Circulating microbial content in myeloid malignancy patients is associated with disease subtypes and patient outcomes

Jakob Woerner, Yidi Huang, Stephan Hutter, Carmelo Gurnari, Jesús María Hernández Sánchez, Janet Wang, Yimin Huang, Daniel Schnabel, Michael Aaby, Wanying Xu, Vedant Thorat, Dongxu Jiang, Babal K. Jha, Mehmet Koyuturk, Jaroslaw P. Maciejewski, Torsten Haferlach and Thomas LaFramboise ()
Additional contact information
Jakob Woerner: Case Western Reserve University
Yidi Huang: Case Western Reserve University
Stephan Hutter: Munich Leukemia Laboratory
Carmelo Gurnari: Cleveland Clinic Foundation
Jesús María Hernández Sánchez: Centro de Investigación del Cáncer
Janet Wang: Case Western Reserve University
Yimin Huang: Case Western Reserve University
Daniel Schnabel: Case Western Reserve University
Michael Aaby: Case Western Reserve University
Wanying Xu: Case Western Reserve University
Vedant Thorat: Case Western Reserve University
Dongxu Jiang: Cleveland Clinic Foundation
Babal K. Jha: Cleveland Clinic Foundation
Mehmet Koyuturk: Case Western Reserve University
Jaroslaw P. Maciejewski: Cleveland Clinic Foundation
Torsten Haferlach: Munich Leukemia Laboratory
Thomas LaFramboise: Case Western Reserve University

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Although recent work has described the microbiome in solid tumors, microbial content in hematological malignancies is not well-characterized. Here we analyze existing deep DNA sequence data from the blood and bone marrow of 1870 patients with myeloid malignancies, along with healthy controls, for bacterial, fungal, and viral content. After strict quality filtering, we find evidence for dysbiosis in disease cases, and distinct microbial signatures among disease subtypes. We also find that microbial content is associated with host gene mutations and with myeloblast cell percentages. In patients with low-risk myelodysplastic syndrome, we provide evidence that Epstein-Barr virus status refines risk stratification into more precise categories than the current standard. Motivated by these observations, we construct machine-learning classifiers that can discriminate among disease subtypes based solely on bacterial content. Our study highlights the association between the circulating microbiome and patient outcome, and its relationship with disease subtype.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28678-x

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DOI: 10.1038/s41467-022-28678-x

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