Prognostic value of a simplified score based on routine parameters in patients with hepatocellular carcinoma treated with systemic therapies: a retrospective cohort study

Introduction

Hepatocellular carcinoma (HCC) is a lethal malignancy with an expected increase in incidence of 55% by 2040 (1). Although screening methods are recommended to detect HCC at an early stage, most patients are diagnosed with advanced-stage disease, when no curative treatments are possible (2).

The management of the advanced stage has evolved over the past decade. In 2008, sorafenib was the first drug that showed improved survival over placebo (3), followed by other tyrosine kinase inhibitors such as lenvatinib (4), regorafenib (5), and cabozantinib (6). After 2020, immunotherapy-based combinations such as atezoliumab plus bevacizumab and durvalumab plus tremelimumab showed improved survival over sorafenib (7,8). More recently, ipilimumab plus nivolumab showed improved survival over sorafenib or lenvatinib, consolidating the role of immunotherapy as the reference standard in front-line treatment of advanced HCC (9).

While the advancements in HCC treatment are promising, there is a pressing need for better prognostic tools. In the long-term follow-up, 25% of the patients selected for durvalumab plus tremelimumab in the pivotal trial were alive at four years (10). Conversely, around 20% of the patients present progressive disease, with a median survival of only 6.8 months (11).

Although several biomarkers and classifications have been proposed to predict treatment benefits and long-term survival, none have been validated to guide treatment decisions. As a result, the management of HCC continues to rely on anatomical and clinical presentation. The only exception is that patients with high alpha-fetoprotein (AFP) benefit from second-line ramucirumab (12). This underscores the importance of more accurately stratifying the prognosis of HCC patients to support risk-guided recommendations and clinical counseling.

A complex clinical and biological context challenges the HCC management. Cirrhosis decompensation during treatment has a negative impact on treatment adherence and survival. The risk of decompensation is associated with untreated etiology and baseline ALBI score (13). Additionally, HCC patients can develop concomitant complications derived from immunosuppression, malnutrition, coagulopathy, and vascular events. The neutrophil-to-lymphocyte ratio (NLR) is a biomarker of systemic inflammation associated with poor prognosis in HCC patients under systemic treatment. NLR may reflect immunological imbalance, protumor inflammatory triggers, and general health fragility (14-16).

In the current study, we aimed to develop a simple and easily applicable prognostic score based on biochemical in patients undergoing systemic treatment for HCC. We present this article in accordance with the STROBE reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2024-988/rc).

Methods

Patients and methods

Data from HCC patients treated at Sao Paulo Cancer Institute (ICESP, Brazil) from July 2009 to June 2024 were retrospectively collected and evaluated. Patients were eligible for inclusion if they had HCC diagnosed by either radiologic or histologic criteria and had initiated first-line systemic therapy in accordance with local treatment protocols. All included patients had either advanced-stage disease (BCLC-C) or intermediate-stage disease (BCLC-B) that was not suitable for locoregional therapies.

Due to the retrospective nature of the study, no preplanned sample size calculation was performed. Instead, a convenience sample was used, including all eligible patients who met the inclusion criteria and had complete baseline clinical and laboratory data available during the study period. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee for Research Project Review of the University of Sao Paulo School of Medicine (No. report 3.807.496). Individual consent for this retrospective analysis was waived.

Data were extracted from medical records and included patient age, sex, performance status (PS) as assessed by the Eastern Cooperative Oncology Group (ECOG) scale (17), underlying disease etiology, BCLC stage, and relevant serum laboratory parameters. The ALBI grade was calculated using the following formula: linear predictor = [log₁₀ bilirubin (µmol/L) × 0.66) + albumin (g/L) × −0.085]. The resulting continuous score was then categorized into three prognostic grades, as previously described: grade 1 (< −2.60), grade 2 (−2.60 to −1.39), and grade 3 (> −1.39) (18). NLR was calculated as the baseline peripheral blood absolute neutrophil count divided by the absolute lymphocyte count. Baseline laboratory parameters were collected locally at the time of initiation of systemic therapy. All measurements were obtained from routine blood tests performed within 7 days before starting treatment. Standardized assays used by the hospital’s clinical laboratory were employed for all biomarker analyses.

