bioRxivCell BiologyPreprint
TMPO promotes cellular dissemination and metastasis in circulating tumor cells
Arianna GiacobbeiD, Aleksandar ObradovićiD, Jinqiu LuiD, Soonbum ParkiD, Carlos Pedraz‐ValduncieliD, Giuseppe Nicolò FanelliiD, Aunika ZhengiD, Jaime Y. KimiD, Maya Stella Dixon, Jung Seung NamiD, Florencia PicechiD, Caroline J. Laplaca, Renu K. VirkiD, Matteo Di BernardoiD, Alexander ChuiiD, Juan Martín ArriagaiD, Stephanie N. AfariiD, Francisca Nunes de AlmeidaiD, Min ZouiD, Helen Garcia, B Robinson, Hongshan Guo, Shyamala MaheswaraniD, Daniel A. HaberiD, David Tomoaki MiyamotoiD, , Cory T. Abate-SheniD
Abstract
Metastasis--the process by which cancer cells spread beyond the primary tumor to distant organs--accounts for the vast majority of cancer-related deaths. To elucidate mechanisms underlying dissemination and metastasis in prostate cancer, we have investigated circulating tumor cells (CTCs) obtained from genetically engineered mouse models (GEMMs). The phenotypic and molecular properties of the CTCs, and organoids derived from these CTCs, closely model the tumor and metastatic phenotypes of their parental GEMMs. Moreover, organoids derived from individual CTCs exhibit molecular and morphological heterogeneity that is associated with distinct metabolic states as well as differences in human prostate cancer outcome. Using computational systems analyses, we have identified TMPO, encoding the nuclear membrane protein lamina-associated polypeptide 2 (Lap2), as a key driver of this heterogeneity. TMPO activity is upregulated in advanced human prostate tumors, metastases, and CTCs, and is associated with adverse clinical outcome. Our findings indicate that TMPO promotes dissemination and metastasis in vivo by enhancing survival in conditions of metabolic stress, and reveal a novel mechanistic link between CTC heterogeneity, stress adaptation, and metastatic potential.
- organism: mouse
- 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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