Efficacy and Safety of Durvalumab Rechallenge in Advanced Hepatocellular Carcinoma Patients Refractory to Prior Anti-PD-1 Therapy

Efficacy and Safety of Durvalumab Rechallenge in Advanced Hepatocellular Carcinoma Patients Refractory to Prior Anti-PD-1 Therapy | 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 Efficacy and Safety of Durvalumab Rechallenge in Advanced Hepatocellular Carcinoma Patients Refractory to Prior Anti-PD-1 Therapy Kuan-Chang Lai, Yen-Hao Chen, Yi-Ping Hung, Nai-Jung Chiang, Ming-Huang Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4659138/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Nov, 2024 Read the published version in Hepatology International → Version 1 posted 5 You are reading this latest preprint version Abstract Background/Purpose: Recently, anti-programmed cell death protein-1 (anti-PD-1) and anti-PD-L1 therapies were approved for hepatocellular carcinoma (HCC). However, the effectiveness of rechallenging with one immune checkpoint inhibitor (ICI) after failure of another remains unclear. This study explores the efficacy and safety of anti-PD-L1 rechallenge in patients who failed anti-PD-1 therapy. Methods: From January 2016 to December 2023, 65 advanced HCC patients previously treated with anti-PD-1 therapy were retrospectively enrolled and rechallenged with durvalumab (480 mg IV every two weeks). Results: Overall, 86.2% of patients received nivolumab and 13.8% pembrolizumab as prior anti-PD-1 therapy. The overall response rate (ORR) to durvalumab was 13.8%. Patients who responded to prior anti-PD-1 had a higher ORR compared to non-responders (31.3% vs. 8.7%, p = 0.04). Patients with any grade of immune-related adverse events (irAEs) from durvalumab had a higher ORR than those without irAEs (35.3% vs. 6.7%, p = 0.01). The median PFS was 5.4 months, and the median OS was 9.6 months. Responders to prior anti-PD-1 showed longer OS (33.9 vs. 8.2 months, p < 0.01) and a trend toward longer PFS (13.8 vs. 4.9 months, p = 0.07) compared to non-responders. Multivariate analysis identified prior anti-PD-1 response (HR: 0.31) as the only protective factor for death. Common irAEs were skin toxicity (13.8%) and hepatitis (7.7%); no correlation was found between irAEs from prior anti-PD-1 and durvalumab treatment. Conclusion: This study provides the first, concrete evidence that durvalumab rechallenge is effective for HCC patients who are refractory to anti-PD-1 therapy, especially for those who previously responded to anti-PD-1 treatment. Hepatocellular carcinoma (HCC) immune checkpoint inhibitor (ICI) anti-programmed cell death protein-1 (anti-PD-1) anti-programmed cell death ligand-1 (anti-PD-L1) nivolumab durvalumab anti-PD-1 refractory rechallenge immune-related adverse event (irAE) predictor Figures Figure 1 Figure 2 Figure 3 Introduction Hepatocellular carcinoma (HCC), the most common primary liver cancer, ranks as the sixth most prevalent cancer worldwide.[ 1 ] It was responsible for over 900,000 new cases and more than 800,000 deaths in 2020.[ 2 ] Historically, treatment options for HCC were limited to the targeted therapy sorafenib[ 3 ] and, despite the development of second-line agents such as regorafenib[ 4 ], cabozantinib[ 5 ], and ramucirumab[ 6 ], the prognosis for advanced HCC remains poor. Recent advancements in immune checkpoint inhibitors (ICIs), including anti-cytotoxic T lymphocyte–associated antigen 4 (anti-CTLA-4) and anti-programmed cell death- (ligand)1 (anti-PD-(L)1), have become the standard treatment, improving outcomes for various cancers, including HCC. Notably, pembrolizumab and the combination of nivolumab with ipilimumab were approved in 2018 and 2020, respectively, for second-line treatment of HCC due to their breakthrough responses. [ 7 , 8 ] Later, combination therapies such as atezolizumab (anti-PD-L1) with bevacizumab[ 9 ] and dual immunotherapy with durvalumab (anti-PD-L1) and tremelimumab (anti-CTLA-4)[ 10 ] were also validated for first-line treatment in HCC. A clinical challenge emerges when patients do not respond to initial anti-PD-1 therapies, such as nivolumab or pembrolizumab; the effectiveness of subsequent treatments with newer combinations like atezolizumab/bevacizumab or durvalumab/tremelimumab, both anti-PD-L1-based therapies, is uncertain. Conversely, with anti-PD-L1 combinations now established as standard first-line therapy, the efficacy of subsequent anti-PD-1 therapy following failure of these initial treatments also remains uncertain. When first ICI therapy proves effective, it may suggest an inflamed tumor microenvironment conducive to successful rechallenging with another ICI. However, acquired resistance due to immune escape could potentially undermine the effectiveness of rechallenges. The answers to these queries are crucial yet currently unresolved. Given the earlier approvals of nivolumab and pembrolizumab for HCC, we collected data from patients who had previously received anti-PD-1 therapy to explore the efficacy of subsequent anti-PD-L1 treatment. Rechallenging was limited to patients using durvalumab to specifically assess the effectiveness and side effects of anti-PD-L1 rechallenge. This design aims to elucidate the potential benefits and risks associated with anti-PD-L1 rechallenge in this patient population. Materials and methods Patient selection This study was a retrospective collection of clinical, pathological and image information retrieved from electronic medical records. It included a total of 65 HCC patients from Taipei Veterans General Hospital (Taipei, Taiwan), from January 2016 to December 2023. The criteria for patient enrollment included aged 18 years or older at the time of diagnosis; cases of advanced HCC, particularly among patients who had experienced anti-PD-1 immunotherapy, and subsequently received anti-PD-L1 with durvalumab treatment (480 mg intravenously, every two weeks). The diagnosis of HCC was established either through histopathological confirmation or clinical interpretation, following the criteria set by the American Association for the Study of Liver Diseases (AASLD).[ 11 ] All the detailed information was available, including age, gender, etiologies, biochemistry and hematology data, underlying comorbidities, tumor marker, tumor stage, comprehensive treatment information, survival time following diagnosis, and cause of death were collected. The presence of underlying cirrhosis was confirmed either through histological examination or by clinical and/or imaging evidence, including ascites, varices, splenomegaly, and thrombosis in the hepatic or portal veins. This study was approved by the Institutional Review Board of Taipei Veterans General Hospital. (TPEVGH IRB No.: 2024-06-018BC) Outcome Assessment Immune-related adverse events (irAEs) were assessed based on main organ involvement and grading by Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Two independent investigators evaluated the treatment outcomes, categorizing them into complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD), based on RECIST and modified RECIST (Response Evaluation Criteria in Solid Tumors, version 1.1) criteria. We classified overall response rate (ORR, proportion of patients with a best overall response of partial or complete response) by using RECIST version 1.1 version criteria by specialist review. Statistical analysis The patient’s baseline characteristics are presented by calculating the arithmetic mean, standard deviation, median, and interquartile range. The associations between durvalumab treatment responses across groups were analyzed using the Fisher exact test and chi-square (χ2) test. The Kaplan–Meier method was used to analyze OS and PFS. Survival curves were evaluated for differences using the log-rank test. A Cox proportional-hazards model was utilized both to calculate hazard ratios with 95% confidence intervals and to evaluate the effects of prognostic factors on progression and death in univariate and multivariate analyses. We selected relevant factors or variables with p value less than 0.1 in the univariate analysis for the multivariate model. All the tests were two-sided, and a p value of less than 0.05 was considered to indicate statistical significance. All the statistical analyses were performed by IBM® SPSS®, version 21.0 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.). Results Patient characteristics A total of 65 patients were diagnosed with HCC who received durvalumab during the study period between January 2016 to December 2023. Among them, forty-eight patients were male. The mean age was 60.5 ± 11.6; 43.1% (28/65) had HBsAg positive, 3.1% (2/65) had positive anti-HCV antibody. Approximately 76.7% of the patients were classified as Child-Pugh score (CPS) A, and 18.3% fell into the Child-Pugh score B category. Albumin-bilirubin (ALBI) grade 1 accounted for 39.1% of patients and ALBI grade 2 for 56.3%. Furthermore, a total 27 patients (41.5%) were diagnosed to have cirrhosis, 28 patients (43.1%) have portal vein thrombosis and 69.2% already had extrahepatic metastasis. Regard to prior systemic treatment, 84.6% of patients has been experienced targeted therapy with lenvatinib, and 86.2% received anti-PD-1 therapy with nivolumab. Moreover, regarding the combination regimen with durvalumab, 63.1% of patients underwent combination treatment with lenvatinib and 16.9% of patients received regorafenib. The median duration between the administration of prior anti-PD-1 and durvalumab was 6 months (interquartile range: 3.0-14.5 months). The detailed demographic and clinical characteristics were shown in Table 1 . Table 1 Patient characteristics Factors Case numbers % Age (Mean+/- SD) 60.5 +/-11.6 M/F 48/17 73.8%/26.2% HBsAg Positive 28 43.1% Anti-HCV Positive 2 3.1% Laboratory data Median IQR Platelet count (10^9/L) 155.0 (109.5-220.8) ALT (U/L) 37.0 (28.0-54.0) ALB (g/dL) 3.8 (3.5-4.0) Total bilirubin (mg/dL) 0.7 (0.5-1.2) PT (second) 11.6 (11.0-12.7) AFP (ng/mL) 690.0 (22.8-7563.0) PIVKA-II (mAL/mL) 4707.0 (577.5-34639.3) CPS score class Case numbers % A 46 76.7% B 11 18.3% C 3 5.0% ALBI grade I 25 39.1% II 36 56.3% III 3 4.7% Radiological factors Cirrhosis 27 41.5% Ascites No 40 61.5% Mild/moderate 24 36.9% Severe 1 1.5% Intra-hepatic tumor numbers ≤ 10 42 64.6% > 10 19 29.2% Infiltrating type 4 6.2% Intra-hepatic tumor size (cm) ≤ 10 49 75.4% > 10 12 18.5% Infiltrating type 4 6.2% Macrovascular invasion 28 43.1% Extrahepatic metastasis 45 69.2% BCLC stage B/C/D 14/48/3 21.5%/73.8%/4.6% Lines of prior systemic therapy 1 15 23.1% 2 27 41.5% ≥ 3 23 35.4% Experienced targeted therapy Lenvatinib 55 84.6% Sorafenib 32 49.2% Regorafenib 12 18.5% Cabozantinib 5 7.7% Experienced anti-PD-1 Nivolumab 56 86.2% Pembrolizumab 9 13.8% Combined drugs with durvalumab Lenvatinib 41 63.1% Regorafenib 11 16.9% Sorafenib 4 6.2% Cabozantinib 4 6.2% Ramucirumab 2 3.1% Bevacizumab 1 1.5% Chemotherapy 1 1.5% IQR = interquartile range; ALT: Alanine Aminotransferase; ALB: Albumin; PT: Prothrombin Time; AFP: Alpha-Fetoprotein; PIVKA-II: Protein Induced by Vitamin K Absence or Antagonist-II; CPS score class: Child-Pugh Score class; ALBI grade: Albumin-Bilirubin grade; BCLC stage: Barcelona Clinic Liver Cancer stage Efficacy and associated factors The ORR to durvalumab was 13.8%, and disease control rate (DCR) was 70.8%, comprising a CR in 4.6%, a PR in 9.2%, and SD in 56.9%. The waterfall plot categorized by etiologies was shown in Fig. 1 . Patients who responded to prior anti-PD-1 therapy had a higher ORR compared to non-responders (31.3% vs. 8.7%, p = 0.04). In addition, patients experiencing any grade of irAEs with durvalumab was associated with a higher ORR compared to those without irAEs (35.3% vs. 6.7%, p = 0.01). However, factors such as sex, etiologies, liver function, tumor size and intrahepatic tumor numbers were not associated with the response (Table 2 ). The ORR of different combined regimens with durvalumab were shown in supplementary table 1 . We present a case in which the patient initially achieved a partial response with combined nivolumab and lenvatinib therapy, subsequently exhibited pulmonary metastasis progression with intrahepatic tumors remained stable. Then, the pulmonary metastasis experienced tumor regression following the substitution of nivolumab with durvalumab (Supplementary Fig. 1). Table 2 Treatment response with durvalumab by RECIST 1.1 criteria CR PR SD PD ORR p value All population 4.6% 9.2% 56.9% 29.3% 13.8% Sex Male 4.4% 8.9% 57.8% 28.9% 13.3% 0.70 Female 5.9% 11.8% 64.7% 17.6% 17.7% HBsAg Positive 2.3% 11.6% 62.8% 23.3% 14.0% 1.0 Negative 10.5% 5.3% 52.6% 31.6% 15.8% Anti-HCV antibody Positive 50.0% 0.0% 50.0% 0.0% 50% 0.27 Negative 3.3% 10.0% 60.0% 26.7% 13.3% CPS classification A 4.4% 11.2% 62.2% 22.2% 15.6% 0.80 B 10.0% 10.0% 50.0% 30.0% 20.0% C 0.0% 0.0% 33.3% 66.7% 0% AIBI Score Grade I 4.3% 4.3% 78.3% 13.1% 8.7% 0.61 Grade II 5.6% 13.8% 50.0% 30.6% 19.4% Grade III 0.0% 0.0% 50.0% 50.0% 0% Intrahepatic tumor number 1–5 7.1% 10.7% 64.3% 17.9% 17.9% 0.60 > 5–10 0.0% 0.0% 71.4% 28.6% 0% > 10 0.0% 11.8% 47.1% 41.1% 11.8% Infiltrating 0.0% 25.0% 50.0% 25.0% 25.0% Intrahepatic tumor size (cm) 1–5 7.4% 11.1% 51.9% 29.6% 18.5% 0.62 > 5–10 0.0% 6.7% 73.3% 20.0% 6.7% > 10 0.0% 10.0% 60.0% 30.0% 10.0% Infiltrating 0.0% 25.0% 50.0% 25.0% 25.0% Prior anti-PD-1 response Responder 12.5% 18.8% 43.7% 25.0% 31.3% 0.04 Non-responder 2.2% 6.5% 65.2% 26.1% 8.7% irAE of durvalumab Nil 2.2% 4.4% 62.2% 31.2% 6.7% 0.01 Any grade 11.8% 23.5% 52.9% 11.8% 35.3% Progression-free survival and overall survival The median PFS of total population was 5.4 months [95% confidence interval (CI): 3.1–7.7], and the median OS 9.6 months [95% CI: 5.5–13.3]. The outcomes under durvalumab therapy and prior anti-PD-1 treatment were shown in swimmers’ plot (Fig. 2 ). By prior anti-PD-1 response, the responders demonstrated longer OS (33.9 vs. 8.2 months, p < 0.01), and a trend of longer PFS (13.8 vs 4.9 months, p = 0.07) than those non-responders (Fig. 3 A, B). According to the etiologies, patients with hepatitis B virus (HBV) infection showed no significant difference in PFS and OS when comparing with those in non-HBV patients (Fig. 3 C, D). ALBI grades and CPS classes showed distinct prognostic impacts, with significant differences observed in survival outcomes. Patients across ALBI grades 1, 2, and 3 experienced varying PFS (7.0 vs. 5.4 vs. 1.6 months, p < 0.05) and OS (23.0 vs. 8.5 vs. 1.6 months, p < 0.05), highlighting ALBI grade as a critical prognostic indicator. While CPS class A patients demonstrated better OS compared to classes B and C (12.7 vs 8.1 vs 3.7 months, p < 0.05), the difference in PFS among CPS classes was not statistically significant (5.8 vs 3.9 vs 1.9 months, p = 0.09). The median PFS and OS of different combined regimens with durvalumab were shown in supplementary Fig. 2. Prognostic factors for progression and death Univariate analysis identified macrovascular invasion, intrahepatic tumor numbers > 10, Alpha–fetoprotein (AFP) levels > 400 ng/mL, and CPS classes B and C as risk factors for progression, while a prior response to anti-PD-1 therapy was protective. However, in the multivariate analysis, none of these factors remained as independent predictors of risk (Table 3 ). Regarding risk factors for death, multivariate analysis indicated that age > 60 years [Hazard ratio (HR): 2.55] and intrahepatic tumor numbers > 10 (HR: 4.06) were independent poor risk factors, while a prior response to anti-PD-1 therapy (HR: 0.31) was the only independent protective factor (Table 4 ). Table 3 Risk factors for progression Variables Univariate Multivariate General factors HR (95% CI) p value HR (95% CI) p value Age > 60 vs. ≤ 60 1.48 (0.82–2.66) 0.19 Sex Male vs. female 1.09 (0.58–2.03) 0.79 HBsAg-positive Yes vs. no 0.78 (0.42–1.44) 0.43 Anti-HCV-positive Yes vs. no 0.63 (0.15–2.62) 0.52 Radiological factors Cirrhosis Yes vs. no 1.17 (0.63–1.99) 0.71 Ascites Yes vs. no 1.25 (0.70–2.22) 0.45 Macrovascular invasion Yes vs. no 1.75 (0.98–3.11) 0.06 1.07 (0.45–2.51) 0.88 Metastasis Yes vs. no 0.88 (0.48–1.61) 0.68 Intrahepatic tumor numbers > 10 vs. ≤10 1.90 (1.04–3.47) 0.04 1.42 (0.72–2.80) 0.32 Intrahepatic tumor size (cm) > 10 vs. ≤10 1.45 (0.77–2.70) 0.25 Lab data ALT (U/L) > 40 vs. ≤ 40 1.54 (0.88–2.71) 0.13 ALBI grade Grade 2, 3 vs. 1 1.49 (0.81–2.73) 0.20 CPS class Class B, C vs. A 1.83 (0.91–3.67) 0.09 1.62 (0.69–3.79) 0.27 AFP (ng/mL) > 400 vs ≤ 400 2.27 (1.20–4.28) 0.01 1.83 (0.84–3.96) 0.13 Prior anti-PD-1 response Responder vs. non-responder 0.53 (0.26–1.07) 0.08 0.65 (0.29–1.47) 0.30 ALT: Alanine Aminotransferase; ALBI grade: Albumin-Bilirubin grade; CPS class: Child-Pugh Score class; AFP: Alpha-Fetoprotein Table 4 Risk factors for death Variables Univariate Multivariate General factors HR (95% CI) p value HR (95% CI) p value Age > 60 vs. ≤ 60 2.18 (1.17–4.07) 0.01 2.55 (1.10–5.95) 0.03 Sex Male vs. female 1.37 (0.68–2.77) 0.38 HBsAg-positive Yes vs. no 0.80 (0.42–1.55) 0.51 Anti-HCV-positive Yes vs. no 0.43 (0.06–3.17) 0.41 Radiological factors Cirrhosis Yes vs. no 1.32 (0.72–2.40) 0.37 Ascites Yes vs. no 1.56 (0.85–2.86) 0.15 Macrovascular invasion Yes vs. no 2.30 (1.24–4.25) 0.01 1.93 (0.76–4.93) 0.17 Metastasis Yes vs. no 1.15 (0.59–2.24) 0.68 Intrahepatic tumor numbers > 10 vs. ≤10 3.56 (1.92–6.63) < 0.01 4.06 (1.87–8.78) 10 vs. ≤10 1.39 (0.74–2.62) 0.31 Lab data ALT (U/L) > 40 vs. ≤ 40 1.29 (0.70–2.37) 0.41 ALBI grade Grade 2, 3 vs. 1 2.18 (1.12–4.21) 0.02 CPS class Class B, C vs. A 2.36 (1.16–4.78) 0.02 *2.09 (0.89–4.88) 0.09 AFP (ng/mL) > 400 vs ≤ 400 1.91 (0.98–3.71) 0.06 1.48 (0.66–3.31) 0.34 Prior anti-PD-1 response Responder vs. non-responder 0.33 (0.14–0.79) 0.01 0.31 (0.10–0.99) 0.04 ALT: Alanine Aminotransferase; ALBI grade: Albumin-Bilirubin grade; CPS class: Child-Pugh Score class; AFP: Alpha-Fetoprotein * Given the interaction between CPS class and ALBI grade, only CPS class was included in the multivariate analysis. Immune-related adverse event In this cohort, the most common irAEs included skin toxicity (13.8%) and hepatitis (7.7%), with other irAEs such as hypothyroidism, fever, pancreatitis, pneumonitis, encephalopathy, diarrhea, diabetes, and myalgia occurring at an incidence rate of less than 5% (Supplementary table 2 ). No correlation was observed between the occurrence of high-grade irAEs and prior anti-PD-1 treatment with subsequent durvalumab therapy (Supplementary table 3 ). Similarly, no association was found for irAEs of any grade between prior anti-PD-1 and durvalumab treatment (Supplementary table 4 ). Discussion The major findings of this study include: 1) Durvalumab is effective in anti-PD-1 refractory HCC; 2) A prior response to anti-PD-1 predicts the response and outcomes to durvalumab; 3) The irAEs from prior anti-PD-1 therapy showed no association with subsequent irAEs from durvalumab. In HCC, the evidence supporting the strategy of rechallenging with a second ICI in patients who were previously refractory to an initial ICI is limited. Wong et al. reported a retrospective cohort study involving 25 HCC patients who had progressed on anti-PD-(L)1 therapy and were subsequently treated with a combination of ipilimumab and anti-PD-1 (nivolumab or pembrolizumab), demonstrating an ORR of 16%. This study found no significant difference in prognosis between cases of primary and acquired resistance to prior anti-PD-(L)1 treatment, marking it as the first to show that combining anti-CTLA-4 with anti-PD-1 could overcome resistance to prior anti-PD-(L)1 treatments.[ 12 ] Similarly, Alden et al. reported a retrospective study delivering salvage therapy with ipilimumab plus nivolumab to 32 patients who had progressed on prior anti-PD-(L)1 treatment, which showed an ORR of 22%.