Clinical Utility of Comprehensive Genomic Profiling for Advanced Pancreatic Cancer: Insights from Real-World Data Analysis

Clinical Utility of Comprehensive Genomic Profiling for Advanced Pancreatic Cancer: Insights from Real-World Data Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical Utility of Comprehensive Genomic Profiling for Advanced Pancreatic Cancer: Insights from Real-World Data Analysis Eiichiro So, Hideyuki Hayashi, Keitaro Shimozaki, Sara Horie, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5054340/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Feb, 2025 Read the published version in International Journal of Clinical Oncology → Version 1 posted 5 You are reading this latest preprint version Abstract Background Precision medicine is a promising therapeutic strategy for pancreatic cancer. However, only a few patients are eligible for genotype-matched treatments because of the low detection rate of actionable genomic alterations, and the clinical application of comprehensive genomic profiling (CGP) in pancreatic cancer has not been completely investigated. CGP provides considerable information, such as data on prognosis and future eligibility of patients for genotype-matched clinical trials, and can eventually guide physicians’ treatment strategies. This study aimed to investigate the contribution of CGP to patient outcomes. Methods This single-center retrospective cohort study enrolled patients diagnosed with recurrent or metastatic pancreatic cancer with adenocarcinoma or adenosquamous carcinoma who underwent systemic chemotherapy between April 2018 and April 2022. We reviewed medical records and collected data on patient characteristics, survival, and genomic information. We compared overall survival (OS) between patients who received CGP (CGP group) and those who did not (non-CGP group). Results Overall, 111 patients were eligible, of which 59 underwent CGP. No significant differences were observed in patient characteristics between the groups. The median OS was significantly longer in the CGP group than in the non-CGP group (25.2 vs. 11.8 months; hazard ratio, 0.49; 95% confidence interval, 0.31–0.76; P = 0.0013). Actionable genomic alterations were detected in 24 patients (40.7%), and six patients (10.2%) underwent genotype-matched treatments. Conclusions OS was extended in patients with pancreatic cancer who underwent CGP, possibly due to its influence on physicians’ treatment strategies. This result highlights the need for proactive and timely CGP for patients with pancreatic cancer. Pancreatic cancer Comprehensive genomic profiling Precision medicine Chemotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Pancreatic cancer is one of the most lethal malignancies. It was the sixth leading cause of cancer-related mortality worldwide in 2022 [ 1 ]. It is more common in older adults, especially in developed countries. It is mainly diagnosed at an advanced stage, with surgical resection being the only radical treatment. Additionally, patients who undergo surgery often experience recurrences. Systemic chemotherapy using 5-fluorouracil, irinotecan, and oxaliplatin (FOLFIRINOX) and nab-paclitaxel plus gemcitabine is the standard treatment. NALIRIFOX (5-fluorouracil, liposomal irinotecan, and oxaliplatin) has been recently reported as a new treatment choice, but the median overall survival (OS) remains less than12 months [ 2 – 4 ]. Recently, comprehensive genomic profiling (CGP) has been applied to various cancers in clinical settings, with widespread applications in precision medicine. CGP provides information regarding the future eligibility of patients for genotype-matched clinical trials and prognosis, and may guide physicians’ treatment strategies. Defects in DNA damage response genes that cause homologous recombination deficiency (HRD) are potential biomarkers for treatment with poly-ADP ribose polymerase (PARP) inhibitors and platinum-based chemotherapy [ 5 – 8 ]. The occurrence rates of HRD, germline, and somatic BRCA1/2 mutations are approximately 20%, 5%, and 10%, respectively [ 9 ]. KRAS G12C is a new potential therapeutic target; however, it is present in < 5% of pancreatic cancer cases [ 10 – 12 ]. Immune checkpoint inhibitors (ICIs) are effective in patients with microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR) genes; however, they are only present in 0.5–1.0% of pancreatic cancer cases [ 13 , 14 ]. The Know Your Tumor Registry Trial reported that the median OS of patients with pancreatic cancer with actionable genomic alterations who underwent genotype-matched treatments was better than that of patients who underwent unmatched treatments or those without actionable genomic alterations [ 15 ], but only 2% of patients received genotype-matched treatments, indicating that CGP application in patients with pancreatic cancer is limited. In addition to providing information on the eligibility for genotype-matched treatments, the CGP provides information regarding the prognosis of pancreatic cancer. An increasing number of driver gene mutations, such as KRAS , TP53 , CDKN2A , and SMAD4 , are associated with poor prognosis [ 12 , 16 , 17 ]. Thus, precision medicine is a promising therapeutic strategy. However, the utility of CGP in clinical applications for all patients with pancreatic cancer remains unknown, owing to the low detection rate of actionable genomic alterations. We hypothesized that CGP would help physicians select optimal treatment strategies for patients with pancreatic cancer based on their eligibility for genotype-matched treatments and prognostic information, and eventually contribute to their treatment outcomes. Therefore, we aimed to investigate the contribution of CGP to patient outcomes. Patients and Methods Patients This was a single-center, retrospective cohort study. Patients diagnosed with recurrent or metastatic pancreatic cancer with adenocarcinoma or adenosquamous carcinoma who underwent systemic chemotherapy at our hospital between April 2018 and April 2022 were enrolled. The data cutoff date was April 30, 2024. Patients with locally advanced pancreatic cancer or other histologic types were ineligible. Patients with other active malignancies or those who underwent local treatments that were not covered by insurance, including irreversible electroporation, were excluded. We reviewed the patient’s medical records and collected data on patient characteristics and the blood tests at the initiation of palliative chemotherapy, survival, treatments, and genomic information. We compared OS between patients who received CGP (CGP group) and those who did not (non-CGP group). The CGP group included cases, in which CGP was conducted during the course of treatment, while the non-CGP group included cases without CGP. Local recurrence was counted as one metastatic site when assessing the number of metastatic sites. Comprehensive genomic profiling We performed CGP using the FoundationOne CDx (Foundation Medicine, Cambridge, MA, USA), FoundationOne Liquid CDx (Foundation Medicine, Cambridge, MA, USA), OncoGuide NCC Oncopanel system (Sysmex Co., Tokyo, Japan), PleSSision (Mitsubishi Electric Software Co., Ltd., Tokyo, Japan), PleSSision-Exome (Mitsubishi Electric Software Co., Ltd., Tokyo, Japan), and Guardant360 CDx (Guardant Health, Inc., USA). The standard process of CGP is as follows: formalin-fixed paraffin-embedded (FFPE) tumor tissue samples are prepared from surgical specimens, endoscopic ultrasound-guided fine needle aspiration biopsy samples from the primary lesion, or biopsy samples from metastatic sites, such as the liver. DNA was extracted from FFPE specimens, and genome sequencing was performed. In cases where adequate tissue collection was complex, blood samples were substituted, and genome sequencing was performed using FoundationOne Liquid CDx or Guardant360 CDx. CGP results were discussed at the Molecular Tumor Board of the hospital, which reported genomic information and recommendations for genotype-matched treatments for each physician. The Molecular Tumor Board reviewed all cases based on the clinical practice guidelines for next-generation sequencing for cancer diagnosis and treatment (edition 2.1). Genomic alterations with evidence levels A–D were defined as actionable genomic alterations [ 18 ]. FoundationOne CDx was approved for use for all solid tumors in 2019 by the Pharmaceuticals and Medical Devices Agency (PMDA), a regulatory authority in Japan. FoundationOne CDx can detect substitutions, insertions, deletions, and copy number alterations across 324 genes, including 36 oncogene rearrangements, MSI, and tumor mutational burden (TMB), using DNA extracted from FFPE tumor tissue specimens. For the analysis, 10 undyed 4–5-µm FFPE specimen sections were prepared using samples with more than 20% tumor content [ 19 ]. FoundationOne Liquid CDx was approved in 2021 by the PMDA for use in all solid tumors when it is difficult to obtain tissue specimens of tumor cells. FoundationOne Liquid CDx can detect substitutions, insertions, deletions, and copy number alterations across 324 genes, including 36 oncogene rearrangements, MSI, and TMB, using DNA extracted from the patient’s peripheral blood. For the analysis, two samples of 8.5 mL of blood were used [ 20 ]. The OncoGuide NCC Oncopanel system was approved by the PMDA in 2019 for use with solid tumors. The NCC Oncopanel can detect mutations, amplifications, and homozygous deletions of 124 genes, including 13 oncogene rearrangements, MSI, and TMB, using DNA extracted from FFPE tumor