TY - JOUR
T1 - Microbiome analyses of blood and tissues suggest cancer diagnostic approach
AU - Poore, Gregory D.
AU - Kopylova, Evguenia
AU - Zhu, Qiyun
AU - Carpenter, Carolina
AU - Fraraccio, Serena
AU - Wandro, Stephen
AU - Kosciolek, Tomasz
AU - Janssen, Stefan
AU - Metcalf, Jessica
AU - Song, Se Jin
AU - Kanbar, Jad
AU - Miller-Montgomery, Sandrine
AU - Heaton, Robert
AU - Mckay, Rana
AU - Patel, Sandip Pravin
AU - Swafford, Austin D.
AU - Knight, Rob
N1 - Funding Information:
Acknowledgements We acknowledge conversations with C. Sepich, C. Martino, R. Bejar, and H. Carter. G.D.P. has been supported by training grants from the National Institutes of Health during the course of this work (5T32GM007198-42; 5T32GM007198-43). S.F. is partially funded through trainee support from Merck KGaA in partnership with the Center for Microbiome Innovation at UC San Diego. Samples acquired for the validation cohort were collected under the following grants: R00 AA020235, R01 DA026334, P30 MH062513, P01 DA012065, and P50 DA026306. The Seven Bridges Cancer Genomics Cloud was used during the course of this work and has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, Contract No. HHSN261201400008C, and ID/IQ Agreement No. 17X146 under Contract No. HHSN261201500003I. This work was supported in part by the Chancellor’s Initiative in the Microbiome and Microbial Sciences (R.K., A.D.S., S.M.-M.) and by Illumina, Inc. through reagent donation in partnership with the Center for Microbiome Innovation at UC San Diego. We thank G. Humphrey and K. Sanders for sample processing, and G. Ackermann, A. Gonzalez, and J. DeReus for assistance with metadata curation and data handling.
Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2020/3/26
Y1 - 2020/3/26
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85081918397&partnerID=8YFLogxK
U2 - 10.1038/s41586-020-2095-1
DO - 10.1038/s41586-020-2095-1
M3 - Article
C2 - 32214244
AN - SCOPUS:85081918397
SN - 0028-0836
VL - 579
SP - 567
EP - 574
JO - Nature
JF - Nature
IS - 7800
ER -