The Prognostic Value of Peripheral Blood Inflammatory Markers in Hepatocellular Carcinoma Treated with Lenvatinib Combined with PD-1 Inhibitors

Purpose: To investigate the prognostic value of inflammatory indexes based on peripheral blood cells in unresectable hepatocellular carcinoma (HCC) patients treated with Lenvatinib combined with PD-1 inhibitors. Methods: This study retrospectively collected baseline inflammatory indexes from HCC patients received Lenvatinib and PD-1 inhibitor-based combination therapy at the Cancer Hospital of the Chinese Academy of Medical Sciences between October 2018 and October 2021. The optimal threshold values for inflammatory indexes determined using X-tile. The factors related to treatment response and survival outcomes were analyzed through logistic regression and Cox regression, respectively. A novel preoperative prognostic nomogram was constructed based on inflammatory indexes, and the predictive efficacy of the nomogram and BCLC staging was compared by the area under the ROC curve. Results: 156 eligible patients with unresectable HCC were included, with median OS and PFS of 23.8 and 11.5 months, respectively, and ORR of 48.7%. The baseline SIRI was an independent factor of treatment response, with a significantly higher ORR for patients with a SIRI <0.8 than for patients with a SIRI >= 0.8 (59.7% vs 41.5%, P=0.03). SIRI and PNI were independent prognostic factors of PFS, and SIRI was an independent prognostic factor of OS. The AUC value of nomogram based on baseline SIRI, PNI, and tumor distribution in predicting the 6-,12- and 18-month PFS of patients was significantly higher than that of traditional BCLC stage, and its prediction performance was substantially better than that of BCLC stage system (C-index, 0.730 vs 0.535). Conclusion: The baseline SIRI could be used as a potential non-invasive biomarker to predict the efficacy and survival benefit of immune combination therapy for HCC. The nomogram based on inflammation indexes could achieve better prediction performance and help clinicians to identify high-risk patients and formulate treatment plans.