[ 13 ] Furthermore, Scheiner et al. described an international, retrospective multicenter study involving 58 HCC patients who received at least two lines of ICI-based therapies at 14 institutions. The therapies included ICI monotherapy, dual ICI regimens, and ICI combined with targeted therapies or anti-vascular endothelial growth factor (anti-VEGF) agents. The ORR for second ICI-based therapies reached up to 26%, despite the heterogeneity of the regimens used.[ 14 ] In summary, in HCC patients resistant to prior anti-PD-(L)1 treatments, rechallenge with dual immunotherapy comprising anti-PD-1 and anti-CTLA-4 has shown some evidence of antitumor efficacy. However, rechallenge with different anti-PD-(L)1 agents still lacks definitive data. As for rechallenging with a second ICI in other cancer types, data remain sparse and the patient contexts are typically heterogeneous. Fujita et al. retrospectively analyzed the limited efficacy of salvage anti-PD-L1 with atezolizumab in 18 non-small lung cancer (NSCLC) patients previously treated with anti-PD-1 (nivolumab, pembrolizumab), noting that none achieved tumor shrinkage.[ 15 ] Similarly, Kitagawa et al. reported an ORR of 5.9% among 17 NSCLC patients undergoing interchange between anti-PD-1 (nivolumab and pembrolizumab) and anti-PD-L1 (atezolizumab) as rechallenge therapy, where 41.2% of cases showed PD-L1 expression greater than 1%.[ 16 ] Furthermore, a meta-analysis incorporating 18 studies of NSCLC involving patients previously treated with anti-PD-(L)1 with or without anti-CTLA-4, and subsequently rechallenged with anti-PD-(L)1 after disease progression, demonstrated a pooled ORR of 8%, indicating a modest benefit.[ 17 ] In metastatic renal cell carcinoma, another meta-analysis that included 10 studies from prospective, retrospective, or ambispective designs revealed therapeutic benefits from reintroducing second-line ICI-containing treatments in patients previously treated with ICIs, with a pooled ORR of 19%.[ 18 ] In summary, evidence regarding the interchange between anti-PD-1 and anti-PD-L1 is limited and the available data appear controversial. The mechanisms underlying primary and acquired resistance to ICIs are complex, and overcoming this resistance remains a significant challenge. Any disruption in the key steps of the immune response against cancer diminishes the effectiveness of ICIs. These key processes include the release of neoantigens from cancer cells, their capture and presentation by antigen-presenting cells, the recognition of these neoantigens and subsequent activation of CD8 + cytotoxic T cells, and the interaction of these T cells with malignant cells.[ 19 – 21 ] Among the critical elements of the immune mechanism, the upregulation of immune checkpoints such as CTLA-4, PD-(L)1, lymphocyte activation gene 3 (LAG-3), T cell immunoglobulin and mucin domain 3 (TIM-3), and V-domain Ig suppressor of T cell activation (VISTA) can inhibit interactions between T cells and malignant cells, contributing to the loss of T cell effector function[ 22 ] Theoretically, when cancer becomes refractory to one type of ICI, other ICIs may still be effective. Clinical data reviewed earlier demonstrate that anti-CTLA-4 can effectively treat PD-(L)1 refractory cancers. Additionally, relatlimab, a novel ICI targeting the LAG-3, was shown to have an ORR of 12.0% when combined with nivolumab in melanoma previously treated with anti-PD-(L)1, according to the phase I/IIa RELATIVITY-020 trial.[ 23 ] This trial offers a rationale for rechallenging with different ICI targets in resistant cases. Anti-PD-1 and anti-PD-L1 inhibitors target the same signaling pathway and are approved for many of the same cancer types, often leading to their consideration as equivalent therapies. However, anti-PD-1 inhibitors block signals through both PD-L1 and PD-L2 ligands, adding a layer of complexity to their mechanism of action.[ 24 ] In contrast, Linhares et al. demonstrated that anti-PD-L1 inhibitors have lower half maximal effective concentrations (EC50) and are more potent than anti-PD-1 inhibitors, as evidenced by functional assays conducted in vitro.[ 25 ] Therefore, despite their similarities, significant differences between these drugs exist. These differences may partially explain why durvalumab, a PD-L1 inhibitor, shows some therapeutic effects in patients with HCC previously refractory to anti-PD-1 therapy in our study. Our study is subject to several limitations. First, as a retrospective analysis, it may be affected by information bias and selection bias. However, in the absence of prospective clinical trials addressing this specific issue, retrospective analysis is the only feasible approach. Second, patient heterogeneity, particularly regarding the number of prior systemic treatments and combinations with different targeted therapies, might bias the analysis of therapeutic efficacy. Finally, our study focused on anti-PD-L1 rechallenge for anti-PD-1 refractory HCC; whether the reverse sequence of administration could offer benefits remains unknown. Conclusion This study provides the first evidence that anti-PD-L1 rechallenge is efficacious for anti-PD-1 refractory HCC, particularly in patients who previously responded to anti-PD-1 therapy, resulting in improved response and prognosis with durvalumab. It offers a rationale for using anti-PD-L1 rechallenge in clinical practice. Declarations Acknowledgment The authors would like to acknowledge the support provided by grants from Taipei Veterans General Hospital (VGHTPE V113C-148) and the Taiwan Clinical Oncology Research Foundation. Ethics approval This study has been approved by the institutional review board of Taipei Veterans General Hospital (TPEVGH IRB No.: 2024-06-018BC), which is the appropriate regulatory agency to review research on both adults and children. All methods were carried out in accordance with relevant guidelines and regulations. Conflict of interest statement The authors declare that they have no conflicts of interest to disclose regarding the publication of this article. Funding sources This work was supported by grants from Taipei Veterans General Hospital (VGHTPE V113C-148), Taiwan Clinical Oncology Research Foundation to San-Chi Chen. Author Contributions Kuan-Chang Lai, San-Chi Chen participated in study conception, study design, data collection, data analysis and interpretation, and drafting of the manuscript. Yen-Hao Chen, Yi‑Ping Hung, Nai-Jung Chiang, Ming‑Huang Chen participated in data analysis and interpretation, and critical revision of the manuscript as the corresponding author. All authors approved the final manuscript for submission and take responsibility for the accuracy and integrity of the work. Clinical trial number NA. Data availability statement Data are available by requesting. References Singal AG, Kanwal F, Llovet JM. 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Zhu AX, Kang YK, Yen CJ, Finn RS, Galle PR, Llovet JM, et al. Ramucirumab after sorafenib in patients with advanced hepatocellular carcinoma and increased alpha-fetoprotein concentrations (REACH-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2019;20(2):282–96. Zhu AX, Finn RS, Edeline J, Cattan S, Ogasawara S, Palmer D, et al. Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial. Lancet Oncol. 2018;19(7):940–52. Yau T, Kang YK, Kim TY, El-Khoueiry AB, Santoro A, Sangro B, et al. Efficacy and Safety of Nivolumab Plus Ipilimumab in Patients With Advanced Hepatocellular Carcinoma Previously Treated With Sorafenib: The CheckMate 040 Randomized Clinical Trial. JAMA Oncol. 2020;6(11):e204564. Finn RS, Qin S, Ikeda M, Galle PR, Ducreux M, Kim TY, et al. Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinoma. N Engl J Med. 2020;382(20):1894–905. Abou-Alfa GK, Lau G, Kudo M, Chan SL, Kelley RK, Furuse J, et al. Tremelimumab plus Durvalumab in Unresectable Hepatocellular Carcinoma. NEJM Evid. 2022;1(8):EVIDoa2100070. Singal AG, Llovet JM, Yarchoan M, Mehta N, Heimbach JK, Dawson LA, et al. AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology. 2023;78(6):1922–65. Wong JSL, Kwok GGW, Tang V, Li BCW, Leung R, Chiu J et al. Ipilimumab and nivolumab/pembrolizumab in advanced hepatocellular carcinoma refractory to prior immune checkpoint inhibitors. J Immunother Cancer. 2021;9(2). Alden SL, Lim M, Kao C, Shu D, Singal AG, Noonan A, et al. Salvage Ipilimumab plus Nivolumab after Anti-PD-1/PD-L1 Therapy in Advanced Hepatocellular Carcinoma. Cancer Res Commun. 2023;3(7):1312–17. Scheiner B, Roessler D, Phen S, Lim M, Pomej K, Pressiani T, et al. Efficacy and safety of immune checkpoint inhibitor rechallenge in individuals with hepatocellular carcinoma. JHEP Rep. 2023;5(1):100620. Fujita K, Uchida N, Yamamoto Y, Kanai O, Okamura M, Nakatani K, et al. Retreatment With Anti-PD-L1 Antibody in Advanced Non-small Cell Lung Cancer Previously Treated With Anti-PD-1 Antibodies. Anticancer Res. 2019;39(7):3917–21. Kitagawa S, Hakozaki T, Kitadai R, Hosomi Y. Switching administration of anti-PD-1 and anti-PD-L1 antibodies as immune checkpoint inhibitor rechallenge in individuals with advanced non-small cell lung cancer: Case series and literature review. Thorac Cancer. 2020;11(7):1927–33. Cai Z, Zhan P, Song Y, Liu H, Lv T. Safety and efficacy of retreatment with immune checkpoint inhibitors in non-small cell lung cancer: a systematic review and meta-analysis. Transl Lung Cancer Res. 2022;11(8):1555–66. Papathanassiou M, Tamposis I, Exarchou-Kouveli KK, Kontou PI, de Paz AT, Mitrakas L, et al. Immune-based treatment re-challenge in renal cell carcinoma: A systematic review and meta-analysis. Front Oncol. 2022;12:996553. Jenkins RW, Barbie DA, Flaherty KT. Mechanisms of resistance to immune checkpoint inhibitors. Br J Cancer. 