tissue specimens and blood cells. For the analysis, five undyed 10-µm or 10 undyed 4–5-µm FFPE tumor tissue sections were prepared. Peripheral blood (2 mL) was collected from the same patient and used as a control to distinguish between somatic and germline mutations [ 21 ]. PleSSision is an outsourced clinical-sequencing system. PleSSision can detect substitutions, insertions, and deletions, as well as copy number alterations, across 160 genes, MSI, and TMB using DNA extracted from FFPE tumor tissue specimens and peripheral blood. PleSSision-Exome can analyze approximately 20,000 genes (19,296 genes), corresponding to almost all human genes, and can detect MSI and TMB. For both analyses, 10 undyed 10-µm FFPE specimen sections were used for DNA extraction. All specimens with more than 30% tumor content were collected within three years. As a control, 2.5 mL of peripheral blood was collected and analyzed using PleSSision and PleSSision-Exome [ 22 ]. Guardant360 CDx was approved for application for all solid tumors in 2022 by PMDA. Guardant 360 can detect 74 gene nucleotide substitutions and insertions/deletions, as well as gene amplification, in 18 genes, six fusion genes, and MSI-H in solid tumors. For analysis, we collected two samples of 10 mL of blood [ 23 ]. Statistical analysis Summary statistics were presented as frequencies and proportions for categorical data and as means and standard deviations for continuous variables to evaluate patient characteristics. We compared patient characteristics using the Fisher’s exact test for categorical variables and the Wilcoxon signed-rank test for continuous variables, as appropriate. OS was calculated from the initiation of palliative chemotherapy to the day of death from any cause. For recurrent cases, the date of palliative chemotherapy initiation after confirmation of relapse was considered the OS starting date. Patients without a recorded date of death were assessed based on their last recorded date. Survival analyses were performed using the Kaplan–Meier method and log-rank test. Hazard ratios (HRs) were estimated using the Cox proportional hazards model. All P values were based on a two-sided hypothesis, and P values < 0.05 indicated statistical significance. All statistical analyses were performed using the SAS JMP Pro version 17.0.0 software (SAS Institute, Cary, NC, USA). Ethical approval statement This study was approved by the Keio University Hospital Institutional Ethics Committee (approval number: 20241038) and was performed in accordance with the Declaration of Helsinki and Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan. Consent for participation was obtained via an opt-out form on the website, ensuring that patients had the opportunity to refuse participation. Results Patient characteristics Overall, 111 patients were enrolled, with 59 and 52 patients in the CGP and non-CGP groups, respectively (Fig. 1 ). The CGP group had a statistically lower median age than the non-CGP group (66 vs. 73 years, P = 0.0013) and a lower proportion of patients aged over 75 years (CGP: 15.3% vs. non-CGP: 38.5%, P = 0.0088). Additionally, the CGP group had a higher proportion of patients with recurrence and a lower proportion of UR-M patients compared to the non-CGP group (UR-M: 52.5% vs. 78.9%, Recurrence: 47.5% vs. 21.2%, P = 0.0051) (Table 1 ). The other variables were not significantly different between the groups. Table 1 Patient characteristics: CGP vs. non-CGP Characteristics CGP (n = 59) non-CGP (n = 52) P value Age, median (range) 66 (26–88) 73 (52–89) 0.0013* Age ≧ 75 9 (15.3%) 20 (38.5%) 0.0088** Sex male 35 (59.3%) 27 (51.9%) 0.45** female 24 (40.7%) 25 (48.1%) ECOG PS 0 30 (50.9%) 23 (44.2%) 0.51** 1 28 (47.5%) 29 (55.8%) ≥ 2 1 (1.7%) 0 (0%) UR-M 31 (52.5%) 41 (78.9%) 0.0051** Recurrence 28 (47.5%) 11 (21.2%) Primary sites Head 23 (39.0%) 20 (38.5%) 1.00** Body/tail 36 (61.0%) 32 (61.5%) Metastatic sites Lymph node 15 (25.4%) 18 (34.6%) 0.31** Liver 31 (52.5%) 29 (55.8%) 0.85** Lung 14 (23.7%) 12 (23.1%) 1.00** Peritoneum 24 (40.7%) 19 (36.5%) 0.70** Bone 4 (6.8%) 3 (5.8%) 1.00** Local 3 (5.1%) 3 (5.8%) 1.00** No. of metastatic sites 1 31 (52.5%) 27 (51.9%) 1.00** ≥ 2 28 (47.5%) 25 (48.1%) Pathology Adenocarcinoma 58 (98.3%) 52 (100.0%) 1.00** Adenosquamous carcinoma 1 (1.7%) 0 (0%) Alb (g/dL), median (range) 3.9 (2.1–4.6) 3.7 (2.4–4.8) 0.06* NLR, median (range) 2.6 (0.7–9.8) 2.8 (0.47–14.7) 0.13* GPS 0 46 (78.0%) 35 (67.3%) 0.12* 1 7 (11.9%) 11 (21.2%) 2 3 (5.1%) 6 (11.5%) NA 3 (5.1%) 0 (0%) CA19-9 (U/mL), median (range) 77.0 (1.0–148791.0) 215.0 (1.0–382392.0) 0.20* CEA (ng/mL), median (range) 4.1 (0.7–3205.0) 6.4 (0.7–225.0) Abbreviations: CGP, comprehensive genomic profiling; ECOG PS, Eastern Cooperative Oncology Group Performance Status; Alb, albumin; NLR; neutrophil to lymphocyte ratio, GPS; Glasgow Prognostic Score. *Wilcoxon, **Fisher`s exact test Genomic profile of patients who underwent CGP In the CGP group, CGP implementation was as follows: FoundationOne CDx, 35.6% (21/59); FoundationOne Liquid CDx, 13.6% (8/59); OncoGuide NCC Oncopanel system, 11.9% (7/59); PleSSision, 23.7% (14/59); PleSSision-Exome, 11.9% (7/59); and Guardant360 CDx, 3.4% (2/59) (Online Resource 1). Nine patients (15.2%) underwent CGP before first-line chemotherapy, 26 patients (44.1%) between first- and second-line therapies, and 24 (40.7%) between second- and third-line therapies (Online Resource 2). The frequencies of genomic alterations in the well-known driver genes KRAS , TP53 , CDKN2A , and SMAD4 were 84.7% (50/59), 66.1% (39/59), 22.0% (13/59), and 13.6% (8/59), respectively (Fig. 2 ). The most common subtypes of KRAS mutations were G12D (44.0%, 22/50), G12V (40.0%, 20/50), and G12R (8.0%, 4/50) (Online Resource 3). The most frequently observed oncogenic alterations were KRAS/TP53 (30.5%, 18/59), followed by only KRAS (22.0%, 13/59), KRAS/TP53/CDKN2A (13.6%, 8/59), and KRAS/TP53/SMAD4 (11.9%, 7/59) (Online Resource 4). Actionable genomic alterations were detected in 24 patients (40.7%) (Fig. 2 ). HRD was detected in seven patients (11.9%). Among these patients, three had germline BRCA2 mutations (5.1%), five had ATM mutations (8.5%), and one had CHEK2 mutation (1.7%). Two patients with MSI-H/dMMR (3.4%) were considered to have Lynch syndrome: a germline MSH2 mutation was observed in one patient and a germline MLH1 mutation in the other. In addition, KRAS G12C mutation was observed in one patient. Six patients (10.2%) underwent genotype-matched treatments (Fig. 2 and Table 2 ). Among these patients, four with HRD underwent platinum-based chemotherapy, one with MSI-H/dMMR underwent PD-1 inhibitor treatment, and one with KRAS G12C mutation underwent KRAS G12C inhibitor treatment (Table 2 , Online Resource 5). Table 2 Actionable genomic alterations and genotype-matched treatments Actionable genomic alterations Genotype-matched treatments Mutated genes n (%) Treatment agents n (%) CDKN2A 13 (18.8) CDK4/6 inhibitor 0 ATM 5 (8.5) Platinum, PARP inhibitor 2 (3.4) BRCA2 3 (5.1) Platinum, PARP inhibitor 2 (3.4) BRAF 2 (3.4) BRAF inhibitor 0 MMR (MSH2, MLH1) 2 (3.4) Immune checkpoint inhibitor 1 (1.7) MDM2 2 (3.4) MDM2 inhibitor 0 PIK3CA 2 (3.4) PI3K/AKT/mTOR inhibitor 0 CHEK2 1 (1.7) Platinum, PARP inhibitor 1 (1.7) PTEN 1 (1.7) AKT/mTOR inhibitor 0 KRAS G12C 1 (1.7) KRAS G12C inhibitor 1 (1.7) Total 6 (10.2) Abbreviations: n, number. Association between CGP and OS of patients with advanced pancreatic cancer We compared the OS between the CGP and non-CGP groups using the Kaplan–Meier method and measured HRs using the Cox proportional hazards model. The patient characteristics were almost well-balanced between the two groups (Table 1 ). OS was statistically higher in the CGP group than in the non-CGP group (median OS, 25.2 vs. 11.8 months; HR, 0.49; 95% confidence interval [CI], 0.31–0.76; P = 0.0013, Fig. 3 ). The median follow-up periods were 45.5 months (95% CI, 32.4–50.6) in the CGP group and 42.0 months (95% CI, 23.3–49.6) in the non-CGP group. The median OS of patients who underwent genotype-matched treatments was 35.5 months (95% CI, 11.4–76.2), which tended to be longer than that of 17.0 months (95% CI, 12.4–24.0) for patients who did not undergo genotype-matched treatments. However, only 10.2% of the patients underwent genotype-matched treatments, suggesting that other factors may have contributed to the high OS in the CGP group. To investigate the reasons for better outcomes in the CGP group, we compared the implementation rate of chemotherapy using key drugs for pancreatic cancer between the two groups. More patients in the CGP group received gemcitabine (GEM)- and 5-fluorouracil (5-FU)-based chemotherapy than those in the non-CGP group (78.0% vs. 38.5%, P < 0.0001) (Table 3 ). A similar trend was observed in the rate of transition to subsequent chemotherapy (76.3% vs. 48.1%, P = 0.0030). Meanwhile, the first-line chemotherapy did not differ between the two cohorts. The implementation rates of treatment regimens sometimes used in vulnerable patients, such as GEM monotherapy and S-1, were similar. Table 3 Implementation rate of chemotherapy Implementation of chemotherapy CGP (n = 59) non-CGP (n = 52) P value 5-FU-based and GEM-based 46 (78.0%) 20 (38.5%) < 0.0001* Subsequent chemotherapy 45 (76.3%) 25 (48.1%) 0.0030* First-line chemotherapy FOLFIRINOX 23 (39.0%) 13 (25.0%) 0.28* GEM + nab-PTX 25 (42.4%) 31 (59.6%) GEM + S-1 2 (3.4%) 0 (0.00%) GEM 5 (8.5%) 4 (7.7%) S-1 4 (6.8%) 4 (7.7%) Abbreviations: FOLFIRINOX, 5-fluorouracil, irinotecan, and