Graphical Abstract
INTRODUCTION
Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, represents a major global health burden as both the fastest rising and second deadliest cancer worldwide [1-4]. Unfortunately, most patients present with intermediate or advanced stage disease, for which few effective therapies exist [1,5]. However, the recent advent of immunotherapy represents a promising avenue for revolutionizing HCC treatment [6-8]. Immunotherapy capitalizes on the body’s immune system to recognize and eliminate cancer cells, offering a targeted and potentially more effective approach [6].
The immune microenvironment in HCC is complex and dynamic, shaped by intricate interactions between cancer cells and the surrounding immune cells [9]. This complexity has fueled the exploration of immunotherapeutic strategies, particularly the advent of immune checkpoint inhibitors (ICIs), which have revolutionized cancer treatment over the past decade [6]. By blocking intrinsic immune-inhibitory ligands or receptors such as programmed death ligand 1 (PD-L1) and cytotoxic T-lymphocyte associated protein 4 (CTLA-4), ICIs release the “brakes” on anti-tumor immunity [10,11]. While ICIs as single agents initially showed marginal benefit in HCC [12-16], the combination of the PD-L1 inhibitor atezolizumab with the anti-angiogenic agent bevacizumab has emerged as a breakthrough treatment regimen, showing unprecedented efficacy in the IMbrave150 trial [17,18]. Based on positive phase III data, this combined immunotherapy gained approval from the US Food and Drug Administration in 2020 as the new first-line standard of care for advanced HCC.
Despite representing a major step forward, the atezolizumab-bevacizumab regimen continues to face significant challenges. Objective response rates remain around 30%, robust predictive biomarkers are lacking, immune-related adverse events can be severe, and prohibitive costs hinder access [16,18,19]. Unraveling the complex interplay between the immune system and liver cancer cells is crucial for identifying predictive biomarkers that can distinguish patients who are likely to derive substantial benefit from those who may not respond as favorably. A comprehensive understanding of the genomic landscape holds the key to unlocking the potential of precision medicine in HCC treatment. While the efficacy of atezolizumab plus bevacizumab is evident, identifying biomarkers capable of predicting response can further refine patient selection, facilitate personalized treatment strategies, optimize therapeutic outcomes, and potentially circumvent unnecessary adverse effects [20].
In the current study, we applied an immune signature score (ISS) prediction model, which was derived from our previous pan-cancer analysis of The Cancer Genome Atlas (TCGA) genomic data [21], to gene expression data from the IMbrave150 cohort [16,18,19] to evaluate the clinical utility of ISS in the context of atezolizumab plus bevacizumab combination immunotherapy. Identifying biomarkers that enrich for responders to such combination regimens would progress precision oncology in HCC. Our approach to analyzing transcriptomic data provides insight around integral immune processes governing immunotherapeutic susceptibility. The findings could ultimately help spare non-responding patients from ineffective treatment and toxicity and guide more judicious, biomarker-directed allocation of emerging immunotherapies for this prevalent, often fatal malignancy.
MATERIALS AND METHODS
Patients and related RNA expression data
The details of IMbrave150 and GO30140 have been published earlier [17,18,22]. IMbrave150 (NCT03434379) was a phase III clinical trial assessing the safety and efficacy of atezolizumab combined with bevacizumab versus that of sorafenib as the primary treatment for unresectable HCC [17,18]. GO30140 (NCT02715531) evaluated the safety and dosage of atezolizumab, both alone and combined with bevacizumab, in patients with HCC [22]. These studies’ primary endpoints were confirmed objective response rate in all patients who received the treatments, and the other key endpoints were overall survival (OS) and progressionfree survival (PFS) in the intention-to-treat population, as evaluated using an impulse residue function according to Response Evaluation Criteria in Solid Tumors, version 1.1 [23]. Combining the two studies’ patient populations yielded 253 patients in the atezolizumab plus bevacizumab treatment arm and 58 patients in the sorafenib treatment arm who had available gene expression data. Those without clinical outcome data were excluded, resulting in the inclusion of 247 patients in the atezolizumab plus bevacizumab arm and 48 patients in the sorafenib arm for the analysis (Fig. 1). Since the majority of the data set originates from the IMbrave150 cohort, we will subsequently denote the combined data set as “IMbrave150plus.”
