Chia Minghao, Pop Mihai, Salzberg Steven L, Nagarajan Niranjan
Genome Institute of Singapore, Singapore, Singapore.
University of Maryland, College Park, College Park, Maryland, United States.
Cancer Res. 2025 Aug 12. doi: 10.1158/0008-5472.CAN-24-3629.
The study of cancer-associated microbiomes has gained significant attention in recent years, spurred by advances in high-throughput sequencing and metagenomic analysis. Microbiome research holds promise for identifying non-invasive biomarkers and possibly new paradigms for cancer treatment. In this review, we explore the key computational challenges and opportunities in analyzing cancer-associated microbiomes (in tumor/normal tissues and other body sites, e.g. gut, oral, skin), focusing on sequencing-driven strategies and associated considerations for taxonomic and functional characterization. The discussion covers the strengths and limitations of current analysis tools for identifying contamination, determining compositional bias, and resolving species and strains, as well as the statistical, metabolic, and network inferences that are essential to uncover host-microbiome interactions. Several key considerations are required to guide the choice of databases used for metagenomic analysis in such studies. Recent advances in spatial and single-cell technologies have provided insights into cancer-associated microbiomes, and AI-driven protein function prediction might enable rapid advances in this field. Finally, we provide a perspective on how the field can evolve to manage the ever-growing size of datasets and to generate robust and testable hypotheses.
近年来，随着高通量测序和宏基因组分析技术的进步，癌症相关微生物组的研究受到了广泛关注。微生物组研究有望识别非侵入性生物标志物，并可能为癌症治疗带来新的模式。在这篇综述中，我们探讨了分析癌症相关微生物组（肿瘤/正常组织以及其他身体部位，如肠道、口腔、皮肤）时的关键计算挑战和机遇，重点关注基于测序的策略以及分类和功能表征的相关注意事项。讨论涵盖了当前分析工具在识别污染、确定组成偏差、解析物种和菌株方面的优缺点，以及揭示宿主-微生物组相互作用所必需的统计、代谢和网络推断。在这类研究中，指导宏基因组分析所用数据库的选择需要考虑几个关键因素。空间和单细胞技术的最新进展为癌症相关微生物组提供了新的见解，人工智能驱动的蛋白质功能预测可能会推动该领域的快速发展。最后，我们就该领域如何发展以管理不断增长的数据集规模并生成可靠且可检验的假设提出了展望。
