BackgroundThe development and progression of estrogen receptor alpha positive (ERα+) breast cancer has been linked epidemiologically to prolactin. However, activation of the canonical mediator of prolactin, STAT5, is associated with more differentiated cancers and better prognoses. We have reported that density/stiffness of the extracellular matrix potently modulates the repertoire of prolactin signals in human ERα + breast cancer cells in vitro: stiff matrices shift the balance from the Janus kinase (JAK)2/STAT5 cascade toward pro-tumor progressive extracellular regulated kinase (ERK)1/2 signals, driving invasion. However, the consequences for behavior of ERα + cancers in vivo are not known.MethodsIn order to investigate the importance of matrix density/stiffness in progression of ERα + cancers, we examined tumor development and progression following orthotopic transplantation of two clonal green fluorescent protein (GFP) + ERα + tumor cell lines derived from prolactin-induced tumors to 8-week-old wild-type FVB/N (WT) or collagen-dense (col1a1 tm1Jae/+) female mice. The latter express a mutant non-cleavable allele of collagen 1a1 “knocked-in” to the col1a1 gene locus, permitting COL1A1 accumulation. We evaluated the effect of the collagen environment on tumor progression by examining circulating tumor cells and lung metastases, activated signaling pathways by immunohistochemistry analysis and immunoblotting, and collagen structure by second harmonic generation microscopy.ResultsERα + primary tumors did not differ in growth rate, histologic type, ERα, or prolactin receptor (PRLR) expression between col1a1 tm1Jae/+ and WT recipients. However, the col1a1 tm1Jae/+ environment significantly increased circulating tumor cells and the number and size of lung metastases at end stage. Tumors in col1a1 tm1Jae/+ recipients displayed reduced STAT5 activation, and higher phosphorylation of ERK1/2 and AKT. Moreover, intratumoral collagen fibers in col1a1 tm1Jae/+ recipients were aligned with tumor projections into the adjacent fat pad, perpendicular to the bulk of the tumor, in contrast to the collagen fibers wrapped around the more uniformly expansive tumors in WT recipients.ConclusionsA collagen-dense extracellular matrix can potently interact with hormonal signals to drive metastasis of ERα + breast cancers.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-017-0801-1) contains supplementary material, which is available to authorized users.

The hepatitis C virus (HCV) infects more than 200 million people globally, with increasing incidence, especially in developing countries. HCV infection frequently progresses to chronic liver disease, creating a heavy economic burden on resourcepoor countries and lowering patient quality of life. Effective HCV diagnosis, treatment selection, and treatment monitoring are important in stopping disease progression. Serological assays, which detect anti-HCV antibodies in the patient after seroconversion, are used for initial HCV diagnosis. Qualitative and quantitative molecular assays are used to confirm initial diagnosis, determine viral load, and genotype the dominant strain. Viral load and genotype information are used to guide appropriate treatment. Various other biomarker assays are performed to assess liver function and enable disease staging. Most of these diagnostic methods are mature and routinely used in high-resource countries with well-developed laboratory infrastructure. Few technologies, however, are available that address the needs of low-resource areas with high HCV prevalence, such as Africa and Southeast Asia.

More than one third of the reports coming from manufacturers did not include information that is considered a limiting factor to evaluate any causal relationship, and can be an issue for the detection of safety signals. To take advantage of this huge amount of potentially important information that is almost useless at present, data mining tools and new algorithms should be developed and tested with the aim of finding formulas to deal with a huge amount of low quality data without losing it, nor generating a number of false associations.

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