2018;118(1):9–16. Schoenfeld AJ, Hellmann MD. Acquired Resistance to Immune Checkpoint Inhibitors. Cancer Cell. 2020;37(4):443–55. Zhou B, Gao Y, Zhang P, Chu Q. Acquired Resistance to Immune Checkpoint Blockades: The Underlying Mechanisms and Potential Strategies. Front Immunol. 2021;12:693609. Fares CM, Van Allen EM, Drake CG, Allison JP, Hu-Lieskovan S. Mechanisms of Resistance to Immune Checkpoint Blockade: Why Does Checkpoint Inhibitor Immunotherapy Not Work for All Patients? Am Soc Clin Oncol Educ Book. 2019;39:147–64. Ascierto PA, Lipson EJ, Dummer R, Larkin J, Long GV, Sanborn RE, et al. Nivolumab and Relatlimab in Patients With Advanced Melanoma That Had Progressed on Anti-Programmed Death-1/Programmed Death Ligand 1 Therapy: Results From the Phase I/IIa RELATIVITY-020 Trial. J Clin Oncol. 2023;41(15):2724–35. Bardhan K, Anagnostou T, Boussiotis VA. The PD1:PD-L1/2 Pathway from Discovery to Clinical Implementation. Front Immunol. 2016;7:550. De Sousa Linhares A, Battin C, Jutz S, Leitner J, Hafner C, Tobias J, et al. Therapeutic PD-L1 antibodies are more effective than PD-1 antibodies in blocking PD-1/PD-L1 signaling. Sci Rep. 2019;9(1):11472. Supplementary Files Supp.Figure1Casepresentation.tif Supp.Figure2PFSandOSbyregimens.tif SupplementalTable.docx Cite Share Download PDF Status: Published Journal Publication published 23 Nov, 2024 Read the published version in Hepatology International → Version 1 posted Editorial decision: Major Revisions Needed 19 Jul, 2024 Reviewers agreed at journal 07 Jul, 2024 Reviewers invited by journal 06 Jul, 2024 Editor assigned by journal 30 Jun, 2024 First submitted to journal 29 Jun, 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-4659138","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323614691,"identity":"ba5c347f-85c0-4572-883a-ff4a47387be2","order_by":0,"name":"Kuan-Chang Lai","email":"","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kuan-Chang","middleName":"","lastName":"Lai","suffix":""},{"id":323614692,"identity":"c33e27bb-2a16-46b9-984a-cc1b89bf363a","order_by":1,"name":"Yen-Hao Chen","email":"","orcid":"","institution":"Chang Gung Memorial Hospital Kaohsiung Branch","correspondingAuthor":false,"prefix":"","firstName":"Yen-Hao","middleName":"","lastName":"Chen","suffix":""},{"id":323614693,"identity":"90e6fb84-0316-4f47-8f8a-f91e1d5ba789","order_by":2,"name":"Yi-Ping Hung","email":"","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi-Ping","middleName":"","lastName":"Hung","suffix":""},{"id":323614694,"identity":"09d5c461-a49e-4e27-878e-86905b30fb12","order_by":3,"name":"Nai-Jung Chiang","email":"","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nai-Jung","middleName":"","lastName":"Chiang","suffix":""},{"id":323614695,"identity":"0f68f7a4-e59c-4105-99dc-1f913136fd0b","order_by":4,"name":"Ming-Huang Chen","email":"","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ming-Huang","middleName":"","lastName":"Chen","suffix":""},{"id":323614696,"identity":"472d75fa-b1aa-401a-83e6-25ea0f330237","order_by":5,"name":"SAN-CHI CHEN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYHACZoYEECXBwMD48Z8NkMXYeIAILQZgLcwSbGkgLQ2EtTBAtTDwsB0GC+HVIj8j97DBgz9/GPhnNx97IMFz3m5t+2GgLTU20bi0GNzIS05IbDNgkLhzLN2gQOJ28rYziUAtx9JyG3BpkcgxPpDYYABimElIGNxONgNyDzA2HMapRX4GUEvCH5CW/G8SPAnnks3OP8SvheFGjnFCAhvYFjYJngMH7MxuELDF4MwbY4PENmMeiRtpZtKSDckJZjeAtiTg8Yt8e46x5I8/cnL8M5KfSX5ssLM3O5/+8MGHGhvcDoMCHhgjEawygYByFGBPiuJRMApGwSgYGQAAGCldWGkGHSEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9436-5400","institution":"Taipei Veterans General Hospital","correspondingAuthor":true,"prefix":"","firstName":"SAN-CHI","middleName":"","lastName":"CHEN","suffix":""}],"badges":[],"createdAt":"2024-06-29 11:42:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4659138/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4659138/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12072-024-10728-9","type":"published","date":"2024-11-23T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62133511,"identity":"196b35ad-ea4b-4ddd-95fe-f01169be9a76","added_by":"auto","created_at":"2024-08-09 15:58:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":626217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWaterfall plot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe waterfall plot illustrates the greatest percentage change in the sum of the longest diameters of target lesions according to RECIST criteria. (Blue represents HBV, Red denotes HCV, and Green indicates non-viral cases.)\u003c/p\u003e","description":"","filename":"Figure1.Waterfallplots.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4659138/v1/cbeae1df0c5689f3b8f91f8c.jpg"},{"id":62133509,"identity":"9b2ba222-5127-4679-8f09-c9bdb16cd4a0","added_by":"auto","created_at":"2024-08-09 15:58:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSwimmers’ plot of prior anti-PD-1 and durvalumab treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSwimmers’ plot depicts PFS of prior anti-PD-1 treatment and durvalumab by each case. The outcomes under durvalumab therapy are also shown.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4659138/v1/1e5c6d5320fcd83cb91b0639.jpg"},{"id":62134580,"identity":"83fc27c4-edd1-4620-ae0f-36522c554060","added_by":"auto","created_at":"2024-08-09 16:06:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":910655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProgression-free survival and overall survival\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe log-rank test indicated that patients who previously responded to anti-PD-1 therapy showed (A) a trend towards longer PFS (13.8 vs. 4.9 months, p = 0.07) and (B) significantly longer OS (33.9 vs. 8.2 months, p \u0026lt; 0.01) compared to non-responders. There was no significant difference in (C) PFS and (D) OS between patients with HBV and those without HBV. The difference in (E) PFS among different Child-Pugh classes was not significant, but was significant in (F) OS. On the other hand, different ALBI grades demonstrated significant differences in both (G) PFS and (H) OS.\u003c/p\u003e","description":"","filename":"Figure3.PFSandOSbyfactors.png","url":"https://assets-eu.researchsquare.com/files/rs-4659138/v1/27092b8337b1390849c2bf52.png"},{"id":69834893,"identity":"53fde77b-5c90-419e-9648-d4bfd5b8bd23","added_by":"auto","created_at":"2024-11-25 16:10:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2488566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659138/v1/ab9e3344-748c-4846-ae12-fbb3471b5646.pdf"},{"id":62134578,"identity":"506a580b-9b23-4dd5-9c92-014a2f89acd8","added_by":"auto","created_at":"2024-08-09 16:06:45","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":5399940,"visible":true,"origin":"","legend":"","description":"","filename":"Supp.Figure1Casepresentation.tif","url":"https://assets-eu.researchsquare.com/files/rs-4659138/v1/011602951c20ac2f72840b4b.tif"},{"id":62133515,"identity":"e0e45c6c-6663-49be-91ea-01ef228fa8ab","added_by":"auto","created_at":"2024-08-09 15:58:45","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":703204,"visible":true,"origin":"","legend":"","description":"","filename":"Supp.Figure2PFSandOSbyregimens.tif","url":"https://assets-eu.researchsquare.com/files/rs-4659138/v1/d6acb5c894d70491d6535664.tif"},{"id":62133513,"identity":"4310661e-e5b3-41fb-9303-68be05244ae6","added_by":"auto","created_at":"2024-08-09 15:58:45","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":24914,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4659138/v1/c0e813a660f3fa6e219e783d.docx"}],"financialInterests":"","formattedTitle":"Efficacy and Safety of Durvalumab Rechallenge in Advanced Hepatocellular Carcinoma Patients Refractory to Prior Anti-PD-1 Therapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC), the most common primary liver cancer, ranks as the sixth most prevalent cancer worldwide.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] It was responsible for over 900,000 new cases and more than 800,000 deaths in 2020.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Historically, treatment options for HCC were limited to the targeted therapy sorafenib[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and, despite the development of second-line agents such as regorafenib[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], cabozantinib[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and ramucirumab[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the prognosis for advanced HCC remains poor.\u003c/p\u003e \u003cp\u003eRecent advancements in immune checkpoint inhibitors (ICIs), including anti-cytotoxic T lymphocyte\u0026ndash;associated antigen 4 (anti-CTLA-4) and anti-programmed cell death- (ligand)1 (anti-PD-(L)1), have become the standard treatment, improving outcomes for various cancers, including HCC. Notably, pembrolizumab and the combination of nivolumab with ipilimumab were approved in 2018 and 2020, respectively, for second-line treatment of HCC due to their breakthrough responses. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Later, combination therapies such as atezolizumab (anti-PD-L1) with bevacizumab[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and dual immunotherapy with durvalumab (anti-PD-L1) and tremelimumab (anti-CTLA-4)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] were also validated for first-line treatment in HCC.\u003c/p\u003e \u003cp\u003eA clinical challenge emerges when patients do not respond to initial anti-PD-1 therapies, such as nivolumab or pembrolizumab; the effectiveness of subsequent treatments with newer combinations like atezolizumab/bevacizumab or durvalumab/tremelimumab, both anti-PD-L1-based therapies, is uncertain. Conversely, with anti-PD-L1 combinations now established as standard first-line therapy, the efficacy of subsequent anti-PD-1 therapy following failure of these initial treatments also remains uncertain. When first ICI therapy proves effective, it may suggest an inflamed tumor microenvironment conducive to successful rechallenging with another ICI. However, acquired resistance due to immune escape could potentially undermine the effectiveness of rechallenges. The answers to these queries are crucial yet currently unresolved.