The prognostic value of systemic immune-inflammation index in patients with unresectable hepatocellular carcinoma treated with immune-based therapy

doi:10.21203/rs.3.rs-4679916/v1

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Research Article

The prognostic value of systemic immune-inflammation index in patients with unresectable hepatocellular carcinoma treated with immune-based therapy

https://doi.org/10.21203/rs.3.rs-4679916/v1

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This study evaluates the Systemic Immune-Inflammation Index (SII) for predicting immune-based therapy response and prognosis in unresectable hepatocellular carcinoma (HCC) patients.A cohort of 268 HCC patients treated with immune-based therapy from January 2019 to March 2023 was studied. The training group of 93 patients received atezolizumab plus bevacizumab (T+A) treatment, while 175 patients in the validation group were treated with tyrosine kinase inhibitors (TKIs) and anti-PD-(L)1 therapy. SII’s cutoff point was determined using X-tile analysis based on overall survival (OS) in the training cohort. Prognostic factors were identified through univariate and multivariate logistic and Cox regression analyses, with survival outcomes compared using Kaplan-Meier methods. The predictive accuracy of SII was assessed using receiver operating characteristic (ROC) curves.

The optimal cutoff for SII was 752*10⁹. Univariate and multivariate logistic regression indicated that SII was a strong predictor for the objective response rate (ORR). The ORR was significantly different between the low SII and high SII subgroups (34.72% vs. 9.52%, P = 0.019), similar results were validated in the validation group (34.09% vs. 16.28%, P = 0.026). SII also demonstrated prognostic significance in Cox regression and Kaplan-Meier analyses. ROC curves showed that the SII’s predictive function was superior among common clinical indicators, with relevance even in AFP-negative patients. Lower SII was associated with a higher T cell ratio, higher CD8⁺T cell and GranzymeB⁺CD8⁺T cell number in peripheral blood.

SII is a viable predictor of the efficacy and prognosis of various kinds of immune-based therapy in HCC patients, potentially serving aiding therapeutic decisions, thus increasing the likelihood of patients benefiting from immune-based therapy.

Hepatocellular carcinoma

Immune-based therapy

Atezolizumab-bevacizumab

Systemic immune-inflammation index (SII)

Prognosis

Primary liver cancer is the sixth most common cancer worldwide, and its incidence is increasing¹. Hepatocellular carcinoma (HCC), the primary type of liver cancer, typically develops in the context of chronic liver disease. Common causes include hepatitis B virus (HBV), hepatitis C virus (HCV) infection, alcohol abuse, or factors related to metabolic syndrome². For early-stage HCC patients, curative treatments include surgical resection, transarterial chemoembolization (TACE), radio-frequency ablation, and liver transplantation³. In more advanced stages, immune checkpoint inhibitors (ICIs) targeting the programmed cell death protein 1 (PD-1) and its ligand PD-L1 have become promising treatments⁴. Atezolizumab and bevacizumab combination therapy, in particular, has shown significant effectiveness and safety for advanced-stage HCC, and is now recommended as a first-line treatment ⁵.

However, the absence of reliable predictive biomarkers for immune-based therapy remains a major challenge. Although tumor tissue biomarkers like tumor PD-L1 expression, tumor mutational burden (TMB), mismatch repair protein (MMR), and microsatellite instability (MSI) are promising, they are limited by the tumor microenvironment's complexity and their lack of specificity and accuracy ^{6 7}. These biomarkers are also expensive and not routinely available. As a result, regular hematological markers associated with inflammation are gaining attention ^{8 9 10}. The systemic immune-inflammatory response is crucial in HCC development and progression ¹¹. This is highlighted by the significance of systemic inflammation markers such as the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR). High levels of NLR or PLR in HCC indicate a pro-inflammatory tumor environment¹² and are linked to poorer survival outcomes in patients receiving T + A treatment ^{13 14}.

