bioRxivCancer Biology
Repurposing Tofacitinib As An Anti-Myeloma Therapeutic To Reverse Growth-Promoting Effects Of The Bone Marrow Microenvironment
C. Lam, M. Murnane, H. Liu, G. A. Smith, S. Wong, J. Taunton, J. O. Liu, C. S. Mitsiades, B. C. Hann, B. T. Aftab, A. P. Wiita
Abstract
The myeloma bone marrow microenvironment promotes proliferation of malignant plasma cells and resistance to therapy. Interleukin-6 (IL-6) and downstream JAK/STAT signaling are thought to be central components of these microenvironment-induced phenotypes. In a prior drug repurposing screen, we identified tofacitinib, a pan-JAK inhibitor FDA-approved for rheumatoid arthritis, as an agent that may reverse the tumor-stimulating effects of bone marrow mesenchymal stromal cells. Here, we validated both in vitro, in stromal-responsive human myeloma cell lines, and in vivo, in orthotopic disseminated murine xenograft models of myeloma, that tofacitinib showed both single-agent and combination therapeutic efficacy in myeloma models. Surprisingly, we found that ruxolitinib, an FDA-approved agent targeting JAK1 and JAK2, did not lead to the same anti-myeloma effects. Combination with a novel irreversible JAK3-selective inhibitor also did not enhance ruxolitinib effects. RNA-seq and unbiased phosphoproteomics revealed that marrow stromal cells stimulate a JAK/STAT-mediated proliferative program in myeloma plasma cells, and tofacitinib reversed the large majority of these pro-growth signals. Taken together, our results suggest that tofacitinib specifically reverses the growth-promoting effects of the tumor microenvironment through blocking an IL-6-mediated signaling axis. As tofacitinib is already FDA-approved, these results can be rapidly translated into potential clinical benefits for myeloma patients.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- assay: proteomics
- research focus: drug response / drug discovery
- research focus: cell signaling & trafficking
- research focus: cancer & tumor microenvironment
- approach: experimental assay
- approach: statistical model
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