNA, with a median TV of 18.9 mL [IQ 8.4–55.2] with detectable ctDNA vs 18.0 mL [IQ 5.7–57. 8] without (p = 0.519). Sixty patients (84.5%) had liver metastases. The number of liver metastases and liver metastases TV were significantly higher in patients with detectable ctDNA, with a median number of lesions of 18 [IQ 6-51.5] with detectable ctDNA vs 1 [IQ 0-5.3] without (p < 0.001) and a median liver metastases TV of 28.3 mL [IQ7.9-141.9] with detectable ctDNA vs 0.4 mL [IQ 0.0-2.6] without (p < 0.001). These data are presented in Table 1 . CtDNA was detected for only one of the 11 patients (9.1%) without liver metastasis, whereas detection rate was 76.7% (46/60) for patients with liver metastases. The patient with detected ctDNA without liver metastasis had a total tumor volume of 253.3 mL (221 mL of lymph node tumor volume). Patients without liver metastasis had mainly lymph node and peritoneal metastasis of small volume (< 10mL). Characteristic of patients without liver metastasis are presented in Supplementary table 1 . Respectively, 27 (38.0%), 16 (22.5%), and 14 (19.7%) patients had lymph node, peritoneal, and lung metastases. In these patients the median numbers and TV were: 2 [IQ 1–6] and 5.8 mL [IQ 3.1–10.5] for lymph node metastases; 5 [IQ 2.5–15.5] and 13.0 mL [IQ 2.3–38.3] for peritoneal metastases; 6.5 [IQ 1.5–41.8] and 2.7 mL [IQ 0.3–4.1] for lung metastases. For these three groups of patients, ctDNA detection was always significatively associated with a higher number of liver metastases and higher liver metastases TV (Supplementary table 2). Correlation between TV and ccfDNA, CA 19 − 9, and TV/ctDNA There was no significant correlation between total TV and ccfDNA quantity (p = 0.112), but a correlation was found between liver metastases TV and ccfDNA quantity, with Spearman's ρ = 0.266 (p = 0.028). There was no significant correlation of CA 19 − 9 at baseline (n = 58) nor with TV (total p = 0.08 and liver p = 0.08), nor with ctDNA quantity (in all of population p = 0.88 or in the subgroup with detectable ctDNA p = 0.44). Correlation between TV and ctDNA There was a correlation between total TV and ctDNA quantity, whether in the whole population with Spearman's ρ = 0.462 (p < 0.001), or in the subgroup of patients with detectable ctDNA Spearman's ρ = 0.353 (p = 0.01) (Fig. 3 a). Similarly, there was a correlation between liver metastases TV and ctDNA quantity, both in the whole population with Spearman's ρ = 0.692 (p < 0.001), and in the subgroup of patients with detectable ctDNA with Spearman's ρ = 0.500 (p < 0.001) (Fig. 3 b). TV threshold allowing the detection of ctDNA The technique of ROC curves was used to establish TV thresholds for the detection of ctDNA. A total TV threshold of 90.1mL allowed the detection of ctDNA with a sensitivity (Se) of 57.4% and a specificity (Sp) of 91.7% (AUC = 0.723) (Fig. 4 a). A liver metastases TV threshold of 3.7mL allowed the detection of ctDNA with a Se of 85.1% and a Sp of 79.2% (AUC = 0.887) (Fig. 4 b). Follow up of mPDAC patients Six patients with initially detectable ctDNA had at least one additional sample taken during follow-up, at one (5 patients) and/or two (2 patients) months of the chemotherapy beginning. For four of them, a decrease in ctDNA quantity on the sample during follow-up was consistent with a decrease in total TV on the evaluation CT scans. For the other two patients, a decrease in ctDNA quantity was discordant, with a slight increase in total TV on the evaluation CT scans (respectively, 6.7 mL and 5.6 mL). These results are presented in Fig. 4 . On the other hand, ctDNA remained undetectable during follow-up for three patients for whom ctDNA was initially undetectable and whose TV decreased on evaluation CT scans. 3 Discussion In the era of personalized medicine for cancer patients ctDNA and TV are two markers which could offer non-invasive methods to evaluate patients with mPDAC. The aim of this study was to evaluate the correlation of these two markers. In our study, the ctDNA detection rate of 66.2% was comparable to the rates reported in the literature for mPDAC patients, where ctDNA was detected by mutations (mainly KRAS mutations), with rates ranging from 46–68% 7,14,17 . This result is interesting because detecting methylated markers with ddPCR is rapid, sensitive, fast, and less expensive compared to strategies based on Next Generation Sequencing (NGS) 18 . A recent large study (1065 patients, including 120 mPDAC) by Dawi et al. , using FoundationOne Liquid CDx assay (Roche), a pan-cancer ccfDNA based comprehensive genomic profiling assay, found 32% of patients having a high (> 10%) tumor fraction (close to MAF but irrespective of a specific mutation). With this assay, a cutoff of 10% was suggested by the manufacturer to refer to specimens that are more likely to contain actionable alterations. Therefore, in this study, values below 10% were automatically adjusted to 0 15 . To our knowledge, our study is the first to demonstrate the correlation between ctDNA quantity obtained using epigenetic markers and TV measured in 3D in a mPDAC population. TV was correlated with ctDNA quantity, with Spearman's ρ of 0.353 (p = 0.01) (with ρ < 0,4 correlation is considered as weak). It confirms the correlation between ctDNA quantity and TV which had been reported in the study by Strijker et al. , in which ctDNA quantity was obtained using NGS and ddPCR for research of KRAS mutations. In a mPDAC population of 58 patients, Strijker et al. showed tumor volume (3D reconstructions from imaging) and ctDNA MAF were correlated with Spearman’s ρ of 0.544 (p < 0.001) 14 . This approach had already been the subject of a few studies in other types of cancers, in particular colorectal and ovarian cancers 19 , 20 , but its transposition to PDAC, due to its dense desmoplastic stroma with relatively few tumor cells 16 , was not obvious. In Dawi et al. a pan-cancer study, tumor fraction helped predict TV (estimated from 2D annotations) in linear model with R 2 = 0.17; p < 0.001 (R 2 < 0.30 considered as very weak predictor) 15 . Interestingly, the correlation between ctDNA quantity and liver metastatic TV was better than with total TV, with Spearman's ρ of 0.500 (p < 0.001) (with ρ < 0,6 correlation is considered as modarate). Moreover, a liver metastatic TV threshold of only 3.7 mL (which corresponds to a spherical lesion of approximately 2 cm in diameter) allowed the detection of ctDNA with a Se of 85.1%. ctDNA detection was significantly associated with the number of liver metastases and liver metastatic TV. This suggest liver could be a metastatic site shedding more ctDNA. These results are consistent with two studies including mPDAC patients, the one of Strijker et al. where ctDNA detection rates differed depending on the location of the metastases, with high rates in the case of liver metastases 14 , and the one of Kirchweger et al. where mutant allele frequency had a stronger correlation with liver metastasis volume (r = 0.6) than with TV (r = 0.473) 21 . Similar results have been reported in other cancers, notably for colorectal cancer in Bachet et al. study, where the presence of liver metastases was significantly associated with the presence of ctDNA 22 , and in the Dawi et al. pan-cancer study where liver lesions volume (volume > 3 cm 3 ) was significantly associated with a contributory liquid biopsy (OR 2.74). A recent study of Nitschke C et al. comparing ctDNA detection between peripheral blood and portal blood provides another hypothesis as if the liver acted as a filter for ccfDNA and ctDNA. Indeed, in matched samples, significantly higher ccfDNA concentration was found in portal vein blood compared to peripheral blood (22.23 ng/mL vs 55.12 ng/mL; p < 0.0001) and KRAS positive portal vein samples had significantly higher mutant copy numbers compared to the peripheral sample with an average of 6.4-fold increase (p < 0.05) 23 . Although ctDNA quantity is correlated with the TV in our study, it should be noted that some patients with high TV had no ctDNA detected. Moreover, correlation between ctDNA quantity and TV was not strong. These observations illustrate well the fact that factors other than TV contribute to the detectability of ctDNA. In our study, patients with detectable ctDNA had tumors with a lower degree of differentiation (p = 0.031). In literature, released mechanisms of ccfDNA and ctDNA implied necrosis and apoptosis, but also cell secretion immunologically mediate released implying, for example, neutrophil extracellular traps 24 , or due to hypoxic conditions 25 . These other mechanisms are potentially less correlated to TV. Other parameters as molecular profile and imaging factors (heterogeneity, proximity to vessels, necrosis) could influence ctDNA quantity and should be assessed in future studies. One of the main limitations to this 3D measurement of TV, whether in current practice or in studies, is its time-consuming nature. Indeed, even if it is a semi-automated measurement, an exhaustive measurement is tedious, in particular because of the number of lesions. Indeed, 25% of the patients in our study had more than 30 liver metastases. The development of fully automated segmentation software and artificial intelligence should, however, make it easier and faster to obtain reliable TV measurements in the years to come. Additionally, pancreatic tumors are difficult to measure due to their ill-defined margins and irregular growth patterns. Measurement errors may have occurred but seem unavoidable for PDAC. However, one of the strengths of this study is that all the TV measurements have been verified by a single radiologist expert in digestive oncology. At baseline, there was no statistically significant correlation between CA 19 − 9 (n = 58) and ctDNA quantity (p = 0.570) or TV (p = 0.873). This result is inconsistent with the study by Perets et al. that showed a correlation between CA 19 − 9 and ctDNA 26 , but consistent with the study by Strijker et al. in which neither ctDNA quantity nor the TV were correlated with CA 19 − 9 14 . Similar results have been reported by Bachet et al. in metastatic colorectal cancer in which no correlation was observed between CEA and ctDNA levels at diagnosis 27 . The correlation between TV and ctDNA quantity is an argument in favor of its use in monitoring patients undergoing treatment. A non-radiological evaluation of the evolution of the disease during treatment would be particularly interesting insofar as, in PDAC, after chemotherapy, imaging makes it difficult to distinguish between viable adenocarcinoma and fibrosis 28 , 29 . In patients who show a mixed response or progression on CT but with a clinical response, ctDNA could help evaluate the tumor response. In this study, the evaluation of follow-up during treatment was only exploratory. Four of the six patients with follow-up showed a decrease in ctDNA consistent with the decrease in TV, but for the last two patients the evolution was discordant. Other authors have shown an inconsistence between ctDNA quantity and the tumor response based on 3D imaging 14 or standard evaluations 30 , 31 , in a minority of patients. The limited number of follow-up samples hampered a better understanding of ctDNA dynamics. To determine whether the clinical implementation of ctDNA might be useful, future studies should determine the additional information obtained by repeated measurement of ctDNA quantity during chemotherapy and at treatment assessment. Encouraging data have been reported on the dynamic evolution of ctDNA under treatment 7 , 30 . Nevertheless, repeated ctDNA measurement only applied for two thirds 7 , 9 of mPDAC patients with ctDNA detectable at baseline. In conclusion, this study demonstrates the correlation between ctDNA quantity and TV, in particular liver metastases TV, in mPDAC. It supports the data that ctDNA could be a surrogate marker of TV at baseline. 4 Methods Patients From January 2011 to December 2018, plasma from all consecutive patients with histologically proven mPDAC receiving first-line chemotherapy was prospectively collected at Pitié-Salpêtrière Hospital (Paris, France). Blood samples were taken just before the first cycle of chemotherapy. Nine patients had at least one additional sample taken during follow-up (at one and/or two months of the chemotherapy beginning). All patients signed an informed consent form, approved by the ethics committee (CPP Ile-de-France 2014/59NICB). All methods were performed in accordance with the relevant guidelines and regulations. To be include, the patients should have a histologically proven mPDAC with an interpretable ctDNA result and a thoraco-abdomino-pelvic computed tomography (CT) scan available at baseline (i.e., before the start of treatment). The following data were collected in a prospective database: clinical characteristics (gender, age, ECOG performance status (PS), medical history, date of diagnosis, location of primary tumor, diameter of primary tumor, grade of tumor differentiation, stage at diagnosis), follow-up data (date and type of chemotherapy, date of death or last follow-up) and biological data before the first cycle of chemotherapy (complete blood count, carcinoembryonic antigen (CEA), CA 19 − 9, albuminemia, and bilirubinemia). Plasma sample preparation, storage, DNA extraction Blood samples (9 mL) were taken from a central catheter and collected in EDTA tubes. The samples were centrifuged at 3500 rpm for 15 minutes at 4°C within 3 hours of blood collection. Plasma was stored at -80°C until further use. Before extraction, plasma samples were centrifugated for 15 min at 5000 rpm. Extraction was then realized from 1 or 2 mL of plasma, respectively, with QIAamp Circulating Nucleic Acid Kit (Qiagen) or Maxwell RSC ccfDNA Plasma Kit. Extraction method is detailed in supplementary data. Extracted DNA was respectively eluted with 50 µL our 60 µL of AVE buffer and stored at -80°C. Droplet-based digital PCR (ddPCR) for methylated markers We used two methylated markers HOXD8 and POU4F1, whose development and validation have been described in our previous studies 10 , 32 and detailed in supplementary method. We used bisulfite conversion (EZ DNA-Metylation Gold kitTM (Zymo Research)) and used droplet-based digital PCR (ddPCR) (BioRad QX200 system) for detection of both target methylated markers. This method is detailed in supplementary data. Quantification of ccfDNA and ctDNA To calculate ctDNA quantity (ng/mL), we used the methylated biomarker for which the number of droplets was the highest and applied a formula (detailed in supplementary method) involving the different volumes used for ddPCR, bisulfite conversion, elution of extracted DNA and plasma extraction. To calculate ccfDNA, the same formula was used with the number of droplets of the albumin un-methylated sequence, used as reference for measuring amplifiable DNA. CtDNA was analyzed both as a continuous variable and categorized as detectable whether greater than 0 ng/mL or not. Mutant allele frequency (MAF) was the ratio between ctDNA quantity and ccfDNA quantity, expressed as a percentage. Tumor volume (TV) The CT scans were performed as part of routine clinical practice. They included at least portal phase acquisition of an injection of iodinated contrast product in the abdominopelvic region and thoracic acquisition. The CT scans were acquired on different systems, but all available exams had slices less than 5 mm thick. All CT scans were analyzed using Syngo.via software (Siemens Healthcare, Forchheim, Germany) (Fig. 1 ). For the measurement of TV, the “MM Oncology” module was used. Segmentation of tumor lesions was semi-automated with manual correction if necessary. The TV of the primary lesion as well as the number and TV of metastases were collected. These