Abstract The clinical relevance of immune landscape intratumoural heterogeneity (immune-ITH) and its role in tumour evolution remain largely unexplored. Here, we uncover significant spatial and phenotypic immune-ITH from multiple tumour sectors and decipher its relationship with tumour evolution and disease progression in hepatocellular carcinomas (HCC). Immune-ITH is associated with tumour transcriptomic-ITH, mutational burden and distinct immune microenvironments. Tumours with low immune-ITH experience higher immunoselective pressure and escape via loss of heterozygosity in human leukocyte antigens and immunoediting. Instead, the tumours with high immune-ITH evolve to a more immunosuppressive/exhausted microenvironment. This gradient of immune pressure along with immune-ITH represents a hallmark of tumour evolution, which is closely linked to the transcriptome-immune networks contributing to disease progression and immune inactivation. Remarkably, high immune-ITH and its transcriptomic signature are predictive for worse clinical outcome in HCC patients. This in-depth investigation of ITH provides evidence on tumour-immune co-evolution along HCC progression. Subject terms: Cancer microenvironment, Hepatocellular carcinoma, Immunoediting __________________________________________________________________ Intratumoural heterogeneity is a feature of liver cancer. Here, the authors demonstrate that heterogeneity exists at the immune cell level in liver cancer and show that tumours with high intratumoural immune heterogeneity demonstrated an immune suppressive microenvironment, which was associated with tumour evolution and a poor prognosis. Introduction Hepatocellular carcinoma (HCC) is known to be a heterogeneous tumour derived primarily from a background of chronic liver inflammation with various etiopathogenesis including chronic viral hepatitis infection, alcoholism and fatty liver diseases^[81]1. Due to the heterogenous nature and hence the limited options for targeted treatment, HCC remains the third leading cause of cancer mortality globally^[82]2. The recent success of immunotherapy in HCC benefits only up to 20% of the patients who would respond to the anti-PD-1 immune-checkpoint blockade (ICB)^[83]3,[84]4. Hence, the current immunotherapy landscape leans towards combination therapy with enhanced clinical efficacy, such as that demonstrated by the recently approved atezolizumab (anti-Programmed death-ligand 1 [PD-L1]) and bevacizumab (anti-Vascular Endothelial Growth Factor A [VEGFA]) combination therapy for advanced HCC from the phase III (IMbrave150) trial^[85]5. Moreover, a recent biomarker study from liver cancer patients treated with ICB demonstrated that low tumour cell transcriptomic diversity and cytolytic activity of CD8+ T cells predict their therapeutic response^[86]6. This warrants a deeper understanding of the complex nature of immune microenvironment and its relationship with tumour genomic profiles in a spatio-temporal manner. The genomic intratumoural heterogeneity (ITH) was previously described as an important hallmark of tumour evolution and cancer progression^[87]7 including in HCC^[88]8,[89]9. On the flip side, the biological and clinical relevance of ITH in the tumour microenvironment (TME), based upon degree of heterogeneity in the spatial distributions and the phenotypes of tumour-infiltrating leukocytes (TILs), is not known. It was previously described that distinct histological TME could impact on clinical outcome of HCC^[90]10,[91]11, whereas multiomic analyses also described intensive ITH in TME of HCC^[92]12. Other recent studies using immunogenomics approach addressed how the immune landscape contributes to genomic ITH in ovarian cancer^[93]13 and HCC^[94]14. Despite all that, the clinical impact and role of immune-ITH in tumour evolution remain unexplored. Furthermore, tumour evolution or immunoediting driven by immunoselective pressure was previously shown in various cancers^[95]15,[96]16; it is however not known whether immune-ITH is linked to TME with different immunoselective pressure, which drives tumour evolution. Given the multistep nature of carcinogenesis and disease progression in HCC, it will be important to study and understand the evolution of its immune microenvironment along with tumour genomic evolution. Our study aims to fill the knowledge gap in the field of tumour ITH and to examine the significance of immune-ITH in tumour evolution and disease progression. Using multi-sectoring and multi-omics approaches on different regions from a single HCC tumour, we found a marked degree of immune-ITH, which is correlated to tumour transcriptomic-ITH. Concurrently, the overall TME shows decreasing immunoselective pressure with increased immune-ITH, indicating an immune evolution towards immune exhaustion/suppression. Along with this differential immunoselective pressure, tumour evolve with distinct escape strategies. We also uncovered immune-ITH-related transcriptome-immune networks and the distinct molecular pathways involved in dictating the disease progression and immune status. The current findings demonstrate the remodelling of immune landscape with increased immune-ITH as another dimension in tumour-immune co-evolution, which can be harnessed as a predictive signature for tumour progression. Results Significant degree of immune-ITH in HCC Based on our previous discovery of significant genomic ITH and its impact on evolution trajectory in HCC^[97]9, we aimed to examine the degree and implication of ITH in the immune landscapes from multiple regions within an HCC tumour. Following strict sampling protocol of two to five regions per tumour (Supplementary Fig. [98]1a, b), we prospectively collected a total of 95 tumour sectors (T) with its adjacent non-tumour liver tissues (N) and peripheral blood (P) from 28 HCC patients who underwent surgical resection as the first-line therapy without any prior