RNA sequencing was conducted using Illumina TruSeq RNA Access and analyzed following established procedures as described in a previous study [19]. The reads from RNA-seq underwent alignment to ribosomal RNA to eliminate ribosomal reads. Subsequently, the remaining reads were aligned to the human reference genome (NCBI Build 38) using GSNAP (v.2013-11-01). Gene expression levels were assessed by counting reads mapped to RefSeq gene exons employing GenomicAlignments. For each cohort, raw gene counts underwent TMM normalization and voom transformation to generate normalized log2 (CPM) data. Gene features were filtered based on expression and variance thresholds. Gene expression data from IMbrave150 and GO30140 are available in the European Genome-Phenome Archive under the accession number EGAD00001 008128.
We also analyzed an independent HCC cohort treated with a different immunotherapy combination. Gene expression data and clinical data from a Taipei HCC cohort were obtained from the NCBI Gene Expression Omnibus site under accession number GSE140901 [24]. The Taipei cohort predominantly consisted of HCC patients treated with either nivolumab monotherapy (n=13) or a combination of nivolumab and ipilimumab (n=9). From the initial 24 patients, we excluded 2 who received a markedly different treatment regimen (spartalizumab and sabatolimab combination), ensuring a more homogeneous treatment group for our analysis. Finally, gene expression data and clinical data including viral etiology from TCGA HCC cohort were obtained from cBioPortal (https://www.cbioportal.org/) [25].
Immune signature
We extracted a previously identified set of 105 genes used in the ISS prediction model [21] from RNA-seq data of HCC tumors in the TCGA project (TCGA-LIHC) [25]. This gene set from LIHC data served as the training set during the development of prediction models. Subsequently, the signature was employed to stratify patients in IMbrave150plus. During the development of prediction models using the IMbrave150plus data set, 8 genes from the original training set were excluded due to variations in RNA-seq data processing. In essence, the expression data for the remaining 97 ISS genes in the training set were combined to construct a classifier using 6 different algorithms to stratify the patients into ISS high or low subtypes.
Data analysis
The mRNA expression data that were collected were normalized and transformed in line with previous studies [26-31]. BRB-ArrayTools (v 4.6.2), a freeware program from the National Institutes of Health (https://brb.nci.nih.gov/BRB-ArrayTools/), was used to construct predictive models to evaluate the clinical relevance of ISS [32]. Cluster (v 3.0) and TreeView (v 1.6) were employed to generate a heatmap of gene expression data [33]. The R language (http://www.r-project.org,v4.1.1) was used for statistical analysis.
Prior to pooling RNA expression data from TCGA-LIHC and IMbrave150plus for predictive purposes, expression data from both data sets were independently converted to z-scores [z=(x−mean)/standard deviation] [34]. Six independent prediction algorithms were used to categorize individual human HCC samples into ISS high or ISS low subtypes [34-36]. These algorithms included linear discriminant analysis, support vector machines, nearest centroid, nearest neighbor, compound covariate predictor, and Bayesian compound covariate predictor (BCCP). In the BCCP prediction, the classifier estimated the probability that an individual patient possessed either a high or low immune signature, utilizing a Bayesian probability of IS score cutoff at 0.5. This cutoff was optimized by comparing response rates to immunotherapies in previous studies [21,37].
Identification of minimum number of genes for prediction model
To determine the minimum number of genes required for our prediction model without compromising its accuracy, we employed two approaches to evaluate the impact of using fewer genes during training and on predicted clinical outcomes. First, we evaluated the miscalculation rate during prediction using a reduced number of genes in the training set. Utilizing the pamr algorithm [38], we assessed the miscalculation rate across different gene counts and observed a significant increase in miscalculation rate when the predictor used fewer than 10 genes. This analysis suggested that 10 genes offered a good balance between model complexity and accuracy.
