bioRxivCancer BiologyPreprint
Regenerative cell turnover coupled to a tumor-hostile niche protects naked mole-rats from cancer
M. Behm, P. Baeza-Centurion, S. Weser, V. F. Busa, P. Zafferani, L. Penso-Dolfin, F. J. Botey, S. Delaunay, N. Hirschmuller, B. Berthet, M.-L. Koch, S. Del Prete, M. Bekavac, D. Sohn, L. Penteskoufi, C. Reifenberg, F. Coraggio, M. Schopp, F. Lammers, L. Sole-Boldo, J. Dutton, R. E. Wagner, S. Blanco, A. Lonsdorf, S. Begall, , M. Frye
Abstract
Long-lived species suppress cancer despite accumulating somatic mutations throughout life, but how this is achieved in renewing tissues remains unclear. Here, we show that naked mole-rat skin uncouples high cellular turnover from cancer risk through coordinated epithelial and stromal mechanisms that constrain clonal outgrowth and suppress tumor-promoting inflammation. Despite elevated epidermal proliferation and mutational burden, naked mole-rat skin maintains tissue integrity through an expanded pool of early committed progenitor (hybrid) cells and replication-coupled genome maintenance pathways. Under chronic carcinogenic stress, undifferentiated basal cells replenish the hybrid progenitor pool. This dilutes initiated clones and permits only limited selective expansion of rare cancer gene-mutant clones. Depletion of the hybrid compartment shifts this protective state toward clonal expansion and inflammatory activation. These expanding clones are further constrained by a highly tumor-suppressive stromal microenvironment, driven by fibroblasts that adopt a metabolically restricted, non-inflammatory program. Together, our data uncover a multi-layered tumor-suppressive strategy that couples turnover-driven regeneration with an anti-permissive stromal niche to prevent malignant progression under mutagenic stress.
- assay: proteomics
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
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