Fig 1.
The domain-based network approach.
(A) Prediction of molecular interactions of fusion C, whose parental proteins are proteins A and B that contain n and q domains respectively, denoted A1~An and B1~Bq. Fusion C comprises domains A1~Am and Bq+1~Bq. Blue and white nodes represent proteins that have protein-protein interactions or genes that have protein-DNA interactions with proteins A or B. Only the proteins or genes represented by the blue nodes can interact with fusion C because the domains of fusion C can bind to their protein domains (for protein-protein interactions) or DNA sequence close to their promoters (for protein-DNA interactions). (B) Calculation of the functional association score of each gene with fusion C (red node) through predicted molecular interaction partners (blue nodes). Genes that are more functionally associated with these predicted partners (e.g., gene 1) are also more functionally associated with fusion C than other genes (e.g., gene 2) are. (C) Prediction of pathways associated with fusion C. Genes are ranked on the basis of their association scores with fusion C. Pathway A, which enriches genes with higher association scores, is more functionally associated with fusion C than is pathway B. (D) Gene expression data from samples containing or lacking a fusion are leveraged to identify pathways deregulated by the fusion.
Fig 2.
Evaluation of p210 BCR-ABL1 prediction using known BCR-ABL1 pathways and benchmark gene sets.
(A) Bar plot of some statistically significant pathways that are associated with BCR-ABL1 in our prediction. A bar in the graph represents the statistically significance of a given pathway in GSEA association analysis, GSEA deregulation analysis, or combination analysis using the truncated product method. The statistically significances are presented in the graph as–log10(p-value). (B) GSEA association plot of the Gleevec pathway in our prediction. GSEA evaluates the genes of the pathway for their distribution in the ordered gene list generated by our association prediction. (C) ROC curves for five benchmark gene sets: 26 Wnt/Ca+/NFAT pathway genes (denoted WNT_CA+_NFAT genes), 1150 genes cited with BCR-ABL1 in the literature (denoted BCR-ABL genes), 1240 genes cited with CML in the literature (denoted CML genes), 328 genes categorized in the KEGG cancer pathways (denoted cancer pathway genes), and 68 target genes of compounds that have been tested in clinical trials or used for the treatment of imatinib-resistant CML (denoted drug targets). Genes in these five gene sets were treated as positive instances, and the remaining genes were treated as negative instances. TPR indicates true positive rate; FPR, false positive rate; and AUC, area under the ROC curve.
Fig 3.
Prediction evaluation of three known BCR-ABL1 variants (p185, p210, and p230).
(A) Number of predicted protein-protein interaction partners of the three BCR-ABL1 variants. (B) Correlations of pathway prediction of the three BCR-ABL1 variants. GSEA association scores of categorized pathways in the MSigDB database (category C2) were used for calculating spearman correlations. (C) Oncogenic impact evaluation of the three BCR-ABL1 variants using genes categorized in the KEGG cancer pathway.
Table 1.
Selected compounds whose targets are in the top 5% of genes functionally associated with BCR-ABL1.
Fig 4.
Prediction evaluation of two undruggable fusions in sarcoma using benchmark gene sets.
(A) EWS-FLI1 fusion. (B) FUS-DDIT3 fusion. Target gene sets of identified sensitive compounds of Ewing sarcoma and Myxoid liposarcoma in high-throughput screening assay are denoted drug screening. Other benchmark gene sets have similar denotations as Fig 2.
Fig 5.
Workflow for the FusionPathway package.