Functional consequences of prolactin signalling in endothelial cells: a potential link with angiogenesis in pathophysiology?

Prolactin is best known as the polypeptide anterior pituitary hormone, which regulates the development of the mammary gland. However, it became clear over the last decade that prolactin contributes to a broad range of pathologies, including breast cancer. Prolactin is also involved in angiogenesis via the release of pro-angiogenic factors by leukocytes and epithelial cells. However, whether prolactin also influences endothelial cells, and whether there are functional consequences of prolactin-induced signalling in the perspective of angiogenesis, remains so far elusive. In the present study, we show that prolactin induces phosphorylation of ERK1/2 and STAT5 and induces tube formation of endothelial cells on Matrigel. These effects are blocked by a specific prolactin receptor antagonist, del1-9-G129R-hPRL. Moreover, in an in vivo model of the chorioallantoic membrane of the chicken embryo, prolactin enhances vessel density and the tortuosity of the vasculature and pillar formation, which are hallmarks of intussusceptive angiogenesis. Interestingly, while prolactin has only little effect on endothelial cell proliferation, it markedly stimulates endothelial cell migration. Again, migration was reverted by del1-9-G129R-hPRL, indicating a direct effect of prolactin on its receptor. Immunohistochemistry and spectral imaging revealed that the prolactin receptor is present in the microvasculature of human breast carcinoma tissue. Altogether, these results suggest that prolactin may directly stimulate angiogenesis, which could be one of the mechanisms by which prolactin contributes to breast cancer progression, thereby providing a potential tool for intervention.     

Functional normalization of 450k methylation array data improves replication in large cancer studies

We propose an extension to quantile normalization that removes unwanted technical variation using control probes. We adapt our algorithm, functional normalization, to the Illumina 450k methylation array and address the open problem of normalizing methylation data with global epigenetic changes, such as human cancers. Using data sets from The Cancer Genome Atlas and a large case–control study, we show that our algorithm outperforms all existing normalization methods with respect to replication of results between experiments, and yields robust results even in the presence of batch effects. Functional normalization can be applied to any microarray platform, provided suitable control probes are available.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0503-2) contains supplementary material, which is available to authorized users.     

How old is this mutation? - a study of three Ashkenazi Jewish founder mutations

Background

Several founder mutations leading to increased risk of cancer among Ashkenazi Jewish individuals have been identified, and some estimates of the age of the mutations have been published. A variety of different methods have been used previously to estimate the age of the mutations. Here three datasets containing genotype information near known founder mutations are reanalyzed in order to compare three approaches for estimating the age of a mutation. The methods are: (a) the single marker method used by Risch et al., (1995); (b) the intra-allelic coalescent model known as DMLE, and (c) the Goldgar method proposed in Neuhausen et al. (1996), and modified slightly by our group. The three mutations analyzed were MSH2*1906 G->C, APC*I1307K, and BRCA2*6174delT.

Results

All methods depend on accurate estimates of inter-marker recombination rates. The modified Goldgar method allows for marker mutation as well as recombination, but requires prior estimates of the possible haplotypes carrying the mutation for each individual. It does not incorporate population growth rates. The DMLE method simultaneously estimates the haplotypes with the mutation age, and builds in the population growth rate. The single marker estimates, however, are more sensitive to the recombination rates and are unstable. Mutation age estimates based on DMLE are 16.8 generations for MSH2 (95% credible interval (13, 23)), 106 generations for I1037K (86-129), and 90 generations for 6174delT (71-114).

Conclusions

For recent founder mutations where marker mutations are unlikely to have occurred, both DMLE and the Goldgar method can give good results. Caution is necessary for older mutations, especially if the effective population size may have remained small for a long period of time.