oxaliplatin; GEM, gemcitabine; nab-PTX, nab-paclitaxel; S-1, tegafur/gimeracil/oteracil; 5-FU, 5-fluorouracil. *Fisher`s exact test OS according to the number of mutated major driver genes ( KRAS , TP53 , CDKN2A , and SMAD4 ) Several studies have suggested that an increasing number of mutated driver genes, including KRAS , TP53 , CDKN2A , and SMAD4 , are associated with poor prognosis in pancreatic cancer [ 11 , 15 , 16 ]. We compared the OS according to the number of altered driver genes to determine whether there was a similar trend in our cohort. The median OS tended to be worse with an increase in the number of mutated driver genes (Fig. 4 a). The median OS of patients with 0–1 mutated driver gene, 2 mutated driver genes, and 3–4 mutated driver genes was 40.1 months, 25.2 months, and 19.0 months, respectively. Moreover, the median OS of patients with 0–2 mutated genes was statistically longer than that of patients with 3–4 mutated genes (median OS, 29.2 vs. 19.0 months; HR, 0.40; 95% CI, 0.20–0.82; P = 0.0093, Fig. 4 b). Patients with three or more mutated driver genes had a higher rate of FOLFIRINOX use during treatment than those with two or fewer mutations (56.3% vs. 34.9%, P = 0.15, Online Resource 6). An important consideration is the difference in the detection of mutated major driver genes ( KRAS, TP53, CDKN2A , and SMAD4 ) between tissue CGP and liquid CGP. Tissue CGP identified a higher number of patients with 3–4 mutated driver genes and fewer patients with 0–2 mutated driver genes compared to liquid CGP (Online Resource 7). Discussion In this study, patients who underwent CGP exhibited longer OS, highlighting the potential benefits of CGP in identifying genotype-matched treatments and providing prognostic information to guide treatment strategies. However, the observed survival benefit may be subject to selection bias, as patients with more favorable baseline characteristics were more likely to receive CGP, whereas those with worse baseline characteristics may have missed this opportunity. While CGP holds promise for clinical benefit, proactive and timely implementation should be considered when managing patients with pancreatic cancer. CGP provides information on genotype-matched treatments for advanced pancreatic cancer. For example, HRD is considered a putative biomarker for PARP inhibitors [ 5 – 7 ]. Several studies have suggested an association between HRD and a better response to platinum-based chemotherapy during any line of treatment [ 24 – 26 ]. Furthermore, the Know Your Tumor Registry Trial showed that the median OS of patients with pancreatic cancer with actionable genomic alterations who underwent genotype-matched treatments was longer than that of those who underwent unmatched treatments or those without actionable genomic alterations [ 15 ]. In addition to providing information on genotype-matched treatments, CGP allows physicians to predict the disease prognosis. KRAS , TP53 , CDKN2A , and SMAD4 are the major driver genes of pancreatic cancer. The number of mutations in these genes (three or more) is considered to be associated with a poor prognosis [ 12 , 16 , 17 ]. In our hospital, for patients with pancreatic cancer who have undergone CGP, eligibility for genotype-matched treatments and prognostic information are discussed among physicians on the Molecular Tumor Board, which is used to guide treatment strategies, including increasing the intensity of treatments for patients with a poor prognosis and good Eastern Cooperative Oncology Group Performance Status (ECOG PS) and providing more conservative treatments for patients with poor prognosis and poor ECOG PS. Moreover, treatment transitioning can be achieved at an appropriate time for patients who are eligible for genotype-matched treatments or clinical trials and even for patients without actionable genomic alterations based on prognosis. Recently, a phase III trial reported that monitoring symptoms via a web application improved the outcomes of patients with lung cancer [ 27 ]. The web application enabled physicians to detect cancer relapse in patients earlier than in controls through frequent and timely notifications of symptoms, leading to a change in the awareness of patients and attending physicians and an improvement in prognosis. We hypothesized that CGP would have similar effects. In our cohort, the median OS in the CGP group was longer than that in the non-CGP group. The median age of the CGP group was statistically younger than that of the non-CGP group, and the CGP group included a higher proportion of patients with recurrent disease. However, other patient characteristics did not differ significantly between the two groups. There are three possible reasons for this. First, the implementation of genotype-matched treatments is considered to contribute to the outcomes. The median OS of patients who underwent genotype-matched treatments tended to be longer than that of patients who did not. The detection rate of actionable genomic alterations and the implementation rate of genotype-matched treatments were similar to those in previous research [ 28 ]. HRD, MSI-H/dMMR, and KRAS G12C were detected in 11.9%, 3.4%, and 1.7% of patients, respectively, whereas in previous studies, HRD was detected in 15–20% [ 29 – 32 ], MSI-H/dMMR in 0.5–1% [ 14 ], and KRAS G12C in less than 5% [ 10 – 12 ] of patients with pancreatic cancer. Meanwhile, they were not sufficiently high to explain the differences in OS. Second, the prognostic value of CGP may have contributed to changing the physicians’ treatment strategies and eventually improving treatment outcomes regardless of the implementation of genotype-matched treatments. Herein, similar to previous studies [ 12 , 16 , 17 ], the median OS tended to worsen with an increasing number of mutated driver genes. Patients with three or more mutated driver genes who is considered to have a worse prognosis were more likely to be treated with FOLFIRINOX, which is regarded as an intensive treatment, than patients with two or fewer mutations. Treatment strategies that consider prognostic predictions may have contributed to patient outcomes. Additionally, the implementation rate of both GEM- and 5-FU-based chemotherapy was higher in the CGP group (78.0%) than in the non-CGP group (38.5%) of the present study, NALIRIFOX cohort of NAPOLI-3 (41%), and nab-paclitaxel plus gemcitabine cohort of NAPOLI-3 (35%) [ 4 ]. Moreover, the rate of transition to subsequent chemotherapy was higher in the CGP group (76.3%) than in the non-CGP group (48.1%) of the present study, NALIRIFOX cohort of NAPOLI-3 (51%), and nab-paclitaxel plus gemcitabine cohort of NAPOLI-3 (54%) [ 4 ]. Thus, while the transition rate was not necessarily low in the non-CGP group, it was comparatively higher in the CGP group. Therefore, treatment transitioning could have been achieved at an appropriate time based on prognostic information and the presence of actionable genomic alterations. Importantly, patients with better baseline may have been more likely to undergo CGP, potentially contributing to the observed survival benefit. This selection bias must be considered when interpreting the results. Our study had some limitations. First, the sample size was relatively small owing to the single-center design. Second, this was a retrospective and observational study; therefore, selection and causative biases were inevitable. For example, patients with better OS are likely to have opportunities to undergo CGP. The median OS of 11.8 months, the rates of GEM- and 5-FU-based chemotherapy use, and the transition rate to subsequent chemotherapy in the non-CGP group align with previous research findings [ 2 – 4 ]. However, there were potential influence of selection bias on these outcomes. While first-line chemotherapy regimens and implementation rates for treatments often prescribed to vulnerable patients, such as GEM monotherapy and S-1, were similar across cohorts, it remains likely that patients with better baseline characteristics were more inclined to receive CGP. These limitations highlight the need for timely and proactive CGP implementation in pancreatic cancer management. Additionally, differing CGP methods with variable detection rates were employed in this study. Notably, the number of major driver genes ( KRAS , TP53 , CDKN2A , and SMAD4 ) detected varied between tissue and liquid CGP, potentially reflecting differences in sensitivity for identifying driver gene alterations. Thus, caution is necessary when interpreting the prognostic value of CGP based solely on the number of detected mutations. Finally, it is challenging to definitively state that CGP’s prognostic predictions were always time-relevant. In our cohort, 59.3% of patients underwent CGP before second-line chemotherapy. This timely implementation of CGP potentially influenced treatment transitioning at an appropriate timing based on prognostic information and the presence of actionable genomic alteration. Nonetheless, some degree of selection bias must be acknowledged when interpreting these results. A prospective study would be necessary to fully understand how CGP influences physicians' treatment strategies. In conclusion, OS was extended in patients who underwent CGP, possibly due to its influence on treatment strategies. Yet, the survival benefit observed may have been affected by selection bias, as patients with more favorable baseline characteristics were more likely to undergo CGP. Despite this limitation, our findings suggest that CGP provides meaningful clinical benefits, underscoring the importance of its proactive application, particularly in pancreatic cancer, where rapid disease progression may restrict treatment options. Further prospective studies are warranted to clarify CGP’s impact on patient outcomes and to refine its role in clinical practice. Declarations Acknowledgments We are deeply indebted to all patients who participated in this study and their families. Funding This work was partly supported by the Japan Society for the Promotion of Science KAKENHI (Grant-in-Aid for Early-Career Scientists) (grant number 21K15959). Conflict of Interests The authors declare no competing financial or nonfinancial interests. Data availability statement The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. 