\u003c/p\u003e \u003cp\u003eGiven the earlier approvals of nivolumab and pembrolizumab for HCC, we collected data from patients who had previously received anti-PD-1 therapy to explore the efficacy of subsequent anti-PD-L1 treatment. Rechallenging was limited to patients using durvalumab to specifically assess the effectiveness and side effects of anti-PD-L1 rechallenge. This design aims to elucidate the potential benefits and risks associated with anti-PD-L1 rechallenge in this patient population.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003eThis study was a retrospective collection of clinical, pathological and image information retrieved from electronic medical records. It included a total of 65 HCC patients from Taipei Veterans General Hospital (Taipei, Taiwan), from January 2016 to December 2023. The criteria for patient enrollment included aged 18 years or older at the time of diagnosis; cases of advanced HCC, particularly among patients who had experienced anti-PD-1 immunotherapy, and subsequently received anti-PD-L1 with durvalumab treatment (480 mg intravenously, every two weeks). The diagnosis of HCC was established either through histopathological confirmation or clinical interpretation, following the criteria set by the American Association for the Study of Liver Diseases (AASLD).[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] All the detailed information was available, including age, gender, etiologies, biochemistry and hematology data, underlying comorbidities, tumor marker, tumor stage, comprehensive treatment information, survival time following diagnosis, and cause of death were collected. The presence of underlying cirrhosis was confirmed either through histological examination or by clinical and/or imaging evidence, including ascites, varices, splenomegaly, and thrombosis in the hepatic or portal veins. This study was approved by the Institutional Review Board of Taipei Veterans General Hospital. (TPEVGH IRB No.: 2024-06-018BC)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Assessment\u003c/h2\u003e \u003cp\u003eImmune-related adverse events (irAEs) were assessed based on main organ involvement and grading by Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Two independent investigators evaluated the treatment outcomes, categorizing them into complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD), based on RECIST and modified RECIST (Response Evaluation Criteria in Solid Tumors, version 1.1) criteria. We classified overall response rate (ORR, proportion of patients with a best overall response of partial or complete response) by using RECIST version 1.1 version criteria by specialist review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe patient\u0026rsquo;s baseline characteristics are presented by calculating the arithmetic mean, standard deviation, median, and interquartile range. The associations between durvalumab treatment responses across groups were analyzed using the Fisher exact test and chi-square (χ2) test. The Kaplan\u0026ndash;Meier method was used to analyze OS and PFS. Survival curves were evaluated for differences using the log-rank test. A Cox proportional-hazards model was utilized both to calculate hazard ratios with 95% confidence intervals and to evaluate the effects of prognostic factors on progression and death in univariate and multivariate analyses. We selected relevant factors or variables with \u003cem\u003ep\u003c/em\u003e value less than 0.1 in the univariate analysis for the multivariate model. All the tests were two-sided, and a \u003cem\u003ep\u003c/em\u003e value of less than 0.05 was considered to indicate statistical significance. All the statistical analyses were performed by IBM\u0026reg; SPSS\u0026reg;, version 21.0 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 65 patients were diagnosed with HCC who received durvalumab during the study period between January 2016 to December 2023. Among them, forty-eight patients were male. The mean age was 60.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6; 43.1% (28/65) had HBsAg positive, 3.1% (2/65) had positive anti-HCV antibody. Approximately 76.7% of the patients were classified as Child-Pugh score (CPS) A, and 18.3% fell into the Child-Pugh score B category. Albumin-bilirubin (ALBI) grade 1 accounted for 39.1% of patients and ALBI grade 2 for 56.3%. Furthermore, a total 27 patients (41.5%) were diagnosed to have cirrhosis, 28 patients (43.1%) have portal vein thrombosis and 69.2% already had extrahepatic metastasis. Regard to prior systemic treatment, 84.6% of patients has been experienced targeted therapy with lenvatinib, and 86.2% received anti-PD-1 therapy with nivolumab. Moreover, regarding the combination regimen with durvalumab, 63.1% of patients underwent combination treatment with lenvatinib and 16.9% of patients received regorafenib. The median duration between the administration of prior anti-PD-1 and durvalumab was 6 months (interquartile range: 3.0-14.5 months). The detailed demographic and clinical characteristics were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase numbers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Mean+/- SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.5 +/-11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM/F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.8%/26.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBsAg Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-HCV Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(109.5-220.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(28.0-54.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.5-4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.5-1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (second)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(11.0-12.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e690.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(22.8-7563.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIVKA-II (mAL/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4707.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(577.5-34639.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPS score class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiological factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild/moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\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\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntra-hepatic tumor numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfiltrating type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntra-hepatic tumor size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfiltrating type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrovascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtrahepatic metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLC stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB/C/D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14/48/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.5%/73.8%/4.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLines of prior systemic therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced targeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLenvatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCabozantinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced anti-PD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNivolumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined drugs with durvalumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLenvatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCabozantinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRamucirumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBevacizumab\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\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\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\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eIQR\u0026thinsp;=\u0026thinsp;interquartile range; ALT: Alanine Aminotransferase; ALB: Albumin; PT: Prothrombin Time; AFP: Alpha-Fetoprotein; PIVKA-II: Protein Induced by Vitamin K Absence or Antagonist-II; CPS score class: Child-Pugh Score class; ALBI grade: Albumin-Bilirubin grade; BCLC stage: Barcelona Clinic Liver Cancer stage\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\u003eEfficacy and associated factors\u003c/h2\u003e \u003cp\u003eThe ORR to durvalumab was 13.8%, and disease control rate (DCR) was 70.8%, comprising a CR in 4.6%, a PR in 9.2%, and SD in 56.9%. The waterfall plot categorized by etiologies was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients who responded to prior anti-PD-1 therapy had a higher ORR compared to non-responders (31.3% vs. 8.7%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). In addition, patients experiencing any grade of irAEs with durvalumab was associated with a higher ORR compared to those without irAEs (35.3% vs. 6.7%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). However, factors such as sex, etiologies, liver function, tumor size and intrahepatic tumor numbers were not associated with the response (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The ORR of different combined regimens with durvalumab were shown in supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We present a case in which the patient initially achieved a partial response with combined nivolumab and lenvatinib therapy, subsequently exhibited pulmonary metastasis progression with intrahepatic tumors remained stable. Then, the pulmonary metastasis experienced tumor regression following the substitution of nivolumab with durvalumab (Supplementary Fig.