Our research group previously introduced the SII score as an innovative biomarker for assessing recurrence risk in HCC post-resection ¹⁵. Initially proposed in our earlier studies, SII has proven its prognostic value in various cancers, including small cell lung cancer, gastroesophageal adenocarcinoma, and esophageal squamous cell carcinoma ^{16 15 17}. SII's non-invasive nature and accessibility through routine blood tests make it a promising tool for predicting immune-based therapy effectiveness in HCC. This study aims to further explore SII's role in guiding therapeutic decisions for HCC patients, particularly in immune-based treatments, offering a significant contribution to precision oncology.

Patients and specimens

This study included 268 hepatocellular carcinoma (HCC) patients from Zhongshan Hospital, Fudan University, China, who received immune-based therapy from January 2019 to March 2023. The detailed dosages for the different immune-based strategies were as follows: bevacizumab or its biosimilar IBI305 at a dose of 15 mg/kg every 3 weeks, rivoceranib 250 mg once daily, or lenvatinib 8 mg/day. The anti-PD(L)-1 antibodies included atezolizumab 1200 mg every 3 weeks, nivolumab 3 mg/kg every 2 weeks, camrelizumab 200 mg every 2 weeks, pembrolizumab 200 mg every 3 weeks, sintilimab 200 mg every 3 weeks, or toripalimab 240 mg every 3 weeks. Most of the patients were over 50 years old (68.28%). The cohort predominantly comprised males (66.42%). Inclusion criteria were: 1) clinical symptoms, imaging features, and serological molecular markers aligning with HCC diagnostic criteria ¹⁸; 2) receipt of at least two courses of anti-PD-(L)1 treatment. Exclusion criteria included: 1) incomplete hematological data; 2) lack of follow-up; 3) receipt of locoregional therapy (e.g., TACE, radiofrequency ablation) at baseline or surgery post immune-based therapy. Considering the prominence of T + A treatment as first-line therapy for unresectable HCC, we assigned 93 patients (from October 2020 to March 2023) receiving this treatment as the training cohort. To mirror real-world immune-based therapy, 175 patients (from January 2019 to December 2021) who received TKIs plus anti-PD-(L)1 therapy were included as the validation cohort. Baseline blood samples for research were collected prior to immune-based therapy initiation and analyzed in the laboratory department.

Tumor stages were assessed using the Barcelona Clinic Liver Cancer (BCLC) staging system ¹⁹. Liver function was evaluated by the Child–Pugh scoring system. Treatment response was measured using the Response Evaluation Criteria in Solid Tumors V.1.1 (RECIST V.1.1). Objective Response Rate (ORR) was defined as the proportion of patients achieving complete response (CR) or partial response (PR). Patients with CR or PR were considered responders, while those with stable disease (SD) or progressive disease (PD) were non-responders. Overall survival (OS) was calculated from the first cycle of immune-based therapy to death due to any cause, or censored on the last follow-up, and progression-free survival (PFS) was measured from the first cycle to the occurrence of disease progression, death from any cause; or censored on the last follow-up.

The study was conducted in accordance with the Declaration of Helsinki and written informed consent was obtained from all patients before inclusion in the study. The study protocol was approved by the Ethics Committee of Zhongshan Hospital (Approval Number B2020-177R) and followed the Declaration of Helsinki.

Data collection and follow-up

Data collected via an electronic medical record system encompassed patient baseline information (age, gender, etc.), laboratory test results (complete blood count, AFP, ALT, etc.), immune-based therapy related data (baseline time, treatment strategy, response state, etc.), and tumor-related information (CNLC, extrahepatic metastasis, etc.). Follow-ups were conducted every 60 days (± 7 days) after the initiation of combination therapy until October 2023 for both cohorts.

Immune-based prognostic scores

The Systemic Immune-Inflammation Index (SII) was calculated using the formula: SII = Platelet count * Neutrophil count / Lymphocyte count, as previously established ¹⁵. The training cohort data was analyzed using X-tile 3.6.1 software (Yale University, New Haven, CT) to determine the optimal SII cutoff for overall survival (Supplementary Fig. S1) ²⁰. Neutrophil-to-Lymphocyte Ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR) were calculated as Neutrophil count / Lymphocyte count and Platelet count / Lymphocyte count, respectively, with previously defined cutoff values ²¹.