treatment (Supplementary Table [99]1). The samples from the same region were analysed by cytometry by time-of-flight (CyTOF), whole genome sequencing (WGS) and RNA sequencing for their immunomic, genomic and transcriptomic profiles, respectively (Fig. [100]1a). CyTOF analysis was performed using 38 surface or intracellular immune markers (Supplementary Table [101]2) as previously described^[102]17. Fig. 1. Significant degree of intratumoural heterogeneity (ITH) in the immune landscapes of HCC. [103]Fig. 1 [104]Open in a new tab a Two to five tumour sectors (T), one adjacent non-tumour sector (N) and one PBMC (P) sample were collected from each of the 28 HCC patients and analysed by CyTOF, RNA sequencing (seq) or whole genome sequencing (WGS) for their genomic, transcriptomic and immunomic profiles. b Global tSNE plots showing 30 immune clusters (0–29) from all tumour sectors (n = 95), each represented by one colour. c Heatmap depiction of 30 immune clusters (rows) with normalized protein markers expression (columns) from all samples. The colour bars on the left correspond to the major immune lineages. d Graphs showing proportions of 30 immune clusters in each tumour sector from five representative HCC patients. e Bar graphs showing proportions of 15 immune subsets (percentages of each immune subsets of total live CD45+ immune cells) in all 95 tumour sectors from 28 HCC patients (each patient labelled and separated by grey colour zone). Bottom, heatmap showing relative immune-ITH scores of 28 HCC patients with respect to its median value. Each bar represents a single tumour sector. f Scatter plot showing correlation between immune-ITH scores calculated from tSNE or manually gated immune clusters. g Scatter plot showing correlation between immune-ITH scores by Spearman’s correlation and Euclidian distance metrics. h Left: representative images from multiplex immunohistochemistry (mIHC) stained for CD8 (green), CD4 (red) and DAPI (blue) on either homogenous (patient A002) or heterogeneous (patient C002) tumours. Scale bar, 50 μm. Right: correlation between immune-ITH calculated by Spearson’s correlation from CyTOF data and standard deviation (SD) of CD4+ and CD8+ T-cell density derived from mIHC data. f–h Spearman’s correlation coefficient, ρ- and p-value were indicated. We employed Phenograph clustering^[105]18 and our in-house CyTOF analytics pipeline^[106]19 on the data generated from all tumour sectors and identified 30 immune cell clusters (Fig. [107]1b). The clusters were assigned to major immune lineages according to their lineage marker expressions (Fig. [108]1c). The varying proportions of these clusters across sectors from the same tumour showed different degree of immune-ITH (Fig. [109]1d and Supplementary Fig. [110]2a). Next, we examined the ITH by manual gating of all major TIL subsets identified as the key global representative of TME in HCC from our previous study^[111]17 (Supplementary Fig. [112]3a). Again, we observed significant variations in the proportions (Fig. [113]1e) and the variances (Supplementary Fig. [114]3b) of these 15 immune subsets in the TME of HCC, indicating varying degree of immune-ITH. Next, to systematically quantify for immune-ITH, we compared the proportions of these 15 immune subsets in pairwise manner across all tumour sectors from each tumour using Spearman’s correlation coefficient (ρ), which measures degree of association or homogeneity^[115]20; the immune-ITH scores (degree of heterogeneity) were reported as 1 − ρ (Supplementary Fig. [116]4a). HCC tumours showed varying degree of immune-ITH (Fig. [117]1e, bottom) and the immune-ITH scores according to t-distributed stochastic neighbor embedding (tSNE) clusters or manual gating of 15 immune subsets showed good concordance (Fig. [118]1f). We also calculated immune-ITH scores using previously described Euclidean distance^[119]21 and demonstrated a high correlation between the two scoring methods (ρ = 0.99, Fig. [120]1g) resulting in the same immune-ITH groupings of HCC tumours according to their respective medians (Supplementary Fig. [121]5a). To validate whether ITH was also reflected by tissue immune cell density, we next examined the heterogeneity in the densities of the CD4^+ and CD8^+ T cells within tumour tissues using multiplex immunohistochemistry (mIHC) (Fig. [122]1h, left). We assigned ITH scoring by calculating the standard deviation (SD) of CD4^+ and CD8^+ T-cell densities from ten regions of each tumour. Indeed, we observed a significant correlation in the degrees of immune-ITH based on CyTOF (proportions of immune subsets) or tissue mIHC (cell densities) (Fig. [123]1h, right), both demonstrating consistent degree of Immune-ITH. Taken together, the above data demonstrated significant immune-ITH within HCC tumours. Tumour evolutionary events are linked to immune-ITH Next, we aim to explore immune-ITH as a hallmark of tumour evolution along tumour mutational trajectory. First, we compared immune-ITH with genomic (DNA)- and transcriptomic (RNA)-ITH, which were shown to be an important hallmark of tumour evolution^[124]7,[125]9. We constructed the phylogenetic trees for the RNA and DNA profiles of the tumours with low or high immune-ITH using median immune-ITH score as the cut off (Fig. [126]2a). The phylogenetic trees illustrated the evolutionary relationships between tumour sectors (T) and the adjacent non-tumour liver tissues (N), based upon similarities and differences in their genetic characteristics^[127]22. In general, we observed concordance between immune-ITH and RNA- or DNA-ITH where tumours with low immune-ITH showed shorter RNA or DNA branch distances between tumour sectors compared to those with high immune-ITH (Fig. [128]2a). Next, we calculated the tumour RNA-ITH, as 1 minus Spearman’s ρ for RNA expression of each gene and DNA-ITH, as ratio of the number of unique DNA mutations to the total number of DNA mutations, with references to