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.
Highlights
Recent work has described the microbiome in solid tumors, microbial content in hematological malignancies is not well-characterized
This class of neoplasms includes acute myeloid leukemia (AML) as well as other diseases that can progress to AML such as myelodysplastic syndrome (MDS), characterized by dysplastic changes of hematopoietic progenitor cells, and myeloproliferative neoplasm (MPN), an over-proliferation of cells
>18,000 solid tumor and matched normal blood samples was able to find microbial signatures in both the solid tissue and blood that could accurately predict tumor type, and the blood signatures could differentiate between cancer patients and healthy individuals
Summary
Recent work has described the microbiome in solid tumors, microbial content in hematological malignancies is not well-characterized. 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. 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. Recent work has investigated the relationships between the microbiome and clinical features in myeloid malignancy patients, though these studies have almost exclusively analyzed the gut microbiome. >18,000 solid tumor and matched normal blood samples was able to find microbial signatures in both the solid tissue and blood that could accurately predict tumor type, and the blood signatures could differentiate between cancer patients and healthy individuals
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