In the second approach, we employed a stepwise iterative methodology to assess the impact of removing genes from the prediction models on the predicted outcomes in the IMbrave150plus data set. Initially, we ranked the 97 genes in the ISS signature based on their expression ratio between the high and low subtypes using the TCGA training data. We began with a 97-gene BCCP model and systematically eliminated the bottom 10 genes from the rank list in each iteration. After removing each set of 10 genes, we refit Cox proportional hazards models and calculated hazard ratios (HRs) for OS prediction. This process enabled us to evaluate the prediction performance across iterative models containing 90, 80, 70 genes, and so on, until we reached a final model with only the top 10 genes. The HRs were used to assess the significance of each gene removal step on survival prediction. By gauging the HRs, we were able to determine the impact of removing specific genes on OS prediction, allowing us to find minimum gene set for accurate prediction.
Analysis of immune cell population using CIBERTSORT
To unravel the composition of immune cell populations within each HCC tumor, we employed CIBERSORT [39], a computational method widely used for analyzing immune cell composition in cancer cells. The deconvolution algorithm of CIBERSORT uses bulk gene expression data to estimate the relative proportions of different immune cell types in a mixed cell population. It relies on a reference gene signature matrix comprising gene expression profiles of 22 functionally distinct immune cell types. By comparing the gene expression profile of a mixed cell population to the reference matrix, CIBERSORT can estimate the relative proportions of each immune cell type present in the sample.
RESULTS
ISS is significantly associated with therapeutic benefit of combination treatment with atezolizumab and bevacizumab
Using an immune signature derived from prior analyses involving pan-cancer-wide examination of genomic data from the TCGA project [21,40-43], we formulated several prediction models. These models were designed to assess the consistency and resilience of predictions, aiming to categorize HCC tumors within the IMbrave150plus cohort into potential groups with high and low response to the combination therapy of atezolizumab and bevacizumab (Fig. 2A).
Kaplan-Meier plots and log-rank tests on OS data revealed that patients identified as high responders by the BCCP model exhibited better OS and PFS outcomes with atezolizumab and bevacizumab treatment compared to sorafenib (Fig. 2B, 2C). This trend persisted across all 6 prediction models (Supplementary Fig. 1). In contrast, patients predicted as low responders showed no significant difference in OS or PFS between the 2 treatment arms. These results demonstrate the ability of gene expression– based predictors to identify patients likely to respond to the combination treatment with atezolizumab and bevacizumab, underscoring the strong association between the ISS and treatment response.
Consistently, Cox proportional hazard regression analysis also revealed a significant advantage of the combination treatment over sorafenib, specifically among patients classified under the ISS high subtype. This advantage was seen in OS among patients classified under the ISS high subtype (HR 0.263; 95% CI 0.135–0.51; P=7.78×10-5) but not those classified under the ISS low subtype (HR 0.875; 95% CI 0.46–1.660.875; P=0.683; Fig. 3). Similar findings were observed in PFS (ISS high subtype, HR 0.496; 95% CI 0.275–0.894; P=0.019; ISS low subtype, HR 0.948; 95% CI 0.599–1.499; P=0.82; Fig. 3).
Overall, these results demonstrate that the combination treatment significantly prolongs OS and PFS compared to sorafenib monotherapy specifically in patients with the ISS high subtype and further suggest that the benefit of the combination treatment might be attributable to differences in the underlying tumor immune microenvironment between subtypes.
Development of ISS10
To accelerate the clinical application of our findings using simpler technology platforms such as real-time PCR or NanoString, we aimed to streamline our prediction models by reducing the number of genes without compromising predictive power. Our goal was to identify the smallest gene signature that maintains strong prediction of clinical outcomes, as smaller gene panels lower the cost and increase the accessibility of data generation on routine clinical platforms.
