Dendritic Cell Targeting mRNA Lipopolyplexes Combine Strong Antitumor T-Cell Immunity with Improved Inflammatory Safety

In vitro transcribed mRNA constitutes a versatile platform to encode antigens and to evoke CD8 T-cell responses. Systemic delivery of mRNA packaged into cationic liposomes (lipoplexes) has proven particularly powerful in achieving effective antitumor immunity in animal models. Yet, T-cell responses to mRNA lipoplexes critically depend on the induction of type I interferons (IFN), potent pro-inflammatory cytokines, which inflict dose-limiting toxicities. Here, we explored an advanced hybrid lipid polymer shell mRNA nanoparticle (lipopolyplex) endowed with a trimannose sugar tree as an alternative delivery vehicle for systemic mRNA vaccination. Like mRNA lipoplexes, mRNA lipopolyplexes were extremely effective in conferring antitumor T-cell immunity upon systemic administration. Conversely to mRNA lipoplexes, mRNA lipopolyplexes did not rely on type I IFN for effective T-cell immunity. This differential mode of action of mRNA lipopolyplexes enabled the incorporation of N1 methyl pseudouridine nucleoside modified mRNA to reduce inflammatory responses without hampering T-cell immunity. This feature was attributed to mRNA lipopolyplexes, as the incorporation of thus modified mRNA into lipoplexes resulted in strongly weakened T-cell immunity. Taken together, we have identified lipopolyplexes containing N1 methyl pseudouridine nucleoside modified mRNA as potent yet low-inflammatory alternatives to the mRNA lipoplexes currently explored in early phase clinical trials.

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.

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.

Proinflammatory characteristics of SMAC/DIABLO-induced cell death in antitumor therapy

Molecular mimetics of the caspase activator second mitochondria-derived activator of caspase (SMAC) are being investigated for use in cancer therapy, but an understanding of in vivo effects remains incomplete. In this study, we offer evidence that SMAC mimetics elicit a proinflammatory cell death in cancer cells that engages an adaptive antitumor immune response. Cancer cells of different histologic origin underwent apoptosis when transduced with lentiviral vectors encoding a cytosolic form of the SMAC mimetic LV-tSMAC. Strikingly, treatment of tumor-bearing mice with LV-tSMAC resulted in the induction of apoptosis, activation of antitumor immunity, and enhanced survival. Antitumor immunity was accompanied by an increase of tumor-infiltrating lymphocytes displaying low PD-1 expression, high lytic capacity, and high levels of IFN-γ when stimulated. We also noted in vivo a decrease in regulatory T cells along with in vitro activation of tumor-specific CD8(+) T cells by dendritic cells (DC) isolated from tumor draining lymph nodes. Last, tumor-specific cytotoxic T cells were also found to be activated in vivo. Mechanistic analyses showed that transduction of cancer cells with LV-tSMAC resulted in exposure of calreticulin but not release of HMGB1 or ATP. Nevertheless, DCs were activated upon engulfment of dying cancer cells. Further validation of these findings was obtained by their extension in a model of human melanoma using transcriptionally targeted LV-tSMAC. Together, our findings suggest that SMAC mimetics can elicit a proinflammatory cell death that is sufficient to activate adaptive antitumor immune responses in cancer.

Preclinical evaluation of TriMix and antigen mRNA-based antitumor therapy

The use of tumor-associated antigen (TAA) mRNA for therapeutic purposes is under active investigation. To be effective, mRNA vaccines need to deliver activation stimuli in addition to TAAs to dendritic cells (DC). In this study, we evaluated whether intranodal delivery of TAA mRNA together with TriMix, a mix of mRNA encoding CD40 ligand, constitutive active Toll-like receptor 4 and CD70, results in the in situ modification and maturation of DCs, hence, priming of TAA-specific T cells. We showed selective uptake and translation of mRNA in vivo by lymph node resident CD11c(+) cells. This process was hampered by codelivery of classical maturation stimuli but not by TriMix mRNA. Importantly, TriMix mRNA induced a T-cell-attracting and stimulatory environment, including recruitment of antigen-specific CD4(+) and CD8(+) T cells and CTLs against various TAAs. In several mouse tumor models, mRNA vaccination was as efficient in CTL induction and therapy response as vaccination with mRNA-electroporated DCs. Together, our findings suggest that intranodal administration of TAA mRNA together with mRNA encoding immunomodulating molecules is a promising vaccination strategy.

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.