Statistical analysis

Statistical analyses were conducted using IBM SPSS software (version 29.0; IBM Corp., Armonk, NY). Continuous variables were tested for normality with the Shapiro-Wilk test and compared using the t-test or Mann-Whitney U test. They were summarized as median and range or mean ± SEM. Categorical variables, presented as counts and percentages, were compared using Fisher’s exact test or Pearson’s χ2 test. OS and PFS were calculated using the Kaplan–Meier method, with differences between groups assessed by the log-rank test. Univariate and multivariate logistic regression identified independent predictors. Univariate and multivariate Cox regression analyses determined risk factors for OS and PFS. Variables with a P value < 0.1 in univariate analyses were included in multivariate analyses. Receiver Operating Characteristic (ROC) curves were used to evaluate sensitivity and specificity, and differences in the area under the curves (AUC) were analyzed using MedCalc version 13.0 (MedCalc Software). A P value < 0.05 was considered statistically significant.

Patient Characteristics

The study included 268 patients meeting the inclusion criteria, detailed in Table 1. Both cohorts, training and validation, were predominantly male (training: 90.32%, validation: 85.14%) and showed a high prevalence of advanced-stage HCC, as classified by BCLC (training: 94.62%, validation: 91.43%). A majority of patients tested positive for AFP and HBV, with compensated liver function. The training cohort had a lower incidence of extrahepatic metastasis (30.11% vs. 47.43%, P = 0.006) but a higher rate of HBsAg positivity (89.25% vs. 74.86%, P = 0.005) and macrovascular tumor thrombus (68.82% vs. 54.29%, P = 0.021) compared to the validation cohort. Additionally, the training group had a higher proportion of patients with lower PLR (76.34% vs. 64.00%, P = 0.039). No significant differences were noted in the distribution of patients based on SII and NLR between the cohorts.

The training cohort showed a slightly higher objective response rate (ORR) of 30.1% compared to 29.7% in the validation cohort (Supplementary Fig. S2A). Median PFS was longer in the training group (7.37 months, 95% CI: 2.76 – 11.96) than in the validation cohort (6.50 months, 95% CI: 4.83 – 8.17), as depicted in Supplementary Figures S2B and S2C. Similarly, the training cohort exhibited a higher median OS of 17.7 months (95% CI: 14.7-29.2) compared to 16.77 months (95% CI: 13.18 – 20.35) in the validation group, as shown in Supplementary Figures S2D and S2E.

Association Between SII and Clinicopathologic Parameters

An optimal cutoff point for the SII of 752*10⁹ was identified in the training cohort using X-Tile analysis. Patients were subsequently categorized into two groups: SII ≤752*10⁹ or >752*10⁹ for further analysis. Higher SII values (>752*10⁹) correlated significantly with extrahepatic metastasis in the training cohort (P = 0.011) and with an increased likelihood of vascular tumor thrombus in the validation cohort (P = 0.019), as detailed in Table 2.

Prognostic Significance of SII in the Training Cohort

Building on previous findings regarding the prognostic value of the NLR in T+A treated HCC patients, this study focused on assessing the reliability of SII in a similar cohort from Zhongshan Hospital, Fudan University. Univariate analysis revealed SII as a significant prognostic factor for both PFS (HR = 2.296, 95% CI = 1.264 - 4.170, P = 0.006, Supplementary table S2) and OS (HR = 2.692, 95% CI = 1.395 - 5.196, P = 0.003, Supplementary table S3), outperforming other clinical factors. PLR and NLR were also significant for OS, as previously reported. Other factors such as age, gender, Child–Pugh score, alanine aminotransferase (ALT) level, α-fetoprotein (AFP), macrovascular tumor thrombus, extrahepatic metastasis, and CNLC stage showed limited prognostic impact for PFS or OS. Multivariate analysis confirmed SII as an independent prognostic factor for both PFS and OS.

