Metagenomic Assay for Identification of Microbial Pathogens in Tumor Tissues

Baldwin, Don A.; Feldman, Michael; Alwine, James C.; Robertson, Erle S.

Averaging probe signals by accession provided a rapid and rather uncomplicated means to summarize the data and collect the strongest detection candidates. The sliding window analysis generally matched AccSig results but provided better ability to distinguish variants within the set of samples and offers the potential to detect candidates represented by only a portion of an accession. As used here, the MAT algorithm did require more labor because it was applied to each sample in separate operations, but this could be addressed by future automated scripting. Analyses at the individual probe level helped to explain how candidates arose in the AccSig and MAT results and are likely the only way in which previously unknown pathogens with some sequence homology to a conserved (or specific) probe can be detected. Thus, a PathoChip screening project can generate a list of candidates prioritized by the magnitude of detection, detection via multiple analysis strategies, and the rate of detection across the sample population. Combining these results with annotations for the virus or pathogenic microorganism such as host range, tissue specificity, or prevalence in the general population will assist in determination of which agents deserve further attention. This approach is likely to provide a signature of a particular cancer or disease with agents with various degrees of contribution. A window into the natural conditions for commensal and pathogenic organisms will greatly enhance our ability to diagnose and treat cancer and other possible diseases not yet linked to specific agents.