Gastric cancer (GC) is a leading contributor to global cancer incidence and mortality. Pioneering genomic studies, focusing largely on primary GCs, revealed driver alterations in genes such as ERBB2, FGFR2, TP53 and ARID1A as well as multiple molecular subtypes. However, clinical efforts targeting these alterations have produced variable results, hampered by complex co-alteration patterns in molecular profiles and intra-patient genomic heterogeneity. In this Review, we highlight foundational and translational advances in dissecting the genomic cartography of GC, including non-coding variants, epigenomic aberrations and transcriptomic alterations, and describe how these alterations interplay with environmental influences, germline factors and the tumour microenvironment. Mapping of these alterations over the GC life cycle in normal gastric tissues, metaplasia, primary carcinoma and distant metastasis will improve our understanding of biological mechanisms driving GC development and promoting cancer hallmarks. On the translational front, integrative genomic approaches are identifying diverse mechanisms of GC therapy resistance and emerging preclinical targets, enabled by technologies such as single-cell sequencing and liquid biopsies. Validating these insights will require specifically designed GC cohorts, converging multi-modal genomic data with longitudinal data on therapeutic challenges and patient outcomes. Genomic findings from these studies will facilitate ‘next-generation’ clinical initiatives in GC precision oncology and prevention.

胃癌 (GC) 是全球癌症发病率和死亡率的主要贡献者。开创性的基因组研究，主要关注原发性 GC，揭示了ERBB2、FGFR2、TP53和ARID1A等基因的驱动改变以及多种分子亚型。然而，针对这些改变的临床努力产生了不同的结果，受到分子谱和患者基因组异质性中复杂的共同改变模式的阻碍。在这篇综述中，我们强调了剖析 GC 基因组图的基础和转化进展，包括非编码变异、表观基因组畸变和转录组改变，并描述了这些改变如何与环境影响、种系因素和肿瘤微环境相互作用。在正常胃组织、化生、原发性癌和远处转移中绘制这些改变在 GC 生命周期中的图谱将提高我们对推动 GC 发展和促进癌症标志的生物学机制的理解。在翻译方面，通过单细胞测序和液体活检等技术，整合基因组方法正在确定 GC 治疗耐药性的不同机制和新兴的临床前靶点。验证这些见解将需要专门设计的 GC 队列，将多模式基因组数据与治疗挑战和患者结果的纵向数据相结合。这些研究的基因组学结果将促进 GC 精准肿瘤学和预防的“下一代”临床计划。将多模式基因组数据与有关治疗挑战和患者结果的纵向数据相结合。这些研究的基因组学结果将促进 GC 精准肿瘤学和预防的“下一代”临床计划。将多模式基因组数据与有关治疗挑战和患者结果的纵向数据相结合。这些研究的基因组学结果将促进 GC 精准肿瘤学和预防的“下一代”临床计划。