Analysis flow. We began the analysis with the hypothesis that the OVOLs impact MET in multiple cancers. We used RNA-Seq to identify sets of genes that are differentially expressed in response to OVOL TFs overexpression in BC and PC models. At the intersection of these sets are genes that are differentially expressed in OVOL Induced MET (OI-MET) across these two cancer models. We test the hypothesis that this set should be enriched for genes annotated for association with cancer, breast cancer, prostate cancer, and MET. We find annotation consistent with this hypothesis, as well as annotation for regulation of gene expression by AP1, STAT1, STAT3, and NFKB1 TFs. Pursuing this secondary hypothesis, we developed the OI-MET-TF model, based on the genes annotated as being regulated by these TFs and the OVOL TFs. Genes in the OI-MET-TF network are even more significantly enriched for cancer, breast cancer, prostate cancer, and MET annotation than the OI-MET set. Within the OI-MET-TF set, we identified genes documented to be drug targets and prioritized them for validation and near-term clinical follow up. Reflecting our inference from the OI-MET-TF model back to the OI-MET model, we found enrichment of AP1/MYC binding motif pairs in the promoters of the OI-MET gene set, suggesting the hypothesis that the AP1/MYC TF pair is important in regulating this gene set. Testing this hypothesis based on ChIP-Seq data, we find significant evidence consistent with this hypothesis.