The intestinal microbiota is a complex and diverse ecological community that fulfills multiple functions and substantially impacts human health. Despite its plasticity, unfavorable conditions can cause perturbations leading to so-called dysbiosis, which have been connected to multiple diseases. Unfortunately, understanding the mechanisms underlying the crosstalk between those microorganisms and their host is proving to be difficult. Traditionally used bioinformatic tools have difficulties to fully exploit big data generated for this purpose by modern high throughput screens. Machine Learning (ML) may be a potential means of solving such problems, but it requires diligent application to allow for drawing valid conclusions. This is especially crucial as gaining insight into the mechanistic basis of microbial impact on human health is highly anticipated in numerous fields of study. This includes oncology, where growing amounts of studies implicate the gut ecosystems in both cancerogenesis and antineoplastic treatment outcomes. Based on these reports and first signs of clinical benefits related to microbiota modulation in human trials, hopes are rising for the development of microbiome-derived diagnostics and therapeutics. In this mini-review, we’re inspecting analytical approaches used to uncover the role of gut microbiome in immune checkpoint therapy (ICT) with the use of shotgun metagenomic sequencing (SMS) data.

肠道菌群是一个复杂多样的生态群落，具有多种功能，对人类健康产生重大影响。尽管具有可塑性，但不利的条件可能会引起扰动，导致所谓的菌群失调，而菌群失调与多种疾病有关。不幸的是，事实证明，了解这些微生物与其宿主之间串扰的机制非常困难。传统使用的生物信息学工具很难充分利用现代高通量屏幕为此目的生成的大数据。机器学习（ML）可能是解决此类问题的潜在手段，但需要勤奋应用才能得出有效的结论。这一点尤其重要，因为许多研究领域都高度期待深入了解微生物对人类健康影响的机制基础。这包括肿瘤学，越来越多的研究表明肠道生态系统与癌症发生和抗肿瘤治疗结果有关。根据这些报告以及人体试验中与微生物群调节相关的临床益处的初步迹象，人们对开发微生物组衍生的诊断和治疗方法的希望越来越大。在这篇小型综述中，我们正在研究利用鸟枪法宏基因组测序 (SMS) 数据揭示肠道微生物组在免疫检查点治疗 (ICT) 中的作用的分析方法。