\u0026nbsp;1).\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\u003eTreatment response with durvalumab by RECIST 1.1 criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eORR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\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\u003eAll population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBsAg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-HCV antibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPS classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIBI Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrahepatic tumor number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfiltrating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrahepatic tumor size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfiltrating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior anti-PD-1 response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-responder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eirAE of durvalumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eProgression-free survival and overall survival\u003c/h2\u003e \u003cp\u003eThe median PFS of total population was 5.4 months [95% confidence interval (CI): 3.1\u0026ndash;7.7], and the median OS 9.6 months [95% CI: 5.5\u0026ndash;13.3]. The outcomes under durvalumab therapy and prior anti-PD-1 treatment were shown in swimmers\u0026rsquo; plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By prior anti-PD-1 response, the responders demonstrated longer OS (33.9 vs. 8.2 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and a trend of longer PFS (13.8 vs 4.9 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07) than those non-responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). According to the etiologies, patients with hepatitis B virus (HBV) infection showed no significant difference in PFS and OS when comparing with those in non-HBV patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). ALBI grades and CPS classes showed distinct prognostic impacts, with significant differences observed in survival outcomes. Patients across ALBI grades 1, 2, and 3 experienced varying PFS (7.0 vs. 5.4 vs. 1.6 months, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and OS (23.0 vs. 8.5 vs. 1.6 months, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), highlighting ALBI grade as a critical prognostic indicator. While CPS class A patients demonstrated better OS compared to classes B and C (12.7 vs 8.1 vs 3.7 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the difference in PFS among CPS classes was not statistically significant (5.8 vs 3.9 vs 1.9 months, p\u0026thinsp;=\u0026thinsp;0.09). The median PFS and OS of different combined regimens with durvalumab were shown in supplementary Fig.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic factors for progression and death\u003c/h2\u003e \u003cp\u003eUnivariate analysis identified macrovascular invasion, intrahepatic tumor numbers\u0026thinsp;\u0026gt;\u0026thinsp;10, Alpha\u0026ndash;fetoprotein (AFP) levels\u0026thinsp;\u0026gt;\u0026thinsp;400 ng/mL, and CPS classes B and C as risk factors for progression, while a prior response to anti-PD-1 therapy was protective. However, in the multivariate analysis, none of these factors remained as independent predictors of risk (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Regarding risk factors for death, multivariate analysis indicated that age\u0026thinsp;\u0026gt;\u0026thinsp;60 years [Hazard ratio (HR): 2.55] and intrahepatic tumor numbers\u0026thinsp;\u0026gt;\u0026thinsp;10 (HR: 4.06) were independent poor risk factors, while a prior response to anti-PD-1 therapy (HR: 0.31) was the only independent protective factor (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eRisk factors for progression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 vs. \u0026le; 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.82\u0026ndash;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\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 vs. female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.58\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBsAg-positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.42\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-HCV-positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.15\u0026ndash;2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiological factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.63\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.70\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrovascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.98\u0026ndash;3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.45\u0026ndash;2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.48\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrahepatic tumor numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 vs. \u0026le;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.04\u0026ndash;3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.72\u0026ndash;2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrahepatic tumor size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 vs. \u0026le;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.77\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLab data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40 vs. \u0026le; 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.88\u0026ndash;2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 2, 3 vs. 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.81\u0026ndash;2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPS class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass B, C vs. A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.91\u0026ndash;3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.69\u0026ndash;3.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;400 vs\u0026thinsp;\u0026le;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.20\u0026ndash;4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.84\u0026ndash;3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior anti-PD-1 response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponder vs. non-responder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.26\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.29\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eALT: Alanine Aminotransferase; ALBI grade: Albumin-Bilirubin grade; CPS class: Child-Pugh Score class; AFP: Alpha-Fetoprotein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factors for death\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 vs. \u0026le; 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.17\u0026ndash;4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.10\u0026ndash;5.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.03\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 vs. female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.68\u0026ndash;2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBsAg-positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.42\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-HCV-positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiological factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.72\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.85\u0026ndash;2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrovascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.24\u0026ndash;4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.76\u0026ndash;4.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.59\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrahepatic tumor numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 vs. \u0026le;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.92\u0026ndash;6.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.87\u0026ndash;8.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrahepatic tumor size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 vs. \u0026le;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.74\u0026ndash;2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLab data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40 vs. \u0026le; 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.70\u0026ndash;2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 2, 3 vs. 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.12\u0026ndash;4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPS class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass B, C vs. A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.16\u0026ndash;4.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.89\u0026ndash;4.