included lymph node metastases (lymph nodes were included when the short axis was greater than 10 mm, according to the definition of pathological lymph nodes reported in RECIST 1.1.), liver metastases, peritoneal metastases, lung metastases, and others (adrenal gland, bone). All primary tumors and metastases were measured regardless of their size. These measurements were carried out by a qualified doctor, then were verified in their entirety by a radiologist expert in digestive oncology. The whole of this collection was carried out blind to the result of the ctDNA. TV, measured in milliliters, was analyzed as a continuous variable. Statistical analysis Patient characteristics were compared using a chi-square test or Fisher's exact test (for categorical variables) and the t-test for independent samples (for normally defined continuous variables). Spearman's rank correlation rho was used to evaluate correlation. ROC (receiver operating feature) curves were used, the threshold value corresponding to the point where the AUC (Area Under Curve) is the highest. All statistical analyzes were performed using r software version 4.1.0. A value of p < 0.05 was considered significant. Declarations Competing financial and/or non-financial interests : JB Bachet has received personal fees from Amgen, AstraZeneca, Bayer, Merck Serono, Pierre Fabre, Roche, Sanofi, Servier, Shire, and non-financial support from Amgen, Merck Serono, and Roche. P Laurent-Puig declares consulting for or personal fees from Amgen, AstraZeneca, Biocartis, BMS, Boehringer-Ingelheim, Lilly, Merck Serono, MSD, Sanofi, Servier, Roche. Fundings: There was no funding for this manuscript except for a grant from the Fondation pour la Recherche Médicale (Grant No. M2R201806006018) awarded to O.C. Author contributions statement: O.C., J-B.B., V.T., and P.L-P. contributed to the study design. O.C. and D.P. were responsible for data acquisition and laboratory handling. M.W. provided expert radiologic measurements. O.C. conducted the statistical analysis. O.C., J-B.B., V.T., P.L-P., D.P., S.D., and L.M. contributed to the interpretation of the results. O.C. wrote the manuscript and generated the tables and figures. J-B.B. and V.T. provided comments and corrections on the manuscript text. All authors reviewed the manuscript. Data Availability Statement: The data that support the findings of this study have been deposited in the Science Data Bank with the following accession link: https://doi.org/10.57760/sciencedb.23515 References Ferlay, J., Partensky, C. & Bray, F. More deaths from pancreatic cancer than breast cancer in the EU by 2017. Acta Oncol. 55 , 1158–1160 (2016). Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J. Clin. 68 , 394–424 (2018). Thierry, C. et al. FOLFIRINOX versus Gemcitabine for Metastatic Pancreatic Cancer. N. Engl. J. Med. 9 (2011). Von Hoff, D. D. et al. Increased Survival in Pancreatic Cancer with nab-Paclitaxel plus Gemcitabine. N. Engl. J. Med. 369 , 1691–1703 (2013). Ballehaninna, U. K. & Chamberlain, R. S. The clinical utility of serum CA 19 – 9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: An evidence based appraisal. J. Gastrointest. 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RAS mutation analysis in circulating tumor DNA from patients with metastatic colorectal cancer: the AGEO RASANC prospective multicenter study. Ann. Oncol. 29 , 1211–1219 (2018). Nitschke, C. et al. Peripheral and Portal Venous KRAS ctDNA Detection as Independent Prognostic Markers of Early Tumor Recurrence in Pancreatic Ductal Adenocarcinoma. Clin. Chem. hvac214 10.1093/clinchem/hvac214 (2023). Association of neutrophil extracellular. traps with the production of circulating DNA in patients with colorectal cancer | Elsevier Enhanced Reader. https://reader.elsevier.com/reader/sd/pii/S2589004222000967?token=2189474A585606D5BFD521E32294A56FA015AE70A75A4EB60947EE8A178AB05409A5B9784038B2DD84B944AE973FF6C3&originRegion=eu-west-1&originCreation=20230212103135 10.1016/j.isci.2022.103826 Ho, A. S. et al. Circulating miR-210 as a Novel Hypoxia Marker in Pancreatic Cancer. Transl Oncol. 3 , 109–113 (2010). Perets, R. et al. Mutant KRAS Circulating Tumor DNA Is an Accurate Tool for Pancreatic Cancer Monitoring. Oncologist 23 , 566–572 (2018). Bachet, J. B. et al. Circulating tumour DNA at baseline for individualised prognostication in patients with chemotherapy-naïve metastatic colorectal cancer. An AGEO prospective study. Eur. J. Cancer . 189 , 112934 (2023). Ferrone, C. R. et al. Radiological and Surgical Implications of Neoadjuvant Treatment With FOLFIRINOX for Locally Advanced and Borderline Resectable Pancreatic Cancer. Ann. Surg. 261 , 12–17 (2015). Wagner, M. et al. CT evaluation after neoadjuvant FOLFIRINOX chemotherapy for borderline and locally advanced pancreatic adenocarcinoma. Eur. Radiol. 27 , 3104–3116 (2017). Kruger, S. et al. Repeated mut KRAS ctDNA measurements represent a novel and promising tool for early response prediction and therapy monitoring in advanced pancreatic cancer. Ann. Oncol. 29 , 2348–2355 (2018). Sugimori, M. et al. Quantitative monitoring of circulating tumor DNA in patients with advanced pancreatic cancer undergoing chemotherapy. Cancer Sci. 10.1111/cas.14245 (2019). Wang-Renault, S., Pietrasz, D., Bachet, J. B., Puig, P. L. & Taly, V. Detection of hypermethylated genes for diagnosing pancreatic cancer. (2019). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 07 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviews received at journal 12 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviewers agreed at journal 22 May, 2025 Reviewers invited by journal 21 May, 2025 Editor assigned by journal 19 May, 2025 Editor invited by journal 18 Apr, 2025 Submission checks completed at journal 15 Apr, 2025 First submitted to journal 15 Apr, 2025 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6422905","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":460331732,"identity":"e700d850-9993-4d4e-a1ee-93abbc54a823","order_by":0,"name":"Olivier Caliez","email":"","orcid":"","institution":"Sorbonne University, Department of Hepato-gastroenterology, Groupe Hospitalier Pitié Salpêtrière, Paris","correspondingAuthor":false,"prefix":"","firstName":"Olivier","middleName":"","lastName":"Caliez","suffix":""},{"id":460331733,"identity":"11fc7656-86f6-4950-ac25-817d0a8bc5db","order_by":1,"name":"Mathilde Wagner","email":"","orcid":"","institution":"Sorbonne University, Department of Radiology, Groupe Hospitalier Pitié Salpêtrière, Paris","correspondingAuthor":false,"prefix":"","firstName":"Mathilde","middleName":"","lastName":"Wagner","suffix":""},{"id":460331734,"identity":"963d4926-7ce5-4d86-a7bc-09f0f49771e7","order_by":2,"name":"Valérie Taly","email":"","orcid":"","institution":"Sorbonne University, Centre de Recherche des Cordeliers, INSERM, CNRS, USPC, Université de Paris, Equipe labellisée Ligue Nationale contre le cancer, CNRS SNC 5096, Paris","correspondingAuthor":false,"prefix":"","firstName":"Valérie","middleName":"","lastName":"Taly","suffix":""},{"id":460331738,"identity":"b725b08a-8792-4077-a12e-7fb1c6ca488c","order_by":3,"name":"Léo Mas","email":"","orcid":"","institution":"Sorbonne University, Department of Hepato-gastroenterology, Groupe Hospitalier Pitié Salpêtrière, Paris","correspondingAuthor":false,"prefix":"","firstName":"Léo","middleName":"","lastName":"Mas","suffix":""},{"id":460331739,"identity":"e915bc98-e68b-46de-aac6-cd36ddc9947f","order_by":4,"name":"Solène Doat","email":"","orcid":"","institution":"Sorbonne University, Department of Hepato-gastroenterology, Groupe Hospitalier Pitié Salpêtrière, Paris","correspondingAuthor":false,"prefix":"","firstName":"Solène","middleName":"","lastName":"Doat","suffix":""},{"id":460331740,"identity":"b68d98b0-6a9b-48b6-a470-0cfbe84cdffc","order_by":5,"name":"Daniel Pietrasz","email":"","orcid":"","institution":"Department of