Research Papers:

Identification of myeloproliferative neoplasm drug agents via predictive simulation modeling: assessing responsiveness with micro-environment derived cytokines

Susumu S. Kobayashi _, Shireen Vali, Ansu Kumar, Neeraj Singh, Taher Abbasi and Peter P. Sayeski

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Oncotarget. 2016; 7:35989-36001. https://doi.org/10.18632/oncotarget.8540

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Susumu S. Kobayashi1,*, Shireen Vali2, Ansu Kumar3, Neeraj Singh3, Taher Abbasi2, Peter P. Sayeski4,*

1Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, USA

2Cellworks Group, Inc., San Jose, CA, USA

3Cellworks Research India Pvt Ltd., Cellworks Group Inc., Bangalore, India

4Department of Physiology and Functional Genomics, University of Florida College of Medicine, Gainesville, FL, USA

*These authors have contributed equally and share senior authorship

Correspondence to:

Peter P. Sayeski, email: [email protected]

Susumu S. Kobayashi, email: [email protected]

Keywords: predictive modeling, simulated signaling, signal transduction, JaK kinase

Received: December 18, 2015    Accepted: March 10, 2016    Published: April 1, 2016


Previous studies have shown that the bone marrow micro-environment supports the myeloproliferative neoplasms (MPN) phenotype including via the production of cytokines that can induce resistance to frontline MPN therapies. However, the mechanisms by which this occurs are poorly understood. Moreover, the ability to rapidly identify drug agents that can act as adjuvants to existing MPN frontline therapies is virtually non-existent. Here, using a novel predictive simulation approach, we sought to determine the effect of various drug agents on MPN cell lines, both with and without the micro-environment derived inflammatory cytokines. We first created individual simulation models for two representative MPN cell lines; HEL and SET-2, based on their genomic mutation and copy number variation (CNV) data. Running computational simulations on these virtual cell line models, we identified a synergistic effect of two drug agents on cell proliferation and viability; namely, the Jak2 kinase inhibitor, G6, and the Bcl-2 inhibitor, ABT737. IL-6 did not show any impact on the cells due to the predicted lack of IL-6 signaling within these cells. Interestingly, TNFα increased the sensitivity of the single drug agents and their use in combination while IFNγ decreased the sensitivity. In summary, this study predictively identified two drug agents that reduce MPN cell viability via independent mechanisms that was prospectively validated. Moreover, their efficacy is either potentiated or inhibited, by some of the micro-environment derived cytokines. Lastly, this study has validated the use of this simulation based technology to prospectively determine such responses.

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