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N Engl J Med 388:33–43. https://doi.org/10.1056/nejmoa2208470 Ou SI, Jänne PA, Leal TA, Rybkin II, Sabari JK, Barve MA et al (2022) First-in-human phase I/IB dose-finding study of adagrasib (MRTX849) in patients with advanced KRASG12C solid tumors (KRYSTAL-1). J Clin Oncol 40:2530–2538. https://doi.org/10.1200/jco.21.02752 Hayashi H, Kohno T, Ueno H, Hiraoka N, Kondo S, Saito M et al (2017) Utility of assessing the number of mutated KRAS, CDKN2A, TP53, and SMAD4 genes using a targeted deep sequencing assay as a prognostic biomarker for pancreatic cancer. Pancreas 46:335–340. https://doi.org/10.1097/mpa.0000000000000760 Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD et al (2015) PD-1 blockade in tumors with mismatch-repair deficiency. 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JAMA Oncol 4:e173420. https://doi.org/10.1001/jamaoncol.2017.3420 Masugi Y, Takamatsu M, Tanaka M, Hara K, Inoue Y, Hamada T et al (2023) Post-operative mortality and recurrence patterns in pancreatic cancer according to KRAS mutation and CDKN2A, p53, and SMAD4 expression. J Pathol Clin Res 9:339–353. https://doi.org/10.1002/cjp2.323 Naito Y, Aburatani H, Amano T, Baba E, Furukawa T, Hayashida T et al (2021) Clinical practice guidance for next-generation sequencing in cancer diagnosis and treatment (edition 2.1). Int J Clin Oncol 26:233–283. https://doi.org/10.1007/s10147-020-01831-6 Takeda M, Takahama T, Sakai K, Shimizu S, Watanabe S, Kawakami H et al (2021) Clinical application of the FoundationOne CDx assay to therapeutic decision-making for patients with advanced solid tumors. Oncologist 26:e588–596. https://doi.org/10.1002/onco.13639 Woodhouse R, Li M, Hughes J, Delfosse D, Skoletsky J, Ma P et al (2020) Clinical and analytical validation of FoundationOne Liquid CDx, a novel 324-gene cfDNA-based comprehensive genomic profiling assay for cancers of solid tumor origin. PLoS ONE 15:e0237802. https://doi.org/10.1371/journal.pone.0237802 Sunami K, Ichikawa H, Kubo T, Kato M, Fujiwara Y, Shimomura A et al (2019) Feasibility and utility of a panel testing for 114 cancer-associated genes in a clinical setting: A hospital-based study. Cancer Sci 110:1480–1490. https://doi.org/10.1111/cas.13969 Hayashi H, Tanishima S, Fujii K, Mori R, Okada C, Yanagita E et al (2020) Clinical impact of a cancer genomic profiling test using an in-house comprehensive targeted sequencing system. 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Oncologist 28:691–698. https://doi.org/10.1093/oncolo/oyad178 Park W, Chen J, Chou JF, Varghese AM, Yu KH, Wong W et al (2020) Genomic methods identify homologous recombination deficiency in pancreas adenocarcinoma and optimize treatment selection. Clin Cancer Res 26:3239–3247. https://doi.org/10.1158/1078-0432.ccr-20-0418 Denis F, Lethrosne C, Pourel N, Molinier O, Pointreau Y, Domont J et al (2017) Randomized trial comparing a web-mediated follow-up with routine surveillance in lung cancer patients. J Natl Cancer Inst 109. https://doi.org/10.1093/jnci/djx029 Hayashi H, Tanishima S, Fujii K, Mori R, Okamura Y, Yanagita E et al (2018) Genomic testing for pancreatic cancer in clinical practice as real-world evidence. Pancreatology 18:647–654. https://doi.org/10.1016/j.pan.2018.07.006 Lowery MA, Wong W, Jordan EJ, Lee JW, Kemel Y, Vijai J et al (2018) Prospective evaluation of germline alterations in patients with exocrine pancreatic neoplasms. J Natl Cancer Inst 110:1067–1074. https://doi.org/10.1093/jnci/djy024 Salo-Mullen EE, O'Reilly EM, Kelsen DP, Ashraf AM, Lowery MA, Yu KH et al (2015) Identification of germline genetic mutations in patients with pancreatic cancer. Cancer 121:4382–4388. https://doi.org/10.1002/cncr.29664 Das S, Cardin D (2020) Targeting DNA damage repair pathways in pancreatic adenocarcinoma. Curr Treat Options Oncol 21:62. https://doi.org/10.1007/s11864-020-00763-7 Heeke AL, Pishvaian MJ, Lynce F, Xiu J, Brody JR, Chen WJ et al (2018) Prevalence of homologous recombination-related gene mutations across multiple cancer types. JCO Precis Oncol 2018. https://doi.org/10.1200/po.17.00286 . PO.17.00286 Supplementary Files IJCOD2400811RevisedSupplementary.docx Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2025 Read the published version in International Journal of Clinical Oncology → Version 1 posted Reviewers agreed at journal 16 Nov, 2024 Reviewers invited by journal 11 Nov, 2024 Editor assigned by journal 03 Nov, 2024 First submitted to journal 02 Nov, 2024 Editorial decision: Minor revisions 11 Oct, 2024 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. We do this by developing innovative software and high quality services for the global research community. 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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-5054340","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376894044,"identity":"89da26db-fcc9-423a-9fd3-c5550c4a5817","order_by":0,"name":"Eiichiro 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23:48:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5054340/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5054340/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10147-025-02713-5","type":"published","date":"2025-02-17T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71562708,"identity":"a6b96248-b626-485e-a379-c00df681393f","added_by":"auto","created_at":"2024-12-16 17:11:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20586,"visible":true,"origin":"","legend":"\u003cp\u003eStudy scheme\u003c/p\u003e\n\u003cp\u003eAbbreviations: CGP, comprehensive genomic profiling\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-5054340/v1/149fd153ba22b1d135ef5cea.png"},{"id":71562703,"identity":"1c0975be-f198-462c-815f-13f00999cc0e","added_by":"auto","created_at":"2024-12-16 17:11:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59070,"visible":true,"origin":"","legend":"\u003cp\u003eGene mutation profiles and genotype-matched treatments for advanced pancreatic cancer\u003c/p\u003e\n\u003cp\u003eAbbreviations: CGP, comprehensive genomic profiling\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-5054340/v1/3711d20cd34cb7598419f126.png"},{"id":71562706,"identity":"53175692-3f54-4142-8768-39837e1dbf8f","added_by":"auto","created_at":"2024-12-16 17:11:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75820,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between CGP levels and OS in patients with advanced pancreatic cancer.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CGP, comprehensive genomic profiling; CI, confidence interval; HR, hazard ratio; OS, overall survival\u003c/p\u003e","description":"","filename":"Figure31.png","url":"https://assets-eu.researchsquare.com/files/rs-5054340/v1/53a572643abc23afe0f8c9a4.png"},{"id":71562707,"identity":"607d922d-18c8-49f9-ac88-8573ffea4669","added_by":"auto","created_at":"2024-12-16 17:11:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65467,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Association between OS and the number of mutated driver genes: 0–1, 2, and 3–4mutated driver genes.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval; HR, hazard ratio; OS, overall survival\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Association between OS and the number of mutated driver genes: 0–2 and 3–4 mutated driver genes. Abbreviations: CI, confidence interval; HR, hazard ratio; OS, overall survival\u003c/p\u003e","description":"","filename":"Figure41.png","url":"https://assets-eu.researchsquare.com/files/rs-5054340/v1/481f85c756f50d2e33e3b3dd.png"},{"id":77052681,"identity":"126771e1-fa13-487d-8e83-f3bcbdd1adad","added_by":"auto","created_at":"2025-02-24 16:23:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1128123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5054340/v1/818b600a-e151-484d-9cd7-ed473e4d28e4.pdf"},{"id":71562710,"identity":"6ebc2ee3-2b80-44e5-ada4-6a91caeca88c","added_by":"auto","created_at":"2024-12-16 17:11:32","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":34781,"visible":true,"origin":"","legend":"","description":"","filename":"IJCOD2400811RevisedSupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-5054340/v1/39ba31a53088b1c0ba9a1001.docx"}],"financialInterests":"","formattedTitle":"Clinical Utility of Comprehensive Genomic Profiling for Advanced Pancreatic Cancer: Insights from Real-World Data Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic cancer is one of the most lethal malignancies. It was the sixth leading cause of cancer-related mortality worldwide in 2022 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is more common in older adults, especially in developed countries. It is mainly diagnosed at an advanced stage, with surgical resection being the only radical