The prevalence of wheat allergy has reached significant levels in many countries. Therefore, wheat is a major global food safety and public health issue. Animal models serve as critical tools to advance the understanding of the mechanisms of wheat allergenicity to develop preventive and control methods. A comprehensive review on the molecular mechanisms of wheat allergenicity using animal models is unavailable at present. There were two major objectives of this study: To identify the lessons that animal models have taught us regarding the molecular mechanisms of wheat allergenicity and to identify the strengths, challenges, and future prospects of animal models in basic and applied wheat allergy research. Using the PubMed and Google Scholar databases, we retrieved and critically analyzed the relevant articles and excluded celiac disease and non-celiac gluten sensitivity. Our analysis shows that animal models can provide insight into the IgE epitope structure of wheat allergens, effects of detergents and other chemicals on wheat allergenicity, and the role of genetics, microbiome, and food processing in wheat allergy. Although animal models have inherent limitations, they are critical to advance knowledge on the molecular mechanisms of wheat allergenicity. They can also serve as highly useful pre-clinical testing tools to develop safer genetically modified wheat, hypoallergenic wheat products, novel pharmaceuticals, and vaccines.
Abstract Cytokines and other immune regulatory molecules are critical players in the immune response against cancer. There is growing interest in testing the potential utility of systemic immune biomarkers to track cancer progression and to use them as predictors of effective responses to cancer therapy. The central hypothesis guiding this project is that specific immune biomarkers will serve as predictors of effective vs. ineffective immunotherapy in patients with malignant diseases. The objective of this study was to establish baseline of immune markers in patients already started treatment with immunotherapy (n=10) (T), patients starting, but not yet treated (S) with immunotherapy (n=10) and subjects without diagnosed malignant disease (W) (n=10). Blood was collected and plasma was isolated and used in the biomarker (100 markers) analysis using a protein microarray method (RayBiotech). The biomarkers in the three groups were analyzed by Principal Component Analysis, heat map with clustering, and differential expression based on p value, and Significance Analysis of Microarrays (SAM). Although 15 biomarkers were significantly different between S vs. W groups, based on SAM, only seven were found differentially expressed. Similarly, although 10 biomarkers were significantly different between T vs. W groups, based on SAM, only one biomarker was found differentially expressed. Furthermore, SAM revealed that responders (n=4) vs. stable (n=5) subgroup of patients within the T group exhibited 22 differentially expressed biomarkers. Future larger studies will be needed to evaluate whether immune markers will be able to predict effective vs. ineffective responses to immunotherapy and whether they may have therapeutic potential.
Background and Objectives: Cytokines and other immune regulatory molecules are critical for mounting an effective immune response against cancer. The gastrointestinal (GI) microbiome plays a significant role in the pathogenesis of cancer and the response to immunotherapy. The central hypothesis guiding this project was that specific immune biomarkers and microbiome profiles will serve as predictors of effective vs. ineffective immunotherapy in patients with malignant diseases. This pilot feasibility study aims to establish baseline immune markers and microbiome profiles in subjects with newly diagnosed malignant solid tumors (n = 10), healthy subjects without diagnosed malignant disease (n = 10), and in existing patients treated with immunotherapy (n = 10). Methods: Parallel blood and stool samples were collected and used in the biomarker and microbiome analysis. The biomarkers in the two groups were analyzed by Principal Component Analysis, heat map with clustering, and differential expression based on P value, and Significance Analysis of Microarrays (SAM). The microbiome analysis was performed using long read 16S rRNA encoding gene sequencing with data visualization and analysis in R. Significant differences in alpha and beta diversity were evaluated between the groups. Results: Several biomarkers that were differentially expressed were identified. Significant taxa differences at the class (Clostridia), order (Clostridiales, Lactobacillillales), family (Eubacteriaceae, Lactobacillaceae), genus and species were identified. Furthermore, a limited analysis of samples from existing patients on immunotherapy who were responders (n = 4) vs. stable non-responders (n = 5) identified differentially expressed immune biomarkers and significant bacterial taxa differences. Conclusion: This study has established the feasibility for conducting a future larger study at the local community cancer center to evaluate whether immune and microbiome markers can predict effective vs. ineffective responses to immunotherapy and whether either or both molecular and microbial markers may have therapeutic potential.
<p><strong>Objective. </strong>This paper describes our experience and outcomes from 54 cases presented to the (Molecular tumor board) MTB.</p><p><strong>Methods. </strong>54 Cases presented between July 2017 and April 2018 were included in this analysis. These patients had different types of cancers that had either failed standard therapy or were expected to fail and physicians were looking for future options for anticipated progression. Patients who had obvious mutations and were candidates for Targeted Agent and Profiling Utilization Registry or Molecular Analysis for Treatment Choice clinical trials were not included. Oncologists presented the cases virtually and Foundation Medicine scientific and clinical team discussed the molecular pathways to find targeted options or trials. Tumor board attendees included oncologists, nurses, pharmacists, mid-level providers, residents and staff of the Cancer Center.</p><p><strong>Results. </strong>Amongst the 54 cases presented 81% had one or more potentially actionable alteration. 12 (22%) patients received genomically matched therapy as per MTB recommendations. Additional 13 (24%) patients have options available when they progress. Out of 12 patients who got treatment six are alive at the time of this analysis<strong>. </strong>Genomically matched therapy or Clinical Trials option were offered to the 46% of patients based on the MTB discussion.</p><p><strong>Conclusion. </strong>More widespread use of molecular diagnostics, better physician education and multidisciplinary collaboration between the staff involved in diagnosis and treatment, as well as third party payers are necessary for consensus on treatment and care of oncology patients.</p>