Digestive Surgery, Hôpital Paul Brousse, Villejuif","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Pietrasz","suffix":""},{"id":460331741,"identity":"1eea0f65-7c06-4f43-a61b-20d79bda7188","order_by":6,"name":"Pierre Laurent-Puig","email":"","orcid":"","institution":"Sorbonne University, Centre de Recherche des Cordeliers, INSERM, CNRS, USPC, Université de Paris, Equipe labellisée Ligue Nationale contre le cancer, CNRS SNC 5096, Paris","correspondingAuthor":false,"prefix":"","firstName":"Pierre","middleName":"","lastName":"Laurent-Puig","suffix":""},{"id":460331742,"identity":"20d635d1-ddb4-4812-916d-55eff156bc62","order_by":7,"name":"Jean-Baptiste Bachet","email":"data:image/png;base64,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","orcid":"","institution":"Sorbonne University, Department of Hepato-gastroenterology, Groupe Hospitalier Pitié Salpêtrière, Paris","correspondingAuthor":true,"prefix":"","firstName":"Jean-Baptiste","middleName":"","lastName":"Bachet","suffix":""}],"badges":[],"createdAt":"2025-04-10 19:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6422905/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6422905/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-13160-7","type":"published","date":"2025-10-07T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83421689,"identity":"bc10cc3d-2194-4a7a-84b0-64f481be4710","added_by":"auto","created_at":"2025-05-26 02:13:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82091,"visible":true,"origin":"","legend":"\u003cp\u003e3D tumor volume measurement with Syngo.via software\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6422905/v1/d3542324e17b9c4ffdbd1b4d.jpg"},{"id":83420965,"identity":"b5eab4ea-618c-4077-9784-7955ed0c4353","added_by":"auto","created_at":"2025-05-26 01:57:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131901,"visible":true,"origin":"","legend":"\u003cp\u003eflow chart\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6422905/v1/67805bd5e5b00e91add3df9e.jpg"},{"id":83420961,"identity":"12cb9c6c-9b1f-42a0-969c-fb367e6bc50a","added_by":"auto","created_at":"2025-05-26 01:57:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":127589,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation plot\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6422905/v1/c09d94de16759cd7649f2699.png"},{"id":83420973,"identity":"145c4a87-a9cf-48b5-923b-f008e23e1a44","added_by":"auto","created_at":"2025-05-26 01:57:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150981,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6422905/v1/a7790ac03e1cae9479bd34ed.png"},{"id":83420971,"identity":"1fb2e764-b35c-4877-9561-15388da947f2","added_by":"auto","created_at":"2025-05-26 01:57:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":296484,"visible":true,"origin":"","legend":"\u003cp\u003efollowed patients\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6422905/v1/7409d44e241e88e8c9b67966.png"},{"id":93420131,"identity":"f5677ce3-35c5-4051-8811-0677a9c3c8be","added_by":"auto","created_at":"2025-10-13 16:09:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1367444,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6422905/v1/def18264-b4a0-41e9-add9-10b2768e4de7.pdf"},{"id":83420974,"identity":"ac5bf3a5-7d71-4ddd-b463-e534df50dcfb","added_by":"auto","created_at":"2025-05-26 01:57:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15372269,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6422905/v1/8591b95fef26270967babe64.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation between circulating tumor DNA quantity assessed by methylated markers and tumor volume in patients with metastatic pancreatic adenocarcinoma.","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) is about to become the third leading cause of cancer death in the European Union \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. GLOBOCAN predicts a worldwide increasing trend in PDAC incidence (+\u0026thinsp;77.7%, with 356,358 new cases) and mortality (+\u0026thinsp;79.9%, with 345,181 deaths) between 2018 and 2040 \u003csup\u003e2\u003c/sup\u003e. Despite recent improvements with new chemotherapy protocols, FOLFIRINOX \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e or Gemcitabine plus Nab-Paclitaxel \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, patients\u0026rsquo; prognosis remains poor.\u003c/p\u003e \u003cp\u003eIn the era of personalized medicine for cancer patients, tools to predict prognosis, personalize treatment, and monitor response are needed. Carbohydrate antigen 19\u0026ndash;9 (CA 19\u0026ndash;9) is currently the only biomarker used for PDAC and has several limitations. It can be elevated in many situations (cholestasis, other cancers) and is constitutively normal in patients with a negative Lewis genotype (5\u0026ndash;10% of the Caucasian population) \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSearch of \u003cem\u003eKRAS\u003c/em\u003e mutations or other frequent mutations in PDAC such as \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, or \u003cem\u003eSMAD4\u003c/em\u003e is used to identify circulating tumor DNA (ctDNA) from circulating cell-free DNA (ccfDNA). It has demonstrated both high specificity \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and prognostic value \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Other methods have been developed for the detection of ctDNA based on epigenetic modifications using the methylation profile of gene promoters. We previously identified and validated two specific methylated markers of PDACs, HOXD8 and POU4F1. The level of ctDNA detected by the presence of HOXD8 and/or POUAF1 in patients with metastatic PDAC (mPDAC) had a strong prognostic value in a prospective cohort and two randomized phase 2 trials \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ctDNA quantity has been validated as a pertinent prognostic marker \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Recent studies suggest that ctDNA could be a surrogate for tumor volume (TV) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, the dense desmoplastic stroma characteristic of PDAC with relatively few tumor cells could impact the correlation between tumor volume (TV) and ctDNA quantity \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we evaluated in patients with mPDAC, the correlation between ctDNA quantity (assessed by methylated markers) and the TV (3D measured). The secondary objectives of this study were to assess TV threshold for ctDNA detection, analyze the correlation between ctDNA quantity and TV for different sites, particularly the primary tumor and liver metastases, examine the correlation between ccfDNA quantity and TV, and investigate the evolution of ctDNA and TV after the initiation of first-line chemotherapy.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003ePopulation\u003c/p\u003e \u003cp\u003eOur cohort prospectively and consecutively included 129 patients with mPDAC. After verification, six patients with advanced non-metastatic tumor and three patients with a histological type other than PDAC (desmoid tumor, intestinal adenocarcinoma, and cholangiocarcinoma) were excluded. Five patients with missing plasma sample and 44 patients without an interpretable baseline CT were excluded. Details are shown in the flowchart (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Seventy-one patients were therefore included in the analysis. Their clinical, tumoral, biological, and therapeutic characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median time between the CT scan and the ctDNA collection was 10 days [Interquartile (IQ) 6\u0026ndash;21]. The median ccfDNA quantity was 11.2 ng/mL [IQ 5.8\u0026ndash;19.5]. ctDNA was detected in 47 patients (66.2%). The median ctDNA quantity in patients with detectable ctDNA was 0.9 ng/mL [IQ 0.2\u0026ndash;3.1]. Patients with detectable ctDNA had significantly more often a tumor with a lower degree of differentiation (p\u0026thinsp;=\u0026thinsp;0.031). Median MAF in patients with detectable ctDNA was 8.3% [IQ 1.2\u0026ndash;33.6].