bioRxivBioengineering
Dormancy-inducing 3D-engineered matrix uncovers mechanosensitive and drug protective FHL2-p21 signaling axis
S. Bakhshandeh, U. Heras, H. M. Taieb, A. R. Varadarajan, S. M. Lissek, S. M. Huecker, X. Lu, D. S. Garske, S. A. E. Young, A. Abaurrea, M. M. Caffarel, A. Riestra, P. Bragado, J. Contzen, M. Gossen, S. Kirsch, J. Warfsmann, K. Honarnejad, C. A. Klein, A. Cipitria
Abstract
Resected tumors frequently relapse with distant metastasis, despite systemic treatment. Cellular dormancy has been identified as an important mechanism underlying such drug resistance enabling late relapse. Nonetheless, hurdles associated with detection and isolation of disseminated cancer cells (DCCs) in disease-free patients urge the need for in vitro models of dormant cells suited for drug discovery. Here, we explore dormancy-inducing 3D-engineered matrices, which generate mechanical confinement and induce growth arrest and survival against chemotherapy in cancer cells. We characterized the dormant phenotype of solitary cells by P-ERKlow:P-p38high dormancy signaling ratio, along with Ki67-expression. As underlying mechanism, we identified stiffness-dependent nuclear localization of the four-and-a-half LIM domains 2 (FHL2) protein, leading to p53-independent high p21Cip1/Waf1 nuclear expression, validated in murine and human tissue. Suggestive of a resistance-causing role, cells in the dormancy-inducing matrix became sensitive against chemotherapy upon FHL2 downregulation. Thus, our biomaterial-based approach will enable systematic screens for novel compounds suited to eradicate potentially relapsing, dormant cancer cells. TeaserUsing semi-synthetic bioengineered hydrogels, we reveal a mechanosensitive and drug protective mechanism of dormant cancer cells in tissues
- organism: mouse
- organism: human
- assay: bulk RNA-seq
- assay: proteomics
- research focus: drug response / drug discovery
- research focus: cell signaling & trafficking
- research focus: cell type annotation
- approach: experimental assay
- approach: statistical model
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