An In Vitro Model of Mast Cell Recruitment and Activation by Breast Cancer Cells Supports Anti-Tumoral Responses.
Angélica Aponte-LópezJennifer EncisoSamira Muñoz-CruzEzequiel M Fuentes-PananaPublished in: International journal of molecular sciences (2020)
Breast cancer (BrC) affects millions of women yearly. Mast cells (MCs) are common components of breast tumors with documented agonistic and antagonistic roles in tumor progression. Understanding the participation of MCs in BrC may lead to new therapies to control tumor growth. In this study, we looked into mechanistic models of MC responses triggered by BrC cells (BrCC), assessing both early degranulation and late transcriptional activities. We used aggressive and non-aggressive BrCC to model the progressive staging of the disease over HMC1 and LAD-2 human MC lines. We found that both MC lines were chemoattracted by all BrCC, but their activation was preferentially induced by aggressive lines, finding differences in their active transcriptional programs, both at basal level and after stimulation. Among those genes with altered expression were down-regulated SPP1, PDCD1, IL17A and TGFB1 and up-regulated KITLG and IFNG. A low expression of SPP1 and a high expression of KITLG and IFNG were associated with increased overall survival of BrC patients from public databases. The set of altered genes is more often associated with tumor stromas enriched with anti-tumoral signals, suggesting that MCs may participate in tumor control.
Keyphrases
- poor prognosis
- transcription factor
- end stage renal disease
- gene expression
- induced apoptosis
- breast cancer cells
- endothelial cells
- newly diagnosed
- long non coding rna
- healthcare
- type diabetes
- lymph node
- ejection fraction
- genome wide
- chronic kidney disease
- binding protein
- peritoneal dialysis
- physical activity
- mental health
- adipose tissue
- metabolic syndrome
- polycystic ovary syndrome
- cell death
- oxidative stress
- young adults
- pregnant women
- cell cycle arrest
- big data
- deep learning
- machine learning
- patient reported outcomes
- heat shock
- artificial intelligence
- induced pluripotent stem cells
- pluripotent stem cells
- heat shock protein
- cervical cancer screening