treatment. Additionally, patients who undergo surgery often experience recurrences. Systemic chemotherapy using 5-fluorouracil, irinotecan, and oxaliplatin (FOLFIRINOX) and nab-paclitaxel plus gemcitabine is the standard treatment. NALIRIFOX (5-fluorouracil, liposomal irinotecan, and oxaliplatin) has been recently reported as a new treatment choice, but the median overall survival (OS) remains less than12 months [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, comprehensive genomic profiling (CGP) has been applied to various cancers in clinical settings, with widespread applications in precision medicine. CGP provides information regarding the future eligibility of patients for genotype-matched clinical trials and prognosis, and may guide physicians\u0026rsquo; treatment strategies. Defects in DNA damage response genes that cause homologous recombination deficiency (HRD) are potential biomarkers for treatment with poly-ADP ribose polymerase (PARP) inhibitors and platinum-based chemotherapy [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The occurrence rates of HRD, germline, and somatic \u003cem\u003eBRCA1/2\u003c/em\u003e mutations are approximately 20%, 5%, and 10%, respectively [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. \u003cem\u003eKRAS\u003c/em\u003e G12C is a new potential therapeutic target; however, it is present in \u0026lt;\u0026thinsp;5% of pancreatic cancer cases [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Immune checkpoint inhibitors (ICIs) are effective in patients with microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR) genes; however, they are only present in 0.5\u0026ndash;1.0% of pancreatic cancer cases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The Know Your Tumor Registry Trial reported that the median OS of patients with pancreatic cancer with actionable genomic alterations who underwent genotype-matched treatments was better than that of patients who underwent unmatched treatments or those without actionable genomic alterations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], but only 2% of patients received genotype-matched treatments, indicating that CGP application in patients with pancreatic cancer is limited. In addition to providing information on the eligibility for genotype-matched treatments, the CGP provides information regarding the prognosis of pancreatic cancer. An increasing number of driver gene mutations, such as \u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, and \u003cem\u003eSMAD4\u003c/em\u003e, are associated with poor prognosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, precision medicine is a promising therapeutic strategy. However, the utility of CGP in clinical applications for all patients with pancreatic cancer remains unknown, owing to the low detection rate of actionable genomic alterations. We hypothesized that CGP would help physicians select optimal treatment strategies for patients with pancreatic cancer based on their eligibility for genotype-matched treatments and prognostic information, and eventually contribute to their treatment outcomes. Therefore, we aimed to investigate the contribution of CGP to patient outcomes.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThis was a single-center, retrospective cohort study. Patients diagnosed with recurrent or metastatic pancreatic cancer with adenocarcinoma or adenosquamous carcinoma who underwent systemic chemotherapy at our hospital between April 2018 and April 2022 were enrolled. The data cutoff date was April 30, 2024. Patients with locally advanced pancreatic cancer or other histologic types were ineligible. Patients with other active malignancies or those who underwent local treatments that were not covered by insurance, including irreversible electroporation, were excluded. We reviewed the patient\u0026rsquo;s medical records and collected data on patient characteristics and the blood tests at the initiation of palliative chemotherapy, survival, treatments, and genomic information. We compared OS between patients who received CGP (CGP group) and those who did not (non-CGP group). The CGP group included cases, in which CGP was conducted during the course of treatment, while the non-CGP group included cases without CGP. Local recurrence was counted as one metastatic site when assessing the number of metastatic sites.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComprehensive genomic profiling\u003c/h3\u003e\n\u003cp\u003eWe performed CGP using the FoundationOne CDx (Foundation Medicine, Cambridge, MA, USA), FoundationOne Liquid CDx (Foundation Medicine, Cambridge, MA, USA), OncoGuide NCC Oncopanel system (Sysmex Co., Tokyo, Japan), PleSSision (Mitsubishi Electric Software Co., Ltd., Tokyo, Japan), PleSSision-Exome (Mitsubishi Electric Software Co., Ltd., Tokyo, Japan), and Guardant360 CDx (Guardant Health, Inc., USA).\u003c/p\u003e \u003cp\u003eThe standard process of CGP is as follows: formalin-fixed paraffin-embedded (FFPE) tumor tissue samples are prepared from surgical specimens, endoscopic ultrasound-guided fine needle aspiration biopsy samples from the primary lesion, or biopsy samples from metastatic sites, such as the liver. DNA was extracted from FFPE specimens, and genome sequencing was performed. In cases where adequate tissue collection was complex, blood samples were substituted, and genome sequencing was performed using FoundationOne Liquid CDx or Guardant360 CDx. CGP results were discussed at the Molecular Tumor Board of the hospital, which reported genomic information and recommendations for genotype-matched treatments for each physician. The Molecular Tumor Board reviewed all cases based on the clinical practice guidelines for next-generation sequencing for cancer diagnosis and treatment (edition 2.1). Genomic alterations with evidence levels A\u0026ndash;D were defined as actionable genomic alterations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e FoundationOne CDx was approved for use for all solid tumors in 2019 by the Pharmaceuticals and Medical Devices Agency (PMDA), a regulatory authority in Japan. FoundationOne CDx can detect substitutions, insertions, deletions, and copy number alterations across 324 genes, including 36 oncogene rearrangements, MSI, and tumor mutational burden (TMB), using DNA extracted from FFPE tumor tissue specimens. For the analysis, 10 undyed 4\u0026ndash;5-\u0026micro;m FFPE specimen sections were prepared using samples with more than 20% tumor content [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. FoundationOne Liquid CDx was approved in 2021 by the PMDA for use in all solid tumors when it is difficult to obtain tissue specimens of tumor cells. FoundationOne Liquid CDx can detect substitutions, insertions, deletions, and copy number alterations across 324 genes, including 36 oncogene rearrangements, MSI, and TMB, using DNA extracted from the patient\u0026rsquo;s peripheral blood. For the analysis, two samples of 8.5 mL of blood were used [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e The OncoGuide NCC Oncopanel system was approved by the PMDA in 2019 for use with solid tumors. The NCC Oncopanel can detect mutations, amplifications, and homozygous deletions of 124 genes, including 13 oncogene rearrangements, MSI, and TMB, using DNA extracted from FFPE tumor tissue specimens and blood cells. For the analysis, five undyed 10-\u0026micro;m or 10 undyed 4\u0026ndash;5-\u0026micro;m FFPE tumor tissue sections were prepared. Peripheral blood (2 mL) was collected from the same patient and used as a control to distinguish between somatic and germline mutations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePleSSision is an outsourced clinical-sequencing system. PleSSision can detect substitutions, insertions, and deletions, as well as copy number alterations, across 160 genes, MSI, and TMB using DNA extracted from FFPE tumor tissue specimens and peripheral blood. PleSSision-Exome can analyze approximately 20,000 genes (19,296 genes), corresponding to almost all human genes, and can detect MSI and TMB. For both analyses, 10 undyed 10-\u0026micro;m FFPE specimen sections were used for DNA extraction. All specimens with more than 30% tumor content were collected within three years. As a control, 2.5 mL of peripheral blood was collected and analyzed using PleSSision and PleSSision-Exome [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Guardant360 CDx was approved for application for all solid tumors in 2022 by PMDA. Guardant 360 can detect 74 gene nucleotide substitutions and insertions/deletions, as well as gene amplification, in 18 genes, six fusion genes, and MSI-H in solid tumors. For analysis, we collected two samples of 10 mL of blood [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSummary statistics were presented as frequencies and proportions for categorical data and as means and standard deviations for continuous variables to evaluate patient characteristics. We compared patient characteristics using the Fisher\u0026rsquo;s exact test for categorical variables and the Wilcoxon signed-rank test for continuous variables, as appropriate. OS was calculated from the initiation of palliative chemotherapy to the day of death from any cause. For recurrent cases, the date of palliative chemotherapy initiation after confirmation of relapse was considered the OS starting date. Patients without a recorded date of death were assessed based on their last recorded date. Survival analyses were performed using the Kaplan\u0026ndash;Meier method and log-rank test. Hazard ratios (HRs) were estimated using the Cox proportional hazards model. All \u003cem\u003eP\u003c/em\u003e values were based on a two-sided hypothesis, and \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance. All statistical analyses were performed using the SAS JMP Pro version 17.0.0 software (SAS Institute, Cary, NC, USA).