Kaplan–Meier analysis demonstrated that lower SII was associated with longer PFS and OS (Fig. 1A - B). Patients with SII ≤ 752*10⁹ had a median PFS of 11.6 months and OS of 25.5 months, whereas those with SII > 752*10⁹ had 5.1 months and 12 months, respectively. Further analysis for PLR and NLR indicated that lower PLR correlated with longer PFS and OS (Supplementary Fig. S3A - B), while lower NLR only showed a significant association with OS (Supplementary Fig. S3C - D). Patients with lower SII had better ORR (34.72% vs. 9.52%; P = 0.019; Fig. 1C; Supplementary Table S1). NLR and PLR had limited predictive ability for ORR (Supplementary Fig. S3E - F). Additionally, responders tended to have lower SII scores than non-responders (337.0 vs. 613.3, P < 0.001, Fig. 1D). The area under the curve (AUC) values for SII, NLR, and PLR in predicting ORR, OS, and PFS suggested that SII was the strongest factor among them (Fig. 1E - G).

Validation of the SII in an Independent Cohort

The prognostic relevance of SII was further validated in an independent cohort of 175 patients treated with TKIs plus PD-(L)1 inhibitors. The validation cohort's findings were consistent with the initial training cohort, confirming the strong association of SII with PFS and OS in both univariate and multivariate analyses (Supplementary Table S2 - 3). Patients with SII lower than 752*10⁹ showed a higher response rate and benefited more from immune-based therapy, evidenced by prolonged PFS and OS (Fig. 2A - C). Responders were characterized by significantly lower SII scores compared to non-responders (427.8 vs. 649.4, P = 0.005, Fig. 2D). The predictive function of PLR for PFS was not observed in this cohort (Supplementary Fig. S4). The AUC values for SII in predicting ORR, PFS, and OS were superior to other clinical indices (Fig. E - G).

The Prognostic Significance of SII in HCC Patients with Negative AFP Subgroups

Further investigation into the prognostic value of SII in AFP-negative HCC patients revealed that lower SII scores in the training cohort significantly improved PFS and OS (Fig. 3A - B). The validation cohort showed similar findings, confirming the prognostic relevance of SII for PFS and OS in AFP-negative patients (Fig. 3C - D). These results highlight SII's potential as a valuable biomarker in this specific patient subgroup.

Patients with Lower SII Scores Tend to Have Higher Effective T Cell Ratio In Peripheral Blood

Exploring the relationship between SII scores and immune response, the study focused on lymphocyte composition, particularly T cells, given their established significance in cancer immune-based therapy ²². Patients with lower SII scores exhibited higher T cell ratios in their lymphocytes in both cohorts (P = 0.008, P < 0.001, respectively, Fig. 3E - F). According to the flow cytometry results in our previous study ²³, we matched 33 patients in TKIs plus PD-(L)1 antibody group. Results showed that patients in lower SII group tended to have higher CD8⁺T cell and GranzymeB⁺CD8⁺T cell number in peripheral blood (P = 0.013, P = 0.034, respectively, Fig. 3G - H). These findings underscore the potential influence of SII on immune functionality, especially considering the crucial role of T cells in mediating anti-tumor effects.

The remarkable progress in immune-based therapy for HCC, particularly with the emergence of combination therapies such as atezolizumab and bevacizumab, represents a substantial advancement in the treatment of advanced-stage HCC ²⁴. Despite this progress, challenges remain, most notably the suboptimal response rate to these therapies. This underscores the urgent need for effective predictive biomarkers ²⁵, making our study's focus on the SII as a prognostic tool both timely and crucial.

In our analysis, we underscore the well-established link between inflammation and anti-tumor immune response ²⁶. The SII, comprising platelets, neutrophils, and lymphocytes, serves as a potential indicator of the balance between inflammation and immune response. Our findings reveal that an elevated SII, often indicative of abnormal inflammatory states and weaker immune responses, is associated with poorer outcomes in immune-based therapy. This is particularly relevant since inflammation plays a pivotal role in tumor progression ²⁷, and components like platelets and neutrophils are known to promote tumor growth and immune evasion, whereas a reduced lymphocyte count usually indicates a weakened immune response ^{28 29 30}.

