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Cancer Res. 2016 Mar 1;76(5):1214-24. doi: 10.1158/0008-5472.CAN-15-2743. Epub 2016 Feb 1.

An Integrated Analysis of Heterogeneous Drug Responses in Acute Myeloid Leukemia That Enables the Discovery of Predictive Biomarkers.

Author information

1
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
2
Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.
3
Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada.
4
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
5
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. Department of Medicine, University of Toronto, Toronto, Ontario, Canada. Division of Medical Oncology and Hematology, University Health Network, Toronto, Ontario, Canada. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
6
Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada. Department of Immunology, University of Toronto, Toronto, Ontario, Canada.
7
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
8
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. Department of Medicine, University of Toronto, Toronto, Ontario, Canada. Division of Medical Oncology and Hematology, University Health Network, Toronto, Ontario, Canada. jwang@uhnresearch.ca.

Abstract

Many promising new cancer drugs proceed through preclinical testing and early-phase trials only to fail in late-stage clinical testing. Thus, improved models that better predict survival outcomes and enable the development of biomarkers are needed to identify patients most likely to respond to and benefit from therapy. Here, we describe a comprehensive approach in which we incorporated biobanking, xenografting, and multiplexed phospho-flow (PF) cytometric profiling to study drug response and identify predictive biomarkers in acute myeloid leukemia (AML) patients. To test the efficacy of our approach, we evaluated the investigational JAK2 inhibitor fedratinib (FED) in 64 patient samples. FED robustly reduced leukemia in mouse xenograft models in 59% of cases and was also effective in limiting the protumorigenic activity of leukemia stem cells as shown by serial transplantation assays. In parallel, PF profiling identified FED-mediated reduction in phospho-STAT5 (pSTAT5) levels as a predictive biomarker of in vivo drug response with high specificity (92%) and strong positive predictive value (93%). Unexpectedly, another JAK inhibitor, ruxolitinib (RUX), was ineffective in 8 of 10 FED-responsive samples. Notably, this outcome could be predicted by the status of pSTAT5 signaling, which was unaffected by RUX treatment. Consistent with this observed discrepancy, PF analysis revealed that FED exerted its effects through multiple JAK2-independent mechanisms. Collectively, this work establishes an integrated approach for testing novel anticancer agents that captures the inherent variability of response caused by disease heterogeneity and in parallel, facilitates the identification of predictive biomarkers that can help stratify patients into appropriate clinical trials.

PMID:
26833125
DOI:
10.1158/0008-5472.CAN-15-2743
[Indexed for MEDLINE]
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