scholarly journals Sprouty4 negatively regulates ERK/MAPK signaling and the transition from in situ to invasive breast ductal carcinoma

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252314
Author(s):  
Ethan J. Brock ◽  
Ryan M. Jackson ◽  
Julie L. Boerner ◽  
Quanwen Li ◽  
Meredith A. Tennis ◽  
...  

Breast ductal carcinoma in situ (DCIS) is a non-obligate precursor of invasive ductal carcinoma (IDC). It is still unclear which DCIS will become invasive and which will remain indolent. Patients often receive surgery and radiotherapy, but this early intervention has not produced substantial decreases in late-stage disease. Sprouty proteins are important regulators of ERK/MAPK signaling and have been studied in various cancers. We hypothesized that Sprouty4 is an endogenous inhibitor of ERK/MAPK signaling and that its loss/reduced expression is a mechanism by which DCIS lesions progress toward IDC, including triple-negative disease. Using immunohistochemistry, we found reduced Sprouty4 expression in IDC patient samples compared to DCIS, and that ERK/MAPK phosphorylation had an inverse relationship to Sprouty4 expression. These observations were reproduced using a 3D culture model of disease progression. Knockdown of Sprouty4 in MCF10.DCIS cells increased ERK/MAPK phosphorylation as well as their invasive capability, while overexpression of Sprouty4 in MCF10.CA1d IDC cells reduced ERK/MAPK phosphorylation, invasion, and the aggressive phenotype exhibited by these cells. Immunofluorescence experiments revealed reorganization of the actin cytoskeleton and relocation of E-cadherin back to the cell surface, consistent with the restoration of adherens junctions. To determine whether these effects were due to changes in ERK/MAPK signaling, MEK1/2 was pharmacologically inhibited in IDC cells. Nanomolar concentrations of MEK162/binimetinib restored an epithelial-like phenotype and reduced pericellular proteolysis, similar to Sprouty4 overexpression. From these data we conclude that Sprouty4 acts to control ERK/MAPK signaling in DCIS, thus limiting the progression of these premalignant breast lesions.

2018 ◽  
Vol 120 ◽  
pp. S152
Author(s):  
Semra Unal ◽  
Tilbe Gokce ◽  
Sema Arslan ◽  
Ayse Mine Yilmaz ◽  
Oguzhan Gunduz ◽  
...  

2018 ◽  
Author(s):  
Ethan J. Brock ◽  
Ryan Jackson ◽  
Julie L. Boerner ◽  
Quanwen Li ◽  
Bonnie F. Sloane ◽  
...  

2016 ◽  
Vol 309 (1) ◽  
pp. 63-69 ◽  
Author(s):  
Tatiana do Nascimento Pedrosa ◽  
Evelyne De Vuyst ◽  
Abdallah Mound ◽  
Catherine Lambert de Rouvroit ◽  
Silvya Stuchi Maria-Engler ◽  
...  

PLoS ONE ◽  
2012 ◽  
Vol 7 (11) ◽  
pp. e49150 ◽  
Author(s):  
Diana Seidel ◽  
Dana Krinke ◽  
Heinz-Georg Jahnke ◽  
Anika Hirche ◽  
Daniel Kloß ◽  
...  

Oncotarget ◽  
2016 ◽  
Vol 7 (43) ◽  
pp. 70336-70352 ◽  
Author(s):  
Scott Walker ◽  
Fiona Foster ◽  
Amber Wood ◽  
Thomas Owens ◽  
Keith Brennan ◽  
...  

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1722 ◽  
Author(s):  
Kristin Calar ◽  
Simona Plesselova ◽  
Somshuvra Bhattacharya ◽  
Megan Jorgensen ◽  
Pilar de la Puente

Lack of efficacy and a low overall success rate of phase I-II clinical trials are the most common failures when it comes to advancing cancer treatment. Current drug sensitivity screenings present several challenges including differences in cell growth rates, the inconsistent use of drug metrics, and the lack of translatability. Here, we present a patient-derived 3D culture model to overcome these limitations in breast cancer (BCa). The human plasma-derived 3D culture model (HuP3D) utilizes patient plasma as the matrix, where BCa cell lines and primary BCa biopsies were grown and screened for drug treatments. Several drug metrics were evaluated from relative cell count and growth rate curves. Correlations between HuP3D metrics, established preclinical models, and clinical effective concentrations in patients were determined. HuP3D efficiently supported the growth and expansion of BCa cell lines and primary breast cancer tumors as both organoids and single cells. Significant and strong correlations between clinical effective concentrations in patients were found for eight out of ten metrics for HuP3D, while a very poor positive correlation and a moderate correlation was found for 2D models and other 3D models, respectively. HuP3D is a feasible and efficacious platform for supporting the growth and expansion of BCa, allowing high-throughput drug screening and predicting clinically effective therapies better than current preclinical models.


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