AFP is the most widely used biomarker for HCC, its predictive ability is limited in AFP-negative patients ³¹. This limitation underscores a significant gap in current diagnostic and prognostic approaches ³². Our study addresses this by demonstrating that SII retains its prognostic value even in AFP-negative patients. We observed distinct differences in OS and PFS among patients with varying SII scores in both training and validation cohorts. These observations reinforce the potential of SII as a valuable prognostic marker in this challenging subgroup, opening new possibilities for patient stratification and management.

Further investigating the mechanistic underpinnings of SII scores, we found that lower SII scores correlate with a higher T cell ratio in lymphocytes. Based on the previous flow cytometry results, we deeply looked into the detailed composition of T cells and gladly found that patients with lower SII had higher CD8⁺T cell and GranzymeB⁺CD8⁺T cell number in peripheral blood. This correlation is significant, considering the vital role of effective T cells in immune-based therapy and anti-tumor responses. A higher number of effective T cells suggests a more potent anti-tumor response. These finding sheds light on how SII may influence patient responses to immune-based therapy and suggests further research, particularly focusing on the subtypes and functional states of T cells ²³.

Our study is pioneering in demonstrating the prognostic value of baseline SII in HCC patients undergoing treatment with the atezolizumab-bevacizumab combination (T + A), a frontline therapy for advanced-stage HCC. We also validated SII's functionality in patients treated with TKIs plus anti-PD-(L)1, suggesting its broad applicability in predicting outcomes and guiding treatment decisions, especially in AFP-negative patients who traditionally lack effective clinical indicators. This finding could significantly refine clinical practice, allowing for a more nuanced approach to HCC management.

While our findings are promising, we acknowledge the limitations of our study, such as its single-center nature and the limited patient sample. To generalize our findings, broader validation across diverse patient populations is essential. Moreover, the complete mechanism behind SII warrants further exploration and validation. Understanding the complex interplay between different components of the immune system and their impact on immune-based therapy outcomes remains a key area for future research.

Additionally, future studies should examine the dynamic changes in SII during treatment to assess their potential predictive value. Investigating these longitudinal variations could offer deeper insights into immune response dynamics under therapeutic pressure and their implications for patient prognosis.

In conclusion, our study underscores the potential of SII as a predictive biomarker for HCC patients undergoing immune-based therapy. The simplicity, cost-effectiveness, and wide-applicability of the SII position it as a promising tool for assessing HCC prognosis in clinical practice. With further validation and exploration, SII could significantly contribute to personalized medicine, aiding in the selection of patients most likely to benefit from specific immune-based therapies.

Data availability

The data and materials used in the current study are available from the corresponding authors upon reasonable request, without undue reservation.

Acknowledgements

The authors wish to thank all patients and their families.

Funding

This work was supported by the National Natural Science Foundation of China (81871929 and 82072667 to C Huang), the Special Research Fund for Liver Cancer Diagnosis and Treatment from the China Anti-Cancer Association (H2020-044 to C Huang), and the Clinical Research Special Fund of Zhongshan Hospital, Fudan University (2020ZSLC71) to H-C Sun.

Competing interests

No conflicts of interest to declare.

Ethics approval and consent to participate

Statement of translational relevance

Immune-based therapy has transformed the treatment patterns for unresectable HCC in recent years. Therefore, the identification of predictive biomarkers has important clinical significances. Our study highlights the potential of SII as a predictive biomarker for HCC patients undergoing immune-based therapy, which is economical and convenient in the clinical practice. We believe SII has a promising future in distinguishing patients who are more likely to benefit from immune-based therapy.

Acknowledgements

The authors thank Ms. Jie Liu, Jinjin Zhu, Meiling Li, Suiyi Wu and Xian Li for their work in coordinating this study and collecting data.

Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers 2021;7(1):6. doi: 10.1038/s41572-020-00240-3 [published Online First: 20210121]
Sangro B, Sarobe P, Hervas-Stubbs S, et al. Advances in immunotherapy for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2021;18(8):525-43. doi: 10.1038/s41575-021-00438-0 [published Online First: 20210413]
Anwanwan D, Singh SK, Singh S, et al. Challenges in liver cancer and possible treatment approaches. Biochim Biophys Acta Rev Cancer 2020;1873(1):188314. doi: 10.1016/j.bbcan.2019.188314 [published Online First: 20191101]
Kudo M. Targeted and immune therapies for hepatocellular carcinoma: Predictions for 2019 and beyond. World J Gastroenterol 2019;25(7):789-807. doi: 10.3748/wjg.v25.i7.789
Casak SJ, Donoghue M, Fashoyin-Aje L, et al. FDA Approval Summary: Atezolizumab Plus Bevacizumab for the Treatment of Patients with Advanced Unresectable or Metastatic Hepatocellular Carcinoma. Clin Cancer Res 2021;27(7):1836-41. doi: 10.1158/1078-0432.CCR-20-3407 [published Online First: 20201102]
Ribas A, Hu-Lieskovan S. What does PD-L1 positive or negative mean? J Exp Med 2016;213(13):2835-40. doi: 10.1084/jem.20161462 [published Online First: 20161130]
Allgauer M, Budczies J, Christopoulos P, et al. Implementing tumor mutational burden (TMB) analysis in routine diagnostics-a primer for molecular pathologists and clinicians. Transl Lung Cancer Res 2018;7(6):703-15. doi: 10.21037/tlcr.2018.08.14
Mantovani A, Allavena P, Sica A, et al. Cancer-related inflammation. Nature 2008;454(7203):436-44. doi: 10.1038/nature07205
Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144(5):646-74. doi: 10.1016/j.cell.2011.02.013
Dolan RD, Lim J, McSorley ST, et al. The role of the systemic inflammatory response in predicting outcomes in patients with operable cancer: Systematic review and meta-analysis. Sci Rep 2017;7(1):16717. doi: 10.1038/s41598-017-16955-5 [published Online First: 20171201]
McMillan DC. Cancer and systemic inflammation: stage the tumour and stage the host. Br J Cancer 2013;109(3):529. doi: 10.1038/bjc.2013.418
Motomura T, Shirabe K, Mano Y, et al. Neutrophil-lymphocyte ratio reflects hepatocellular carcinoma recurrence after liver transplantation via inflammatory microenvironment. J Hepatol 2013;58(1):58-64. doi: 10.1016/j.jhep.2012.08.017 [published Online First: 20120825]
Bruix J, Cheng AL, Meinhardt G, et al. Prognostic factors and predictors of sorafenib benefit in patients with hepatocellular carcinoma: Analysis of two phase III studies. J Hepatol 2017;67(5):999-1008. doi: 10.1016/j.jhep.2017.06.026 [published Online First: 20170704]
Mouchli M, Reddy S, Gerrard M, et al. Usefulness of neutrophil-to-lymphocyte ratio (NLR) as a prognostic predictor after treatment of hepatocellular carcinoma." Review article. Ann Hepatol 2021;22:100249. doi: 10.1016/j.aohep.2020.08.067 [published Online First: 20200905]
Hu B, Yang XR, Xu Y, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res 2014;20(23):6212-22. doi: 10.1158/1078-0432.CCR-14-0442 [published Online First: 20140930]
Jomrich G, Paireder M, Kristo I, et al. High Systemic Immune-Inflammation Index is an Adverse Prognostic Factor for Patients With Gastroesophageal Adenocarcinoma. Ann Surg 2021;273(3):532-41. doi: 10.1097/SLA.0000000000003370
Geng Y, Shao Y, Zhu D, et al. Systemic Immune-Inflammation Index Predicts Prognosis of Patients with Esophageal Squamous Cell Carcinoma: A Propensity Score-matched Analysis. Sci Rep 2016;6:39482. doi: 10.1038/srep39482 [published Online First: 20161221]
Zhou J, Sun H, Wang Z, et al. Guidelines for the Diagnosis and Treatment of Hepatocellular Carcinoma (2019 Edition). Liver Cancer 2020;9(6):682-720. doi: 10.1159/000509424 [published Online First: 20201111]
Reig M, Forner A, Rimola J, et al. BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. J Hepatol 2022;76(3):681-93. doi: 10.1016/j.jhep.2021.11.018 [published Online First: 20211119]
Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res 2004;10(21):7252-9. doi: 10.1158/1078-0432.CCR-04-0713
Jia G, Qiu L, Zheng H, et al. Nomogram for predicting survival in patients with advanced hepatocellular carcinoma treated with PD-1 inhibitors: incorporating pre-treatment and post-treatment clinical parameters. BMC Cancer 2023;23(1):556. doi: 10.1186/s12885-023-11064-1 [published Online First: 20230616]
Paijens ST, Vledder A, de Bruyn M, et al. Tumor-infiltrating lymphocytes in the immunotherapy era. Cell Mol Immunol 2021;18(4):842-59. doi: 10.1038/s41423-020-00565-9 [published Online First: 20201102]
Huang C, Xu B, Zhu XD, et al. Peripheral naïve CD8(+) T cells as a predictive biomarker of response to lenvatinib plus an anti-PD-1 antibody in advanced hepatocellular carcinoma: a biomarker study. Cancer Commun (Lond) 2022;42(11):1226-30. doi: 10.1002/cac2.12350 [published Online First: 20220822]
Finn RS, Qin S, Ikeda M, et al. Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinoma. N Engl J Med 2020;382(20):1894-905. doi: 10.1056/NEJMoa1915745
Xing M, Wang X, Kiken RA, et al. Immunodiagnostic Biomarkers for Hepatocellular Carcinoma (HCC): The First Step in Detection and Treatment. Int J Mol Sci 2021;22(11) doi: 10.3390/ijms22116139 [published Online First: 20210607]
Lochowski M, Chalubinska-Fendler J, Zawadzka I, et al. The Prognostic Significance of Preoperative Platelet-to-Lymphocyte and Neutrophil-to-Lymphocyte Ratios in Patients Operated for Non-Small Cell Lung Cancer. Cancer Manag Res 2021;13:7795-802. doi: 10.2147/CMAR.S317705 [published Online First: 20211009]
Deets KA, Vance RE. Inflammasomes and adaptive immune responses. Nat Immunol 2021;22(4):412-22. doi: 10.1038/s41590-021-00869-6 [published Online First: 20210218]
Ma C, Fu Q, Diggs LP, et al. Platelets control liver tumor growth through P2Y12-dependent CD40L release in NAFLD. Cancer Cell 2022;40(9):986-98 e5. doi: 10.1016/j.ccell.2022.08.004 [published Online First: 20220901]
Xiong S, Dong L, Cheng L. Neutrophils in cancer carcinogenesis and metastasis. J Hematol Oncol 2021;14(1):173. doi: 10.1186/s13045-021-01187-y [published Online First: 20211021]
Mantovani A, Cassatella MA, Costantini C, et al. Neutrophils in the activation and regulation of innate and adaptive immunity. Nat Rev Immunol 2011;11(8):519-31. doi: 10.1038/nri3024 [published Online First: 20110725]
Huang C, Zhu XD, Shen YH, et al. Radiographic and alpha-fetoprotein response predict pathologic complete response to immunotherapy plus a TKI in hepatocellular carcinoma: a multicenter study. BMC Cancer 2023;23(1):416. doi: 10.1186/s12885-023-10898-z [published Online First: 20230509]
Bruix J, Sherman M, American Association for the Study of Liver D. Management of hepatocellular carcinoma: an update. Hepatology 2011;53(3):1020-2. doi: 10.1002/hep.24199

