Dendritic Cells Crosspresent Antigens from Live B16 Cells More Efficiently than from Apoptotic Cells and Protect from Melanoma in a Therapeutic Model

Dendritic cells (DC) are able to elicit anti-tumoral CD8+ T cell responses by cross-presenting exogenous antigens in association with major histocompatibility complex (MHC) class I molecules. Therefore they are crucial actors in cell-based cancer immunotherapy. Although apoptotic cells are usually considered to be the best source of antigens, live cells are also able to provide antigens for cross-presentation by DC. We have recently shown that prophylactic immunotherapy by DC after capture of antigens from live B16 melanoma cells induced strong CD8+ T-cell responses and protection against a lethal tumor challenge in vivo in C57Bl/6 mice. Here, we showed that DC cross-presenting antigens from live B16 cells can also inhibit melanoma lung dissemination in a therapeutic protocol in mice. DC were first incubated with live tumor cells for antigen uptake and processing, then purified and irradiated for safety prior to injection. This treatment induced stronger tumor-specific CD8+ T-cell responses than treatment by DC cross-presenting antigens from apoptotic cells. Apoptotic B16 cells induced more IL-10 secretion by DC than live B16 cells. They underwent strong native antigen degradation and led to the expression of fewer MHC class I/epitope complexes on the surface of DC than live cells. Therefore, the possibility to use live cells as sources of tumor antigens must be taken into account to improve the efficiency of cancer immunotherapy

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

A Fair Opportunistic Access Scheme for Multiuser OFDM Wireless Networks

We propose a new access scheme for efficient support of multimedia services in OFDM wireless networks, both in the uplink and in the downlink. This scheme further increases the benefits of opportunistic scheduling by extending this cross-layer technique to higher layers. Access to the medium is granted based on a system of weights that dynamically accounts for both the experienced QoS and the transmission conditions. This new approach enables the full support of multimedia services with the adequate traffic and QoS differentiation while maximizing the system capacity and keeping a special attention on fairness. Performance evaluation shows that the proposed access technique outperforms existing wireless access schemes and demonstrates that choosing between high fairness and high system throughput is no more required.</p

Mixed-Effects Estimation in Dynamic Models of Plant Growth for the Assessment of Inter-individual Variability

Modeling inter-individual variability in plant populations is a key issue to understand crop heterogeneity and its variations in response to the environment. Being able to describe the interactions among plants and explain the variability observed in the population could provide useful information on how to control it and improve global plant growth. We propose here a method to model plant variability within a field, by extending the so-called GreenLab functional-structural plant model from the individual to the population scale via nonlinear mixed-effects modeling. Parameter estimation of the population model is achieved using the stochastic approximation expectation maximization algorithm, implemented in the platform for plant growth modeling and analysis PyGMAlion. The method is first applied on a set of simulated data and then on a real dataset from a population of 34 winter oilseed rape plants at the rosette stage. Results show that our method allows for a good characterization of the variability in the population with only a limited number of parameters, which is a key point for plant models. Results on simulated data show that parameters associated with a low sensitivity index are inaccurately estimated by the algorithm when considered as random effects, but a good stability of the results can be obtained by considering them as fixed effects. These results open new ways for the analysis of inter-plant variability within a population and the study of plant–plant competition.Supplementary materials accompanying this paper appear online. © 2018, International Biometric Society

Apoptotic cell capture by DCs induces unexpectedly robust autologous CD4(+) T-cell responses

Item does not contain fulltextApoptotic cells represent an important source of self-antigens and their engulfment by dendritic cells (DCs) is usually considered to be related to tolerance induction. We report here an unexpectedly high level of human CD4(+) T-cell proliferation induced by autologous DCs loaded with autologous apoptotic cells, due to the activation of more than 10% of naive CD4(+) T cells. This proliferation is not due to an increase in the costimulatory capacity of DCs, but is dependent on apoptotic cell-associated material processed through an endo-lysosomal pathway and presented on DC MHC class II molecules. Autologous CD4(+) T cells stimulated with apoptotic cell-loaded DCs exhibit suppressive capacities. However, in the presence of bacterial lipopolysaccharide, apoptotic cell-loaded DCs induce the generation of IL-17-producing cells. Thus, apoptotic cell engulfment by DCs may lead to increased autologous responses, initially generating CD4(+) T cells with suppressive capacities able to differentiate into Th17 cells in the presence of a bacterial danger signal such as LPS