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatients\u0026rsquo; characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients\u0026rsquo; characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative ctDNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive ctDNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.3 [60.1, 72.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.2 [57.7, 74.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.0 [61.9, 71.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;Male (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2 (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.1 [20.2, 25.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.1 [19.3, 25.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.4 [21.0, 25.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG PS\u0026thinsp;=\u0026thinsp;2 (%) (vs 0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead and isthmus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation grade (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA 19\u0026thinsp;\u0026minus;\u0026thinsp;9, U/ml (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1218.5 [142.3, 4827.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167.3 [61.0, 3716.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1795.0 [213.0, 9598.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA 19\u0026thinsp;\u0026minus;\u0026thinsp;9\u0026thinsp;\u0026gt;\u0026thinsp;1000U/mL (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA, \u0026micro;g/L (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3 [3.0, 35.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2 [2.3, 50.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 [3.7, 20.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin, \u0026micro;mol/L (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5 [8.0, 26.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9 [7.2, 39.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 [8.8, 25.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet, Giga/L (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257.0 [210.0, 317.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254.0 [215.5, 337.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e260.1 [209.5, 309.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucocytes, Giga/L (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.1 [5.3, 11.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8 [5.1, 7.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2 [5.5, 14.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.0 [34.0, 40.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.9 [38.5, 42.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.0 [31.0, 39.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCcfDNA quantity (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2 [5.8, 19.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2 [5.9, 12.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.4 [5.6, 23.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCtDNA quantity (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 [0.2, 3.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3 [1.2, 33.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst line chemotherapy protocol (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOLFIRINOX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEMCITABINE-NAB PACLITAXEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal tumor volume (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.6 [22.9, 193.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8 [14.8, 80.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.5 [34.9, 243.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary lesion tumor volume (mL) (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9 [7.4, 56.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.0 [5.7, 57.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9 [8.4, 55.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of liver lesions (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 [2.0, 30.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 [0.0, 5.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.0 [6.0, 51.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver tumor volume (mL) (median [IQ])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2 [1.2, 100.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4 [0.0, 2.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.3 [7.9, 141.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTV and number of metastases\u003c/p\u003e \u003cp\u003eThe median total TV was 69.6 mL [IQ 22.9-193.2]. It was significantly higher in patients with detectable ctDNA with 129.5 mL [IQ 34.9-243.3] vs 31.8 mL [IQ 14.8\u0026ndash;80.1] (p\u0026thinsp;=\u0026thinsp;0.002). The TV of the primary lesion was not significantly different in patients with detectable or undetectable ctDNA, with a median TV of 18.9 mL [IQ 8.4\u0026ndash;55.2] with detectable ctDNA vs 18.0 mL [IQ 5.7\u0026ndash;57. 8] without (p\u0026thinsp;=\u0026thinsp;0.519). Sixty patients (84.5%) had liver metastases. The number of liver metastases and liver metastases TV were significantly higher in patients with detectable ctDNA, with a median number of lesions of 18 [IQ 6-51.5] with detectable ctDNA vs 1 [IQ 0-5.3] without (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a median liver metastases TV of 28.3 mL [IQ7.9-141.9] with detectable ctDNA vs 0.4 mL [IQ 0.0-2.6] without (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. CtDNA was detected for only one of the 11 patients (9.1%) without liver metastasis, whereas detection rate was 76.7% (46/60) for patients with liver metastases. The patient with detected ctDNA without liver metastasis had a total tumor volume of 253.3 mL (221 mL of lymph node tumor volume). Patients without liver metastasis had mainly lymph node and peritoneal metastasis of small volume (\u0026lt;\u0026thinsp;10mL). Characteristic of patients without liver metastasis are presented in Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eRespectively, 27 (38.0%), 16 (22.5%), and 14 (19.7%) patients had lymph node, peritoneal, and lung metastases. In these patients the median numbers and TV were: 2 [IQ 1\u0026ndash;6] and 5.8 mL [IQ 3.1\u0026ndash;10.5] for lymph node metastases; 5 [IQ 2.5\u0026ndash;15.5] and 13.0 mL [IQ 2.3\u0026ndash;38.3] for peritoneal metastases; 6.5 [IQ 1.5\u0026ndash;41.8] and 2.7 mL [IQ 0.3\u0026ndash;4.1] for lung metastases. For these three groups of patients, ctDNA detection was always significatively associated with a higher number of liver metastases and higher liver metastases TV (Supplementary table 2).\u003c/p\u003e \u003cp\u003eCorrelation between TV and ccfDNA, CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9, and TV/ctDNA\u003c/p\u003e \u003cp\u003eThere was no significant correlation between total TV and ccfDNA quantity (p\u0026thinsp;=\u0026thinsp;0.112), but a correlation was found between liver metastases TV and ccfDNA quantity, with Spearman's ρ\u0026thinsp;=\u0026thinsp;0.266 (p\u0026thinsp;=\u0026thinsp;0.028). There was no significant correlation of CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9 at baseline (n\u0026thinsp;=\u0026thinsp;58) nor with TV (total p\u0026thinsp;=\u0026thinsp;0.08 and liver p\u0026thinsp;=\u0026thinsp;0.08), nor with ctDNA quantity (in all of population p\u0026thinsp;=\u0026thinsp;0.88 or in the subgroup with detectable ctDNA p\u0026thinsp;=\u0026thinsp;0.44).