A Novel Unsupervised Method to Identify Genes Important in the Anti-viral Response: Application to Interferon/Ribavirin in Hepatitis C Patients

Background: Treating hepatitis C with interferon/ribavirin results in a varied response in terms of decrease in viral titer and ultimate outcome. Marked responders have a sharp decline in viral titer within a few days of treatment initiation, whereas in other patients there is no effect on the virus (poor responders). Previous studies have shown that combination therapy modifies expression of hundreds of genes in vitro and in vivo. However, identifying which, if any, of these genes have a role in viral clearance remains challenging. Aims: The goal of this paper is to link viral levels with gene expression and thereby identify genes that may be responsible for early decrease in viral titer. Methods: Microarrays were performed on RNA isolated from PBMC of patients undergoing interferon/ribavirin therapy. Samples were collected at pre-treatment (day 0), and 1, 2, 7, 14 and 28 days after initiating treatment. A novel method was applied to identify genes that are linked to a decrease in viral titer during interferon/ribavirin treatment. The method uses the relationship between inter-patient gene expression based proximities and inter-patient viral titer based proximities to define the association between microarray gene expression measurements of each gene and viral-titer measurements. Results: We detected 36 unique genes whose expressions provide a clustering of patients that resembles viral titer based clustering of patients. These genes include IRF7, MX1, OASL and OAS2, viperin and many ISG's of unknown function. Conclusion: The genes identified by this method appear to play a major role in the reduction of hepatitis C virus during the early phase of treatment. The method has broad utility and can be used to analyze response to any group of factors influencing biological outcome such as antiviral drugs or anti-cancer agents where microarray data are available. © 2007 Brodsky et al

Systematic Bias in Genomic Classification Due to Contaminating Non-neoplastic Tissue in Breast Tumor Samples

Abstract Background Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. Methods To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Results Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Conclusions Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor

Pretreatment serum albumin as a predictor of cancer survival: A systematic review of the epidemiological literature

<p>Abstract</p> <p>Background</p> <p>There are several methods of assessing nutritional status in cancer of which serum albumin is one of the most commonly used. In recent years, the role of malnutrition as a predictor of survival in cancer has received considerable attention. As a result, it is reasonable to investigate whether serum albumin has utility as a prognostic indicator of cancer survival in cancer. This review summarizes all available epidemiological literature on the association between pretreatment serum albumin levels and survival in different types of cancer.</p> <p>Methods</p> <p>A systematic search of the literature using the MEDLINE database (January 1995 through June 2010) to identify epidemiologic studies on the relationship between serum albumin and cancer survival. To be included in the review, a study must have: been published in English, reported on data collected in humans with any type of cancer, had serum albumin as <it>one of the </it>or <it>only </it>predicting factor, had survival as one of the outcome measures (primary or secondary) and had any of the following study designs (case-control, cohort, cross-sectional, case-series prospective, retrospective, nested case-control, ecologic, clinical trial, meta-analysis).</p> <p>Results</p> <p>Of the 29 studies reviewed on cancers of the gastrointestinal tract, all except three found higher serum albumin levels to be associated with better survival in multivariate analysis. Of the 10 studies reviewed on lung cancer, all excepting one found higher serum albumin levels to be associated with better survival. In 6 studies reviewed on female cancers and multiple cancers each, lower levels of serum albumin were associated with poor survival. Finally, in all 8 studies reviewed on patients with other cancer sites, lower levels of serum albumin were associated with poor survival.</p> <p>Conclusions</p> <p>Pretreatment serum albumin levels provide useful prognostic significance in cancer. Accordingly, serum albumin level could be used in clinical trials to better define the baseline risk in cancer patients. A critical gap for demonstrating causality, however, is the absence of clinical trials demonstrating that raising albumin levels by means of intravenous infusion or by hyperalimentation decreases the excess risk of mortality in cancer.</p

European micronutrient recommendations aligned: a general framework developed by EURRECA

