Cell Communication Network Factor 4 (CCN4/WISP1) Shifts Melanoma Cells from a Fragile Proliferative State to a Resilient Metastatic State

Deng, Wentao; Fernández, Audry; McLaughlin, Sarah L.; Klinke, David J.

doi:10.1007/s12195-019-00602-2

Cited by 14 publications

(21 citation statements)

References 37 publications

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“…The single cell suspensions were aliquoted among three antibody panels to characterize the tumor infiltrating lymphocytes by flow cytometry (see Figures S4-S6 for gating strategies). While the B16F0 and YUMM1.7 KO variants were generated using a double nickase CRISPR/Cas9 approach, similar results were obtained using a homology directed repair strategy (34,51). Additional controls for puromycin selection of CRIPSR/Cas9 edited cells using B16F0 cells transfected with a pBabe-puromycin retrovirus also behaved functionally similar in vitro and in vivo as wild-type B16F0 cells (34).…”

Section: Resultsmentioning

confidence: 89%

“…To illustrate the approach, we focused on Cell Communication Network factor 4 (CCN4/WISP1), as it is upregulated in invasive breast cancer (Klinke, 2014) and correlates with a lower overall survival in patients diagnosed with primary melanoma (Deng et al, 2019). Functionally, CCN4 promotes metastasis in melanoma by promoting a process similar to the epithelial-mesenchymal transition (Deng et al, 2019(Deng et al, , 2020.…”

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confidence: 99%

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Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Klinke

Fernández

Deng

et al. 2020

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1 ingly relies on mechanistic mathematical modeling and simu-2 lation. In immuno-oncology, models that capture causal rela-3 tions among genetic drivers of oncogenesis, functional plastic-4 ity, and host immunity provide an important complement to wet 5 experiments, given the cellular complexity and dynamics within 6 the tumor microenvironment. Unfortunately, formulating such 7 mechanistic cell-level models currently relies on hand curation 8 by experts, which can bias how data is interpreted or the pri-9 ority of drug targets. In modeling molecular-level networks, 10 rules and algorithms have been developed to limit a priori bi-11 ases in formulating mechanistic models. To realize an equivalent 12 approach for cell-level networks, we combined digital cytome-13 try with Bayesian network inference to generate causal models 14 that link an increase in gene expression associated with onco-15 genesis with alterations in stromal and immune cell subsets di-16 rectly from bulk transcriptomic datasets. To illustrate, we pre-17 dicted how an increase in expression of Cell Communication 18 Network factor 4 (CCN4/WISP1) altered the tumor microenvi-19 ronment using data from patients diagnosed with breast cancer 20 and melanoma. Network predictions were then tested using two 21 immunocompetent mouse models for melanoma. In contrast to 22 hand-curated approaches, we posit that combining digital cy-23 tometry with Bayesian network inference provides a less biased 24 approach for elaborating mechanistic cell-level models directly 25 from data. 26 Heterocellular networks | digital cytometry | deconvolution | anti-tumor 27 immunity | Bayesian network inference | functional plasticity Introduction 30 Tissues are dynamic structures where different cell types or-31 ganize to maintain function in a changing environment. For 32 instance, the mammary epithelium reorganizes during dis-33 tinct stages of the ovarian cycle in preparation for lactation 34 (Klinke, 2016). At the same time, immune cells clear dead 35 cells and defend against pathogens present in the tissue mi-36 croenvironment. Ultimately, the number and functional ori-37 entation of different cell types within a tissue interact to cre-38 ate a network, that is a heterocellular network. This hetero-39 cellular network is essential for creating and maintaining tis-40 sue homeostasis. While we know that tissue homeostasis is 41 disrupted during oncogenesis, our understanding of how ge-42 netic alterations quantitatively and dynamically influence the 43 heterocellular network within malignant tissues in humans 44 is not well developed despite large efforts, like The Cancer 45 Genome Atlas (TCGA), to characterize the genomic and tran-46 scriptomic landscape in human malignancy (Hoadley et al., 47 2018; Wells and Wiley, 2018). In parallel with these large 48 scale data gathering efforts, two informatic developments, 49 namely digital cytometry and Bayesian network inference, 50 may be helpful in interrogating these datasets. 51 87 Increases in size and information content of t...