The data were last updated on June 12, 2024.

Treatment and assessments

During the study period, sorafenib was the first-line treatment available to patients who were candidates for systemic therapy at the institution. Sorafenib was administered orally at an initial dose of 400 mg twice daily, which could be modified upon the development of adverse events according to type and severity. Between 2020 and 2024, a subset of patients received immunotherapy-based regimens, depending on the temporary availability of the drugs, as a first-line treatment after physicians’ indication. The dose and schedule of immunotherapy were based on the local prescribing information for each drug. Treatment was continued until symptomatic or radiologic disease progression, unacceptable toxicity, or death. Clinical and laboratory assessments were performed at baseline and then monthly, while radiologic evaluations were conducted every two months.

Statistical analysis

Continuous variables were summarized using the median and interquartile range (IQR, 25th to 75th percentiles). Categorical variables were reported as counts and corresponding percentages (%). Comparisons between categorical variables were performed using Fisher’s exact test. Time-to-event outcomes were estimated by the Kaplan-Meier method, presenting medians along with 95% confidence intervals (CIs). Survival time was calculated from the start of treatment until either death or the last follow-up date. Survival functions were compared using the log-rank test. A Cox regression model estimated hazard ratios (HRs) and 95% CI. Tests were two-sided with a significance level of 0.05. Analyses were performed using Stata software version 15.1 (StataCorp, College Station, Texas, USA).

Score modelling

Clinical features examined in univariate and multivariate analyses included BCLC stage, ECOG PS, extrahepatic metastasis, macrovascular invasion, and Child-Pugh class. The selected baseline biochemical features included neutrophils, lymphocytes, platelets, albumin, bilirubin, and AFP. We performed a univariate analysis using the biochemical variables in both continuous and categorical forms. To establish a simplified and reproducible score using the selected variables, we analyzed them using composite variables such as NLR (which included neutrophil and lymphocytes counts) and ALBI-score (which included albumin and bilirubin). Serum AFP and platelet counts were analyzed separately, as they are neither part of ALBI nor NLR. The variables were dichotomized into NLR ≥3 or <3, AFP <200 or ≥200 ng/mL, ALBI-1 vs. ALBI-2 or 3, and platelets < or ≥150,000/mm³ based on the median value in the SOR cohort and previously published data (16,19-21). These variables were evaluated for their prognostic significance in a multivariate analysis adjusted for the BCLC stage.

A simplified point score was devised by assigning one point to overall survival (OS)’s independent baseline negative prognostic risk factors after multivariate analysis. For example, NLR ≥3, AFP ≥200 ng/mL, and ALBI 2–3 scored one point each. Subgroups were divided based on the sum of points (0, 1, 2, or 3) and compared for OS. The score was modeled in the cohort of patients treated with first-line sorafenib (SOR cohort) and validated in the cohort of patients treated with first-line immunotherapy (IO cohort). An IO cohort was selected as a validation set due to the recent incorporation of IO as a standard therapy in HCC and its unique mechanism of oncologic action compared to sorafenib and other tyrosine kinase inhibitors.

To analyze how the score performs among subgroups of responders vs. progressors with each treatment applied (sorafenib and immunotherapy), we defined responders as patients with confirmed partial responses or stable disease based on bimonthly radiological evaluations. Progressors were defined as patients with radiologic progressive disease or symptomatic progression without a prior stable or partial response. Subsequently, we analyzed the correlation between prognostic subgroups and response, and compared survival outcomes among these subgroups.

Results

Baseline characteristics

A total of 440 patients were included in the SOR cohort. Most were male (73.4%), and hepatitis C was the predominant etiology (49.7%). Considering baseline staging, 82.7% were Child-Pugh A, and 73.6% were classified as BCLC-C stage. In 41.6% and 40.7% of the patients, macrovascular invasion and extrahepatic metastasis were found, respectively. Previous local or locoregional treatments were delivered for 52.8% of the patients in this group, mainly transarterial chemoembolization.

