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Precision medicine risk prediction

Petra Bachour, Stephen T Sonis
PURPOSE OF REVIEW: The goals of this review are to describe the complexity of factors influencing the risk of cancer regimen-related mucosal injury (CRRMI), to evaluate the contribution of the innate immune response to CRRMI risk, to compare the concordance of genome analytics in describing mechanism and risk, and to determine if common biological pathways are noted when CRRMI is compared to a disease with a similar phenotype. RECENT FINDINGS: The pathogenesis of and risk for CRRMI are complex and influenced by multiple intrinsic and extrinsic factors...
March 14, 2018: Current Opinion in Supportive and Palliative Care
Haiyan Gao, Mei Yang, Xiaolan Zhang
The present study aimed to investigate potential recurrence-risk biomarkers based on significant pathways for Luminal A breast cancer through gene expression profile analysis. Initially, the gene expression profiles of Luminal A breast cancer patients were downloaded from The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were identified using a Limma package and the hierarchical clustering analysis was conducted for the DEGs. In addition, the functional pathways were screened using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and rank ratio calculation...
April 2018: Oncology Letters
K R Goethals
Personalised medicine promises to provide us with a diagnostic predictive system of stratification that is based on a wide variety of tests; these can include biological, cognitive, demographic, psychopathological tests and other clearly defined tests. The purpose of forensic psychiatry is not only to take care of and treat mentally impaired patients but also to engage in risk assessment and risk management.<br/> AIM: To explain risk assessment in forensic psychiatry as a nomothetic approach to personalised medicine, and also to demonstrate the link with offence paralleling behaviour, which is an illustration of the ideographic approach...
2018: Tijdschrift Voor Psychiatrie
Christina Kraniotou, Vasiliki Karadima, George Bellos, George Th Tsangaris
The global incidence of metabolic disorders like type 2 diabetes mellitus (DM2) has assumed epidemic proportions, leading to adverse health and socio-economic impacts. It is therefore of critical importance the early diagnosis of DM2 patients and the detection of those at increased risk of disease. In this respect, Precision Medicine (PM) is an emerging approach that includes practices, tests, decisions and treatments adapted to the characteristics of each patient. With regard to DM2, PM manages a wealth of "omics" data (genomic, metabolic, proteomic, environmental, clinical and paraclinical) to increase the number of clinically validated biomarkers in order to identify patients in early stage even before the prediabetic phase...
March 5, 2018: Journal of Proteomics
Robert M Maier, Zhihong Zhu, Sang Hong Lee, Maciej Trzaskowski, Douglas M Ruderfer, Eli A Stahl, Stephan Ripke, Naomi R Wray, Jian Yang, Peter M Visscher, Matthew R Robinson
Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts...
March 7, 2018: Nature Communications
Fangying Xie, Juliana C N Chan, Ronald C W Ma
Diabetes has become a major burden of healthcare expenditure. Diabetes management following a uniform treatment algorithm is often associated with progressive treatment failure and development of diabetic complications. Recent advances in our understanding in the genomic architecture of diabetes and its complications have provided the framework for development of precision medicine to personalize diabetes prevention and management. In this review, we summarized recent advances in the understanding of the genetic basis of diabetes and its complications...
March 2, 2018: Journal of Diabetes Investigation
Harald Hampel, Andrea Vergallo, Lisi Flores Aguilar, Norbert Benda, Karl Broich, A Claudio Cuello, Jeffrey Cummings, Bruno Dubois, Howard J Federoff, Massimo Fiandaca, Remy Genthon, Marion Haberkamp, Eric Karran, Mark Mapstone, George Perry, Lon S Schneider, Lindsay A Welikovitch, Janet Woodcock, Filippo Baldacci, Simone Lista
The complex multifactorial nature of polygenic Alzheimer's disease (AD) presents significant challenges for drug development. AD pathophysiology is progressing in a non-linear dynamic fashion across multiple systems levels - from molecules to organ systems - and through adaptation, to compensation, and decompensation to systems failure. Adaptation and compensation maintain homeostasis: a dynamic equilibrium resulting from the dynamic non-linear interaction between genome, epigenome, and environment. An individual vulnerability to stressors exists on the basis of individual triggers, drivers, and thresholds accounting for the initiation and failure of adaptive and compensatory responses...
