Team Project

Lung Reference Set B Application: Eddie Adams-Micronoma (2021)

Lung Ref Set B App: Adams (2021)
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Adams, EddieMicronoma, Inc.
Feng, ZidingFred Hutchinson Cancer Center
Taxonomically assigned cell-free microbial DNA detected by shotgun metagenomic sequencing. Our method leverages the power of next generation sequencing and machine learning to use hundreds of disease-associated features.
No design specified.
UNKNOWN
Lung and Upper Aerodigestive Cancers Research Group

Our objective is to use the EDRN Lung Cancer Reference Set B to perform a small-scale blinded validation of our diagnostic assay that utilizes the disease-associated abundance of cell-free, plasma-derived microbial DNA. … (Collaborative Groups: Lung and Upper Aerodigestive Cancers Research Group)

Specific Aims: I.   Isolate total cell-free DNA from the EDRN Lung Cancer Reference Set B plasma samples and generate next generation sequencing quality libraries. II.   Sequence the NGS libraries from (I.) and process the sequencing reads through our metagenomics computational pipeline to identify circulating microbial DNA sequences and their taxonomic abundance. III.   Use our trained and tuned machine learning model to analyze and predict the lung cancer status of the samples based on the sequencing data in (II.).
The Oncobiota™Lung Assay utilizes shotgun metagenomic sequencing of patient samples at a target read depth of 20 million reads/sample, a sequencing depth commonly employed in cfDNA copy number variation analyses and shallow whole genome sequencing analyses of somatic mutations. Sequencing in this manner (i.e., non-targeted) gives us access to all taxonomic sources of DNA fragments in circulation (human, bacterial, fungal, viral, and archaeal) and enables the discovery and downstream employment of complex, multi-analyte diagnostic signatures. Total cell-free DNA (human and non-human DNA) is isolated from 400 microliters of K2- or K3-EDTA plasma alongside DNA extraction (blank) controls and used to prepare libraries for Illumina sequencing. Raw sequencing reads are mapped to the human genome and all non-human reads (nominally of microbial origin, are processed through our decontamination metagenomics pipeline to remove historically common DNA extraction kit microbial contaminants and any contaminants identified in DNA extraction controls. The filtered remaining reads are taxonomically assigned using a proprietary workflow against a microbial genome database containing all complete refseq genomes from viruses, bacteria, fungi, and archaea. Feature tables consisting of assigned taxa and their associated abundances are inputted into a trained and tuned machine learning model for analysis and generation of a cancer probability score.

There are currently no biomarkers annotated for this protocol.

No datasets are currently associated with this protocol.


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