癌症
微生物群
计算生物学
前列腺癌
生物
转录组
疾病
基因组
肺癌
DNA测序
基因组
生物信息学
医学
肿瘤科
内科学
DNA
遗传学
基因
基因表达
作者
Gregory D. Sepich‐Poore,Evguenia Kopylova,Qiyun Zhu,Carolina S. Carpenter,Serena Fraraccio,Stephen Wandro,Tomasz Kościółek,Stefan Janssen,Jessica L. Metcalf,Se Jin Song,Jad Kanbar,Sandrine Miller‐Montgomery,Robert K. Heaton,Rana R. McKay,Sandip Pravin Patel,Austin D. Swafford,Rob Knight
出处
期刊:Nature
[Springer Nature]
日期:2020-03-11
卷期号:579 (7800): 567-574
被引量:800
标识
DOI:10.1038/s41586-020-2095-1
摘要
Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions1–10, we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas11 (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia–IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration. Microbial nucleic acids are detected in samples of tissues and blood from more than 10,000 patients with cancer, and machine learning is used to show that these can be used to discriminate between and among different types of cancer, suggesting a new microbiome-based diagnostic approach.
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