Management of hepatocellular carcinoma after progression on first-line systemic treatment: defining the optimal sequencing strategy in second line and beyond

The New Era of Systemic Treatment for Hepatocellular Carcinoma: From the First Line to the Optimal Sequence

Hepatocellular carcinoma (HCC) represents the most common primary liver cancer and is considered a major global health problem as one of the leading causes of cancer-related death in the world. Due to the increase in life expectancy and the epidemiological growth of specific risk factors, such as metabolic dysfunction-associated steatotic liver disease (MASLD), the incidence of HCC is growing globally, and mortality rates are still high. Moreover, patients frequently present at an intermediate or advanced tumor stage, when curative treatments, such as surgical resection, liver transplantation or ablation are no longer applicable. In these cases, trans-arterial chemoembolization (TACE), trans-arterial radioembolization (TARE), and systemic therapy are the only suitable options to achieve disease control. The multi-kinase inhibitor Sorafenib has been the only systemic treatment available for unresectable advanced HCC for almost a decade, but in the last couple of years new therapeutic options have emerged. Recent advances in understanding the interactions between the tumor and its microenvironment, especially cancer immune escape, led to the advent of immunotherapy. Currently, first-line systemic treatment for HCC is represented by the combination of the immune checkpoint inhibitor (ICI) Atezolizumab plus Bevacizumab, an anti-vascular endothelial growth factor (VEGF) monoclonal antibody, but many other ICIs have been investigated, such as Nivolumab, Pembrolizumab, Durvalumab and Ipilimumab. However, the problem of second- and third-line therapies, and the correct sequence of treatments remains open and is not addressed in most studies. This explains the urge to find new systemic treatments that can improve the survival and quality of life in patients that can go beyond the first line of treatment. The aim of this paper is to offer a complete overview of the most recent innovations in systemic treatments for unresectable locally advanced and metastatic HCC, including emerging therapies, with a particular focus on treatment sequences. Moreover, we will provide an outlook on possible future approaches to patients who progress beyond first-line therapies.

Gemcitabine-facilitated modulation of the tumor microenvironment and PD-1/PD-L1 blockade generate a synergistic antitumor effect in a murine hepatocellular carcinoma model

BACKGROUND

Gemcitabine can alter the immunogenic microenvironments, and the effect of gemcitabine plus programmed death-1 (PD-1)/ programmed death-ligand 1 (PD-L1) blockade in hepatocellular carcinoma (HCC) is investigated.

METHODS

Subcutaneous H22-green fluorescent protein (GFP) cells inoculation model was constructed and treated with gemcitabine, anti-PD-1 antibody (αPD-1), or the combination every four days when the tumor volumes reached about 50 mm³. Four days after the final treatment, primary tumor tissues were resected and dissociated, which were further subcutaneously injected on the contralateral side to construct the HCC relapse model. The infiltrated proportion of immune cells and PD-1 expression were quantified by flow cytometry. The relative content of transforming growth factor (TGF)-β, interleukin (IL)-12p70, and interferon (IFN)-γ were detected by the enzyme-linked immunosorbent assay (ELISA). Tumor volume and the number of tumor-free mice were evaluated.

RESULTS

Gemcitabine treatment can effectively increase the total proportion of infiltrating immune cells, reduce the proportion of myeloid-derived suppressor cells (MDSCs) and macrophages, and increase T cells proportion without significant growth inhibition. While after gemcitabine treatment, PD-L1 expression on tumor cells and PD-1 on T cells were significantly up-regulated. Subcutaneous tumors volume were reduced considerably after gemcitabine plus αPD-1 treatment compared with gemcitabine (P<0.01) or αPD-1 monotherapy (P<0.001) with the increased proportion of IL-2⁺CD8⁺T, CD8⁺T central memory cells (TCM), CD4 TCM, up-regulated IL12p70 and IFN-γ secretion, and down-regulated TGF-β. Gemcitabine plus αPD-1 blockade could inhibit the relapse tumor model as indicated with down-regulated tumor volume and increased number of tumor-free mice.

