Gut microbiome, big data and machine learning to promote precision medicine for cancer

被引:168
|
作者
Cammarota, Giovanni [1 ]
Ianiro, Gianluca [1 ]
Ahern, Anna [2 ,3 ]
Carbone, Carmine [4 ]
Temko, Andriy [5 ,6 ]
Claesson, Marcus J. [2 ,3 ]
Gasbarrini, Antonio [1 ]
Tortora, Giampaolo [4 ]
机构
[1] Univ Cattolica Sacro Cuore, Fdn Policlin Univ Agostino Gemelli IRCCS, Gastroenterol Dept, Rome, Italy
[2] Univ Coll Cork, Sch Microbiol, Cork, Ireland
[3] Univ Coll Cork, APC Microbiome Ireland, Cork, Ireland
[4] Univ Cattolica Sacro Cuore, Fdn Policlin Univ Agostino Gemelli IRCCS, Oncol Dept, Rome, Italy
[5] Univ Coll Cork, Sch Engn, Cork, Ireland
[6] Qualcomm ML R&D, Cork, Ireland
基金
爱尔兰科学基金会;
关键词
FECAL MICROBIOTA; INTESTINAL MICROBIOTA; COLORECTAL-CANCER; INDUCED DIARRHEA; MULTI-OMICS; EFFICACY; THERAPY; CHEMOTHERAPY; PREVENTION; PREDICTION;
D O I
10.1038/s41575-020-0327-3
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer. Large-scale datasets of increasing size and complexity are being produced in the microbiome and oncology field. This Perspective discusses the potential to harness gut microbiome analysis, big data and machine learning in cancer, and the potential and limitations with this approach.
引用
收藏
页码:635 / 648
页数:14
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