Our assessment of miscalculation rates during training identified 10 genes as the optimal number for constructing the predictor (Supplementary Fig. 2A). Next, our stepwise iterative gene reduction process yielded the ISS10 gene signature (Fig. 4A). Patients in the ISS10 high subtype maintained an OS advantage with the combination treatment versus sorafenib (HR 0.2805; 95% CI 0.145–0.54; P=1.4×10-4), consistent with the full 97-gene model (HR 0.2628; 95% CI 0.135–0.51; P=7.78×10-5; Supplementary Fig. 2B). Conversely, models with more than 10 genes did not yield superior outcomes in predicting responders to the combination treatment.
Subsequent analysis with predicted outcomes of ISS10 showed non-inferior outcomes to the prediction with the full gene set. Kaplan–Meier plots and log-rank tests on OS data revealed that patients identified as high responders by the ISS10 prediction model exhibited enhanced OS with atezolizumab and bevacizumab treatment compared to sorafenib (Fig. 4B). In contrast, patients predicted as low responders showed no significant difference in overall OS between the two treatment arms. Likewise, we observed a similar significant difference in PFS between predicted high and low responders (Fig. 4B). In contrast with the ISS10 high subtype, there was no discernible OS benefit observed from the combination treatment among patients classified under the ISS10 low subtype (HR 0.883; 95% CI 0.46–1.71; P=0.712; Fig. 4C). Similar findings were observed in PFS (ISS10 high subtype, HR 0.5045; 95% CI 0.2875–0.8854, P=0.017; ISS10 low subtype, HR 0.938; 95% CI 0.5844–1.508; P=0.79).
In alignment with these findings, when patients were categorized into ISS10 high and low subtypes in the context of responders and non-responders, those in the high subtype group showed significantly better responses only when treated with the combination of atezolizumab and bevacizumab (P=8.6×10-5 by χ2-test in Table 1 and P=5.0×10-4 by χ2-test in Supplementary Table 1). These results underscore the potential of the ISS10 signature as a predictive biomarker for treatment response, particularly in patients receiving atezolizumab and bevacizumab combination therapy. The ISS10 signature effectively stratifies patients into high and low responders, demonstrating its utility in identifying individuals likely to benefit from this treatment regimen. Taken together, these results demonstrate that the ISS10 model sufficiently identifies combination treatment responders without sacrificing accuracy compared to the full gene signature.
Significance of ISS10 in HCC cohort treated with nivolumab and ipilimumab
To validate the predictive capacity of ISS10 in HCC patients treated with different ICIs, we analyzed gene expression data from the Taipei cohort. This cohort comprised HCC patients treated with either nivolumab monotherapy (n=13) or a combination of nivolumab and ipilimumab (n=9). Our analysis revealed that patients classified with the ISS10 high subtype exhibited significantly prolonged OS and a higher proportion with partial response or stable disease (Fig. 5A and 5C, Supplementary Table 2). Notably, the predictive power of ISS10 was particularly pronounced in patients receiving the combination therapy, as demonstrated by more distinct survival curve separation (Fig. 5B) and an increased fraction of patients responding to the treatment (Supplementary Table 2). These findings further validate ISS10’s robust predictive capability across different ICI regimens in HCC patients, potentially offering valuable guidance for clinical decision-making.