Of the 32 patients in the IO cohort, 68.8% were male, and Hepatitis C was the etiology in 50%. All patients had Child-Pugh A, and 73.6% had BCLC-C stage disease. Regarding the IO regimen, 19 patients received combination treatments (13 treated with nivolumab-based combinations, 5 with durvalumab-based combinations, and 1 with atezolizumab-based combination). Additionally, 13 patients received single-agent immunotherapy (10 treated with nivolumab and 3 with durvalumab).

Patients in the IO cohort were older than patients in the SOR cohort, had a higher prevalence of Child-Pugh A and ECOG-PS 0–1, and had a lower prevalence of macrovascular invasion. There were no significant differences in etiology or previous treatment between groups, except that patients in the IO cohort had more previous liver resection than those in the SOR cohort. The baseline features of both cohorts are shown in Table 1.

OS and risk factors

The median OS of the SOR cohort was 9.5 months (95% CI: 8.5–10.6) after a median follow-up of 9.1 (IQR: 4.1–18.8) months. At the last update, 382 (86.8%) had died. Baseline clinical characteristics associated with worse prognosis included BCLC stage C (vs. B), Child-Pugh B class (vs. A), presence of macrovascular invasion (vs. no macrovascular invasion), and performance status ≥2 (vs. 0–1) (Table 2).

In the univariate analysis, the biochemical parameters that were significant for an adverse prognosis included NLR, ALBI score, platelets, and AFP, both as continuous variables and after dichotomization using preplanned cutpoints (Table 3). In the multivariate analysis using dichotomized variables, platelet count was not associated with prognosis, while AFP, NLR, and ALBI score showed a statistically significant association with prognosis, with an HR between 1–2 for OS (Table 4).

Risk groups

The three independent risk factors were used to create the risk model: ALBI grade 2–3, high AFP (≥200 ng/mL), and high NLR (≥3). A Cox model was fitted using the categorical versions of the variables. Each patient was then categorized into one of three risk groups: those with zero or one risk factor, those with two risk factors, and those with three risk factors. There was a significant difference in the survival profiles of the three risk groups (P<0.0001). The median survival time for the 179 patients with 0 or 1 risk factor was 17.4 months. For patients with 2 and 3 risk factors, the median OS was 7.9 and 4.2 months, respectively (Table 5 and Figure 1). When comparing patients with 0–1 vs. 2–3 points in the SOR cohort, the HR for worse survival in patients with 2–3 factors was 2.2; 95% CI: 1.8–2.7; P<0.001. A bootstrap validation was conducted, and the risk ratios were comparable to those obtained in the original multivariate model, offering evidence of the robustness of the model-building process.

Figure 1 Survival curves stratified by subgroups defined by the number of risk factors in the SOR cohort. SOR cohort, cohort of patients treated with first-line sorafenib.

External validation in the IO cohort

We validated the prognostic role of the risk groups in a cohort of 32 patients who received immunotherapy in the first-line (IO cohort). In this group, the median OS was 24.5 months (95% CI: 10.7–34.3) after a median follow-up of 14.5 (IQR: 8.1–22.5) months. Due to the small sample size, we grouped patients into two categories: those with 0 or 1 and 2 or 3 factors. The median OS for patients with 0 or 1 risk factor was 26.7 months (95% CI: 10.7–37.6), while the median OS for those with 2 or 3 risk factors was 15.1 months (95% CI: 4.2–21.9). There was a significant difference in OS between groups, with an HR =2.22 (95% CI: 1.12–4.91) (Table 6 and Figure 2).

Figure 2 Survival curves stratified by subgroups defined by the number of risk factors in the IO cohort. IO cohort, cohort of patients treated with first-line immunotherapy.

Outcomes according to response to systemic treatment

We found a positive correlation between response and score classification in the SOR (P<0.001) but not in the IO cohort (P=0.075). Thus, we performed a multivariate Cox regression to analyze whether the score classification was independently associated with response (as a time-dependent covariate). We found that in both the SOR and IO cohorts, the score was associated with prognosis, with an HR of 4.6 (95% CI: 1.28–17.06; P=0.02) in the IO cohort and an HR of 1.4 (95% CI: 1.15–1.79; P=0.001) in the SOR cohort. In the SOR cohort, the median survival in responders vs. progressors differed according to each score classification (0–1 vs. 2–3 risk factors). In the IO cohort, the subgroups were small, which prevents reaching a definitive conclusion. However, we observed that the difference in the score group was more prominent among the progressors (HR =6.13; 95% CI: 1.15–32.78; P=0.034), while it was negligible between the responders (HR =1.62; 95% CI: 0.23–11.57; P=0.63). This may lead to the hypothesis that response to immunotherapy mitigates the negative impact of the prognostic score.