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;400 vs\u0026thinsp;\u0026le;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.98\u0026ndash;3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.66\u0026ndash;3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior anti-PD-1 response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponder vs. non-responder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.14\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.10\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eALT: Alanine Aminotransferase; ALBI grade: Albumin-Bilirubin grade; CPS class: Child-Pugh Score class; AFP: Alpha-Fetoprotein\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e* Given the interaction between CPS class and ALBI grade, only CPS class was included in the multivariate analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmune-related adverse event\u003c/h2\u003e \u003cp\u003eIn this cohort, the most common irAEs included skin toxicity (13.8%) and hepatitis (7.7%), with other irAEs such as hypothyroidism, fever, pancreatitis, pneumonitis, encephalopathy, diarrhea, diabetes, and myalgia occurring at an incidence rate of less than 5% (Supplementary table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No correlation was observed between the occurrence of high-grade irAEs and prior anti-PD-1 treatment with subsequent durvalumab therapy (Supplementary table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, no association was found for irAEs of any grade between prior anti-PD-1 and durvalumab treatment (Supplementary table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe major findings of this study include: 1) Durvalumab is effective in anti-PD-1 refractory HCC; 2) A prior response to anti-PD-1 predicts the response and outcomes to durvalumab; 3) The irAEs from prior anti-PD-1 therapy showed no association with subsequent irAEs from durvalumab.\u003c/p\u003e \u003cp\u003eIn HCC, the evidence supporting the strategy of rechallenging with a second ICI in patients who were previously refractory to an initial ICI is limited. Wong et al. reported a retrospective cohort study involving 25 HCC patients who had progressed on anti-PD-(L)1 therapy and were subsequently treated with a combination of ipilimumab and anti-PD-1 (nivolumab or pembrolizumab), demonstrating an ORR of 16%. This study found no significant difference in prognosis between cases of primary and acquired resistance to prior anti-PD-(L)1 treatment, marking it as the first to show that combining anti-CTLA-4 with anti-PD-1 could overcome resistance to prior anti-PD-(L)1 treatments.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Similarly, Alden et al. reported a retrospective study delivering salvage therapy with ipilimumab plus nivolumab to 32 patients who had progressed on prior anti-PD-(L)1 treatment, which showed an ORR of 22%.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Furthermore, Scheiner et al. described an international, retrospective multicenter study involving 58 HCC patients who received at least two lines of ICI-based therapies at 14 institutions. The therapies included ICI monotherapy, dual ICI regimens, and ICI combined with targeted therapies or anti-vascular endothelial growth factor (anti-VEGF) agents. The ORR for second ICI-based therapies reached up to 26%, despite the heterogeneity of the regimens used.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] In summary, in HCC patients resistant to prior anti-PD-(L)1 treatments, rechallenge with dual immunotherapy comprising anti-PD-1 and anti-CTLA-4 has shown some evidence of antitumor efficacy. However, rechallenge with different anti-PD-(L)1 agents still lacks definitive data.\u003c/p\u003e \u003cp\u003eAs for rechallenging with a second ICI in other cancer types, data remain sparse and the patient contexts are typically heterogeneous. Fujita et al. retrospectively analyzed the limited efficacy of salvage anti-PD-L1 with atezolizumab in 18 non-small lung cancer (NSCLC) patients previously treated with anti-PD-1 (nivolumab, pembrolizumab), noting that none achieved tumor shrinkage.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Similarly, Kitagawa et al. reported an ORR of 5.9% among 17 NSCLC patients undergoing interchange between anti-PD-1 (nivolumab and pembrolizumab) and anti-PD-L1 (atezolizumab) as rechallenge therapy, where 41.2% of cases showed PD-L1 expression greater than 1%.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Furthermore, a meta-analysis incorporating 18 studies of NSCLC involving patients previously treated with anti-PD-(L)1 with or without anti-CTLA-4, and subsequently rechallenged with anti-PD-(L)1 after disease progression, demonstrated a pooled ORR of 8%, indicating a modest benefit.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] In metastatic renal cell carcinoma, another meta-analysis that included 10 studies from prospective, retrospective, or ambispective designs revealed therapeutic benefits from reintroducing second-line ICI-containing treatments in patients previously treated with ICIs, with a pooled ORR of 19%.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] In summary, evidence regarding the interchange between anti-PD-1 and anti-PD-L1 is limited and the available data appear controversial.\u003c/p\u003e \u003cp\u003eThe mechanisms underlying primary and acquired resistance to ICIs are complex, and overcoming this resistance remains a significant challenge. Any disruption in the key steps of the immune response against cancer diminishes the effectiveness of ICIs. These key processes include the release of neoantigens from cancer cells, their capture and presentation by antigen-presenting cells, the recognition of these neoantigens and subsequent activation of CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells, and the interaction of these T cells with malignant cells.[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Among the critical elements of the immune mechanism, the upregulation of immune checkpoints such as CTLA-4, PD-(L)1, lymphocyte activation gene 3 (LAG-3), T cell immunoglobulin and mucin domain 3 (TIM-3), and V-domain Ig suppressor of T cell activation (VISTA) can inhibit interactions between T cells and malignant cells, contributing to the loss of T cell effector function[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Theoretically, when cancer becomes refractory to one type of ICI, other ICIs may still be effective. Clinical data reviewed earlier demonstrate that anti-CTLA-4 can effectively treat PD-(L)1 refractory cancers. Additionally, relatlimab, a novel ICI targeting the LAG-3, was shown to have an ORR of 12.0% when combined with nivolumab in melanoma previously treated with anti-PD-(L)1, according to the phase I/IIa RELATIVITY-020 trial.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] This trial offers a rationale for rechallenging with different ICI targets in resistant cases.\u003c/p\u003e \u003cp\u003eAnti-PD-1 and anti-PD-L1 inhibitors target the same signaling pathway and are approved for many of the same cancer types, often leading to their consideration as equivalent therapies. However, anti-PD-1 inhibitors block signals through both PD-L1 and PD-L2 ligands, adding a layer of complexity to their mechanism of action.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] In contrast, Linhares et al. demonstrated that anti-PD-L1 inhibitors have lower half maximal effective concentrations (EC50) and are more potent than anti-PD-1 inhibitors, as evidenced by functional assays conducted in vitro.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Therefore, despite their similarities, significant differences between these drugs exist. These differences may partially explain why durvalumab, a PD-L1 inhibitor, shows some therapeutic effects in patients with HCC previously refractory to anti-PD-1 therapy in our study.\u003c/p\u003e \u003cp\u003eOur study is subject to several limitations. First, as a retrospective analysis, it may be affected by information bias and selection bias. However, in the absence of prospective clinical trials addressing this specific issue, retrospective analysis is the only feasible approach. Second, patient heterogeneity, particularly regarding the number of prior systemic treatments and combinations with different targeted therapies, might bias the analysis of therapeutic efficacy. Finally, our study focused on anti-PD-L1 rechallenge for anti-PD-1 refractory HCC; whether the reverse sequence of administration could offer benefits remains unknown.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first evidence that anti-PD-L1 rechallenge is efficacious for anti-PD-1 refractory HCC, particularly in patients who previously responded to anti-PD-1 therapy, resulting in improved response and prognosis with durvalumab. It offers a rationale for using anti-PD-L1 rechallenge in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the support provided by grants from Taipei Veterans General Hospital (VGHTPE V113C-148) and the Taiwan Clinical Oncology Research Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by the institutional review board of Taipei Veterans General Hospital (TPEVGH IRB No.: 2024-06-018BC), which is the appropriate regulatory agency to review research on both adults and children. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest to disclose regarding the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from Taipei Veterans General Hospital (VGHTPE V113C-148), Taiwan Clinical Oncology Research Foundation to San-Chi Chen.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKuan-Chang Lai, San-Chi Chen participated in study conception, study design, data collection, data analysis and interpretation, and drafting of the manuscript. Yen-Hao Chen, Yi‑Ping Hung, Nai-Jung Chiang, Ming‑Huang Chen participated in data analysis and interpretation, and critical revision of the manuscript as the corresponding author. All authors approved the final manuscript for submission and take responsibility for the accuracy and integrity of the work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available by requesting.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSingal AG, Kanwal F, Llovet JM. Global trends in hepatocellular carcinoma epidemiology: implications for screening, prevention and therapy. Nat Rev Clin Oncol. 