Umbilical cord blood-derived haematopoietic stem cells (HSCs) are essential for many life-saving regenerative therapies. However, despite their advantages for transplantation, their clinical use is restricted because HSCs in cord blood are found only in small numbers. Small molecules that enhance haematopoietic stem and progenitor cell (HSPC) expansion in culture have been identified, but in many cases their mechanisms of action or the nature of the pathways they impinge on are poorly understood. A greater understanding of the molecular circuitry that underpins the self-renewal of human HSCs will facilitate the development of targeted strategies that expand HSCs for regenerative therapies. Whereas transcription factor networks have been shown to influence the self-renewal and lineage decisions of human HSCs, the post-transcriptional mechanisms that guide HSC fate have not been closely investigated. Here we show that overexpression of the RNA-binding protein Musashi-2 (MSI2) induces multiple pro-self-renewal phenotypes, including a 17-fold increase in short-term repopulating cells and a net 23-fold ex vivo expansion of long-term repopulating HSCs. By performing a global analysis of MSI2-RNA interactions, we show that MSI2 directly attenuates aryl hydrocarbon receptor (AHR) signalling through post-transcriptional downregulation of canonical AHR pathway components in cord blood HSPCs. Our study gives mechanistic insight into RNA networks controlled by RNA-binding proteins that underlie self-renewal and provides evidence that manipulating such networks ex vivo can enhance the regenerative potential of human HSCs.

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Circulating DNA is cell-free DNA (cfDNA) in serum or plasma that can be used for non-invasive prenatal testing, as well as cancer diagnosis, prognosis, and stratification. High-throughput sequence analysis of the cfDNA with next-generation sequencing technologies has proven to be a highly sensitive and specific method in detecting and characterizing mutations in cancer and other diseases, as well as aneuploidy during pregnancy. This unit describes detailed procedures to extract circulating cfDNA from human serum and plasma and generate sequencing libraries from a wide concentration range of circulating DNA. © 2016 by John Wiley & Sons, Inc.
Copyright © 2016 John Wiley & Sons, Inc.

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More accurate diagnostic methods are pressingly needed to diagnose breast cancer, the most common malignant cancer in women worldwide. Blood-based metabolomics is a promising diagnostic method for breast cancer. However, many metabolic biomarkers are difficult to replicate among studies.
We propose that higher-order functional representation of metabolomics data, such as pathway-based metabolomic features, can be used as robust biomarkers for breast cancer. Towards this, we have developed a new computational method that uses personalized pathway dysregulation scores for disease diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods.
The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under the Curve, a receiver operating characteristic curve) of 0.968 and 0.934, sensitivities of 0.946 and 0.954, and specificities of 0.934 and 0.918. These two metabolomics-based pathway models are further validated by RNA-Seq-based TCGA (The Cancer Genome Atlas) breast cancer data, with AUCs of 0.995 and 0.993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer.
We have successfully developed a new type of pathway-based model to study metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease diagnosis.

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To assess the impact of a variety of methodological parameters on the association between six drug classes and five key adverse events in multiple databases.
The selection of Drug-Adverse Event pairs was based on public health impact, regulatory relevance, and the possibility to study a broad range of methodological issues. Common protocols and data analytical specifications were jointly developed and independently and blindly executed in different databases in Europe with replications in the same and different databases.
The association between antibiotics and acute liver injury, benzodiazepines and hip fracture, antidepressants and hip fracture, inhaled long-acting beta2-agonists and acute myocardial infarction was consistent in direction across multiple designs, databases and methods to control for confounding. Some variation in magnitude of the associations was observed depending on design, exposure and outcome definitions, but none of the differences were statistically significant. The association between anti-epileptics and suicidality was inconsistent across the UK CPRD, Danish National registries and the French PGRx system. Calcium channel blockers were not associated with the risk of cancer in the UK CPRD, and this was consistent across different classes of calcium channel blockers, cumulative durations of use up to >10 years and different types of cancer.
A network for observational drug effect studies allowing the execution of common protocols in multiple databases was created. Increased consistency of findings across multiple designs and databases in different countries will increase confidence in findings from observational drug research and benefit/risk assessment of medicines. Copyright © 2016 John Wiley & Sons, Ltd.
Copyright © 2016 John Wiley & Sons, Ltd.

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Patient-reported outcome (PRO) data are increasingly being implemented in oncology clinical trial research to evaluate treatment benefit, such as disease-related symptoms, treatment-related adverse events, and health-related quality of life impacts. However, only a small amount of PRO data collected is used to support labeling claims, leaving a substantial amount of data that could be shared by sponsors to further convey treatment benefit from the patient perspective.
This paper describes how pharmaceutical sponsors can realize the value of PRO data derived from oncology trials with regard to the following stakeholders: payers, health care providers (HCPs), and patient advocacy groups. Further, ideas are presented for integrating PRO data and implementing PRO assessments within oncology, by stakeholder type. Finally, a summary is provided to describe how PRO data can benefit the patient by facilitating better, more symptom-focused care and enhancing treatment decisions.
With the goal of motivating further use of PRO assessments in oncology, we present examples of how payers utilize PRO data to inform reimbursement decisions (eg, PRO data inform decisions made by Germany׳s Institute for Quality and Efficiency in Health Care and the United Kingdom׳s National Institute for Health and Care Excellence); how communication of results with patient advocacy groups can lead to a better understanding of what is important to patients; and how HCPs can use PRO instruments to inform patient treatment decisions through real-world application.
Integrating PRO data can enhance health care by allowing the patient’s voice to carry beyond regulatory decisions and into those made by payers and HCPs, which are crucial to quality care and assessing the value of care. Utilizing PRO assessments and communicating results to key stakeholders in the oncology space can allow sponsors to report treatment benefit and, more importantly, can provide valuable insight into the patient treatment experience.
Copyright © 2016 Elsevier HS Journals, Inc. All rights reserved.

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