Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.
Microsoft Research, Redmond, WA, USA.
Sci Rep. 2023 Mar 21;13(1):4599. doi: 10.1038/s41598-023-31210-w.
Recent studies revealed that gut microbiota modulates the response to cancer immunotherapy and fecal microbiota transplantation has clinical benefits in melanoma patients during treatment. Understanding how microbiota affects individual responses is crucial for precision oncology. However, it is challenging to identify key microbial taxa with limited data as statistical and machine learning models often lose their generalizability. In this study, DeepGeni, a deep generalized interpretable autoencoder, is proposed to improve the generalizability and interpretability of microbiome profiles by augmenting data and by introducing interpretable links in the autoencoder. DeepGeni-based machine learning classifier outperforms state-of-the-art classifier in the microbiome-driven prediction of responsiveness of melanoma patients treated with immune checkpoint inhibitors. Moreover, the interpretable links of DeepGeni elucidate the most informative microbiota associated with cancer immunotherapy response. DeepGeni not only improves microbiome-driven prediction of immune checkpoint inhibitor responsiveness but also suggests potential microbial targets for fecal microbiota transplant or probiotics improving the outcome of cancer immunotherapy.
最近的研究表明，肠道微生物群调节了对癌症免疫疗法的反应，粪便微生物群移植在治疗期间对黑色素瘤患者具有临床益处。了解微生物群如何影响个体反应对于精准肿瘤学至关重要。然而，由于统计和机器学习模型通常会失去通用性，因此用有限的数据来识别关键的微生物类群具有挑战性。在这项研究中，提出了 DeepGeni，这是一种深度广义可解释自动编码器，通过增加数据和在自动编码器中引入可解释的链接来提高微生物组谱的通用性和可解释性。基于 DeepGeni 的机器学习分类器在基于微生物组的黑色素瘤患者对免疫检查点抑制剂治疗反应的预测中优于最先进的分类器。此外，DeepGeni 的可解释链接阐明了与癌症免疫治疗反应最相关的微生物群。DeepGeni 不仅提高了微生物组驱动的免疫检查点抑制剂反应性预测，还为改善癌症免疫治疗结果的粪便微生物群移植或益生菌提供了潜在的微生物靶标。