2023;20(12):864\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc JF, et al. Sorafenib in advanced hepatocellular carcinoma. N Engl J Med. 2008;359(4):378\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruix J, Qin S, Merle P, Granito A, Huang YH, Bodoky G, et al. Regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment (RESORCE): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2017;389(10064):56\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbou-Alfa GK, Meyer T, Cheng AL, El-Khoueiry AB, Rimassa L, Ryoo BY, et al. Cabozantinib in Patients with Advanced and Progressing Hepatocellular Carcinoma. N Engl J Med. 2018;379(1):54\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu AX, Kang YK, Yen CJ, Finn RS, Galle PR, Llovet JM, et al. Ramucirumab after sorafenib in patients with advanced hepatocellular carcinoma and increased alpha-fetoprotein concentrations (REACH-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2019;20(2):282\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu AX, Finn RS, Edeline J, Cattan S, Ogasawara S, Palmer D, et al. Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial. Lancet Oncol. 2018;19(7):940\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYau T, Kang YK, Kim TY, El-Khoueiry AB, Santoro A, Sangro B, et al. Efficacy and Safety of Nivolumab Plus Ipilimumab in Patients With Advanced Hepatocellular Carcinoma Previously Treated With Sorafenib: The CheckMate 040 Randomized Clinical Trial. JAMA Oncol. 2020;6(11):e204564.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinn RS, Qin S, Ikeda M, Galle PR, Ducreux M, Kim TY, et al. Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinoma. N Engl J Med. 2020;382(20):1894\u0026ndash;905.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbou-Alfa GK, Lau G, Kudo M, Chan SL, Kelley RK, Furuse J, et al. Tremelimumab plus Durvalumab in Unresectable Hepatocellular Carcinoma. NEJM Evid. 2022;1(8):EVIDoa2100070.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingal AG, Llovet JM, Yarchoan M, Mehta N, Heimbach JK, Dawson LA, et al. AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology. 2023;78(6):1922\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong JSL, Kwok GGW, Tang V, Li BCW, Leung R, Chiu J et al. Ipilimumab and nivolumab/pembrolizumab in advanced hepatocellular carcinoma refractory to prior immune checkpoint inhibitors. J Immunother Cancer. 2021;9(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlden SL, Lim M, Kao C, Shu D, Singal AG, Noonan A, et al. Salvage Ipilimumab plus Nivolumab after Anti-PD-1/PD-L1 Therapy in Advanced Hepatocellular Carcinoma. Cancer Res Commun. 2023;3(7):1312\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheiner B, Roessler D, Phen S, Lim M, Pomej K, Pressiani T, et al. Efficacy and safety of immune checkpoint inhibitor rechallenge in individuals with hepatocellular carcinoma. JHEP Rep. 2023;5(1):100620.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujita K, Uchida N, Yamamoto Y, Kanai O, Okamura M, Nakatani K, et al. Retreatment With Anti-PD-L1 Antibody in Advanced Non-small Cell Lung Cancer Previously Treated With Anti-PD-1 Antibodies. Anticancer Res. 2019;39(7):3917\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitagawa S, Hakozaki T, Kitadai R, Hosomi Y. Switching administration of anti-PD-1 and anti-PD-L1 antibodies as immune checkpoint inhibitor rechallenge in individuals with advanced non-small cell lung cancer: Case series and literature review. Thorac Cancer. 2020;11(7):1927\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai Z, Zhan P, Song Y, Liu H, Lv T. Safety and efficacy of retreatment with immune checkpoint inhibitors in non-small cell lung cancer: a systematic review and meta-analysis. Transl Lung Cancer Res. 2022;11(8):1555\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapathanassiou M, Tamposis I, Exarchou-Kouveli KK, Kontou PI, de Paz AT, Mitrakas L, et al. Immune-based treatment re-challenge in renal cell carcinoma: A systematic review and meta-analysis. Front Oncol. 2022;12:996553.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJenkins RW, Barbie DA, Flaherty KT. Mechanisms of resistance to immune checkpoint inhibitors. Br J Cancer. 2018;118(1):9\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchoenfeld AJ, Hellmann MD. Acquired Resistance to Immune Checkpoint Inhibitors. Cancer Cell. 2020;37(4):443\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou B, Gao Y, Zhang P, Chu Q. Acquired Resistance to Immune Checkpoint Blockades: The Underlying Mechanisms and Potential Strategies. Front Immunol. 2021;12:693609.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFares CM, Van Allen EM, Drake CG, Allison JP, Hu-Lieskovan S. Mechanisms of Resistance to Immune Checkpoint Blockade: Why Does Checkpoint Inhibitor Immunotherapy Not Work for All Patients? Am Soc Clin Oncol Educ Book. 2019;39:147\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAscierto PA, Lipson EJ, Dummer R, Larkin J, Long GV, Sanborn RE, et al. Nivolumab and Relatlimab in Patients With Advanced Melanoma That Had Progressed on Anti-Programmed Death-1/Programmed Death Ligand 1 Therapy: Results From the Phase I/IIa RELATIVITY-020 Trial. J Clin Oncol. 2023;41(15):2724\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBardhan K, Anagnostou T, Boussiotis VA. The PD1:PD-L1/2 Pathway from Discovery to Clinical Implementation. Front Immunol. 2016;7:550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Sousa Linhares A, Battin C, Jutz S, Leitner J, Hafner C, Tobias J, et al. Therapeutic PD-L1 antibodies are more effective than PD-1 antibodies in blocking PD-1/PD-L1 signaling. Sci Rep. 2019;9(1):11472.\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":"hepatology-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hepi","sideBox":"Learn more about [Hepatology International](https://www.springer.com/journal/12072)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/hepi/default.aspx","title":"Hepatology International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hepatocellular carcinoma (HCC), immune checkpoint inhibitor (ICI), anti-programmed cell death protein-1 (anti-PD-1), anti-programmed cell death ligand-1 (anti-PD-L1), nivolumab, durvalumab, anti-PD-1 refractory, rechallenge, immune-related adverse event (irAE), predictor","lastPublishedDoi":"10.21203/rs.3.rs-4659138/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4659138/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground/Purpose:\u003c/h2\u003e \u003cp\u003eRecently, anti-programmed cell death protein-1 (anti-PD-1) and anti-PD-L1 therapies were approved for hepatocellular carcinoma (HCC). However, the effectiveness of rechallenging with one immune checkpoint inhibitor (ICI) after failure of another remains unclear. This study explores the efficacy and safety of anti-PD-L1 rechallenge in patients who failed anti-PD-1 therapy.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eFrom January 2016 to December 2023, 65 advanced HCC patients previously treated with anti-PD-1 therapy were retrospectively enrolled and rechallenged with durvalumab (480 mg IV every two weeks).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eOverall, 86.2% of patients received nivolumab and 13.8% pembrolizumab as prior anti-PD-1 therapy. The overall response rate (ORR) to durvalumab was 13.8%. Patients who responded to prior anti-PD-1 had a higher ORR compared to non-responders (31.3% vs. 8.7%, p\u0026thinsp;=\u0026thinsp;0.04). Patients with any grade of immune-related adverse events (irAEs) from durvalumab had a higher ORR than those without irAEs (35.3% vs. 6.7%, p\u0026thinsp;=\u0026thinsp;0.01). The median PFS was 5.4 months, and the median OS was 9.6 months. Responders to prior anti-PD-1 showed longer OS (33.9 vs. 8.2 months, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and a trend toward longer PFS (13.8 vs. 4.9 months, p\u0026thinsp;=\u0026thinsp;0.07) compared to non-responders. Multivariate analysis identified prior anti-PD-1 response (HR: 0.31) as the only protective factor for death. Common irAEs were skin toxicity (13.8%) and hepatitis (7.7%); no correlation was found between irAEs from prior anti-PD-1 and durvalumab treatment.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThis study provides the first, concrete evidence that durvalumab rechallenge is effective for HCC patients who are refractory to anti-PD-1 therapy, especially for those who previously responded to anti-PD-1 treatment.\u003c/p\u003e","manuscriptTitle":"Efficacy and Safety of Durvalumab Rechallenge in Advanced Hepatocellular Carcinoma Patients Refractory to Prior Anti-PD-1 Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 15:58:40","doi":"10.21203/rs.3.rs-4659138/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2024-07-19T19:45:12+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-07-07T05:35:09+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-07T03:56:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-30T07:24:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Hepatology International","date":"2024-06-29T07:42:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"hepatology-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hepi","sideBox":"Learn more about [Hepatology International](https://www.springer.com/journal/12072)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/hepi/default.aspx","title":"Hepatology International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"538ce8f4-b150-489e-ac15-f0dbe0bef3bf","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T16:02:02+00:00","versionOfRecord":{"articleIdentity":"rs-4659138","link":"https://doi.org/10.1007/s12072-024-10728-9","journal":{"identity":"hepatology-international","isVorOnly":false,"title":"Hepatology International"},"publishedOn":"2024-11-23 15:57:37","publishedOnDateReadable":"November 23rd, 2024"},"versionCreatedAt":"2024-08-09 15:58:40","video":"","vorDoi":"10.1007/s12072-024-10728-9","vorDoiUrl":"https://doi.org/10.1007/s12072-024-10728-9","workflowStages":[]},"version":"v1","identity":"rs-4659138","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4659138","identity":"rs-4659138","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}