\u003c/p\u003e \u003cp\u003eCorrelation between TV and ctDNA\u003c/p\u003e \u003cp\u003eThere was a correlation between total TV and ctDNA quantity, whether in the whole population with Spearman's ρ\u0026thinsp;=\u0026thinsp;0.462 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), or in the subgroup of patients with detectable ctDNA Spearman's ρ\u0026thinsp;=\u0026thinsp;0.353 (p\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Similarly, there was a correlation between liver metastases TV and ctDNA quantity, both in the whole population with Spearman's ρ\u0026thinsp;=\u0026thinsp;0.692 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and in the subgroup of patients with detectable ctDNA with Spearman's ρ\u0026thinsp;=\u0026thinsp;0.500 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTV threshold allowing the detection of ctDNA\u003c/p\u003e \u003cp\u003eThe technique of ROC curves was used to establish TV thresholds for the detection of ctDNA. A total TV threshold of 90.1mL allowed the detection of ctDNA with a sensitivity (Se) of 57.4% and a specificity (Sp) of 91.7% (AUC\u0026thinsp;=\u0026thinsp;0.723) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). A liver metastases TV threshold of 3.7mL allowed the detection of ctDNA with a Se of 85.1% and a Sp of 79.2% (AUC\u0026thinsp;=\u0026thinsp;0.887) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFollow up of mPDAC patients\u003c/p\u003e \u003cp\u003eSix patients with initially detectable ctDNA had at least one additional sample taken during follow-up, at one (5 patients) and/or two (2 patients) months of the chemotherapy beginning. For four of them, a decrease in ctDNA quantity on the sample during follow-up was consistent with a decrease in total TV on the evaluation CT scans. For the other two patients, a decrease in ctDNA quantity was discordant, with a slight increase in total TV on the evaluation CT scans (respectively, 6.7 mL and 5.6 mL). These results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. On the other hand, ctDNA remained undetectable during follow-up for three patients for whom ctDNA was initially undetectable and whose TV decreased on evaluation CT scans.\u003c/p\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eIn the era of personalized medicine for cancer patients ctDNA and TV are two markers which could offer non-invasive methods to evaluate patients with mPDAC. The aim of this study was to evaluate the correlation of these two markers.\u003c/p\u003e \u003cp\u003eIn our study, the ctDNA detection rate of 66.2% was comparable to the rates reported in the literature for mPDAC patients, where ctDNA was detected by mutations (mainly \u003cem\u003eKRAS\u003c/em\u003e mutations), with rates ranging from 46\u0026ndash;68% \u003csup\u003e7,14,17\u003c/sup\u003e. This result is interesting because detecting methylated markers with ddPCR is rapid, sensitive, fast, and less expensive compared to strategies based on Next Generation Sequencing (NGS) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. A recent large study (1065 patients, including 120 mPDAC) by \u003cem\u003eDawi et al.\u003c/em\u003e, using FoundationOne Liquid CDx assay (Roche), a pan-cancer ccfDNA based comprehensive genomic profiling assay, found 32% of patients having a high (\u0026gt;\u0026thinsp;10%) tumor fraction (close to MAF but irrespective of a specific mutation). With this assay, a cutoff of 10% was suggested by the manufacturer to refer to specimens that are more likely to contain actionable alterations. Therefore, in this study, values below 10% were automatically adjusted to 0 \u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo our knowledge, our study is the first to demonstrate the correlation between ctDNA quantity obtained using epigenetic markers and TV measured in 3D in a mPDAC population. TV was correlated with ctDNA quantity, with Spearman's ρ of 0.353 (p\u0026thinsp;=\u0026thinsp;0.01) (with ρ\u0026thinsp;\u0026lt;\u0026thinsp;0,4 correlation is considered as weak). It confirms the correlation between ctDNA quantity and TV which had been reported in the study by Strijker \u003cem\u003eet al.\u003c/em\u003e, in which ctDNA quantity was obtained using NGS and ddPCR for research of \u003cem\u003eKRAS\u003c/em\u003e mutations. In a mPDAC population of 58 patients, Strijker \u003cem\u003eet al.\u003c/em\u003e showed tumor volume (3D reconstructions from imaging) and ctDNA MAF were correlated with Spearman\u0026rsquo;s ρ of 0.544 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This approach had already been the subject of a few studies in other types of cancers, in particular colorectal and ovarian cancers \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, but its transposition to PDAC, due to its dense desmoplastic stroma with relatively few tumor cells \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, was not obvious. In \u003cem\u003eDawi et al.\u003c/em\u003e a pan-cancer study, tumor fraction helped predict TV (estimated from 2D annotations) in linear model with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.17; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.30 considered as very weak predictor) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, the correlation between ctDNA quantity and liver metastatic TV was better than with total TV, with Spearman's ρ of 0.500 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (with ρ\u0026thinsp;\u0026lt;\u0026thinsp;0,6 correlation is considered as modarate). Moreover, a liver metastatic TV threshold of only 3.7 mL (which corresponds to a spherical lesion of approximately 2 cm in diameter) allowed the detection of ctDNA with a Se of 85.1%. ctDNA detection was significantly associated with the number of liver metastases and liver metastatic TV. This suggest liver could be a metastatic site shedding more ctDNA. These results are consistent with two studies including mPDAC patients, the one of \u003cem\u003eStrijker et al.\u003c/em\u003e where ctDNA detection rates differed depending on the location of the metastases, with high rates in the case of liver metastases \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and the one of \u003cem\u003eKirchweger et al.\u003c/em\u003e where mutant allele frequency had a stronger correlation with liver metastasis volume (r\u0026thinsp;=\u0026thinsp;0.6) than with TV (r\u0026thinsp;=\u0026thinsp;0.473) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Similar results have been reported in other cancers, notably for colorectal cancer in \u003cem\u003eBachet et al.\u003c/em\u003e study, where the presence of liver metastases was significantly associated with the presence of ctDNA \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and in the \u003cem\u003eDawi et al.\u003c/em\u003e pan-cancer study where liver lesions volume (volume\u0026thinsp;\u0026gt;\u0026thinsp;3 cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e) was significantly associated with a contributory liquid biopsy (OR 2.74). A recent study of \u003cem\u003eNitschke C et al.\u003c/em\u003e comparing ctDNA detection between peripheral blood and portal blood provides another hypothesis as if the liver acted as a filter for ccfDNA and ctDNA. Indeed, in matched samples, significantly higher ccfDNA concentration was found in portal vein blood compared to peripheral blood (22.23 ng/mL vs 55.12 ng/mL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and \u003cem\u003eKRAS\u003c/em\u003e positive portal vein samples had significantly higher mutant copy numbers compared to the peripheral sample with an average of 6.4-fold increase (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough ctDNA quantity is correlated with the TV in our study, it should be noted that some patients with high TV had no ctDNA detected. Moreover, correlation between ctDNA quantity and TV was not strong. These observations illustrate well the fact that factors other than TV contribute to the detectability of ctDNA. In our study, patients with detectable ctDNA had tumors with a lower degree of differentiation (p\u0026thinsp;=\u0026thinsp;0.031). In literature, released mechanisms of ccfDNA and ctDNA implied necrosis and apoptosis, but also cell secretion immunologically mediate released implying, for example, neutrophil extracellular traps \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, or due to hypoxic conditions \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. These other mechanisms are potentially less correlated to TV. Other parameters as molecular profile and imaging factors (heterogeneity, proximity to vessels, necrosis) could influence ctDNA quantity and should be assessed in future studies.