Table1. The clinicopathologic characteristics of patients in the training and validation cohorts

Variables	Training cohort		Validation cohort
	N=93	%	N=175	%	P-value
Age(y)
≤50	32	34.40%	53	30.29%
>50	61	65.60%	122	69.71%	0.490
Sex
Female	9	9.68%	26	14.86%
Male	84	90.32%	149	85.14%	0.231
Child-Pugh
A	85	91.40%	156	89.14%
B+C	8	8.60%	19	10.86%	0.559
HBsAg
Negative	10	10.75%	44	25.14%
Positive	83	89.25%	131	74.86%	0.005
ALT(U/L)
≤50	73	78.49%	126	72.00%
>50	20	21.51%	49	28.00%	0.247
AFP (ng/mL)
≤20	34	36.56%	52	29.71%
>20	59	63.44%	123	79.29%	0.253
Macrovascular tumor thrombus
No	29	31.18%	80	45.71%
Yes	64	68.82%	95	54.29%	0.021
Extrahepatic metastasis
No	65	69.89%	92	52.57%
Yes	28	30.11%	83	47.43%	0.006
BCLC
A	5	5.38%	15	8.57%
B+C	88	94.62%	160	91.43%	0.343
NLR
≤3	66	70.97%	108	61.71%
>3	27	29.03%	67	38.29%	0.131
PLR
≤150	71	76.34%	112	64.00%
>150	22	23.66%	63	36.00%	0.039
SII
≤752	72	77.42%	132	75.43%
>752	21	22.58%	43	24.57%	0.716

Abbreviation: ALT, albumin; AFP, alpha‐fetoprotein; BCLC, Barcelona Clinic Liver Cancer; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune-inflammation index

Table 2. The correlation between SII and clinicopathologic characteristics in training and validation cohorts

Variables	Training cohort（n=93）					Validation cohort（n=175）
	SII≤752	%	SII>752	%	P-value	SII≤752	%	SII>752	%	P-value
Age(y)
≤50	25	34.72%	7	33.33%		39	29.55%	14	32.56%
>50	47	65.28%	14	66.67%	0.906	93	70.45%	29	67.44%	0.709
Sex
Female	7	9.72%	2	9.52%		19	14.39%	7	16.28%
Male	65	90.28%	19	90.48%	0.978	113	85.61%	36	83.72%	0.763
Child-Pugh
A	66	91.67%	19	90.48%		118	89.39%	38	88.37%
B+C	6	8.33%	2	9.52%	0.864	14	10.61%	5	11.63%	0.852
HBsAg
Negative	7	9.72%	3	14.29%		36	27.27%	8	18.60%
Positive	65	90.28%	18	85.71%	0.553	96	72.73%	35	81.40%	0.255
ALT(U/L)
≤50	55	76.39%	18	85.71%		92	69.70%	34	79.07%
>50	17	23.61%	3	14.29%	0.360	40	30.30%	9	20.93%	0.235
AFP (ng/mL)
≤20	25	34.72%	9	42.86%		39	29.55%	14	32.56%
>20	47	65.28%	12	57.14%	0.496	93	70.45%	29	67.44%	0.709
Macrovascular tumor thrombus
No	19	26.39%	10	47.62%		67	50.76%	13	30.23%
Yes	53	73.61%	11	52.38%	0.065	65	49.24%	30	69.77%	0.019
Extrahepatic metastasis
No	55	76.39%	10	47.62%		74	56.06%	18	41.86%
Yes	17	23.61%	11	52.38%	0.011	58	43.94%	25	58.14%	0.105
BCLC
A	3	4.17%	2	9.52%		14	25.76%	1	2.33%
B+C	69	95.83%	19	90.48%	0.338	118	74.24%	42	97.67%	0.092

No competing interests reported.

20240416supplementary.docx
Supplementary Figure 1.The optimal cutoff value for the SII was selected by X-tile 3.6.1 software (Yale University, New Haven, CT, USA). Supplementary Figure 2. The ORR in training and validation cohort (A). The Kaplan–Meier analysis of PFS and OS in the training and validation cohorts (B - E). Supplementary Figure 3. The Kaplan–Meier analysis of PFS (A) and OS (B) for the PLR in the training cohort. The Kaplan–Meier analysis of PFS (C) and OS (D) for the NLR in the training cohort. Comparison of ORR for the NLR and PLR in the training cohort (E-F). Supplementary Figure 4. Comparison of ORR for the NLR and PLR in the validation cohort (A-B). The Kaplan–Meier analysis of PFS and OS for the PLR and NLR in the validation cohort (C - F).

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The prognostic value of systemic immune-inflammation index in patients with unresectable hepatocellular carcinoma treated with immune-based therapy

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Abstract

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Introduction

Patients and methods

Patients and specimens

Data collection and follow-up

Immune-based prognostic scores

Statistical analysis

Results

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Supplementary Files

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