Background: In Europe, micronutrient recommendations have been established by (inter)national committees of experts and are used by public health-policy decision makers to monitor and assess the adequacy of the diets of population groups. Current micronutrient recommendations are, however, heterogeneous, whereas the scientific basis for this is not obvious. Alignment of setting micronutrient recommendations is necessary to improve the transparency of the process, the objectivity and reliability of recommendations that are derived by diverse regional and (inter)national bodies. Objective: This call for alignment of micronutrient recommendations is a direct result of the current sociopolitical climate in Europe and uncovers the need for an institutional architecture. There is a need for evidence-based policy making, transparent decision making, stakeholder involvement and alignment of policies across Europe. Results: In this paper, we propose a General Framework that describes the process leading from assessing nutritional requirements to policy applications, based on evidence from science, stakeholder interests and the sociopolitical context. The framework envisions the derivation of nutrient recommendations as scientific methodology, embedded in a policy-making process that also includes consumer issues, and acknowledges the influences of the wider sociopolitical context by distinguishing the principal components of the framework: (a) defining the nutrient requirements for health, (b) setting nutrient recommendations, (c) policy options and (d) policy applications. Conclusion: The General Framework can serve as a basis for a systematic and transparent approach to the development and review of micronutrient requirements in Europe, as well as the decision making of scientific advisory bodies, policy makers and stakeholders involved in this process of assessing, developing and translating these recommendations into public health nutrition policy. European Journal of Clinical Nutrition (201 0) 64, S2-510; doi:10.1038/ejcn.2010.5

Shedding Light on Fish Otolith Biomineralization Using a Bioenergetic Approach

Otoliths are biocalcified bodies connected to the sensory system in the inner ears of fish. Their layered, biorhythm-following formation provides individual records of the age, the individual history and the natural environment of extinct and living fish species. Such data are critical for ecosystem and fisheries monitoring. They however often lack validation and the poor understanding of biomineralization mechanisms has led to striking examples of misinterpretations and subsequent erroneous conclusions in fish ecology and fisheries management. Here we develop and validate a numerical model of otolith biomineralization. Based on a general bioenergetic theory, it disentangles the complex interplay between metabolic and temperature effects on biomineralization. This model resolves controversial issues and explains poorly understood observations of otolith formation. It represents a unique simulation tool to improve otolith interpretation and applications, and, beyond, to address the effects of both climate change and ocean acidification on other biomineralizing organisms such as corals and bivalves

Differential impact of LPG-and PG-deficient Leishmania major mutants on the immune response of human dendritic cells

<div><p>Background</p><p><i>Leishmania major</i> infection induces robust interleukin-12 (IL12) production in human dendritic cells (hDC), ultimately resulting in Th1-mediated immunity and clinical resolution. The surface of <i>Leishmania</i> parasites is covered in a dense glycocalyx consisting of primarily lipophosphoglycan (LPG) and other phosphoglycan-containing molecules (PGs), making these glycoconjugates the likely pathogen-associated molecular patterns (PAMPS) responsible for IL12 induction.</p><p>Methodology/Principal Findings</p><p>Here we explored the role of parasite glycoconjugates on the hDC IL12 response by generating <i>L</i>. <i>major</i> Friedlin V1 mutants defective in LPG alone, (FV1 <i>lpg1-</i>), or generally deficient for all PGs, (FV1 <i>lpg2-</i>). Infection with metacyclic, infective stage, <i>L</i>. <i>major</i> or purified LPG induced high levels of <i>IL12B</i> subunit gene transcripts in hDCs, which was abrogated with FV1 <i>lpg1-</i> infections. In contrast, hDC infections with FV1 <i>lpg2-</i> displayed increased <i>IL12B</i> expression, suggesting other PG-related/<i>LPG2</i> dependent molecules may act to dampen the immune response. Global transcriptional profiling comparing WT, FV1 <i>lpg1-</i>, FV1 <i>lpg2-</i> infections revealed that FV1 <i>lpg1-</i> mutants entered hDCs in a silent fashion as indicated by repression of gene expression. Transcription factor binding site analysis suggests that LPG recognition by hDCs induces IL-12 in a signaling cascade resulting in Nuclear Factor κ B (NFκB) and Interferon Regulatory Factor (IRF) mediated transcription.</p><p>Conclusions/Significance</p><p>These data suggest that <i>L</i>. <i>major</i> LPG is a major PAMP recognized by hDC to induce IL12-mediated protective immunity and that there is a complex interplay between PG-baring <i>Leishmania</i> surface glycoconjugates that result in modulation of host cellular IL12.</p></div