\u003c/p\u003e \u003cp\u003eOne of the main limitations to this 3D measurement of TV, whether in current practice or in studies, is its time-consuming nature. Indeed, even if it is a semi-automated measurement, an exhaustive measurement is tedious, in particular because of the number of lesions. Indeed, 25% of the patients in our study had more than 30 liver metastases. The development of fully automated segmentation software and artificial intelligence should, however, make it easier and faster to obtain reliable TV measurements in the years to come. Additionally, pancreatic tumors are difficult to measure due to their ill-defined margins and irregular growth patterns. Measurement errors may have occurred but seem unavoidable for PDAC. However, one of the strengths of this study is that all the TV measurements have been verified by a single radiologist expert in digestive oncology.\u003c/p\u003e \u003cp\u003eAt baseline, there was no statistically significant correlation between CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (n\u0026thinsp;=\u0026thinsp;58) and ctDNA quantity (p\u0026thinsp;=\u0026thinsp;0.570) or TV (p\u0026thinsp;=\u0026thinsp;0.873). This result is inconsistent with the study by \u003cem\u003ePerets et al.\u003c/em\u003e that showed a correlation between CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9 and ctDNA \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, but consistent with the study by \u003cem\u003eStrijker et al.\u003c/em\u003e in which neither ctDNA quantity nor the TV were correlated with CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9 \u003csup\u003e14\u003c/sup\u003e. Similar results have been reported by \u003cem\u003eBachet et al.\u003c/em\u003e in metastatic colorectal cancer in which no correlation was observed between CEA and ctDNA levels at diagnosis \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe correlation between TV and ctDNA quantity is an argument in favor of its use in monitoring patients undergoing treatment. A non-radiological evaluation of the evolution of the disease during treatment would be particularly interesting insofar as, in PDAC, after chemotherapy, imaging makes it difficult to distinguish between viable adenocarcinoma and fibrosis \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In patients who show a mixed response or progression on CT but with a clinical response, ctDNA could help evaluate the tumor response. In this study, the evaluation of follow-up during treatment was only exploratory. Four of the six patients with follow-up showed a decrease in ctDNA consistent with the decrease in TV, but for the last two patients the evolution was discordant. Other authors have shown an inconsistence between ctDNA quantity and the tumor response based on 3D imaging \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e or standard evaluations \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, in a minority of patients. The limited number of follow-up samples hampered a better understanding of ctDNA dynamics. To determine whether the clinical implementation of ctDNA might be useful, future studies should determine the additional information obtained by repeated measurement of ctDNA quantity during chemotherapy and at treatment assessment. Encouraging data have been reported on the dynamic evolution of ctDNA under treatment \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Nevertheless, repeated ctDNA measurement only applied for two thirds\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e of mPDAC patients with ctDNA detectable at baseline.\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrates the correlation between ctDNA quantity and TV, in particular liver metastases TV, in mPDAC. It supports the data that ctDNA could be a surrogate marker of TV at baseline.\u003c/p\u003e"},{"header":"4 Methods","content":"\u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eFrom January 2011 to December 2018, plasma from all consecutive patients with histologically proven mPDAC receiving first-line chemotherapy was prospectively collected at Piti\u0026eacute;-Salp\u0026ecirc;tri\u0026egrave;re Hospital (Paris, France). Blood samples were taken just before the first cycle of chemotherapy. Nine patients had at least one additional sample taken during follow-up (at one and/or two months of the chemotherapy beginning). All patients signed an informed consent form, approved by the ethics committee (CPP Ile-de-France 2014/59NICB). All methods were performed in accordance with the relevant guidelines and regulations. To be include, the patients should have a histologically proven mPDAC with an interpretable ctDNA result and a thoraco-abdomino-pelvic computed tomography (CT) scan available at baseline (i.e., before the start of treatment). The following data were collected in a prospective database: clinical characteristics (gender, age, ECOG performance status (PS), medical history, date of diagnosis, location of primary tumor, diameter of primary tumor, grade of tumor differentiation, stage at diagnosis), follow-up data (date and type of chemotherapy, date of death or last follow-up) and biological data before the first cycle of chemotherapy (complete blood count, carcinoembryonic antigen (CEA), CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9, albuminemia, and bilirubinemia).\u003c/p\u003e \u003cp\u003ePlasma sample preparation, storage, DNA extraction\u003c/p\u003e \u003cp\u003eBlood samples (9 mL) were taken from a central catheter and collected in EDTA tubes. The samples were centrifuged at 3500 rpm for 15 minutes at 4\u0026deg;C within 3 hours of blood collection. Plasma was stored at -80\u0026deg;C until further use. Before extraction, plasma samples were centrifugated for 15 min at 5000 rpm. Extraction was then realized from 1 or 2 mL of plasma, respectively, with QIAamp Circulating Nucleic Acid Kit (Qiagen) or Maxwell RSC ccfDNA Plasma Kit. Extraction method is detailed in supplementary data. Extracted DNA was respectively eluted with 50 \u0026micro;L our 60 \u0026micro;L of AVE buffer and stored at -80\u0026deg;C.\u003c/p\u003e \u003cp\u003eDroplet-based digital PCR (ddPCR) for methylated markers\u003c/p\u003e \u003cp\u003eWe used two methylated markers HOXD8 and POU4F1, whose development and validation have been described in our previous studies \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and detailed in supplementary method. We used bisulfite conversion (EZ DNA-Metylation Gold kitTM (Zymo Research)) and used droplet-based digital PCR (ddPCR) (BioRad QX200 system) for detection of both target methylated markers. This method is detailed in supplementary data.\u003c/p\u003e \u003cp\u003eQuantification of ccfDNA and ctDNA\u003c/p\u003e \u003cp\u003eTo calculate ctDNA quantity (ng/mL), we used the methylated biomarker for which the number of droplets was the highest and applied a formula (detailed in supplementary method) involving the different volumes used for ddPCR, bisulfite conversion, elution of extracted DNA and plasma extraction. To calculate ccfDNA, the same formula was used with the number of droplets of the albumin un-methylated sequence, used as reference for measuring amplifiable DNA.