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval statement \u003c/strong\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e This study was approved by the Keio University Hospital Institutional Ethics Committee (approval number: 20241038) and was performed in accordance with the Declaration of Helsinki and Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan. Consent for participation was obtained via an opt-out form on the website, ensuring that patients had the opportunity to refuse participation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eOverall, 111 patients were enrolled, with 59 and 52 patients in the CGP and non-CGP groups, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The CGP group had a statistically lower median age than the non-CGP group (66 vs. 73 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0013) and a lower proportion of patients aged over 75 years (CGP: 15.3% vs. non-CGP: 38.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0088). Additionally, the CGP group had a higher proportion of patients with recurrence and a lower proportion of UR-M patients compared to the non-CGP group (UR-M: 52.5% vs. 78.9%, Recurrence: 47.5% vs. 21.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0051) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The other variables were not significantly different between the groups.\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\u003ePatient characteristics: CGP vs. non-CGP\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGP\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enon-CGP\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (26\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (52\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;≧\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0088**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (51.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (48.1%)\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\u003eECOG PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (50.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (47.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (55.8%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\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\u003eUR-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (78.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0051**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (47.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (21.2%)\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\u003ePrimary sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody/tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (61.5%)\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\u003eMetastatic sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLymph node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeritoneum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of metastatic sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (51.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (47.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (48.1%)\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\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenosquamous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\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\u003eAlb (g/dL), \u003c/p\u003e \u003cp\u003emedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9 (2.1\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7 (2.4\u0026ndash;4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR, median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (0.7\u0026ndash;9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8 (0.47\u0026ndash;14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (78.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (21.2%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (11.5%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\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\u003eCA19-9 (U/mL), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.0 \u003c/p\u003e \u003cp\u003e(1.0\u0026ndash;148791.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215.0 \u003c/p\u003e \u003cp\u003e(1.0\u0026ndash;382392.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (ng/mL), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1 \u003c/p\u003e \u003cp\u003e(0.7\u0026ndash;3205.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4 \u003c/p\u003e \u003cp\u003e(0.7\u0026ndash;225.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: CGP, comprehensive genomic profiling; ECOG PS, Eastern Cooperative Oncology Group Performance Status; Alb, albumin; NLR; neutrophil to lymphocyte ratio, GPS; Glasgow Prognostic Score.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Wilcoxon, **Fisher`s exact test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenomic profile of patients who underwent CGP\u003c/h2\u003e \u003cp\u003eIn the CGP group, CGP implementation was as follows: FoundationOne CDx, 35.6% (21/59); FoundationOne Liquid CDx, 13.6% (8/59); OncoGuide NCC Oncopanel system, 11.9% (7/59); PleSSision, 23.7% (14/59); PleSSision-Exome, 11.9% (7/59); and Guardant360 CDx, 3.4% (2/59) (Online Resource 1). Nine patients (15.2%) underwent CGP before first-line chemotherapy, 26 patients (44.1%) between first- and second-line therapies, and 24 (40.7%) between second- and third-line therapies (Online Resource 2). The frequencies of genomic alterations in the well-known driver genes \u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, and \u003cem\u003eSMAD4\u003c/em\u003e were 84.7% (50/59), 66.1% (39/59), 22.0% (13/59), and 13.6% (8/59), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most common subtypes of \u003cem\u003eKRAS\u003c/em\u003e mutations were G12D (44.0%, 22/50), G12V (40.0%, 20/50), and G12R (8.0%, 4/50) (Online Resource 3). The most frequently observed oncogenic alterations were \u003cem\u003eKRAS/TP53\u003c/em\u003e (30.5%, 18/59), followed by only \u003cem\u003eKRAS\u003c/em\u003e (22.0%, 13/59), \u003cem\u003eKRAS/TP53/CDKN2A\u003c/em\u003e (13.6%, 8/59), and \u003cem\u003eKRAS/TP53/SMAD4\u003c/em\u003e (11.9%, 7/59) (Online Resource 4). Actionable genomic alterations were detected in 24 patients (40.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). HRD was detected in seven patients (11.9%). Among these patients, three had germline \u003cem\u003eBRCA2\u003c/em\u003e mutations (5.1%), five had \u003cem\u003eATM\u003c/em\u003e mutations (8.5%), and one had \u003cem\u003eCHEK2\u003c/em\u003e mutation (1.7%). Two patients with MSI-H/dMMR (3.4%) were considered to have Lynch syndrome: a germline \u003cem\u003eMSH2\u003c/em\u003e mutation was observed in one patient and a germline \u003cem\u003eMLH1\u003c/em\u003e mutation in the other. In addition, \u003cem\u003eKRAS\u003c/em\u003e G12C mutation was observed in one patient. Six patients (10.2%) underwent genotype-matched treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these patients, four with HRD underwent platinum-based chemotherapy, one with MSI-H/dMMR underwent PD-1 inhibitor treatment, and one with \u003cem\u003eKRAS\u003c/em\u003e G12C mutation underwent KRAS G12C inhibitor treatment (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Online Resource 5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActionable genomic alterations and genotype-matched treatments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eActionable genomic alterations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGenotype-matched treatments\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCDKN2A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCDK4/6 inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatinum, PARP inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBRCA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatinum, PARP inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBRAF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBRAF inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMMR (MSH2, MLH1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmune checkpoint inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMDM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDM2 inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePI3K/AKT/mTOR inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCHEK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatinum, PARP inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePTEN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAKT/mTOR inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKRAS G12C\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKRAS G12C inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: n, number.