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Leuk Res. 2017 Jan;52:1-7. doi: 10.1016/j.leukres.2016.11.004. Epub 2016 Nov 6.

Computational drug treatment simulations on projections of dysregulated protein networks derived from the myelodysplastic mutanome match clinical response in patients.

Author information

1
Department of Hematology Oncology: 1600 SW Archer Rd. PO box 100278, Gainesville, FL 32610, USA.
2
Department of Biostatistics: 2004 Mowry Road, PO box 117450, Gainesville, FL 32611, USA.
3
Department of Pharmacotherapy and Translational Research: 1225 Center Drive, PO box 100486, Gainesville, FL 32610 USA.
4
Cellworks Group, Inc., 2033 Gateway Place, Suite 500, San Jose, CA, 95110, USA.
5
Leukemia Program, Cleveland Clinic, Cleveland: 9500 Euclid Ave, Mail Code R35, Cleveland, OH 44195 USA.
6
MDS Research Group, Institut de Recerca Contra la Leucemia Josep Carreras, ICO-Hospital Germans Trias i Pujol, Universitat Autònoma de Barcelona, 08916 Badalona, Barcelona, Spain.
7
Division of Hematology and Oncology, UC San Diego Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093 USA.
8
Department of Hematology Oncology: 1600 SW Archer Rd. PO box 100278, Gainesville, FL 32610, USA. Electronic address: christopher.cogle@medicine.ufl.edu.

Abstract

Although the majority of MDS patients fail to achieve clinical improvement to approved therapies, some patients benefit from treatment. Predicting patient response prior to therapy would improve treatment effectiveness, avoid treatment-related adverse events and reduce healthcare costs. Three separate cohorts of MDS patients were used to simulate drug response to lenalidomide alone, hypomethylating agent (HMA) alone, or HMA plus lenalidomide. Utilizing a computational biology program, genomic abnormalities in each patient were used to create an intracellular pathway map that was then used to screen for drug response. In the lenalidomide treated cohort, computer modeling correctly matched clinical responses in 37/46 patients (80%). In the second cohort, 15 HMA patients were modeled and correctly matched to responses in 12 (80%). In the third cohort, computer modeling correctly matched responses in 10/10 patients (100%). This computational biology network approach identified GGH overexpression as a potential resistance factor to HMA treatment and paradoxical activation of beta-catenin (through Csnk1a1 inhibition) as a resistance factor to lenalidomide treatment. We demonstrate that a computational technology is able to map the complexity of the MDS mutanome to simulate and predict drug response. This tool can improve understanding of MDS biology and mechanisms of drug sensitivity and resistance.

KEYWORDS:

Computational biology; Hma; Lenalidomide; Mutanome; Myelodysplastic syndromes; Response prediction

PMID:
27855285
DOI:
10.1016/j.leukres.2016.11.004
[Indexed for MEDLINE]
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