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Section: Resultsmentioning

confidence: 89%

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confidence: 99%

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Klinke

Fernández

Deng

et al. 2020

Preprint

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“…Recent literature connects the epithelial-mesenchymal transition (EMT) with tumor immune escape (20, 28, 29). We previously found that CCN4 activates EMT-associated genes in melanoma cells thus increasing metastatic potential (11, 12). Motivated by encouraging clinical correlates, we report that melanoma-derived CCN4 also stimulated tumor-induced immunosuppression in mice, particularly by directly suppressing antigen-induced IFN γ release by CD8 + T cells and by expanding and recruiting G-MDSC.…”

Section: Discussionmentioning

confidence: 99%

“…CCN4-knockout (CCN4-KO) B16F0 and YUMM1.7 cells were generated using a double nickase-based CRISPR/Cas9 approach as previously described (11). Additionally, for the B16F0 model, DNMT3A- and CCN4-knockout cells were obtained through transfection with a mix of CRISPR/Cas9 KO and Homology-Directed Repair (HDR) plasmids, followed by puromycin selection (12). A control cell, B16F0-Ctr, was also created using a pBabe-puro retrovirus (12).…”

Section: Methodsmentioning

confidence: 99%

“…Metastatic dissemination is attributed to re-engaging developmental pathways that enable malignant cells to decouple from their tissue niche, migrate via the circulation, ex-travasate into a peripheral tissue, and establish metastatic colonies (10). In previous work, Cell Communication Network factor 4 (CCN4), a secreted matricellular protein produced by activating the Wnt/ β -catenin pathway, promotes metastatic dissemination in melanoma by engaging the epithelial-mesenchymal transition (11, 12). Interestingly, we observed slower tumor growth rates of CCN4 knock-out variants in immunocompetent (C57BL/6) compared with immunocompromised (NSG) hosts while the parental cell lines exhibited no difference in growth rates.…”

Section: Introductionmentioning

confidence: 99%

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Cell Communication Network factor 4 promotes tumor-induced immunosuppression in melanoma

Fernández

Deng

McLaughlin

et al. 2021

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Immune cell composition within the tumor microenvironment is regulated by tumor-derived factors. Cell Communication Network factor 4 (CCN4/WISP1) is a matricellular protein secreted by cancer cells that promotes metastasis by inducing the epithelial-mesenchymal transition. While metastatic dissemination limits patient survival, the absence of anti-tumor immunity also associates with poor patient outcomes with recent work suggesting these two clinical correlates are linked. Motivated by finding that CCN4 was associated with a dampened anti-tumor immune contexture in patients diagnosed with primary melanoma, we tested for a direct causal link by knocking out CCN4 (CCN4-KO) in the B16F0 and YUMM1.7 mouse models for melanoma. Tumor growth was significantly reduced when CCN4-KO melanoma cells were implanted subcutaneously in immunocompetent C57BL/6 mice but not in immunodeficient NSG mice. Correspondingly, the frequency of total CD45+ tumor-infiltrating leukocytes was significantly increased in CCN4-KO tumors, with increased natural killer (NK) and effector CD8+ T cells and reduced myeloid-derived suppressor cells (MDSC). Additionally, the absence of tumor-derived CCN4 was associated with an impaired splenic generation of suppressive granulocytic MDSC. Among mechanisms linked to local immunosuppression, we found CCN4 directly suppressed antigen-induced IFNγ release by CD8+ T cells, promoted glycolysis and consequent lactate release by melanoma cells, and enhanced tumor secretion of MDSC-attracting chemokines like CCL2 and CXCL1. Finally, CCN4- KO in B16F0 and YUMM1.7 melanoma cells potentiated the anti-tumor effect of immune checkpoint blockade (ICB) therapy. Overall, our results suggest that CCN4 promotes tumor-induced immunosuppression and is a potential target for therapeutic combinations with ICB.

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Transcriptome Analysis of Host Anti-Vibrio harveyi Infection Revealed the Pathogenicity of V. harveyi to American Eel (Anguilla rostrata)

Chen,

Wan,

et al. 2024

Mar Biotechnol

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Cell Communication Network Factor 4 (CCN4/WISP1) Shifts Melanoma Cells from a Fragile Proliferative State to a Resilient Metastatic State

Cited by 14 publications

References 37 publications

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Cell Communication Network factor 4 promotes tumor-induced immunosuppression in melanoma

Transcriptome Analysis of Host Anti-Vibrio harveyi Infection Revealed the Pathogenicity of V. harveyi to American Eel (Anguilla rostrata)

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