Discussion

Our study suggests that advanced-stage HCC can be further stratified according to biochemical parameters such as AFP, NLR, and the ALBI score. We proposed a simple prognostic score based on these three variables, which was applied in two cohorts of patients treated with first-line sorafenib and immunotherapy. The median OS of patients in the most favorable group was 17.4 months, while that of patients in the least favorable group was 4.2 months among those treated with sorafenib. In the groups treated with immunotherapy, the median OS was 26.7 and 15.1 months, respectively.

Staging systems have historically been determined based on anatomic criteria such as tumor burden, metastasis, and vascular invasion. In the case of HCC, liver function is also a key determinant of the stage as it impacts treatment indications (22). The Child-Pugh classification is the most widely used method for assessing liver function. However, the Child-Pugh score relies on subjective measures such as the grade of ascites and encephalopathy. Laboratory measurements of albumin, bilirubin, and prothrombin time are graded in fixed cut-offs. On the other hand, the ALBI score is based solely on albumin and bilirubin levels, eliminating the need for subjective assessment of ascites and encephalopathy. The ALBI score was initially developed for HCC patients and validated in different stages, achieving accuracy in detecting small changes in liver function (18,23-25). The prognostic value of the ALBI score was evaluated in patients with compensated cirrhosis, meaning patients without uncontrolled ascites, encephalopathy, or recent gastrointestinal bleeding due to portal hypertension (18,23-25).

AFP is widely adopted in the management of HCC and can guide treatment decisions in HCC. In the REACH-2 trial, patients with AFP ≥400 ng/mL who had progressed on sorafenib showed improved survival with ramucirumab over placebo, while patients with low AFP did not demonstrate benefit (12). AFP is associated with several processes in HCC carcinogenesis. AFP inhibits apoptosis and suppresses anti-tumor immunity by limiting T lymphocytes and natural killer cell activity while promoting the activation of T suppressor cells (26,27). AFP is also associated with upregulation of vascular endothelial growth factor (VEGF) signaling, which regulates angiogenesis and immunosuppressive functions, such as checkpoint proteins, in the tumor microenvironment (28).

Immune cells in the tumor microenvironment may reflect the prognosis and treatment response in patients with HCC undergoing immunotherapy. However, no validated method exists to translate immunologic features from the microenvironment into clinical practice. Cancer-associated systemic inflammation is crucial to carcinogenesis (29). It reflects the direct interaction of immune and tumor cells and hematopoiesis activation in lymphoid organs, establishing a relationship between systemic and local inflammatory response (30-32). Circulating tumor cells clustered with neutrophils are suggested to be associated with metastatic spread (33-35). Tumor-infiltrating lymphocytes are associated with better outcomes in several tumor types, including HCC (36,37). High NLR is associated with worse survival across several solid tumors. Our group has previously reported that a high baseline NLR is associated with worse survival in HCC patients undergoing systemic treatment (16,21). Other groups reported the same finding (14,38,39).

In this study, we propose a simple score that uses biochemical parameters routinely obtained in clinical practice for patients with HCC. This score has the advantage of being based on objective numerical measurements, which are reproducible without subjective judgment, such as performance status scales, ascites severity, and encephalopathy grade. The score could discriminate between different subgroups, even when adjusting for the BCLC stage. The most favorable subgroup presented a 1-year survival rate of 61.7%, compared to 11% in the worst prognostic group.

