Development and validation of a modified albumin-bilirubin grade and α-fetoprotein score (mALF score) for hepatocellular carcinoma patients receiving atezolizumab and bevacizumab

CRAFITY score as a predictive marker for refractoriness to atezolizumab plus bevacizumab therapy in hepatocellular carcinoma: a multicenter retrospective study

BACKGROUND

Although atezolizumab plus bevacizumab (Atezo/Bev) therapy has been used as the preferred first-line treatment for advanced hepatocellular carcinoma (HCC), up to 26% of patients do not achieve disease control, suggesting alternative treatments might be more beneficial for such patients. We investigated key predictors for refractoriness to Atezo/Bev therapy, particularly in the first-line setting.

METHODS

We retrospectively analyzed 302 patients with HCC who received Atezo/Bev therapy between October 2020 and September 2022 across nine hospitals in Japan. Refractoriness was defined as best overall response (BOR) of progressive disease or stable disease and a progression-free survival (PFS) of < 180 days (RECIST v1.1). Clinical benefit was defined as BOR of partial/complete response or stable disease with PFS of ≥ 180 days. Baseline characteristics and potential predictors, identified through literature review, were compared between these groups. Stratifications of overall survival (OS), and PFS were also assessed.

RESULTS

Refractoriness was observed in 126 (41.7%) patients, while 154 (51.0%) achieved clinical benefit. Due to a significant association between the treatment line and refractory rate, the subsequent analysis focused on the first-line cohort (n = 214; 72 [33.6%] patients showed refractoriness). Among 13 potential predictors, the CRP and AFP in immunotherapy (CRAFITY) score had the best predictive performance, with refractory rates of 24.6%, 44.6%, and 57.9% in CRAFITY-0, 1, and 2 patients, respectively (p < 0.001). OS and PFS were also well-stratified by this scoring system.

CONCLUSIONS

Approximately one-third of patients were refractory to first-line Atezo/Bev therapy. The CRAFITY score demonstrated superior performance in predicting refractoriness.

A Machine Learning Algorithm Facilitates Prognosis Prediction and Treatment Selection for Barcelona Clinic Liver Cancer Stage C Hepatocellular Carcinoma

PURPOSE

Given its heterogeneity and diverse clinical outcomes, precise subclassification of Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) is required for appropriately determining patient prognosis and selecting treatment.

EXPERIMENTAL DESIGN

We recruited 2,626 patients with BCLC-C HCC from multiple centers, comprising training/test (n = 1,693) and validation cohorts (n = 933). The XGBoost model was chosen for maximum performance among the machine learning (ML) models. Patients were categorized into low-, intermediate-, high-, and very high-risk subgroups based on the estimated prognosis, and this subclassification was named the CLAssification via Machine learning of BCLC-C (CLAM-C).

RESULTS

The areas under the receiver operating characteristic curve of the CLAM-C for predicting the 6-, 12-, and 24-month survival of patients with BCLC-C were 0.800, 0.831, and 0.715, respectively-significantly higher than those of the conventional models, which were consistent in the validation cohort. The four subgroups had significantly different median overall survivals, and this difference was maintained among various patient subgroups and treatment modalities. Immune-checkpoint inhibitors and transarterial therapies were associated with significantly better survival than tyrosine kinase inhibitors (TKI) in the low- and intermediate-risk subgroups. In cases with first-line systemic therapy, the CLAM-C identified atezolizumab-bevacizumab as the best therapy, particularly in the high-risk group. In cases with later-line systemic therapy, nivolumab had better survival than TKIs in the low-to-intermediate-risk subgroup, whereas TKIs had better survival in the high- to very high-risk subgroup.

CONCLUSIONS

ML modeling effectively subclassified patients with BCLC-C HCC, potentially aiding treatment allocation. Our study underscores the potential utilization of ML modeling in terms of prognostication and treatment allocation in patients with BCLC-C HCC.

