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 co-targeting 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 anti-leukemia 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 (Ph+) 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.
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.
Philadelphia chromosome-like B-acute lymphoblastic leukemia (Ph-like ALL) is a common leukemia subtype associated with high relapse rates and poor overall survival. Ph-like ALL has a kinase-activated gene expression signature similar to that of BCR-ABL1-rearranged ALL, but is driven by alternative mutations in JAK or ABL kinase signaling pathway genes. Preclinical studies using tyrosine kinase inhibitor (TKI) monotherapy in Ph-like ALL models demonstrate incomplete efficacy, emphasizing the need for combination therapy to improve cure rates. In these studies, we use a novel patient ‘omics data-driven approach to nominate pathways for optimal synergistic targeting in Ph-like ALL. We utilized our OptiCon algorithm (Hu et al. Nat. Comm. 2019) to analyze publicly-available genomic and transcriptomic data from patient leukemia samples. We identified synergistic gene pairs that control a maximal number of deregulated genes (for optimal efficacy) and a minimal number of unperturbed genes (to minimize toxicity) in Ph-like ALL, including a top-ranked synergistic control pair STAT5B and BAG1. STAT5B is hyperactive in different subclasses of Ph-like ALL and BAG1 (BCL2-associated athanogene1) enhances the anti-apoptotic effect of BCL-2 likely via degradation prevention. We then validated the anti-leukemic efficacy of co-targeting this pairing in Ph-like ALL cell lines and patient-derived xenograft (PDX) models. We assessed the in vitro and in vivo effects of combining the BCL-2 inhibitor venetoclax with the JAKi ruxolitinib or ABL1i dasatinib in 3 Ph-like ALL cell lines (CRLF2-rearranged/JAK-mutant MUTZ5 and MHH-cALL-4, immortalized ABL1-rearranged TVA-1) and 3 Ph-like ALL PDX models (CRLF2-rearranged/JAK-mutant ALL4364 and UP_ALL4988, ABL1-rearranged TVA-1). Co-treatment of the 3 cell lines with venetoclax and ruxolitinib or dasatinib showed synergy at all dose combinations tested, versus monotherapy (CI <1.0). Venetoclax and ruxolitinib co-treatment of UP_ALL4988 and ALL4364 PDX mice significantly inhibited leukemia proliferation in vivo when compared to vehicle or single-agent inhibitors. Similar findings were observed in TVA-1 PDX mice treated with venetoclax and dasatinib. These preclinical results provide compelling rationale for potential combination therapy using BCL-2 inhibitor with TKIs in treatment of patients with Ph-like ALL, and represent an unbiased approach to the identification and testing of synergistic targets as candidates for multi-agent precision medicine therapy. Citation Format: Yang Y. Ding, Kellyn Madden, Joseph P. Loftus, Hannah Kim, Chia-hui Chen, Sarah K. Tasian, Kai Tan. Novel synergistic targets for combination therapy in Philadelphia chromosome-like acute lymphoblastic leukemia [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3893.
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