Sugar alcohol provides imaging contrast in cancer detection

Clinical imaging is widely used to detect, characterize and stage cancers in addition to monitoring the therapeutic progress. Magnetic resonance imaging (MRI) aided by contrast agents utilizes the differential relaxivity property of water to distinguish between tumorous and normal tissue. Here, we describe an MRI contrast method for the detection of cancer using a sugar alcohol, maltitol, a common low caloric sugar substitute that exploits the chemical exchange saturation transfer (CEST) property of the labile hydroxyl group protons on maltitol (malCEST). In vitro studies pointed toward concentration and pH-dependent CEST effect peaking at 1?ppm downfield to the water resonance. Studies with control rats showed that intravenously injected maltitol does not cross the intact blood-brain barrier (BBB). In glioma carrying rats, administration of maltitol resulted in the elevation of CEST contrast in the tumor region only owing to permeable BBB. These preliminary results show that this method may lead to the development of maltitol and other sugar alcohol derivatives as MRI contrast agents for a variety of preclinical imaging applications

The Self Model and the Conception of Biological Identity in Immunology

The self/non-self model, first proposed by F.M. Burnet, has dominated immunology for sixty years now. According to this model, any foreign element will trigger an immune reaction in an organism, whereas endogenous elements will not, in normal circumstances, induce an immune reaction. In this paper we show that the self/non-self model is no longer an appropriate explanation of experimental data in immunology, and that this inadequacy may be rooted in an excessively strong metaphysical conception of biological identity. We suggest that another hypothesis, one based on the notion of continuity, gives a better account of immune phenomena. Finally, we underscore the mapping between this metaphysical deflation from self to continuity in immunology and the philosophical debate between substantialism and empiricism about identity

The influence of diet on anti‑cancer immune responsiveness

Immunotherapy has matured into standard treatment for several cancers, but much remains to be done to extend the reach of its effectiveness particularly to cancers that are resistant within each indication. This review proposes that nutrition can affect and potentially enhance the immune response against cancer. The general mechanisms that link nutritional principles to immune function and may influence the effectiveness of anticancer immunotherapy are examined. This represents also the premise for a research project aimed at identifying the best diet for immunotherapy enhancement against tumours (D.I.E.T project). Particular attention is turned to the gut microbiota and the impact of its composition on the immune system. Also, the dietary patterns effecting immune function are discussed including the value of adhering to a healthy diets such as the Mediterranean, Veg, Japanese, or a Microbiota-regulating diet, the very low ketogenic diet, which have been demonstrated to lower the risk of developing several cancers and reduce the mortality associated with them. Finally, supplements, as omega-3 and polyphenols, are discussed as potential approaches that could benefit healthy dietary and lifestyle habits in the context of immunotherapy

Influence of quercetin-rich food intake on microRNA expression in lung cancer tissues

BACKGROUND: Epidemiologic studies have reported that frequent consumption of quercetin-rich foods is inversely associated with lung cancer incidence. A quercetin-rich diet might modulate microRNA (miR) expression; however, this mechanism has not been fully examined. METHODS: miR expression data were measured by a custom-made array in formalin-fixed paraffin-embedded tissue samples from 264 lung cancer cases (144 adenocarcinomas and 120 squamous cell carcinomas). Intake of quercetin-rich foods was derived from a food-frequency questionnaire. In individual-miR-based analyses, we compared the expression of miRs (n=198) between lung cancer cases consuming high-versus-low quercetin-rich food intake using multivariate ANOVA tests. In family-miR-based analyses, we used Functional Class Scoring (FCS) to assess differential effect on biologically functional miRs families. We accounted for multiple testing using 10,000 global permutations (significance at p-value(global) <0.10). All multivariate analyses were conducted separately by histology and by smoking status (former and current smokers). RESULTS: Family-based analyses showed that a quercetin-rich diet differentiated miR expression profiles of the tumor suppressor let-7 family among adenocarcinomas (p-value(FCS)<0.001). Other significantly differentiated miR families included carcinogenesis-related miR-146, miR-26, and miR-17 (p-values(FCS)<0.05). In individual-based analyses, we found that among former and current smokers with adenocarcinoma, 33 miRs were observed to be differentiated between highest-and-lowest quercetin-rich food consumers (23 expected by chance; p-value(global) = 0.047). CONCLUSIONS: We observed differential expression of key biologically functional miRNAs between high-versus-low consumers of quercetin-rich foods in adenocarcinoma cases. Impact: Our findings provide preliminary evidence on the mechanism underlying quercetin-related lung carcinogenesis

The Ovarian Cancer Chemokine Landscape Is Conducive to Homing of Vaccine-Primed and CD3/CD28-Costimulated T Cells Prepared for Adoptive Therapy.

