Chronological change in serum albumin as a prognostic factor in patients with hepatocellular carcinoma treated with lenvatinib: proposal of albumin simplified grading based on the modified albumin-bilirubin score (ALBS grade)

Lenvatinib and immune-checkpoint inhibitors in hepatocellular carcinoma: mechanistic insights, clinical efficacy, and future perspectives

Lenvatinib is a multi-target tyrosine kinase inhibitor widely used in the treatment of hepatocellular carcinoma (HCC). Its primary mechanism of action involves inhibiting signal pathways such as vascular endothelial growth factor receptors (VEGFR) and fibroblast growth factor receptors (FGFR), thereby reducing tumor cell proliferation and angiogenesis and affecting the tumor's immune microenvironment. In the treatment of liver cancer, although lenvatinib monotherapy has shown good clinical effect, the problem of drug resistance is becoming more and more serious. This resistance may be caused by a variety of factors, including genetic mutations, signaling pathway remodeling, and changes in the tumor microenvironment. In order to overcome drug resistance, the combination of lenvatinib and other therapeutic strategies has gradually become a research hotspot, and it is worth noting that the combination of lenvatinib and immune checkpoint inhibitors (ICIs) has shown a good application prospect. This combination not only enhances the anti-tumor immune response but also helps improve therapeutic efficacy. However, combination therapy also faces challenges regarding safety and tolerability. Therefore, studying the mechanisms of resistance and identifying relevant biomarkers is particularly important, as it aids in early diagnosis and personalized treatment. This article reviews the mechanisms of lenvatinib in treating liver cancer, the mechanisms and efficacy of its combination with immune checkpoint inhibitors, the causes of resistance, the exploration of biomarkers, and other novel combination therapy strategies for lenvatinib. We hope to provide insights into the use and research of lenvatinib in clinical and scientific settings, offering new strategies for the treatment of liver cancer.

Disease Etiology Impact on Outcomes of Hepatocellular Carcinoma Patients Treated with Atezolizumab plus Bevacizumab: A Real-World, Multicenter Study

Introduction

The impact of etiology on response to immunotherapy in advanced hepatocellular carcinoma (HCC) is being debated, with contrasting findings between early and recent post hoc analyses of IMbrave-150 and metanalyses of clinical trials of PD-1/PD-L1 blockers. As a results, it is not clear whether the first-line systemic treatment atezolizumab plus bevacizumab (A + B) is equally effective in viral and nonviral patients.

Methods

We retrospectively analyzed 885 HCC patients treated with the first-line A + B from multiple centers from Eastern and Western countries, 53.9% having viral and 46.1% nonviral etiology. Baseline clinical and laboratory characteristics were analyzed with uni- and multivariate models to explore potential differences on overall survival (OS), time-to-progression (TTP), disease control rates (DCRs) based on etiology and to identify putative prognostic factors in etiology subgroups. Treatment toxicities and access to the second-line treatments and outcomes were also reported and compared between etiologies.

Results

Overall, no statistically significant differences were found in median OS (mOS: viral 15.9 months; nonviral 16.3 months), TTP (mTTP: viral 8.3 months; nonviral 7.2 months), and DCRs (viral 78.1%; nonviral 80.8%) based on etiology. Prognostic factors of survival and progression were mainly shared between viral and nonviral etiologies, including alpha-fetoprotein, aspartate transaminase, neutrophil-to-lymphocyte ratio (NLR) and ALBI score. Exploratory analyses highlighted a possible stronger association of immunological factors, i.e., NLR and eosinophil count, to treatment outcomes in viral patients. The toxicity profile, the access to and type of the second-line treatments and their outcome in terms of OS almost overlap in the two etiology subgroups.

Conclusion

Atezolizumab plus bevacizumab efficacy does not vary according to underlying etiology of HCC in a multicenter, real-world population, matching recent post hoc findings from the IMbrave-150 trial. Preliminary analyses suggest that some prognostic factors differ between viral and nonviral patients, potentially due to biological and immunological differences. Prospective and comparative trials stratifying by etiology are warranted to validate these findings and guide clinical practice.

Identifying risk factors of dose reduction or treatment discontinuation due to fatigue or gastrointestinal symptoms in patients receiving lenvatinib treatment for hepatocellular carcinoma

INTRODUCTION

Lenvatinib (LEN) is the standard treatment for hepatocellular carcinoma (HCC). In clinical practice, gastrointestinal (GI) symptoms such as fatigue and loss of appetite often lead to dose reduction or treatment discontinuation. This study aimed to identify the predictors of patients who will experience dose reduction or treatment discontinuation owing to fatigue or GI symptoms during LEN treatment for HCC.