Outcomes of patients with hepatocellular carcinoma treated with atezolizumab plus bevacizumab in real-world clinical practice who met or did not meet the inclusion criteria for the phase 3 IMbrave150 trial

BACKGROUND

Atezolizumab plus bevacizumab (Atezo/Bev) is frequently selected as the primary systemic therapy for hepatocellular carcinoma (HCC).

AIMS

To investigate the outcomes of patients with HCC treated with Atezo/Bev in a real-world setting based on whether they met the inclusion criteria for the phase 3 IMbrave150 trial.

METHODS

A total of 936 patients were enrolled. There were 404 patients who met the inclusion criteria of the phase 3 IMbrave150 trial (IMbrave150 group) and 532 who did not (non-IMbrave150 group).

RESULTS

Median progression-free survival (PFS) in the IMbrave150 and non-IMbrave150 groups was 7.4 months and 5.6 months (p = 0.002). Multivariable analysis revealed that non-B, non-C HCC aetiology (hazard ratio [HR], 1.173), α-fetoprotein ≥100 ng/mL (HR, 1.472), Barcelona Clinic Liver Cancer stage ≥ C (HR, 1.318), and modified albumin-bilirubin (mALBI) grade 2b or 3 (HR, 1.476) are independently associated with PFS. Median overall survival (OS) in the IMbrave150 and non-Imbrave150 groups was 26.5 and 18.8 months (p < 0.001). Multivariable analysis revealed that Eastern Cooperative Oncology Group performance status ≥2 (HR, 1.986), α-fetoprotein ≥100 ng/mL (HR, 1.481), and mALBI grade 2b or 3 (HR, 2.037) are independently associated with OS. In subgroup analysis, there were no significant differences in PFS or OS between these groups among patients with mALBI grade 1 or 2a.

CONCLUSIONS

Patients who are treated with Atezo/Bev and meet the inclusion criteria for the phase 3 IMbrave150 trial, as well as those who do not meet the inclusion criteria but have good liver function, have a good prognosis for survival.

Neoadjuvant therapies in resectable hepatocellular carcinoma: Exploring strategies to improve prognosis

Hepatocellular carcinoma (HCC), a challenging malignancy, often necessitates surgical intervention, notably liver resection. However, the high recurrence rate, reaching 70% within 5 years post-resection, significantly impacts patient outcomes. Neoadjuvant therapies aim to preoperatively address this challenge, reducing lesion size, improving surgical resection rates, deactivating potential micro-metastases, and ultimately lowering postoperative recurrence rates. This review concentrates on advances in research on and clinical use of neoadjuvant therapies for HCC, with particular attention to the use of immune checkpoint inhibitors (ICIs) targeting programmed cell death-1 (PD-1), programmed cell death ligand-1 (PD-L1), and cytotoxic T-lymphocyte-associated protein-4 (CTLA-4). Ongoing clinical studies exploring immunotherapy combined with a tyrosine kinase inhibitor (TKI), interventional therapy, radiotherapy, and other modalities offer promising insights into overcoming resistance to monotherapies. In summary, neoadjuvant therapies hold significant promise in terms of improving the prognosis for patients with HCC and enhancing long-term survival, particularly through innovative combination strategies.

Cost-Effectiveness Analysis of Camrelizumab Plus Rivoceranib Versus Sorafenib as a First-Line Therapy for Unresectable Hepatocellular Carcinoma in the Chinese Health Care System

BACKGROUND AND OBJECTIVES

Camrelizumab plus rivoceranib showed significant clinical benefits in progression-free survival and overall survival compared to sorafenib in patients with unresectable hepatocellular carcinoma (HCC). This study aimed to assess its cost effectiveness from the perspective of Chinese health care system.

METHODS

A Markov state-transition model was developed based on the Phase 3 randomized CARES-310 clinical trial data. Health state utility values were obtained from the CARES-310 clinical trial, and direct medical costs were derived from the relevant literature and local charges. The measured outcomes included quality-adjusted life-years (QALYs) and the incremental cost-effectiveness ratio (ICER). Probabilistic and one-way sensitivity analyses were performed to assess the uncertainty of the model.

