Downregulation of Stat3 in melanoma: reprogramming the immune microenvironment as an anticancer therapeutic strategy.

Persistent activation of the transcription factor, signal transducer and activator of transcription 3 (Stat3) has been shown to mediate several oncogenic features in many types of cancers, including melanoma. In this study, we investigated whether lentiviral (LV) delivery of Stat3-targeting short hairpin RNA (shRNA; LV-shStat3) to K1735-C4 melanoma cells modulates antitumor immunity. Three shStat3 sequences, starting at the position 446, 830 and 1412, were cloned into a mir30 cassette. A shRNA with scrambled sequence served as a control. Transduction with LV-shStat3 resulted in downregulation of Stat3 in vitro. The latter coincided with low cell viability, a reduced expression of survivin and matrix metalloproteinase (MMP)-2. A single injection of LV-shStat3 in K1735-C4 tumors efficiently downregulated Stat3 in vivo and resulted in reduction of both vascular endothelial growth factor secretion and in myeloid-derived suppressor cell (MDSC) numbers. In contrast, we observed an increase in interleukin-6 and interferon-γ secretion, mature dendritic cells (DCs) and CD8(+) T cells. Both DCs and CD8(+) T cells displayed enhanced activity, whereas granulocytic MDSCs lost their suppressive capacity upon Stat3 downregulation. Importantly, a single injection of LV-shStat3 was sufficient to reduce tumor growth, hence prolong survival of tumor-bearing mice. These data demonstrate that Stat3 downregulation in melanoma reinvigorates existing antitumor immunity.

Lentiviral vectors: a versatile tool to fight cancer.

Over the years, there has been an exponential increase in the number of gene therapy approaches that are under investigation for the treatment of cancer. This can be attributed to our growing understanding of the molecular mechanisms that contribute to the onset and maintenance of cancer as well as to the development of gene delivery vectors. In this review, we will focus on the use of lentiviral vectors (LVs) in immuno gene therapy of cancer, as these efficacious gene delivery vehicles have come to the fore front because of their many attractive features. LVs have been successfully applied to generate potent dendritic cell based anti-cancer vaccines and to deliver cancer-specific receptors to T-cells. Moreover, LVs are under investigation for the modulation of cancer cells. We will describe various strategies of this 'genuine' cancer gene therapy, amongst which transfer of suicide genes, modulation of pro- and anti-apoptotic molecules, strategies to optimize chemo- and radiotherapy, expression of molecules that affect angiogenesis or affect the immunogenicity of tumor cells. These will be discussed in view of our current knowledge of tumor immunology. Finally we will discuss some important issues and future directions to push the field forward.

Marker-based estimation of the coefficient of coancestry in hybrid breeding programmes.

Molecular markers allow to estimate the pairwise relatedness between the members of a breeding pool when their selection history is no longer available or has become too complex for a classical pedigree analysis. The field of population genetics has several estimation procedures at its disposal, but when the genotyped individuals are highly selected inbred lines, their application is not warranted as the theoretical assumptions on which these estimators were built, usually linkage equilibrium between marker loci or even Hardy-Weinberg equilibrium, are not met. An alternative approach requires the availability of a genotyped reference set of inbred lines, which allows to correct the observed marker similarities for their inherent upward bias when used as a coancestry measure. However, this approach does not guarantee that the resulting coancestry matrix is at least positive semi-definite (psd), a necessary condition for its use as a covariance matrix. In this paper we present the weighted alikeness in state (WAIS) estimator. This marker-based coancestry estimator is compared to several other commonly applied relatedness estimators under realistic hybrid breeding conditions in a number of simulations. We also fit a linear mixed model to phenotypical data from a commercial maize breeding programme and compare the likelihood of the different variance structures. WAIS is shown to be psd which makes it suitable for modelling the covariance between genetic components in linear mixed models involved in breeding value estimation or association studies. Results indicate that it generally produces a low root mean squared error under different breeding circumstances and provides a fit to the data that is comparable to that of several other marker-based alternatives. Recommendations for each of the examined coancestry measures are provided.

Support vector machine regression for the prediction of maize hybrid performance.

Accurate prediction of the phenotypical performance of untested single-cross hybrids allows for a faster genetic progress of the breeding pool at a reduced cost. We propose a prediction method based on epsilon-insensitive support vector machine regression (epsilon-SVR). A brief overview of the theoretical background of this fairly new technique and the use of specific kernel functions based on commonly applied genetic similarity measures for dominant and co-dominant markers are presented. These different marker types can be integrated into a single regression model by means of simple kernel operations. Field trial data from the grain maize breeding programme of the private company RAGT R2n are used to assess the predictive capabilities of the proposed methodology. Prediction accuracies are compared to those of one of today's best performing prediction methods based on best linear unbiased prediction. Results on our data indicate that both methods match each other's prediction accuracies for several combinations of marker types and traits. The epsilon-SVR framework, however, allows for a greater flexibility in combining different kinds of predictor variables.