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Background Numerous studies have used multi-region sampling approaches to characterize intra-tumor heterogeneity (ITH) in hepatocellular carcinoma (HCC). However, conventional multi-region sampling strategies do not preserve the spatial details of samples, and thus, the potential influences of spatial distribution on patient-wise ITH (represents the overall heterogeneity level of the tumor in a given patient) have long been overlooked. Furthermore, gene-wise transcriptional ITH (represents the expression pattern of genes across different intra-tumor regions) in HCC is also under-explored, highlighting the need for a comprehensive investigation. Methods To address the problem of spatial information loss, we propose a simple and easy-to-implement strategy called spatial localization sampling (SLS). We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. Normalized diversity score and integrated heterogeneity score (IHS) were then developed to measure patient-wise and gene-wise ITH, respectively. Results A significant correlation between spatial and molecular heterogeneity was uncovered, implying that spatial distribution of sampling sites did influence ITH estimation in HCC. We demonstrated that the normalized diversity score had the ability to overcome sampling location bias and provide a more accurate estimation of patient-wise ITH. According to this metric, HCC tumors could be divided into two classes (low-ITH and high-ITH tumors) with significant differences in multiple biological properties. Through IHS analysis, we revealed a highly heterogenous immune microenvironment in HCC and identified some low-ITH checkpoint genes with immunotherapeutic potential. We also constructed a low-heterogeneity risk stratification (LHRS) signature based on the IHS results which could accurately predict the survival outcome of patients with HCC on a single tumor biopsy sample. Conclusions This study provides new insights into the complex phenotypes of HCC and may serve as a guide for future studies in this field.

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Helicobacter pylori (HP) infection is the greatest risk factor for gastric cancer (GC). Increasing evidence has clarified that tumor immune microenvironment (TIME) is closely related to the prognosis and therapeutic efficacy of HP−positive (HP+) GC patients. In this study, we aimed to construct a novel immune−related signature for predicting the prognosis and immunotherapy efficacy of HP+ GC patients. A total of 153 HP+ GC from three different cohorts were included in this study. An Immune−Related prognostic Signature for HP+ GC patients (IRSHG) was established using Univariate Cox regression, the LASSO algorithm, and Multivariate Cox regression. Univariate and Multivariate analyses proved IRSHG was an independent prognostic predictor for HP+ GC patients, and an IRSHG−integrated nomogram was established to quantitatively assessthe prognostic risk. The low−IRSHG group exhibited higher copy number load and distinct mutation profiles compared with the high−IRSHG group. In addition, the difference of hallmark pathways and immune cells infiltration between the two groups was investigated. Notably, tumor immune dysfunction and exclusion (TIDE) analysis indicated that the low−IRSHG group had a higher sensitivity to anti−PD−1 immunotherapy, which was validated by an external pabolizumab treatment cohort. Moreover, 98 chemotherapeutic drugs and corresponding potential biomarkers were identified for two groups, and several drugs with potential ability to reverse IRSHG score were identified using CMap analysis. Collectively, IRSHG may serve as a promising biomarker for survival outcome as well as immunotherapy efficacy. Furthermore, it can also help to prioritize potential therapeutics for HP+ GC patients, providing new insight for the personalized treatment of HP−infected GC.