Network Analysis Reveals Synergistic Genetic Dependencies for Rational Combination Therapy in Philadelphia Chromosome-like Acute Lymphoblastic Leukemia

Abstract

Systems biology approaches can identify critical targets in complex cancer signaling networks to inform therapy combinations and overcome conventional treatment resistance. Herein, we 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 high-risk leukemia subtype associated with hyperactive signal transduction and chemoresistance. Integrated analysis of 1,046 childhood B-ALL cases identified 14 dysregulated network nodes in Ph-like ALL involved in aberrant JAK/STAT, Ras/MAPK, and apoptosis pathways and other critical processes. Consistent with network controllability theory, combination small molecule inhibitor therapy targeting a pair of key nodes shifted the transcriptomic state of Ph-like ALL cells to become less like kinase-activated BCR-ABL1-rearranged (Ph+) B-ALL and more similar to prognostically-favorable childhood B-ALL subtypes. Functional validation experiments further demonstrated enhanced anti-leukemia efficacy of combining the BCL-2 inhibitor venetoclax with tyrosine kinase inhibitors ruxolitinib or dasatinib in vitro in human Ph-like ALL cell lines and in vivo in multiple patient-derived xenograft models. Our study represents a broadly-applicable conceptual framework for combinatorial drug discovery, based on systematic interrogation of synergistic vulnerability pathways with pharmacologic targeted validation in sophisticated preclinical human leukemia models.