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between CGP and OS of patients with advanced pancreatic cancer\u003c/h3\u003e\n\u003cp\u003eWe compared the OS between the CGP and non-CGP groups using the Kaplan\u0026ndash;Meier method and measured HRs using the Cox proportional hazards model. The patient characteristics were almost well-balanced between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). OS was statistically higher in the CGP group than in the non-CGP group (median OS, 25.2 vs. 11.8 months; HR, 0.49; 95% confidence interval [CI], 0.31\u0026ndash;0.76; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0013, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The median follow-up periods were 45.5 months (95% CI, 32.4\u0026ndash;50.6) in the CGP group and 42.0 months (95% CI, 23.3\u0026ndash;49.6) in the non-CGP group. The median OS of patients who underwent genotype-matched treatments was 35.5 months (95% CI, 11.4\u0026ndash;76.2), which tended to be longer than that of 17.0 months (95% CI, 12.4\u0026ndash;24.0) for patients who did not undergo genotype-matched treatments. However, only 10.2% of the patients underwent genotype-matched treatments, suggesting that other factors may have contributed to the high OS in the CGP group. To investigate the reasons for better outcomes in the CGP group, we compared the implementation rate of chemotherapy using key drugs for pancreatic cancer between the two groups. More patients in the CGP group received gemcitabine (GEM)- and 5-fluorouracil (5-FU)-based chemotherapy than those in the non-CGP group (78.0% vs. 38.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A similar trend was observed in the rate of transition to subsequent chemotherapy (76.3% vs. 48.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0030). Meanwhile, the first-line chemotherapy did not differ between the two cohorts. The implementation rates of treatment regimens sometimes used in vulnerable patients, such as GEM monotherapy and S-1, were similar.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImplementation rate of chemotherapy\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eImplementation of chemotherapy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGP (n\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enon-CGP (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e5-FU-based and GEM-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (78.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSubsequent chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (76.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0030*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-line chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFOLFIRINOX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGEM\u0026thinsp;+\u0026thinsp;nab-PTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (59.6%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGEM\u0026thinsp;+\u0026thinsp;S-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (7.7%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: FOLFIRINOX, 5-fluorouracil, irinotecan, and oxaliplatin; GEM, gemcitabine; nab-PTX, nab-paclitaxel; S-1, tegafur/gimeracil/oteracil; 5-FU, 5-fluorouracil.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Fisher`s exact test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOS according to the number of mutated major driver genes (\u003c/b\u003e \u003cb\u003eKRAS\u003c/b\u003e, \u003cb\u003eTP53\u003c/b\u003e, \u003cb\u003eCDKN2A\u003c/b\u003e, \u003cb\u003eand\u003c/b\u003e \u003cb\u003eSMAD4\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSeveral studies have suggested that an increasing number of mutated driver genes, including \u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, and \u003cem\u003eSMAD4\u003c/em\u003e, are associated with poor prognosis in pancreatic cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. We compared the OS according to the number of altered driver genes to determine whether there was a similar trend in our cohort. The median OS tended to be worse with an increase in the number of mutated driver genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The median OS of patients with 0\u0026ndash;1 mutated driver gene, 2 mutated driver genes, and 3\u0026ndash;4 mutated driver genes was 40.1 months, 25.2 months, and 19.0 months, respectively. Moreover, the median OS of patients with 0\u0026ndash;2 mutated genes was statistically longer than that of patients with 3\u0026ndash;4 mutated genes (median OS, 29.2 vs. 19.0 months; HR, 0.40; 95% CI, 0.20\u0026ndash;0.82; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0093, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Patients with three or more mutated driver genes had a higher rate of FOLFIRINOX use during treatment than those with two or fewer mutations (56.3% vs. 34.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15, Online Resource 6). An important consideration is the difference in the detection of mutated major driver genes (\u003cem\u003eKRAS, TP53, CDKN2A\u003c/em\u003e, and \u003cem\u003eSMAD4\u003c/em\u003e) between tissue CGP and liquid CGP. Tissue CGP identified a higher number of patients with 3\u0026ndash;4 mutated driver genes and fewer patients with 0\u0026ndash;2 mutated driver genes compared to liquid CGP (Online Resource 7).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, patients who underwent CGP exhibited longer OS, highlighting the potential benefits of CGP in identifying genotype-matched treatments and providing prognostic information to guide treatment strategies. However, the observed survival benefit may be subject to selection bias, as patients with more favorable baseline characteristics were more likely to receive CGP, whereas those with worse baseline characteristics may have missed this opportunity. While CGP holds promise for clinical benefit, proactive and timely implementation should be considered when managing patients with pancreatic cancer.\u003c/p\u003e \u003cp\u003eCGP provides information on genotype-matched treatments for advanced pancreatic cancer. For example, HRD is considered a putative biomarker for PARP inhibitors [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several studies have suggested an association between HRD and a better response to platinum-based chemotherapy during any line of treatment [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, the Know Your Tumor Registry Trial showed that the median OS of patients with pancreatic cancer with actionable genomic alterations who underwent genotype-matched treatments was longer than that of those who underwent unmatched treatments or those without actionable genomic alterations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition to providing information on genotype-matched treatments, CGP allows physicians to predict the disease prognosis. \u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, and \u003cem\u003eSMAD4\u003c/em\u003e are the major driver genes of pancreatic cancer. The number of mutations in these genes (three or more) is considered to be associated with a poor prognosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In our hospital, for patients with pancreatic cancer who have undergone CGP, eligibility for genotype-matched treatments and prognostic information are discussed among physicians on the Molecular Tumor Board, which is used to guide treatment strategies, including increasing the intensity of treatments for patients with a poor prognosis and good Eastern Cooperative Oncology Group Performance Status (ECOG PS) and providing more conservative treatments for patients with poor prognosis and poor ECOG PS. Moreover, treatment transitioning can be achieved at an appropriate time for patients who are eligible for genotype-matched treatments or clinical trials and even for patients without actionable genomic alterations based on prognosis. Recently, a phase III trial reported that monitoring symptoms via a web application improved the outcomes of patients with lung cancer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The web application enabled physicians to detect cancer relapse in patients earlier than in controls through frequent and timely notifications of symptoms, leading to a change in the awareness of patients and attending physicians and an improvement in prognosis. We hypothesized that CGP would have similar effects.\u003c/p\u003e \u003cp\u003eIn our cohort, the median OS in the CGP group was longer than that in the non-CGP group. The median age of the CGP group was statistically younger than that of the non-CGP group, and the CGP group included a higher proportion of patients with recurrent disease. However, other patient characteristics did not differ significantly between the two groups. There are three possible reasons for this. First, the implementation of genotype-matched treatments is considered to contribute to the outcomes. The median OS of patients who underwent genotype-matched treatments tended to be longer than that of patients who did not. The detection rate of actionable genomic alterations and the implementation rate of genotype-matched treatments were similar to those in previous research [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. HRD, MSI-H/dMMR, and \u003cem\u003eKRAS\u003c/em\u003e G12C were detected in 11.9%, 3.4%, and 1.7% of patients, respectively, whereas in previous studies, HRD was detected in 15\u0026ndash;20% [\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], MSI-H/dMMR in 0.5\u0026ndash;1% [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and \u003cem\u003eKRAS\u003c/em\u003e G12C in less than 5% [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] of patients with pancreatic cancer. Meanwhile, they were not sufficiently high to explain the differences in OS. Second, the prognostic value of CGP may have contributed to changing the