bioRxivCancer BiologyPreprint
Melanoma evolution in the lymph node shapes systemic outcomes
Katherine S. VentreiD, Tara MuijlwijkiD, Robert StagnittaiD, Shi QiuiD, Ze CheniD, Irineu Illa-BochacaiD, Ata S. MoshiriiD, Teresa DavoliiD, Iman OsmaniD, Itai YanaiiD, Markus SchöberiD, Amanda W. LundiD
Abstract
Lymph node (LN) metastasis predicts poor patient outcomes, but the mechanistic drivers that shape metastatic fitness, immune evasion, and clinical impact remain elusive. While preclinical models indicate that active tumor adaptation is necessary for LN metastasis, observations of clonal heterogeneity in human tumors has supported a stochastic model of passive and continuous seeding. Reconciling LN metastasis as a passive or active process is essential to understanding if LN metastasis is simply a marker of disease progression or a clinically informative therapeutic target. Here, we report evidence that LNs are active niches that facilitate ongoing melanoma evolution to progressively subvert immune surveillance and enable progression. To construct a spatial trajectory of LN metastasis, we examined paired primary melanomas and metastatic sentinel LNs through integrated genomic, phenotypic, and immunologic analyses. In contrast to a model of continuous seeding, we observe that early dissemination from the primary tumor is followed by extensive intra-nodal diversification, indicating that metastatic outgrowth requires ongoing adaptation within the LN. As clones evolve in the LN, they re-differentiate towards a melanocytic state and reprogram the microenvironment for immune exclusion. In further evolved clones, loss of inflammatory interferon signaling and induction of p53 and mitochondrial stress are associated with decreased overall survival. Collectively, these results implicate the LN as a critical battleground for melanoma progression, where tumor evolution drives adaptation and immune escape to biologically link regional metastasis to patient survival.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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