CONCLUSION

Gemcitabine up-regulates the proportion of intratumor CD8⁺T and the relative expression of PD-1/PD-L1, and the combination of PD-1/PD-L1 blockade can further inhibit the growth and the relapse of HCC.

Conceptual Framework and Methodological Challenges for Modeling Effectiveness in Oncology Treatment Sequence Models

In this review, we summarize the challenges faced by existing oncology treatment sequence decision models and introduce a general framework to conceptualize such models. In the proposed framework, patients with cancer receive at least two lines of therapy (LOTs) followed by palliative care throughout their lifetime. Patients cycle through progression-free and progressive disease health states in each LOT before death. Under this framework, four broad aspects of modeling effectiveness of treatment sequences need exploration. First, disease progression, treatment discontinuation, and the relationship between the two events should be considered. Second, the effectiveness of each LOT depends on its placement in a treatment sequence as the effectiveness of later LOTs may be influenced by the earlier LOTs. Third, the treatment-free interval (TFI; time between discontinuation of earlier LOT and initiation of later LOT) may impact a therapy's effectiveness. Fourth, in the absence of head-to-head trials directly comparing LOTs, indirect treatment comparison (ITC) of outcomes for a specific LOT or even for the entire treatment sequence is important to consider. A search of decision models that estimated effectiveness of at least two lines of oncology therapy was conducted in PubMed (N = 20) and technology appraisals by the National Institute for Health and Care Excellence (N = 26) to assess four methodological aspects related to the model framework: (1) selection of outcomes for effectiveness in a treatment sequence, (2) approaches to adjust the efficacy of a treatment in consideration of its place in the sequence, (3) approaches to address TFIs between LOTs, and (4) incorporation of ITCs to estimate comparators' effectiveness in the absence of direct head-to-head evidence. Most models defined health states based on disease progression on different LOTs while estimating treatment duration outside of the main model framework (30/46) and used data from multiple data sources in different LOTs to model efficacy of a treatment sequence (41/46). No models adjusted efficacy for the characteristics of patients who switched from an earlier LOT to a later LOT or adjusted for the impact of prior therapies, and just six models considered TFIs. While 11 models applied ITC results to estimate efficacy in comparator treatment sequences, the majority limited the ITC to one LOT in the sequence. Thus, there is substantial room to improve the estimation of effectiveness for treatment sequences using existing data when comparing effectiveness of alternative treatment sequences.

Absent in melanoma 1-like (AIM1L) serves as a novel candidate for overall survival in hepatocellular carcinoma

Identifying biomarkers for hepatocellular carcinoma (HCC) survival is of great importance for the early detection, monitoring, and predicting for prognosis. This study aimed to investigate the candidate biomarkers for predicting overall survival (OS) in HCC patients. Using RTCGAToolbox, top 50 upregulated differential expressed genes (DEGs) were identified. The least absolute shrinkage and selection operator (LASSO) and Cox models were used to select powerful candidate genes, and log rank method was used to address the survivor functions of potential biomarkers. Selected by LASSO model, ANLN, TTK, AIM1L and person neoplasm cancer status might be candidate parameters associated with OS in HCC patients. After adjusting person neoplasm cancer status, ANLN and TTK levels in Cox model, AIM1L was identified as a risk factor for predicting OS in HCC patients (HR = 1.5, P = 0.037). Validated in The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) series, AIM1L was significantly overexpressed in tumor tissues compared to nontumor tissues (all P < 0.0001). HCC patients with high AIM1L in tumor tissues had significantly unfavorable OS compared to those with low AIM1L in TCGA, ICGC, Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier Plotter datasets (all P < 0.05). Conclusively, AIM1L is upregulated in tumor samples and serves as a novel candidate for predicting unfavorable OS in HCC patients.