This article is part of the Topical Collection on Myelodysplastic Syndromes
This review discusses the need for computational modeling in myelodysplastic syndromes (MDS) and early test results.
As our evolving understanding of MDS reveals a molecularly complicated disease, the need for sophisticated computer analytics is required to keep track of the number and complex interplay among the molecular abnormalities. Computational modeling and digital drug simulations using whole exome sequencing data input have produced early results showing high accuracy in predicting treatment response to standard of care drugs. Furthermore, the computational MDS models serve as clinically relevant MDS cell lines for pre-clinical assays of investigational agents.
MDS is an ideal disease for computational modeling and digital drug simulations. Current research is focused on establishing the prediction value of computational modeling. Future research will test the clinical advantage of computer-informed therapy in MDS.
Cogle CR, Komrokji R, List AF. Myelodysplastic syndromes. In: Perry MC, editor. The chemotherapy source book. 5th ed. The Netherlands: Wolters Kluwer; 2012. p. 619–38. ISBN-13: 978-1451101454 ISBN-10: 1451101457
Chevallier P. Sur la terminologie des leucosis et les affections-frontiéres: Les odoleucoses. Sang. 1943;15:587–93.
Greenberg P, Cox C, LeBeau MM, Fenaux P, Morel P, Sanz G, et al. International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood. 1997;89(6):2079–88. PubMed
• Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sole F, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012;120(12):2454–65. This is the most recent international prognostic scoring system that enables quantification of MDS CrossRefPubMedPubMedCentral
• Bejar R, Lord A, Stevenson K, Bar-Natan M, Perez-Ladaga A, Zaneveld J, et al. TET2 mutations predict response to hypomethylating agents in myelodysplastic syndrome patients. Blood. 2014;124(17):2705–12. This retrospective study found significant correlation between a genetic biomarker (TET2) and clinical outcomes after hypomethylating agent treatment CrossRefPubMedPubMedCentral
Della Porta MG, Galli A, Bacigalupo A, Zibellini S, Bernardi M, Rizzo E, et al. Clinical effects of driver somatic mutations on the outcomes of patients with myelodysplastic syndromes treated with allogeneic hematopoietic stem-cell transplantation. J Clin Oncol. 2016;34(30):3627–37. https//:doi.org/10.1200/JCO.2016.67.3616
•• Drusbosky L, Medina C, Martuscello R, Hawkins KE, Chang M, Lamba JK, et al. Computational drug treatment simulations on projections of dysregulated protein networks derived from the myelodysplastic mutanome match clinical response in patients. Leuk Res. 2017;52:1–7. This was the first study of a computational method that uses multi-gene/multi-drug matching in MDS CrossRefPubMed
Drusbosky L, Wise E, Vali S, Abbasi T, Kumar A, Kumar Singh N, et al. Icare 1: a prospective clinical trial to predict treatment response based on mutanome-informed computational biology in patients with AML and MDS. Blood. 2016;128(22):594.
Drusbosky L, Abbasi T, Vali S, Radhakrishnan S, Kumar Singh N, Usmani S, et al. A genomic signature predicting venetoclax treatment response in AML identified by protein network mapping and validated by ex vivo drug sensitivity testing: a beat AML project study. Blood. 2016;128(22):1713.
Drusbosky L, Kumar Singh N, Tiwari P, Vali S, Abbasi T, Sarkaria S, et al. A genomic rule predicting HMA treatment response in MDS identified by protein network mapping and validated by clinical trial simulation. Blood. 2016;128(22):3151.
- Computational Modeling and Treatment Identification in the Myelodysplastic Syndromes
Leylah M. Drusbosky
Christopher R. Cogle
- Springer US
Neu im Fachgebiet Onkologie
Mail Icon II