||5U01CA230551-02 Interpret this number
||Graduate School Of Public Health And Health Policy
||Exploiting Public Metagenomic Data to Uncover Cancer-Microbiome Relationships
Project Summary / Abstract
The microbiome is now known to influence the onset, progression, and treatment of cancers both within the
gastrointestinal tract and systemically. Whole-metagenome shotgun sequencing (WMS) provides the highest
resolution profiling of the human bacterial, archeal, and viral microbiome available, however, it remains
expensive to perform. Furthermore, the analysis of all microbiome data, including WMS and the less expensive
16S ribosomal RNA amplicon sequencing, is hindered by inability to systematically compare the results to
previous studies and experiments. Thus, high quality re-analysis of public microbiome data offers the opportunity
for rapid and cost-effective elucidation of the roles of bacteria, viruses, and microbial function in the etiology and
progression of cancer. This proposal efficiently extracts new value from published microbiome research through
three aims. First, it improves the interpretability of cancer-linked microbiome profiles by translating concepts from
Gene Set Enrichment Analysis and developing microbial signature resources. Second, it develops new methods
to identify strain-level microbial features, fungi, human viruses, and bacteriophages from WMS data and applies
these to thousands of available cancer-associated metagenomes and controls. Finally, it identifies microbiota,
community structure and functions relevant in the development or inhibition of cancer by pooled analysis and
meta-analysis of publicly available human microbiome profiles, and makes these newly processed data and
manually curated clinical data conveniently available to the cancer research community for further interrogation.
This contribution is significant because it increases the likelihood of identifying new microbiome correlates of
cancer, of correctly distinguishing causal factors from artifacts of confounding or technical batches, and of
developing effective public health interventions based on the human microbiome. The proposed research is
innovative because it identifies and corrects important deficiencies in how microbiome data are processed,
interpreted, and made available for re-use on a large scale by other research teams.
Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma.
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, Ramos M.
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Cancer research, 2020-10-15; 80(20), p. 4335-4345.
Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data.
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Nature medicine, 2020 06; 26(6), p. 964-973.
Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0.
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, Manara S.
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, May U.
, et al.
Nature communications, 2020-05-19; 11(1), p. 2500.