RESULTS

In the base-case analysis, the incremental effectiveness and cost of camrelizumab plus rivoceranib versus sorafenib were 0.41 QALYs and $13,684.84, respectively, resulting in an ICER of $33,619.98/QALY, lower than the willingness-to-pay threshold of China ($35,864.61/QALY). Subgroup analyses revealed that the ICERs of camrelizumab plus rivoceranib versus sorafenib were $35,920.01 and $29,717.98 in patients with ALBI grade 1 and grade 2, respectively. One-way sensitivity analyses indicated that the cost of camrelizumab, the proportion of patients receiving subsequent treatment in the camrelizumab plus rivoceranib group, and the cost of rivoceranib were the most significant factors in the base-case analysis. Probabilistic sensitivity analysis suggested that the probabilities of cost effectiveness of camrelizumab plus rivoceranib were 61.27%, 51.46%, and 82.78% for any grade, and ALBI grade 1 and grade 2, respectively.

CONCLUSIONS

Camrelizumab plus rivoceranib was more cost effective than sorafenib as first-line therapy for unresectable HCC in the Chinese setting.

Predicting Outcomes of Atezolizumab and Bevacizumab Treatment in Patients with Hepatocellular Carcinoma

A combination of atezolizumab with bevacizumab (AB) is the first regimen that has shown superiority compared to sorafenib and is now being used as the systemic treatment of choice for hepatocellular carcinoma (HCC) patients with Barcelona Liver Cancer Clinic stage C. However, a considerable number of patients do not achieve survival or significant responses, indicating the need to identify predictive biomarkers for initial and on-treatment decisions in HCC patients receiving AB. In this manuscript, we summarized the current data from both experimental and clinical studies. This review will be beneficial for both clinicians and researchers in clinical practice as well as those designing experimental, translational, or clinical studies.

Response Prediction Model of Atezolizumab plus Bevacizumab in Patients with Unresectable Hepatocellular Carcinoma: Multicenter Retrospective Study

INTRODUCTION

Atezolizumab plus bevacizumab (ATZ+BV) treatment has become the first-line regimen for unresectable hepatocellular carcinoma (u-HCC). Prediction of response to it might be clinically beneficial. Using peripheral blood parameters, we aimed to construct a prediction model for ATZ+BV treatment.

METHODS

Clinical records of 119 patients with u-HCC treated by ATZ+BV were retrospectively analyzed. The primary outcome measurement was defined as any-size reduction at the initial image evaluation. Using baseline values of peripheral blood parameters, a prediction model was constructed by univariate and multivariate logistic regression analysis. Validation was performed internally by bootstrap method.

RESULTS

The primary outcome was achieved in 46 patients. Univariate analysis showed that C-reactive protein (CRP), alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin (DCP), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were possible predictors. CRP and DCP, and NLR and PLR had correlation (correlation coefficient >0.3), so we used CRP and NLR as representative factors, respectively. Multivariate analysis constructed the following prediction model: Logit = 1.62-0.61×[CRP] -0.38×[Log10AFP] -0.37×[NLR]. Bootstrapped median (95% confidence interval) of coefficients of CRP, Log10AFP, NLR were -0.64 (-1.46 ∼ -0.11), -0.40 (-0.82 ∼ -0.03), and -0.38 (-0.74 ∼ -0.05), respectively. The area under the receiver operating characteristic curve (95% confidence interval) was 0.73 (0.60-0.80). Median overall survival of the favorably and unfavorably predicted groups were 17.0 and 11.0 months (p = 0.03), respectively.

DISCUSSION

In patients with u-HCC treated by ATZ+BEV, a prediction model constructed using baseline values of CRP, AFP, and NLR had impact on any-size reduction at the initial image evaluation and on prognosis.