METHODS

We retrospectively identified 99 patients who received LEN at the Ogaki Municipal Hospital (Ogaki, Japan) between April 2018 and December 2023. To investigate the risk factors for treatment discontinuation or dose reduction due to fatigue or GI symptoms during LEN administration, patients were divided into two groups based on whether treatment discontinuation or dose reduction occurred due to fatigue or GI symptoms during LEN administration (37 patients) or not (62 patients). We compared baseline characteristics between the two groups.

RESULTS

Multivariate analysis revealed that body weight (odds ratio 4.310, 95% confidence interval 1.380-13.500; P = 0.002) was an independent risk factor that significantly contributed to treatment discontinuation or dose reduction owing to fatigue or GI symptoms during LEN administration. The cut-off value calculated using the body weight curve was 55.0 kg. Using this cutoff value, the sensitivity and specificity of body weight to detect treatment discontinuation or dose reduction due to fatigue or GI symptoms during LEN administration were 83.9% and 56.8%, respectively.

CONCLUSION

In clinical practice, patients weighing less than 55 kg who start with a full dose will likely experience weight loss or discontinuation during treatment.

Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma

BACKGROUND

Lenvatinib is a first-line agent for advanced hepatocellular carcinoma (HCC), but individual responses to treatment are highly heterogeneous. The aim of this study was to investigate the clinical parameters that influence the efficacy of Lenvatinib and to develop a prognostic model.

METHODS

We retrospectively enrolled 333 Lenvatinib-treated patients with HCC with a median age of 57 years. Two hundred nd sixty-three of these patients had BCLC (2022) stage C. The median overall survival (mOS) time within the cohort was 12.1 months, and the median progression-free survival (mPFS) time was 4.7 months. Univariate Cox regression, best subset regression, and Lasso regression were used to screen primary variables for possible contribution to OS, multivariate Cox analysis was used to fit selected models, and the final model was selected using the maximum area under the curve (AUC) and minimum AIC. Receiver operating curves (ROC), calibration curves, and decision curve analysis were plotted to assess model performance, and 5-fold cross-validation was performed for internal validation. X-tile software was used to select the best cutoff points and to divide the study cohort into 3 different risk groups.

RESULTS

Seven variables were included in the final model: BCLC stage, prior transarterial chemoembolization and immunotherapy history, tumor number, prognostic nutritional index, log (alpha-fetoprotein), and log (platelet-to-lymphocyte ratio). We named this final model the "multivariate prognostic model for Lenvatinib" (MPML), and a nomogram was constructed to predict the probability of survival at 6, 9, and 12 months. The MPML had good discrimination, calibration, and applicability. Cross-validation showed mean AUC values of 0.7779, 0.7738, and 0.7871 at 6, 9, and 12 months, respectively. According to nomogram points, mOS time was 21.57, 8.70, and 5.37 months in the low, medium, and high-risk groups, respectively (P < .001), and these differences were also observed in the PFS survival curve (P < .001).

CONCLUSIONS

The MPML stratified patients according to baseline clinical characteristics had a strong performance in predicting Lenvatinib efficacy and has the potential for use as an auxiliary clinical tool for individualized decision-making.

Current Status and Future Direction of Albumin-Bilirubin in Hepatocellular Carcinoma: A Bibliometric Analysis

INTRODUCTION

Hepatocellular carcinoma (HCC) is a common malignant tumor, so we need a convenient and objective way to diagnose and treat HCC. We discuss the current situation, progress, hotspots, and existing problems of Albumin-Bilirubin (ALBI) in HCC, which can provide new ideas for the prevention, diagnosis, and treatment of HCC.

METHODS

We adopt Excel 2019 software and visual analysis tools based on Web of Science database search. This manuscript uses VOSviewer, Co-Occurrence13.3 (COOC13.3) software to conduct overall trend analysis, synonym merging, frequency of countries, journals, institutions, funds, dissimilarity matrices, co-occurrence matrices, bimodal matrices, coupling matrices, cluster analysis of topic evolution time zone graphs.

RESULTS

A total of 610 papers were included, and the number of papers output showed an overall upward trend. ALBI has been valued by the industry in HCC and plays an important role in diagnosing and treating HCC, even better than the classic Child-Pugh (C-P) grade. At the same time, hot spots in the treatment of HCC and other applications of ALBI were discovered.

CONCLUSION

ALBI score is a convenient and objective liver function evaluation index, which plays an important role in the prediction of patient survival rate and prognosis. Promoting the ALBI score in HCC can help doctors judge the patient's condition and improve the diagnosis and precise treatment effect.