February 16, 2018: Pharmacological Research: the Official Journal of the Italian Pharmacological Society
Kate Sutherland, Fernanda R Almeida, Philip de Chazal, Peter A Cistulli
Obstructive sleep apnoea (OSA) is common disorder, under-diagnosed, and can be difficult to treat adequately across the lifespan. OSA is a heterogeneous disorder with different risk factors, clinical presentations, pathophysiology and morbidity. Prediction has an important role in OSA recognition and management, embodied in screening methods to circumvent the need for diagnosis by overnight sleep studies and prediction of treatment efficacy and adherence. Other opportunities exist in predicting susceptibility to comorbidity and health outcomes...
February 17, 2018: Expert Review of Respiratory Medicine
Michela Franchini, Stefania Pieroni, Claudio Passino, Michele Emdin, Sabrina Molinaro
Modern medicine remains dependent on the accurate evaluation of a patient's health state, recognizing that disease is a process that evolves over time and interacts with many factors unique to that patient. The CARPEDIEM project represents a concrete attempt to address these issues by developing reproducible algorithms to support the accuracy in detection of complex diseases. This study aims to establish and validate the CARPEDIEM approach and algorithm for identifying those patients presenting with or at risk of heart failure (HF) by studying 153,393 subjects in Italy, based on patient information flow databases and is not reliant on the electronic health record to accomplish its goals...
2018: Frontiers in Public Health
James E Andruchow, Peter A Kavsak, Andrew D McRae
This article synthesizes current best evidence for the evaluation of patients with suspected acute coronary syndrome (ACS) using high-sensitivity troponin assays, enabling physicians to effectively incorporate them into practice. Unlike conventional assays, high-sensitivity assays can precisely measure blood cardiac troponin concentrations in the vast majority of healthy individuals, facilitating the creation of rapid diagnostic algorithms. Very low troponin concentrations on presentation accurately rule out acute myocardial infarction (AMI) and enable the discharge of approximately 20% of patients after a single test, whereas an additional 30%-40% of patients can be safely discharged after short-interval serial sampling in as little as 1 or 2 hours...
February 2018: Canadian Journal of Cardiology
Maryellen L Giger
Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data...
March 2018: Journal of the American College of Radiology: JACR
Alok A Khorana, Charles W Francis
Cancer-associated venous thromboembolism (VTE) has major consequences for patients, including morbidity and risk of mortality. However, there is substantial variation in risk depending on a multitude of clinical risk factors and many cancer patients are at low risk for VTE. This critical concept of risk variation has led to efforts to identify patients at high or low risk for developing VTE. Our research group and others have focused on understanding and predicting risk of cancer-associated VTE. This narrative review describe research efforts conducted over the past decade, beginning with the 2008 publication of the first validated risk assessment tool in this setting...
January 26, 2018: Thrombosis Research
Sherry-Ann Brown, Naveen Pereira
Variability in response to antiplatelet therapy can be explained in part by pharmacogenomics, particularly of the CYP450 enzyme encoded by CYP2C19. Loss-of-function and gain-of-function variants help explain these interindividual differences. Individuals may carry multiple variants, with linkage disequilibrium noted among some alleles. In the current pharmacogenomics era, genomic variation in CYP2C19 has led to the definition of pharmacokinetic phenotypes for response to antiplatelet therapy, in particular, clopidogrel...
January 30, 2018: Journal of Personalized Medicine
Chee Lee, Maneesh Sharma, Svetlana Kantorovich, Ashley Brenton
Purpose: The purpose of this study was to determine the clinical utility of an algorithm-based decision tool designed to assess risk associated with opioid use in the primary care setting. Methods: A prospective, longitudinal study was conducted to assess the utility of precision medicine testing in 1822 patients across 18 family medicine/primary care clinics in the United States. Using the profile, patients were categorized into low, moderate, and high risk for opioid use...