In Silico Ascription of Gene Expression Differences to Tumor and Stromal Cells in a Model to Study Impact on Breast Cancer Outcome

Breast tumors consist of several different tissue components. Despite the heterogeneity, most gene expression analyses have traditionally been performed without prior microdissection of the tissue sample. Thus, the gene expression profiles obtained reflect the mRNA contribution from the various tissue components. We utilized histopathological estimations of area fractions of tumor and stromal tissue components in 198 fresh-frozen breast tumor tissue samples for a cell type-associated gene expression analysis associated with distant metastasis. Sets of differentially expressed gene-probes were identified in tumors from patients who developed distant metastasis compared with those who did not, by weighing the contribution from each tumor with the relative content of stromal and tumor epithelial cells in their individual tumor specimen. The analyses were performed under various assumptions of mRNA transcription level from tumor epithelial cells compared with stromal cells. A set of 30 differentially expressed gene-probes was ascribed solely to carcinoma cells. Furthermore, two sets of 38 and five differentially expressed gene-probes were mostly associated to tumor epithelial and stromal cells, respectively. Finally, a set of 26 differentially expressed gene-probes was identified independently of cell type focus. The differentially expressed genes were validated in independent gene expression data from a set of laser capture microdissected invasive ductal carcinomas. We present a method for identifying and ascribing differentially expressed genes to tumor epithelial and/or stromal cells, by utilizing pathologic information and weighted t-statistics. Although a transcriptional contribution from the stromal cell fraction is detectable in microarray experiments performed on bulk tumor, the gene expression differences between the distant metastasis and no distant metastasis group were mostly ascribed to the tumor epithelial cells of the primary breast tumors. However, the gene PIP5K2A was found significantly elevated in stroma cells in distant metastasis group, compared to stroma in no distant metastasis group. These findings were confirmed in gene expression data from the representative compartments from microdissected breast tissue. The method described was also found to be robust to different histopathological procedures

Transcriptomic landscape of breast cancers through mRNA sequencing

Breast cancer is a heterogeneous disease with a poorly defined genetic landscape, which poses a major challenge in diagnosis and treatment. By massively parallel mRNA sequencing, we obtained 1.2 billion reads from 17 individual human tissues belonging to TNBC, Non-TNBC, and HER2-positive breast cancers and defined their comprehensive digital transcriptome for the first time. Surprisingly, we identified a high number of novel and unannotated transcripts, revealing the global breast cancer transcriptomic adaptations. Comparative transcriptomic analyses elucidated differentially expressed transcripts between the three breast cancer groups, identifying several new modulators of breast cancer. Our study also identified common transcriptional regulatory elements, such as highly abundant primary transcripts, including osteonectin, RACK1, calnexin, calreticulin, FTL, and B2M, and “genomic hotspots” enriched in primary transcripts between the three groups. Thus, our study opens previously unexplored niches that could enable a better understanding of the disease and the development of potential intervention strategies

No common denominator for breast cancer lymph node metastasis

The axillary lymph node status is the most powerful prognostic factor for breast cancer patients to date. The molecular mechanisms that control lymph node metastasis, however, remain poorly understood. To define patterns of genes or gene regulatory pathways that drive breast cancer lymph node metastasis, we compared the gene expression profiles of 15 primary breast carcinomas and their matching lymph node metastases using microarrays. In general, primary breast carcinomas and lymph node metastases do not differ at the transcriptional level by a common subset of genes. No classifier or single gene discriminating the group of primary tumours from those of the lymph node metastases could be identified. Also, in a series of 295 breast tumours, no classifier predicting lymph node metastasis could be developed. However, subtle differences in the expression of genes involved in extracellular-matrix organisation and growth factor signalling are detected in individual pairs of matching primary and metastatic tumours. Surprisingly, however, different sets of these genes are either up- or downregulated in lymph node metastases. Our data suggest that breast carcinomas do not use a shared gene set to accomplish lymph node metastasis