In pivotal trials with immunotherapy, the shape of the OS and progression-free survival curves suggest that some patients progress early while others achieve long-term benefits. In the Himalaya trial, the median PFS of the patients treated with durvalumab and tremelimumab is 3.8 months, the median OS is 16.4 months, and 25% are alive at four years (10). The same long-term plateau in the tail of the curve was observed in the Checkmate-9DW (9). There are no biomarkers to predict which patients will achieve long-term survivorship. At the same time, clinical trials stratify patients with only a few stratification factors, such as macrovascular invasion, metastasis, etiology, and AFP (7,9,10). More comprehensive and objective prognostic factors are relevant to exploring outlier features and discussing strategies to increase the proportion of patients with systemic treatment benefits.

We validated the score in a small cohort of patients under first-line immunotherapy. Due to the limited number of patients, they were stratified into only two groups, but a clear separation of the curves was observed. In the IO cohort, the prognostic difference among the score groups was more pronounced in the progressors, while it was minimal among the responders. This leads to the hypothesis that response to immunotherapy mitigates the negative impact of the prognostic score. A potential explanation may involve the expansion of lymphocytes in IO responses (40), which counteracts the negative influence of NLR as a prognostic driver. This same pattern was not observed in the SOR cohort, where both responders and progressors were differentiated by the score. However, we acknowledge that this hypothesis requires further validation and exploration in a larger cohort treated with immunotherapy. The small sample size in the IO cohort limited our ability to explore the interaction between the prognostic score and treatment response. Future studies should evaluate how this score performs in identifying responders to specific treatments, particularly immunotherapy, where long-term survival benefits are observed in selected patients.

Several recent studies have explored the prognostic value of gene signatures derived from distinct biological pathways in HCC, offering novel insights into tumor behavior and potential therapeutic targets. Yan et al. established a prognostic model based on cellular senescence-related genes, demonstrating significant associations with clinical features, immune infiltration, and tumor progression pathways (41). Similarly, Chen et al. developed a pyroptosis-related gene signature that stratified HCC patients into distinct risk groups with differing survival outcomes and immune profiles, emphasizing the role of inflammation-related cell death in HCC prognosis (42). Jin et al. identified necrosis-related genes driven by mitochondrial permeability transition and constructed a robust risk model involving genes such as LMNB1, BAK1, and CASP7 and shown to correlate with poor survival (43). While these studies focused primarily on molecular mechanisms, Tian et al. addressed the prognostication of patients with renal cell carcinoma by developing a clinically applicable nomogram incorporating standard demographic and pathological variables. Similar to HCC, renal cell carcinoma lacks predictive therapeutic biomarkers (44).

These findings align with the growing emphasis on integrating routine clinical parameters and biologically relevant gene expression profiles to refine risk stratification in HCC. The current study builds on this foundation by proposing a simplified prognostic score based on routinely available clinical and laboratory data, aiming to provide a practical tool for clinicians managing patients undergoing systemic therapy. Unlike models that rely on high-throughput genomic platforms, our score is designed to be readily applicable in real-world settings while maintaining strong predictive performance.

Our study has several limitations, including the retrospective nature and the limited sample size of patients in the immunotherapy cohort. Most patients received sorafenib, which is not the current standard therapy. However, as part of the public health system in Brazil, the availability of immunotherapy is restricted in our center. This study was not powered to detect how each treatment (IO and SOR) impact on prognosis, once baseline features are not balanced and the retrospective nature hampers this conclusion. Additionally, our study does not provide new insights into novel biomarkers for treatment tailoring and does not endorse the score as a predictive tool for treatment response. The study focuses on integrating three already known markers into a composite prognostic score. Despite these limitations, the results suggest that treatment escalation should be considered for patients in the worst-prognosis group, both in clinical research and daily practice. We recognize that the score must be validated widely and across different contexts, and efforts are being carried out to validate the score in external cohorts.

Conclusions

In conclusion, readily available and low-cost biochemical biomarkers can help select prognostic subgroups in addition to established staging systems. This can identify patients predicted to have long-term survival, improve trial interpretation, and support the development of risk-based therapeutic strategies. This study can be a starting point for risk stratification using simple parameters, and further work is needed to integrate predictive markers and explore treatment-specific interactions.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2024-988/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2024-988/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2024-988/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2024-988/coif). L.G.D.F. reports receiving speaker fees from Bayer, Roche and Astrazeneca. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee for Research Project Review of the University of Sao Paulo School of Medicine (No. report 3.807.496). Individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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