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 out 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.
- Received October 1, 2015.
- Revision received December 14, 2015.
- Accepted December 17, 2015.
- Copyright © 2016, American Association for Cancer Research.