Gene expression profiles contribute to robustly predicting prognosis in hepatocellular carcinoma

There have been major advances in the armamentarium for hepatocellular carcinoma (HCC) since the last official update of the Barcelona Clinic Liver Cancer prognosis and treatment strategy published in 2018. Whilst there have been advances in all areas, we will focus on those that have led to a change in strategy and we will discuss why, despite being encouraging, data for select interventions are still too immature for them to be incorporated into an evidence-based model for clinicians and researchers. Finally, we describe the critical insight and expert knowledge that are required to make clinical decisions for individual patients, considering all of the parameters that must be considered to deliver personalised clinical management.

Hepatocellular carcinoma (HCC) is one of the main cancer-related causes of death worldwide. Thus, there is a constant search for improvement in screening, diagnosis, and treatment strategies to improve the prognosis of this malignancy. The identification of useful biomarkers for surveillance and early HCC diagnosis is still deficient, with available serum biomarkers showing low sensitivity and heterogeneous specificity despite different cut-off points, even when assessed longitudinally, or with a combination of serum biomarkers. In contrast, HCC biomarkers used for prognostic (when associated with clinical outcomes) or predictive purposes (when associated with treatment response) may have an increased clinical role in the near future. Furthermore, some serum biomarkers are already implicated as a treatment selection tool, whether to provide access to certain therapies or to assess clinical benefit after treatment. In the present review we will discuss the clinical utility and foreseen future of HCC biomarkers implicated in surveillance, diagnosis, prognosis, and post-treatment assessment.

Background The tumor immunological microenvironment (TIME) has a prominent impact on prognosis and immunotherapy. However, the heterogeneous TIME and the mechanisms by which TIME affects immunotherapy have not been elucidated in hepatocellular carcinoma (HCC). Methods A total of 2195 eligible HCC patients from TCGA and GEO database were collected. We comprehensively explored the different heterogeneous TIME phenotypes and its clinical significance. The potential immune escape mechanisms and what genomic alterations may drive the formation of different phenotypes were further investigated. Results We identified three phenotypes in HCC: TIME-1, the “immune-deficiency” phenotype, with immune cell depletion and proliferation; TIME-2, the “immune-suppressed” phenotype, with enrichment of immunosuppressive cells; TIME-3, the “immune-activated phenotype”, with abundant leukocytes infiltration and immune activation. The prognosis and sensitivity to both sorafenib and immunotherapy differed among the three phenotypes. We also underlined the potential immune escape mechanisms: lack of leukocytes and defective tumor antigen presentation capacity in TIME-1, increased immunosuppressive cells in TIME-2, and rich in immunoinhibitory molecules in TIME-3. The different phenotypes also demonstrated specific genomic events: TIME-1 characterized by TP53, CDKN2A, CTNNB1, AXIN1 and FOXD4 alterations; TIME-2 characterized by significant alteration patterns in the PI3K pathway; TIME-3 characterized by ARID1A mutation. Besides, the TIME index (TI) was proposed to quantify TIME infiltration pattern, and it was a superior prognostic and immunotherapy predictor. A pipeline was developed to classify single patient into one of these three subtypes and calculated the TI. Conclusions We identified three TIME phenotypes with different clinical outcomes, immune escape mechanisms and genomic alterations in HCC, which could present strategies for improving the efficacy of immunotherapy. TI as a novel prognostic and immunotherapeutic signature that could guide personalized immunotherapy and clinical management of HCC.