Immune cell subpopulations in ISS10 subtypes
To gain insights into the biological basis for the better response of the ISS10 high subtype of HCC tumors to the combination therapy, we conducted an in-depth analysis of the immune cell subpopulations present in these tumors. Specifically, we used the CIBERSORT algorithm to estimate the percentage of immune cells in these tumors by analyzing their gene expression data (Fig. 6). Our analysis revealed that the ISS10 high subtype had a significantly higher fraction of anti-tumor macrophage M1, while the ISS10 low subtype had a significantly higher proportion of pro-tumor macrophage M2 and undifferentiated macrophage M0 (Supplementary Fig. 3). These findings suggest that the absence of active anti-cancer macrophages may contribute to the poor response of ISS10 low tumors to the combination therapy. Furthermore, the ISS10 low subtype was characterized by a higher fraction of resting mast cells and natural killer (NK) cells, indicating a more immunologically quiescent and less inflamed tumor microenvironment. This quiescent state may lead to less active anti-tumor immunity in this subtype, leading to the poor response to checkpoint inhibition. In contrast, the ISS10 high subtype was characterized by a higher fraction of CD8 T-cells, CD4 memory activated T-cells, and follicular helper T-cells. These findings indicate that the immune microenvironment in the ISS10 high subtype is primed to recognize and eliminate tumor cells. Therefore, ICI treatment may further stimulate anti-tumor immunity, leading to a more robust response to the combination therapy. Overall, our analysis suggests that the differential distribution of immune cell subpopulations in the ISS10 subtypes may underlie their distinct responses to the combination therapy. These findings highlight the importance of understanding the tumor microenvironment and the immune landscape in guiding the development of effective immunotherapeutic strategies for HCC.
Biological and etiological characteristics in ISS10 high subtypes
To gain in-depth understanding of the biological processes at play in the ISS10 high subgroup, we employed Ingenuity Pathway Analysis to identify genes that were differentially expressed between ISS10 high and low HCC tumors in both the TCGA and IMbrave150plus cohorts (Fig. 7A). As expected, the analysis revealed a strong emphasis on immune response pathways in the ISS10 high subtype. These pathways included NK cell signaling, communication between dendritic cells and NK cells, interactions between immune and non-immune cells, interferon gamma signaling, T helper cell differentiation (Th1/Th2), and signaling pathways mediated by interleukin-27, interleukin-10, and macrophage activation (Fig. 7B). These findings align with the notion that the ISS10 high subtype exhibits a heightened immune response, potentially explaining its improved response to the combination therapy. Further exploration of these pathways has the potential to unveil additional insights into HCC biology and guide the development of novel and targeted immunotherapies.
Finally, given that hepatitis B virus (HBV) and hepatitis C virus (HCV) are prevalent causes of HCC in various geographical regions [44,45], we investigated the potential relationship between viral etiology and ISS10 subtype using gene expression and clinical data from the TCGA study [25]. Our analysis revealed a notable association between viral etiology and the ISS10 high subtype (Supplementary Fig. 4A and Supplementary Table 3). This association was particularly pronounced in patients with HCV infection and even more striking in cases of dual infection with both HBV and HCV. These findings suggest that HCC patients with viral etiologies, especially those related to HCV or dual HBV and HCV infections, may derive greater benefit from ICI therapy compared to patients with non-viral HCC etiologies. To further validate this observation, we examined the relationship between viral etiology and ISS10 subtype in the Taipei cohort. Consistent with our findings in the TCGA cohort, we observed a trend where a larger proportion of HCC patients with HCV infection were classified into the ISS10 high subtype (Supplementary Fig. 4B and Supplementary Table 4). This corroborating evidence strengthens the potential link between viral etiology, particularly HCV, and the ISS10 high subtype, which may have implications for predicting responsiveness to ICI therapy in HCC patients.
DISCUSSION
In this study, we explored the clinical utility of ISS derived from TCGA pan-cancer genomic analyses within the context of atezolizumab plus bevacizumab combination therapy in HCC. Our analysis, based on gene expression data from the IMbrave150plus cohort, aimed to identify predictive biomarkers that could refine patient selection and optimize therapeutic outcomes. Our findings demonstrate a significant association between ISS and the therapeutic benefit of atezolizumab plus bevacizumab combination treatment. Multiple prediction models consistently identified an ISS high subgroup of HCC patients exhibiting improved OS and PFS on atezolizumab and bevacizumab compared to sorafenib. In contrast, ISS low HCCs failed to show survival advantage with the immunotherapy combination over sorafenib. These findings highlight the integral role of the tumor immune microenvironment in determining therapeutic outcomes and the potential of the ISS to prospectively identify patients likely to benefit from this emerging first-line regimen.
