Vitamin D deficiency and prognostics among patients with pancreatic adenocarcinoma

BACKGROUND: The prevalence of vitamin D deficiency among patients with cancer has been previously reported. Because vitamin D is fat soluble, patients with pancreatic adenocarcinoma may have an especially high risk of vitamin D deficiency in association with ongoing and varying degrees of malabsorption. However, little is known about the correlation between vitamin D status and prognosis in these patients. METHODS: We conducted a retrospective review of vitamin D status in patients with pancreatic adenocarcinoma who were treated at Siteman Cancer Center. Patients’ demographic information, clinical staging at the time of vitamin D assessment, vitamin D levels, and survival data were collected. Vitamin D deficiency was defined as a serum 25-hydroxyvitamin D (25[OH]D) level of less than 20 ng/mL, and vitamin D insufficiency was defined as a 25(OH)D level of between 20 ng/mL and 30 ng/mL. RESULTS: Between December 2007 and June 2011, 178 patients with pancreatic adenocarcinoma had their vitamin D levels checked at the time of initial visit at this center. Of these 178 patients, 87 (49%) had vitamin D deficiency, and 44 (25%) had vitamin D insufficiency. The median 25(OH)D level was significantly lower among nonwhite patients and among patients with stage I and II disease. A 25(OH)D level of less than 20 ng/mL was found to be associated with poor prognosis (p = 0.0019) in patients with stage III and IV disease. CONCLUSIONS: Vitamin D insufficiency and deficiency were prevalent among patients with pancreatic adenocarcinoma. The vitamin D level appears to be prognostic for patients with advanced pancreatic adenocarcinoma, and its effects should be further examined in a prospective study

Phase II trial of levocetirizine with capecitabine and bevacizumab to overcome the resistance of antiangiogenic therapies in refractory metastatic colorectal cancer

Background: Despite the clinical success of vascular endothelial growth factor (VEGF) blockade in metastatic colorectal cancers (mCRC), resistance to anti-angiogenic drugs invariably develops. IL-8 and other cytokines have been implicated in development of resistance to anti-angiogenic therapy. Levocetirizine is a second generation H1 antihistamine with anti-inflammatory and IL-8 suppression properties. We conducted a phase II trial combining levocetirizine with capecitabine and bevacizumab to potentially overcome anti-angiogenic therapy resistance in patients with refractory mCRC. Methods: This was a single-center open-label prospective trial in refractory mCRC patients. Treatment consisted of oral capecitabine 850 mg/m Results: Forty-seven patients were enrolled in the trial to have 36 evaluable patients. Arm A enrolled 23 patients and Arm B enrolled 24 patients. Fifty percent of patients had progressive disease and 62% of patients had stable disease in each arm as best response. There was no demonstrable difference in PFS between the two arms (log-rank test P=0.83). Median time to progression was 3.4 months in Arm A and 3.5 months in Arm B. Conclusions: Median PFS in the trial was comparable to and appeared to be better than other regimens used in the refractory setting (e.g., median PFS of 1.9 months for regorafenib). Cytokine measurement with IL-8 levels did not show any correlation with progression free survival but patients with stable disease showed overall lower levels of IL-8 as compared to patients with progressive disease in the cytokine analysis

Visualizing dimensionality reduction of systems biology data

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system and which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21

An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

<p>Abstract</p> <p>Background</p> <p>Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches.</p> <p>Results</p> <p>In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay.</p> <p>Conclusions</p> <p>By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at <url>http://www.laurenzi.net</url>.</p

Extracellular vesicle sorting of α-Synuclein is regulated by sumoylation

Extracellular α-Synuclein has been implicated in interneuronal propagation of disease pathology in Parkinson’s Disease. How α-Synuclein is released into the extracellular space is still unclear. Here, we show that α-Synuclein is present in extracellular vesicles in the central nervous system. We find that sorting of α-Synuclein in extracellular vesicles is regulated by sumoylation and that sumoylation acts as a sorting factor for targeting of both, cytosolic and transmembrane proteins, to extracellular vesicles. We provide evidence that the SUMO-dependent sorting utilizes the endosomal sorting complex required for transport (ESCRT) by interaction with phosphoinositols. Ubiquitination of cargo proteins is so far the only known determinant for ESCRT-dependent sorting into the extracellular vesicle pathway. Our study reveals a function of SUMO protein modification as a Ubiquitin-independent ESCRT sorting signal, regulating the extracellular vesicle release of α-Synuclein. We deciphered in detail the molecular mechanism which directs α-Synuclein into extracellular vesicles which is of highest relevance for the understanding of Parkinson’s disease pathogenesis and progression at the molecular level. We furthermore propose that sumo-dependent sorting constitutes a mechanism with more general implications for cell biology.Instituto de Investigaciones Bioquímicas de La Plat

Amyloid imaging in the differential diagnosis of dementia: review and potential clinical applications

In the past decade, positron emission tomography (PET) with carbon-11-labeled Pittsburgh Compound B (PIB) has revolutionized the neuroimaging of aging and dementia by enabling in vivo detection of amyloid plaques, a core pathologic feature of Alzheimer's disease (AD). Studies suggest that PIB-PET is sensitive for AD pathology, can distinguish AD from non-AD dementia (for example, frontotemporal lobar degeneration), and can help determine whether mild cognitive impairment is due to AD. Although the short half-life of the carbon-11 radiolabel has thus far limited the use of PIB to research, a second generation of tracers labeled with fluorine-18 has made it possible for amyloid PET to enter the clinical era. In the present review, we summarize the literature on amyloid imaging in a range of neurodegenerative conditions. We focus on potential clinical applications of amyloid PET and its role in the differential diagnosis of dementia. We suggest that amyloid imaging will be particularly useful in the evaluation of mildly affected, clinically atypical or early age-at-onset patients, and illustrate this with case vignettes from our practice. We emphasize that amyloid imaging should supplement (not replace) a detailed clinical evaluation. We caution against screening asymptomatic individuals, and discuss the limited positive predictive value in older populations. Finally, we review limitations and unresolved questions related to this exciting new technique

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Genetics of Gastric Cancer

Gastric cancer represents a major cause of cancer mortality worldwide despite a declining incidence. New molecular classification schemes developed from genomic and molecular analyses of gastric cancer have provided a framework for understanding this heterogenous disease, and early findings suggest these classifications will be relevant for designing and implementing new targeted therapies. The success of targeted therapy and immunotherapy in breast cancer and melanoma, respectively, has not been duplicated in gastric cancer, but trastuzumab and ramucirumab have demonstrated efficacy in select populations. New markers that predict therapeutic response are needed to improve patient selection for both targeted and immunotherapies