physicians\u0026rsquo; treatment strategies and eventually improving treatment outcomes regardless of the implementation of genotype-matched treatments. Herein, similar to previous studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the median OS tended to worsen with an increasing number of mutated driver genes. Patients with three or more mutated driver genes who is considered to have a worse prognosis were more likely to be treated with FOLFIRINOX, which is regarded as an intensive treatment, than patients with two or fewer mutations. Treatment strategies that consider prognostic predictions may have contributed to patient outcomes. Additionally, the implementation rate of both GEM- and 5-FU-based chemotherapy was higher in the CGP group (78.0%) than in the non-CGP group (38.5%) of the present study, NALIRIFOX cohort of NAPOLI-3 (41%), and nab-paclitaxel plus gemcitabine cohort of NAPOLI-3 (35%) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, the rate of transition to subsequent chemotherapy was higher in the CGP group (76.3%) than in the non-CGP group (48.1%) of the present study, NALIRIFOX cohort of NAPOLI-3 (51%), and nab-paclitaxel plus gemcitabine cohort of NAPOLI-3 (54%) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, while the transition rate was not necessarily low in the non-CGP group, it was comparatively higher in the CGP group. Therefore, treatment transitioning could have been achieved at an appropriate time based on prognostic information and the presence of actionable genomic alterations. Importantly, patients with better baseline may have been more likely to undergo CGP, potentially contributing to the observed survival benefit. This selection bias must be considered when interpreting the results.\u003c/p\u003e \u003cp\u003eOur study had some limitations. First, the sample size was relatively small owing to the single-center design. Second, this was a retrospective and observational study; therefore, selection and causative biases were inevitable. For example, patients with better OS are likely to have opportunities to undergo CGP. The median OS of 11.8 months, the rates of GEM- and 5-FU-based chemotherapy use, and the transition rate to subsequent chemotherapy in the non-CGP group align with previous research findings [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, there were potential influence of selection bias on these outcomes. While first-line chemotherapy regimens and implementation rates for treatments often prescribed to vulnerable patients, such as GEM monotherapy and S-1, were similar across cohorts, it remains likely that patients with better baseline characteristics were more inclined to receive CGP. These limitations highlight the need for timely and proactive CGP implementation in pancreatic cancer management. Additionally, differing CGP methods with variable detection rates were employed in this study. Notably, the number of major driver genes (\u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, and \u003cem\u003eSMAD4\u003c/em\u003e) detected varied between tissue and liquid CGP, potentially reflecting differences in sensitivity for identifying driver gene alterations. Thus, caution is necessary when interpreting the prognostic value of CGP based solely on the number of detected mutations. Finally, it is challenging to definitively state that CGP\u0026rsquo;s prognostic predictions were always time-relevant. In our cohort, 59.3% of patients underwent CGP before second-line chemotherapy. This timely implementation of CGP potentially influenced treatment transitioning at an appropriate timing based on prognostic information and the presence of actionable genomic alteration. Nonetheless, some degree of selection bias must be acknowledged when interpreting these results. A prospective study would be necessary to fully understand how CGP influences physicians' treatment strategies.\u003c/p\u003e \u003cp\u003eIn conclusion, OS was extended in patients who underwent CGP, possibly due to its influence on treatment strategies. Yet, the survival benefit observed may have been affected by selection bias, as patients with more favorable baseline characteristics were more likely to undergo CGP. Despite this limitation, our findings suggest that CGP provides meaningful clinical benefits, underscoring the importance of its proactive application, particularly in pancreatic cancer, where rapid disease progression may restrict treatment options. Further prospective studies are warranted to clarify CGP\u0026rsquo;s impact on patient outcomes and to refine its role in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply indebted to all patients who participated in this study and their families.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partly supported by the Japan Society for the Promotion of Science KAKENHI (Grant-in-Aid for Early-Career Scientists) (grant number 21K15959).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial or nonfinancial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConception and Design: \u003c/strong\u003eES, HH\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollection and Assembly of Data:\u003c/strong\u003e ES, KS, HH\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis and Interpretation: \u003c/strong\u003eAll authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManuscript Writing: \u003c/strong\u003eES, HH\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinal Approval of Manuscript: \u003c/strong\u003eAll authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccountable for All Aspects of the Work: \u003c/strong\u003eHH\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe International Agency for Research on Cancer (IARC) (2024) Global Cancer Observatory. 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PO.17.00286\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pancreatic cancer, Comprehensive genomic profiling, Precision medicine, Chemotherapy","lastPublishedDoi":"10.21203/rs.3.rs-5054340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5054340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrecision medicine is a promising therapeutic strategy for pancreatic cancer. However, only a few patients are eligible for genotype-matched treatments because of the low detection rate of actionable genomic alterations, and the clinical application of comprehensive genomic profiling (CGP) in pancreatic cancer has not been completely investigated. CGP provides considerable information, such as data on prognosis and future eligibility of patients for genotype-matched clinical trials, and can eventually guide physicians\u0026rsquo; treatment strategies. This study aimed to investigate the contribution of CGP to patient outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis single-center retrospective cohort study enrolled patients diagnosed with recurrent or metastatic pancreatic cancer with adenocarcinoma or adenosquamous carcinoma who underwent systemic chemotherapy between April 2018 and April 2022. We reviewed medical records and collected data on patient characteristics, survival, and genomic information. We compared overall survival (OS) between patients who received CGP (CGP group) and those who did not (non-CGP group).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 111 patients were eligible, of which 59 underwent CGP. No significant differences were observed in patient characteristics between the groups. The median OS was significantly longer in the CGP group than in the non-CGP group (25.2 vs. 11.8 months; hazard ratio, 0.49; 95% confidence interval, 0.31\u0026ndash;0.76; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0013). Actionable genomic alterations were detected in 24 patients (40.7%), and six patients (10.2%) underwent genotype-matched treatments.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOS was extended in patients with pancreatic cancer who underwent CGP, possibly due to its influence on physicians\u0026rsquo; treatment strategies. This result highlights the need for proactive and timely CGP for patients with pancreatic cancer.\u003c/p\u003e","manuscriptTitle":"Clinical Utility of Comprehensive Genomic Profiling for Advanced Pancreatic Cancer: Insights from Real-World Data Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 17:11:27","doi":"10.21203/rs.3.rs-5054340/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-11-17T02:24:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-12T01:09:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-04T04:26:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Clinical Oncology","date":"2024-11-02T12:00:14+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor revisions","date":"2024-10-11T21:47:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ae3adb92-4bbd-4d3e-b7ee-f945e573ab15","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T16:04:14+00:00","versionOfRecord":{"articleIdentity":"rs-5054340","link":"https://doi.org/10.1007/s10147-025-02713-5","journal":{"identity":"international-journal-of-clinical-oncology","isVorOnly":false,"title":"International Journal of Clinical Oncology"},"publishedOn":"2025-02-17 15:57:58","publishedOnDateReadable":"February 17th, 2025"},"versionCreatedAt":"2024-12-16 17:11:27","video":"","vorDoi":"10.1007/s10147-025-02713-5","vorDoiUrl":"https://doi.org/10.1007/s10147-025-02713-5","workflowStages":[]},"version":"v1","identity":"rs-5054340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5054340","identity":"rs-5054340","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}