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

Tyrosine kinase inhibitor response of ABL-class acute lymphoblastic leukemia: the role of kinase type and SH3 domain

ABSTRACT

Acute lymphoblastic leukemia (ALL) with fusions of ABL-class tyrosine kinase genes other than BCR::ABL1 occurs in ∼3% of children with ALL. The tyrosine kinase genes involved in this BCR::ABL1-like (Ph-like) subtype include ABL1, PDGFRB, ABL2, and CSF1R, each of which has up to 10 described partner genes. ABL-class ALL resembles BCR::ABL1-positive ALL with a similar gene expression profile, poor response to chemotherapy, and sensitivity to tyrosine kinase inhibitors (TKIs). There is a lack of comprehensive data regarding TKI sensitivity in the heterogeneous group of ABL-class ALL. We observed variability in TKI sensitivity within and among each ABL-class tyrosine kinase gene subgroup. We showed that ALL samples with fusions for any of the 4 tyrosine kinase genes were relatively sensitive to imatinib. In contrast, the PDGFRB-fused ALL samples were less sensitive to dasatinib and bosutinib. Variation in ex vivo TKI response within the subset of samples with the same ABL-class tyrosine kinase gene was not associated with the ALL immunophenotype, 5' fusion partner, presence or absence of Src-homology-2/3 domains, or deletions of IKZF1, PAX5, or CDKN2A/B. In conclusion, the tyrosine kinase gene involved in ABL-class ALL is the main determinant of TKI sensitivity and relevant for specific TKI selection.

JAK/STAT in leukemia: a clinical update

Over the past three decades, considerable efforts have been expended on understanding the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway in leukemia, following the identification of the JAK2V617F mutation in myeloproliferative neoplasms (MPNs). The aim of this review is to summarize the latest progress in our understanding of the involvement of the JAK/STAT signaling pathway in the development of leukemia. We also attempt to provide insights into the current use of JAK/STAT inhibitors in leukemia therapy and explore pertinent clinical trials in this field.

Gene essentiality in cancer is better predicted by mRNA abundance than by gene regulatory network-inferred activity

Gene regulatory networks (GRNs) are often deregulated in tumor cells, resulting in altered transcriptional programs that facilitate tumor growth. These altered networks may make tumor cells vulnerable to the inhibition of specific regulatory proteins. Consequently, the reconstruction of GRNs in tumors is often proposed as a means to identify therapeutic targets. While there are examples of individual targets identified using GRNs, the extent to which GRNs can be used to predict sensitivity to targeted intervention in general remains unknown. Here we use the results of genome-wide CRISPR screens to systematically assess the ability of GRNs to predict sensitivity to gene inhibition in cancer cell lines. Using GRNs derived from multiple sources, including GRNs reconstructed from tumor transcriptomes and from curated databases, we infer regulatory gene activity in cancer cell lines from ten cancer types. We then ask, in each cancer type, if the inferred regulatory activity of each gene is predictive of sensitivity to CRISPR perturbation of that gene. We observe slight variation in the correlation between gene regulatory activity and gene sensitivity depending on the source of the GRN and the activity estimation method used. However, we find that there is consistently a stronger relationship between mRNA abundance and gene sensitivity than there is between regulatory gene activity and gene sensitivity. This is true both when gene sensitivity is treated as a binary and a quantitative property. Overall, our results suggest that gene sensitivity is better predicted by measured expression than by GRN-inferred activity.

Network controllability solutions for computational drug repurposing using genetic algorithms

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdős-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.

Network analytics for drug repurposing in COVID-19

To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein–protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.

The Emerging Role of Suppressors of Cytokine Signaling (SOCS) in the Development and Progression of Leukemia

Cytokines are pleiotropic signaling molecules that execute an essential role in cell-to-cell communication through binding to cell surface receptors. Receptor binding activates intracellular signaling cascades in the target cell that bring about a wide range of cellular responses, including induction of cell proliferation, migration, differentiation, and apoptosis. The Janus kinase and transducers and activators of transcription (JAK/STAT) signaling pathways are activated upon cytokines and growth factors binding with their corresponding receptors. The SOCS family of proteins has emerged as a key regulator of cytokine signaling, and SOCS insufficiency leads to constitutive activation of JAK/STAT signaling and oncogenic transformation. Dysregulation of SOCS expression is linked to various solid tumors with invasive properties. However, the roles of SOCS in hematological malignancies, such as leukemia, are less clear. In this review, we discuss the recent advances pertaining to SOCS dysregulation in leukemia development and progression. We also highlight the roles of specific SOCS in immune cells within the tumor microenvironment and their possible involvement in anti-tumor immunity. Finally, we discuss the epigenetic, genetic, and post-transcriptional modifications of SOCS genes during tumorigenesis, with an emphasis on leukemia.