To expedite clinical application, the original 97-gene signature was refined and streamlined down to 10 key genes (ISS10). Rigorous stepwise gene reduction and misclassification analyses verified that 10 was the minimum number of genes required to maintain accurate predictive power. The ISS10 model could effectively identify responders to the immunotherapy combination without sacrificing precision compared to the larger signature. This more focused 10-gene biomarker panel offers a practical and accessible clinical tool to guide more precise application of atezolizumab and bevacizumab versus sorafenib based on the tumor immune profile. Therefore, the ISS10 model will lower cost barriers and technical complexity, providing a more feasible approach for clinical implementation compared to the full multi-gene signature.
Importantly, the predictive power of ISS10 extends beyond the atezolizumab and bevacizumab combination therapy. Among HCC patients treated with nivolumab alone or in combination with ipilimumab, those classified with the ISS10 high subtype demonstrated significantly longer survival and better response to their therapy. Notably, this association was more pronounced in patients receiving the nivolumab and ipilimumab combination. This finding is particularly significant given that this combination was approved for second-line HCC treatment following the Check-Mate 040 trials [46], and recent interim analysis of the CheckMate 9DW phase III trial indicated the combination’s superiority to lenvatinib or sorafenib in first-line treatment [47]. These results suggest that ISS10 could serve as a valuable tool for guiding ICI treatment selection in HCC patients across different immunotherapy regimens.
Our examination of immune cell subpopulations unveiled contrasting profiles between ISS10 high and low subtypes. The ISS10 high subtype exhibited enrichment in anti-tumor M1 macrophages and activated T-cell subsets, implying a microenvironment conducive to effective immunity in baseline tumor tissues. Conversely, the ISS10 low subtype demonstrated an abundance of immunosuppressive M2 macrophages and quiescent NK cells, indicating immune quiescence and potentially explaining their limited response to the combination therapy. In-depth pathway analysis of genes correlated with two subtypes further elucidated the immune-related pathways enriched in ISS10 high tumors, providing insights into the molecular mechanisms underlying treatment response. These pathways, including NK cell signaling and interferon gamma signaling, underscore the importance of immune activation in mediating therapeutic efficacy. Moreover, our analysis into the biological characteristics of ISS10 subtypes uncovered associations with HS cell traits, highlighting the interplay between immune activity and tumor biology. ISS10 high tumors exhibited elevated HS cell features, suggesting that genetic traits of HS cells may trigger the activation of immune cells in the tumor microenvironment. These findings shed light on the underlying biology of HCC and offer insights into potential mechanisms driving treatment response.
Our investigation into the relationship between viral etiology and the ISS10 subtype in HCC revealed intriguing results. We found a significant correlation between viral etiology, particularly HCV infection, and the ISS10 high subtype. This association was even more pronounced in cases of co-infection with both HCV and HBV. These findings suggest that HCC patients with viral backgrounds, especially those linked to HCV or dual HBV/HCV infections, might be more responsive to ICI therapy. Our observations are consistent with previous studies that demonstrated improved survival rates in HBV- or HCV-related HCC patients receiving ICI treatment compared to those with non-viral HCC [48,49]. However, the literature on this topic presents some inconsistencies. While our findings and some earlier studies suggest a potential advantage for patients with viral HCC in ICI treatment outcomes, more recent meta-analyses did not find a clear connection. A comprehensive review of randomized clinical trials of ICIs in advanced HCC, along with a separate analysis of multiple ICI trials, failed to establish a clear link between viral etiology and ICI treatment efficacy [50-52]. These conflicting results underscore the complexity of the relationship between HCC etiology and immunotherapy outcomes, suggesting the presence of additional, as-yet-unidentified factors influencing treatment response.