January 2018: Health Services Research and Managerial Epidemiology
Meng Wang, Yuan Yao, Shanqun Jiang, Fang Tao, Rui Tang, Jinlu Sun
Asthma is one of the most significant diseases worldwide and causes overwhelming costs physically and economically. The heterogeneity of asthma has been a hot topic, and an increasing amount of research has been conducted on this issue. The study of asthma has revealed various groups of asthma patients who share phenotypic characteristics that naturally elicit the need for personalized asthma therapy. Clinical evidence has shown that a 'one size fits all' approach does not apply to all asthma patients because of the large variability in treatment responses to asthma medication...
January 28, 2018: Current Drug Metabolism
Ashley Brenton, Chee Lee, Katrina Lewis, Maneesh Sharma, Svetlana Kantorovich, Gregory A Smith, Brian Meshkin
Purpose: The purpose of this study was to determine the clinical utility of an algorithm-based decision tool designed to assess risk associated with opioid use. Specifically, we sought to assess how physicians were using the profile in patient care and how its use affected patient outcomes. Patients and methods: A prospective, longitudinal study was conducted to assess the utility of precision medicine testing in 5,397 patients across 100 clinics in the USA. Using a patent-protected, validated algorithm combining specific genetic risk factors with phenotypic traits, patients were categorized into low-, moderate-, and high-risk patients for opioid abuse...
2018: Journal of Pain Research
Na You, Shun He, Xueqin Wang, Junxian Zhu, Heping Zhang
Common diseases including cancer are heterogeneous. It is important to discover disease subtypes and identify both shared and unique risk factors for different disease subtypes. The advent of high-throughput technologies enriches the data to achieve this goal, if necessary statistical methods are developed. Existing methods can accommodate both heterogeneity identification and variable selection under parametric models, but for survival analysis, the commonly used Cox model is semiparametric. Although finite-mixture Cox model has been proposed to address heterogeneity in survival analysis, variable selection has not been incorporated into such semiparametric models...
January 22, 2018: Biometrics
Khader Shameer, Kipp W Johnson, Benjamin S Glicksberg, Joel T Dudley, Partho P Sengupta
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform...
January 19, 2018: Heart: Official Journal of the British Cardiac Society
Jessica McAlpine, Alicia Leon-Castillo, Tjalling Bosse
Endometrial cancer is a clinically heterogeneous disease and it is becoming increasingly clear that this heterogeneity may be a function of the diversity of the underlying molecular alterations. Recent large scale genomic studies have revealed that endometrial cancer can be divided into at least four distinct molecular subtypes, with well described underlying genomic aberrations. These subtypes can be reliably delineated and carry significant prognostic as well as predictive information; embracing and incorporating them into clinical practice is thus attractive...
January 17, 2018: Journal of Pathology
Elisa Izquierdo, Lina Yuan, Sally George, Michael Hubank, Chris Jones, Paula Proszek, Janet Shipley, Susanne A Gatz, Caedyn Stinson, Andrew S Moore, Steven C Clifford, Debbie Hicks, Janet C Lindsey, Rebecca M Hill, Thomas S Jacques, Jane Chalker, Khin Thway, Simon O'Connor, Lynley Marshall, Lucas Moreno, Andrew Pearson, Louis Chesler, Brian A Walker, David Gonzalez De Castro
The implementation of personalised medicine in childhood cancers has been limited by a lack of clinically validated multi-target sequencing approaches specific for paediatric solid tumours. In order to support innovative clinical trials in high-risk patients with unmet need, we have developed a clinically relevant targeted sequencing panel spanning 311 kb and comprising 78 genes involved in childhood cancers. A total of 132 samples were used for the validation of the panel, including Horizon Discovery cell blends (n=4), cell lines (n=15), formalin-fixed paraffin embedded (FFPE, n=83) and fresh frozen tissue (FF, n=30) patient samples...
December 19, 2017: Oncotarget
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