PURPOSE: Chemokines are implicated in T-cell trafficking. We mapped the chemokine landscape in advanced stage ovarian cancer and characterized the expression of cognate receptors in autologous dendritic cell (DC)-vaccine primed T cells in the context of cell-based immunotherapy. EXPERIMENTAL DESIGN: The expression of all known human chemokines in patients with primary ovarian cancer was analyzed on two independent microarray datasets and validated on tissue microarray. Peripheral blood T cells from five HLA-A2 patients with recurrent ovarian cancer, who previously received autologous tumor DC vaccine, underwent CD3/CD28 costimulation and expansion ex vivo. Tumor-specific T cells were identified by HER2/neu pentamer staining and were evaluated for the expression and functionality of chemokine receptors important for homing to ovarian cancer. RESULTS: The chemokine landscape of ovarian cancer is heterogeneous with high expression of known lymphocyte-recruiting chemokines (CCL2, CCL4, and CCL5) in tumors with intraepithelial T cells, whereas CXCL10, CXCL12, and CXCL16 are expressed quasi-universally, including in tumors lacking tumor-infiltrating T cells. DC-vaccine primed T cells were found to express the cognate receptors for the above chemokines. Ex vivo CD3/CD28 costimulation and expansion of vaccine-primed Tcells upregulated CXCR3 and CXCR4, and enhanced their migration toward universally expressed chemokines in ovarian cancer. CONCLUSIONS: DC-primed tumor-specific T cells are armed with the appropriate receptors to migrate toward universal ovarian cancer chemokines, and these receptors are further upregulated by ex vivo CD3/CD28 costimulation, which render T cells more fit for migrating toward these chemokines. Clin Cancer Res; 21(12); 2840-50. ©2015 AACR

Perspectives in melanoma: meeting report from the Melanoma Bridge (November 29th-1 December 1st, 2018, Naples, Italy).

Diagnosis of melanocytic lesions, correct prognostication of patients, selection of appropriate adjuvant and systemic therapies, and prediction of response to a given therapy remain very real challenges in melanoma. Recent studies have shown that immune checkpoint blockade that represents a forefront in cancer therapy, provide responses but they are not universal. Improved understanding of the tumor microenvironment, tumor immunity and response to therapy has prompted extensive translational and clinical research in melanoma. Development of novel biomarker platforms may help to improve diagnostics and predictive accuracy for selection of patients for specific treatment. There is a growing evidence that genomic and immune features of pre-treatment tumor biopsies may correlate with response in patients with melanoma and other cancers they have yet to be fully characterized and implemented clinically. For example, advancements in sequencing and the understanding of the tumor microenvironment in melanoma have led to the use of genome sequencing and gene expression for development of multi-marker assays that show association with inflammatory state of the tumor and potential to predict response to immunotherapy. As such, melanoma serves as a model system for understanding cancer immunity and patient response to immunotherapy, either alone or in combination with other treatment modalities. Overall, the aim for the translational and clinical studies is to achieve incremental improvements through the development and identification of optimal treatment regimens, which increasingly involve doublet as well as triplet combinations, as well as through development of biomarkers to improve immune response. These and other topics in the management of melanoma were the focus of discussions at the fourth Melanoma Bridge meeting (November 29th-December 1st, 2018, Naples, Italy), which is summarised in this report

Parametric POMDPs for planning in continuous state spaces

This thesis is concerned with planning and acting under uncertainty in partially-observable continuous domains. In particular, it focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian estimation to maintain a probability distribution over possible robot poses. In contrast, control algorithms often base their decisions on the assumption that a single state, such as the mode of this distribution, is correct. In scenarios involving significant uncertainty, this can lead to serious control errors. It is generally agreed that the reliability of navigation in uncertain environments would be greatly improved by the ability to consider the entire distribution when acting, rather than the single most likely state. The framework adopted in this thesis for modelling navigation problems mathematically is the Partially Observable Markov Decision Process (POMDP). An exact solution to a POMDP problem provides the optimal balance between reward-seeking behaviour and information-seeking behaviour, in the presence of sensor and actuation noise. Unfortunately, previous exact and approximate solution methods have had difficulty scaling to real applications. The contribution of this thesis is the formulation of an approach to planning in the space of continuous parameterised approximations to probability distributions. Theoretical and practical results are presented which show that, when compared with similar methods from the literature, this approach is capable of scaling to larger and more realistic problems. In order to apply the solution algorithm to real-world problems, a number of novel improvements are proposed. Specifically, Monte Carlo methods are employed to estimate distributions over future parameterised beliefs, improving planning accuracy without a loss of efficiency. Conditional independence assumptions are exploited to simplify the problem, reducing computational requirements. Scalability is further increased by focussing computation on likely beliefs, using metric indexing structures for efficient function approximation. Local online planning is incorporated to assist global offline planning, allowing the precision of the latter to be decreased without adversely affecting solution quality. Finally, the algorithm is implemented and demonstrated during real-time control of a mobile robot in a challenging navigation task. We argue that this task is substantially more challenging and realistic than previous problems to which POMDP solution methods have been applied. Results show that POMDP planning, which considers the evolution of the entire probability distribution over robot poses, produces significantly more robust behaviour when compared with a heuristic planner which considers only the most likely states and outcomes