Computational modeling of early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) to identify personalized therapy using genomics. (March 2019)

• Record Type: Journal Article
• Authors: Kumar, Ansu; Drusbosky, Leylah M.; Meacham, Amy; Turcotte, Madeleine; Bhargav, Priyanka; Vasista, Sumanth; Usmani, Shahabuddin; Pampana, Anusha; Basu, Kabya; Tyagi, Anuj; Lala, Deepak; Rajagopalan, Swaminathan; Birajdar, Shivgonda C.; Alam, Aftab; Ghosh Roy, Kunal; Abbasi, Taher; Vali, Shireen; Sengar, Manju; Chinnaswamy, Girish; Shah, Bijal D.; Cogle, Christopher R.
• Is Part Of: Leukemia research. Volume 78(2019)
• Page Start: 3
• Page End: 11
• Publication Date: 2019-03
• Subjects: ETP-ALL early T-cell precursor acute lymphoblastic Leukemia -- GEO geneexpression omnibus -- CCND1 cyclin D1 -- AKT AKT serine/threonine kinase -- BIRC5 baculoviral IAP repeat containing 5 -- ETV6 ETS variant 6 -- LEF-1 lymphoid enhancer binding factor 1 -- JAK3j Janus kinase 3 -- NPM1 nucleophosmin 1 -- mTORC1 mammalian target of rapamycin complex 1 -- FOXM1 forkhead box M1 -- CEBPA CCAAT enhancer binding protein alpha -- HIF1A hypoxia inducible factor 1 subunit alpha -- TET2 Tet methylcytosine dioxygenase 2 -- FLT3 Fms related tyrosine kinase 3 -- CDKN2A cyclin dependent kinase inhibitor 2A -- CDKN2B cyclin dependent kinase inhibitor 2B -- TSC1 TSC complex subunit; ETP-ALL -- Genomics -- Drug response -- Computational-modeling -- Leukemia -- Biomarkers; Leukemia -- Periodicals; Leukemia -- Periodicals; Leucémie -- Périodiques; Leukemia; Periodicals; Electronic journals; Electronic journals; 616.9941905
• DOI: 10.1016/j.leukres.2019.01.003
• Languages: English
• ISSNs: 0145-2126

Abstract

Graphical abstract: Highlights: Determine unique genomic characteristics of ETP-ALL using computational modeling. Generated disease-specific protein network maps for ETP-ALL patients. Genomics-based classification of ETP-ALL had sensitivity and specificity of 93% and 87%. Computational simulation of 62 ETP-ALL patients identified 87 combination therapies. Shortlisted combinations validated by in vitro chemosensitivity assays. Abstract: Early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) is an aggressive hematological malignancy for which optimal therapeutic approaches are poorly characterized. Using computational biology modeling (CBM) in conjunction with genomic data from cell lines and individual patients, we generated disease-specific protein network maps that were used to identify unique characteristics associated with the mutational profiles of ETP-ALL compared to non-ETP-ALL (T-ALL) cases and simulated cellular responses to a digital library of FDA-approved and investigational agents. Genomics-based classification of ETP-ALL patients using CBM had a prediction sensitivity and specificity of 93% and 87%, respectively. This analysis identified key genomic and pathway characteristics that are distinct in ETP-ALL including deletion of nucleophosmin-1 ( NPM1 ), mutations of which are used to direct therapeutic decisions in acute myeloid leukemia. Computational simulations based on mutational profiles of 62 ETP-ALL patient models identified 87 unique targeted combination therapies in 56 of the 62 patients despite actionable mutations being present in only 37% of ETP-ALL patients. Shortlisted two-drug combinations were predicted to be synergistic in 11 profiles and were validated by in vitro chemosensitivity assays. In conclusion, computational modeling was able to identify unique biomarkers and pathways for ETP-ALL, and identify new drug combinations for potential clinical testing.