Clinical Outcomes of Cabozantinib in Patients Previously Treated with Atezolizumab/Bevacizumab for Advanced Hepatocellular Carcinoma-Importance of Good Liver Function and Good Performance Status

(1) Background: This study aimed to investigate clinical outcomes for cabozantinib in clinical practice in patients with advanced hepatocellular carcinoma (HCC) previously treated with atezolizumab plus bevacizumab (Atz/Bev), with a focus on whether patients met criteria of Child-Pugh Class A and Eastern Cooperative Oncology Group performance status (ECOG-PS) score 0/1 at baseline. (2) Methods: Eleven patients (57.9%) met the criteria of both Child-Pugh class A and ECOG-PS score 0/1 (CP-A+PS-0/1 group) and eight patients (42.1%) did not (Non-CP-A+PS-0/1 group); efficacy and safety were retrospectively evaluated. (3) Results: Disease control rate was significantly higher in the CP-A+PS-0/1 group (81.1%) than in the non-CP-A+PS-0/1 group (12.5%). Median progression-free survival, overall survival and duration of cabozantinib treatment were significantly longer in the CP-A+PS-0/1 group (3.9 months, 13.4 months, and 8.3 months, respectively) than in the Non-CP-A+PS-0/1 group (1.2 months, 1.7 months, and 0.8 months, respectively). Median daily dose of cabozantinib was significantly higher in the CP-A+PS-0/1 group (22.9 mg/day) than in the non-CP-A+PS-0/1 group (16.9 mg/day). (4) Conclusions: Cabozantinib in patients previously treated with Atz/Bev has potential therapeutic efficacy and safety if patients have good liver function (Child-Pugh A) and are in good general condition (ECOG-PS 0/1).

Prognostic effect of albumin-to-alkaline phosphatase ratio on patients with hepatocellular carcinoma: a systematic review and meta-analysis

The prognostic value of albumin-to-alkaline phosphatase ratio (AAPR) in patients with hepatocellular carcinoma (HCC) remains controversial. This meta-analysis aims to evaluate the prognostic role of AAPR in patients with HCC. The databases of Web of Science, Embase, Cochrane Library and PubMed were comprehensively searched from inception to April 25, 2022. Pooled hazard ratio (HR) and 95% confidence interval (CI) were calculated with Stata 16.0 software for the assessment of the relationship between AAPR and overall survival (OS) as well as recurrence-free survival (RFS) in patients with HCC. A total of 2634 patients from 12 cohorts were included in this meta-analysis. The pooled results showed that lower AAPR predicted poorer OS (HR 2.02, 95% CI 1.78-2.30). Similarly, pooled results demonstrated that lower AAPR also predicted poorer RFS (HR 1.88, 95% CI 1.37-2.57). The heterogeneity for RFS by multivariate analytic results and the publication bias for OS existed, however, the subgroup analysis, meta-regression analysis as well as adjustment using trim-and-fill analysis confirmed an association between AAPR and OS as well as RFS. This meta-analysis proves that lower AAPR in patients with HCC predicted inferior survival outcomes, and AAPR might be a promising indicator for the prognosis of HCC.

Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma

BACKGROUND

Lenvatinib and transarterial chemoembolization (TACE) are first-line treatments for unresectable hepatocellular carcinoma (HCC), but the objective response rate (ORR) is not satisfactory. We aimed to predict the response to lenvatinib combined with TACE before treatment for unresectable HCC using machine learning (ML) algorithms based on clinical data.

METHODS

Patients with unresectable HCC receiving the combination therapy of lenvatinib combined with TACE from two medical centers were retrospectively collected from January 2020 to December 2021. The response to the combination therapy was evaluated over the following 4-12 weeks. Five types of ML algorithms were applied to develop the predictive models, including classification and regression tree (CART), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The performance of the models was assessed by the receiver operating characteristic (ROC) curve and area under the receiver operating characteristic curve (AUC). The Shapley Additive exPlanation (SHAP) method was applied to explain the model.

RESULTS

A total of 125 unresectable HCC patients were included in the analysis after the inclusion and exclusion criteria, among which 42 (33.6%) patients showed progression disease (PD), 49 (39.2%) showed stable disease (SD), and 34 (27.2%) achieved partial response (PR). The nonresponse group (PD + SD) included 91 patients, while the response group (PR) included 34 patients. The top 40 most important features from all 64 clinical features were selected using the recursive feature elimination (RFE) algorithm to develop the predictive models. The predictive power was satisfactory, with AUCs of 0.74 to 0.91. The SVM model and RF model showed the highest accuracy (86.5%), and the RF model showed the largest AUC (0.91, 95% confidence interval (CI): 0.61-0.95). The SHAP summary plot and decision plot illustrated the impact of the top 40 features on the efficacy of the combination therapy, and the SHAP force plot successfully predicted the efficacy at the individualized level.

CONCLUSIONS

A new predictive model based on clinical data was developed using ML algorithms, which showed favorable performance in predicting the response to lenvatinib combined with TACE for unresectable HCC. Combining ML with SHAP could provide an explicit explanation of the efficacy prediction.

Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy

Background

Lenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.

Methods

304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.

Results

Multivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.

Conclusion

The PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients.