Predicting cancer prognosis and drug response from the tumor microbiome

被引:37
作者
Hermida, Leandro C. [1 ,2 ]
Gertz, E. Michael [1 ]
Ruppin, Eytan [1 ]
机构
[1] Natl Canc Inst NCI, Natl Inst Hlth NIH, Canc Data Sci Lab CDSL, Bethesda, MD 20814 USA
[2] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA
基金
美国国家卫生研究院;
关键词
DIFFERENTIAL EXPRESSION ANALYSIS; CEDECEA-DAVISAE; BACTEREMIA; PATIENT; REGULARIZATION; INFECTION; SELECTION;
D O I
10.1038/s41467-022-30512-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Tumor gene expression is predictive of patient prognosis in some cancers. However, RNA-seq and whole genome sequencing data contain not only reads from host tumor and normal tissue, but also reads from the tumor microbiome, which can be used to infer the microbial abundances in each tumor. Here, we show that tumor microbial abundances, alone or in combination with tumor gene expression, can predict cancer prognosis and drug response to some extent-microbial abundances are significantly less predictive of prognosis than gene expression, although similarly as predictive of drug response, but in mostly different cancer-drug combinations. Thus, it appears possible to leverage existing sequencing technology, or develop new protocols, to obtain more non-redundant information about prognosis and drug response from RNA-seq and whole genome sequencing experiments than could be obtained from tumor gene expression or genomic data alone. Computational approaches have been developed to estimate tumor microbial abundances from whole genomic and RNA-sequencing datasets. Here the authors report the predictive value of tumor microbial abundance, alone or in combination with gene expression data, for cancer prognosis and drug response.
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页数:15
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