\u003c/p\u003e \u003cp\u003eCtDNA was analyzed both as a continuous variable and categorized as detectable whether greater than 0 ng/mL or not.\u003c/p\u003e \u003cp\u003eMutant allele frequency (MAF) was the ratio between ctDNA quantity and ccfDNA quantity, expressed as a percentage.\u003c/p\u003e \u003cp\u003eTumor volume (TV)\u003c/p\u003e \u003cp\u003eThe CT scans were performed as part of routine clinical practice. They included at least portal phase acquisition of an injection of iodinated contrast product in the abdominopelvic region and thoracic acquisition. The CT scans were acquired on different systems, but all available exams had slices less than 5 mm thick. All CT scans were analyzed using Syngo.via software (Siemens Healthcare, Forchheim, Germany) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the measurement of TV, the \u0026ldquo;MM Oncology\u0026rdquo; module was used. Segmentation of tumor lesions was semi-automated with manual correction if necessary. The TV of the primary lesion as well as the number and TV of metastases were collected. These included lymph node metastases (lymph nodes were included when the short axis was greater than 10 mm, according to the definition of pathological lymph nodes reported in RECIST 1.1.), liver metastases, peritoneal metastases, lung metastases, and others (adrenal gland, bone). All primary tumors and metastases were measured regardless of their size. These measurements were carried out by a qualified doctor, then were verified in their entirety by a radiologist expert in digestive oncology. The whole of this collection was carried out blind to the result of the ctDNA. TV, measured in milliliters, was analyzed as a continuous variable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStatistical analysis\u003c/p\u003e \u003cp\u003ePatient characteristics were compared using a chi-square test or Fisher's exact test (for categorical variables) and the t-test for independent samples (for normally defined continuous variables). Spearman's rank correlation rho was used to evaluate correlation. ROC (receiver operating feature) curves were used, the threshold value corresponding to the point where the AUC (Area Under Curve) is the highest. All statistical analyzes were performed using r software version 4.1.0. A value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting financial and/or non-financial interests :\u003c/p\u003e\n\u003cp\u003eJB Bachet has received personal fees from Amgen, AstraZeneca, Bayer, Merck Serono, Pierre Fabre, Roche, Sanofi, Servier, Shire, and non-financial support from Amgen, Merck Serono, and Roche.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eP Laurent-Puig declares consulting for or personal fees from Amgen, AstraZeneca, Biocartis, BMS, Boehringer-Ingelheim, Lilly, Merck Serono, MSD, Sanofi, Servier, Roche.\u003c/p\u003e\n\u003cp\u003eFundings:\u003c/p\u003e\n\u003cp\u003eThere was no funding for this manuscript except for a grant from the Fondation pour la Recherche M\u0026eacute;dicale (Grant No. M2R201806006018) awarded to O.C.\u003c/p\u003e\n\u003ch3\u003eAuthor contributions statement:\u003c/h3\u003e\n\u003cp\u003eO.C., J-B.B., V.T., and P.L-P. contributed to the study design.\u003c/p\u003e\n\u003cp\u003eO.C. and D.P. were responsible for data acquisition and laboratory handling.\u003c/p\u003e\n\u003cp\u003eM.W. provided expert radiologic measurements.\u003c/p\u003e\n\u003cp\u003eO.C. conducted the statistical analysis.\u003c/p\u003e\n\u003cp\u003eO.C., J-B.B., V.T., P.L-P., D.P., S.D., and L.M. contributed to the interpretation of the results.\u003c/p\u003e\n\u003cp\u003eO.C. wrote the manuscript and generated the tables and figures.\u003c/p\u003e\n\u003cp\u003eJ-B.B. and V.T. provided comments and corrections on the manuscript text.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the manuscript.\u003c/p\u003e\n\u003ch3\u003eData Availability Statement:\u003c/h3\u003e\n\u003cp\u003eThe data that support the findings of this study have been deposited in the Science Data Bank with the following accession link: https://doi.org/10.57760/sciencedb.23515\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFerlay, J., Partensky, C. \u0026amp; Bray, F. 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(2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metastatic pancreatic adenocarcinoma, circulating tumor DNA, tumor volume, tumor biomarkers, cancer imaging ","lastPublishedDoi":"10.21203/rs.3.rs-6422905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6422905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCirculating tumor DNA (ctDNA) is a promising marker with a strong prognostic value in metastatic pancreatic ductal adenocarcinoma (mPDAC). However, ctDNA is not detected in about a third of patients with mPDAC. The objective of this study was to assess the correlation between ctDNA and tumor volume (TV). In this monocentric cohort of mPDAC patients naïve for chemotherapy, ctDNA quantity at baseline was assessed by droplet-based digital PCR targeting two methylated markers (HOXD8 and POU4F1). TV was measured in 3D, for the primary lesion and the metastatic sites, from baseline thoraco-abdomino-pelvic computed tomography (CT) scan. Among the 71 included patients, ctDNA was detected in 47 patients (66.2%). There was a significant correlation of total and liver TV with ctDNA quantity, with Spearman's ρ of 0.353 (p = 0.01) and 0.500 (p \u0026lt; 0.001), respectively. Total and liver TV were higher in patients with detected ctDNA (129.5 vs 31.8 mL, p = 0.002 and 18 vs 1 mL, p \u0026lt; 0.001, respectively). Total and liver TV thresholds of 90.1 and 3.7 mL were associated with ctDNA detection, respectively. This study demonstrates the correlation between ctDNA and TV in mPDAC, especially for liver metastases. It supports the data that ctDNA could be a surrogate marker of TV in patients with mPDAC and liver metastases.\u003c/p\u003e","manuscriptTitle":"Correlation between circulating tumor DNA quantity assessed by methylated markers and tumor volume in patients with metastatic pancreatic adenocarcinoma.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 01:57:52","doi":"10.21203/rs.3.rs-6422905/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-24T08:51:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-13T03:09:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133430986296630013972762010378157384551","date":"2025-06-06T16:20:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T19:53:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69090257641019488366217317565045257316","date":"2025-05-22T08:44:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-21T13:51:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T10:23:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-18T05:22:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-15T21:54:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-15T21:53:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"505b6ad7-4def-405d-9948-fe7506d2293a","owner":[],"postedDate":"May 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48896800,"name":"Health sciences/Oncology/Cancer/Gastrointestinal cancer/Pancreatic cancer"},{"id":48896801,"name":"Health sciences/Oncology/Cancer/Tumour biomarkers"},{"id":48896802,"name":"Biological sciences/Cancer/Cancer imaging"}],"tags":[],"updatedAt":"2025-10-13T16:07:55+00:00","versionOfRecord":{"articleIdentity":"rs-6422905","link":"https://doi.org/10.1038/s41598-025-13160-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-07 15:57:31","publishedOnDateReadable":"October 7th, 2025"},"versionCreatedAt":"2025-05-26 01:57:52","video":"","vorDoi":"10.1038/s41598-025-13160-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-13160-7","workflowStages":[]},"version":"v1","identity":"rs-6422905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6422905","identity":"rs-6422905","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}