While our ISS originated independently from the IMbrave150 study, derived from the analysis of the TCGA data set in a pan-cancer manner, the performance of the ISS in identifying potential responders to combination therapy in the IMbrave150plus cohort was highly comparable to that of the atezolizumab plus bevacizumab response signature (ABRS) [19]. Notably, ABRS genes were identified through differential expression analysis between responders and non-responders in the GO30140 and IMbrave150 trial. Because the genes in ABRS were identified through differential expression analysis between responders and non-responders in the GO30140 and IMbrave150 trial, it is expected that patients with high expression of ABRS genes would have longer PFS (HR 0.49; 95% CI 0.25–0.97; P=0.041) and OS (HR 0.26; 95% CI 0.11–0.58; P=0.0012) than those with low expression. Interestingly, our ISS10 achieved similar efficacy in predicting response, showing non-inferior outcomes to those of ABRS with pre-selected responder genes (PFS, HR 0.5045; 95% CI 0.2875– 0.8854; P=0.017; OS, HR 0.2805; 95% CI 0.145–0.54; P=1.4×10-4), suggesting its ability to effectively capture underlying immunological features in the tumor microenvironment. Interestingly, despite similar efficacy, none of the genes in ISS10 (ITGAL, IKZF3, CD8A, TOX, GBP5, PRF1, CCL5, LCK, SLAMF7, and CXCL9) overlap with those in ABRS (CXCR2P1, ICOS, TIMD4, CTLA4, PAX5, KLRC3, FCRL3, AIM2, GBP5, and CCL4) [19]. This suggests that ISS10 captures distinct, yet potentially complementary, aspects of the tumor immune microenvironment, contributing to its comparable effectiveness in predicting response to the combination therapy with atezolizumab and bevacizumab and to the combination with nivolumab and ipilimumab.
This study has several limitations that should be considered when interpreting the results. First, the findings require prospective validation in additional independent cohorts to firmly establish the clinical utility of the 10-gene signature. The study was conducted retrospectively on data from previously completed clinical trials. Validating the signature’s predictive power prospectively is essential before routine clinical use. Second, the study population included only advanced HCC patients eligible for first-line systemic therapy. Whether the signature predicts immunotherapy benefit in patients with earlier-stage disease merits further investigation. Third, the current study used transcriptomic data alone for prediction. Incorporating complementary biomarkers from immunohistochemical analysis to measure protein expression could potentially enhance accuracy. Fourth, the association of ISS10 with the clinical outcome of the combination therapy with nivolumab and ipilimumab requires further validation using data from a randomized clinical trial like CheckMate 9DW, as the number of patients in the Taipei cohort is very small and do not represent a randomized trial. Overall, further clinical validation cohorts, experimental functional studies, and assay standardization will maximize clinical impact moving forward. However, the current study provides proof-of-concept for an immunogenomic biomarker predicting immunotherapy outcomes in HCC patients.
In summary, this research illustrates that the ISS can function as a predictive biomarker for identifying HCC patients who are likely to experience improved survival outcomes in response to atezolizumab and bevacizumab combination therapy compared to sorafenib and in response to nivolumab and ipilimumab combination therapy. By narrowing down the signature to 10 key genes, a concise panel has been established that maintains accuracy in guiding treatment decisions and is easily applicable in clinical settings. This biomarker also revealed valuable biological insights by highlighting distinct immune cell and etiology characteristics linked to the response to combination therapy. In future studies, it will be crucial to validate the ISS10 panel prospectively in additional patient groups and to conduct head-to-head comparisons with other proposed signatures to firmly establish its usefulness. Overall, the clinical translation of biomarkers that identify immunotherapy responders remains essential for enhancing survival rates in advanced HCC by precisely matching patients with treatments based on their biology. Subsequent investigations can expand upon these findings by refining and prospectively validating the predictive model while exploring actionable mechanisms associated with primary immunotherapy resistance.
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