IntroductionHepatocellular carcinoma (HCC) is the fourth leading cause of cancer-associated mortality globally. Immune-checkpoint blockade (ICB) is one of the systemic therapy options for HCC. However, response rates remain low, necessitating robust predictive biomarkers. In the present study, we examined the expression of CD38, a molecule involved in the immunosuppressive adenosinergic pathway, on immune cells present in the tumor microenvironment. We then investigated the association between CD38 and ICB treatment outcomes in advanced HCC.MethodsClinically annotated samples from 49 patients with advanced HCC treated with ICB were analyzed for CD38 expression using immunohistochemistry (IHC), multiplex immunohistochemistry/immunofluorescence (mIHC/IF) and multiplex cytokine analysis.ResultsIHC and mIHC/IF analyses revealed that higher intratumoral CD38+ cell proportion was strongly associated with improved response to ICB. The overall response rates to ICB was significantly higher among patients with high proportion of total CD38+cells compared with patients with low proportion (43.5% vs 3.9%, p=0.019). Higher responses seen among patients with a high intratumoral CD38+cell proportion translated to a longer median progression-free survival (mPFS, 8.21 months vs 1.64 months, p=0.0065) and median overall survival (mOS, 19.06 months vs 9.59 months, p=0.0295). Patients with high CD38+CD68+macrophage density had a better mOS of 34.43 months compared with 9.66 months in patients with low CD38+CD68+ macrophage density. CD38hi macrophages produce more interferon γ (IFN-γ) and related cytokines, which may explain its predictive value when treated with ICB.ConclusionsA high proportion of CD38+ cells, determined by IHC, predicts response to ICB and is associated with superior mPFS and OS in advanced HCC.

Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.