Network Analysis Reveals Synergistic Genetic Dependencies for Rational

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Network Analysis Reveals Synergistic Genetic Dependencies for Rational Published OnlineFirst July 1, 2021; DOI: 10.1158/1078-0432.CCR-21-0553 CLINICAL CANCER RESEARCH | TRANSLATIONAL CANCER MECHANISMS AND THERAPY Network Analysis Reveals Synergistic Genetic Dependencies for Rational Combination Therapy in Philadelphia Chromosome–Like Acute Lymphoblastic Leukemia Yang-Yang Ding1,2,3, Hannah Kim4, Kellyn Madden1, Joseph P. Loftus1, Gregory M. Chen5, David Hottman Allen1, Ruitao Zhang1, Jason Xu5, Chia-Hui Chen1, Yuxuan Hu6, Sarah K. Tasian1,2,7, and Kai Tan1,2,3,7 ABSTRACT ◥ Purpose: Systems biology approaches can identify critical in vitro. Pharmacologic inhibition with dual small molecule inhib- targets in complex cancer signaling networks to inform new itor therapy targeting this pair of key nodes further demonstrated therapy combinations that may overcome conventional treat- enhanced antileukemia efficacy of combining the BCL-2 inhib- ment resistance. itor venetoclax with the tyrosine kinase inhibitors ruxolitinib or Experimental Design: We performed integrated analysis of 1,046 dasatinib in vitro in human Ph-like ALL cell lines and in vivo in childhood B-ALL cases and developed a data-driven network con- multiple childhood Ph-like ALL patient-derived xenograft mod- trollability-based approach to identify synergistic key regulatortargets els. Consistent with network controllability theory, co-inhibitor in Philadelphia chromosome–like B-acute lymphoblastic leukemia treatment also shifted the transcriptomic state of Ph-like ALL (Ph-like B-ALL), a common high-risk leukemia subtype associated cells to become less like kinase-activated BCR-ABL1–rearranged with hyperactive signal transduction and chemoresistance. (Phþ) B-ALL and more similar to prognostically favorable child- Results: We identified 14 dysregulated network nodes in Ph-like hood B-ALL subtypes. ALL involved in aberrant JAK/STAT, Ras/MAPK, and apoptosis Conclusions: Our study represents a powerful conceptual frame- pathways and other critical processes. Genetic cotargeting of the work for combinatorial drug discovery based on systematic inter- synergistic key regulator pair STAT5B and BCL2-associated atha- rogation of synergistic vulnerability pathways with pharmacologic nogene 1 (BAG1) significantly reduced leukemia cell viability inhibitor validation in preclinical human leukemia models. Introduction and developed in an ad hoc manner. More rational identification of new targets in human cancers for combination drug regimens is an Cancer cells exploit multiple deregulated pathways to evade the essential next step. There is growing interest in identifying synergistic selective pressure of single-agent drugs, promoting therapeutic resis- genetic interactions as targets for combination therapy (1), but large- tance and clinical relapse. However, combination therapy regimens for scale experimental screening for genetic interactions has been tech- cancer have traditionally been nonspecific with broad toxicity profiles nically challenging and expensive given the large number of candidate gene pairs one has to screen. As a result, existing RNA-interference and CRISPR-based screenings have been limited to only a few hundred 1Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, genes (2, 3), far from saturating the search space of all possible Philadelphia, Pennsylvania. 2Department of Pediatrics, University of Pennsylvania, (4Â108) pairwise interactions in the human genome. Given the Philadelphia, Pennsylvania. 3Department of Biomedical and Health Informatics, above challenges, we developed a systems biology approach that 4 Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania. Institute enables efficient in silico genetic screening and prioritization of co- for Genomics and Evolutionary Medicine, Temple University, Philadelphia, 5 targetable pathways for combinatorial therapeutics followed by rig- Pennsylvania. Graduate Group in Genomics and Computational Biology, in vitro in vivo fi University of Pennsylvania, Philadelphia, Pennsylvania. 6School of Computer orous and pharmacologic validation in a dif cult-to- Science and Technology, Xidian University, Xi’an, Shaanxi, China. 7Abramson cure subtype of leukemia. Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania. Philadelphia chromosome–like acute lymphoblastic leukemia Note: Supplementary data for this article are available at Clinical Cancer (Ph-like ALL) comprises 15% to 30% of high-risk B-ALL cases in Research Online (http://clincancerres.aacrjournals.org/). children and adolescents/young adults (AYA) and 20% to 40% in older – S.K. Tasian and K. Tan contributed equally as the co-senior authors of this article. adults (4 6), and is associated with high rates of conventional chemo- therapy resistance and poor clinical outcomes (6, 7). Ph-like ALL is Corresponding Authors: Kai Tan, Pediatrics/Oncology, The Children’s Hospital defined by a kinase-activated transcriptomic signature resembling that of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104. E-mail: þ BCR- [email protected]; and Sarah K. Tasian, 3501 Civic Center Boulevard, CTRB 3056, of Philadelphia chromosome-positive (Ph ) ALL, but lacks the Philadelphia, PA 19104. E-mail: [email protected] ABL1 rearrangement (8). Ph-like ALL is instead driven by alternative Clin Cancer Res 2021;XX:XX–XX genetic alterations in two major subclasses: (i) JAK/STAT pathway alterations involving CRLF2, JAK2, EPOR, IL7R,orSH2B3 rearrange- doi: 10.1158/1078-0432.CCR-21-0553 ments or indels and (ii) ABL-class kinase fusions involving ABL1, ABL2, This open access article is distributed under Creative Commons Attribution- CSF1R,orPDGFRB rearrangements (7). Preclinical studies of tyrosine NonCommercial-NoDerivatives License 4.0 International (CC BY-NC-ND). kinase inhibitor (TKI) monotherapy in Ph-like ALL models have Ó2021 The Authors; Published by the American Association for Cancer Research expectedly demonstrated incomplete anti-leukemia activity (9–12) AACRJournals.org | OF1 Downloaded from clincancerres.aacrjournals.org on September 28, 2021. © 2021 American Association for Cancer Research. Published OnlineFirst July 1, 2021; DOI: 10.1158/1078-0432.CCR-21-0553 Ding et al. samples were downloaded from publicly available databases as spec- Translational Relevance ified in “Source Details” of Supplementary Table S1. The drug/gene We performed unbiased integrated network analysis of large- databases were downloaded from the Therapeutic Target Database scale patient genomic and transcriptomic datasets to identify (TTD;ref.15),DrugBank(16),andDGIdb(17).TheRNAsequenc- previously unrecognized targetable synergistic regulators in human ing data of untreated human B-ALL cell lines (except TVA-1) were Ph-like ALL. We then queried drug databases for clinically avail- downloaded from the Broad Institute Cancer Cell Line Encyclope- able drugs and discovered synergistic efficacy of cotargeting the dia (CCLE) available at https://portals.broadinstitute.org/ccle. The pair of top-ranked regulators BCL-2 and STAT5 with venetoclax RNA sequencing data generated in this study is deposited at Gene and ruxolitinib or dasatinib, respectively, in vitro in human Ph-like Expression Ominbus (GEO) under the accession number ALL cell lines and in vivo in Ph-like ALL patient-derived xenograft GSE161939. All software supporting the analysis in this study can models. This dual inhibitor precision medicine strategy is immi- be found in public repositories. Software package implementing the nently translatable to the clinic given the established therapeutic OptiCon algorithm has been deposited at GitHub (https://github. dosing of these drugs and the high rates of chemoresistance and com/tanlabcode/OptiCon). relapse in patients with Ph-like ALL. Our combinatorial genetic target discovery and pharmacologic validation approach may be Prediction of candidate combination therapeutic targets using broadly applicable to interrogation of other human cancers. OptiCon We used our recently developed computational algorithm, Opti- Con (13), to nominate candidate combination therapeutic targets. Inputs to the algorithm consists of a gene regulatory network, genetic likely via compensatory signaling mechanisms, emphasizing the need mutation data, and gene expression data. A high-quality gene regu- for more rationally-designed combination therapy approaches to latory network was generated by combining known expert-curated achieve cure. In the present studies, we hypothesized that an unbiased pathway annotations from three public pathway databases: KEGG systems biology approach could effectively elucidate optimal target (1,597 pathways; ref. 18), Reactome (195 pathways; ref. 19), and NCI pairings. Our network-based analysis is optimal to address the unique Pathway Interaction Database (PID; 745 pathways; Supplementary challenges of Ph-like ALL given its known dysregulation of multiple Table S5; ref. 20). All pathways were downloaded in the Simple intracellular pathways that maintain a high degree of crosstalk. Interaction Format from Pathway Commons 252. We also removed A main goal of effective multiagent therapy is identifying drug undirected, redundant, and small-molecule-associated interactions to combinations with synergistic efficacies, but not synergistic toxicities. generate a regulatory network comprising 5959 nodes (genes) and We recently reported our Optimal Control algorithm (OptiCon) (13) 108,281 directed edges (regulatory links). Mutation information used that is capable of discovering novel disease-specific synergistic regula- included
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