Network analysis reveals synergistic genetic dependencies for rational combination therapy in Philadelphia chromosome⇓like acute lymphoblastic leukemia

Purpose: Systems biology approaches can identify critical targets in complex cancer signaling networks to inform new therapy combinations that may overcome conventional treatment resistance. Experimental Design: We performed integrated analysis of 1,046 childhood B-ALL cases and developed a data-driven network controllability-based approach to identify synergistic key regulator targets in Philadelphia chromosome–like B-acute lymphoblastic leukemia (Ph-like B-ALL), a common high-risk leukemia subtype associated with hyperactive signal transduction and chemoresistance. Results: We identified 14 dysregulated network nodes in Ph-like ALL involved in aberrant JAK/STAT, Ras/MAPK, and apoptosis pathways and other critical processes. Genetic cotargeting of the synergistic key regulator pair STAT5B and BCL2-associated athanogene 1 (BAG1) significantly reduced leukemia cell viability in vitro. Pharmacologic inhibition with dual small molecule inhibitor therapy targeting this pair of key nodes further demonstrated enhanced antileukemia efficacy of combining the BCL-2 inhibitor venetoclax with the tyrosine kinase inhibitors ruxolitinib or dasatinib in vitro in human Ph-like ALL cell lines and in vivo in multiple childhood Ph-like ALL patient-derived xenograft models. Consistent with network controllability theory, co-inhibitor treatment also shifted the transcriptomic state of Ph-like ALL cells to become less like kinase-activated BCR-ABL1–rearranged (Phþ) B-ALL and more similar to prognostically favorable childhood B-ALL subtypes. Conclusions: Our study represents a powerful conceptual framework for combinatorial drug discovery based on systematic interrogation of synergistic vulnerability pathways with pharmacologic inhibitor validation in preclinical human leukemia models.